diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md new file mode 100644 index 0000000..f66126d --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -0,0 +1,29 @@ +--- +name: Bug report +about: Create a report to help us improve +title: "[BUG]" +labels: bug +assignees: '' + +--- + +## Describe the bug +- + +## To Reproduce +- + +## Expected behavior +- + +## Screenshots +- + +## Additional context +- + +## Possible Solution +- + +## Your Environment +- diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md new file mode 100644 index 0000000..840bcc6 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/feature_request.md @@ -0,0 +1,18 @@ +--- +name: Feature request +about: Suggest an idea for this project +title: "[FEAT]" +labels: enhancement +assignees: '' + +--- + +## Background +- + +## Todo +- [ ] Todo 1 +- [ ] Todo 2 + +## See also +- # diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md new file mode 100644 index 0000000..df34995 --- /dev/null +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -0,0 +1,12 @@ +## Overview +- + +## Change Log +- + +## To Reviewer +- + +## Issue Tags +- Closed | Fixed: # +- See also: # diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..f2f728d --- /dev/null +++ b/.gitignore @@ -0,0 +1,7 @@ +__pycache__ +*.csv +wandb/* +!wandb/.gitkeep +checkpoints/* +!checkpoints/.gitkeep +!streamlit/assets/*.csv diff --git a/README.md b/README.md new file mode 100644 index 0000000..50310df --- /dev/null +++ b/README.md @@ -0,0 +1,189 @@ +
+ + # ๐Ÿ† LV.2 NLP ํ”„๋กœ์ ํŠธ : ์ˆ˜๋Šฅํ˜• ๋ฌธ์ œ ํ’€์ด ๋ชจ๋ธ ์ƒ์„ฑ + +
+

+ +## โœ๏ธ ๋Œ€ํšŒ ์†Œ๊ฐœ + +| ํŠน์ง•ใ€€ใ€€ใ€€ใ€€| ์„ค๋ช… | +|:------:| --- | +| ๋Œ€ํšŒ ์ฃผ์ œ | ๋„ค์ด๋ฒ„ ๋ถ€์ŠคํŠธ์บ ํ”„ AI Tech 7๊ธฐ NLP Track์˜ Level 2 ๋„๋ฉ”์ธ ๊ธฐ์ดˆ ๋Œ€ํšŒ '์ˆ˜๋Šฅํ˜• ๋ฌธ์ œ ํ’€์ด ๋ชจ๋ธ ์ƒ์„ฑ'์ž…๋‹ˆ๋‹ค. | +| ๋Œ€ํšŒ ์„ค๋ช… | AI ๋ชจ๋ธ๋กœ ํ•œ๊ตญ์–ด ์ˆ˜๋Šฅ ๊ตญ์–ด ๋ฐ ์‚ฌํšŒ ๊ณผ๋ชฉ์˜ ๋ฌธ์ œ๋ฅผ ํ’€์–ด ๋Œ€ํ˜• ์–ธ์–ด ๋ชจ๋ธ๋“ค์„ ๋Šฅ๊ฐ€ํ•˜๋Š” ์„ฑ๋Šฅ์„ ๋ชฉํ‘œ๋กœ ํ•˜๋Š” ๋Œ€ํšŒ์ž…๋‹ˆ๋‹ค. | +| ๋ฐ์ดํ„ฐ ๊ตฌ์„ฑ | ๋ฐ์ดํ„ฐ๋Š” ์ˆ˜๋Šฅ ๊ตญ์–ดยท์‚ฌํšŒ์™€ ์œ ์‚ฌํ•œ ๋ฌธ์ œ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ KMMLU(ํ•œ๊ตญ์‚ฌ), MMMLU(๊ณ ๊ต ์—ญ์‚ฌยท๊ฒฝ์ œยท์ •์น˜ ๋“ฑ), KLUE MRC(๊ฒฝ์ œยท๊ตญ์ œยท์‚ฌํšŒ ๋“ฑ) ๋ฐ์ดํ„ฐ๋ฅผ ํฌํ•จํ•ฉ๋‹ˆ๋‹ค. | +| ํ‰๊ฐ€ ์ง€ํ‘œ | ๋ชจ๋ธ์ด ๋งž์ถ˜ ๋ฌธ์ œ ์ˆ˜๋ฅผ ์ „์ฒด ๋ฌธ์ œ ์ˆ˜๋กœ ๋‚˜๋ˆˆ ์ •ํ™•๋„(Accuracy)๋ฅผ ๊ธฐ์ค€์œผ๋กœ ํ•ฉ๋‹ˆ๋‹ค.| +| ๊ฒฐ๊ณผ๋ฌผ | [WrapUp Report](https://github.com/boostcampaitech7/level2-nlp-generationfornlp-nlp-05-lv3/blob/main/assets/NLP%E1%84%80%E1%85%B5%E1%84%8E%E1%85%A9%E1%84%83%E1%85%A2%E1%84%92%E1%85%AC_NLP_%E1%84%90%E1%85%B5%E1%86%B7%20%E1%84%85%E1%85%B5%E1%84%91%E1%85%A9%E1%84%90%E1%85%B3(05%E1%84%8C%E1%85%A9).pdf), [Presentation Material](https://github.com/boostcampaitech7/level2-nlp-generationfornlp-nlp-05-lv3/blob/main/assets/%5B5%E1%84%8C%E1%85%A9%5DLv2_%E1%84%89%E1%85%AE%E1%84%82%E1%85%B3%E1%86%BC%E1%84%86%E1%85%AE%E1%86%AB%E1%84%8C%E1%85%A6%E1%84%91%E1%85%AE%E1%86%AF%E1%84%8B%E1%85%B5_%E1%84%91%E1%85%B3%E1%84%85%E1%85%A9%E1%84%8C%E1%85%A6%E1%86%A8%E1%84%90%E1%85%B3_%E1%84%87%E1%85%A1%E1%86%AF%E1%84%91%E1%85%AD%E1%84%8C%E1%85%A1%E1%84%85%E1%85%AD.pdf) | + +

+ +## ๐ŸŽ–๏ธ Leader Board + +ํ”„๋กœ์ ํŠธ ๊ฒฐ๊ณผ Public ๋ฆฌ๋”๋ณด๋“œ 1๋“ฑ, Private ๋ฆฌ๋”๋ณด๋“œ 1๋“ฑ์„ ๊ธฐ๋กํ•˜์˜€์Šต๋‹ˆ๋‹ค. + +### ๐Ÿฅ‡ย Public Leader Board (1์œ„) + +![image](https://github.com/user-attachments/assets/778831bc-2ed6-4090-a1a0-49ce38c71bc6) + +### ๐Ÿฅ‡ Private Leader Board (1์œ„) + +![image](https://github.com/user-attachments/assets/8757896e-8e93-4bb2-9798-14bf764259ae) + +

+ +## ๐Ÿ‘จโ€๐Ÿ’ป ๋‚˜์•ผ, ์ž, ์—ฐ์–ดํŒ€ ๋ฉค๋ฒ„ +
+ +| ๊ณฝํฌ์ค€ [](https://github.com/gwaksital) | ๊น€์ •์€ [](https://github.com/wjddms4299) | ๊น€์ง„์žฌ [](https://github.com/jin-jae) | ์˜ค์ˆ˜ํ˜„ [](https://github.com/ocean010315) | ์œค์„ ์›… [](https://github.com/ssunbear) | ์ •๋ฏผ์ง€ [](https://github.com/minjijeong98) +|:-:|:-:|:-:|:-:|:-:|:-:| +| ![๊ณฝํฌ์ค€](https://avatars.githubusercontent.com/u/80732503) | ![๊น€์ •์€](https://avatars.githubusercontent.com/u/121777522) | ![๊น€์ง„์žฌ](https://avatars.githubusercontent.com/u/97018331) | ![์˜ค์ˆ˜ํ˜„](https://avatars.githubusercontent.com/u/91974779) | ![์œค์„ ์›…](https://avatars.githubusercontent.com/u/117508164) | ![์ •๋ฏผ์ง€](https://avatars.githubusercontent.com/u/162319450) | + +
+ +

+ +## ๐Ÿ‘ผ ์—ญํ•  ๋ถ„๋‹ด + +
+ +|ํŒ€์›ใ€€ใ€€| ์—ญํ•  | +|:--------:| -------------- | +|๊ณฝํฌ์ค€| ๋ฐ์ดํ„ฐ์…‹ ๋ ˆ์ด๋ธ”๋ง, EDA, ์™ธ๋ถ€ ๋ฐ์ดํ„ฐ์…‹ ํƒ์ƒ‰, GPT๋ฅผ ํ†ตํ•œ ๋ฐ์ดํ„ฐ์…‹ ์ฆ๊ฐ• ์‹คํ—˜, ์ฝ”๋“œ ๋ฆฌํŒฉํ† ๋ง, LLM ํ•™์Šต ๋ฐฉ๋ฒ• ์„ค๊ณ„, Fine-Tuning, RAG ํŒŒ์ดํ”„๋ผ์ธ ๊ตฌ์ถ•, ์ตœ์ข… ์ฝ”๋“œ ์ •๋ฆฌ | +|๊น€์ •์€| ๋ฐ์ดํ„ฐ์…‹ ๋ ˆ์ด๋ธ”๋ง, ๋ชจ๋ธ ํƒ์ƒ‰ ๋ฐ Fine-Tuning, ๋ฐ์ดํ„ฐ์…‹ ํฌ๋กค๋ง ๋ฐ ์ „์ฒ˜๋ฆฌ(๊ณต๋ฌด์› ๊ธฐ์ถœ, khan), ๋ฐ์ดํ„ฐ์…‹ ํ’ˆ์งˆ ํ…Œ์ŠคํŠธ, RAG ์‹œ์Šคํ…œ ๊ตฌ์ถ• ๋ฐ ์‹คํ—˜, ํ”„๋กฌํ”„ํŠธ ์—”์ง€๋‹ˆ์–ด๋ง | +|๊น€์ง„์žฌ| ์ดˆ๊ธฐ ํŒ€ ํ™˜๊ฒฝ ๊ตฌ์ถ• ๋ฐ ๋Œ€์‹œ๋ณด๋“œ ์ œ์ž‘, ๋ฐ์ดํ„ฐ์…‹ ๋ ˆ์ด๋ธ”๋ง, ๋ฐ์ดํ„ฐ ํƒ์ƒ‰, RAG ๋ฐ์ดํ„ฐ ์ „์ฒ˜๋ฆฌ(๋ฒˆ์—ญ), Retrieval ๊ตฌ์ถ• ๋ฐ ์‹คํ—˜ (Sparse) | +|์˜ค์ˆ˜ํ˜„| ์ดˆ๊ธฐ ๋ฒ ์ด์Šค๋ผ์ธ ์ฝ”๋“œ ๊ตฌ์ถ•, ๋ฐ์ดํ„ฐ์…‹ ๋ ˆ์ด๋ธ”๋ง, ๋ฐ๋ชจ ํŽ˜์ด์ง€ ์ œ์ž‘ | +|์œค์„ ์›…| ๋ฐ์ดํ„ฐ์…‹ ๋ ˆ์ด๋ธ”๋ง, ๋ชจ๋ธ ํƒ์ƒ‰ ๋ฐ Fine-Tuning, Unsloth ์„ธํŒ…, ํ”„๋กฌํ”„ํŠธ ์—”์ง€๋‹ˆ์–ด๋ง, ๋ฐ์ดํ„ฐ์…‹ ํฌ๋กค๋ง(๊ณต๋ฌด์› ๊ธฐ์ถœ, khan), ๋ฐ์ดํ„ฐ์…‹ ํ’ˆ์งˆ ํ…Œ์ŠคํŠธ, LoRA ํŠœ๋‹, ์•™์ƒ๋ธ” | +|์ •๋ฏผ์ง€| ๋ฐ์ดํ„ฐ์…‹ ๋ ˆ์ด๋ธ”๋ง, ๋ฒกํ„ฐ์Šคํ† ์–ด ๋ฐ์ดํ„ฐ ํฌ๋กค๋ง ๋ฐ ์ „์ฒ˜๋ฆฌ (OpenStax, Wikipedia, ์šฐ๋ฆฌ์—ญ์‚ฌ๋„ท), Retrieval ์„ฑ๋Šฅ ํ‰๊ฐ€ ๋ฐ์ดํ„ฐ์…‹ ๋ฐ ์ง€ํ‘œ ๊ตฌ์„ฑ, RAG ์‹œ์Šคํ…œ ๊ตฌ์ถ• ๋ฐ ์‹คํ—˜ (Chunk size, Dense Retrieval, Reranking) | + +
+ +

+ +## ๐Ÿƒ ํ”„๋กœ์ ํŠธ ์„ค๋ช… + +### ๐Ÿ–ฅ๏ธ ํ”„๋กœ์ ํŠธ ๊ฐœ์š” + +| ๊ฐœ์š” | ์„ค๋ช… | +|:--------:| --- | +| ์ฃผ์ œ | Generation for NLP - ์ˆ˜๋Šฅํ˜• ๋ฌธ์ œ ํ’€์ด ๋ชจ๋ธ ์ƒ์„ฑ | +| ๊ตฌ์กฐ | LLM Fine-Tuned Foundation Model + RAG | +| ํ‰๊ฐ€ ์ง€ํ‘œ | Accuracy = correct / total | +| ๊ฐœ๋ฐœ ํ™˜๊ฒฝ | `GPU` : Tesla V100 Server 4๋Œ€, `IDE` : VsCode, Jupyter Notebook | +| ํ˜‘์—… ํ™˜๊ฒฝ | Jira&Confluence(์ง„ํ–‰ ์ƒํ™ฉ ๊ณต์œ ), Github(์ฝ”๋“œ ๋ฐ ๋ฐ์ดํ„ฐ ๊ณต์œ ), Zoom&Slack(์‹ค์‹œ๊ฐ„ ์†Œํ†ต) | + +
+ +### ๐Ÿ“… ํ”„๋กœ์ ํŠธ ํƒ€์ž„๋ผ์ธ + +- ํ”„๋กœ์ ํŠธ๋Š” 2024-11-11 ~ 2024-11-28๊นŒ์ง€ ์ง„ํ–‰๋˜์—ˆ์Šต๋‹ˆ๋‹ค. + +![image](./assets/timeline.png) + +
+ +### ๐Ÿ•ต๏ธ ํ”„๋กœ์ ํŠธ ์ง„ํ–‰ + +- ํ”„๋กœ์ ํŠธ๋ฅผ ์ง„ํ–‰ํ•˜๋ฉฐ ๋‹จ๊ณ„๋ณ„๋กœ ์‹คํ—˜ํ•˜์—ฌ ์ ์šฉํ•œ ๋‚ด์šฉ๋“ค์€ ์•„๋ž˜์™€ ๊ฐ™์Šต๋‹ˆ๋‹ค. + +| ํ”„๋กœ์„ธ์Šคใ€€| ์„ค๋ช… | +|:--------:| --- | +| ๋ฐ์ดํ„ฐ | EDA, Fine-Tuning ๋ฐ์ดํ„ฐ์…‹ ๊ตฌ์„ฑ (๋ฐ์ดํ„ฐ ํ’ˆ์งˆ ๊ฐœ์„ , ๋ฐ์ดํ„ฐ ์ฆ๊ฐ•), RAG ๋ฐ์ดํ„ฐ ๊ตฌ์„ฑ (๋ฒกํ„ฐ ์Šคํ† ์–ด ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘ ๋ฐ ์ „์ฒ˜๋ฆฌ, chunking) | +| ๋ชจ๋ธ๋ง | ๋ชจ๋ธ ์„ ์ • ๋ฐ ํŠœ๋‹, LoRA ํŠœ๋‹, ํ”„๋กฌํ”„ํŠธ ํŠœ๋‹ | +| RAG | Vector Store ๊ตฌ์ถ•, Retriever ํ‰๊ฐ€์šฉ ๋ฐ์ดํ„ฐ์…‹ ๊ตฌ์ถ•, Retriever ํŒŒ๋ผ๋ฏธํ„ฐ ์„ค์ •, RAFT(Retrieval Augmented Fine-Tuning) | +| ์•™์ƒ๋ธ” | Weighted Voting Ensemble | + +
+ +### ๐Ÿค– Ensemble + +์ •์ œ, ์ฆ๊ฐ•์„ ๋‹ค์–‘ํ•˜๊ฒŒ ์ ์šฉํ•œ ๋ฐ์ดํ„ฐ์…‹๊ณผ LoRA ํŠœ๋‹์„ ํ†ตํ•ด `itsmenlp/unsloth_qwen_2.5_32B_bnb_4bit_finetuned`๋กœ ์ถ”๋ก ํ•œ output์˜ accuracy Top 5๋กœ weighted voting ensemble์„ ์ง„ํ–‰ํ•œ ๊ฒฐ๊ณผ, ์ตœ์ข… Public Accuracy **0.8341**์„ ๋‹ฌ์„ฑํ–ˆ์Šต๋‹ˆ๋‹ค. + +
+ +| Output | Accuracy | Weight | +|:--------:| --- | --- | +| Top 5 | 0.8180 | 0.1 | +| Top 4 | 0.8180 | 0.1 | +| Top 3 | 0.8203 | 0.2 | +| Top 2 | 0.8272 | 0.2 | +| Top 1 | 0.8295 | 0.4 | + +
+ +
+ +### ๐Ÿ“ƒ KSAT Results + +๋ณธ ํ”„๋กœ์ ํŠธ์—์„œ ๊ฐœ๋ฐœํ•œ sLLM์„ ํ™œ์šฉํ•œ 2025ํ•™๋…„๋„ ์ˆ˜๋Šฅ์˜ ๊ตญ์–ด(ํ™”๋ฒ•๊ณผ ์ž‘๋ฌธ), ํ•œ๊ตญ์‚ฌ, ์‚ฌํšŒ ํƒ๊ตฌ ์˜์—ญ ํ’€์ด ๊ฒฐ๊ณผ์ž…๋‹ˆ๋‹ค. + +![image](https://github.com/user-attachments/assets/ca280ffb-8598-4112-81f6-8f5fd04fb4dd) + +

+ +## ๐ŸŽฅ 2025ํ•™๋…„๋„ ์ˆ˜๋Šฅ ๋ฌธ์ œ ํ’€์ด ๋ฐ๋ชจ ์˜์ƒ + +https://github.com/user-attachments/assets/4448f058-6571-4037-9fb9-dfd8f86d5291 + +

+ +## ๐Ÿ“ ํ”„๋กœ์ ํŠธ ๊ตฌ์กฐ + +ํ”„๋กœ์ ํŠธ ํด๋” ๊ตฌ์กฐ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™์Šต๋‹ˆ๋‹ค. + +``` +level2-nlp-generationfornlp-nlp-05-lv3/ +โ”œโ”€โ”€ checkpoints/ # ๋ชจ๋ธ ์ฒดํฌํฌ์ธํŠธ ์ €์žฅ ํด๋” +โ”‚ โ””โ”€โ”€ (experiment_name)/ # ์‹คํ—˜ ์ด๋ฆ„ +โ”‚ โ”œโ”€โ”€ checkpoint-1111 # ๋ชจ๋ธ ์ฒดํฌํฌ์ธํŠธ +โ”‚ โ””โ”€โ”€ checkpoint-2222 +โ”œโ”€โ”€ config/ +โ”‚ โ””โ”€โ”€ config.yaml # ์„ค์ • ๊ด€๋ฆฌ ํŒŒ์ผ +โ”œโ”€โ”€ notebooks/ +โ”‚ โ”œโ”€โ”€ eda.ipynb # EDA +โ”‚ โ”œโ”€โ”€ demo_data_preprocessing.ipynb # ๋ฐ๋ชจ ๋ฐ์ดํ„ฐ ์ „์ฒ˜๋ฆฌ ์ฝ”๋“œ +โ”‚ โ””โ”€โ”€ ft_data_processing.ipynb # Fine-Tuning ๋ฐ์ดํ„ฐ ์ „์ฒ˜๋ฆฌ ์ฝ”๋“œ +โ”œโ”€โ”€ prompt/ +โ”‚ โ”œโ”€โ”€ prompt_templates.yaml # ํ”„๋กฌํ”„ํŠธ ํ…œํ”Œ๋ฆฟ ๊ด€๋ฆฌ ํŒŒ์ผ +โ”œโ”€โ”€ src/ +โ”‚ โ”œโ”€โ”€ dataset.py # ๋ฐ์ดํ„ฐ ๋กœ๋“œ ๋ฐ ์ „์ฒ˜๋ฆฌ ๊ด€๋ จ ์ฝ”๋“œ +โ”‚ โ”œโ”€โ”€ ensemble.py # ์•™์ƒ๋ธ” ๊ธฐ๋ฒ• ๊ตฌํ˜„ ์ฝ”๋“œ +โ”‚ โ”œโ”€โ”€ model.py # ๋ชจ๋ธ ์ •์˜ ๋ฐ ํ•™์Šต ๊ด€๋ จ ์ฝ”๋“œ +โ”‚ โ”œโ”€โ”€ preprocessing.py # ๋ฒกํ„ฐ ์Šคํ† ์–ด ๊ตฌ์ถ•์šฉ ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘ ๋ฐ ์ „์ฒ˜๋ฆฌ ์ฝ”๋“œ +โ”‚ โ”œโ”€โ”€ retrieval_dense.py # Dense Retrieval ๊ตฌํ˜„ ์ฝ”๋“œ +โ”‚ โ”œโ”€โ”€ retrieval_sparse.py # Sparse Retrieval ๊ตฌํ˜„ ์ฝ”๋“œ +โ”‚ โ””โ”€โ”€ utils.py # ๋ณด์กฐ ํ•จ์ˆ˜ ๋ฐ ์œ ํ‹ธ๋ฆฌํ‹ฐ ์ฝ”๋“œ +โ”œโ”€โ”€ streamlit/ # Streamlit ๊ด€๋ จ ์›น ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜ ๋””๋ ‰ํ† ๋ฆฌ +โ”œโ”€โ”€ main.py # ํ”„๋กœ์ ํŠธ ์‹คํ–‰์˜ ๋ฉ”์ธ ์Šคํฌ๋ฆฝํŠธ +โ”œโ”€โ”€ .gitignore +โ”œโ”€โ”€ README.md +โ””โ”€โ”€ requirements.txt + +``` + +
+ +### ๐Ÿ’พ ํ”„๋กœ์ ํŠธ ์„ค์น˜ ๋ฐ ์‹คํ–‰ + +- OS: Ubuntu-20.04.6 LTS +- Python: 3.11 ์ด์ƒ +- ํ•„์ˆ˜ ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ: `requirements.txt` ์ฐธ๊ณ  +- **GPU**: NVIDIA V100 32GB + +```bash +git clone https://github.com/boostcampaitech7/level2-nlp-generationfornlp-nlp-05-lv3.git +pip install -r requirements.txt + +python src/retrieval_dense.py # ํ˜น์€ python src/retrieval_sparse.py +python main.py --config {config_path} --mode {train/test} +``` +**config.yaml** +[github](https://github.com/boostcampaitech7/level2-nlp-generationfornlp-nlp-05-lv3/blob/main/config/config.yaml)์—์„œ ํ™•์ธ + +
+ +### ๐Ÿ’พ Demo ์‹คํ–‰ + +```bash +cd streamlit +streamlit run home.py +``` diff --git a/assets/KSAT.jpg b/assets/KSAT.jpg new file mode 100644 index 0000000..dccdc17 Binary files /dev/null and b/assets/KSAT.jpg differ diff --git "a/assets/NLP\341\204\200\341\205\265\341\204\216\341\205\251\341\204\203\341\205\242\341\204\222\341\205\254_NLP_\341\204\220\341\205\265\341\206\267 \341\204\205\341\205\265\341\204\221\341\205\251\341\204\220\341\205\263(05\341\204\214\341\205\251).pdf" "b/assets/NLP\341\204\200\341\205\265\341\204\216\341\205\251\341\204\203\341\205\242\341\204\222\341\205\254_NLP_\341\204\220\341\205\265\341\206\267 \341\204\205\341\205\265\341\204\221\341\205\251\341\204\220\341\205\263(05\341\204\214\341\205\251).pdf" new file mode 100644 index 0000000..f693181 Binary files /dev/null and "b/assets/NLP\341\204\200\341\205\265\341\204\216\341\205\251\341\204\203\341\205\242\341\204\222\341\205\254_NLP_\341\204\220\341\205\265\341\206\267 \341\204\205\341\205\265\341\204\221\341\205\251\341\204\220\341\205\263(05\341\204\214\341\205\251).pdf" differ diff --git a/assets/Private Leader Board.png b/assets/Private Leader Board.png new file mode 100644 index 0000000..cfcca10 Binary files /dev/null and b/assets/Private Leader Board.png differ diff --git a/assets/Public Leader Board.png b/assets/Public Leader Board.png new file mode 100644 index 0000000..babb45a Binary files /dev/null and b/assets/Public Leader Board.png differ diff --git "a/assets/[5\341\204\214\341\205\251]Lv2_\341\204\211\341\205\256\341\204\202\341\205\263\341\206\274\341\204\206\341\205\256\341\206\253\341\204\214\341\205\246\341\204\221\341\205\256\341\206\257\341\204\213\341\205\265_\341\204\221\341\205\263\341\204\205\341\205\251\341\204\214\341\205\246\341\206\250\341\204\220\341\205\263_\341\204\207\341\205\241\341\206\257\341\204\221\341\205\255\341\204\214\341\205\241\341\204\205\341\205\255.pdf" "b/assets/[5\341\204\214\341\205\251]Lv2_\341\204\211\341\205\256\341\204\202\341\205\263\341\206\274\341\204\206\341\205\256\341\206\253\341\204\214\341\205\246\341\204\221\341\205\256\341\206\257\341\204\213\341\205\265_\341\204\221\341\205\263\341\204\205\341\205\251\341\204\214\341\205\246\341\206\250\341\204\220\341\205\263_\341\204\207\341\205\241\341\206\257\341\204\221\341\205\255\341\204\214\341\205\241\341\204\205\341\205\255.pdf" new file mode 100644 index 0000000..7f4528d Binary files /dev/null and "b/assets/[5\341\204\214\341\205\251]Lv2_\341\204\211\341\205\256\341\204\202\341\205\263\341\206\274\341\204\206\341\205\256\341\206\253\341\204\214\341\205\246\341\204\221\341\205\256\341\206\257\341\204\213\341\205\265_\341\204\221\341\205\263\341\204\205\341\205\251\341\204\214\341\205\246\341\206\250\341\204\220\341\205\263_\341\204\207\341\205\241\341\206\257\341\204\221\341\205\255\341\204\214\341\205\241\341\204\205\341\205\255.pdf" differ diff --git a/assets/demo_ksat.mp4 b/assets/demo_ksat.mp4 new file mode 100644 index 0000000..5f4031d Binary files /dev/null and b/assets/demo_ksat.mp4 differ diff --git a/assets/timeline.png b/assets/timeline.png new file mode 100644 index 0000000..8bc5c57 Binary files /dev/null and b/assets/timeline.png differ diff --git a/checkpoints/.gitkeep b/checkpoints/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/config/config.yaml b/config/config.yaml new file mode 100644 index 0000000..26c20b8 --- /dev/null +++ b/config/config.yaml @@ -0,0 +1,61 @@ +model: + experiment_name: &experiment_name "Experiment_Sample" # Define common experiment name + train: + train_model_name: "unsloth/Qwen2.5-32B-Instruct-bnb-4bit" # Model name for training + train_csv_path: "data/rag_results/train_rag_rerank3_v2_list.csv" # Path to train CSV file + train_checkpoint_path: "checkpoints/{experiment_name}" # Path to save training checkpoints + test: + test_checkpoint_path: "checkpoints/{experiment_name}/checkpoint-298" # Path to inference checkpoint + test_csv_path: "data/rag_results/test_rag_rerank3_v2_list.csv" # Path to test CSV file + test_output_csv_path: "data/outputs/{experiment_name}.csv" # Path for leaderboard submission CSV file + + max_seq_length: 4096 # Maximum sequence length for the model + prompt_name: "BASE_PROMPT" # Name of the prompt template in the prompt file + rag: True # Enable retrieval-augmented generation + uniform_answer_distribution: True # Ensure uniform answer distribution + train_valid_split: True # If True, split train and validation datasets (0.9/0.1) + +seed: 3407 # Seed for reproducibility + +FastLanguageModel: + # model_name -> Set to 'train_model_name' + # max_seq_length -> Set to 'max_seq_length' + # dtype -> Hardcoded to None + # load_in_4bit -> Hardcoded to True + +peft: + # model_name -> Set to 'train_model_name' + r: 64 + lora_alpha: 32 + lora_dropout: 0 + target_modules: ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj",] + bias: "none" + use_gradient_checkpointing: "unsloth" + # random_state -> Set to 'seed' + use_rslora: True + # loftq_config -> Hardcoded to None + +UnslothTrainingArguments: + # do_train -> Hardcoded to True + # do_eval -> Automatically set based on 'train_valid_split' + per_device_train_batch_size: 2 + per_device_eval_batch_size: 2 + gradient_accumulation_steps: 8 + warmup_ratio: 0.1 + num_train_epochs: 2 + learning_rate: 5e-5 + embedding_learning_rate: 1e-6 + # fp16 -> Hardcoded to not is_bfloat16_supported() + # bf16 -> Hardcoded to is_bfloat16_supported() + # logging_steps -> Hardcoded to 1 + optim: "adamw_8bit" + weight_decay: 0.01 + lr_scheduler_type: "linear" + # seed -> Set to 'seed' + # max_seq_length -> Set to 'max_seq_length' + # output_dir -> Set to 'train_checkpoint_path' + save_strategy: "epoch" + # eval_strategy: "no" -> Automatically set based on 'do_eval' + save_total_limit: 2 + save_only_model: True + # report_to -> Hardcoded to 'wandb' \ No newline at end of file diff --git a/main.py b/main.py new file mode 100644 index 0000000..81fecd6 --- /dev/null +++ b/main.py @@ -0,0 +1,58 @@ +import os +import yaml +import argparse +import shutil +import pandas as pd + +from src.model import MyModel +from src.dataset import MyDataset +from src.utils import set_seed, reset_token, update_paths + + +if __name__ == "__main__": + # Parse command-line arguments + parser = argparse.ArgumentParser() + parser.add_argument("--config", "-c", type=str, default="config/config.yaml") + parser.add_argument("--mode", "-m", type=str, default="train") + args = parser.parse_args() + + # Load YAML configuration file + with open(args.config) as f: + config = yaml.full_load(f) + + # Update paths based on the experiment name + config = update_paths(config) + + # Set random seed for reproducibility + set_seed(config["seed"]) + + # Initialize dataset and model + dataset = MyDataset(config["model"]) + model = MyModel(config, args.mode) + + base_path = "../contest_baseline_code" + + if args.mode == "train": + # Training mode + checkpoint_dir = config["model"]["train"]["train_checkpoint_path"] + os.makedirs(checkpoint_dir, exist_ok=True) + + # Save configuration file in the checkpoint directory + shutil.copy(args.config, os.path.join(checkpoint_dir, "config.yaml")) + + # Process training data and train the model + train_df = pd.read_csv(os.path.join(base_path, config["model"]["train"]["train_csv_path"])) + processed_train = dataset.process(train_df, "train") + model.train(processed_train) + + # Reset tokenizer token configurations + reset_token(config["model"]["experiment_name"]) + + elif args.mode == "test": + # Testing mode + test_df = pd.read_csv(os.path.join(base_path, config["model"]["test"]["test_csv_path"])) + processed_test = dataset.process(test_df, "test") + + # Run inference and save results + model.inference(processed_test, output_dir=os.path.join(base_path, config["model"]["test"]["test_output_csv_path"]), + ) diff --git a/notebooks/demo_data_processing.ipynb b/notebooks/demo_data_processing.ipynb new file mode 100644 index 0000000..1d6ffc8 --- /dev/null +++ b/notebooks/demo_data_processing.ipynb @@ -0,0 +1,154 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### test ํ˜•์‹ ์ „ํ™˜ ์ฝ”๋“œ###\n", + "\n", + "import pandas as pd\n", + "\n", + "# ๋ฐ์ดํ„ฐ ๋กœ๋“œ\n", + "data = pd.read_csv(\"2025ํ•™๋…„๋„ ์ˆ˜๋Šฅ - ๊ตญ์–ด.csv\")\n", + "\n", + "# DataFrame์œผ๋กœ ๋ณ€ํ™˜\n", + "df = pd.DataFrame(data)\n", + "\n", + "# ๋ณ€ํ™˜ ๋ฐ์ดํ„ฐ๋ฅผ ๋‹ด์„ ๋ฆฌ์ŠคํŠธ\n", + "transformed_data = []\n", + "\n", + "# DataFrame์˜ ๊ฐ ํ–‰์„ ์ˆœํšŒํ•˜๋ฉฐ ๋ฐ์ดํ„ฐ ๋ณ€ํ™˜ ์ˆ˜ํ–‰\n", + "for idx, row in df.iterrows():\n", + "\n", + " # ๋ณด๊ธฐ ๋ฐ์ดํ„ฐ๋ฅผ ๋ฆฌ์ŠคํŠธ๋กœ ๊ตฌ์„ฑ\n", + " choices = [row[\"choice1\"], row[\"choice2\"], row[\"choice3\"], row[\"choice4\"], row[\"choice5\"]]\n", + " \n", + " # ๋ฌธ์ œ ๋ฐ์ดํ„ฐ ๊ตฌ์„ฑ\n", + " if \"question_plus\" in row and pd.notna(row[\"question_plus\"]):\n", + " problem = {\n", + " \"question\": row[\"question\"],\n", + " \"choices\": choices,\n", + " \"answer\": '',\n", + " \"question_plus\": row[\"question_plus\"]\n", + " }\n", + " else:\n", + " problem = {\n", + " \"question\": row[\"question\"],\n", + " \"choices\": choices,\n", + " \"answer\": ''\n", + " }\n", + " \n", + " # ์ตœ์ข… ๋ฐ์ดํ„ฐ ๊ตฌ์„ฑ\n", + " transformed_data.append({\n", + " \"id\": row[\"id\"],\n", + " \"paragraph\": row[\"paragraph\"],\n", + " \"problems\": problem,\n", + " \"question_plus\": row[\"question_plus\"]\n", + " })\n", + "\n", + "# ๋ณ€ํ™˜๋œ ๋ฐ์ดํ„ฐํ”„๋ ˆ์ž„ ์ƒ์„ฑ\n", + "transformed_df = pd.DataFrame(transformed_data)\n", + "\n", + "# ํŒŒ์ผ ์ €์žฅ\n", + "output_file = \"2025ksat_korean.csv\"\n", + "transformed_df.to_csv(output_file, index=False)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### output ํ˜•์‹ ์ „ํ™˜ ์ฝ”๋“œ ###\n", + "\n", + "import pandas as pd\n", + "\n", + "# ๋ฐ์ดํ„ฐ ๋กœ๋“œ\n", + "data = pd.read_csv('2025ํ•™๋…„๋„ ์ˆ˜๋Šฅ - ๊ตญ์–ด.csv')\n", + "\n", + "# DataFrame์œผ๋กœ ๋ณ€ํ™˜\n", + "df = pd.DataFrame(data)\n", + "\n", + "# ๋ณ€ํ™˜ ๋ฐ์ดํ„ฐ๋ฅผ ๋‹ด์„ ๋ฆฌ์ŠคํŠธ\n", + "transformed_data = []\n", + "\n", + "# DataFrame์˜ ๊ฐ ํ–‰์„ ์ˆœํšŒํ•˜๋ฉฐ ๋ฐ์ดํ„ฐ ๋ณ€ํ™˜ ์ˆ˜ํ–‰\n", + "for idx, row in df.iterrows():\n", + "\n", + " # ์ตœ์ข… ๋ฐ์ดํ„ฐ ๊ตฌ์„ฑ\n", + " transformed_data.append({\n", + " \"id\": row[\"id\"],\n", + " \"answer\": row[\"answer\"]\n", + " })\n", + "\n", + "# ๋ณ€ํ™˜๋œ ๋ฐ์ดํ„ฐํ”„๋ ˆ์ž„ ์ƒ์„ฑ\n", + "transformed_df = pd.DataFrame(transformed_data)\n", + "\n", + "# ํŒŒ์ผ ์ €์žฅ\n", + "output_file = \"2025ksat_korean_answer.csv\"\n", + "transformed_df.to_csv(output_file, index=False)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### ์ „์ฒด ๋ฐ์ดํ„ฐ ๋ณ‘ํ•ฉ ์ฝ”๋“œ ###\n", + "\n", + "import pandas as pd\n", + "\n", + "# CSV ํŒŒ์ผ ๊ฒฝ๋กœ ์ง€์ •\n", + "file1 = '2025ํ•™๋…„๋„ ์ˆ˜๋Šฅ - ๊ตญ์–ด (1).csv'\n", + "file2 = '2025ํ•™๋…„๋„ ์ˆ˜๋Šฅ - ๋™์•„์‹œ์•„์‚ฌ (1).csv'\n", + "file3 = '2025ํ•™๋…„๋„ ์ˆ˜๋Šฅ - ์‚ฌํšŒ๋ฌธํ™” (1).csv'\n", + "file4 = '2025ํ•™๋…„๋„ ์ˆ˜๋Šฅ - ์ƒํ™œ๊ณผ ์œค๋ฆฌ.csv'\n", + "file5 = '2025ํ•™๋…„๋„ ์ˆ˜๋Šฅ - ์„ธ๊ณ„์‚ฌ (1).csv'\n", + "file6 = '2025ํ•™๋…„๋„ ์ˆ˜๋Šฅ - ์œค๋ฆฌ์™€ ์‚ฌ์ƒ (1).csv'\n", + "file7 = '2025ํ•™๋…„๋„ ์ˆ˜๋Šฅ - ์ •์น˜์™€๋ฒ• (1).csv'\n", + "file8 = '2025ํ•™๋…„๋„ ์ˆ˜๋Šฅ - ํ•œ๊ตญ์‚ฌ (1).csv'\n", + "\n", + "# ๋ฐ์ดํ„ฐํ”„๋ ˆ์ž„ ์ „ํ™˜\n", + "df1 = pd.read_csv(file1)\n", + "df2 = pd.read_csv(file2)\n", + "df3 = pd.read_csv(file3)\n", + "df4 = pd.read_csv(file4)\n", + "df5 = pd.read_csv(file5)\n", + "df6 = pd.read_csv(file6)\n", + "df7 = pd.read_csv(file7)\n", + "df8 = pd.read_csv(file8)\n", + "\n", + "# ๋ฐ์ดํ„ฐํ”„๋ ˆ์ž„์„ ํ•˜๋‚˜๋กœ ๋ณ‘ํ•ฉ\n", + "merged_df = pd.concat([df1, df2, df3, df4, df5, df6, df7, df8], ignore_index=True)\n", + "\n", + "# ํŒŒ์ผ ์ €์žฅ\n", + "merged_df.to_csv('train_ksat.csv', index=False)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/eda.ipynb b/notebooks/eda.ipynb new file mode 100644 index 0000000..34eb2b9 --- /dev/null +++ b/notebooks/eda.ipynb @@ -0,0 +1,2810 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idparagraphquestionchoicesansweranswer_lenํ•„์š” ์ง€์‹์„ธ๋ถ„๋ฅ˜(๋ชจ๋ธ)์„ธ๋ถ„๋ฅ˜(์‚ฌ๋žŒ)๋ณด๊ธฐ ์—ฌ๋ถ€๋ฐ์ดํ„ฐ์ €ํ’ˆ์งˆ-paragraph๋ฐ์ดํ„ฐ์ €ํ’ˆ์งˆ-question๋ฐ์ดํ„ฐ์ €ํ’ˆ์งˆ-choices๋ฐ์ดํ„ฐ์ €ํ’ˆ์งˆ-answer๋น„๊ณ 
0generation-for-nlp-425์ƒ์†Œํ•˜์—ฌ ์•„๋ขฐ๊ธฐ๋ฅผ , โ€œ์‹ ์ด ์ขŒ์ฐธ ์ฐฌ ์†ก์ค€๊ธธ์ด ์˜ฌ๋ฆฐ ์ฐจ์ž๋ฅผ ๋ณด์•˜๋Š”๋ฐ , ์ƒ๋ณต(ๅ–ชๆœ)...์ƒ์†Œํ•œ ์ธ๋ฌผ์ด ์†ํ•œ ๋ถ•๋‹น์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ๋งŒ์„ ๋ชจ๋‘ ๊ณ ๋ฅด๋ฉด?['ใ„ฑ, ใ„ด', 'ใ„ฑ, ใ„ท', 'ใ„ด, ใ„น', 'ใ„ท, ใ„น']24์™ธ๋ถ€์ง€์‹์ •์น˜์™€ ๋ฒ•ํ•œ๊ตญ์‚ฌTrue๋„์–ด์“ฐ๊ธฐ ์˜ค๋ฅ˜์ •์ƒ์ •์ƒ์ •์ƒNaN
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" + ], + "text/plain": [ + " id paragraph \\\n", + "0 generation-for-nlp-425 ์ƒ์†Œํ•˜์—ฌ ์•„๋ขฐ๊ธฐ๋ฅผ , โ€œ์‹ ์ด ์ขŒ์ฐธ ์ฐฌ ์†ก์ค€๊ธธ์ด ์˜ฌ๋ฆฐ ์ฐจ์ž๋ฅผ ๋ณด์•˜๋Š”๋ฐ , ์ƒ๋ณต(ๅ–ชๆœ)... \n", + "\n", + " question choices \\\n", + "0 ์ƒ์†Œํ•œ ์ธ๋ฌผ์ด ์†ํ•œ ๋ถ•๋‹น์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ๋งŒ์„ ๋ชจ๋‘ ๊ณ ๋ฅด๋ฉด? ['ใ„ฑ, ใ„ด', 'ใ„ฑ, ใ„ท', 'ใ„ด, ใ„น', 'ใ„ท, ใ„น'] \n", + "\n", + " answer answer_len ํ•„์š” ์ง€์‹ ์„ธ๋ถ„๋ฅ˜(๋ชจ๋ธ) ์„ธ๋ถ„๋ฅ˜(์‚ฌ๋žŒ) ๋ณด๊ธฐ ์—ฌ๋ถ€ ๋ฐ์ดํ„ฐ์ €ํ’ˆ์งˆ-paragraph \\\n", + "0 2 4 ์™ธ๋ถ€์ง€์‹ ์ •์น˜์™€ ๋ฒ• ํ•œ๊ตญ์‚ฌ True ๋„์–ด์“ฐ๊ธฐ ์˜ค๋ฅ˜ \n", + "\n", + " ๋ฐ์ดํ„ฐ์ €ํ’ˆ์งˆ-question ๋ฐ์ดํ„ฐ์ €ํ’ˆ์งˆ-choices ๋ฐ์ดํ„ฐ์ €ํ’ˆ์งˆ-answer ๋น„๊ณ  \n", + "0 ์ •์ƒ ์ •์ƒ ์ •์ƒ NaN " + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "from ast import literal_eval\n", + "import matplotlib.pyplot as plt\n", + "\n", + "train = pd.read_csv('../../contest_baseline_code/data/EDA/241116_eda_train_labeled.csv')\n", + "test = pd.read_csv('../../contest_baseline_code/data/raw/test.csv')\n", + "\n", + "train.head(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded Font: NanumGothic\n" + ] + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import matplotlib.font_manager as fm\n", + "\n", + "# ๋‚˜๋ˆ”๊ณ ๋”• ํฐํŠธ ๊ฒฝ๋กœ ์„ค์ •\n", + "font_path = '/opt/conda/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/NanumGothic.ttf'\n", + "font_prop = fm.FontProperties(fname=font_path)\n", + "plt.rcParams['font.family'] = font_prop.get_name()\n", + "\n", + "# ํ…Œ์ŠคํŠธ์šฉ ์ฝ”๋“œ\n", + "print(f\"Loaded Font: {font_prop.get_name()}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train ๋ฐ์ดํ„ฐ ์ˆ˜: (2031, 15)\n", + "test ๋ฐ์ดํ„ฐ ์ˆ˜: (869, 4)\n" + ] + } + ], + "source": [ + "# ๋ฐ์ดํ„ฐ ์ˆ˜ - train, test\n", + "''' AI Stages '''\n", + "print('train ๋ฐ์ดํ„ฐ ์ˆ˜:', train.shape)\n", + "print('test ๋ฐ์ดํ„ฐ ์ˆ˜:', test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "# train์—์„œ kmmlu, mmmlu, mrc ๋ฐ์ดํ„ฐ ์ˆ˜, ๋น„์œจ\n", + "'''\n", + "(from Confluence)\n", + "์„ธ๋ถ„๋ฅ˜ ์ข…๋ฅ˜\n", + "1. KMMLU: ํ•œ๊ตญ์‚ฌ - 1๊ฐœ\n", + "2. MMMLU: ์—ญ์‚ฌ(์„ธ๊ณ„), ์—ญ์‚ฌ(๋ฏธ๊ตญ), ์—ญ์‚ฌ(์œ ๋Ÿฝ), ๊ฒฝ์ œ(๋ฏธ์‹œ), ๊ฒฝ์ œ(๊ฑฐ์‹œ), ์ •์น˜, ์ง€๋ฆฌ, ์‹ฌ๋ฆฌ, (+ํ†ต๊ณ„) - 8๊ฐœ\n", + "3. KLUE-MRC: ๊ฒฝ์ œ, ๊ต์œก์‚ฐ์—…, ๊ตญ์ œ, ๋ถ€๋™์‚ฐ, ์‚ฌํšŒ, ์ƒํ™œ, ์ฑ…๋งˆ์„ - 7๊ฐœ\n", + "'''\n", + "dataset_dict = {\n", + " 'ํ•œ๊ตญ์‚ฌ': 'KMMLU',\n", + " '์—ญ์‚ฌ(์„ธ๊ณ„)': 'MMMLU',\n", + " '์—ญ์‚ฌ(๋ฏธ๊ตญ)': 'MMMLU',\n", + " '์—ญ์‚ฌ(์œ ๋Ÿฝ)': 'MMMLU',\n", + " '๊ฒฝ์ œ(๋ฏธ์‹œ)': 'MMMLU',\n", + " '๊ฒฝ์ œ(๊ฑฐ์‹œ)': 'MMMLU',\n", + " '์ •์น˜': 'MMMLU',\n", + " '์ง€๋ฆฌ': 'MMMLU',\n", + " '์‹ฌ๋ฆฌ': 'MMMLU',\n", + " 'ํ†ต๊ณ„': 'MMMLU',\n", + " '๊ฒฝ์ œ': 'KLUE-MRC',\n", + " '๊ต์œก์‚ฐ์—…': 'KLUE-MRC',\n", + " '๊ตญ์ œ': 'KLUE-MRC',\n", + " '๋ถ€๋™์‚ฐ': 'KLUE-MRC',\n", + " '์‚ฌํšŒ': 'KLUE-MRC',\n", + " '์ƒํ™œ': 'KLUE-MRC',\n", + " '์ฑ…๋งˆ์„': 'KLUE-MRC',\n", + "}\n", + "\n", + "train['dataset'] = train['์„ธ๋ถ„๋ฅ˜(์‚ฌ๋žŒ)'].map(dataset_dict)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dataset\n", + "KLUE-MRC 1239\n", + "MMMLU 719\n", + "KMMLU 73\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train['dataset'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_3515290/2975742761.py:23: FutureWarning: \n", + "\n", + "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", + "\n", + " sns.barplot(\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# ๊ณตํ†ต ํ•จ์ˆ˜ ์ •์˜: ๋ง‰๋Œ€ ์œ„ ๊ฐ’ ํ‘œ์‹œ\n", + "def add_values_to_bars(ax, values):\n", + " ymax = ax.get_ylim()[1] # y์ถ• ์ตœ๋Œ€๊ฐ’ ๊ฐ€์ ธ์˜ค๊ธฐ\n", + " for index, value in enumerate(values):\n", + " ax.text(index, value + ymax * 0.02, str(value), ha='center', fontsize=12) # y์ถ•์˜ 2% ์œ„์— ๊ฐ’ ํ‘œ์‹œ\n", + "\n", + "# ๋ฐ์ดํ„ฐ์…‹ ๋ถ„ํฌ ๊ณ„์‚ฐ\n", + "dataset_counts = train['dataset'].value_counts()\n", + "dataset_counts = dataset_counts.reindex(['KMMLU', 'MMMLU', 'KLUE-MRC'])\n", + "\n", + "colors = sns.color_palette(\"coolwarm\", 4)\n", + "custom_palette = {\n", + " 'KMMLU': colors[0], # ์ฒซ ๋ฒˆ์งธ ์ƒ‰์ƒ\n", + " 'MMMLU': colors[2], # ์ค‘๊ฐ„ ์ƒ‰์ƒ\n", + " 'KLUE-MRC': colors[3] # ๋งˆ์ง€๋ง‰ ์ƒ‰์ƒ\n", + "}\n", + "\n", + "# ๋ง‰๋Œ€ ๊ทธ๋ž˜ํ”„ ์‹œ๊ฐํ™”\n", + "plt.figure(figsize=(10, 6))\n", + "sns.barplot(\n", + " x=dataset_counts.index,\n", + " y=dataset_counts.values,\n", + " palette=custom_palette\n", + ")\n", + "\n", + "# ๊ทธ๋ž˜ํ”„ ์ œ๋ชฉ ๋ฐ ์ถ• ๋ผ๋ฒจ ์„ค์ •\n", + "plt.title('Dataset Distribution', fontsize=20, fontweight='bold')\n", + "plt.xlabel('Dataset', fontsize=14)\n", + "plt.ylabel('Count', fontsize=14)\n", + "\n", + "add_values_to_bars(plt.gca(), dataset_counts.values)\n", + "\n", + "# ๋ˆˆ๊ธˆ ๋ฐ ๋ ˆ์ด์•„์›ƒ ์กฐ์ •\n", + "plt.xticks(fontsize=12)\n", + "plt.yticks(fontsize=12)\n", + "plt.grid(True, linestyle='--', linewidth=0.5, alpha=0.7)\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "kmmlu = train[train['dataset'] == 'KMMLU']\n", + "mmmlu = train[train['dataset'] == 'MMMLU']\n", + "klue_mrc = train[train['dataset'] == 'KLUE-MRC']\n", + "\n", + "# ํ…Œ์ด๋ธ”์„ matplotlib์œผ๋กœ ์‹œ๊ฐํ™”\n", + "fig, ax = plt.subplots(figsize=(6, 3))\n", + "ax.axis('tight')\n", + "ax.axis('off')\n", + "\n", + "# ๋ฐ์ดํ„ฐ์…‹ ์ธ๋ฑ์Šค ๊ตฌ๊ฐ„ ํ…Œ์ด๋ธ” ๋ฐ์ดํ„ฐ\n", + "table_data = [\n", + " [\"KMMLU\", kmmlu.index[0], kmmlu.index[-1]],\n", + " [\"MMMLU\", kmmlu.index[-1]+1, mmmlu.index[-1]],\n", + " [\"KLUE-MRC\", mmmlu.index[-1]+1, klue_mrc.index[-1]]\n", + "]\n", + "\n", + "# ํ…Œ์ด๋ธ” ์ƒ์„ฑ ๋ฐ ์‹œ๊ฐํ™”\n", + "table = ax.table(cellText=table_data, colLabels=[\"Dataset\", \"Start Index\", \"End Index\"], loc='center', cellLoc='center')\n", + "\n", + "# ํ…Œ์ด๋ธ” ์Šคํƒ€์ผ ์กฐ์ •\n", + "table.auto_set_font_size(False)\n", + "table.set_fontsize(10)\n", + "table.auto_set_column_width(col=list(range(len(table_data[0]))))\n", + "\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Calculating Similarities: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 2061465/2061465 [03:50<00:00, 8958.03it/s] " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Exact Match Duplicates:\n", + "(282, 16)\n", + "\n", + "Similarity Results:\n", + "(375, 3)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# ํ•„์š”ํ•œ ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ ์„ค์น˜ ๋ฐ ์žฌ์ˆ˜ํ–‰\n", + "from rapidfuzz import fuzz\n", + "from itertools import combinations\n", + "from tqdm import tqdm\n", + "\n", + "# 1. Exact Match ํ™•์ธ\n", + "exact_duplicates = train[train.duplicated(subset=['paragraph'], keep=False)]\n", + "\n", + "# 2. Similarity ํ™•์ธ (RapidFuzz)\n", + "similarity_threshold = 90 # ์œ ์‚ฌ๋„๋ฅผ ํŒ๋‹จํ•  ๊ธฐ์ค€\n", + "similar_pairs = []\n", + "\n", + "# Wrap combinations with tqdm\n", + "combinations_with_progress = tqdm(\n", + " combinations(train['paragraph'].items(), 2), \n", + " desc=\"Calculating Similarities\", \n", + " total=(len(train['paragraph']) * (len(train['paragraph']) - 1)) // 2\n", + ")\n", + "\n", + "for (idx1, para1), (idx2, para2) in combinations_with_progress:\n", + " similarity = fuzz.ratio(para1, para2)\n", + " if similarity >= similarity_threshold:\n", + " similar_pairs.append((idx1, idx2, similarity))\n", + "\n", + "# ์œ ์‚ฌ์„ฑ ๊ฒฐ๊ณผ ๋ฐ์ดํ„ฐํ”„๋ ˆ์ž„ ์ƒ์„ฑ\n", + "paragraph_similarity_results = pd.DataFrame(similar_pairs, columns=['Index 1', 'Index 2', 'paragraph_similarity'])\n", + "\n", + "# Exact Match ์‹œ๊ฐํ™”\n", + "print(\"Exact Match Duplicates:\")\n", + "print(exact_duplicates.shape)\n", + "\n", + "# ์œ ์‚ฌ์„ฑ ๊ฒฐ๊ณผ ์‹œ๊ฐํ™”\n", + "print(\"\\nSimilarity Results:\")\n", + "print(paragraph_similarity_results.shape)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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375 rows ร— 3 columns

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" + ], + "text/plain": [ + " Index 1 Index 2 paragraph_similarity\n", + "0 1 10 100.000000\n", + "1 20 67 100.000000\n", + "2 27 48 99.719888\n", + "3 31 47 100.000000\n", + "4 32 60 100.000000\n", + ".. ... ... ...\n", + "370 758 767 100.000000\n", + "371 760 777 100.000000\n", + "372 776 787 99.858557\n", + "373 779 781 100.000000\n", + "374 783 786 100.000000\n", + "\n", + "[375 rows x 3 columns]" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "paragraph_similarity_results" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [], + "source": [ + "# # 1. Exact Match ํ™•์ธ\n", + "# exact_duplicates_question = train[train.duplicated(subset=['question'], keep=False)]\n", + "\n", + "# # 2. Similarity ํ™•์ธ (RapidFuzz)\n", + "# similarity_threshold = 90 # ์œ ์‚ฌ๋„๋ฅผ ํŒ๋‹จํ•  ๊ธฐ์ค€\n", + "# similar_pairs_question = []\n", + "\n", + "# # Wrap combinations with tqdm\n", + "# combinations_with_progress_question = tqdm(\n", + "# combinations(train['question'].items(), 2), \n", + "# desc=\"Calculating Similarities for Questions\", \n", + "# total=(len(train['question']) * (len(train['question']) - 1)) // 2\n", + "# )\n", + "\n", + "# for (idx1, ques1), (idx2, ques2) in combinations_with_progress_question:\n", + "# similarity = fuzz.ratio(ques1, ques2)\n", + "# if similarity >= similarity_threshold:\n", + "# similar_pairs_question.append((idx1, idx2, similarity))\n", + "\n", + "# # ์œ ์‚ฌ์„ฑ ๊ฒฐ๊ณผ ๋ฐ์ดํ„ฐํ”„๋ ˆ์ž„ ์ƒ์„ฑ\n", + "# similarity_results_question = pd.DataFrame(similar_pairs_question, columns=['Index 1', 'Index 2', 'Similarity'])\n", + "\n", + "# # Exact Match ์‹œ๊ฐํ™”\n", + "# print(\"Exact Match Duplicates (Questions):\")\n", + "# print(exact_duplicates_question.shape)\n", + "\n", + "# # ์œ ์‚ฌ์„ฑ ๊ฒฐ๊ณผ ์‹œ๊ฐํ™”\n", + "# print(\"\\nSimilarity Results (Questions):\")\n", + "# print(similarity_results_question.shape)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Paragraph-Question Similarity Results:\n", + "(9, 8)\n" + ] + } + ], + "source": [ + "from rapidfuzz import fuzz\n", + "\n", + "# ์œ ์‚ฌํ•œ paragraph์— ํ•ด๋‹นํ•˜๋Š” question ์œ ์‚ฌ๋„ ๋ฐ ๋™์ผ ์—ฌ๋ถ€ ํ™•์ธ\n", + "paragraph_question_similarity_check = []\n", + "for _, row in paragraph_similarity_results.iterrows():\n", + " idx1, idx2 = row['Index 1'], row['Index 2']\n", + " question1 = train.loc[idx1, 'question']\n", + " question2 = train.loc[idx2, 'question']\n", + " \n", + " # question์˜ ์œ ์‚ฌ๋„ ๊ณ„์‚ฐ\n", + " similarity = fuzz.ratio(question1, question2)\n", + "\n", + " if similarity >= 90:\n", + " paragraph_question_similarity_check.append({\n", + " 'Index 1': int(row['Index 1']),\n", + " 'Index 2': int(row['Index 2']),\n", + " 'Paragraph 1': train.loc[idx1, 'paragraph'],\n", + " 'Paragraph 2': train.loc[idx2, 'paragraph'],\n", + " 'paragraph_similarity': row['paragraph_similarity'],\n", + " 'Question 1': question1,\n", + " 'Question 2': question2,\n", + " 'question_similarity': similarity,\n", + " })\n", + "\n", + "# ๊ฒฐ๊ณผ๋ฅผ ๋ฐ์ดํ„ฐํ”„๋ ˆ์ž„์œผ๋กœ ์ •๋ฆฌ\n", + "paragraph_question_similarity_results = pd.DataFrame(paragraph_question_similarity_check)\n", + "\n", + "# ๊ฒฐ๊ณผ ์ถœ๋ ฅ\n", + "print(\"Paragraph-Question Similarity Results:\")\n", + "print(paragraph_question_similarity_results.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Index 1Index 2Paragraph 1Paragraph 2paragraph_similarityQuestion 1Question 2question_similarity
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12067์ด์ œ ์‚ดํŽด๋ณด๊ฑด๋Œ€, ์‹ ๋ผ๊ฐ€ ์ฃผโ€ค๊ตฐ์„ ์„ค์น˜ํ•  ๋•Œ ๊ทธ ์ „์ •(็”ฐไธ), ํ˜ธ๊ตฌ(ๆˆถๅฃ)๊ฐ€ ํ˜„์˜ ...์ด์ œ ์‚ดํŽด๋ณด๊ฑด๋Œ€, ์‹ ๋ผ๊ฐ€ ์ฃผโ€ค๊ตฐ์„ ์„ค์น˜ํ•  ๋•Œ ๊ทธ ์ „์ •(็”ฐไธ), ํ˜ธ๊ตฌ(ๆˆถๅฃ)๊ฐ€ ํ˜„์˜ ...100.000000ใ‰ , ใ‰ก์˜ ๊ฑฐ์ฃผ๋ฏผ์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?ใ‰ , ใ‰ก์˜ ๊ฑฐ์ฃผ๋ฏผ์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?100.0
22748ํƒ‘๊ณจ๊ณต์›์— ๋ชจ์ธ ์ˆ˜๋งŽ์€ ํ•™์ƒ๊ณผ ์‹œ๋ฏผ์ด ๋…๋ฆฝ์„ ์–ธ์‹์„ ๊ฑฐํ–‰ํ•˜๊ณ  ๋งŒ์„ธ๋ฅผ ๋ถ€๋ฅด๋ฉฐ ๊ฑฐ๋ฆฌ๋ฅผ ํ–‰...ํƒ‘๊ณจ๊ณต์›์— ๋ชจ์ธ ์ˆ˜๋งŽ์€ ํ•™์ƒ๊ณผ ์‹œ๋ฏผ์ด ๋…๋ฆฝ์„ ์–ธ์‹์„ ๊ฑฐํ–‰ํ•˜๊ณ  ๋งŒ์„ธ๋ฅผ ๋ถ€๋ฅด๋ฉฐ ๊ฑฐ๋ฆฌ๋ฅผ ํ–‰...99.719888(๊ฐ€) ๋‹จ์ฒด์˜ ํ™œ๋™์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?(๊ฐ€) ๋‹จ์ฒด์˜ ํ™œ๋™์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?100.0
33147(๊ฐ€)์‹ ๋ผ์˜ ์šฐ์‚ฐ๊ตญ ๋ณต์† (๋‚˜)๊ณ ๊ตฌ๋ ค์˜ ์„œ ์•ˆํ‰ ์ ๋ น(๋‹ค) ๋ฐฑ์ œ์˜ ๋Œ€์•ผ์„ฑ ์ ๋ น (๋ผ...(๊ฐ€)์‹ ๋ผ์˜ ์šฐ์‚ฐ๊ตญ ๋ณต์† (๋‚˜)๊ณ ๊ตฌ๋ ค์˜ ์„œ ์•ˆํ‰ ์ ๋ น(๋‹ค) ๋ฐฑ์ œ์˜ ๋Œ€์•ผ์„ฑ ์ ๋ น (๋ผ...100.000000๋‹ค์Œ ์‚ฌ๊ฑด์„ ์‹œ๊ธฐ ์ˆœ์œผ๋กœ ๋ฐ”๋ฅด๊ฒŒ ๋‚˜์—ดํ•œ ๊ฒƒ์€?๋‹ค์Œ ์‚ฌ๊ฑด์„ ์‹œ๊ธฐ ์ˆœ์œผ๋กœ ๋ฐ”๋ฅด๊ฒŒ ๋‚˜์—ดํ•œ ๊ฒƒ์€?100.0
43260(๊ฐ€)๋†๋ฏผ๊ตฐ์ด ์ •๋ถ€์™€ ์ „์ฃผํ™”์•ฝ์„ ๋งบ์—ˆ๋‹ค. (๋‚˜) ๋†๋ฏผ๊ตฐ์ด ์šฐ๊ธˆ์น˜์—์„œ ์ „ํˆฌ๋ฅผ ๋ฒŒ์˜€๋‹ค....(๊ฐ€)๋†๋ฏผ๊ตฐ์ด ์ •๋ถ€์™€ ์ „์ฃผํ™”์•ฝ์„ ๋งบ์—ˆ๋‹ค. (๋‚˜) ๋†๋ฏผ๊ตฐ์ด ์šฐ๊ธˆ์น˜์—์„œ ์ „ํˆฌ๋ฅผ ๋ฒŒ์˜€๋‹ค....100.000000์ด๋ฅผ ์‹œ๊ธฐ ์ˆœ์œผ๋กœ ๋ฐ”๋ฅด๊ฒŒ ๋‚˜์—ดํ•œ ๊ฒƒ์€?์ด๋ฅผ ์‹œ๊ธฐ ์ˆœ์œผ๋กœ ๋ฐ”๋ฅด๊ฒŒ ๋‚˜์—ดํ•œ ๊ฒƒ์€?100.0
5141179\"๋ฒ„ํ‚น์—„ ๊ถ์ „, 1839๋…„ 5์›” 10์ผ. ์—ฌ์™•์€ ์ง€๋‚œ ๋‚˜ํ˜ ๋™์•ˆ ์ž์‹ ์ด ํ•ด์•ผ ํ•œ ๋งŽ...๋ฒ„ํ‚น์—„ ๊ถ์ „, 1839๋…„ 5์›” 10์ผ. ์—ฌ์™•์€ ์ง€๋‚œ ๋‚˜ํ˜ ๋™์•ˆ ์ž์‹ ์ด ํ•ด์•ผ ํ•œ ๋งŽ์€...94.025974์ด ์ง€๋ฌธ์— ๋‚˜ํƒ€๋‚œ ์ •๋ณด๋Š” ๋ฌด์—‡์ž…๋‹ˆ๊นŒ?์ด ์ง€๋ฌธ์— ๋‚˜ํƒ€๋‚œ ์ •๋ณด๋Š” ๋ฌด์—‡์ž…๋‹ˆ๊นŒ?100.0
6323384์‹œ๋‚˜๋ฆฌ์˜ค: ํ›„๋ฐ˜๊ธฐ ์ž์‹ ์˜ ๊ณ ์ „์  ์กฐ๊ฑดํ™” ์‹คํ—˜์—์„œ ํŒŒ๋ธ”๋กœํ”„(Ivan Pavlov)์˜ ...์‹œ๋‚˜๋ฆฌ์˜ค: ํ›„๋ฐ˜๊ธฐ ์ž์‹ ์˜ ๊ณ ์ „์  ์กฐ๊ฑดํ™” ์‹คํ—˜์—์„œ ํŒŒ๋ธ”๋กœํ”„(Ivan Pavlov)์˜ ...100.000000๋‹ค์Œ ์‹œ๋‚˜๋ฆฌ์˜ค๋ฅผ ๊ฐ€์žฅ ์ž˜ ์„ค๋ช…ํ•˜๋Š” โ€˜๊ณ ์ „์  ์กฐ๊ฑดํ™”โ€™(classical conditi...๋‹ค์Œ ์‹œ๋‚˜๋ฆฌ์˜ค๋ฅผ ๊ฐ€์žฅ ์ž˜ ์„ค๋ช…ํ•˜๋Š” โ€˜๊ณ ์ „์  ์กฐ๊ฑดํ™”โ€™(classical conditi...100.0
7524583\"๊ตญ๊ฐ€์  ๋ฌธ์ œ๊ฐ€ ์—„์ค‘ํ•œ ์‹œ๊ธฐ, ๊ตญ๊ฐ€์˜ ์ •์˜ ์˜์‹์œผ๋กœ ํƒ„์ƒํ•œ ๊ตญ๋ฏผ์˜ ์–‘์‹ฌ์œผ๋กœ ์ƒˆ๋กœ์šด ...\"๊ตญ๊ฐ€์  ๋ฌธ์ œ๊ฐ€ ์—„์ค‘ํ•œ ์‹œ๊ธฐ, ๊ตญ๊ฐ€์˜ ์ •์˜ ์˜์‹์œผ๋กœ ํƒ„์ƒํ•œ ๊ตญ๋ฏผ์˜ ์–‘์‹ฌ์œผ๋กœ ์ƒˆ๋กœ์šด ...92.756350์ด ์ง€๋ฌธ์— ๋‚˜ํƒ€๋‚œ ์ •๋ณด๋Š” ๋ฌด์—‡์ž…๋‹ˆ๊นŒ?์ด ์ง€๋ฌธ์— ๋‚˜ํƒ€๋‚œ ์ •๋ณด๋Š” ๋ฌด์—‡์ž…๋‹ˆ๊นŒ?100.0
8679682ํฌ๋ฅดํˆฌ๊ฐˆ์ธ๋“ค์ด ์ค‘๊ตญ์˜ ๋งˆ์นด์˜ค์—์„œ ์ผ๋ณธ์œผ๋กœ ๊ฐˆ ๋•Œ, ํฐ ๋น„๋‹จ๊ณผ ๊ธˆ, ์‚ฌํ–ฅ, ๋„์ž๊ธฐ ๋“ฑ...\"ํฌ๋ฅดํˆฌ๊ฐˆ์ธ๋“ค์ด ์ค‘๊ตญ์˜ ๋งˆ์นด์˜ค์—์„œ ์ผ๋ณธ์œผ๋กœ ๊ฐˆ ๋•Œ, ํฐ ๋น„๋‹จ๊ณผ ๊ธˆ, ์‚ฌํ–ฅ, ๋„์ž๊ธฐ ...96.216216์„œ๊ธฐ 1750-1900์— ์ผ์–ด๋‚œ ๋‹ค์Œ ์‚ฌ๊ฑด ์ค‘ ์ฒซ ๋ฒˆ์งธ ๊ธ€์—์„œ ์–ธ๊ธ‰ํ•œ ๋ฌด์—ญ ํŒจํ„ด์„ ...์„œ๊ธฐ 1450-1750์— ์ผ์–ด๋‚œ ๋‹ค์Œ ์‚ฌ๊ฑด ์ค‘ ์ฒซ ๋ฒˆ์งธ ๊ธ€์—์„œ ์–ธ๊ธ‰ํ•œ ๋ฌด์—ญ ํŒจํ„ด์„ ...95.0
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(๋‚˜) ๋†๋ฏผ๊ตฐ์ด ์šฐ๊ธˆ์น˜์—์„œ ์ „ํˆฌ๋ฅผ ๋ฒŒ์˜€๋‹ค.... 100.000000 \n", + "5 ๋ฒ„ํ‚น์—„ ๊ถ์ „, 1839๋…„ 5์›” 10์ผ. ์—ฌ์™•์€ ์ง€๋‚œ ๋‚˜ํ˜ ๋™์•ˆ ์ž์‹ ์ด ํ•ด์•ผ ํ•œ ๋งŽ์€... 94.025974 \n", + "6 ์‹œ๋‚˜๋ฆฌ์˜ค: ํ›„๋ฐ˜๊ธฐ ์ž์‹ ์˜ ๊ณ ์ „์  ์กฐ๊ฑดํ™” ์‹คํ—˜์—์„œ ํŒŒ๋ธ”๋กœํ”„(Ivan Pavlov)์˜ ... 100.000000 \n", + "7 \"๊ตญ๊ฐ€์  ๋ฌธ์ œ๊ฐ€ ์—„์ค‘ํ•œ ์‹œ๊ธฐ, ๊ตญ๊ฐ€์˜ ์ •์˜ ์˜์‹์œผ๋กœ ํƒ„์ƒํ•œ ๊ตญ๋ฏผ์˜ ์–‘์‹ฌ์œผ๋กœ ์ƒˆ๋กœ์šด ... 92.756350 \n", + "8 \"ํฌ๋ฅดํˆฌ๊ฐˆ์ธ๋“ค์ด ์ค‘๊ตญ์˜ ๋งˆ์นด์˜ค์—์„œ ์ผ๋ณธ์œผ๋กœ ๊ฐˆ ๋•Œ, ํฐ ๋น„๋‹จ๊ณผ ๊ธˆ, ์‚ฌํ–ฅ, ๋„์ž๊ธฐ ... 96.216216 \n", + "\n", + " Question 1 \\\n", + "0 (๊ฐ€)์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์ง€ ์•Š์€ ๊ฒƒ์€? \n", + "1 ใ‰ , ใ‰ก์˜ ๊ฑฐ์ฃผ๋ฏผ์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€? \n", + "2 (๊ฐ€) ๋‹จ์ฒด์˜ ํ™œ๋™์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€? \n", + "3 ๋‹ค์Œ ์‚ฌ๊ฑด์„ ์‹œ๊ธฐ ์ˆœ์œผ๋กœ ๋ฐ”๋ฅด๊ฒŒ ๋‚˜์—ดํ•œ ๊ฒƒ์€? \n", + "4 ์ด๋ฅผ ์‹œ๊ธฐ ์ˆœ์œผ๋กœ ๋ฐ”๋ฅด๊ฒŒ ๋‚˜์—ดํ•œ ๊ฒƒ์€? \n", + "5 ์ด ์ง€๋ฌธ์— ๋‚˜ํƒ€๋‚œ ์ •๋ณด๋Š” ๋ฌด์—‡์ž…๋‹ˆ๊นŒ? \n", + "6 ๋‹ค์Œ ์‹œ๋‚˜๋ฆฌ์˜ค๋ฅผ ๊ฐ€์žฅ ์ž˜ ์„ค๋ช…ํ•˜๋Š” โ€˜๊ณ ์ „์  ์กฐ๊ฑดํ™”โ€™(classical conditi... \n", + "7 ์ด ์ง€๋ฌธ์— ๋‚˜ํƒ€๋‚œ ์ •๋ณด๋Š” ๋ฌด์—‡์ž…๋‹ˆ๊นŒ? \n", + "8 ์„œ๊ธฐ 1750-1900์— ์ผ์–ด๋‚œ ๋‹ค์Œ ์‚ฌ๊ฑด ์ค‘ ์ฒซ ๋ฒˆ์งธ ๊ธ€์—์„œ ์–ธ๊ธ‰ํ•œ ๋ฌด์—ญ ํŒจํ„ด์„ ... \n", + "\n", + " Question 2 question_similarity \n", + "0 (๊ฐ€)์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์ง€ ์•Š์€ ๊ฒƒ์€? 100.0 \n", + "1 ใ‰ , ใ‰ก์˜ ๊ฑฐ์ฃผ๋ฏผ์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€? 100.0 \n", + "2 (๊ฐ€) ๋‹จ์ฒด์˜ ํ™œ๋™์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€? 100.0 \n", + "3 ๋‹ค์Œ ์‚ฌ๊ฑด์„ ์‹œ๊ธฐ ์ˆœ์œผ๋กœ ๋ฐ”๋ฅด๊ฒŒ ๋‚˜์—ดํ•œ ๊ฒƒ์€? 100.0 \n", + "4 ์ด๋ฅผ ์‹œ๊ธฐ ์ˆœ์œผ๋กœ ๋ฐ”๋ฅด๊ฒŒ ๋‚˜์—ดํ•œ ๊ฒƒ์€? 100.0 \n", + "5 ์ด ์ง€๋ฌธ์— ๋‚˜ํƒ€๋‚œ ์ •๋ณด๋Š” ๋ฌด์—‡์ž…๋‹ˆ๊นŒ? 100.0 \n", + "6 ๋‹ค์Œ ์‹œ๋‚˜๋ฆฌ์˜ค๋ฅผ ๊ฐ€์žฅ ์ž˜ ์„ค๋ช…ํ•˜๋Š” โ€˜๊ณ ์ „์  ์กฐ๊ฑดํ™”โ€™(classical conditi... 100.0 \n", + "7 ์ด ์ง€๋ฌธ์— ๋‚˜ํƒ€๋‚œ ์ •๋ณด๋Š” ๋ฌด์—‡์ž…๋‹ˆ๊นŒ? 100.0 \n", + "8 ์„œ๊ธฐ 1450-1750์— ์ผ์–ด๋‚œ ๋‹ค์Œ ์‚ฌ๊ฑด ์ค‘ ์ฒซ ๋ฒˆ์งธ ๊ธ€์—์„œ ์–ธ๊ธ‰ํ•œ ๋ฌด์—ญ ํŒจํ„ด์„ ... 95.0 " + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "paragraph_question_similarity_results" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ํฌ๋ฅดํˆฌ๊ฐˆ์ธ๋“ค์ด ์ค‘๊ตญ์˜ ๋งˆ์นด์˜ค์—์„œ ์ผ๋ณธ์œผ๋กœ ๊ฐˆ ๋•Œ, ํฐ ๋น„๋‹จ๊ณผ ๊ธˆ, ์‚ฌํ–ฅ, ๋„์ž๊ธฐ ๋“ฑ์„ ๋งŽ์ด ๊ฐ€์ ธ๊ฐ€์ง€๋งŒ, ์ผ๋ณธ์—์„œ๋Š” ์€๋งŒ ๊ฐ€์ ธ๊ฐ„๋‹ค. ์ด๋“ค์€ ๋งค๋…„ ์ผ๋ณธ์œผ๋กœ ๊ฐ€๋Š” ๊ฑฐ๋Œ€ํ•œ ๋ฌด์žฅ ์ƒ์„ ์ด ์žˆ๋Š”๋ฐ, ์ด ์ƒ์„ ์€ ๋งค๋…„ ์ผ๋ณธ์—์„œ ์•ฝ 600๊ฐœ์˜ ์€ํ™”๋ฅผ ๊ฐ€์ ธ์˜จ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ผ๋ณธ์—์„œ ๊ฐ€์ ธ์˜จ ์€ํ™”์™€ ๋งค๋…„ ์ธ๋„์—์„œ ๊ฐ€์ ธ์˜ค๋Š” 20๋งŒ ๊ฐœ์˜ ์€ํ™”๋ฅผ ์ค‘๊ตญ์— ํŒ”์•„ ์ด์ต์„ ๋‚จ๊ธด๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ค‘๊ตญ์—์„œ ๋‹ค์‹œ ๊ธˆ, ์‚ฌํ–ฅ, ์‹คํฌ, ๊ตฌ๋ฆฌ, ๋„์ž๊ธฐ์™€ ๋งค์šฐ ๊ฐ’๋น„์‹ธ๊ณ  ํ™”๋ คํ•œ ๋งŽ์€ ๋‹ค๋ฅธ ๋ฌผ๊ฑด๋“ค์„ ๊ฐ€์ ธ์˜จ๋‹ค. ํฌ๋ฅดํˆฌ๊ฐˆ์ธ๋“ค์ด ๊ฒฝ์œ ์ง€๋กœ ์ค‘๊ตญ ๊ด€๋™์— ์™”์„ ๋•Œ ๊ทธ ๊ณณ์—์„œ ๋ฉฐ์น  ๊ฐ„ ๋จธ๋ฌผ๋Ÿฌ์•ผ ํ–ˆ๋‹ค. ์ด๋“ค์ด ๋„์‹œ ๊ด€๋ฌธ์œผ๋กœ ๋‚˜์™”์„ ๋•Œ ์ž์‹ ๋“ค์˜ ์ด๋ฆ„์„ ๊ธฐ๋กํ•ด์•ผ ํ–ˆ๊ณ  ๋ฐค์— ๋‚˜๊ฐˆ ๋•Œ์—๋„ ์ด๋ฆ„์„ ๊ธฐ๋กํ•ด์•ผ ํ–ˆ๋‹ค. ๋ฐค์ƒˆ๋„๋ก ๋งˆ์„์— ๋จธ๋ฌด๋ฅผ ์ˆ˜๋Š” ์—†์—ˆ๊ณ , ๋ฐ–์— ์žˆ๋Š” ์ž์‹ ๋“ค์˜ ๋ฐฐ์—์„œ ๋ฐค์„ ๋ณด๋‚ด์•ผ ํ–ˆ๋‹ค. ์ฃผ์–ด์ง„ ์‹œ๊ฐ„์ด ๊ฒฝ๊ณผํ–ˆ์„ ๋•Œ ๋„์‹œ์— ๋‚จ์•„์žˆ๋Š” ์ž๊ฐ€ ์žˆ๋‹ค๋ฉด ๊ฐ์˜ฅ์— ์ˆ˜๊ฐ๋˜์—ˆ๋‹ค.\n", + "\"ํฌ๋ฅดํˆฌ๊ฐˆ์ธ๋“ค์ด ์ค‘๊ตญ์˜ ๋งˆ์นด์˜ค์—์„œ ์ผ๋ณธ์œผ๋กœ ๊ฐˆ ๋•Œ, ํฐ ๋น„๋‹จ๊ณผ ๊ธˆ, ์‚ฌํ–ฅ, ๋„์ž๊ธฐ ๋“ฑ์„ ๋งŽ์ด ๊ฐ€์ ธ๊ฐ€์ง€๋งŒ, ์ผ๋ณธ์—์„œ๋Š” ์€๋งŒ ๊ฐ€์ ธ๊ฐ„๋‹ค. ์ด๋“ค์€ ๋งค๋…„ ์ผ๋ณธ์œผ๋กœ ๊ฐ€๋Š” ๊ฑฐ๋Œ€ํ•œ ๋ฌด์žฅ ์ƒ์„ ์ด ์žˆ๋Š”๋ฐ, ์ด ์ƒ์„ ์€ ๋งค๋…„ ์ผ๋ณธ์—์„œ ์•ฝ 600๊ฐœ์˜ ์€ํ™”๋ฅผ ๊ฐ€์ ธ์˜จ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ผ๋ณธ์—์„œ ๊ฐ€์ ธ์˜จ ์€ํ™”์™€ ๋งค๋…„ ์ธ๋„์—์„œ ๊ฐ€์ ธ์˜ค๋Š” 20๋งŒ ๊ฐœ์˜ ์€ํ™”๋ฅผ ์ค‘๊ตญ์— ํŒ”์•„ ์ด์ต์„ ๋‚จ๊ธด๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ค‘๊ตญ์—์„œ ๋‹ค์‹œ ๊ธˆ, ์‚ฌํ–ฅ, ์‹คํฌ, ๊ตฌ๋ฆฌ, ๋„์ž๊ธฐ์™€ ๋งค์šฐ ๊ฐ’๋น„์‹ธ๊ณ  ํ™”๋ คํ•œ ๋งŽ์€ ๋‹ค๋ฅธ ๋ฌผ๊ฑด๋“ค์„ ๊ฐ€์ ธ์˜จ๋‹ค. ํฌ๋ฅดํˆฌ๊ฐˆ์ธ๋“ค์ด ๊ฒฝ์œ ์ง€๋กœ ์ค‘๊ตญ ๊ด€๋™์— ์™”์„ ๋•Œ ๊ทธ ๊ณณ์—์„œ ๋ฉฐ์น  ๊ฐ„ ๋จธ๋ฌผ๋Ÿฌ์•ผ ํ–ˆ๋‹ค. ์ด๋“ค์ด ๋„์‹œ ๊ด€๋ฌธ์œผ๋กœ ๋‚˜์™”์„ ๋•Œ ์ž์‹ ๋“ค์˜ ์ด๋ฆ„์„ ๊ธฐ๋กํ•ด์•ผ ํ–ˆ๊ณ  ๋ฐค์— ๋‚˜๊ฐˆ ๋•Œ์—๋„ ์ด๋ฆ„์„ ๊ธฐ๋กํ•ด์•ผ ํ–ˆ๋‹ค. ๋ฐค์ƒˆ๋„๋ก ๋งˆ์„์— ๋จธ๋ฌด๋ฅผ ์ˆ˜๋Š” ์—†์—ˆ๊ณ , ๋ฐ–์— ์žˆ๋Š” ์ž์‹ ๋“ค์˜ ๋ฐฐ์—์„œ ๋ฐค์„ ๋ณด๋‚ด์•ผ ํ–ˆ๋‹ค. ์ฃผ์–ด์ง„ ์‹œ๊ฐ„์ด ๊ฒฝ๊ณผํ–ˆ์„ ๋•Œ ๋„์‹œ์— ๋‚จ์•„์žˆ๋Š” ์ž๊ฐ€ ์žˆ๋‹ค๋ฉด ๊ฐ์˜ฅ์— ์ˆ˜๊ฐ๋˜์—ˆ๋‹ค.โ€ Ralph Fitch, ๊ทน๋™์œผ๋กœ์˜ ์—ฌํ–‰๊ธฐ, 1599 C.E\n" + ] + } + ], + "source": [ + "print(paragraph_question_similarity_results.at[8, 'Paragraph 1'])\n", + "print(paragraph_question_similarity_results.at[8, 'Paragraph 2'])" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Paragraph-Question Similarity Results:\n", + "(1, 10)\n" + ] + } + ], + "source": [ + "from rapidfuzz import fuzz\n", + "\n", + "# ์œ ์‚ฌํ•œ paragraph์— ํ•ด๋‹นํ•˜๋Š” question ์œ ์‚ฌ๋„ ๋ฐ ๋™์ผ ์—ฌ๋ถ€ ํ™•์ธ\n", + "paragraph_question_choices_similarity_check = []\n", + "for _, row in paragraph_question_similarity_results.iterrows():\n", + " idx1, idx2 = row['Index 1'], row['Index 2']\n", + " choices1 = train.loc[idx1, 'choices']\n", + " choices2 = train.loc[idx2, 'choices']\n", + " \n", + " # question์˜ ์œ ์‚ฌ๋„ ๊ณ„์‚ฐ\n", + " similarity = fuzz.ratio(choices1, choices2)\n", + "\n", + " if similarity >= 90:\n", + " paragraph_question_choices_similarity_check.append({\n", + " 'Index 1': int(row['Index 1']),\n", + " 'Index 2': int(row['Index 2']),\n", + " 'Paragraph 1': train.loc[idx1, 'paragraph'],\n", + " 'Paragraph 2': train.loc[idx2, 'paragraph'],\n", + " 'paragraph_similarity': row['paragraph_similarity'],\n", + " 'Question 1': row['Question 1'],\n", + " 'Question 2': row['Question 2'],\n", + " 'question_similarity': similarity,\n", + " 'Choices 1': choices1,\n", + " 'Choices 2': choices2,\n", + " })\n", + "\n", + "# ๊ฒฐ๊ณผ๋ฅผ ๋ฐ์ดํ„ฐํ”„๋ ˆ์ž„์œผ๋กœ ์ •๋ฆฌ\n", + "paragraph_question_choices_similarity_results = pd.DataFrame(paragraph_question_choices_similarity_check)\n", + "\n", + "# ๊ฒฐ๊ณผ ์ถœ๋ ฅ\n", + "print(\"Paragraph-Question Similarity Results:\")\n", + "print(paragraph_question_choices_similarity_results.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Index 1Index 2Paragraph 1Paragraph 2paragraph_similarityQuestion 1Question 2question_similarityChoices 1Choices 2
0323384์‹œ๋‚˜๋ฆฌ์˜ค: ํ›„๋ฐ˜๊ธฐ ์ž์‹ ์˜ ๊ณ ์ „์  ์กฐ๊ฑดํ™” ์‹คํ—˜์—์„œ ํŒŒ๋ธ”๋กœํ”„(Ivan Pavlov)์˜ ...์‹œ๋‚˜๋ฆฌ์˜ค: ํ›„๋ฐ˜๊ธฐ ์ž์‹ ์˜ ๊ณ ์ „์  ์กฐ๊ฑดํ™” ์‹คํ—˜์—์„œ ํŒŒ๋ธ”๋กœํ”„(Ivan Pavlov)์˜ ...100.0๋‹ค์Œ ์‹œ๋‚˜๋ฆฌ์˜ค๋ฅผ ๊ฐ€์žฅ ์ž˜ ์„ค๋ช…ํ•˜๋Š” โ€˜๊ณ ์ „์  ์กฐ๊ฑดํ™”โ€™(classical conditi...๋‹ค์Œ ์‹œ๋‚˜๋ฆฌ์˜ค๋ฅผ ๊ฐ€์žฅ ์ž˜ ์„ค๋ช…ํ•˜๋Š” โ€˜๊ณ ์ „์  ์กฐ๊ฑดํ™”โ€™(classical conditi...96.875['๋ณ€๋ณ„', '์ž๋ฐœ์  ํšŒ๋ณต', 'ํ”์ ์กฐ๊ฑดํ™”', '์ผ๋ฐ˜ํ™”']['์ฐจ๋ณ„', '์ž๋ฐœ์  ํšŒ๋ณต', 'ํ”์ ์กฐ๊ฑดํ™”', '์ผ๋ฐ˜ํ™”']
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" + ], + "text/plain": [ + " Index 1 Index 2 Paragraph 1 \\\n", + "0 323 384 ์‹œ๋‚˜๋ฆฌ์˜ค: ํ›„๋ฐ˜๊ธฐ ์ž์‹ ์˜ ๊ณ ์ „์  ์กฐ๊ฑดํ™” ์‹คํ—˜์—์„œ ํŒŒ๋ธ”๋กœํ”„(Ivan Pavlov)์˜ ... \n", + "\n", + " Paragraph 2 paragraph_similarity \\\n", + "0 ์‹œ๋‚˜๋ฆฌ์˜ค: ํ›„๋ฐ˜๊ธฐ ์ž์‹ ์˜ ๊ณ ์ „์  ์กฐ๊ฑดํ™” ์‹คํ—˜์—์„œ ํŒŒ๋ธ”๋กœํ”„(Ivan Pavlov)์˜ ... 100.0 \n", + "\n", + " Question 1 \\\n", + "0 ๋‹ค์Œ ์‹œ๋‚˜๋ฆฌ์˜ค๋ฅผ ๊ฐ€์žฅ ์ž˜ ์„ค๋ช…ํ•˜๋Š” โ€˜๊ณ ์ „์  ์กฐ๊ฑดํ™”โ€™(classical conditi... \n", + "\n", + " Question 2 question_similarity \\\n", + "0 ๋‹ค์Œ ์‹œ๋‚˜๋ฆฌ์˜ค๋ฅผ ๊ฐ€์žฅ ์ž˜ ์„ค๋ช…ํ•˜๋Š” โ€˜๊ณ ์ „์  ์กฐ๊ฑดํ™”โ€™(classical conditi... 96.875 \n", + "\n", + " Choices 1 Choices 2 \n", + "0 ['๋ณ€๋ณ„', '์ž๋ฐœ์  ํšŒ๋ณต', 'ํ”์ ์กฐ๊ฑดํ™”', '์ผ๋ฐ˜ํ™”'] ['์ฐจ๋ณ„', '์ž๋ฐœ์  ํšŒ๋ณต', 'ํ”์ ์กฐ๊ฑดํ™”', '์ผ๋ฐ˜ํ™”'] " + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "paragraph_question_choices_similarity_results" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(4, 4)" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "idx1 = paragraph_question_choices_similarity_results['Index 1'].values[0]\n", + "idx2 = paragraph_question_choices_similarity_results['Index 2'].values[0]\n", + "train.at[idx1, 'answer'], train.at[idx2, 'answer']" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Detailed Type Checking:\n", + "\n", + "Type Distribution Summary:\n", + " id paragraph question choices answer answer_len \\\n", + " NaN NaN NaN NaN NaN NaN \n", + " NaN NaN NaN NaN NaN NaN \n", + " NaN NaN NaN NaN 2031.0 2031.0 \n", + " 2031.0 2031.0 2031.0 2031.0 NaN NaN \n", + "\n", + " ํ•„์š” ์ง€์‹ ์„ธ๋ถ„๋ฅ˜(๋ชจ๋ธ) ์„ธ๋ถ„๋ฅ˜(์‚ฌ๋žŒ) ๋ณด๊ธฐ ์—ฌ๋ถ€ ๋ฐ์ดํ„ฐ์ €ํ’ˆ์งˆ-paragraph \\\n", + " NaN NaN NaN 2031.0 NaN \n", + " NaN 3.0 NaN NaN NaN \n", + " NaN NaN NaN NaN NaN \n", + " 2031.0 2028.0 2031.0 NaN 2031.0 \n", + "\n", + " ๋ฐ์ดํ„ฐ์ €ํ’ˆ์งˆ-question ๋ฐ์ดํ„ฐ์ €ํ’ˆ์งˆ-choices ๋ฐ์ดํ„ฐ์ €ํ’ˆ์งˆ-answer ๋น„๊ณ  \\\n", + " NaN NaN NaN NaN \n", + " NaN NaN NaN 2024.0 \n", + " NaN NaN NaN NaN \n", + " 2031.0 2031.0 2031.0 7.0 \n", + "\n", + " dataset \n", + " NaN \n", + " NaN \n", + " NaN \n", + " 2031.0 \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_3515290/1057604122.py:5: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", + " type_check = train.applymap(type)\n", + "/tmp/ipykernel_3515290/1057604122.py:8: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", + " type_summary = type_check.applymap(lambda x: str(x)).apply(lambda col: col.value_counts())\n" + ] + } + ], + "source": [ + "# ๊ฒฐ์ธก์น˜ ํ™•์ธ: empty string, None, NaN, space\n", + "\n", + "# ๋ชจ๋“  ๋ฐ์ดํ„ฐ์˜ ํƒ€์ž…์„ ์—ด๋ณ„๋กœ ํ™•์ธ\n", + "print(\"\\nDetailed Type Checking:\")\n", + "type_check = train.applymap(type)\n", + "\n", + "# ์—ด๋ณ„ ๋ฐ์ดํ„ฐ ํƒ€์ž…์˜ ๋ถ„ํฌ ํ™•์ธ (type์„ ๋ฌธ์ž์—ด๋กœ ๋ณ€ํ™˜ ํ›„ ์ฒ˜๋ฆฌ)\n", + "type_summary = type_check.applymap(lambda x: str(x)).apply(lambda col: col.value_counts())\n", + "\n", + "print(\"\\nType Distribution Summary:\")\n", + "print(type_summary)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_3515290/802159129.py:4: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", + " train_null_check = train_null_check.applymap(lambda x: None if isinstance(x, str) and x.strip() == \"\" else x)\n" + ] + }, + { + "data": { + "text/plain": [ + "id 0\n", + "paragraph 0\n", + "question 0\n", + "choices 0\n", + "answer 0\n", + "answer_len 0\n", + "ํ•„์š” ์ง€์‹ 0\n", + "์„ธ๋ถ„๋ฅ˜(๋ชจ๋ธ) 3\n", + "์„ธ๋ถ„๋ฅ˜(์‚ฌ๋žŒ) 0\n", + "๋ณด๊ธฐ ์—ฌ๋ถ€ 0\n", + "๋ฐ์ดํ„ฐ์ €ํ’ˆ์งˆ-paragraph 0\n", + "๋ฐ์ดํ„ฐ์ €ํ’ˆ์งˆ-question 0\n", + "๋ฐ์ดํ„ฐ์ €ํ’ˆ์งˆ-choices 0\n", + "๋ฐ์ดํ„ฐ์ €ํ’ˆ์งˆ-answer 0\n", + "๋น„๊ณ  2024\n", + "dataset 0\n", + "dtype: int64" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# ๊ฒฐ์ธก์น˜ ํ™•์ธ: empty string, None, NaN, space\n", + "\n", + "train_null_check = train.replace({\"\": None, \" \": None})\n", + "train_null_check = train_null_check.applymap(lambda x: None if isinstance(x, str) and x.strip() == \"\" else x)\n", + "\n", + "train_null_check.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['ใ„ฑ, ใ„ด', 'ใ„ฑ, ใ„ท', 'ใ„ด, ใ„น', 'ใ„ท, ใ„น']" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = \"['ใ„ฑ, ใ„ด', 'ใ„ฑ, ใ„ท', 'ใ„ด, ใ„น', 'ใ„ท, ใ„น']\"\n", + "\n", + "# ๋ฌธ์ž์—ด์„ ๋ฆฌ์ŠคํŠธ๋กœ ๋ณ€ํ™˜\n", + "converted_list = literal_eval(data)\n", + "converted_list" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "str" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(train.at[0, 'choices'])" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idparagraphquestionchoicesansweranswer_lenํ•„์š” ์ง€์‹์„ธ๋ถ„๋ฅ˜(๋ชจ๋ธ)์„ธ๋ถ„๋ฅ˜(์‚ฌ๋žŒ)๋ณด๊ธฐ ์—ฌ๋ถ€๋ฐ์ดํ„ฐ์ €ํ’ˆ์งˆ-paragraph๋ฐ์ดํ„ฐ์ €ํ’ˆ์งˆ-question๋ฐ์ดํ„ฐ์ €ํ’ˆ์งˆ-choices๋ฐ์ดํ„ฐ์ €ํ’ˆ์งˆ-answer๋น„๊ณ datasetparagraph_lengthquestion_lengthchoices_lengthtotal_length
0generation-for-nlp-425์ƒ์†Œํ•˜์—ฌ ์•„๋ขฐ๊ธฐ๋ฅผ , โ€œ์‹ ์ด ์ขŒ์ฐธ ์ฐฌ ์†ก์ค€๊ธธ์ด ์˜ฌ๋ฆฐ ์ฐจ์ž๋ฅผ ๋ณด์•˜๋Š”๋ฐ , ์ƒ๋ณต(ๅ–ชๆœ)...์ƒ์†Œํ•œ ์ธ๋ฌผ์ด ์†ํ•œ ๋ถ•๋‹น์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ๋งŒ์„ ๋ชจ๋‘ ๊ณ ๋ฅด๋ฉด?[ใ„ฑ, ใ„ด, ใ„ฑ, ใ„ท, ใ„ด, ใ„น, ใ„ท, ใ„น]24์™ธ๋ถ€์ง€์‹์ •์น˜์™€ ๋ฒ•ํ•œ๊ตญ์‚ฌTrue๋„์–ด์“ฐ๊ธฐ ์˜ค๋ฅ˜์ •์ƒ์ •์ƒ์ •์ƒNaNKMMLU3533716406
1generation-for-nlp-426(๊ฐ€)์€/๋Š” ์˜๋ณ‘๊ณ„์—ด๊ณผ ์• ๊ตญ๊ณ„๋ชฝ ์šด๋™ ๊ณ„์—ด์˜ ๋น„๋ฐ€๊ฒฐ์‚ฌ๊ฐ€ ๋ชจ์—ฌ ๊ฒฐ์„ฑ๋œ ์กฐ์ง์œผ๋กœ, ์ด์‚ฌ...(๊ฐ€)์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์ง€ ์•Š์€ ๊ฒƒ์€?[๊ณ ๋ ค ๋ฌธ์ข… ๋•Œ์— ๋‚จ๊ฒฝ(ๅ—ไบฌ)์œผ๋กœ ์Šน๊ฒฉ๋˜์—ˆ๋‹ค., ์ข…๋ฃจ(้˜ๆจ“), ์ดํ˜„, ์น ํŒจ ๋“ฑ์—์„œ ...14์™ธ๋ถ€์ง€์‹๋น„๋ฌธํ•™ํ•œ๊ตญ์‚ฌFalse์ •์ƒ์ •์ƒ์ •์ƒ๋ฌธ์ œ์™€ ์„ ์ง€๊ฐ€ ์—‡๊ฐˆ๋ฆผNaNKMMLU7522116213
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" + ], + "text/plain": [ + " id paragraph \\\n", + "0 generation-for-nlp-425 ์ƒ์†Œํ•˜์—ฌ ์•„๋ขฐ๊ธฐ๋ฅผ , โ€œ์‹ ์ด ์ขŒ์ฐธ ์ฐฌ ์†ก์ค€๊ธธ์ด ์˜ฌ๋ฆฐ ์ฐจ์ž๋ฅผ ๋ณด์•˜๋Š”๋ฐ , ์ƒ๋ณต(ๅ–ชๆœ)... \n", + "1 generation-for-nlp-426 (๊ฐ€)์€/๋Š” ์˜๋ณ‘๊ณ„์—ด๊ณผ ์• ๊ตญ๊ณ„๋ชฝ ์šด๋™ ๊ณ„์—ด์˜ ๋น„๋ฐ€๊ฒฐ์‚ฌ๊ฐ€ ๋ชจ์—ฌ ๊ฒฐ์„ฑ๋œ ์กฐ์ง์œผ๋กœ, ์ด์‚ฌ... \n", + "\n", + " question \\\n", + "0 ์ƒ์†Œํ•œ ์ธ๋ฌผ์ด ์†ํ•œ ๋ถ•๋‹น์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ๋งŒ์„ ๋ชจ๋‘ ๊ณ ๋ฅด๋ฉด? \n", + "1 (๊ฐ€)์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์ง€ ์•Š์€ ๊ฒƒ์€? \n", + "\n", + " choices answer answer_len \\\n", + "0 [ใ„ฑ, ใ„ด, ใ„ฑ, ใ„ท, ใ„ด, ใ„น, ใ„ท, ใ„น] 2 4 \n", + "1 [๊ณ ๋ ค ๋ฌธ์ข… ๋•Œ์— ๋‚จ๊ฒฝ(ๅ—ไบฌ)์œผ๋กœ ์Šน๊ฒฉ๋˜์—ˆ๋‹ค., ์ข…๋ฃจ(้˜ๆจ“), ์ดํ˜„, ์น ํŒจ ๋“ฑ์—์„œ ... 1 4 \n", + "\n", + " ํ•„์š” ์ง€์‹ ์„ธ๋ถ„๋ฅ˜(๋ชจ๋ธ) ์„ธ๋ถ„๋ฅ˜(์‚ฌ๋žŒ) ๋ณด๊ธฐ ์—ฌ๋ถ€ ๋ฐ์ดํ„ฐ์ €ํ’ˆ์งˆ-paragraph ๋ฐ์ดํ„ฐ์ €ํ’ˆ์งˆ-question \\\n", + "0 ์™ธ๋ถ€์ง€์‹ ์ •์น˜์™€ ๋ฒ• ํ•œ๊ตญ์‚ฌ True ๋„์–ด์“ฐ๊ธฐ ์˜ค๋ฅ˜ ์ •์ƒ \n", + "1 ์™ธ๋ถ€์ง€์‹ ๋น„๋ฌธํ•™ ํ•œ๊ตญ์‚ฌ False ์ •์ƒ ์ •์ƒ \n", + "\n", + " ๋ฐ์ดํ„ฐ์ €ํ’ˆ์งˆ-choices ๋ฐ์ดํ„ฐ์ €ํ’ˆ์งˆ-answer ๋น„๊ณ  dataset paragraph_length \\\n", + "0 ์ •์ƒ ์ •์ƒ NaN KMMLU 353 \n", + "1 ์ •์ƒ ๋ฌธ์ œ์™€ ์„ ์ง€๊ฐ€ ์—‡๊ฐˆ๋ฆผ NaN KMMLU 75 \n", + "\n", + " question_length choices_length total_length \n", + "0 37 16 406 \n", + "1 22 116 213 " + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def calculate_choices_length(row):\n", + " cnt = 0\n", + " for choice in row:\n", + " cnt += len(choice)\n", + " return cnt\n", + "\n", + "train['choices'] = train['choices'].apply(lambda x: literal_eval(x))\n", + "train['paragraph_length'] = train['paragraph'].apply(len)\n", + "train['question_length'] = train['question'].apply(len)\n", + "train['choices_length'] = train['choices'].apply(calculate_choices_length)\n", + "train['total_length'] = train['paragraph_length'] + train['question_length'] + train['choices_length']\n", + "train.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 244, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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iIkpebJQiIiJqopo3vy1btoxqv7Fjx+q///nnnzj22GPxww8/NGtsAKIa5lafESNGhK0bMmRISANPXU+wi4eSkhJs3LhRXx4+fDi6d+8eVk4URVx55ZX6sqZp+Pbbb2s97hFHHBG2f/Wb8PLy8gbF+f333+u/p6en1zrsbNKkSSHL33zzTYPqiYXqsQPA1VdfHbFcc8Ve1ZC7fPly3HLLLTjxxBPRqVMnZGVlhT35sq45xGJt9erV+u82mw1btmzR57qq+qk+b9SWLVtC5gmrrn379mjVqlXIunbt2oUs17zmql/3AMIa8QDg5JNPDnu6ZXOINNdU+/btQ5ZrmyOLiIhSD+eUIiIiaqKtW7eGLEfbw+nBBx/EN998oz/yft26dRg8eDAmT56MmTNnokWLFs0SX81eN41RvSdRFVEU0aFDB31i92geT2+kHTt2hCz379+/1rI1t23btq3WstV7SVVxuVz674qiRBsiAIRMbt61a1c4nc4mx2iU6rFnZ2ejY8eOEcvl5+cjKytLb4xobOy//fYbLr300jrnQ6pSvQeX0ar3BAoEAhEnM69OVVUcOnQo4ntHfdcbEH7NlZaW1nsMh8OBVq1aGZK31XuJAZU92YiIiAD2lCIiImoSWZZDhspYLBZ069Ytqn0zMjKwYsUK3HDDDXqvCVVV8dJLL6FPnz56Y1VT2e32Jh+jtoaS6j2lPB5Pk+tpTlWTa1dp27ZtrWVrbmtob6emqB5nXTFmZ2eH/C2NjLE20cZec3tjYt+0aROOPfbYqBqk4q3mtReN5syfmk+MrO3YjYmzOcSzwZCIiBILG6WIiIia4J133gm5wf6///u/Bj3dLi0tDU8//TTWrl2L4447Tl+/Z88enHzyyc0y90pzDNGp7ea1+vxZNXtDRENV1UbHVJ/q8zwBdT+qvua2xj6hsDGqx1lXjAcPHoTf79eXjYyxNtHGDgB79+7Vf29M7Ndff33IsK+jjz4ar776Kn7//XeUl5eHPEUx3mr2ZKpPRkZGs/WMBMKH90Uajrp169ao578jIiKKFTZKERERNZLH4wmbgPiss85q1LF69+6N5cuX46qrrtLXlZaW4sEHHwwpF4s5YKJRc44aoLJBaefOnfpy9UfSA5WToVdXc4iR3++PaU+N/Pz8kPO1du3aWsuuX78+ZLn6pO6xVr2uTZs21dqrJZ4x1qZ6DIcOHap1WN727dtDGpRqxl7fdb1//3589tln+vLgwYPx/fffY8KECejZsyfS09PrnJzeaNXnGMvKyoKiKNA0rdafsrKyZm1k/L//+7+Q5VmzZoWVeeKJJ+o9Trzeb4iIKHWwUYqIiKgRgsEgrrzySmzYsEFf16pVK0yZMqXRx7RYLHjhhRdChv+tWbMmpEzNhh6jfPTRR2Hrvvnmm5CeOzWfulazp1LNRoM1a9ZENYynsa85OzsbvXr10pe/+uor/PHHH2HlVFXFa6+9pi8LgoDBgwc3qs7GGDJkiP672+3Gm2++GbFczYm8q+8XLzVj+Ne//hWxXH2x1/c33rx5c8i1MnLkyLAhaitWrKg3XqMMGDBA//3QoUMhE58bYfTo0SFDa//73//iiiuuwL59+3Dw4EE8/PDDeP755+s9Trzeb4iIKHWwUYqIiKiB1q9fj1NOOSXssfZ33313WENMXSINd9I0rc5JgGvOD1W990ksh8ItWrQIP//8s76sKAruvvvukDKnnHJKyHKXLl1Clj/55JOQ/e+7776o6q7+mqu/3mgmFL/44ov13zVNw7XXXhvSkAYADzzwQMhk9aecckpYr69Yqh4jAMyYMSNskvb//ve/WLhwob6cnZ2NM844w5D46jJu3LiQp609++yzYU9h/OWXX/D000/ry5Ik4cILLwwpU9d1rSgKbDZbyPZdu3aFLP/000+YN29eo15DLNT821x77bURewXu27cPN9xwQ7PPx9aqVStMnTo1ZN3cuXPRtm1btGzZErfffnvY+0XNcwzE7/2GiIhSB5++R0REVI958+ahoqICO3fuxJdffolvvvkmrIfPxIkTcf3110d9TFVVkZ+fj4KCApx22mno3LkzMjIy8OGHH4Y0SNR84lrNp3Pdd999WL16Nb799luceeaZUTf0NFQwGMRJJ52EadOmoXXr1njzzTdD5qnp1KkThg8fHrJPzeXbb78d+/fvR6dOnTB//nx8/fXXUdXdunVrfVjY119/jYkTJ+LgwYNYv359xGGF1V111VV48sknceDAAQCVjTvHHHMMrrjiCrhcLnz88cchvcAEQcBdd90VVVzNZejQoTjxxBPx5ZdfAqice2nAgAG4+uqrkZ+fj59++gmvvPIKZFnW97nllltqnXzeSPn5+bj00ksxd+5cAJU9vYYNG4arrroKPXv2xIYNG/DSSy+FzLs2YcIEdOjQIeQ4Na/rF154AUVFRVi/fj06d+6M2bNnw+l06nMgvfrqq3A4HDjiiCPw+++/46WXXkIgEIjxq6307bffhvXSqnL00Uejd+/eGDVqFLp27ao/mfKnn35Cr169MGHCBBQUFKCkpATfffcdPvroI/h8PkyfPh1paWnNGue9996LH3/8EcuWLau1jM1m089bVlZW2PZ4vd8QEVHqYKMUERFRPS699NI6t0+ZMgXPPvtsg45ZXFwMWZaxcePGWhtWBEEIGw54xBFHwGKx6A0UmzdvxgsvvAAA6NOnT4NiaKjS0lLcc889Ebc99thjYUN9+vXrhxNOOAFfffUVACAQCGDmzJkNrveYY47RG6UURcGrr74KABBFEcFgsM59W7VqhTlz5uCCCy7Qe1b98ssvuOWWWyKWv+2220ImnDfKyy+/jCFDhqC4uBhA5fVx//33Ryx7wgkn4NZbbzUyvDo98cQTWLVqld4AU1FRgccffzxi2d69e+PRRx8NW9+pUye0bt1af/1FRUX6da1pGhwOBy677DK89NJLACqvg4bmXHOZPXs2Zs+eHXHbhAkT8Oqrr0IURbz++us48cQT9VzduXNnrX/TWLDb7fjoo49w33334ZlnngnpqdW6dWs8+OCDmDFjBnbv3g0AYQ2FQHzfb4iIKDVw+B4REVEjDRo0CJ988gleeOGFBs+9UlpaWm+ZWbNmYejQoSHrWrRogUmTJkUsX9/Tz5qisLAw4npBEPDggw/i/PPPj7j95ZdfDnsSWJWsrCzk5eXVW/e0adMinl9VVbFv37569x89ejQWLlwYsSdIFUmScOedd+Khhx6q93ixUFhYiP/+978h84lFcuaZZ+Kjjz6C1Wo1KLL6tWzZEsuXL8fAgQPrLDdkyBAsW7YM2dnZYdsEQcDNN98ccb+q63rWrFno27dvrcc/6aSTog86RqoPaRs6dCjef//9Oq87oLJBLtI5aQ52ux0PPvggioqK8OWXX+Ltt9/GypUrsWvXLgwaNEhvkAIqG39ritf7DRERpQ72lCIiIqqh5g2iIAjIzMxEy5Yt0adPHwwZMgRnnnlm2NC6SNLS0kJ6GlTdoPbo0QP/+c9/sHjxYqxevRqbN2+G3+9Hhw4dcMQRR+CGG27ACSecEPGYzz33HPLy8jBnzhzs27cPbdu2xcCBA/U5ZFwuF0RR1G+Qa7vhrX6zbLfbQyZGrv4kMEmS8OGHH+Ljjz/G66+/jk2bNqFFixYYOnQobrzxxjp7FnXt2hU//fQTZs6ciY8++gg7d+5ETk4OzjjjDEyfPh1TpkzR5weqbfjS0KFD8fnnn+OOO+7Azz//jIyMDHTt2hXjxo1D+/btQ15H1d+qpgsuuADHHnssXnjhBXz44YfYtm0bgsEg8vLycPLJJ+Oqq64Km6i9Sk5Ojv67xWKJOG9Y9aetNbaBoW/fvli3bh3mzJmDt99+Gxs2bMChQ4eQm5uLwYMH47LLLsPZZ59d6/7Vz4MkSQ2a36w2OTk5ei+1li1b1lquQ4cO+Pbbb/Hmm29iwYIFWL9+PUpKSpCTk4MjjzwSF110ES655JKQ+adq+uc//4msrCw8+eST2LZtG1q3bo3+/fvj6quv1l/ft99+i6effhpLlizRHzJw9NFH44YbbkDnzp31hjFBEEKGO7pcrpC6ai7XpU2bNhAEIapJ+Ws2QI0aNQobN27Es88+i48//hh//fUXPB4PcnJy0K9fP5x11lm48sorQ3IPqP+8Z2dnhwy9i3TN/fXXX9i6dStGjBgBp9MZ8n5SWloa0tgkimKtTw6N5v3GarXqvRar50uVFi1ahNTVnE8aJCIicxO0aD5hiYiIiIjINObOnYsrrrgCXbp0wamnnorevXvD5XJh06ZNmDt3bkgvqbFjx4Y9uIGIiMgI7ClFRERERJRkqv7fefPmzfjXv/5Va7k2bdrgmWeeMSosIiKiEJxTioiIiIgoydhstnrLdOvWDStWrAgZfkpERGQkDt8jIiIiIkoyiqJg2bJl+OSTT/DNN99g586d2L9/P7Kzs9GvXz+MGTMGkyZNiqrxioiIKFbYKEVERERERERERIbj8D0iIiIiIiIiIjIcG6WIiIiIiIiIiMhwfPpeAx08eBCyLDf7ca1WK4LBYLMftybRvw9puxfA0/5iqHZOaknUGEblK5mcz4fW55wDACh+/33A4WjsYXDOOa0BAO+/X9zYw6Qk5iqROTBXicyBuWqMZLlnt1gsaNGiRf3lDIglqciyHJNEVFUViqI0+3HDiC3h73Bd5e98QyFqFMPylUxN8PuBn38GAMh+PzRJatRx/H6h6jDw+2VIEqeCjBZzlcgcmKtE5sBcNUiK3bNz+F6CcDqdxlSkeGFxbwAUrzH1ESUhw/KViJqEuUpkDsxVInNgrhokxe7Z2SiVYqyeTchdcxKsnk3xDoWIKKlpgoDAEUcgcMQR0AQh3uEQERERkQmk2j07h+8lCK83NVpBiZIB85Wi4nRi/7//He8oUhpzlcgcmKtE5sBcpVhgT6kEYbGwfZDILJivRObAXCUyB+YqkTkwVykW2CiVIKxWa7xDIKIoMV+JzIG5SmQOzFUic2CuUiywUSrFaBCgCTZo4PwmRESxJHi9yB08GLmDB0Ngd3ciIiIiikKq3bOz/12CqKioMKQeOaMv9pywxZC6iJKVUflKJqdpsOzcqf9OxmOuEpkDc5WSnaqq8Hq9UBQl3qE0SVlZWbxDSBH5KDlqLaABSNBzLkkSnE4nRLHp/ZzYKJUgXC4X3G53vMMgoigwX4nMgblKZA7MVUpmwWAQHo8HaWlpsFgsEEz8RF5RFKGqarzDoDjTNA2yLKO8vBxpaWlNHtbJ4XsJwqg3J4t7I1r9cBos7o2G1EeUjMz8ZYIolTBXicyBuUrJzOv1Ij09HVarldc6RUfxweL+E1B88Y4kIkEQYLVakZ6e3ixPZGSjVIKQZdmQegTVB1vFLxDUxLzAiczAqHwloqZhrhKZA3OVkp0kSfEOoVlonI7AEAJUCKoXAhK7V1pzXddslEoQgUAg3iEQUZSYr0TmwFwlMgfmKpE5sFGKYoGNUgkiLS0t3iEQUZSYr0TmwFwlMgfmKpE5NMek1kQ18aoiIiKKBUFAsHt3BLt3BziHBBEREVGz2bt3Lzp37qwvr1u3Dv369cOuXbviFxQ1SkI3Si1duhTjxo3D5s2bI24vLy/HG2+8galTp2L8+PG44oor8Pjjj4eUUVUVixcvxpQpUzB+/HhMnz4d69evNyL8BvH5jJnjSXZ0REnvf0F2dDSkPqJkZFS+krlpTieKly9H8fLl0JzOeIeTkpirRObAXCUyh5pP3luyZAny8vJw/vnn17vvmDFjkJeXh48//rhZYgkGgwgGg/qyy+VCYWEh7HZ7sxy/McaPH4+8vLyIPyNGjAgpu3fvXtxwww3o378/CgsLMXLkSCxfvhwAoAk2yI5O0AQbSktLcd9992HQoEEoLCzE0KFD8dhjj0U9wfjrr7+O4447DoWFhRgxYgTef//9JpWLBYthNTWAqqp45ZVXsHHjRv1xgzXt3bsX99xzDwYMGIBrr70Wubm5CAQCKC4uDin3xhtvYO3atZg6dSratGmD77//HrNmzcKMGTPQtWtXo15SvSRJMmSSR82aDV/uWTGvhyiZGZWvRNQ0zFUic2CuEpmDIAgh80oFg0G0bdsWa9euxebNm9GlS5eI+23atAm//vorWrVqFbNcLywsxNKlS2NybAC49tprsWPHDnzwwQe1lpkzZ07EOfIef/xxFBUV6ctlZWU477zzUFhYiDfeeAOtWrXCsmXLcPXVV2PevHkYOHAgNDEbfr8f559/PjIzM/H8888jPz8fP/30E+6880789ttvePXVV+uM+eWXX8YLL7yAxx9/HH369MG3336LW2+9FTabDSNHjmxwuVhJyEap999/H3v27MG9996Lyy+/PGy7pml45plnMHLkSJx77rkh23Jzc/Xf9+/fj88++wyzZs1Cfn4+AOD0009HUVERFi5ciLvuuiumr6MhrFYr/H5/zOsRA8Vw7lsCb5vzoNpax7w+omRkVL4SUdMwV4nMgblKZA41G6WAyh5Kw4YNw8KFC3HHHXdE3O+dd97BWWedhRUrVhgRZtw4nU44a/SODwQCWLp0KV544QV93ZtvvglJkvDqq6/CarUCAK644goEg0E8/fTTmPfGaxDlUnz15Q/YvHkz1q1bh/T0dADAyJEj4XQ6cckll6C0tBTZ2dkRY/F6vXj88cfx7LPPYvjw4QCAc845ByUlJXjggQf0xqZoy8VSQg7fGzlyJKZPnx72B62yYcMGHDhwAGeccUadx/nhhx/QuXNnvUGqyoknnohffvkFHo+n2WI2C8m/F1l/3QfJvzfeoRARJTXB60Xr4cPRevhwCFF2sSYiIiIym4svvhiLFy+O2AtKURQsXrwYY8eOjbjvli1bcOmll6Jr167o168fpk+fHnafvmnTJowfPx7dunVD7969ce2114aNkNq9ezc6dOiAAwcO6OsWLlyIU089Fd27d0efPn0wfvx4/PXXX/p2WZb13kcXXXQRunXrhqOOOgq33npr2PC4559/vs5eUrX56KOPkJWVheOOO05ft2bNGowYMUJvkKpy1llnYdWqVQj63ZD8u2GVKhv9qhqkqrRp0wYWi6XOoYqrVq2C1WoNGzY4ZswYbN++HRs2bGhQuVhKyEYph8MBi6X2TlxVk5ht3LgR9957L6666ircddddWLlyZUi5rVu3oqCgIGz/jh07wmKxYPv27bXWEQwG4fF49J9ox2w2VkVFRUyPT0TNh/lKUdE0WP/8E9Y//wT4COW4YK4SmQNzlcgcas4pVWXQoEHIzMzEsmXLwratWLECLpcLAwcODNu2f/9+nHfeeejcuTM+/fRTLFiwAL///jtuvfVWvUxZWRnGjh0LQRDwwQcf4PPPP0dOTg6uuuqqkGMpigJN06Aoir7u66+/xj/+8Q988cUX+OCDD2C323HVVVfpr8NisUBRFFx99dUYOnQoPv/8c7z55ptYv349HnjggUado5rmzp2L8ePHh6wLBoMRG5RcLhcCgQC2bd8BABh67P8hMzMTCxYs0MvIsoxZs2bhyiuvrLUTDwD88ssv6Nu3L4QaD9vJzMxEp06d8OuvvzaoXCwl5PC9+uzcuROlpaWYPXs2LrnkErRr1w4bNmzAnDlzsHfvXn2itYMHD6KwsDBsf0EQkJmZiYMHD9Zax3vvvYfFixfrywUFBZg1axacTifsdjsqKiqQlpYGURShKAp8Ph9cLhcA6N2Pqy40t9sNh8MBSZKgqiq8Xq9eNhAIQNM0ZGRkIBAIwOPxwG6362U9Ho/eMhoIBKCqKhwOBwDA4/HAZrPBYrFA0zS43W69bDAYhKIoYWVtyt8XbvWysizrF7XX64XFYtFbbisqKuByuSAIAmRZRjAY1Mv6fD5IklRr2UAgoD/m1+fzQRRF2Gw2vWz1c+j3+/Wyfr8fgiDoZd1uN5xOZ7Od76qysTrfXq8XVqu11rINOd81z2Gin+9oz2FTz3fNc1h1vqtea1XZmtdsQ893pHPocDjg8XgadL4T/Zpt6Pnme0T957v6//LZbDYoFkuj3iN8vr//U8TlckHT+B4R7TWblZWFQCBg+HuEKIoNPt+JcM3yPSJxzneqfY/IysqCLMsp8z2C7xGp9R4hiiJEsbIviKqq+u+apgFud8g2QRD0xgEFgHg43nrLahrEwzE0tKymabWWrRlv9YaLqrIAIIoiLrroIixcuBCjRo0KOe7bb7+NcePG6WWr7/P888+jX79+uP/++/V6XnrpJQwZMgQ7duxAp06dsGTJEsiyjJdffhlOpxOqquKBBx7A1q1bsXv3bv1YVfsLgqD//sILL4TEXzVheFWvqqpYTjvtNFx33XV62XvvvReXXHIJZs6cGdV5qe0c/vrrr/jll18wd+5ciKKoly0sLMTPP/+sx1t13G+//RYAUFZeDsAFu92BRYsWYeLEidi6dSvOOecc3HfffejRowfuuuuuWmPQNA1FRUVo06YNRFEMiz83NxdFRUUQRTGkXM34c3NzsW/fvnpfa3p6esS8r9nQVRtTNkp5PB5s3rwZTz31FFq1agUA6NChA5xOJ55//nmMGjUKaWlpCAaDtfa4stlsIbP11zR69GiceeaZ+nLVCfV6vfp+NbsV1vxfnurHr9nTqmbZQCCgr6uvbPXlmk8rqa+s4vEiPYqyVW/cVdxud61lZVmOuiyAkMnf6juHDSnbkPPdkLKNPd/VW+mjKZvM57sp12y0ZWN1voHw8yKKoh5bMl2zDT3fyXzNNsf5rv4xHAgEoFksjbpmVfXvI7ndbqSlJd/5jtU1W/2z1cj3iOYqy/eIyPWkyvlOpe8RsiyHbE/27xHNVTbRrlm+R0Quq6pqSA+j6r+379YNtfGddBJK3nxTX27bvz/EWkbv+IcMwYFqHSraDB4MqaQkYtnAEUdg/7//HbKuekxVDQ+RtgmCEFa2qswFF1yARx99FLt27UK7du0AACUlJVi2bBnuuecevWz1fZYtW4abb745pJ7c3FwUFBTgp59+QseOHfHjjz/ipJNOgt1u18uoqoqxY8fiiy++0JertmmaVuv5zsvLg9Vqxa5du9ChQwc9lmHDhoWU7dmzJzweD4qKivT2hrrOS23n8LXXXsOoUaOQnZ0dsn38+PE4/fTT8eyzz+LKK6+E3W7HN998g0ceeQR2ux2aWlWPhtatW2PMmDF44403sG/fPuzduxfXXHON3thUWwx+vx9WqzXkvFTFb7fb4fV6oapqWLnqZe12O3w+X72vtbb375rDE2uTkMP3otG/f3/9AqkyePBgqKqqjxO1Wq21zu4fCAT0lvFIrFYr0tLS9J+6usY1B6OeOKJaMuDLOQWqJcOQ+oiSEZ8QRGQOzFUic2CuEplDzUnOq8vJycGIESOwaNEifd3SpUsxZMgQtG3bNuI+O3fuxK233opevXqF/Pz555/Yt28fgMohfnl5eWH7Rpqmp6ZNmzbh9ttvx6hRozBgwAD06dNH7+VXM/bqMjMzAYQ3PjZEWVkZ3nvvPVx22WVh27p374758+fj448/Rs+ePVFYWIh77rkHDz30EAKBADKzsqFKmajw+HDOOeegoqICy5cvr5wEfd48PPXUU7VOKl/FZrNFfBIgUNlgVdW+EW25WDJlTymXy6V3kaxOkiSkp6frrdxZWVkoLS0NK6dpGsrKypCVlRXrUKNW24XQ3BRnZ5T0e92QuoiSlVH5SkRNw1wlMgfmKqWqPRs31rpNE0P7j+xbt672sjWGSRV9/33UZRuirkYpALjkkktw++23Y+rUqRAEAe+88w6uvfbaOveZOXMmBg0aFLa+qqHI4XBErLe+WH799Vecc845OP744zFx4kQUFhYiKytLf8JcrL3zzjvIz8+POJcWAAwZMgSffvopPB4PfD4fWrZsiU2bNkGSJOR37grFbscLjz6KFi1a4KabbtL369ixI15//XUce+yxGDlyZMgE6tXl5uZizZo1EbdV7wEWbblYMmWjVF5eXsRZ4GVZRllZmf5YxPz8fKxatSqs3I4dOyDLMjp06BDrUKOWlpYW1u0zJtQgRLkMqiUTEKPrTkdEoQzLVyJqEuYqkTkwVylVaYfnjIpn2YaINGSsumHDhkFRFKxcuRK5ubnYsWMHTjnllFrLt23bFl6vFx07dqy1TF5eHnbt2hW2/rfffqsz1ldeeQUnnHACXnnlFX1dcXFxnVP41GXKlCnYsWMHPvroo6jKv/HGG7jiiivqLVc1Mguo7Fk2cOBA2G1WQJWxbt069O/fP2yfrKwsFBYWYv369bU2SvXs2ROvv/562FxgZWVl2LZtG3r27NmgcrFkyuF7Rx99NH755Zewi3PFihXIzMzUJzcfMGAANm/eHPaUvS+//BI9e/ZERkbqDWGzuv9A22/6w+r+I96hEBElN0GA3KED5A4dgCb8ryQRERGRGYiiiAsvvBALFy7E22+/jdGjR9c5r9Cxxx6L+fPn19nrafjw4Vi+fHnIUDpVVfFmtfm2IikuLkb37t1D1n3yySdRvpJw1SdTr8/KlSuxa9cujBkzJurjb9++Ha+//jomTJgAQfXB6v4Vee1y8c0334QNcS4pKcGff/6JNm3a1Hq8oUOHwuPx6PNuVXn33XfRrl079OnTp0HlYsmUjVJdu3bF0UcfjccffxwbNmxAaWkpVqxYgTfeeAOXX365Prl5u3btMHz4cDz55JN6uU8//RSfffYZxo4dG+dXEarmxIBElLiYrxQNzelE0fffo+j776EZMB6fwjFXicyBuUpkDnX1kqoybtw4/Oc//8E777yDcePG1Vn2mmuuwV9//YUJEybgjz/+QHFxMdasWYNXX31VL3PSSSehffv2mDx5Mv744w/s3LkT06ZNgyRJdR574MCBWLx4MdasWYNdu3ZhwYIFmD17dsT5qaLx/PPP44MPPoiq7Ny5c3HOOefoc1PVVFxcjNWrV+PAgQPYtm0b3nzzTZx55pk4/fTTccYZZ+jlplw1CZs3b8YVV1yBH3/8Efv27cOKFSswfvx4tGvXDiNHjqw1hszMTFx77bW49dZb8eWXX6K4uBjvv/8+Hn74Ydx66616r6hoy8VSwg/fkyQp4gU3depULFiwAI899hg8Hg86dOiAKVOmYPDgwSHlJk2ahLfffhtPPPEEKioq0KFDB9x0002GtPg1RLStrkQUf6mUr1OnXI6K0r1xjSE9uy2efnFuXGMgc0qlXCUyM+YqkTkIghDSq8nhcMBut4eUad++PYYOHYqSkhL07t07ZJvdbg8pX1hYiCVLlmDWrFk499xzEQgE0K5dO1x88cUhdb7xxhu4++67cc4550CSJJx55pl47LHHcOqpp+rlJEmCIAh628GUKVNQUVGB6667DgcOHMDAgQPx2muv4dprrw2Z6NxiseidWqpzOBxRPz2uuv3792PZsmVYunRprWWKioowbdo07Nq1C06nE3369MGMGTMwevTokHKdO3fCJ598gmeffRbXXHMN9u/fj3bt2mHUqFG4+uqrQyYhv//++9G2bVtMnjxZXzd16lQ4HA7cfvvt2Lt3L/Lz83Hffffh/PPPD6kn2nKxImj1zRBGIZoyDrUu6enphoylt5avR+sfT0fxgE8RzOgX8/qIkpFR+ZoIJl50Gt6bHvmJKUYZ/dBevLLws7jGEE8ej4Bu3Sofrbxx4x6kpfFjO1qplKtEZsZcpWRWVlZWa48Zs6lvTilqHoLigcWzEXJaN2hSdPODTZo0CR06dMCMGTNiG1wNdV3fVqsVrVu3rvcYCd9TioiIyJS8XrQ6PJfA/nffBTiEj4iIiIhi4OWXX453CI3GRqkEYdT/DgXTe2PPcX9E3eJKROH4v7kUDUHTYFu7Vv+d/ZuMx1wlMgfmKpE5sJeUMTTRiWB6X5h0CvAGS41XaQJpMXpsZxhBgmbJAIS6J4YjotoZlq9E1CTMVSJzYK4SmQPnfzOIIFTer6fI05t5VSUIoxJc8mxGy7UXQ/JsNqQ+omTED2Qic2CuEpkDc5WIqBrFX3m/rvjjHYkh+AmQIKo/ASCWRMUNx8GvICpuQ+ojSkZG5SsRNQ1zlcgcmKtE5sBnpBlDgAJRKYeA1HhvZKNUgvD7U6MVlCgZMF+JzIG5SmQOzFUic2CjFMUCG6USBMfSE5kH85XIHJirRObAXCUyBw61pVjg0/eIiIhiRGnZMt4hEBERERElLDZKJQijui0r9vYo7fYgFHt7Q+ojSkYcZkDR0NLSsG/9+niHkdKYq0TmwFwlMgcO3zOGJtig2POgCbZ4h2IINkolCMGgxz2qthx48q4wpC6iZGVUvhJR0zBXicyBuUpEVI1ogWprFe8oDMNBoQnCZjOmFVQIHoRz77sQggcNqY8oGRmVr0TUNMxVInNgrhKZAxuQDaLKlffrqhzvSAzBRqkUY/HtRIs/boDFtzPeoRARJTevFznnn4+c888HvN54R0NERETUrJYsWYK8vDycf/759ZYdM2YM8vLy8PHHHxsQWdNt3LgRV199Nfr3749u3brh9NNPx9tvvx1SZvfu3bjuuuswePBgdOnSBf3798fFF1+M5cuXRzxmSUkJ7rvvPgwbNgyFhYXo1asXJk+eHFZO0AKw+LZD0AIh61esWIFevXrhX//6V8Tjl5eX44477sCAAQNQUFCA4cOHY/HixY08A8bh8L0E4Xa74x0CEUWJ+UrREDQN9m+/1X/nLAzGY64SmQNzlcgcVFUNWQ4Gg2jbti3Wrl2LzZs3o0uXLhH327RpE3799Ve0atUKshzf3j9nn302OnbsiOeff77WMn/99RfOOussnH322Xj33XeRlpaGTz75BLfeeisOHTqkNyQFAgEUFhZi4sSJyMvLw/79+7FkyRJceumleOWVV3Daaafpx9yyZQvOP/98jBgxAk899RTy8/Ph9Xqxc2d0nUUWLVqEmTNnIj09PeI5lGUZF110EQDg+eefR8eOHbFmzRrccccdsNvtOOussxpymgzFRqkE4XQ64fF44h0GEUWB+UpkDsxVInNgrhKZgyiKYQ1TLpcLw4YNw8KFC3HHHXdE3O+dd97BWWedhRUrVhgRZpPNmzcPPXr0wCOPPKKvmzRpEoqLi/H222/rjVKdO3fGtGnT9DK5ubno3bs3SktL8e677+qNUpqm4frrr8eVV16Ja6+9NqSu/Pz8euNZs2YNHn/8cSxevBi33XZbxDIff/wxtmzZglWrViE7OxsAkJeXB4fDgZkzZyZ0oxSH7yUIUeSfgsgsmK9E5sBcJTIH5iqRuV188cVYvHhxxB48iqJg8eLFGDt2bMR9t2zZgksvvRRdu3ZFv379MH369LBG6ueeew4nnngiunbtiiOPPBJXX301ioqK9O3btm1Djx49sHbtWpx11lkoLCzEoEGD8PDDD0NRlJBjffDBB3X2kgIAi8WC3NzcsPVt2rRBWlpanfsClU8Ubdu2rb68Zs0a7N69G5MmTap330iOOeYY/Oc//0HXrl1rLfPDDz9gyJAheoNUlVNPPRU7duzAjh07GlW3EfgJkCBqJkusqJITgcyjoUpOQ+ojSkZG5SsRNQ1zlcgcmKuUajQN8HiEhPnRopxjQKul4KBBg5CZmYlly5aFbVuxYgVcLhcGDhwYtm3//v0477zz0LlzZ3z66adYsGABfv/9d9x66616GbfbjV9//RX33XcfvvrqKyxcuBB79uzBLbfcopcRRRE+nw9TpkzBuHHj8NVXX+H555/H+++/jzlz5kT34qoZO3YsVq1ahV9//VVfV1xcjDlz5uCaa66JuI+qqti4cSNmzpyJtWvX4oYbbgg5B8cddxx+/vlnnH/++RgwYADOPfdcLF26NOKxNIjQxDRoh5trBEEIa2yqKRAIwG63h60XRRFOpxObNm2q51XHD4fvJQifz2dIPUpaV+w/+kND6iJKVkblK1X6a/M2TLzotPoLxlh6dls8/eLcuMbwj6n/D76KrXGNAUiMcxEN5iqROTBXKdV4vQK6dWsX7zB0GzfuQVpa/S1TtTVKAcBFF12EBQsW4PTTTw9Zv2jRIowbNy7iPs899xz69u2L+++/X1/3r3/9C8ceeyy2bduGTp06weVy4cUXX9S35+XlYfr06bjgggsgyzIslsomDVmWcdlll2H8+PEAgA4dOuDmm2/G888/j6uvvrre11Zdt27d8MILL+Dqq6/Gtddei/bt22PGjBm46aabQuaJAgCPx4MBAwagvLwcmqZh6NCheP/999GiRQu9zMaNG1FUVIRbb70Vd9xxBwoKCvDDDz/gtttuw5YtW0KGAAIAJAdkV7cGxVxYWIi5c+dCURRIkqSv/+2331BaWopDhw416HhGYqNUgnC5XKioqIh3GEQUBearsSxCEO9Nb1t/wRgb/dDeeIeAikP78OFdPBfRYq4SmQNzlcgcIs0pVeWCCy7Ao48+ij179qBdu8oGt9LSUnzxxReYMWNGxH2++OIL3HzzzSHr2rRpg4KCAvzvf/9Dp06dIu6Xn58PRVGwb98+5OXl6etPOOGEkHK9evXC9u3bo315Ifr06YPBgwdj4cKFyMrKQm5uLo466qiwcmlpaVi2bBkOHTqEv/76Cy+//DImTpyIt956CzabDQBQVlaGdevWYcWKFXq83bt3h8vlwk033YSJEyciMzOzUXFWGTNmDJ5++mncfffd+Mc//oHMzEysW7cOt912W0gDWSJio1SKsZavR+sfT0fxgE8RzOgX73CIiJKa6uRQaSIiIgrldGrYuHFPvMPQOZ1Nf0ZwTk4ORowYgUWLFuHGG28EACxduhRDhgwJmV+pup07d+LWW2/F7bffHrLe7XZj3759+vL//vc/zJ07F7/++iv279+v966s2UCWk5MTspyZmdmonpi//vorJk2ahLvvvhuPPfYYAODbb7/FZZddhltuuQXnnXdeSPm8vDzk5eWhd+/eOOWUU3D66adj4cKFuPzyy/Uyxx9/fEgDGgCcccYZuOGGG7B27VoMGzZMXy8oHlg8GyGndYMm1T+HFVD52t955x3cfffd6NevHyRJQps2bfDggw/in//8Z5MbvWKJjVIJwu/3xzsEIooS85WioaWlYW8Cj99PBcxVInNgrlKqEQRENVwu0dQ1fA8ALrnkEtx+++2YOnUqBEHAO++8E/a0uZpmzpyJQYMGha2vamBavnw5rrjiCpx33nm44YYbkJ+fD7/fj3PPPbfRr6M+d911Fy666CKMHDlSXzdkyBA88cQTuPTSS3HqqaciPT094r4OhwPHH388Vq9erTdKZWVlRWwUslgsaNGiBcrLy5sl7t69e2Px4sXw+/0oLy9HTk4OPB4P9u/fj8LCwmapIxbYKEVERERERERETTJs2DAoioKVK1ciNzcXO3bswCmnnFJr+bZt28Lr9aJjx461lnnhhRdwxRVX4N5779XX/fTTT42O8cwzz0THjh1D5qmqad26dZg6dWrY+qOOOgperxebNm3CkUceWev+siyH9OLq1q0bVq9eHVYuEAjgwIEDaN26dcNeRD3sdrs+6flHH32Edu3aIT8/v1nraE58+l6CiDRTPhElJuYrkTkwV4nMgblKZA6CINS5XRRFXHjhhVi4cCHefvttjB49Glartdbyxx57LObPn19nD6zi4mL06NEjZN2nn37asMBrxCiKdTeDtG/fHl999VXY+u+//x4AkJubW+u+JSUl+Pe//40TTzxRX3fSSSfhm2++CXsC3pIlS9CyZUscccQRDXgF0Tt48CCeeeYZXHHFFfX+7eKJPaWIiIhiwedDy8mTAQAlc+YADkecAyIiIiKKrXHjxuH4449HWloa3nrrrTrLXnPNNRg5ciQmTJiA2267DTk5Odi6dSvWr1+PK6+8EgAwaNAgvP766+jfvz+ysrLw0UcfYcWKFXA08nvVBx98UG+Zf/7zn5gyZQo0TcPFF1+MtLQ0fPfdd7jvvvswduxYtG/fHkDlkwJ79OiBrl27QpIk/Pzzz3jooYfQvXt3nH/++frxjjrqKIwYMQKTJ0/GI488gs6dO+Orr77Cvffei4ceekifEL0pvF4v1qxZg169esHv92PNmjV49NFH0aVLF0yaNKnJx48lNkolCLfbbUg9wbRu2DdoJRR74jyClMhsjMpXMjdBVeH473/13803c4T5MVeJzIG5SmQONScWdzgcYT0d27dvj6FDh6KkpAS9e/cO2VZ9WBkAFBYWYsmSJZg1axbOPfdcBAIBtGvXDhdffLFe5p577sG9996Lyy67DG63G8cffzxef/11nHTSSVAUBQAgSRIEQYAkSSH1Wa3WRjVenXHGGXjnnXfw/PPP4/zzz4fX60WXLl0wbdo0XHrppXq5/fv3480338TevXuhqioKCgowefJkXHrppWGxPP/885g5cyYmTZqE8vJydOvWDY8//jhGjRoVUu66667DqaeegrPPOB0Qw3uZSZIEiyW8GaeiogJ33303tm/fDqvVim7duumxRCqfSAStvtnKKERxcTGCwWCzH9fpdMLr9Tb7cYmo+aVSvk686DS8Nz3yE1OMcvTklfhpznFxjQEARj+0F68s/Czq8oLHg3bdugEA9mzcCC0tuqen1OTxCOjWrfI/Ek4eOggf3pXVqOM0p4aei3hJpVwlMjPmKiWzsrKyhH7yWUMIglDvZOfUNKeffjrOP//8hO/dVKWu69tqtUY1X1ZiN5mlkJotqTGrx7sdGVseRXnBP6A4E3eyM6JEZlS+ElHTMFeJzIG5SmQObJSKvU8//RRQ/ZC826HY2wBi8s+5x4nOE0TNrpCxIsqHkFa0BKJ8yJD6iJKRUflKRE3DXCUyB+YqEdHfBE2BKB+EoCnxDsUQbJRKEB6PJ94hEFGUmK9E5sBcJTIH5iqRObABmWKBjVIJIj09Pd4hEFGUmK9E5sBcJTIH5iqROYgimw+o+fGqIiIiIiIiIiIiw3Gi8wQRCAQMqUex5aK8001QbLmG1EeUjIzKV0osf23ehokXndawnY7vW/nvxNGNrldWnABWAwB27dgOoF+jj5VqmKtE5sBcJTIHTnJuDE2wQrG1gSZY4x2KIdgolSCMGp+r2tugvOBmQ+oiSlYcT5+aLEIQ701va3i9bp8DrVZV/q4qQcPrNzPmKpE5MFeJzIGNUgYRrVDtxn/njBcO30sQDofDkHoEuRz2ki8hyOWG1EeUjIzKVyJqGuYqkTkwV4nMgXNKGURTKu/X+fQ9SkYW71bkrLsEFu/WeIdCRJTcAgqsD6yD9YF1QCA1vlQQERERUdMIqh8W72YIqj/eoRiCjVIJgo/CJTIP5itFRQWklUWQVhYBHJkSF8xVInNgrhKZQ3MPtX311Vdx6qmnNusxyXzYKJUgbDZbvEMgoigxX4nMgblKZA7MVSJzEAQhZHnJkiXIy8vD+eefX+++Y8aMQV5eHj7++GN9XU5ODgoKCpo9zprx1fwpLCyMWP6PP/7A9ddfjwEDBqCgoABHHHEEXnjhhZjFR5U40XmCsFj4pyAyC+ZrEtI0QPVBVL2V4/c1GcLhfwEBmuhA7zwVUAOAYAVqfCmjxMRcJTIH5iqROQiCEDLZeTAYRNu2bbF27Vps3rwZXbp0ibjfpk2b8Ouvv6JVq1aQZVlff8455+Ccc85pcBx79uzByJEjMW3aNFx++eW1lgsGgxg4cCDmzp0bsl6SpLCyn376KW6++WZcddVVuO6669CqVSvs378fwSAfMhNr/ARIEEY9dUQTbZAdnaGJ/B8posbiU4KSgKZBUNwQ5VKIwYMQ5dJ6x+0vvh7AwRXQIEGTXFBtOVCsraBZMtlIlaCYq0TmwFwlMi+Xy4Vhw4Zh4cKFuOOOOyKWeeedd3DWWWdhxYoVzVZvtE8CtFgsyMrKqrNMcXExbrzxRrz55psYOHCgvj4nJ6dJMTaWBhGaYIeWIgPbUuNVmoBRY+llVw8U/d8qyK4ehtRHlIw494WJqQFI3i2wl66E/dB3sLr/gBTYB0H1Q4MAVUyDasmEYm0JxdYair0dFFsbqJYsFJdVHkKAAlEpg8W7BfayNbAf/BqWil8hBooAjTdWiYS5SmQOzFUic6itAfniiy/G4sWLQ3pBVVEUBYsXL8bYsWPDtr377rs46aST9OVFixbh8ssvx+LFizFs2DAUFhZi+PDhWLp0ach+7dq1w9q1a+vsJdUQ8+fPx7Bhw0IapOJKckBO7wlIqfFkUvaUShDp6emoqKiIdxhEFAXmq/kIcjkk3w5I/r0QDs86rkGCas2CasmGZs2GaskChPDu3FVOuGUlfpo9BILqgxg8BDG4H2LwAAQtAIt/D+DfA1V0QnHmQ7G3N+qlUR2Yq0TmwFwlMgdRFCM2TA0aNAiZmZlYtmwZTj/99JBtK1asgMvlitjgI8tyWEPWunXr8Ndff+G+++5Dz549sXr1atx88836HE+x8PXXX+OCCy7A/Pnz8dprr+HQoUPo3bs3brzxRhx11FExqZP+xkapFGOp+A05a8fhwBGLIKf3jnc4REQxJShuWNwbIAVL9HWqlAHZmQ/V1gYQGthhWKgcuqdILiiO9oCmVg4BDBRD8u+FqHohujfA4tkMBWyYIiIiotqJ/n2QAkUh61RLFhRnPqD4YPVsDNsnmNEPACB5NkFUvCHbZEcHaNYWEAMHIPl3hx5XckFJ6wJoCqwVv+nrFVsuVHubJr+Wiy66CAsWLAhrlFq0aBHGjRsX9XGKiorwySefoH///gCAc889F19//TUWLVrUqEapTZs2YdSoUdi9ezdat26NoUOH4sYbb0R2drZeZuPGjXj33XdhsVgwc+ZMOJ1OfPjhhzjvvPMwb948DB06tMH1NonihcW7GbKzCyA5ja07DtgolSACgYAh9QiaAilYUjmBLxE1ilH5Sk2gqZC8W2HxboEArXJoni0XsqMjNEtW880BJYhQrS2hWltCTusKyb8bknc7RNULi7YN2isC5LTOAKfxiwvmKpE5MFcpVbl2z0PGtidC1nlyz0Np72ch+feg9Y+nh+2z+8RdAIAWf0yDreynkG0Hez4Db9sxcBR/iOyNofM7+VqcgJIjFkBQPCHHLe90E8oLbo4q3rrmcbrgggvw6KOPYs+ePWjXrh0AoLS0FF988QVmzJgR1fEBIDs7W2+QqtKrVy989dVXUR+jyuDBg3Hfffeha9eusFqt+Ouvv/Dcc89h1KhR+Oyzz5CRkQEAKCsrQ2lpKT777DOIYuV/WPbt2xeHDh3Cww8/jA8//LDBdTeFAA2CJh/+Dpv82CiVIDjBI5F5MF8TmxAshdX9O0TFDQBQrDmQXT2gSWkxrliC4ugIxd4BYqAIFu82iEIZrOoWSGVFkF09oFpbxjYGCsFcJTIH5iqlKnf78fC1OjVknWqpnJRbsbdD8YBPa933YM8nI/aUAgBf67NQnDkg9LiSCwCgSWkhx1VsuVHHW1ejVE5ODkaMGIFFixbhxhtvBAAsXboUQ4YMQdu2baOuI9Lk4hkZGfB6vRFK161z587o3LmzvtytWzcMHToUw4YNw9tvv42JEyfq2y644AK9QarK2Wefjfnz5yMQCMBm4/8wxgobpRKEw+HgWHoik2C+JihNhcXzJyTfTggANMGKoKvH4WF6Bj4dTxCg2tsgYMuFGNgLq3sjRMUNW9lPUGxtEXR1B/gEVEMwV4nMgblKqUq1t6l96Jzk0IfqRaKkdUVtY19UWw5UWy1PjhOkOo9bl9rmlKpyySWX4Pbbb8fUqVMhCALeeecdXHvttY2qK1YyMjIwcOBAbNiwQV+XlZWF3Nzwxrnc3Fyoqgq3281GqRji0/eIiMj81ABsZT/CcrhBSra3gz97CFR7W2MbpKoLapCeLYH6agvIYh40AFJgL+yl30D0F9W7OxEREZGZDBs2DIqiYOXKldiwYQN27NiBU045pdnr2bNnD/r164fXX3+9UfvLsgyXy6Uvd+vWDVu3bg0rt3v3bjgcDmRmZjYyUooGG6UShFGPwpWdXVB81PuVk6YRUaPw0dWJRZArYDu0BqJ8CJogIZBxJOT0PvHvjaRqkJbtgbRsH2RndwQyB0KVMiBoMmwV62Bx/wloHLISS8xVInNgrhKZQ31DbUVRxIUXXoiFCxfi7bffxujRo2G1Wps9DkEQIIpi2HC7aOzZswerV6/GkCFD9HUnnXQSFi1aFDa/3eLFi3HiiSdCkmp/OnMsaKIdsrMrNNFuaL3xwuF7CcJms8Hn88W8Hs3iQjDrmJjXQ5TMjMpXqp8YOABrxToImgJVdCKYcQQ0S3q8w4pIs2YhkDUQFs9fsPi2weLbDlE+hEB6P0ByxDu8pMRcJTIH5iqROQiCUOe8UgAwbtw4HH/88UhLS8Nbb70Vkzjatm2LtWvX1lvuk08+gSiK6Nu3LwDg559/xqOPPoojjjgipAfX+PHj8dprr+Gaa67BbbfdBqfTifnz5+Pzzz83fJJzAJVPe7a46i+XJNgolSAsFmP+FKJvN9J3zkZFh/8H1cHHlRM1hlH5SnWTfDthcW+AAA2qJRuBjP7x7x1VH0GE7OoG1ZoFa8VvEOVDsB/6HsH0vrXP/UCNxlwlMgfmKpE51GyUcjgcsNtDe/O0b98eQ4cORUlJCXr37h2yzW63h5S3WCwh+V9zua56oqFpGmbOnIkdO3ZAVVV06tQJ5513HqZMmQKh2vQOmZmZeOeddzBjxgycccYZUFUVgwYNwrvvvouuXbs2uN4mUwMQA/uh2lol/nfbZsBPgARRX4tzc5GCB5C+cw68bcawUYqokYzKV6qd5N0Oq+dPAJVPpwm6egGCeUakq7ZcBLLSYS1fD1Eph7X8Z8hp3aE48+MdWlJhrhKZA3OVyJzOOeccnHPOOWHr586dG7H8ihUrQpbHjBmDMWPG1LpcXz31GTVqFEaNGhVV2c6dOzd6jqrmJmgypGAxNGs2NCR/o5R5vsEnObfbHe8QiChKzNf4kny79AYp2VmAoKu3qRqkqmhSGgJZx0C250EAYPX8CYt7E8Cbs2bDXCUyB+YqkTnUN6cUUWOY71t8kkpPT8w5UIgoHPM1fkT/XljcvwMAZEenyoc2xOvpes1BkCC7eiKYVggAsPi2wuL+jROgNxPmKpE5MFeJzKExE4sT1YdXFRERmYIY2A9rxa8QAMj2PMhpXc3dIFVFEKA4CxB09YIGARb/HljL1wGaEu/IiIiIiIhiinNKJYhgMGhIPaq1JdztL4dqbWlIfUTJyKh8pb+JwRJYy9dBgAbF1hayq2fiN0jZRfjeOl7/vT6KIw+aaIO1fD2k4H4IZT8hkHEkAD6Zr7GYq0TmwFwlMgfO/2YMTbBAteZAE1KjuYY9pRKELMuG1KM48nCo+0NQHHmG1EeUjIzKV6rUrY0Ka9laCFChWFshmN478RukgMoYs22VP1HGq9paI5B5NDTBAlE+BFvZz4DK662xmKtE5sBcJTIHNkoZRLRBcXRIiSfvAWyUShhOp9OQegTFC2v5egiK15D6iJKRUflKAFQZz1wahAAFiqUFghn9TDmpeUNo1mwEMo+BJlghKmWwla+Ld0imxVwlMgfmKpE5cE4pg2gqBMWTMnOM8qpKMRbPJrT+8XRYPJviHQoRUd00DdaKX9GplQZNdBxukJLiHVX0Aiosz/0By3N/AIGGfanQLOkIZB51uMdUaWziIyIiIqKEI6g+WDwbIai+eIdiCDZKJQivlz2XiMyC+WoMybcVUrAY/iAQyOhvvi7MqgbLRzth+WgnoDa8u7tmyUQg4yho+Lshziqx23xDMFeJzIG5SmQOqpoaPXfIWAndKLV06VKMGzcOmzdvrrNcUVERLr30Utx///1h2wKBAF577TVMnjwZl156Ke69915s2bIlViE3mtVqjXcIRBQl5mvsiYEDsHj+AgDc/74FmiUzzhHFh2bNQiCzv778wJhgynTlbg7MVSJzYK4SmYNghjk9yXQSslFKVVXMmTMH33zzDTRNq3fyw5dffhkFBQVQlPDHZz/zzDPYvHkzpk+fjqeeegq9evXCjBkzsH///liF3ygWS2rMrE+UDJivMab4YK34BQIA2d4eS35I7fOtWVvov5/cV4XFvQHgRKNRYa4SmQNzlcgc2ChFsZCQjVLvv/8+9uzZg3vvvbfesqtWrYLX68Xw4cPDtm3YsAFr167FLbfcgoKCAuTk5GDs2LHo378/Fi9eHIvQE54GAaqUDg18QyGiBKSpsJWvg6AFoUqZkF094h1RQlFVwOLfBcm3Pd6hEBERUYpbsmQJ8vLycP7559dbdsyYMcjLy8PHH39sQGThNE3D4sWLcf7556NPnz7o06cPLrnkEvzyyy+17rNjxw4MHz4cl19+ea3HnD17No4//ngUFBRgwIABePjhhxEMBkPKzZs3D2eddRb69OmDwsJCHHfccXjooYdw6NChyMeFAA1SytyzJ2Sj1MiRIzF9+vR6n8Thdrsxb948TJ48OWKr7erVq3HUUUchKysrZP2JJ56IH374oVljbqqKigpD6pEz+mLvsA2QM/oaUh9RMjIqX1ORxbMZolIGTbAiYLaJzQ3wxGeVvQksno0QA0VxjibxMVeJzIG5SmQONeeUCgaDaNu2LdauXVvnlDubNm3Cr7/+ilatWtU7Cqox5s6diyOPPBJ79uyptYzf78dbb72FCy+8EB988AE++ugjtGvXDueddx62bw//z75169bh3HPPhSRJtcZ833334cUXX8Qtt9yClStX4rnnnsOyZctwzz33hJQLBAKYPHkyPvroI3z99de466678Pnnn2PcuHHQIvV+l5yV9+tSajyZNCEbpRwOR1TdeOfPn4+hQ4ciPz8/4vatW7eioKAgbH1BQQHKyspQUlJS67GDwSA8Ho/+E+sJGNPT02N6fCJqPszX2BCChyD5tgIAgum9UuaDuCEWfitBtneAAMBa/gsEuSzeISU05iqROTBXicxBFMObD1wuF8444wwsXLiw1v3eeecdnHXWWXA4HDGLLWLjTjUOh0PvKVVYWIiCggI88sgjaNeuHd57772Qsl6vF5dddhnuv/9+jBw5MuLx9uzZg1deeQUvvvgizj77bOTl5WHIkCGYP38+Fi9eHNJAduWVV+Lss89GQUEB2rdvj9NOOw3z58/H+vXr8eeffzb9xZucaQdwb9iwAevWrcPjjz9ea5mSkhK0aNEibH12dra+vWXLlhH3fe+990KG+BUUFGDWrFlwOp2w2+2oqKhAWloaRFGEoijw+XxwuVwAKlthAcButwOo7NHlcDggSRJUVYXX69XLBgIBaJoGm82G9PR0eDwe2O12vazH49E/qAOBAFRV1ZPZ4/HAZrPBYrFA0zS43W69bDAYhKIoYWVt3k1w/ng5DvR6EY42R+tlZVnWe6Z5vV5YLBZ90smKigq4XC4IggBZlhEMBvWyPp8PkiTVWjYQCCAtLU0vK4oibDabXrb6OfT7/XpZv98PQRD0sm63G06ns9nOd1XZWJ1vr9cLq9Vaa9mGnO+a5zDRz3e057Cp57vmOaw631WvtapszWu2oec70jm02WxwOBwNOt+Jfs3WVlaUJIiiCE3TIIqVvZZUVYEgCBCEyi8miiJDkiQAAjRNg6apUZdVVbVyWVMguX+DAEC1t4PgbA9UlRUEiKL0d1lU/k+dAEAQazuuAkmy6GWBv79IKYoMUZQO97DVoCh/l9VUFVpIWQWiKFaWPdwhN7Ts3+clpCw0KPh7nkNREKAdfh1/nxcxcgyaGnK+Q3oCCyJkV3eImhdi4ABs5WvhzxwEyeZq2PmuKguhjnNY+/kG0Ojr28j3iKrPVqPfI0RRbPB7slnfI6qfb36P4PeIxp5vq9WK9PT0lPkewfeI1HqPEEVR/16hqqr+e+V3Ji1kW+VnuJCwZQGElQWASy65BP/v//0/3H777fo1UHVcRVGwePFivPTSS1ixYoW+jyiKCAaDePjhh/Hxxx9j3759aNGiBUaMGIG77roLmZmVD7m57LLLkJ6ejhdeeEGv9+6778a+ffswe/ZsaJqGCRMmYMKECQ1+rZIkoUePHnpnlaptaWlpWL58OXJycvDHH38AQNhx//e//yEjIwNDhgwJOW6bNm0wYMAArFy5EuPGjas1hmAwCEmS0Lp16/DrQ/FB9GyB4uwEwZLW4L9j1d+qetn6/q7Rlo10vaSnp0fM+2jnIDNlo5Qsy5g9ezauuOIKPeFrKxepx5UoipAkKWysZ3WjR4/GmWeeqS9XnVCv16vv5/F4Qvap2fW4+vFr9rSqWdbj8cDn80VVtvpy1T7RllUqDiK94g8Iqr/OslVv3FXcbnetZWVZjrosUPlmXaW+c9iQsg053w0p29jzXXPi/VQ+3025ZqMtG6vzDYSfF1VV9diS6ZqNVFZVFL1RR1H+7rpc+QH2dxfumue/oWUt7k0QFTc0wYZAWnfg8P6KogCHG5nCjgsASnQxVC7/XbbqeA0qq9VWVg4vCwA2Ef7Xh1buahUATatxXuqLQT5crtrnmKYBgoiAqx9syhqIihu28p8RyDoGEKoatRrwt4EW9Tmseb7N8B5R/bPVyPeI5iprhveI2paT/XON3yOaXrbmOax+bpL9e0RzlU20a5bvEZHLqqoaMuyt5hC46stVN/2JXLb6tqrfBw4ciMzMTPznP//B6aefHrLvl19+CZfLhWOOOSZkH1VVsWXLFhw6dAhPPfUU8vPzsWvXLkybNg0zZ87Egw8+CACYMWMGRowYgW+//RaDBw/Gb7/9hrfeegvLli0LOVZjXquqqvjll18wYsSIsG0tWrSAqqr6sWoeNxAIwG63R4zB5XJh48aN+rrq2w4dOoTVq1dj1qxZuOOOO9CyZcuwmARVhqD6AE1t9Gurfq6j2bchZWvGVP36r/57tE9WTcjhe/X58MMP0bZtW/3Cro3FYok4/lNVVSiKoreMR2K1WpGWlqb/1De/VVPV1UBGRImF+dq8Qoft9QTEJHk0uChAa+uE1tYJiM08UaVoQSDjSGiCDaJSAWvFb3wiXwTMVSJzYK4SmUNdQ+QuuugiLFiwIGz9okWLMG7cuIj7dO3aFY888ggGDRqEtm3bYsCAAbjhhhvw+eef62U6d+6M6667DnfffbfeS2rKlCm1TuHTEB988AECgQDOPffcBu9bWFiIoqIi7Ny5M2S91+vFzz//jNLS0pD1jz/+OAoKCtC7d29cc801uPbaa3HVVVc1IfrkYbpGqaKiInz88ceYMGFCvWWzs7Nx8ODBsPVVF0jNCdDjKdaNXkTUfJivzUhTYD08bE+xtYVqy413ROYhORHIOAIaBEiBIj6RLwLmKpE5MFcpVYn+fbCWrw/5kbyHP88VX9g2a/l6fV/JsylsmxCsvPcVAwfCj+s5PBG5poSsF/37oo83wpxSVS644AKsWrUqZC6l0tJSfPHFFxgzZkzUdXTq1ClswvJrrrkGHo8HkydPxu7duzFlypSoj1ebkpIS3HPPPbj99tvrHH1Vm759+2LQoEGYOnUqtm7dCgDYtWsXrr/++ojlr7rqKixfvhxLlizB5MmTceedd+LTTz9tyktIGqYbvrdlyxa43W7ccsstIetlWYYsy7jiiitwwgknYMKECcjPz8eWLVsiHsPlciEnJ8eosImIKAKLZ4s+bC/o6h7vcJpXUIXl9b8AAPIVhYC1+f8fSLNmQXZ1h9W9ARbPJqiWTGjW8LkUiYiIKPG4ds9DxrYnQtZ5cs9Dae9nIfn3oPWPp4fts/vEXQCAFn9Mg63sp5BtB3s+A2/bMXAUf4jsjXeEbPO1OAElRyyAoHhCjlve6SaUF9zc5NeSk5ODESNGYNGiRbjxxhsBAEuXLsWQIUPQtm3bWvdbsWIF3nrrLWzYsAElJSX6VBnV2e12TJ8+HZMmTcKzzz7b5AnTFUXB9ddfj+HDhzeowayml19+Gffddx+GDx+ux3nNNdfA6XSGdYBJT09Heno6OnfujMGDB6Nz58644447cPLJJ0f1kLdkZrpXP2DAADzzzDNhXQe/++47fP/995g6dao+0dwxxxyDRx55BGVlZfpEaQDw5ZdfYsCAAVFPvGWEWD/dr4rsyMeBvq9BdjS9uyNRqjIqX5OdIJfVGLZX+5BqU1I0WN7dBgCQL+0CxGhUomLvADF4CFJgL2zl6+HPHgyIDf8fv2TEXCUyB+YqpSp3+/HwtTo1ZJ1qqWzMUOztUDyg9p40B3s+CVEJzR3Z0QEA4Gt9FoozB4QeV6q8R9aktJDjKg3opV6zsaimSy65BLfffjumTp0KQRDwzjvv4Nprr621/Pz583HnnXdi/Pjx+Oc//4n27dtjy5YtuPrqq8PKfvLJJ8jMzMRnn32G8847L+qYI5kxYwbKy8vx6quvNuk4LVu2xFNPPYUnnngC+/fvR8uWLWGxWHDmmWdi6NChde572mmnYdq0adi2bRsKCwtDtmmCDbKjMzQhyb4b18J0jVIWiwWtW7cOW5+ZmQmr1Yrc3L+Tql+/fujevTsee+wxXHnllcjMzMSyZcvwv//9Dw8//LCRYdfLYrGETbAYC5o1C/4ab3xE1DBG5WtS0zRY3RsOD9trw2F7TSEICKb3gnCo/PDE5+sRyDwaEEw3Qr/ZMVeJzIG5SqlKtbeBam8TeaPkQDCjX637KmldUVvWqLYcqLZaRgUJUp3HrYsgCHXOKzVs2DAoioKVK1ciNzcXO3bswCmnnFJr+eeeew533HEHJk2apK/766+/wsqtXr0an3/+Od577z2MHj0aq1atCmn0ef311/H444/jP//5D9q1a1fna5gzZw4+//xzfPjhh40atheJKIp6O8S2bduwdu1a/WmBtama+zri+RQt0MTEmWoo1hL+G6skSfojqetitVojzu5+8803o2PHjrj//vtx/fXXY/369bjrrruQl5cXi3AbLdqZ6ZtK9BchfduzEP1FhtRHlIyMytdkJgb2QpQPQYOUfMP24kGQEMzoD02QIMqlsHjCv9ClIuYqkTkwV4nMob6RRqIo4sILL8TChQvx9ttvY/To0XXmd1FREbp3D/0eWHOeJUVRMH36dNx4443o2bMnrr32Wtx9990hDzQTBAGiKNYb36effoqnn34ab775ZsSOLk2laRoeeOABnHTSSfVOxD5v3jx06NAhrJcUAEANVs71pabGQyASvqfUwoULoyo3dOjQiF3k0tLSMHnyZEyePLm5QzMlKbAPmVsehr/liVDt7JlARHGgybC6NwEA5LTOHGrWTDTJhaCrN2wV62HxbYNqzWIPNCIiIjLUuHHjcPzxxyMtLQ1vvfVWnWUHDRqEF198Ee3bt4coili4cCF27NgRUubVV1+FoiiYOHEiAGDSpEl4/fXXMXfuXH3d5Zdfjssvv7zOutauXYsbbrgBTz31FHJzc3Ho0CF9m9VqRVpaWoNf64oVK1BYWAiLxYINGzbgueeew/bt27FkyRK9zC+//IJvv/0Wxx13HFq1aoXi4mIsWLAACxYswKuvvhqxIU3QgpACe6FZMqDFav6HBJLwjVKpoqKiIt4hEFGUmK9NY/FuhaD5oYpOKJzfrlmp9jaQ5UOw+LbDWvE7/NlZKd3ox1wlMgfmKpE51JxTyuFwhA2Ba9++PYYOHYqSkhL07t07ZJvdbg8p/+yzz2L69OkYPXo0VFXFyJEj8eKLL2Lo0KEIBoOoqKjAU089hdmzZ+uTgTscDtx+++246667cMEFF4TMHV2X9957D263O2JnlYKCAqxcuTLifnWN3HrllVfw7bffQlEU5OXl4bTTTsNLL72EFi1CHzrz2Wef4amnnkJFRQUyMjJw3HHH4fPPP4/cSyoFCVpdg0IpTHFxMYLB5u9G53K54Ha7m/24NVnL16P1j6ejeMCnjR5LTJTqjMrXRDDxotPw3vTan5jSUILiga30WwjQEMjoH1VPnqMnr8RPc45rthgaq8Fx+BQ4zl1e+evS4YCj/qHokbh9DrQ690MAQI+8HPzvlaPr3kFTYTu0BqJSDsXaEsGMo4BmfrDH6If24pWFnzXrMWMhlXKVyMyYq5TMaj50y8xEUax3snNqOkHxwOLZCDmtGzSp4T24jFTX9W21WqMaJpnwc0qlikR6EiAR1Y352ngW90YI0KBYW0K1Nv9YfgIgiAhm9IUGEVKwBJJve7wjihvmKpE5MFeJiFIXh+8liOoTtcWSasmEt/UZUC3J0VpPFA9G5WuyEQMHIAWLoUGAnNa92XvvJBybCP+//k//3Uia5ILs6g6r+w9YPJugWltCs2QYGkMiYK4SmQNzlcgcOMjKGJogQbVkQRMa18vebNhTKkEEAgFD6lGcnXCwz2wozk6G1EeUjIzK16SiqbB4/gQAKI4O0CzpcQ7IAKIArXM6tM7pgGh8A5xiz4NibQUBGqwVvwBa6j1unblKZA7MVSJzYKOUQUQ7FGfnlJkXlI1SCaIxs/03ihqA6NsNqPzwJ2osw/I1iUj+3RAVNzTBCtnZJd7hpAZBQDC9NzTBBlFxw+LZGO+IDMdcJTIH5iqROYgimw8MoamV9+taaszfxasqxVjdG9D2u4GwujfEOxQiShWaAotnMwBUNkiJyf9oWwBAUIXlzb9gefMvIBinLxWiDcH0PgAAi28nxMD++MRBRERERFERVB+s7t8hqL54h2IINkolCJ8vNS44omTAfG0YybcTghaAJjqgOPLiHY5xFA2W+Vtgmb8FUOLX3V215UB2dAQAWCt+B9Tmf4JsomKuEpkDc5XIHPjkPYoFNkolCHaFJDIP5msDaDIs3q0AANlZAAg8d/Egp3WFKqZB0Pz63F6pgLlKZA7MVUp2ipIc8zrySZlUXXNd1/wESBA2my3eIRBRlJiv0ZO8OyBoQaiiE4q9XbzDSV2CVDm/FACLf0/KDONjrhKZA3OVkpnT6URFRQWCwaDpJwpnoxQBlRPeB4NBVFRUwOl0Nvl4lmaIiYiIKJwahMW3DQAgpxWyl1ScadZsKI6OsPh2wOr+HX7LEEDk1wAiIqJYslqtyMjIgNfrhdfrjXc4TSJJUtL0+kpkglwOa9kOBIOtoFnkeIcTkSRJyMjIaJaervw2miAqKioMqSeY3ge7j98MCCky0TBRDBiVr2Zn8W2HoMlQJRdUW5t4h0OoHMYnBvZDVL2weDZCTu8V75BiirlKZA7MVUp2oijC5XLFOwwyCy0dyG4Fh2BNif/UTf5XaBKGPQpXEAHRnhIXN1Gs8NHVUVADkLzbAQCysxBgd+/EIEh6Q5TFvwti4ECcA4ot5iqROTBXicyBuWqQFLtnZ0+pBGHUBI+S5y9kb/gnSnvMgpJWaEidkUydcjkqSvfGrf4q6dlt8fSLc+MdBpkMJ2Stn8W7FQIUqFIGVFvreIdD1ajWlpDtHWDx76wcxmf9P0BIzq8DzFUic2CuEpkDc9UYiXLPbpTk/BZqQrJszFhRUfHAfuhbiIoH8RwNXFG6F+9NbxvHCCqNfij+DWNkPkblq2mpfki+nQCq5pJK0V5SVhH+pwfqvycS2dUVYnA/RNUHi2cTZFfPeIcUE8xVInNgrhKZA3PVGIlyz26UxPqWnMICgUC8QyCiKDFf61bZS0qFasmCas2JdzjxIwnQemRB65EFSAnWMCdYILsqh/FJvp0QgofiHFBsMFeJzIG5SmQOzFWKBTZKJQiOzyUyD+ZrHdQAJN8uAIDs7JK6vaRMQLXlQLG1hQDA6v4d0NR4h9TsmKtE5sBcJTIH5irFAhuliIio2Ui+nZW9pKR0qNaW8Q4nvoIqpHe2QnpnKxBMzAafoKs7NMECUamA5NsR73CIiIiIKMWwUSpB+Hw+Q+pR7Hko7f4oFHueIfURJSOj8tV0NAWWww0bsrMze0kpGqyvbIL1lU2AosU7mshEG+S0bgAAi2czoCTXtc1cJTIH5iqROTBXjZFq9+xslEoQRj3JQLW1hKf9xVBtKd6DgagJ+OSRyCTfLghaEKrohGrLjXc4FCXF3h6qJQsCFFjdG+IdTrNirhKZA3OVyByYq8ZItXt2XlUJwmazGVKPGChB2u4FEAMlhtRHlIyMyldT0VRYfNsBAIqzEyDw48U0BAFBVy9oECAFiyEGiuIdUbNhrhKZA3OVyByYq8ZItXt23jWkGMm/C9l//gOSf1e8QyGiJCIG9kFQfdAEGxR7u3iHQw2kWdIrGxOByt5SGh/5TERERBQPqXbPzkapBFFRURHvEIgoSszXGjQNFu9WAIDszAcEKb7xUKPIzgKoohOC6q+cXyoJMFeJzIG5SmQOzFWKBTZKJQg+XpPIPJivocTgfoiKG5ogpcyEjElJkCC7egAAJN8OCLL5v3gyV4nMgblKZA7MVYoFNkolCE4aR2QezNdQVb2kFHsHQLTGNxhqEtXWCoq1NQRosHj+BLQEfWpglJirRObAXCUyB+YqxYIl3gFQJUVRDKlHldLgzxoCVWIrN1FjGZWvZiAESyHKh6BBqBy6R3+zigjMOlr/3SxkV3eIpQcgBUugBIqg2tvEO6RGY64SmQNzlcgcmKvGSLV7djZKJQifz2dIPUpaIQ4ctdiQuoiSlVH5agYW3zYAgGJvD4j2OEeTYCQB6hHme5SvJjmhODvB4t0Cq2cj/LZWpp0njLlKZA7MVSJzYK4aI9Xu2c3zX7dJzuVyGVORpgKqv/JfImoUw/I1wQmKF2KgGACgODrGORpqTrKzMzTRAUH16cMzzYi5SmQOzFUic2CuGiTF7tnZKJVirBW/ov2KLrBW/BrvUIjI5CTfTggAFGtLaJb0eIeTeGQV0gc7IH2wA5BN9qVCkBBM6w4AkLzbICieOAdERERElBpS7Z6djVIJwu/3xzsEIooS8xWApkDy7wLAXlK1kjVYX9gA6wsbANl8E4arttZQrC0hQIXF/We8w2kU5iqROTBXicyBuUqxwEYpIiJqMMm/B4ImQxWdUK2t4h0OxYIgQE7rAQ0CpOB+iIH98Y6IiIiIiJIMG6UShN3OCYKJzCLl81XTIPl2AAAURwdAEOIcEMWKZnHpPeEs7j9NN7dByucqkUkwV4nMgblKscBGKSIiahBRPghRcUODVPnUPUpqsrMLNMEGUfVA8u2MdzhERERElETYKJUg3G63IfUEXT2w9//WIOjqYUh9RMnIqHxNVJL3cC8peztAtMY5Goo50QI5rQsAwOLdDKjBOAcUvVTPVSKzYK4SmQNz1Ripds/ORqkE4XA4jKlItEF1tAdEmzH1ESUhw/I1AQmKF2KwGAAnOE8lij0PqpQOQZMrG6ZMIpVzlchMmKtE5sBcNUiK3bOzUSpBSJJkTD3ebWjx6/+D5N1mSH1EyciofE1Ekm8nBACKtSU0iyve4ZBRBAFyWjcAlddAXpY5nr6TyrlKZCbMVSJzYK4aI9Xu2dkolSBU1ZjJY0W5DM7ijyHKZYbUR5SMjMrXhKMpkPy7ALCXVFSsAgL3HonAvUcCVvNPBq/acqBYW0GAhslD9sY7nKikbK4SmQxzlcgcmKvGSLV7djZKJQiv1xvvEIgoSqmar5J/DwRNhio6oVpbxTucxCeJUAe3gjq4FSAlx8et7OoGDQIGdyqHrWRFvMOpV6rmKpHZMFeJzIG5SrFgiXcAVMnlcqGioiLeYRBRFIzI16lTLkdFafx7o+zYvg1AW0DT9CevKY4OgGD+nj/UcJrkguLoAItvB7L+ug/FLT4DhMTtys/PViJzYK4SmQNzlWKBjVJERAmoonQv3pveNt5h4OjJmwAAglwGUamABhGKvX2cozIJWYX038qGReWktoAlSXpLObvAW7obGfgdaXsWwtN+fLxDIiIiIiKTSo5vyEkgEAgYUo9ia4Oygtug2NoYUh9RMjIqXxNJ1VxSqi0XEK1xjsYkZA3WJ36D9YnfAFmLdzTNR7Ri/o+tAQAZWx6FICfu/5imYq4SmRFzlcgcmKvGSLV7djZKJQhNM+aGRbXnoqLT9VDtuYbUR5SMjMrXhKHKkPyVPX5kR16cg6FE8NFvOZCdBZCC+5G+48V4h1OrlMtVIpNirhKZA3PVGKl2z85GqQRht9sNqUcIHoJ9/38gBA8ZUh9RMjIqXxOFFNgLASpUyQXNkh3vcCgBKKqAsi63AwBcO16C6I///GeRpFquEpkVc5XIHJirxki1e3Y2SqUYi287cn6ZAItve7xDISJTqDbBuT2PE5yTztdqFAKZAyCqXmRsfTze4RARERElhVS7Z2ejVILweDzxDoGIopRK+dqvg1ZtgvN28Q6HEokgoKzwLgBA2p63YHFviHNA4VIpV4nMjLlKZA7MVYoFNkolCHaFJDKPVMrXCwYrADjBOUUWyBoIb6uREKAi868H4x1OmFTKVSIzY64SmQNzlWKBjVIJQpKkeIdARFFKmXxVZYw6orJRihOcU23KutwODRIcJV/AdnBVvMMJkTK5SmRyzFUic2CuUiywUSpBqKpqSD2aaEcwrTs0ka3cRI1lVL7GmxTYizQbOMF5Y1kFBKb3Q2B6P8CavHNxKWmF8LQfDwDI/OsBQEuc/EiVXCUyO+YqkTkwV42RavfsbJRKEEaNz5Vd3VE8aDlkV3dD6iNKRikxnl7jBOdNJolQj28D9fg2gJTcH7flnW+CKrlgq1gHZ9H78Q5HlxK5SpQEmKtE5sBcNUaq3bMn97dkE0lPT493CEQUpVTIV0Eug6hUwB8EJzineqm2VqjoeA0AIGPLLED1xzmiSqmQq0TJgLlKZA7MVYoFNkqlGEv5L2j7dQ9Yyn+JdyhElMAk/y4AwGfrRU5w3liKCnHFPogr9gFK8nd3d3e8CoqtDSy+HUjbvSDe4RARERGZUqrds7NRKkEEAgFD6hFQ+Xh3AZoh9RElI6PyNW40BVJgHwBg8RpLnIMxsaAG20PrYXtoPRBM/vdcTXKivNNUAEDGtqcgyO44R5QCuUqUJJirRObAXDVGqt2zs1EqQXDSOCLzSPZ8FQNFEDQFqujED1s4lxRFz9PuYsiOzpCC++HaOSfe4SR9rhIlC+YqkTkwVykW2CiVIBwOR7xDIKIoJXu+Sr7dAKrmkmKjFDWAaEV5wT8AAOk7/gUxUBLXcJI9V4mSBXOVyByYqxQLbJQiIiKdoHghyQehgROcU+N4c89G0NUbolKO9O3PxTscIiIiIkpgbJRKEEY9XlNO64riAZ9CTutqSH1EySiZH4cr+vcAAFRrS0ByxjkaMiVBRFmX2wEArl2vQzzc8y4ekjlXiZIJc5XIHJirxki1e3Y2SiUIm81mSD2a5EQwox803mwSNZpR+Wo4TYN0uFGKvaSoKfwth8Of9X8QND8ytj0ZtziSNleJkgxzlcgcmKvGSLV79oR+rNLSpUuxcOFCzJw5E126dNHXezwe/Oc//8GqVauwd+9euFwuHHnkkbjooouQlZUVcgxVVbFkyRJ88cUXKC8vR35+Pi666CL069fP6JdTJ4vFmD+F5NuF9O3PoyL/WiiOPEPqJEo2RuWr0UT5IETVC02QoNpy4x0OmZkgoKzLbWj987lI2/MW3B2uguwy/n/7kjVXiZINc5XIHJirxki1e/aE7CmlqirmzJmDb775BpqmQZblkO1bt27F5s2bcfHFF+PJJ5/EP/7xD+zYsQMPPPBA2BMB3njjDaxatQpTp07Fs88+i+OPPx6zZs3Cpk2bjHxJ9dI0Yx73KAZL4No9F2IwvpPPEpmZUflqNL2XlK0tIEhxjiYJWAQEb+qN4E29AUvqTRgfzBoIX84pEKAiY+sjcYkhWXOVKNkwV4nMgblqjFS7Z0/IRqn3338fe/bswb333htxe+/evXHTTTfhqKOOQqtWrVBYWIibb74Z27dvx59//qmX279/Pz777DNMmzYNPXv2RIsWLXD66afj1FNPxcKFC416OVFxu93xDoGIopSU+arJEP37AHDoXrOxiFBObQ/l1PaAJSE/bmOurOCf0CDAWfwxrGVrDa8/KXOVKAkxV4nMgblKsZCQ35JHjhyJ6dOnw+mMfgxly5YtkZ6ejrKyMn3dDz/8gM6dOyM/Pz+k7IknnohffvkloSZqS09Pj3cIRBSlZMxXyb8PAlSoYho0S1b9OxBFQU7vBW+b8wAAGVseNrz+ZMxVomTEXCUyB+YqxUJCNko5HI4Gj1ctLi5GRUUFOnfurK/bunUrCgoKwsp27NgRFosF27dvr/V4wWAQHo9H//F6vQ2Kh4jITPShe472gJB6Q81iQlEhfr8f4vf7AUWtv3ySKu98CzTBCsfBFbAd/Dre4RARERFRAkmamcqWLl2KwYMHIzf378l5Dx48iMLCwrCygiAgMzMTBw8erPV47733HhYvXqwvFxQUYNasWXA6nbDb7aioqEBaWhpEUYSiKPD5fHC5XAAAv98PALDb7QAquzk6HA5IkgRVVeH1evWygUAAmqbBYrEgPT0dHo8HdrtdL+vxePQW6UAgAFVV4XA4AFRO+G6z2WCxWKBpGtxut142GAxCUZSwslYpH/6Ca6BYc0LKyrKs90zzer2wWCywWq0AgIqKCrhcLgiCAFmWEQwG9bI+nw+SJNVaNhAIIC0tTS8riiJsNhtEqXK+GkmSAAjQNA2qqh5erpxXTAAgiJXtpooi1yirQJIselkAEKuVFUUJgiAA0KAof5fVVBVatbKAAKfT2ezn2+v1wmq11lq2Iee75jlszPmuKlv9mvX7/XpZv98PQRD0sm63G06ns1HXd7TnsKnnu+Y5rDrfVa+1qmzNa7ah5zvSObRYLHA4HA063w19jxAlCZJkCbtmFUWBKIp1XN8aRFGqv6ymQtMOl5XdEOVSaADg7ABJkv4uKwgQRfHvsgBUVYEgCBCEyPmpaWrUZUPzXoEAITzvBQGiKMXlPSLkHB5uq4v6fPsU2O75X+X1/f5J0CxCjfMSxd8GOFzmsMO/13kOm3q+o3hPBhD99S10gj9/AhzbZiN766Moyj4O6RkZleclxu8RVZ+tRr9HiKLY4Pfkpn6PqCob6+8RzfG51hzfIyKdw1h9rsXjfKfa9whBEJCenp4y3yP4HsH3iKae73i9R8iyrNfDe43YvUdIUj58naZAs7eO+nwn4jUb8h22DoKW4LOVjR07Fg888AC6d+9ea5nffvsNM2fOxKxZs9C+fXt9/X333Ye+ffvivPPOC9tn6tSpGDNmDI4//viIxwwGgwgGg/qyIFQ2XhQXF4esby4WiyVsQvdkNvGi0/De9LbxDgOjH9qLVxZ+Fu8wyGSMyFcjc8Ti2QSLdysUaw6CmUeFbDt68kr8NOc4Q+KoTSLE0Kg4fAoc5y6v/HXpcMDRuMnj3T4HWp37IQCgR14O/vfK0Y06TnNq6HunGChG7nfHQlQ9KOnzMnytR8Ywur+l2mcrkVkxV4nMgblKDWG1WtG6det6yyXk8L2GOHjwIJ599llMnDgxpEEKqDwJtSVNIBDQWxkjsVqtSEtL038aMr9VY1S1NsaaILthPfQDBJmT1BE1llH5aghNg+TfC4ATnFPsqLbWcHecDADI2DILUI35QptUuUqUxJirRObAXDVGqt2zm7pRKhAI4NFHH8WQIUNw4oknhm3PyspCaWlp2HpN01BWVoasrNSbzNfi3YzWP58Di3dzvEMhogQgyKUQVB80QYJqq/9/Mogaq6Lj1VAt2bB6NsK57914h0NERESUkFLtnt20jVKqquKZZ55BZmYmxo8fH7FMfn4+tmzZErZ+x44dkGUZHTp0iHWYUUukJwESUd2SKV/1XlK2NoDQuOFlRNHQLJko73Q9ACBj62OA4ot5ncmUq0TJjLlKZA7MVYoF0zZKvfnmmyguLsaNN95YbdLqUAMGDMDmzZvDnrL35ZdfomfPnsg4PNFqIqhrKCERJZakyVdNgRTYBwBQ7fGf442Sn7v95VDs7WDx74Zr95sxry9pcpUoyTFXicyBuUqxYMpGqc8++wwrV67E9ddfD0VR4Ha79Z/qk5C3a9cOw4cPx5NPPokNGzagtLQUn376KT777DOMHTs2jq8gXNVM/kSU+JIlX8XAfgiaDE20Q7W0iHc4lAokJ8o73QQASN/+DAS5IqbVJUuuEiU75iqROTBXKRYS/qqSJEl/JHWV//73vzh06BBuuummsPIjRozAVVddpS9PmjQJb7/9Np544glUVFSgQ4cOuOmmm9CnT5+Yx94QRj0EURMkKNaW0DhMh6jREvyhpVH7e+heOyDKR7ZSA1gEBK/pof9OlTxtxyJ9x4uweDfDtXMOKjpPi1ldyZKrRMmOuUpkDsxVY6TaPXvCN0otXLgwbN2sWbOi3t9iseDiiy/GxRdf3JxhNTu325iZ9eX03tg3dL0hdRElK6PyNabUAMTgfgCAwqF7sWERoZzdMd5RJB7RgrKCW9Dyt2uQvuMluPMuh2ZtGZOqkiJXiVIAc5XIHJirxki1e3ZTDt9LRunp6fEOgYiilAz5KgWKIECDKmVAs5j/9ZC5+FqfhaCrN0SlHOnbX4xZPcmQq0SpgLlKZA7MVYoFNkqlGIt7A3K/GwqLe0O8QyGiOJL8ewCwl1RMKRrEtSUQ15YACru7hxBElBXcCgBw7XoVon9fnAMiIiIiSgypds/ORqkEUX2C9lgS1AAsvq0Q1IAh9RElI6PyNVYExQNRPgQNbJSKqaAK2z9/gu2fPwFBNd7RJBx/zskIZA6AqPqQse2ZmNRh9lwlShXMVSJzYK4aI9Xu2dkolSBkWY53CEQUJbPnq3h4gnPV2hIQ7XGOhlKWIKCs4J8AgLQ98yF5dzR7FWbPVaJUwVwlMgfmKsUCG6UShNPpjHcIRBQlU+erplUbutcuzsFQqgu0GAp/i2EQtCAytj3R7Mc3da4SpRDmKpE5MFcpFtgoRUSUQgS5DKLqhQYRqq11vMMh0ntLOfcuhsW9Kc7REBEREZGRLPEOgCp5vV5D6pGdnXGg/3zIzs6G1EeUjIzK11io6iWl2nIBgR8B1Dh/bd6GiRed1mzHu+vUDBxbUI4/3joLM5flR7VPenZbPP3i3DrLmDlXiVIJc5XIHJirxki1e3bekSQIi8UCRVFiXo9myYC/5Ykxr4comRmVr81OUyEFKp9yxgnOqSksQhDvTW++a0iQ06Ed+g7HF5Zh8NFp0CyZ9e4z+qG99ZYxba4SpRjmKpE5MFeNkWr37By+lyCsVqsh9Yj+fcjY8jgfv03UBEbla3MTgyUQtCA0wVo5yTlRgtAs6VBtlY1cFs9fzXZcs+YqUaphrhKZA3PVGKl2z85GqRQjBYqQse0JSIGieIdCRAbTe0nZ2gAC3/5jThIQnNgVwYldAUmIdzQJT07rAg0CpOABCMHSeIdDREREFBepds/O4XsJoqKiIt4hEFGUTJmvmgLRX/nBxqF7BrGKUC7oHO8oTEOT0qDY28Pi3wWrZxMCmQMAoWmNeabMVaIUxFwlMgfmKsUC/6s8QbhcrniHQERRMmO+ioH9EKBAEx3QLFnxDocoItlZAA0iRLkUYrCkycczY64SpSLmKpE5MFcpFtgolSCEJv5vMBEZx4z5KgUqJ4VWbG2b3PuEoqRoEDYcgrDhEKBo8Y7GHCQHFEcegMNzS2lNO29mzFWiVMRcJTIH5irFAhulEoQsy4bUo1qy4Mk9Dyp7ShA1mlH52mzUIMTAfgAcumeooAr71DWwT10DBNV4R2Malb2lJIhKGcRAcdOOZbZcJUpRzFUic2CuGiPV7tk5p1SCCAaDhtSjOPNR2vtZQ+oiSlZG5WtzkQLFEKBBlVzQLOnxDoeobqINirMjLN6tsHj/QsDWutG9+8yWq0SpirlKZA7MVWOk2j07e0olCKfTaUxFig+SZwug+IypjygJGZavzUSsPnSPyARkRydoggWi4tav38YwW64SpSrmKpE5MFcNkmL37GyUSjFWz0a0WX0crJ6N8Q6FiIyg+vUJo1V7mzgHQxQl0QrZ2QkAYPFsBjQOfyQiIqLUkGr37GyUShA+X2q0ghIlAzPlq+TfBwGVY9M1KS3e4RBFTXHkQxNsEFUvJP/uRh3DTLlKlMqYq0TmwFylWGCjVIKQJCneIRBRlMyUr38/dY+9pMhkBAmyszMAwOLdAmhKgw9hplwlSmXMVSJzYK5SLLBRKkFYrdZ4h0BEUTJLvgqKB6JcBg2AwqF7ZEKKowM00Q5B9UPy7Wzw/mbJVaJUx1wlMgfmKsUCn75HRJSkRP8+AIBqbQmI9jhHk4IkAfIlBfrv1AiCCNnZBVb377B4t0Jx5AECv7oQERERJQt+s0sQFRUVhtQTzOiH3SfuMqQuomRlVL42VdXQPZVP3YsPqwj50sJ4R2F6ir0dJO82iKoHknc7lLQuUe9rllwlSnXMVSJzYK4aI9Xu2Tl8L0G4XK54h0BEUTJDvgpyBUTFDQ0CFFvreIdD1HiCCPlwQ5TFtw1Qg1HvaoZcJSLmKpFZMFcpFtgolSAEwZihHZJnE1r9dBYkzyZD6iNKRkbla1NIgaqhezmAyPH/caFqELZWQNhaAahavKMxNdXWBqqUDkFTYPFujXo/M+QqETFXicyCuWqMVLtnZ6NUgpBl2ZB6RMULW9lPEBWvIfURJSOj8rXRNE2fT0qxc+he3ARU2K/+DvarvwMCaryjMTdBgJxWORRS8u0AVH9UuyV8rhIRAOYqkVkwV42RavfsbJRKEIFAIN4hEFGUEj1fBaUcouqBBhGqrVW8wyFqFqq1FVRLFgSosHi3RLVPoucqEVVirhKZA3OVYoGNUgkiLS0t3iEQUZQSPV+lqqfu2VrxSWWUPEJ6S+1Cbnr9X4wTPVeJqBJzlcgcmKsUC2yUIiJKJpqmzyel2NrEORii5qVaW0KxtoQADZcMKIp3OERERETURGyUShA+n8+QemRHBxzs+QxkRwdD6iNKRkbla2MIchkE1QcNEofuUVKSnZW9pUZ0L4XFXfcEoImcq0T0N+YqkTkwV42RavfsbJRKEKJozJ9Cs7aAt+0YaNYWhtRHlIyMytfGkAJ7AQCqrTUgSHGOhqj5adYsKNZWkEQgY+tjdZZN5Fwlor8xV4nMgblqjFS7Z+dVlSBsNpsh9YiBA0jb9TrEwAFD6iNKRkbla4Npmj6flGLn0D1KXnJaIVQNcBZ/CEv5L7WWS9hcJaIQzFUic2CuGiPV7tnZKJViJP9uZG+8A5J/d7xDIaJmJsilELQANMEC1ZoT73BIEiCP6QR5TCdAEuIdTVLRLBn4alMWACBzyyNxjoaIiIio+aTaPTsbpRJERUVFvEMgoiglar7qvaRsrQGBb+9xZxUhT+4GeXI3wMq/R3Ob90MuNEhwlHwB66E1Ecskaq4SUSjmKpE5MFcpFvgtOUHw8ZpE5pGQ+aqp+lP3VFvbOAdDFHu7y+zwtBsHAMjcPAvQtLAyCZmrRBSGuUpkDsxVigU2SiUIThpHZB6JmK9i8CAELQhNsEJNkUkRE56qQdjrhbDXC6jhDSbUdOWdboQm2GA/9C3sB78O256IuUpE4ZirRObAXKVY4FWVIBRFMaQeVXLB1+IEqJLLkPqIkpFR+doQYqBq6F4uh+4lioAK+xWrYL9iFRBQ4x1NUlIdeXC3vxQAkLHl4bDeUomYq0QUjrlKZA7MVWOk2j0771wShN/vN6QeJa0LSo5YACWtiyH1ESUjo/I1apoKKVAEAFD51D1KMRWdrocqpsFWvhaO/Z+FbEu4XCWiiJirRObAXDVGqt2zs1EqQRg2PldTIMjlgMZWbqLGSrTx9GLwAARNhibYoFo4dI9Si2prDXeHiQCAjC2PhHy+JVquElFkzFUic2CuGiTF7tnZKJVirBW/od3KnrBW/BbvUIiomehP3bO3AQQhztEQGa+i49VQLVmwejbAWfR+vMMhIiIiarRUu2dno1SCYFdIIvNIqHzVFIiBYgCAYuPQPUpNmjUbFR2nAAAytjwGqEEACZarRFQr5iqROTBXKRbYKJUgBPZuIDKNRMpXMXAAAhRoogOaJSve4RDFjbvDRCjWVrD4tiFt7yIAiZWrRFQ75iqROTBXKRbYKJUgbDZbvEMgoiglUr5Kgb0ADveS4hcFSmGalIaKTjcAADK2PgkovoTKVSKqHXOVyByYqxQLbJQiIjIrTYYY2A/g8HxSlFhEAfKZHSCf2QEQ2WBoBHf78ZDt7SEF9sK1+414h0NERERE9WCjVIJwu92G1BN09cTeY9ch6OppSH1EyciofK2PGNgPASpU0QlNyoh3OFSTTYR8XU/I1/UEbPy4NYRoR0XnmwAA6dufhefQvjgHRETRSJTPVSKqG3PVGKl2z85vyQnC6XQaU5FohWrLAUSrMfURJSHD8rUeVU/dU/nUPSKdp80FkJ0FkIIlyNo3N97hEFEUEuVzlYjqxlw1SIrds7NRKkGIojF/Csm7FS3XXwHJu9WQ+oiSkVH5Wic1CDF4eOierW2cg6GINA0oDVT+aFq8o0kdogVlnf8BAHBsfhZC8GCcAyKi+iTE5yoR1Yu5aoxUu2fnVZUgFEUxpB5RLofjwOcQ5XJD6iNKRkbla13EQDEEaFAlFzTJFe9wKBK/CseFK+C4cAXgV+MdTUrx5Z6FoKsXBLkM6dtfjHc4RFSPRPhcJaL6MVeNkWr37GyUShA+ny/eIRBRlBIhX6VA5dA9PnWPKAJBRFnBrQAA165XIPqL4hwQEdUlET5Xiah+zFWKBTZKJQiXiz0diMwi7vmqBiAGSyp/5VP3iCLy55wCOfsYiKoP6dufiXc4RFSHuH+uElFUmKsUC2yUIiIyGSlQdHjoXjqH7hHVRhDg73k3AMC1ex4k3844B0RERERENbFRKkH4/X5D6lHsbXGo8G4odk6MTNRYRuVrbcTDT91jHhPVzZPxf/BnD4WgBZGx9Yl4h0NEtYj35yoRRYe5aoxUu2dno1SKUW2t4e54FVRb63iHQkSNofohypVPE1NtHLpHVJ+ygn8CAJx734Hk2RTnaIiIiIjqlmr37GyUShB2u92QeoRgKRxFH0IIlhpSH1EyMipfI5H8+yAAUC1Z0CRn3OIgMgO73Y5g1gD4ck6BABWZWx6Ld0hEFEE8P1eJKHrMVWOk2j07G6VSjMW3Ay1/uxoW3454h0JEjRDy1D1KbKIA5eR2UE5uB4h8QmI8VT2Jz1n8ISzlv8Q5GiIiIqLapdo9OxulEoTb7Y53CEQUpXjlq6B4IcqHoAFQ+NS9xGcTEbylD4K39AFs/LiNh6pcldN7w5N7LgAgc8sjcYyIiCLh92Aic2CuUizwW3KCcDgc8Q6BiKIUr3wVD/eSUi0tAJHdp4nqUz1XyzvfDA0SHCVfwFa6Oo5REVFN/B5MZA7MVYoFNkolCEmS4h0CEUUpXvkq+fcCAFT2kjIHTQN8SuWPpsU7mpRUPVeVtC7wtLsQAJC5+QH+TYgSCL8HE5kDc5ViwRLvAOqydOlSLFy4EDNnzkSXLl1CtgUCAcyfPx/ffPMNfD4funbtissuuwwFBQWNKhdvqqoaUo8mOhBI7wtNZCs3UWMZla/VCYobolIBDQIUW67h9VMj+FU4zl0OAPAtHQ44+EXOaDVztbzzTXDuexe2sh/h2P8pfK1HxikyIqouHp+rRNRwzFVjpNo9e0L2lFJVFXPmzME333wDTdMgy3JYmWeeeQabN2/G9OnT8dRTT6FXr16YMWMG9u/f36hy8eb1eg2pR3Z1w/5jPoPs6mZIfUTJyKh8rU70Hx66Z20JiDbD6ycyo5q5qtrbwt3h/wEAMjc/BKjBeIRFRDXE43OViBqOuWqMVLtnT8hGqffffx979uzBvffeG3H7hg0bsHbtWtxyyy0oKChATk4Oxo4di/79+2Px4sUNLpcIXC5XvEMgoigZnq+aBilQOXSPE5wTRS9SrlbkXwPF2hIW72ak7VkYh6iIqCZ+DyYyB+YqxUJCNkqNHDkS06dPh9PpjLh99erVOOqoo5CVlRWy/sQTT8QPP/zQ4HKpxFL+C9p9VcBHYhOZiKBUQFQ80CBCtXLoHlFTaJYMVHS6EQCQsfUJCDKfJERERESJI9Xu2ROyUcrhcMBiqX26q61bt0acE6qgoABlZWUoKSlpULlIgsEgPB6P/hPrroqBQCCmx68iQIOgBSCAE7wSNZZR+VpFn+DclgOICT0VIFFCqS1X3e0vhezoBClYDNfO2QZHRUQ1Gf25SkSNw1w1Rqrds5vy7qakpAQtWrQIW5+dna1vb9myZdTlInnvvfdChvgVFBRg1qxZcDqdsNvtqKioQFpaGkRRhKIo8Pl8endGv98PALDbKx/Z7na74XA4IEkSVFWF1+vVywYCAWiaBqfTCZvNBo/HA7vdrpf1eDxIT0/Xy6qqqj+K0+PxwGazwWKxQNM0uN1uvWwwGISiKGFlbcrfvc+ql5VlWe+Z5vV6YbFYYLVaAQAVFRVwuVwQBAGyLCMYDOplfT4fJEmqtWwgEEBaWppeVhRF2Gw2iIef3FD5BAcBmqZBVVX9iQ6qqkIAIIiV7aaKItcoq0CSLHpZABCrlRVFCYIgANCgKH+X1VQVWrWygACn09ns59vr9cJqtdZatiHnu+Y5bMz5ripb/Zr1+/16Wb/fD0EQ9LJutxtOp7NR13e057Cp57vmOaw631WvtapszWu2oec70jm0Wq0QRbFB57uh7xGiJEGSLNAUBVKgcj4pzZEHQIAoinVc3xpEUTqcC0rtZTUVmhZFWaGyvuplVVWBIAgQhMj5qWlq1GVD816BACE87wUBoijF5T0i5LwIAIDozzcUVBEFAdrh1/H3eYnub1NZ5rDDv9d5Dpt6vqN4Tw6PoSnnO8prtkZZQND/E6uu9wibzRbxPSLQ+x5YfroSGTtehL/D5bBn5wNonvcIURQb/J7c1O8RVWVj/T2iOT7XmuN7RKRzGKvPtXic71T7HmGz2WCz2VLmewTfI/ge0dTzHa/3CE3T9Hp4rxG79wjL4Xt2QRSiPt+JeM2GfIetgykbpWRZjtiTShRFSJKEYDDYoHKRjB49Gmeeeaa+XHVCvV6vvp/H4wnZp6KiImS5+vFr9rSqWVZRFH1dfWWrL/t8vgaVVTxepEdRtuqNpIrb7a61rCzLUZcFDl/MiqLXU52i/D2pvQYAilptW+1lK5f/Lquq0ZbVYna+a8Ybz/Ndpb5rtiFlG3J9N+WajbZsrM43EH5e0tPT9dhidQ5VRYGiyBCChyCoPmiQIFtaANCiuL7/Xm5y2cONEzXLapoGILr8bFBZaOF5r/39mo1/j6hWVqutbO3nUF+vaYCm1Tgv0f1tNK3a55imHd4Ww/Nda1nE8Hw35prV6n2PqP7ZWjPe0oxTYMk4ArbytUjb/BgOdX8oZD+zvc8m4ntyMn+uGXG+U+l7RM3tyf49ornKJto1y/eIxpVNtGu2rvOdnp6ekOc72d4jrIfv2TVVM/U1W9UwV5+EHL5XH4vFEvGJfKqqQlEUvfUw2nKRWK1WpKWl6T+1zW9FRBRLVROcq7bWgCDVU5oSiggox+VCOS7XpJ+2SU4QUdblDgBA2p75kDyb4xwQERERUeox5dfk7OxsHDx4MGx9aWkpAOgTm0dbLhHUbOWMlWBaVxQN/C+CaV0NqY8oGRmVr9A0SP7KoXt86p4J2SQE7+yP4J39ARsbFOOhvlwNtBgKX8uTIGgyMrc8bFBURFSTYZ+rRNQkzFVjpNo9uykbpfLz87Fly5aw9f+fvfsOs6sq9wf+XbucPmdqJpOeSSMJCYQkVEHQiyKKYkRQUEHlonIVy0W9GuQC8gOlqGADsYB6Y0XBgiKC0gQJJaElkN4zSSZTT99l/f6YkqnJmTnn7HLO9/M88yQzs+asd/bsd++91lll69atiEajqK+vH1M5L+ibw1lyahhm9ChA5cgvovFyKl8Vsx1C5iCFBlv3zvWKyC/yydWuWSshIRA+8AD0zucdiIqIhnLsOZiICsJcdUiFtdl92Sm1fPlyrFmzBl1dXYO+/uijj2LZsmX96z/lW84L+haTLXk9mV2ofu3zUDO7HKmPqBw5la9K3yipQCMgfHm5JnJVPrlqxhYg3XQ+ACC+5Yb+dbuIyDlO3VeJqDDMVWdUWpvdl62cxYsXY968ebj11luxbds2tLW14Te/+Q3Wrl2LFStWjLmcF/QtJFxqitGOaMsvoRjDpzUSUX6cyFdVkVBz+3vqC3Dqni9lLITe9jBCb3sYyIy8ADqVVr652jXz85BKCMHOZxA8+PcSR0VEQzn1HExEhWGuOqPS2uye331PVdURe2SvvPJKrFq1Ctdffz0ymQxmzZqFq6++GlOmTBlXObdxfi6RfziRr0unJCCkASkCsPW6ktdHVI7yzVU7NAWJKZeiauf3EN/yNRyoezOgeP4Riahs8DmYyB+Yq1QKnn/i+uUvfzni1yORCC677DJcdtllh/35fMu5baTtNYnIm5zI1zfO6QQAWMFGwENTjYn8ZCy5mpj+SUT3roKe2oBIy2+QmnxRiaMjoj58DibyB+YqlYIvp+8REZU1K41TZvashWcFmlwOhqgySL0a3TM+DQCo2vYNCCvtckRERERE5Y+dUh6Ry+UcqccKNKB7+qdgBRocqY+oHJU6X0Nt/0QkYEMqQUituqR1EZWzseZqcsqHYYamQc21ILrrhyWKioiGcuo5mIgKw1x1RqW12dkp5RFOLRpnByehe9aXYQcnOVIfUTkqdb6G998PoHeUFKfuEY3bmHNVCaK7+YsAgNiO70HJHSxBVEQ0FBdPJvIH5qozKq3Nzk4pjwiFQo7UI8wEAu1PQZicC0w0XqXMV2EmEDr4CADACnLXPaJCjCdX043vRi62CIqVQNW2b5YgKiIayqnnYCIqDHPVGZXWZmenVIXR0lvR8OL50NJb3Q6FiEYQOvgQhJ3Bro4ApFrldjhUCAWwjq+HdXw977Z+IhR0zb4aABDZ83NoyQ0uB0RERESVpNLa7HxM9ghur0nkH6XM1/D+PwAAHt9czal7fhdQYVx/HIzrjwMCqtvRVKTx5mqu9lSk698KAQvxzdcXOSoiGorPwUT+wFylUmCnlEcEAgG3QyCiPJUqX4XRjmDbYwCAxzZxgXOiQhWSq12zvwIpNITa/tGfl0RUGnwOJvIH5iqVAjulPELTNLdDIKI8lSpfwwf+CiENGNEF2NHBOftEhSokV63IbCSnfBgAEN90HWCbRYqKiIbiczCRPzBXqRTYKeURUkpn6hEarEATpOAFhWi8SpWvfVP30o3nluT1yWEZC8Fz/4Hguf8AMpbb0VSkQnO1e8bnYGs10FOvI9LyyyJFRURDOfUcTESFYa46o9La7OyU8ohkMulIPWZsAfad8jzM2AJH6iMqR6XIVyXbgkDHUwCAdOO7iv765A6RtSGy3D7ZLYXmqtRr0D3zvwEAVVtvgTC7ihEWEQ3h1HMwERWGueqMSmuzs1PKI2KxmNshEFGeSpGv4f1/gICNXHwZrPCMor8+USUqRq4mJ18MIzwbqnEQse3fKUJURDQUn4OJ/IG5SqXATqkKoyXWY+JTy6Al1rsdChENEN53HwAgNfE9LkdCRIMoOrpmXw0AiO36EdT0DpcDIiIionJWaW12dkp5hGEYjtQjpAk11wIhuWAr0XgVO1+15EYEEi9DCg2ZCe8s6msTVbJi5Wq2/kxka06FkDnEt9xQlNckokOceg4mosIwV51RaW12dkp5hGVxEVwivyh2vob3/R4AkK09HXagvqivTVTJiparQqBzzjWQUBA+8GcEOlYX53WJCACfg4n8grlKpcBOKY8Ihbj9O5FfFDVfpUR4f9/UvfOK97pEVNRcNWMLkZp0IQAgvvk6QHIBe6Ji4XMwkT8wV6kU2ClFROSiQNdz0DI7YatRZBve6nY4VEwCsBfXwF5cAwi3g6Fi6G7+Amw1ikD32v514IiIiIho/Ngp5RHpdNqResxwM1qP/S3McLMj9RGVo2Lma9/UvUzD2ZBquGivSx4QVJG7ZTlytywHgqrb0VSkYt9b7cAEJKZfAQCIb/0ahOXMvZuo3Dn1HExEhWGuOqPS2uzslPIIXdcdqUdqMeRqT4HUuJ0n0XgVLV/tHML7/wgASHPXPaKiK8W9NTH1MpjBqVCzexHdeWfRX5+oEjn1HExEhWGuOqPS2uzslPIITdMcqUfJ7kXVlq9Bye51pD6iclSsfA22PQrF7IAVaES25g1FeU0iOqQk91Y1hK7ZKwEAsR3fg5JtKX4dRBXGqedgIioMc9UZldZmZ6eUR0gpHalHzbWiasd3oeZaHamPqBwVK18jvWvSpBvfBSi8yZedjIXg+x5D8H2PARnuVuOGUt1bMxPehVx8GRQ7jfjWm0pSB1Elceo5mIgKw1x1RqW12dkK8ohkMul2CBVp85btuPTCs1yNIVbThNvv+KmrMdDYFCNfhdmN0MGHAABp7rpXtkSn4XYIFa1k91Yh0DnnWkx44Z0It/wWySkfhVG1uDR1EVUAPgcT+QNzlUqBnVIeEYvFkEgk3A6j4mjCwH0rm1yNYcWNnPrhN8XI11DrXyHsDIzwbBgxNmaJSqGU91YjvhSpxhWI7L8P8Y1X4+Bx9wGC2ywSjQefg4n8gblKpcDpe0RELoj07rqXnvgeNmSJfKpr1krYShjBrmf7d9IkIiIiovyxU8ojDMOZKR62Xotk04Ww9VpH6iMqR4Xmq5Ldi0D7vwAA6YkrihESEY2g1PdWOzQZiRmfAQDEt9wAYfLdY6LxcOo5mIgKw1x1RqW12dkp5RGmaTpSjxWais75t8IKTXWkPqJyVGi+Rvb9HgI2stUnwArPKFJURDSUE/fWxLSPwQzPhJrbh6rtt5W8PqJy5NRzMBEVhrnqjEprs7NTyiPC4bAzFVlpaMnXASvtTH1EZaigfJUS4ZbfAgDSE88vUkRENBJH7q1KEJ1zrgMARHf9CGpqU+nrJCozjj0HE1FBmKsOqbA2OzulKoye2oTGZ98MnQ/NRK7Qu9dCT22ErYSQbjzH7XColARgz43DnhsHuGxYWcvWn4lM3X9ASAPVG68BuGU2ERERjVOltdkL2n2vvb0dtbWHn+e4adMmbN68GWeddVYhVZW9dLoyekGJykEh+RrpHSWVaTgbUosXKyTyoqCK3HdOcDuKsrV5y3ZceuERni2EKHkHUaymCbff8VN0zrkOwWefQKj9UYQOPoRMA597iPLF52Aif2CuUikU1Cn1iU98At/4xjcwderocx1N08Tvfvc7dkodgaZpsCzL7TCIKA/jzlc7i/D+PwAA0k2cukdUCE0YuG9l02HLKIoC27ZLGseKG1sAAFakGYlpH0PVju8ivulaZGrfCKic5kCUDz4HE/kDc5VKoeTT9/bv349UKlXqanxP13W3QyCiPI03X0MHH4ZidsAKNCFbe2qRoyKioYRwdpWCxPRPwwo0QcvsQGznnY7WTeRnfA4m8gfmKpXCmEdK3XDDDYNW3f/+97+PYDA4YtlMJoNt27bhuOOOG3+EVFQSAlIEILnACZHjIi2/AQCkJp4HCNXlaKjkMhaCH3saAJC962QgxL95uZNaFJ2z/xd16/8LsR3fRbrp/IrZOYeIiIiKo9La7GPulGpoaBg0ZK+2thbRaHTEssFgEKeffjrOOOOMcQdYKRKJhCP1mFWLsPf0rY7URVSuxpOvSu4Aggf/CYBT9yqJ2J9xO4SKZlnOb12daXwXsnt+jmDn04hv/iraj77L8RiI/Map52AiKgxz1RmV1mYfc6fUxz/+8f7/P/bYY7jwwgsPu6YU5ScajSKZTLodBhHlYTz5Gt53HwQs5KqOgxmdW6LIiGggVVWdX/tCCHTO/SomPHcWwgceQLL9CeRqT3M2BiKf4XMwkT8wV6kUClps4ZhjjkEkEilWLBVNCGeG5mnJjWh47ixoyY2O1EdUjsaTr/1T95reW+xwiGhU7gx7N2MLkZxyCQCgeuP/ArbhShxEfuHUczARFYa56oxKa7MX1Cl11VVXoa6urlixVLSB63SVkrAzCCRegbA5pYRovMaar1r3K9CT6yFFAOnGc0sUFRENJaV0re7umZ+HpddBT21AdPc9rsVB5AdOPQcTUWGYq86otDa7s9vS0KhyuZzbIRBRnsaar5F9vwUAZOrPhNRrSxESEY3Atm3X6pZ6DbqbvwQAqNr2DSi5A67FQuR1fA4m8gfmKpXCmNeUGuqll17CH/7wB+zcuROdnZ0jV6JpWLVqVaFVlbVIJMKF44h8Ykz5ahsI77sPAJBquqCEURHRUD1rSrn3rm5q0vsR2fN/CCReQnzL19Ax/5uuxULkZXwOJvIH5iqVQkGdUk888QS++93vYsmSJXjXu96FWCw2ciVawX1fRES+FDr4d6jGQVj6BGTrznA7HHKYPX3k3WmpQggVnXP/HyaseRciLb9GcvIHYcSXuh0VERERkWcU1Ft0//3346yzzsJHP/rRYsVTsTIZZ+aLmqFpaFt4J8zQNEfqIypHY8nXyN5fAugdJaXopQqJvCikInfXyW5HUdFs2+Gd90ZgVC9DqukCRFp+g+oNX0br0gcAhW/WEQ3k1HMwERWGueqMSmuzF7SmVEtLC97whjcUK5aKpqqqI/VIvQaZxndC6jWO1EdUjvLNVyWzG8G2fwLomcZDRM7yyi5BXbOugq1VI5B4BdE997gdDpHnOPUcTESFYa46o9La7AV1SgWDQaTT6WLFUtF03ZkRFEruAKI7f8AFV4kKkG++Rlp+DQGJbM3JsCKzShwVEQ0lhDf2c7EDDeiatRIAULX1ZijZvS5HROQtTj0HE1FhmKvOqLQ2e0FPa0cddRT+/e9/FysWcoCabUH15q9Czba4HQpReZMWInt/BQBITbrI5WDIFRkLgY89jcDHngYy7k8jI3elJl2EXHwpFCuJ6k3XuB0OEREReVSltdkL6pS65JJL8OKLL+JnP/sZWlpaXN162e+4i4GDpARso+dfonHIJ1+D7U9Ay+6GrVUj3XC2A1GRFyk7klB2JN0Oo2K5ufPeMEJBx7yvQ0JF+MADCB58xO2IiDyDz8FE/sBcpVIoaKXNm2++GaZp4oEHHsADDzwweiWahlWrVhVSVdmLRqNIJtlwKQkpIawEFKMditkBxeiAkDlICDy6UiLQ8QykEoRUgrADjbD1OsAj65CQN+WTr5E9Pde81MTzADXsRFhENISqqrAs74xSM2NHIzn1PxHb9QNUb/wKDtScAsnrAxGfg4l8grlKpVBQp9S73vWu/CrRuMvMkXhlMdayIk1oqa1Qs7sh5PB3ywUkGuMArO6eDwDI7oathGGFpsAKTgaUgLMxky8cKV+VXCtCBx8CAKQmXehESEQ0Iu/dW7tnXonQgT9By+xAbPtt6J71ZbdDInIdn4OJ/IG5SqVQUG/RGWecUaQwyDSdmWJga1XI1L8FtlblSH2ukBJKbh/05AYImev5klBhazU9H3oNpFYF2CY+eP1q/GLlAgg7B2F2Q83thWKnoaQ2QUtthh1ohBmZBalGXf6lyEuOlK/hlnshpIlc1XEwYwsdioqIhpIOTNPevGU7Lr3wrDH9zEkzNFzzNiC89bv4/K0PYkd7qKAYYjVNuP2Onxb0GkRucuo5mIgKw1x1RkW02QfgECaPyOVyjtRjhWeibfE9jtTlBmF2Q0++DsXsAADYShhmdB5svR4YuguTqmHdbgV2YEL/l0w5F2q2BWp2NxSzC2puH5TcAZiR2bBC0zmtjwAcIV+lRGTvLwBwlBSR25xY61ITBu5b2TTGn2qC1ZWChlbccVErcvFlBd1fVtxYGQuhUvly6jmYiArDXHVGubfZhyqoU2rDhg159ZZqmoZ58+YVUlXZi0QiziwcZxtQzC7YWhxQymhLTymhprdCS2+BACChwAw3wwpPB4Sa/+sItWfqXmgKhNkFLbUZqnEQemoj1NwBGLGFkGqkZL8G+cPh8jXQuRp6ejNsJYJ047kOR0ZEA/WsKeXNd3WN6FFQOtqgmB1Qs3thhSa7HRKRaxx7DiaigjBXHVKubfZRFNQpdfXVV+dXCRc69ww9+RomPP82HFj2IIyqxW6HUxxSQktthJbZAQCwAo0wIvMAtbDpEFKLw6haAju7B1pyAxSzA4GOf8OMzoUVnMpRUzSivlFS6cZzIbWYy9GQ22RjYdchKmNqGGZkFvTUJmipjbACDVzHkIiIiMqzzX4YBXVK3XHHHSPuatPe3o5Nmzbh73//O5YuXYoPfvCDhVRTETKZjNsh+JOU0JKvQ8vuAgAYkXk9o6OKRQhYoSmw9TpoiXVQzfae6YFGO4zY0WMbhUVlY7R8FUYHQgf+DIBT9whASEX2Z6e6HUVFs23v7Lw3Eis0HWp2LxQrCS21iWvQUcXiczCRPzBXqRSUIxcZXV1dHSZMmDDsY968eXj729+Or33ta1i3bh1+//vfFyvesqUoBf0pKpOU0JLroGV3QQIwoguK2yE1sCo1DCO+FEbkKEgIqLn90LvWArY3p4VQaY2Wr5GW30CxMzCi82HElzocFRENJTy4+94gQoERXQAA0LJ7IIx2lwMicgefg4n8gblKpVDSsyoUCuEDH/gAHn300VJWUxYCAQ7ZHxNpQ0+8Ai27FxICRuxoWKEppa1TCFjhaTDix0EKFarZjkDXc4CdLW295Dkj5qu0Ed3Ts/tVcvIlnN5J5AHCBw/PUq+BGexZT0pPvgbI0i/OTuQ1fA4m8gfmKpVCyZ/W4vE42traSl0NVRIpoSfWQc3t6+2QWgw7OMmx6m29Drn4MkgRgGIlEOh8DsJKOVY/eVOw/XFo6W2w1SqkJ57ndjjkBVkLgStWI3DFaiDr7Wlk5C4zMgdS6FCsJNTe9RGJiIiIKkHJO6VWr16NmpqaUlfje07tYmDEFmLvqa/B8PG6FWp2F9RcS0+HVNWxsIONjscgtThy1cthK2EodhqBzmchzC7H4yB3jJSv0d33AABSTRdAalGHIyJPkoCysQvKxi5Auh1MZfLqznvDKAEY0bkAAC21BcJKuxwQkbO4mxeRPzBXnVEObfaxKGih8w0bNsA0hz/w2baNzs5OrF27Fk8++SRWrFhRSDUVIRKJIJVyYLSNUCG1qtLXUyLC7IKW3ACg551lO9DgWixSjSBXvRyBrrVQrG4EutYgV70cUmWHRLkbmq9qeieCBx8GACSnXOxWWEQ0hKqqI27I4kV2YBJsbQ8UswNa8jUYVUs4DZgqhmPPwURUEOaqQ3zeZh+rgjqlrr766sN+PxaL4dxzz8X5559fSDUVwalF49TUFlRv/Ao65/4/WJFZjtRZNLYJvftlCEhY+gRYodIsaj4mShC5+DIEul6AYnUh0LUG2fhyQOU28OVsaL5G9vwcAhLZ2tNgRea4FBURDeejTh0hYMQWINDxb6jGQVi5FkenphO5iYsnE/kDc9UZvm6zj0NBnVJ33HHHiO9ACiEQCAQQj8cLefmK4tQ7uYqVRKj9MXRbSfjjveNeUkJPvgrFTsNWQj1DGb3yDrKiIRdfgkDnc1DsFALda5CLLwcU3e3IqEQG5auVQWTvLwAAyckfdicgIhqRlP6aNynVKMzwLOjpzdCTG5DV6wGFi8pS+fPLiEaiSsdcdYZv2+zjVFCnVF1dXbHiGJddu3bht7/9LV599VVks1lMnjwZZ599Ns4444xB5XK5HFatWoWnnnoKmUwGc+bMwcUXX4zm5mZ3Ah9BNssd3A5HzeyEmjvQu47UYu91+CgBGPHjejqmrCQC3WuRiy8FhOp2ZFQCA/M1fOBPUM12mMHJyNSf6WJURDSUbftvJzsrPANqbh8UKwE9+XrPPY+ozPE5mMgfmKtUCkUdf5fL5dDe3o5cLlfMlx3Rnj17cNVVVyESieDaa6/Ft771LZx++un4wQ9+gAceeGBQ2W9/+9vYsmULVq5cidtuuw0LFizAtddei9bW1pLHma9IJOJ2CJ4ljE5oqY0AADMyF1KrdjmikUk1jFz8OEihQTE7oXe/zK29y9TAfO1f4HzyhwCloH5+IioyVfXhGwNCgRFbCAnR0zmV2+92REQlx+dgIn9grlIpFNyCklLioYcewkMPPYTdu3dDSgkhBGbNmoVzzz0XJ554YjHiHObhhx/G9OnT8fGPf7z/a29/+9vR0dGBRx99FO94xzsAAK+//jpefPFFfPe730V1dU9nxgUXXICdO3fi3nvvxSc+8YmSxEdFIm3oiVd71pEKNMIKTXM7osOSWgy5qiUIdL0A1WiFTL4GM7rAO1MNqaj0rrUIdK+FFAGkJl3kdjjkQbLaY6M6yRekFocVmgEtsw164jVka2q9N0KYiIiIqAgK6pSyLAu33HIL1qxZg6VLl+Lss89GdXU1Ojs78fzzz+Ob3/wmjj/+eFx55ZUQRW6UK4qCmpqaYV+vra1FKHRokenVq1fjuOOO6++Q6nPGGWfgjjvuKGpMhXBqKKQVnIyOuTfACk52pL5CqZkdUOwUpAjA8EnnjtRrYFQdA717LbTsHkg1BivsgUXZqWj68rVvlFS68RxXd4IkjwqpyP76dLejqGh+nL7Xx4w0Q8nth2KnoKU2wqyQbaGpMnFKEJE/MFed4bc2e6EK6pR68MEH8dprr+H666/HvHnzBn3vzDPPxGuvvYavfe1r+Pvf/463vvWtBQU61BlnnIGrr74a27Ztw8yZMwEAHR0deOCBB3DJJZf0l9u2bRsWLVo07Oebm5vR1dWFtrY219fGAlD0TrvR2IF6pKZ82JG6CmZloKW2AgCM6FxfvUtsBxpgRuZCT22EltoIqcVg6+6fZ1QcQggouTaE9/8RABc4J/IqAcBfS50PIFQYsQUIdj0PLbsHdrCJ9xEqW049BxNRYZirzvBVm70IClpT6tFHH8V73vOeYR1SfebPn49zzz0XjzzySCHVjGjq1Kn4zGc+g29961v4xz/+gZdeegnXX389zj//fBx//PH95dra2lBbWzvs5/tGWbW1tY34+oZhIJVK9X+k0+mi/w4DBQLO7K4jjHaEW34HYbQ7Ul8h9NRGCFiwtWrYgSa3wxkzKzQdVqAJAhJ698sQVmnPIXJOIBBAZM9PIWQWudgxMOJL3Q6JiEYgfL51tdRrYQanAgC0xHpAVsIePFSJnHoOJqLCMFed4ac2ezEUNFKqpaUFRx999GHLHHPMMbjvvvsKqWZUM2fOxIIFC/DPf/4T0WgUNTU1mDNnzqAypmlC04b/moqiQFVVGIYx4mvfd999uPfee/s/b25uxk033YRwOIxgMIhEIoFIJAJFUWBZFjKZDKLRKIBDwxqDwSAAIJlMIhQKQVVV2LaNdDrdXzaXy0FKiUAggFgshlQqhWAw2F82lUohFov1l7Vtu396YiqVQiAQgKZpkFIimUz2lzUMA5ZlDSsbsA4i9tqncWDZgwjWTusva5omwuEwACCdTkPTNOh6z8ikRCKBaDQKIQRM04RhGP1lM5kMVFUdtWwul+tfEC+TyUBRFAQCASi9i8/2LEIrIKWEbdv9i9LKTCvU3D5IAFbV0YAQQ8paUNWev2vf9Aylt/FhWSYURe3tyZewrENlpW1DDijb83OHKyuhKGrv61pQFGXkstKGlIfK2rYFoaiwqxdDtKegmF0IJF6CWXsiJJRBZSEEgsFg3sdwPMe7r+zAczabzfaXzWazEEL0l00mkwiHw+M6v/M9Zws9v4ees7qu9+d6IpHoLzv0nD3S+Z3PMQyoNgJ77ul5/bmfQayq6ojHe6zXCEVVoarasHP2sOdhAefsqGWFgKIow89vISDEoZwbmJ9S2nmXHZj3tm1BQPR3JPSXFQKKog4pa0MAw8sW+Rox6Lj0vjmY9/FO5RC4em3PeXLDUsiAMuS45Pe3GfSuZO//D3sMCz3eI5YdfLyHx1DI8c7znB1StqeOUV63/xgqALQhx7vvd+0rW9g5O/CcKOx4j3wMZdV8SOMAFDsNPb0FdtX8YcdFCNF/zSv1c0S+1+RSP0f0lXXivjbSc1tf2VId74H3tUKPtx+eI3RdRywWq5jnCEVRxny8vX7O8hpRGdeIgfcbtjVKd43Qujch9tqn0Xr83xDobbP78RqR78i6gjqlVFU94rzSXC43YqdQobZt24Zbb70VF198cf9i5evWrcPXv/51XHDBBTjttNMAAJqmwTTNYT9v2zYsyxq1t3fFihU455xz+j/vO6DpdLq/IyuVSg36mUQiMejzgR1eQ0daDS3b3t4OKWVeZQd+nslkxlTWSqURy6Ns34WkTzKZHLWsaZp5lwV6T2bL6q9nIMsyAWkjkFzf83lwKiwlMnrZQZ8fWjukr6FQnLLmuMrK3ne0rdgxCHauhjC7oXS+BCO2GBDiUFkpkc1mS3q8+xzpnB1L2bGc34Wcs/mWHXp+FOv8BkY4Lnt/gVCuFWZwCjqqzgQGlC/WMbQta9D55MQ5O2LZ3ob10LI91yt7QNnR6xlTWUjAGlK2t8E+vCyGlz3s71rgNUKOVnaUYygB5aWed7hsywakGPEaMXoMZm+5AffQ3vtESY/3qGVRwuNdxHN2AMM4lI/5Hu+esvkfw6GfF+t493zeU1ZGFyDQvRZKejtMvRHQq4fFO/D64pVrcjnf15y4B5bqvubF493R0dH/HDxS2bJ7jihSWa+ds7xGjK+s187Zwx3vbDY7KFcBbxzvcrtG6L1tdmlLX5+zfR1zR1LQuPbm5mY8+eSThy3zxBNPYPbs2YVUM6K7774bb37zm3HCCSf0f23hwoW4/PLLcdddd/UfyJqaGrS3Dx/21tHRAQDDFkDvo+s6IpFI/0dfz2eplPr1/UTN7IJiJSGFDjNS/HPHcWoIuapjerf33g81s83tiKgQ0kbVrrsAAMmp/wkoxe90J6Li6Bul5Hd2oKF3OjigJ9cB0r8LuBONhM/BRP7AXKVSKKhT6uyzz8YjjzyC+++/f9g0OMMw8Pvf/x7//Oc/B404KpYtW7aM2Nk1d+5c5HI57N69GwAwffp0bN26dVi5rVu3IhqNor6+vuixjcfA6WQVzc5CS28GAJiROb5a3PxwpF4DM3oUAEBLba6Y+cHlKHjwYajJjbDVOFKTLnI7HCI6rPJZkNWIzoMUAShWElp6+HMNkZ/xOZjIH5irVAoFvcV/wgkn4Pzzz8cvf/lLPPDAAzjmmGMQj8fR1dWFl19+GYlEAhdddBGWLFlSpHAPqa+vx4svvohjjz120NfXr++Z9tW3uPny5ctx8803o6urC/F4vL/co48+imXLlnlmB4GhwwFLxVbDyMWXwla92cutpTZBSAu2Gi+7LTCt0FQoZifU7F4Eul9GtuZEQAm6HRaNUWxn7yipyR+E1GJHKE1Ebho6xcDXlACM6FEIJF6Gmt4GK9AIqVW5HRVRUTj1HExEhWGuOsPrbfZiK3jeyXvf+14sXboUDz/8MLZu3YpNmzYhGo3ilFNOwTve8Q5MmDChGHEO8/73vx+33XYbAODNb34zQqEQ1q1bh5///Oc4/fTT+0dALV68GPPmzcOtt96Kj370o4jH43j44Yexdu1afP3rXy9JbOMxdC5mqViROWhd+idH6horYSWhZvcCAIzoUf0L+pYTIzofwuyCYiWhJ16FUXWc2yHRGOhdaxHsfBpSaEhO/ajb4RDREQxdc8rv7EAjLH0CVOMA9MR65KqXA4LvWpP/OfUcTESFYa46w8tt9lIoymIos2bNwsc+9rFivFTeTjrpJFxzzTX4wx/+gOuuuw7ZbBaTJk3Ceeedh7e+9a2Dyl555ZVYtWoVrr/+emQyGcyaNQtXX301pkyZ4mjMhxONRoctIFZptNRWCACW3gCpj7zWl+8JFUZsMQKdq6EabbDTWwFE3I6K8hTb+QMAgDHlfNjBSS5HQ0RHoqrasMXDfU0IGLH5UDraoVhdUDM7YYVnuB0VUcH4HEzkD8xVKoUxdUrdcMMNaG5uxkUX5b+Oyq9//Wvs27cPn/70p8cc3JEsWLAACxYsOGK5SCSCyy67DJdddlnRY/AbvftlTHj+bTiw7EEYVYvdDqefsFJQci0AADMyy+VoSktqMRjR+Qgk10FLb8Exk2e6HRLlQU3vROjAnwEAuVmfdDka8gsZ5CgWKjIlCDMyF3pyPbTUZth6g9sRERERURF5tc1eKmN6Wt66dSvmzJkzpgrmzp2LDRs2jOlnKtHALSMrkZruGyVVD6nFj1je7+zQZJjBSRAA/ufNu6DkDrgdEh1BdPePIGAjU/tGpIPz3A6H/CCkIvuHNyP7hzcDofLYBc5vbLs8d6mzgpNh6fUQsKEnXoUiymjtLKpIlf4cTOQXzFUqhTF1SnV3d6Ourm5MFcTjcbS3c6cxGp2wUlCzvaOkwuU9SmogMzofthpFXdRE7foruMW3hwmjA5E9vwAAJKd9wuVoiKjiCQEjugBSaFCsLpy/hG9sEBERkT+NqVNKVVVo2tiWoRpr+UoVDFbuLmxqehsEZM8oqXJdS2okQoUROwYZQyDY/gRiO+9wOyIaRWzXj6HYKRjRBcjWvrGi85XIT8p662o11LMpCIAPLDsALbHO5YCIxo/3VSJ/YK5SKYzpaa2qqmrMo546OjoQi3HbdBqZsNL9O+6Z4WaXo3Ge1KK441+TAQBVW2+G3vWCyxHRUMLoRHTXjwAA3TM+U5a7QlKJ5CzoV6+BfvUaIFdeu8CRN9iBJlj6BOiqRO36zwB2zu2QiIiIiMZkTJ1Ss2fPxquvvjqmCtatW4fm5srrbBirZDLpSD1GZC72nfAkjMhcR+o7kkOjpOog9Rq3w3HFQ6/XID3hXRDSRO26T0KYXW6HRANEd/8EitUFI3IUMhPeAcC5fCWfswH12YNQnz0IcHauK8pq572R9O7G15lWoSfXoWr77W5HRDQuvK8S+QNz1Rlea7OX2pg6pU466SQ88sgjeW8DmUwm8cgjj+DEE08cV3CVJBQKOVORGoIVaQZUh+o7HCsDNbsHQGWOkjpEoOOom2CGpkHL7ED1hi8BkovWeoEwuxDb9UMAQPfMzwCi55LpWL4SUUEUpQIWmFeC+N6TPSNuY9u/A73rRZcDIho73leJ/IG56hAvtdkdMKZOqVNPPRUTJkzAN77xDWQymcOWzWaz+OY3v4l4PI7TTz+9oCArgao68+CspnegZt0VUNM7HKnvcLS+UVJaLaRe63Y4rpJaHO0LvgcJFZH9f0C45Tduh0QAort+AsXshBGZi8yEc/q/7lS+ElFhRIVMt31iS3XPiFtYqHntM4CVdjskojHhfZXIH5irzvBSm90JY+qUUhQFX/jCF9DS0oIvfOELePzxx4d1TmUyGTz++OP44he/iB07duALX/hCeS80WiRObVutmJ2I7P89FLPTkfpGEw+Z/aOkrEglj5I6xKhehu7mLwAAqjdeBS25yeWIKpswu/tHSSVmfBYQh27C5brNPFH5qZxRpx3zboAVaISe2oj4lq+5HQ7RmPC+SuQPzFVneKXN7pQxb403YcIE3Hzzzbj77rvx/e9/H3fccQcmTJiAcDiMVCqF1tZW2LaNpUuX4rLLLkNdXV0p4i47qVTK7RAc9bb57RCwYatVsLXKHiU1UGL6JxFsfxLBjidRu+5yHFj6p4oZtuk10d0/gWJ2wIjMQbrxnYO+V2n5SuRXllU5C8xLvQ4dR30D9S9/CLHdP0a2/kxk697odlhEeeF9lcgfmKtUCmPulAJ6duH79Kc/jYsuuggvvvgi9uzZg1QqhXA4jKamJixatAiTJ08udqxlLRaL5b1Wl+/ZJs45+iAAwAxP425mAwkF7Qtux4Tn3gI9uQ7xLTeia+5X3Y6q4ggzgdjOuwAMHyUFVFi+EvmYqmrlv9j5ANn6NyM5+RJE9/wUNa99DvuPf7jip8eTP/C+SuQPzFUqhXF1SvVpaGjAf/zHfxQrFqoQodYHURczIYUOO9DkdjieYweb0DH/NtS/fHHPu921pyHb8Ba3w6oo0d1394ySCs9GuvFdbodDRJS3rtlXI9j+BLT0FtRsWIn2hd/nmz9ERETkWVzsySNyuZwj9ViBRnTP+G9YgUZH6htJdPdPemIJTe3fzYwGy9b/BxJT/xMAUPPa56BkW1yOqHIIM4nozh8AABIzPjNslBTgXL6Sz4VUZB48E5kHzwRCXBjUDbIC176QahjtC74DCRXhA39EeP/9bodEdES8rxL5A3PVGV5oszuJPQIe4dSicXZwIrqbr4QdnOhIfUNp3a8g2PkMTAswg1NcicEvumatRC62CKrZjtr1VwCyctZGcVNs551QzXaY4WakG88dsQwXeSTyB1lBC50PZMSXoHvm5wAA1RtWQs3sdjkiosPjfZXIH5irznC7ze40dkp5RCjkzGLWwuxGsO1RCLPbkfqGiu6+GwDwr63VXMD7SJQg2hd+H7YSQbDjKcR2fM/tiMqekm1BdOcdAICu5i8BysgznJ3KVyIqjKJU7gi1xPQrkKs6DorVhZrXPsM3NsjTeF8l8gfmqjPcbrM7jZ1SFUZLb0P9Sx+Alt7meN1Krg2RffcBAP7wSr3j9fuRFZmNznk3AACqtt4KvfNZlyMqb1XbvgHFTiMXX4bMhHe4HQ75Xc6C/v9egv7/XgJy7BAghyka2hd8u/eNjacR23GH2xERERFRHtxss7uBnVIeUQnba0b2/gJCZpGLHYP1+8Juh+Mb6YnnI9W4AgIWatd9CsLodDuksqQl1iOy91cAgM7ZVx92YeBKyFcqAhtQn9wP9cn9AEe7u8KyKrsz0IrMQufc/wcAqNp2C/SuNS5HRDQy3leJ/IG5SqVQ0O57VDyBQACZTMax+q5b+UlsbnWuY0gREndfuAHxKuA7fzqInTu6AExyrH5fEwKd876GQNcL0DLbUbPhi2hfeCd3Uyqy+JYbIGAjPeEdMKqPP2xZp/OViMZHURTYdmV3TKWbLkCo7Z8IH/gTatd9CgeW/w1Si7kdFtEgvK8S+QNzlUqBnVIeoWnO/im+8dF6SC3uWH1Kdh8CCQNS6PjkefPw47897Vjd5UBqVWhf+D00rHk3wgf+jOzeXyI1+SK3wyobwbbHEWr7J6TQ0dX85SOWdzpfiWh8BDvvASHQMe/r0LtegJbZhuqNX0HHgtvcjopoEN5XifyBuUqlwOl7HuHUTgZSCWBPZwBO/+m1zE4AgBWaCojKXXi2EEb8uJ7FtwHEN10NLbnR5YjKhLQQ3/xVAEBy8sWwIs1H/BHuPELkF5W5+95QUq9Bx4LvQEJBZN9vEd53v9shEQ3C+yqRPzBXnSGVAMzQTEgl4HYojmCnlEc4NT/XjB6FS381z9Gh+8JKQjE7ICFgBqc4Vm85Sk77ODK1b4RiZ1C77nLA4vDZQoVb7oWeXA9bjaN75mfz+hnOpyfyh0pfU2qgXM2JSMz4DACgesOXoKZ3uBwR0SG8rxL5A3PVGWb0KOw/6V8wo0e5HYoj2CnlEbFY+a7voGb2AABsvR5QuY1oQYSCjvm3w9IboCfXI77l/7kdka8JK4341psBAN0zPg2p1+X1c+Wcr0TlRFU5zWCg7hmfRS6+DIrVjdr1nwRsw+2QiADwvkrkF8xVKgV2SlUYLbEOv7p4PYTZ7UyF0oaa3QsAsEKTnamzzNnBRnTMvw0AENt9N4KtD7kbkI/Ftt8GNdcCMzgVySkfcTscIqLSUjS0L/gebDWOQNcLqNp6i9sRERER0RBaYh0m/msxtMQ6t0NxBDulPCKXyzlSj5AWqsMWnFpnQzEOQsgcpAjA1hscqbMSZOvfhMTUjwMAal/7HNT0Tpcj8h8tsR6xnXcCALrmXDemUXxO5Sv5XFBB5v43IXP/m4Agb7dukFz7YhgrPA0dR/V0RlXt/B6CB//pckREvK8S+QVz1RlCWlCNNghZGcsQ8CnZI8p10bi+qXtWsAkQPN2KqWvWl5CrOg6K2YHaVz/G9aXGQtqo2fA/ENJEuuFtyEx425h+vFzzlYpMCCCk9nxwFzhXSC50PqJM4zlITr4EAFDz2qeh9I5oJnIL76tE/sBcpVJgL4FHhEJluNaSnYVitAIArCCn7hWdEkD70T+ApdUikHgJ1Zuudjsi34js+T8Eup6HrcbQOef6Mf98WeYrURlSFO72OprO2f8LI3Y0VKMNtes+Bdim2yFRBeN9lcgfmKtUCuyUopJRsy0QkLC1uKO7/VUSKzQF7Qu/DwkF0b2/QGTvL90OyfOU7D7Et3wNANDd/D+wudYZlUrOhn7rq9BvfRXI8Z1F8hg1hLaFd8JWowh2/htV229zOyIiIiKqQOyU8ginttc0w7PwuftnQaqR0lYkJdRs39S9KaWtq8Ll6t6I7uYvAACqN1wFvfsllyPytupN/wvF6kKuagmSUy4Z12twO1zKiy2hPrwX6sN7AZvTyNxgWZWxFsN4WZFZ6JzXswNpbPttCLQ/4XJEVKl4XyXyB+aqM8zwLBw47g8ww7PcDsUR7JTyiEAg4Eg9UovitX0RQJR2m2xhdkGxkpBQYAUmlrQuAhLTP4VM/VsgZBa1r34MwmhzOyRPCh58GOEDf4aEio55NwFifFN7nMpXIiqMovAx50jSE9+N5KSLICBRu+4KKNl9bodEFYj3VSJ/YK46Q2pRGNXLIbWo26E4gk9rHqFppe0k6qNk9uCyk/eWfFHsvlFSdnAioDjzu1U0oaB9/u0wQzOhZXaidt0VQIXs1pAvYaVQveEqAEBy2mUwqxaN+7WcylciKozgAvN56ZrzVRjRBVCNA6hddzlgG26HRBWG91Uif2CuOkPJ7EF807VQejcNK3fslPIIKZ2Z2qEaB/GeYw5CyBJu5yktqLkWAIDJBc4dI/VqtC36IWwlhFD7o4hv/qrbIXlKfNM10LK7YAanonvmlQW9llP5SkSFYq7mQ6phtB39A9hqDMHOZxDf+nW3Q6IKw/sqkT8wV52hGgcR2/VDqMZBt0NxBDulPCKZTLodQtEouX0Q0oKthCG1GrfDqShmbCE65t8OAIjt+hEiu3/mckTeENr/B0T3/gISAh3zv1nwmmrllK9E5YxrSuXPisxGx/xvAQBiO+9E6MBfXI6IKgnvq0T+wFylUmCnlEfEYuWzO52W6VvgfDLAqROOyzSeg67m/wEAVG/8CgJtj7sckbvU9A7UvN5zPBLTr0Cu9g0Fv2Y55StROVNVTjMYi8yEtyMx9eMAgJrXPgc1tdnliKhS8L5K5A/MVSoFdkpRUQkrDcXsgARgBSe5HU7FSky/AqmJ50HAQt2rH4eW3OR2SO6wDdSu/yQUqxu5+PKCp+0REZW7rllfRrb6RChWAnWvfhzCSrsdEhEREZUxdkp5hGE4s6iordfhT6/WQYrS7Jyg9K4lZet1gBoqSR2UByHQcdQtyFafAMXqQt3LF0PJVd6OfFXbbkWg6wXYahztC79XtEX3ncpX8rmggsyv3ojMr94IBHm7dYOUttsh+I+io33hHbD0CdCT61G94X8AriFCJcb7KpE/MFedYet1SE6+pKdNXQH4lOwRpmk6Uo8VmoLvPzm5ZB1Gara3UyowsSSvT2OgBNF+9I9hhmZAy2xH7auXAhX0jneg/QnEdnwPANBx1C2wQlOL9tpO5Sv5nBBATaDng1OZXcEFWcfHDk5E+9F3QEJFZN/vEN19t9shUZnjfZXIH5irzrBCU9A570ZYoSluh+IIdkp5RDgcdqQeYaUxuyENyOIv/irMBBQrCQkBK9BY9NensbMDdWhb/FPYahzBztWoe/XjgF3CnRc9Qsm1onb9pyEgkZz0AWQazynq6zuVr0RUGEVR3Q7Bt3I1J6Nr9lUAgPimaxHoeNrliKic8b5K5A/MVWcIKw29++WKmULPTqkKo6U24bvnbYawir9zwqFRUg2Aohf99Wl8zOhctC2+B7YSQqjtEdSu/3RJOiW9Qlhp1L38Yai5/TAi89A15zq3Q6JKlbOhffc1aN99DchxGhn5T3Lqx5BqfA8ELNS++nGomd1uh0RERFT2tNQmTHj+bdBSlbEuMDulPCKd9nkvqJRQe9eTsgJNLgdDQ+VqTkT7oh9DCh3hA39C9etfBMpxrRVpoWb9FQh0r4Gt1aBt0Q8h1eK/o+P7fCVn2BLan3dB+/MuwOY0MjfYdvl2wDtCCHQedTOM2NFQjYOofaWypoGTc3hfJfIH5iqVAjulPELX/T2ySJidEHYGUqg9I6XIc7J1Z6B94fchoSLa8ivEN11bdovXxjd/FeHWv0KKANoW/QRWZE5J6vF7vhJVCiH4mFMoqYbRdvSPYWm1CCReRg0XPqcS4H2VyB+Yq1QKfFrzCE0rzq5gbumbumcFGgHBNTy8KjPh7eiY/00AQGz3j1G17ZayaVxEd/0YsV0/AgC0z78NuZoTS1aX3/OVqFIILjBfFFZ4GtqPvnPAwuc/djskKjO8rxL5A3OVSoGdUhVGQiCVUwAU8UFd2lBz+wAANqfueV666b3omHsDAKBq++2Ib77O91P5QgceRHzTNQCAruYvIzPxXJcjIiIqL7naU9E1+2oAQHzTVxFse9zliIiIiMqThICtxiCL2Wb3MHZKeUQikXCkHrNqEc67eyGkVlW011SMNghpQIoAbL22aK9LpZOa8mF09i4AHtv1Q9Ss/7Rvd+ULdPwbNes/2b/TXmL6J0tep1P5SkSFsSxuXV1Myan/idTE9/YvfK4lK2MBVio93leJ/IG56gyzahFaTnsdZtUit0NxBDulPCIWi7kdwrj1T90LTgS4fodvJKf+J9rnfxtSaIjsvw91L38Ywiz+roylFGz9O+pf+gAUO4NM3ZvROfdGwIHpOn7OV6JKoqqcZlBUQqDjqJuRiy+HYnWh7pVLIIx2t6OiMsD7KpE/MFepFNiDUGG05Abcef5GCLNIvdzSgpI7AIC77vlRuuk8tC26B7YSRqj9MdS/eAGU3EG3w8pLuOVe1L1yKYSdQab+TLQdfRegsAFKRFRSShBti34MMzgFWnob6l79OGAbbkdFRERUNrTkBkxY/SZoyQ1uh+IIdkp5hGk6M8VA2FnMqMsCKM4aQkruAAQs2EoYUosX5TXJWdn6N+Hgkt/07KzUvRYNa86FlljvdliHFd31I9S+9hkIWEhNfA/ajv4RoIYdq9+pfCWfCyjI3vMGZO95AxDg7dYNskw2cvAaO9CAtsX3wFajCHb8C9Ubv1I2m2aQO3hfJfIH5qozhJ2FntoAYWfdDsURfEr2CMPw57uMfVP37GCTI9OmqDSM+FIcPO7+3ne+t2LCC+cgsmeV9xoZUqJq6y2o7l3UPDHlUnTMvx1QnN2e1q/5Sg5TBGRTGLIpDCi8PrpB+nwTBy8zYwvRvuC7kBCI7v0/RHff7XZI5GO8rxL5A3OVSoGdUh4RDjs3yqNobAOK0TPVywpy6p7fmdE5aF32V2Tq3gxhZ1Cz4Ys9C4ib3W6HBgAQRhtq130cVdtvAwB0zfwCuuZc58o6Zr7MV6IKpCiq2yGUtWzDW9E16yoAQHzTNQi2/t3liMiveF8l8gfmKpUCO6Vo3NTcfgjInu0q1ajb4VAR2IF6tC3+KTpnfaV3AfQ/YMJzb4Pe/ZKrcQXbHkPjs2cifOABSKGhY+6NSMz8LEfnkbcZNrQfboT2w42AwRE7VJ6S0z6BZNP7IWCjdt3lrt8viIiIyF+4KrBHpNNpR+oxQ9Nx7YPT8aULC+/lVnL7AQBWYGLBr0UeIhQkp1+OXPUJqF33X9Ay29DwwjuRnHwxEjM+BztQV7KqP3P5JUh0tPR/HlBtfPTEFpy7uA0AsKM9iFv+MRWbWn8B4BcliyNW04Tb7/jpqN93Kl/J5ywJ7XfbAQDmh2YBzs4yJQC2bbkdQvkTAp3zvg41uweh9sdR9/IlaD3uj7DC09yOjHyE91Uif2CuOsMMTcfBRXfDDE13OxRHsFPKIzRNg2WV/uFZ6tV4Znu88DV4bAOK0dNRYAcbixAZeY1RvQwHlv8NNRu+iPCBBxDb/RNEWu5F94zPIDn1I4ASLHqdiY4W3LeyCZASitEGLbUBipUEAJjBqWicPRe3zCn9dJwVN7Yc9vtO5SsRFUYIwcXOnaDoaD/6LjSsWQE9uR51L38IrcfdD6nXuB0Z+QTvq0T+wFx1htSrkW14q9thOIbT9zxC1515C13J7scFSw4ABa7kr+YOcOpeBZB6DdqPvgutx/4KRnQhFKsL1VuuR+PqMxDedz9g54pan4CEkt2PQOezCHSvgWIlIUUAuaolMGPzAeGN9WGcylciKoxwYc25SiW1Khxc/DNYgSboqY2oe/U/C37WoMrB+yqRPzBXnaFk9yO2/TtQsvvdDsURfFqrMGpuHz5y4r6Ct5dUcvsAAFaAo6QqQa72NBxY/iDaj/omrEATtMwO1K7/JCY+vQzxjV+B3rW2sJ36rDTCLb/DHedvQiDxEhSrCxIKzNA0ZGtOgh1oKNrvQkREpWGHJuPgMT+DrcYQ7HgaNa9dCXAHRCIiojFRc/sQ3/p1qL1t7nLH6XsekUgk3A4hf4Om7nE9qYohVKQnvQ+ZxnciuvMHiO7+KVTjAGK770Zs990wwrORaTwXuarFMCNzYYWnjz6yyTahd69FsP1JBDv+hUDn8xAyi9o6QAoVVmhazxxqJeDs75gnX+UrUQWzLNPtEByxect2XHrhWW6H0b8eX/vRd6HupQ8hsv8+2MGJ6Jp9tduhkcfxvkrkD8xVKgV2SnlENBpFMpl0O4y8KEbf1L0op+5VIKlGkJj5OSSmX4Fg+xMI77sXodYHoac3Q9/+zUPlRBBmZBbM0DQIOwthpaBYSQg7BSW7H4qdGvS6VnASfv6EhfeftRBQvH1p8lO+ElUyVVUrYu0LTRg96/G5rG89vmzd6eiYfytqX/scYjvvhKU3IDn9cpejIy/jfZXIH5irVArebvlVEOGjre3VLHfdIwCKhmz9m5CtfxOE2Y3Qgb8g2P4E9NQGaKnNEHYGenI99OT6EX/c1mqQrTkF2dpTka19A6zwbPz622/D+8/2/mXJT/lKVNmYq25JN10AJdeG6i3Xo3rL/4Ot1yE96X1uh0UexfsqkT8wV6kUvN/6qxCm6cwUA1uL44nNcZywbJx/etuEYhzs+S/Xk6JeUqtCetL7DjU4pAU1swtacgPU7F5INQzZO7JOqhHYWg3MyGzAp4sQO5Wv5HMBBdk7T+r/PzmPO++5Kzn9E1CNVsR23oGa178AW6+tqN2EKH+8rxL5A3PVGbYWR3rCO2BrcbdDcQQ7pTwilyvuLmajscIzcOPD03HfCZFx/fygqXtarMjRUdkQKqzwDFjhGW5HUhJO5Sv5nCIgZ/I66Sbb5iLbbuuadRUU4yAiLb9B3brLcfCYXyBXc6LbYZHH8L5K5A/MVWdY4RloP/out8NwjO87pVpbW/H73/8eL774Ijo6OhAMBnHaaafhIx/5CICexFm1ahWeeuopZDIZzJkzBxdffDGam5tdjnywSCTizMJxdg4NUaNnN5xxjFJRsz07AHCUFFUyx/KViArSs6YU39V1lRDomHcLhNGB8MGHUPfyh9G65Lcwqxa5HRl5CO+rRP7AXHWInYOSa+3ZgdyjGz8Vk687pTZs2ICbbroJb3rTm/D5z38edXV1SCaTgxLl29/+Njo7O7Fy5UrE43E88sgjuPbaa/GNb3wDDQ2Vt828nnwdP//g68ha1ZBjHQ5om/277nE9KSKiIzBsaL/aCgAw398M6JzCRxVK0dC+8PtQXvoAgp3PoP6lC3Fwyb0wo0e5HRkREZHn6MnXMeH5t+HAsgdhVC12O5yS8+0Tci6Xw7e+9S1ceuml+OAHP4jm5mZUV1dj8uTJmDdvHgDg9ddfx4svvojPf/7zaG5uRn19PS644AIcc8wxuPfee13+DQbLZDJuh3BEPVP3bNhqhLvuUUXzQ76SB1gS2qqt0FZtBSyubeQG2y7/nfd8Qw2jbfFPkataAtVoQ/2L74ea2uJ2VOQRvK8S+QNzlUrBt51S//73vxGPx3HKKaeMWmb16tU47rjjUF1dPejrZ5xxBp577rlShzgmiuL9P0Xfrnt2YCLAnReogvkhX4kIENx9z1OkVoWDx/wfjOgCqLn9qH/xfVAzu9wOizyA91Uif2CuUin4dvreSy+9hKVLl2L16tW4//770dbWhsmTJ+Nd73oXlixZAgDYtm0bFi0avmZBc3Mzurq60NbWhrq6OocjH1kgEPD2wnHy0K57FteTogrn+XwlIgCAUBTA4mLnXiL1Whw89leoX3se9NQm1K+9AK3H/Q52cJLboZGLeF8l8gfmKpWCb7s6d+/ejddeew2/+93vcOGFF2LlypVYsmQJbrnlFjz++OMAgLa2NtTW1g772Zqamv7vj8YwDKRSqf6PdDpdkt/DL5TcwZ6pe0oEUuVuUkRERDQ+dqABB4/9NczQTGiZ7ah/8f1QekdjExERUWXx7UipVCqFtrY23H777QiFQgCA6dOnw7ZtrFq1CqeddhpM04SmDf8VFUWBqqowDGPU17/vvvsGrTvV3NyMm266CeFwGMFgEIlEApFIBIqiwLIsZDIZRKM96yxls1kAQDAYBAAkk0mEQiGoqgrbtpFOp/vL5nI5SNmz1kgsFkMqlUIwGOwvm0qlEIvF+svatt3/+6ZSKQQCAWiaBiklkslkf1nDMGBZ1vCy0RNx7o8X49dfqIKq9hwbKW1IKaEoKoCeNTiEEBC9u/NZlgnNONBTNjgRQlEOW1ZVVQACUkrYtt37eW9ZiJ53rnun/41e1oZA77vcI76u1R9/35bfyoCyiqJCCAFAwrIOlZW2DYnBQ08PX/bQcbEsC4qijFx2xGOYX1kIgWAwCF3XAQCJRALRaBRCCJimiVwuh0gkAqBnHreqqnmXVRQFgUCgv+zAczabzfaXzWazEEL0l00mkwiHw+M6v/M9Z0cqCyH6j9P4j3ff+dJXVkJKe0znrKKqiMViox5DAAiFQmM63mO9RiiqClXVhp2zhz0uBZyzo5YVAoqiHPEaMfAYjvV4j3iNGFhWCCiK6so1YtBx6Z0JlvfxxqH1jBQhIHt/j+Hn7OH/NmLgdOkjXjuLcLzzuCYPj6GQ453nOTukbE8do7xu7zHs+3sV+xox8BgOPCcKO97jP4aDY3D4GjGwbO+1M51OQ9O0Ue9VhmEgXD8H6VP+hMi/3gY9tQkTXn4fUif9Cd1mzHP3tZGe2/rKluq5LZ1OQ9f1UcuapolwONxf9nDH2w/PEZZlIRaLFfwcMZbjPfQY9h3vvt+1r2z/OTvO4z3aMRzr8fb6OTvW433Ea0RvWa+es24fb7euEQPj92NbwzfXiOiJaHvrHliGRCya3/H24jk76Bn2MHzbKQUAJ510Uv8B6XPKKafgF7/4Bfbt2wdN02Caw7eCtm0blmX1/0FHsmLFCpxzzjn9n/cd0HQ63d+ZlUqlBv3M0O0xB3Z6DR1pNbSsruv9r3eksgM/H7rYXD5lc6YEhBi2TfbAz3se5nunPEgbItfTKWXqDZBSjl4WPQ+uo74uZM9Uit7GwuHLYtC0i8OV7fn8UNmhi9sWVtYcV1kp8ywrJbLZbP/FA+i5cAw08O9ommbeZQEMGmJ7pHN2LGXHcn7nfc4OObdKcrxx5HPWtqxBcQ09LpFIpP/3KNUxtC1rUMxOnLMjlu1tWA8tO6a8H881YmDZ3gb78LJw9hohRys7+jHs/7qUw87vfM9ZKQfcqvO5dhZ6vEctixIe7yKes4PI/jiLeY043M8W63j3fO7+MRxL2YHXzr4GSZ+R71V1SC35DerXng8tsQHhf52N5JLfIommEcoe4oX7WknugUM+H3p+HKmsn58jhBCDvl/sZ998ypbqeAOVc86O9Xj7+Zz1wvF24xoRiUQ8ebx5jRi5rNvnbF/H3JH4dvpeNBrtn4Y3UN/XUqkUampq0N7ePqxMR0cHAAxbAH0gXdcRiUT6P/p6PkvFqUXj1NRm3PTOLRBW8siFeylGG4S0IEUQUouXMDoif+Aij0R+wYXOvc4Kz8TBJb+DGZwKLb0FDWvfCyWzx+2wyGG8rxL5A3PVGWpqM+rXvBdqarPboTjCt2fVlClTsG/fvmFf71snqqamBtOnT8fWrVuHldm6dSui0Sjq6+tLHme+RhrRVQqKlcIxk1OAHPnd/BF/JtezzoMVmMBd94jgXL6Sz+kKsrcfj+ztxwO6b2+3vtY3hY+8zQpPx8El98IMTYOW3oqGtedDyex2OyxyEO+rRP7AXHWGYqUQ7HwaipU6cuEy4Nun5OOOOw5PP/00uru7B339sccew8yZM1FXV4fly5djzZo16OrqGlTm0UcfxbJly/Ke4+gEz+5iICXU3ql7dpC77hEBHs5X8hZVQB5VDXlUNaB6535TSfqmnpL3WeFpPSOmQjOgZbahYe17oaZ3uh0WOYT3VSJ/YK5SKfi2U+qkk05CU1MTbrnlFmzbtg1tbW34y1/+gj//+c/48Ic/DABYvHgx5s2bh1tvvbW/zG9+8xusXbsWK1ascPcXGKJvsTOvEWYHhDQghQZbq3E7HCJP8Gq+EtFgfYuMkz9YoSloXfLb3l35dqBhzbuhJTe4HRY5gPdVIn9grlIp+Hahc0VRsHLlSvzsZz/Dddddh1wuh9mzZ+PLX/4yFi5c2F/uyiuvxKpVq3D99dcjk8lg1qxZuPrqqzFlyhQXo/cPtXfqnh2YAAjf9mESETnPsKHevwMAYL17OqfwEeXBDk1B63G/Q/2LF0FPvY76Ne9B2zGrYMSPdTs0IiIiKgHfdkoBPQuVX3HFFYctE4lEcNlll+Gyyy5zKKrxGbpqfalYwSm47bHJ+MS7Q0cuPGDqnhWYUOLIiPzDqXwln7Mk9B9v6vnvO6cB+W1AQkU02m6I5G12sAmtx92L+pcuRqB7DepfvABti+5GrvYUt0OjEuF9lcgfmKvOsIJT0DHvFljByhhIw7dtPcKpnQzsQB3+9lodoASOWFZY3RB2BhIKbN07i8ITuY07jxD5g+Due74l9TocPPZXyNa8AYqVQP1LH0Sw9SG3w6IS4X2VyB+Yq86wA3VITb4IdqDO7VAcwbPKIwKBI3cSFYOSa8NZ89sA+8iL1B2autcACK7LQdTHqXwlosIIPjz7mtRiOLj4Z0jXnwUhs6h75T8R3vtrt8OiEuB9lcgfmKvOUHJtiOz5BZRcm9uhOIJPaxVGze7GZ0/fA2Efeeilwql7RERE5CY1hPaj70Jq4nshYKH29f9GbNu3ACndjoyIiKgk1Oxu1Gz4AtTsbrdDcQQ7pTwikUi4HcIgwkpCsZKQELD1BrfDIfIUr+UrEY3Msky3Q6BiUDR0zL8N3dM/BQCIb7sV1a9/AbANlwOjYuF9lcgfmKtUCuyU8givba/ZN0rK1usAhavzEg3ktXwlopGpKqeelw0h0D3ry+iYeyMkFERbfom6Vz4CYSbdjoyKgPdVIn9grlIpsFPKI7y2aNyh9aQ4dY9oKK/lKxGNhgudl5vUlEvQtujHsJUQQm3/RP3a86BkW9wOiwrE+yqRPzBXqRQ0twOgHpblzLbVthrBS3siOKrmMO8eWxkoZhckuJ4U0UicylfyOV1B7qal/f8n50muO+SozVu249ILz3KkrnkTJuO6s7ejBi9D+/uJuO5vM7CpNYxYTRNuv+OnjsRAxcP7KpE/MFedYasRZKtPhq1Wxsg0dkp5RCZz5IXHi8GKzMb//GkW7lscHbWMavRM3ZNaNaAEHYmLyE+cylfyOVXAPrYytvL1Ktvmw7OTNGHgvpVNjtUnrCbY3S+iIZbEt8/bCiO2EOd+g6Om/Ij3VSJ/YK46w4rMxsHj7nU7DMfwrVuPiEZH7yQqKmlDV+zD7lrDXfeIDs+xfCWigqgq33srZ1KNIBc/HpZeDwEbgcQr+NDyfYC03Q6Nxoj3VSJ/YK46RNqAna2Y+xk7pSqMnngVf7xsHYTVPXIB24RitPf8l51SRETjZ9pQ/7gT6h93AmZlPFQQOU7RYFQtgRmaAQC4aNkB1L76cS6ATkREvqUnXsXkx2dBT7zqdiiOYKeUR2SzWbdDAAAoRisEJGwlAqmyJ5xoJF7JV/I4U0L//uvQv/86YHJtIzfYNjsDK4IQMKNzkYsuhGEJhFv/goYXzoGW3OR2ZJQn3leJ/IG5SqXATikaRO2dusdRUkREROQndmgy/udPzbACTdBTG9DwwtsR2v8nt8MiIiKiw2CnlEcEgx5YUFzaUIxWAFxPiuhwPJGvRHRE3Lq68qzfF8GB5Q8iW3MyFCuJunWfQHzTNYBtuB0aHQbvq0T+wFylUuDTGvVTjHYIaUGKQM/Oe0REREQ+Ywcm4OAxv0L3tE8CAGK7foT6tedDyexxOTIiIiIaip1SHpFMOrMgpxE9Ch/6v6Mg1diw7x3ada8BEMKReIj8yKl8JaLCWJbpdgjkFkVD9+yVaDv6x7DVKgS7nkXjc29B6MCDbkdGI+B9lcgfmKvOMKJHoeWkZ2FEj3I7FEewU8ojQqGQMxUpAbQmdUAM+dNLCdXgelJE+XAsX4moIIqiuh0CuSwz4W04sOyvyMWOgWJ2oO7VS1G94UsQVtrt0GgA3leJ/IG56hAlADs0GVACbkfiCM3tAKiHqjrz4Kymt2PlmTsgrDikGun/urC6IewsJFTYep0jsRB51eYt23HphWeN+n1FVWFbVklj2LljO4CmktZBVO4ER/0SACvSjNalf0B8682I7bwD0T0/R6DjGbQv/B7M2EK3wyM49xxMRIVhrjpDTW9HfMsN6Jp1FazwDLfDKTl2SnmEU9tWK2YXTpvdhawcPKXh0K57dYDgxYYqmyYM3Ldy9A4hVVVhlbhTaull3Mrc93SB3HVL+v9PbpBuB0BeoQTQNfsryNS+EbWvfRZ6agMmPH8OumZ9Ecmpl/HZx2VOPQcTUWGYq85QzC6EDzyAxPQrUNoWhzdw+p5HpNPuDiM/tJ4Up+4RHUmpO6SoTKgK7BMbYJ/YAKi83bqBuUpD5ereiAPL/45M/VsgZBbVm69Hw5oVUFN8I8BNbj8HE1F+mKtUCnxK9ohoNOpa3cJKQ7ESkBCw9QbX4iDyC1XlIFMiP2Cu0kjsQD3aFt2NjqNuha1WIdD1PBqfOwvRHXcCkh2ZbnDzOZiI8sdcpVLg0xr1j5KSWnXFLKbmJUdav8gpsZom3H7HT90Og6h8mDbUf7QAAKw3NwEa3wciKrWx3lMbopPwmdMllk9LoHrL9djz9K247dEp2NFR2GK+vKcSERHlh51SHpHL5RypxwpMxN3PTMSFbwv2f+3Q1L1GR2KgwY60fpFTVtzY4nYIviE5n57yYUro31wHALDeOJF3XBcwVyvPuO6pchqM7B5oqQ1YMDGNO9+3GVZoBsxI87jXmuI9dWyceg4mosIwV51hBSaiq/lLsAIT3Q7FEXzb1iOkdGYxVjvYiN+snQAovZ1Sdg6K2dHzX64nRZQXLp1M5A/MVcqLELBCU5CtPhmW3gABCS2zDcGOp/vfuKPScuo5mIgKw1x1hh1sRGLGFbCDlTFohJ1SHhEMBo9cqAiE0YkTZ3QBtgEAUIxWCEjYagxSDTsSA5HfKQovnUR+wFylMVFDMOJLkKs6FlIJQdgZBLpfhN61FsLi4r6l5NRzMBEVhrnqDGF0Itj6EITR6XYojuDTWoXRMjtw7dt2QNg9D1dq7zuAHCVFRERE1PNMlK05GWZoBiQEVKMVgY6noSU3ArbpdnhERFTmtMwO1L/yEWiZHW6H4gh2SnlEKpVyvlJpQcm1AQAsdkoR5Y3bzBP5A3OVxk2oMKNzkas+EZZWCwEbWmY7gh3/gprZCUiuV1ZMrjwHE9GYMVepFNgp5RFuDIVUjHYIWJBKEFKtcrx+Ir/ilCAif2CuUqGkFoMRX4pc1RLYagRCGtCTryPQ8W8o2f0A11cpCk4JIvIH5iqVAvcC8ghVHd/uLoXo33VPnwAI4Xj9RH4lmC9EvsBcpaIQAnagATm9Dmp2N7TUFih2CoHES7DVKpiRWbD1Bj5LjeIzl1+CRMfhdyNUVBV2CUc2xmqacPsdPy3Z6xNVCjfarFT+2CnlEbZD21ZLJYjtbUE0VQuuJ0U0bnxnnPKgC+RWLu7/P7mBuUpFJBRYoWmwApOgZbZBTe+EYnUj0P0ibDXe2zlVz86pIRIdLbhvZdNhy6iqWtLptituPHynGBHlx6k2a6WTShBGZB6kUhkj09gp5RFOzc81o/Pwid/Oxf1fsCBkDlKosPVaR+omKhdcp4byoiqw3zjR7SgqGnOVSkLRYEbmwAxNh5beATWzE4rVhUD32p6RU+EZUAQ7RMeCuUrkD1xTyhlmdB4OnPBPt8NwDBdb8IhYLOZoff2jpPQGQPA0IBoLVWV/PpEfMFeppJQAzOgcZGvf0LtTn9IzcirxCn5y4QZEd/4Qwky4HaUvMFeJ/MHpNitVBvZGVBit+xX87iProOT2AeCue0REJWPZUB7fB+XxfYDF4e5EZUsJwIzORbb2VBjhWZBCx8QqA9Wbr8XEf5+A+Obroaa2uB0lERH5hNb9CpqeOApa9ytuh+IIdkp5RC6Xc6QeAYlIwIZiZyAhekZKEdGYSM6np3wYEoEbX0bgxpcBg1N53MBcJUcpAViRWcjWnopvPz4ZZngWFLMTsZ13YuLq01C/9gKE9v8JsJ155vMT5iqRPzjVZq10AhKKlYCokLUx2SnlEW4sGmfrtYDC4dJEYyUr5AZB5HfMVXKFUPHX9XXYf8JjOLjoHmTq/gMSAsGOf6Fu3Scw8enjEd98fc874JLnKMBcJfILLnROpcAeCY8IhUJIJJxdd4C77hGNj6KosCzT7TCI6AiYq+QqoSDb8BZkG94CNbMLkb2/QGTvr6Dm9iG2807Edt4JIzIP6YnvRrpxBazwdLcjdg1zlcgf3GizUvnjSKkKI4z2/v9bOjuliIiIiErNCk1Fd/MXse+kZ9B29I+Rbng7pAhCT21AfOvNmPjMyWh4/hzEtn8bWvJ1jqAiIqKKwZFSHuHU9ppaeisAwFZigBpypE6icsOtq4n8gblKnqPoyEx4GzIT3gZhdCLU+ldE9v0egY6nEOheg0D3GsS33gQzNBOZhrciU38mcvFlZf/Mxlwl8gen2qyVzozMwYFlD8KMzHE7FEewU8ojAoEAMplMyesJtf0DAGAFG0teF1G5UhQFts0HaCKvY66Sl0m9GulJ70d60vuhZPchdPAhhFofQrD9X9Ay2xDbdRdiu+6CVELIVp+AXO1pPTv8xY4GhOp2+EXFXCXyB6farJVOqmEYVYvdDsMx7JTyCE0r/Z9CWCkE254AANhavOT1EZUrIYTbIRBRHpir5Bd2cCJSkz+E1OQPQZhJBNsf7e2gehxqbj9C7Y8j1P54T1k1jlx8KXLVy2DElyMXPw5Sqxr2mp+5/BIkOlqc/lWG2bljO4Cmw5ZhrhL5gxNtVgLUzG7EdnwPiemfhBWa4nY4JcezyiOkA2sHBNseg5DZnk+EXvL6iMoX1/qgPGgCxn8v7P8/uYG5Sv4jtSgyE96BzIR3AFJCS21EsP0JBNufQKDjaShWF0LtjyLU/mhPeQiYkbkwYotgxI6GEVsIM7YIiY4W3Lfy8J1BTlh62aY8SjFXifzAiTYrAYrRhuienyI16UJ2SpFzkslkyeswovORbLoI0ZZfAHxHimjcuPYF5UVTYL11sttRVDTmKvmeEDCj82BG5yE59VLANqElX0Og6zkEOp9HoOt5aJnt0FMboKc2APt/3/+j//dBDXrnbkg12vsRga1GASXouedA5iqRPzjRZqXKw04pj4jFYiXfXtOKNCM15eKeTimiITZv2Y5LLzzL1RjyGeLvBaqqcetqIh9grlLZUTSYVYtgVi1CasqHe76U3Q898TL0xCvQE69CT7wKLb0N9VETMNt7PgaQEJBKqOdDDUEq4d7/9/zb02nl7AbdzFUif3CizUqVh51SRAQA0ITh+jD//Ib4E/mEZUN5rg0AYC+vA1RnG3lEVBnsYCOywf9Atv4/+r8mzG7cdOXbccsHQxBWEsJK9fxrpyEgIew0YKeBEfqBJASgBCFH+Dj09VDZLbZORETuYKeURxiG4Ug9ll6P379Uj7e/MeBIfUTlSErb7RDIDwyJwDVrAQCZ+98EsP3mOOYqVSqpVeH1/RFYoSFvNkkJ2FkIO9PTQWX1/mtnIKzefyEBOwNhH36HLSnU3hFXweGdWKKvEyu/503mKpE/ONVmrXSWXo/E1Mtg6fVuh+IIdkp5hFNz6e3QZPzw6Ul4+5tCjtRHVI64yCORPzBXiYYQAlB7p+2hZvj3pQRkrrezKgNhZ/s/MOD/AhaEtCCsJGCNvsaMBPDPLwOBjtUjj7jqnTIopbfWuCKikXH9N2fYocnomnOt22E4hp1SHhEKhRyZnyvMJOZPTAHSBAT//ETjoSgq174g8gHmKtEYCQH0jnKSqB69nG32dE7JbO+oqiyE1fP5oU6sHAQkJlYDsLqAUdqyEgJQw7CVcE8nlRqFrcYg1WjeI62IyBlOtVkrnTCT0JLrYUYXQGpRt8MpOfZKVBgtvQXfevcWZK0GSC3udjhERERE5DeKBqlokDhMY6l31NWHvvoMVq2cD2HneqYE9o+4yvSMyIINWCmoVgoYMjNICr2ng0qLwdbikGoVpBpxfCF2IiInaektmLDmXBxY9iCMqsVuh1Ny7JTyiFQq5XYIRJQnDl0m8gfmKpGLekddvbpbgR1oHLlM7xpXip3uXYy9d0F2K9nzNWlAHbKDoIQCqVXB1uKwtRrYWjWgclkKIiewzUqlwE4pjwgEAshkDr+gJBF5g6IosG02dom8jrlK5HG9a1xBj8Iamqu9a1YJKwHF7IZidkOY3T3rWZmdUMxOADt7iioh2Fo1bL0Wtl7XM5qKiIqObVYqBXZKeYSm8U9B5BdCcEFWIj9grhL5w4i5KlRILQ6pxWEHe78mJYSdgjC7oRgdUMxOCKsbws5AzWWg5vYBAGwlDFuv6/8gouJgm5VKgWeVRzi1Q5AUKjrTKkLVfFAnGj/u6EV50ASM/zqq///kBuYqkT/kmatCQKrRnsXQg029P2pCMbugGO1QjPaeUVR2Gkp2N5DdDQmBm98ZRnTnncjUvwVWZHbpfg2iMsddbZ0hhQpLr4MUqtuhOIKdUh6RTI6+nW4xmbGFuORnC3DfyipH6iMqR1ynhvKiKbDeNc3tKCoac5XIHwrKVaENHhElzZ5RVEYbFOMgFCuJxZNTwObrUb35ehjh2chMOBvpxnfBjC7smUJIRHlxqs1a6czYQux7w8tuh+EYbl3hEbFYzO0QiChPqsr+fCI/YK4S+UNRc1VosAMNMKPzkKs5GdmaN+COJychW3sapNCgpzejasd30fjcWzFh9emo2voNaMmNxaufqIyxzUqlwE6pCqMlX8eP378Bwky4HQoRUXmzJJQX26C82AZYHO5OROQGqYbxx1frcfDYX6HlDS+jbcH3kW44G1IEezqotn8Tjc+egYbnzkJ0108gjDa3QyaiCqclX0fjv98ALfm626E4gm8heoRhGI7UI+wcJlfnkIXtSH1E5UhK5g/lwbAR+J8XAACZ+98EqJWxLoCXMFeJ/MGpXJVaHJmJ5yIz8VwIsxuh1r8hvP+PCLY/hkDiFQQ2vYL45uuRaXgLUk3vQ7b2dEBhc4moj1Nt1kon7By0zDYIO+d2KI7gVdYjTNN0OwQiyhMXeSTyB+YqkT+4katSq0K66b1IN70XwmhDZN/9CLf8GoHEKwgfeADhAw/ACk5CctIHkZr8AdiBCY7HSOQ1bLNSKXD6nkeEw2G3QyCiPCkKR7wQ+QFzlcgf3M5VqdchOfWjaF3+N+xf9jckplwKS6uFmt2L+LZbMPHp41Gz7pMIdD4LsLObKhjbrFQKZTNSasOGDfjf//1fnHHGGfjEJz7R//VEIoF77rkHL7zwAizLwsKFC/GRj3wEjY2NLkZLREREREReY1YtQlfVInTNWonwgT8juvseBLrXILL/fkT2349cbBGSUz+GdOO7AEV3O1wiIt8ri5FSpmnirrvuwrx58wZtKWvbNm644Qak02l89atfxS233ILa2lpcc801SKVSLkY8XDqddqQeMzwTVz0wA1KJOFIfUTmybW4zT+QHzFUif/BkrqohpJvei9Zlf8aBZX9Fsun9kEoIgcQrqH3t05j4zMmI7rgTwuxyO1IixzjVZq10ZngmDh6zCmZ4ptuhOKIsRkr9+c9/xrRp0zBp0iQcOHCg/+tPPfUUOjo6cN111yEQCAAALrvsMqxcuRJ//etfcd5557kV8jCapg3qUCsVqVXhhV1VXLSRqABCCK5VQ+QDzFVyy+Yt23HphWe5GsPOHdsBNLkaQ75KnavF+ntUBWfiHQvb8M5FbajDXlRvuR76azfgL+vr8PsX69GePvzIqVhNE26/46cFx0HkFqfarJVOalXI1p3hdhiO8X3PxP79+/HXv/4VN910Ex566KFB31u9ejVOOeWU/g4poOemd/rpp+Oxxx7zVKeUruvIZrMlr0fJ7sMHlu0D7FpACZa8PqJyJIQCcAdLIs9jrpJbNGHgvpXudggtvWyTq/WPRalztbh/j6mAtGBkW6BmdiASSOK9x7bivGPbYIUmwwzNBNTQiD+54saWIsVA5A6n2qyVTsnuQ3TP/yE5+YOwgxPdDqfkfD9974c//CHOO+881NTUDPve1q1b0dzcPOzrzc3N2L59O2y78h5U1dx+fHD5AQibFxMiopJSBYxL58C4dA6gCrejISKiYhEqrNAU5KpPQq5qCWytGgI2tMwuBDv+BS2xHsLy1lIhROQfam4/qrZ/E2puv9uhOMLXI6WefPJJJJNJnHnmmSN+v729HbW1tcO+XlNTA9M0kUgkEI/HR/xZwzBgGEb/50KIku42kEgkSvbaRFRclsXtcCkPugLr/JluR1HRmKtE/uDbXBUCdqABOb0eitkONbUVqtkOLbsbanYPrOBkmOHmUUdOEfkN26xUCr7tlEomk/i///s/fOlLX4KijDzgyzAMaNrwX1HXe+Z753K5UV//vvvuw7333tv/eXNzM2666SaEw2EEg0EkEglEIhEoigLLspDJZBCNRgGgf0hjMBjsjzUUCkFVVdi2jXQ63V82l8tBSomqqirkcjmkUikEg8H+sqlUCrFYrL+sbdsIhXpubKlUCoFAAJqmQUqJZDLZX9YwDFiWNaxswDrUsaaqPcdGShtSyv7teG3bghCidyh1z4OCqqoAeub7S2nnXda27d7Pe8tCQCgKIERvDKOVtSGAnrIjvq7VH3/fiDdlQFlFUSGEACBhWYfKStuGHFC25+cOV/bQcbEsC4qijFx2xGOYX9m+2PM+huM53kcsaw86J4p7vPM8hsNiGO/x7ou/r+zYz1kIAVXVRj2GiqLCsqwxHu+xHcO+GIaes0c6huM9Z0ctKwQURXH+GjGwrBBQFNWVa8Sg49I72Mnpa0Tfed37SW8Mzl8jBh7v4TE4c40YWLanjlFet/cYapoO27aLfo0YeAwHnhOFHe/xH8PBMTh8jRhQdtC108lrxIjXbxeuEUOvnUJx5Box6jEc8drpzDViYNmh186Rjreu6bClLOycPVzZ/mtniZ4jNA3QGmEFJ8DMHoSe2QYl19rfOWWHp8GOzAaA/vaDaZrI5XKIRHo2H8pkMlAUpX/ZkWK3NfrKlrqtMVpZ0zT73+BPp9PQNK2/TZZIJBCNRiGEgGmaMAyjv2wmk4GqqqOWHcsxzGaz/WWz2SyEEP1lk8kkwuGwb453Op2GrutFOd5Dj+HhjvfAtrVbxzvfY1jo8R56DPuOd9/v2ld26Dk71uM90jHUetvsQhH99fjxGjHoGfYwfNsptWrVKpx00kmYOXPmqGV0XYdpDn/npW8E1MC1poZasWIFzjnnnP7P+w5oOp3u//mhO/gN7TkeONJq6E4FQ8vmcrn+rx2p7MDPM5nMmMpaqTRivZ8PfVdq4Oc9i03aA75nFacsJGDZQO9ilocvi56yebxuz+eHyg7dxaWwsua4yko5lte1UdLjnUfZoZ8X93iP7xiW6ngf6RhCyv7yIx9D0R9bYcd79GM4MIYjlS3p8e5r6KGA87DQc7a3ATm8LJy9RsjRyo5yDC0J+/WDPT86Jw6oYlznrJQDbtX5XDtLdI0o7fEuzTXCtu0Bx7F414jxXjuLeY1w4zp7pLKDrp1OXiNGiwFw7zlCSthy+LXT0fvaaNdOp68Ro147BxzDYfe8Ip+z/ddOB54j9Brk9CUQRge01GaoZjvU9A4o6V346Il1SHfugdRr+ouP1CboU8y2xljKFtLWyLdsX6dFn2QyOWpZ0zTzLguM7Rj6+XgPzUcnjncsFvPk8XbqnB1L2ULOWb23zS5t6etztq9j7kh8uabUhg0bsGbNGrzvfe87bLmamhq0t7cP+3pHRwdUVe3vxRuJruuIRCL9H6WcugdgxM6zUrC1avxjYzUg8jtBiGg47uZFeTFsBD/zLIKfeRYwKm8NQy9grhL5QznmqtRrYFQvQy6+tH/NqfOXtGLiM29AdNePAHv0GRtEXuVUm7XS2Vo1Uo3vga1Vux2KI3w5UmrDhg3o7OzE5ZdfPujrhmHAtm08++yzOO+88zB9+nRs3boVp5566qByW7duxdSpU0ed9ueGgT2TpWSFp+OWf0zDKSeVtpONqJxJyQ4GIj9grhL5Qznnqq3XIRevhWIcxJ7t6zGzrgPVm65BdNfd6Jr1ZWQmvKN/eiGR1znVZq10Vng6OhZ+x+0wHOPLTqm3vOUtOOGEE4Z9/S9/+QsOHjyID33oQ4jH44hGo/jtb3+L973vff1T9aSUeOyxx7B8+XKnwz6scDjszMJxVgaT4llAWoBQj1yeiIbpWVOK7xQReR1zlcgfyj5XexdE/+S9c7Dq1g+jatst0DLbULfu48jFl6Fz9v/CqPZW24RoJI61WSudlYGa3QsrOKkiNkrwzlChMQgGg2hsbBz2EYlEEAqF0NjYiFAohNNOOw2RSAS333479uzZg/379+OHP/whDh48iLPPPtvtX8MVemojfnLhRggreeTCRERERERUFLYUSE2+CPtPeBLdM/4bthJGoOt5TFhzLmpf/TjU9Da3QyQiD9BTGzFx9anQUxvdDsURvuyUGk0gEBi0mJau6/jKV74CXddx1VVX4corr0RrayuuueYaxONxFyMdbugCYUTkXcMWJSciT2KuEvlDpeWq1KLobr4S+098EsmmCyEhED7wZzSuPgPxTddCGMPXxCXyArZZqRR8OX1vNO9+97uHfa22thaf/exnHY9lrFRV5cJxRD4hhCjLRVmJyg1zlcgfKjVX7WATOuffiuTUjyK++QaE2h9FbNcPEWn5Dbqav4jUpA8CSlk118jn2GalUiirkVJ+lu92iUTkPiF46STyA+YqkT9Ueq6asYVoO3YVDh7zCxjRBVDMTtRsvAoTnj8bgY7VbodH1I9tViqFyr4DEBERlYoqYH6gGeYHmgGVOysREdHhZetOx4Hlf0PH3BthazXQk+vQsHYFatZfASW7z+3wiIhKgp1SHuHULgZG1WKc/YNFkJq31tQi8pOy3iGIikdXYH5oNswPzQZ03m7dwFwl8gfm6gBCRWrKJdh/whNITvoAJAQi+36PxtWnIbrzTsA23I6QKhh33nOGUbUYe87YDaNqsduhOIJPyR4RjUbdDoGI8qSqqtshEFEemKtE/sBcHc4O1KHzqJvRuvQB5KqOg2IlUb35ekx47i0ItD3udnhUodhmpVJgp5RHCOHM1A41tQnffPdmCCvpSH1E5YlTsSgPtoTYloDYlgDsylvA1xuYq0T+wFwdjRE/Fq1L/4j2o74JS6+HntqIhpcuRO2rH4Oa2e12eFRhnGqzVjo1tQkNL7wTamqT26E4gp1SHuHULgaKlcaCiWlAVtbWu0TFVIk7BNE45GwEP/FvBD/xbyBnux1NRWKuEvkDc/UIhIL0pPdh/wlPIDHlUkgoCB94ABNWvxGxHd/jlD5yDHfec4ZipRHoegGKlXY7FEewU8ojcrmc2yEQUZ5smx0MRH7AXCXyB+ZqfqReja65X8WB5X9DtvokKHYG8S03YsJzZyHQ+azb4VEFYJuVSoGdUh4RiUTcDoGI8sS1L4j8gblK5A/M1bExYwtxcMm9aJ//LVh6HfTU62hY825Uv/4FCKPd7fCojLHNSqXATikiIiIiIiI/EQLppguw/4THkGy6EAAQ3fsLNK5+I8ItvwU4JZKIfEJzOwDqkclkHKnHDE3Fzf+Yik+fF3akPqJyZNtck43ID5irRP5QKbm6ect2XHrhWSV57aObmnHFaXswo64Nta99FjseuQrffXIydnUEB5WL1TTh9jt+WpIYqPw51WatdGZoKtrnfxtmaKrboTiCnVIeoSjODFqTei3+ubEGn1Z0R+ojKkcCAhJ8B5LI65irRP5QKbmqCQP3rWwqXQWyGUZmB7TUFhw7JYm73rcJVngmzPBMQPRMkVxxY0vp6qey51SbtdJJvRbppvPcDsMxPKs8IhAIOFKPkjuIc44+CNhcpI5ovARvyES+wFwl8gfmapEIBVZ4JnI1J8PS6yEgoaW3ItDxbyi5g25HR2XAqTZrpVNyBxHZfU/F5C1HSlUYNbsHnzx1L7L2NEiFFxUiopJRBczzZvT/n4iIyAlSDcOoWgIrtx96cgMUO41A9xpYgSZUh2rcDo+IjkDN7kHNxqtwIL4MdqDe7XBKjp1SHpFIJNwOgYjyZFmm2yGQH+gKzMvmuh1FRWOuEvkDc7UEhIAdnIisXg8tvQlqZhfUXAvuet8BWHt/jXTTBYDgGyY0NmyzUilwrKxHcHtNIv/g1tVE/sBcJfIH5moJKRrM6Hzk4sfDVmOIhyzUvv7fqH/xAqipzW5HRz7DNiuVAjulPIKLxhH5Cd9ZpDzYEqIlDdGSBuzyX8DXm5irRP7AXC01qVcjV30CfvLvibCVEIIdT6Hx2bcgtu02rjVLeWOblUqBZ5VHWJYzW+HaahTP74wBgjM3icZLSnYwUB5yNoIf/heCH/4XkLPdjqYiMVeJ/IG56hCh4LcvTsCB4/+BTO3pEDKL+LZbMOG5sxDoWO12dOQDTrVZK52tRpGpPR22GnU7FEewU8ojstmsI/VYkVn4yl9mQqocekk0XrbNDgYiP2CuEvkDc9VZVngG2o5ZhfYF34Wl10NPbUDD2hWofv2LEEan2+GRhznVZq10VmQW2o79BazILLdDcQQ7pTzCsfm50kJEtwC+I0U0blz7gsgfmKtE/sBcdYEQSE9cgf0nPIZk0/sBANG9q9D47BkI7f8j2wo0Iq4p5RBpQZjdgKyMkWnslKowemIdfvfR9RBWt9uhEBERERGRi6Rei87530DrknthhGdDze1H3brLUffyxVAzu9wOj6gi6Yl1mPTkfOiJdW6H4gh2SnkEh0IS+QenGRD5A3OVyB+Yq+7L1ZyMA8f/Hd0z/htSBBBq+wcmrD4D0Z0/AGzT7fDII9hmpVJgp5RHCMFdR4j8gtlK5A/MVSJ/YK56hBJEd/OVOLD8IWSrT4Rip1G9+atoeOEc6N0vuR0deQDbrFQK7JTyiEAg4HYIRJQnwe1wiXyBuUrkD8xVbzGjc3Fwyb3omHcLbK0agcTLaHj+HYhvugbCTLodHrmIbVYqBd4BiIiISkERMM+ZCvOcqYDCdxaJiMhHhILU5Iuw/4THkGp8NwRsxHb9CBOefROCrX93OzoiKiOa2wFQj2TSmXcdjOh8vP+n83HP52KO1EdUjiyLaytQHgIKzE/NdzuKisZcJfIH5qpzNm/ZjksvPGvMP7ds2gx86tQ9aIrvRv0rH8YTm+O446lJaE/p44ojVtOE2+/46bh+ltzjVJu10hnR+Wg55SXYWtztUBzBTimPCIfDSKVSpa9I0dGZ0QDBQXJE46WqKiyrMrZoJfIz5iqRPzBXnaMJA/etbBrHTzYBchbM1BaomR04bXYXTp2ThBmZCys4BRjjWkMrbmwZRwzkNsfarJVO0WEH6t2OwjHsmfAIxaG59Gp6G645azuExYsJ0fhxKhblQUqgI9fzIaXb0VQo5iqRPzBXfUGoMKNzkas+AbYah5AW9ORrCHQ9B2Em3I6OHOBUm7XSqeltqHv5w1DT29wOxRE8qzzCqXeHFLMbJ83sBiSHSRONl2QHA+UjayP0/scRev/jQJbbnbuBuUrkD8xVf5FaFXLVx8OIzIOECsXsRKDzGWipTYDkiLdyxhGNzlDMboQO/h2K2e12KI5gp5RHZDIZt0MgojzZNm/IRH7AXCXyB+aqDwkBKzwd2ZqTYekTICChpbch0PFvKEab29FRibDNSqXATimPiEajbodARHlSVS7HR+QHzFUif2Cu+pgaghE/FrnYMZAiCMVOI9D1AvTEq4Cdczs6KjK2WakUeAcgIiIiIiKicbODjcjqddDSm6BmdkHN7oWSa4URmQs7OGnMC6ETUeXgSCmPyGazjtRjBZtw11NNkErQkfqIypFtc30gIj9grhL5A3O1TCgazOh85OLHw1ZjENJAILkOetcL3GSpTDjVZq10VrAJnbP/F1ZwPDtl+g87pSqMHZiA+15uANgpRURERERERSb1auSqT4ARmQMJBarZjkDHv6GmtgKSHZBER2IHJiA57eOwAxPcDsUR7JTyiGDQmU4iYXTg1FmdgG04Uh9ROeJ2uET+wFwl8gfmahkSCqzwTORqToKl10HAhp7ejEDnMxBGh9vR0Tg51WatdMLoQGj/nyomV3gHqDBaZieuestOCDvtdihEROVNJV0mCQAAOGBJREFUEbDOnATrzEmAwrU0iIio8kg1AqPqOORiR0MKHYqVRLDrOXzq1D0QRqfb4RF5kpbZibp1n4CW2el2KI7gQucekUwm3Q6BiPJkWabbIZAfBBQYnz/a7SgqGnOVyB+Yq2VOCNjBScjq9dBSm6Bl9+AdR7fBevYMdM75KjITzuFC6D7BNiuVAkdKeUQoFHI7BCLKk6KobodARHlgrhL5A3O1QigBmLGFyMWXYldHAGpuP+rWfQJ1L18CNbPL7egoD2yzUimwU8ojVJU3YyK/EHw3j/IhJZCxej6kdDuaisRcJfIH5mplsfU6/Ne9c9A943OQQkeo7RFMWP0mRHfeBdgcNedlbLNSKbBTyiOc2gpXKiFsOhACwAsK0fixg4HykLURevc/EXr3P4EsdxtyB3OVyB+Yq5XGsBR0N38eB5b/HdnqE6HYKVRvvg4NL5wDvftlt8OjUTjVZq10UgkhF1sEqVTGyDR2SnlEOu3MwuNmdC6u+P0cSC3qSH1E5ciyLLdDIKI8MFeJ/IG5WrnM6FwcXHIvOubdAlurRiDxMhqefzvim66DMLl+kdc41WatdGZ0LlqX/w1mdK7boTiCnVIeEY2yk4jIL1SVe0QQ+QFzlcgfmKsVTihITb4I+49/FKnGcyFgI7brLkx49k0IHnzY7ehoALZZqRR4B6gwWvcr+ON/vgrbjEBqcbfDISIiIiKiCrN5y3ZceuFZI35v2bQZ+NSpe9AU3436ly/B45vjuPOpSWhP6UWNIVbThNvv+GlRX5OoGLTuVzDhhXfiwNI/waxa5HY4JcdOKY/I5XKO1CMgoasSWUdqIypPkvPpiXyBuUrkD8zVyqMJA/etbBrlu02AnAUztQVqZgfeOLsLp81JwYzMgRWcAhRpYfwVN7YU5XUqiVNt1konICFkDqJC1tvj9D2PkNyZicg3mK1E/sBcJfIH5ioNI1SY0bnIVZ8AW41DSBN68jUEup6DMLvdjq5isc1KpcBOKY8IBoNuh0BEeVIUXjqJ/IC5SuQPzFUajdSqkKs+HkZkHiRUKGYnAp3PQEu+DtiG2+FVHLZZqRQ4fY+IiKgUFMA6tbH//0RERDQOQsAKT4cVaISe2gg1tw9aZifU7D4Y0TmwA5OKNqWPiJzHx2SPSKVSjtRjRObg47+ZA6ly5wSi8eLW1ZSXgArjK8fA+MoxQEB1O5qKxFwl8gfmKuVFDcGoWoxcfClsNQIhcwgk1iHQ9Tyn9DnEqTZrpTMic7D/+H/AiMxxOxRHsFPKIxwbCqmGsaM9BAg2kIjGi9MMiPyBuUrkD8xVGgtbr0Ou+iQYkTm9U/o6EOhc3Tulz3Q7vLLG6XsOUcMwo0cBatjtSBzBO4BHqKoznURqZhc+88bdgJV2pD6iciQ4RJzIF5irRP7AXKUxEwqs8Exka06GFWiEgISW2Ylgx1NQsnsBLshdEk61WSudmtmF6tc+DzWzy+1QHMFOKY+wHdoKVzHa8bYF7RCSCwMSjR8fdCgPGQuhtz2M0NseBjKcmuIO5iqRPzBXaZzUEIyqY5CrOg620jel79XeKX0Jt6MrO061WSudYrQj2vJLKEa726E4gp1SHsH5uUT+wbUviPyBuUrkD8xVKpQdqEeupm9Kn9I7pe8ZaMkNnNJXRGyzUimwU8ojYrGY2yEQUZ5UlRuXEvkBc5XIH5irVBT9U/pOGTClb0fvlL4WTukrArZZqRTYKUVERERERETlYcQpfa9A73qBU/qIPIidUh6Ry+UcqccKNODXaxogRcCR+ojKkeR8eiJfYK4S+QNzlUqhf0pfeDYkFKhmOwKd/4aWfA2wub7ueDjVZq10VqAB3dM/BSvQ4HYojmCnlEc4tWicHZyEe1Y3AWrIkfqIypHkgqxEvsBcJfIH5iqVjFBgRZoH7NIHaJldCHY8hbMXtAGS65mNBRc6d4YdnITuWV+GHZzkdiiOYKeUR4RCznQSCTOBxZMSgOSCf0TjpSjcDpfID5irRP7AXKWSU8M9U/riS2GrUQhp4NNv3IOG59+OQOezbkfnG061WSudMBMItD9VMdNN2SlVYbT0Vtz8rm0QFndOICIqKQWwjq+HdXw977ZEREQeYOt1yFWfCCMyD91ZBYHEK2hY827UrLsCSnav2+ERAehpsze8eD609Fa3Q3GEb7e66OjowIMPPohnnnkGBw4cQHV1NU488UScf/75CIfD/eVyuRxWrVqFp556CplMBnPmzMHFF1+M5uZmF6MfjttrEvkHt66mvARUGNcf53YUFY25SuQPzFVylFBghafjsjsU/PhLyxDZ+wtE9v8eodYHkZj5WSSm/iegBN2O0pPYZqVS8O17t6+88gra29tx6aWX4tvf/jY++clP4oUXXsDtt98+qNy3v/1tbNmyBStXrsRtt92GBQsW4Nprr0Vra6tLkY8sEODC40R+oSi+vXQSVRTmKpE/MFfJDZ0ZDZ1H3YzWZX9BLr4Mip1CfMuNaHz2zQgefNjt8DyJbVYqBd/eAU499VRcfvnlWLRoEerq6rBw4UL813/9F1544QW0tbUBAF5//XW8+OKL+PznP4/m5mbU19fjggsuwDHHHIN7773X5d9gME3z7aA1ooojhHA7BCLKA3OVyB+Yq+Qmo+oYtB73B7TP/zaswERo6W2of/kS1L30IaipzW6H5ylss1Ip+LZTaiTTp08HAHR1dQEAVq9ejeOOOw7V1dWDyp1xxhl47rnnHI/vcKR0ZtcRKTS0JjSU2Z+eyGHcJYjykLEQPPcfCJ77DyDDqSnuYK4S+QNzlVwmBNJN52H/CY+je9p/QQodobZ/oPHZ/0DV5hsqZsHpI3GqzVrppNBgBZogRWV0ApZVz8SWLVsQDAYxaVLP1onbtm0bce2o5uZmdHV19Y+o8oJkMulIPWZsAT60aj6kFnOkPqJyxLUvKF8ia0NkuX2yW5irRP7AXCWvkFoM3bOvwv7jH0Gm7s0Q0kDVzu+jcfUbEW75HVDhnTJOtVkrnRlbgH2nPA8ztsDtUBxRVp1S999/P9761rciGOxZmK6trQ21tbXDytXU1PR/fzSGYSCVSvV/pNPpksTcJxZjJxGRX6hqZbxrQeR3zFUif2CuktdYkdloO+bnOLj4pzDDM6Hm9qH2tU+jYc250Ltfcjs817DNSqVQNneAxx9/HFu3bsUVV1zR/zXTNEec96ooClRVhWEYo77efffdN2jdqebmZtx0000Ih8MIBoNIJBKIRCJQFAWWZSGTySAajQIAstksAPR3jiWTSYRCIaiqCtu2kU6n+8vmcjlIKREIBBCLxZBKpRAMBvvLplKp/uTP5XKwbRuhUAhAz+4HgUAAmqZBSolkMtlf1jAMWJY1rGwg9Tp+/oHXIcwYlGANAEBKG1JKKIoKALBtC0IICNHTZ2lZJlRVBSAgpYSUdt5lbdvu/by3LASEogC9aweMXtaGAHrKjvi6Vv8DjG3b/X/XvrKKovauTyBhWYfKStuGxOAFNQ9f9tBxsSwLiqKMXHbEY5hf2b7Y8z6G4zneRyxr9/49tBIc7zyP4bAYxnu8++LvKzv2cxZCQFW1UY+hoiiQUh3j8R7bMeyLYeg5e6RjON5zdtSyQvT+vg5fIwaWFQKKMvR4O3ONGHRcepc8yft449A7/4oQkL2/x/Bz9vB/m0FrrRzx2lm6a8TA4z08BmeuEQPL9tQxyuv2H0MFgFb0a8TAYzjwnCjseI//GA6OweFrxICyg66dTl4jRrx+u3CNGHrtFIojzxGjHsMRr53OXCMGlh167Rz5eAv0NEuKd40YfP0Wg2Io9XPEaMewJwbnrxFOPPuO9fwe+Pdw8hox6JwVSn/bKZlMIhwOD2/bxd6N9sY3I7z9B4hu/RYCXc+j4fm3IzftQ8jNvwaWXjdi266vHViqtl06nYau66OWNU2zf0f6dDoNTdOg6zoAIJFIIBqNQggB0zSRy+UQiUQAAJlMBqqqjlpWCNFfTyaTgaIo/YufD20fZ7PZ/tfNZrMQQvSXHfV448ht6XyPYaHHe+gx7Dvefb9rX1nTNGEYxriP90jHUEusR+SZ89B27CoEGpfldbwL7Y8oxTmb73qBZdEptXPnTtxzzz347//+b1RVVfV/XdM0mKY5rLxt27As67C7B6xYsQLnnHNO/+d9BzSdTvd3Zg3dEjORGDzXeGCn19CRVkPLJpPJ/pPnSGUHfp7JZMZU1kp2YULMQBY2LGvwsRn4ec98YXvA96zilIUELLt/6Ovhy6KnbB6v2/P5obJ9N7vilDXHVVbKsbyujZIe7zzKDv28uMd7fMewVMf7SMcQUvaXH+kY9jd+hr7umI/36MdwYAxHKlvS4z3a71rqa8TAsr0P48PLwtlrhByt7OjHsP/rUg77m+Z7zko54Fadz7WzRNeI0h7v0l0j+s7fYl4jxnvtLOY1wo3r7JHKDrp2OnmNGC0GwL3nCClhy+HXTkfva6NdO52+Rox67Rxcti9XRy5b4Dnbf+105jli/MewlNcIb11Phn7uyn1N2oPaSqO37RQYUy9HYsK7Ed98IyL7f4/gzp9B3/sHdM/4HOSUDyORGDytbyztwPG27Yb+bkcq29fWBIZPwxtY1jTNUctKKQd9D+jpqOhzpPbxWMo6cQy9cLyB4cdFT3Yhlt0LaeUcO4alON59HXNH4vvpe11dXbjppptw/vnnY9GiRYO+V1NTg/b29mE/09HRAQDDFkAfSNd1RCKR/o++ns9S4Vx6Iv/gIo9E/sBcJfIH5ir5gR2chI6F30HrkvuQiy2CYnaievO1mPDcmQge/Ifb4TmCbVYqBV93SuVyOdx8881YsmQJzj777GHfnz59OrZu3Trs61u3bkU0GkV9fb0TYealbwgcEXnfwOlDRORdzFUif2Cukp/kak5A67K/oGPezbD0euipTah/+UOoe+lD0JKb3A6vpNhmpVLwbaeUlBLf+c53EI1G8dGPfnTEMsuXL8eaNWvQ1dU16OuPPvooli1blvccRyIiojETgL24Bvbimv41VYiIiKgMCBWpyR/A/hOfRGLqxyGFjlDbPzDhuf9AfNM1EEan2xES+YZvO6VWrVqFnTt34mMf+xjS6TSSyWT/R986UosXL8a8efNw6623Ytu2bWhra8NvfvMbrF27FitWrHD5Nxis1Lv79THDzfjiH2dCqhFH6iMqR6OtG0Q0SFBF7pblyN2yHAhyFIAbmKtE/sBcJb+SWhxdc/4X+4//BzL1b4GQJmK7foTG1acisvtngCyvc9upNmulM8PNaD32tzDDzW6H4gjfLnT+yCOPIJlM4vLLLx/2vfe///14z3veAwC48sorsWrVKlx//fXIZDKYNWsWrr76akyZMsXpkA9L13VH5uhKLYaX98YA4ds/PZHrhFCGLYhKRN7DXCXyB+Yq+Z0VmYW2xfcg2PYY4puuhZ7agJqNX0Z0z8/QOec65Grf4HaIReFUm7XSSS2GXO0pbofhGN/2TNx99915lYtEIrjssstw2WWXlTiiwvRtL1lqSnYvPnxCC2DVACrnBBONB6f+EvkDc5XIH5irVC6ydafjwPKHEN3zc1Rt+wb05Ho0vHgB0g1vR9fsr8AKz3A7xII41WatdEp2L6K770FyyodhBye5HU7J+Xb6XrlxatcRNdeK9x3XCiFzRy5MRKPgLkGUh4yF4PseQ/B9jwEZvqvoDuYqkT8wV6mMKDqSUz+KfSc+geTkD0NCRbj1L2hcfQaqtnwNwky4HeG4cadMZ6i5VlTt+C7UXKvboTiCnVIekUwm3Q6BiPLEYcuUL9FpQHQabodRsZirRP7AXKVyJPU6dM67AQeWP4Rs7WkQMoeqHd9F4+rTEN77a0Daboc4ZmyzUilw/J1HxGIxJBL+7TUnqiSqqsGyTLfDIKIjYK4S+QNzldywect2XHrhWQ7VJnHijOm47OQWTKnej9rX/xsHnrgKdz41CQ8+14UZ05ocimNksZom3H7HT49cjm1WKgF2ShEREREREVFF0YSB+1Y62Rk0CZBzYGR2QEtvxbzGNL757i1480wFZ54x39X1flfc2OJa3UScvucRhuHM9A5br8WD62shhe5IfUTlSPpwuDVRJWKuEvkDc5UqhlBghWciW3MKzOBkSADnLLER7HgKamoL4PFdKJ1qs1Y6W69FsulC2Hqt26E4gp1SHmGazgxZtkJTcfvjUwA17Eh9ROWIizwS+QNzlcgfmKtUcZQgzNhC5KpPwPNbBQRs6OktCHY8BSXbAng0J5xqs1Y6KzQVnfNvhRWa6nYojmCnlEeEww51EllpTK/NeL4XnsjLFEV1OwQiygNzlcgfmKtUqaQWx4d+EEAuthhSCUHYWQQSryDQ9RyE2eV2eMM41matdFYaWvJ1wEq7HYkj2ClVYfTUJvzggk0QFndOICIqKQHYc+Ow58YB4XYwRERE5E0CdnAisjUnwwjPgoQCxexEoHM1tMSrgJ11O0BymJ7ahMZn3ww9tcntUBzBhc49Ip2ujF5QonJg2xxpSHkIqsh95wS3o6hozFUif2CuEgEQKqzILFjBydBTm6DmWqBl90LN7YcZboYVmg4Id8eUsM1KpcCRUh6haewfJPILITjshcgPmKtE/sBcJRpADcGoWoRsfDlsNQ4hLeipTQh0PA0lt9/V9abYZqVSYKeUR+g6d8Mj8gvh8rtURJQf5iqRPzBXiYaTeg1y1ccjF1sIKQJQ7DQC3S9B714DYXa7EhPbrFQKvANUGAkBw+K7UUREJZexELz4SQQvfhLIcGoKERERjZEQsIOTka09BWZ4JiQUqEYbAp3PQEus53pTZUpCQIoAZIUsSspOKY9IJBKO1GNWLcK7fnQ0pBZ3pD6icmRZ3A6X8iP2ZyD2Z9wOo2IxV4n8gblKdARCgxmZg1zNybACjRAAtOxuBNufgpre6tjO6k61WSudWbUIe0/fCrNqkduhOIKdUh4RjUbdDoGI8qSq3LqayA+Yq0T+wFwlyo9UwzCqjjm03hQs6KnNCHY8DSXbUvL1pthmpVJgp5RHOLXAo5bciO+8ZxOEmXSkPqLyVBlDaYn8j7lK5A/MVaKxOLTe1NGQShDCziCQeAWBrucgjI6S1ctNCZyhJTei4bmzoCU3uh2KI9gp5RGm6cywZWFnMGdCBgDXNyEaL+niridElD/mKpE/MFeJxkEI2MFJyNacAiM8CxIqFLMTwa7noHe/DGGli16lU23WStfXySjsylgCgns6ekQul3M7BCLKk23bbodARHlgrhL5A3OVqABChRWZBSs0BVpqM9TsHqi5fVByB2CFpsMMzwSU4jT72WalUuBIKY+IRCJuh0BEeeLaF0T+wFwl8gfmKlERKEGYsYXIVZ8IS6uFgA0tsw3BjqegZnYBsvDOX7ZZqRQ4UoqIiKhE7OlcEJSIiIicI7UqGPGlsIxWaMmNUOwU9ORrUDM7YUbmwQ7Uux0i0SAcKeURmYwz80XN0DTc8PdpkErYkfqIypFtc002ykNIRe6uk5G762QgxFEAbmCuEvkDc5WoyISAHZiAXM1JMCLzIIUGxUoi0L0GetcaCLN7XC/rVJu10pmhaWhbeCfM0DS3Q3EEO6U8wqlhy1KvwZNbqgFFd6Q+onLEnUeI/IG5SuQPzFWiEhEKrPB0ZGveADM0HRICqnEQgc5noCVeBayxdTJxqq0zpF6DTOM7IfUat0NxBDulPELXnekkUnIHsGJxK2BnHamPqBwJwUsnkR8wV4n8gblKVGKKDjM6D7mak2EFJkIA0LJ7Eex4ClpyE6KB/EYrOtVmrXRK7gCiO38AJXfA7VAcwTtAhVGzLfjYKS0Q7JQiIiqtjIXAx55G4GNPAxlOTSEiIiJ3STUCo2oxsvHjYWs1/Yuh/+TCDYju/CEHLniEmm1B9eavQs22uB2KI9gp5RGJRMLtEIgoT5Zluh0C+YSyIwllR9LtMCoWc5XIH5irRM6SejVy8WXIVR0LW40iHrJQvflaNK4+A+F994+6Ux/brFQK7JTyiGiUOzQR+QXn0xP5A3OVyB+Yq0Qu6FsMvfpE3PbYZFiBidAyO1C7/pP/v737j46qvvM//rozk/mVX0MSAoQYTYDwo4Ag+IuCgLa24H7tAoLw3XWt9tiKYm1dtLtwXEXB3XZlLe5pvyKnFVo4KGUpaiuCimA5iKhgEC0/JOGXgAn5QX5O5uf3jyyjaYIMkMydmzwf53AOufcz974z7cvLvOd+Plc5H94iZ/W2Ni/hMys6A02pJMECj4CVkFfAGsgqYA1kFTCNYdPGfVkqv3abagsfUcSeJmf9HuWU3K7sj25XSu2uL4fymRWdgKZUkgiFEnPbcsSRrh2H0yXDkZDzAV1RNBo1uwQAcSCrgDWQVcB8UbtX9Zc/qPJrt6u+792KGk65arap567/ox4f3y1H/V8T9pm1u4s40uXP/rYijnSzS0kImlJJIhAIJOQ8Yc8VWrDxckXt3oScD+iKIpH259kDSC5kFbAGsgokj4gzW7UDnlT5tX9RY+/bFZVNnsqN6vnBt+XZfY/sTYfNLrHLC3uuUNWw5Qp7rjC7lISgKZUkvN4ENYkiQWW6Q+dcvA7A+bH2BWANZBWwBrIKJJ+wO181g/5LFVe/raaefydDUXm+WKfcneOVuf9nsjWfNLvErisSlC1QKUWCZleSEDSlupmUhn168c59MsI8OQEAOls0161ortvsMgAAAC5KKLW/qr+xVBWjXlew57dlRENKPblSvd4bq4zPnpAtUGV2iV1OSsM+9d4+XCkN+8wuJSFoSiUJv99vdgkA4hSJhM0uAVbgtqv5d2PV/Luxkpu7AMxAVgFrIKtA8gumD1P1lSt1esQ6NWdeIyPiV9rxpcrdca3SDy2SLXDa7BJhUTSlkoTNxv8UgFUYPCUIsASyClgDWQWswWazKeC7VpUj1qly2EoF0obJFmlU+rFfK3fHtcr47HHZmr8wu0xYDJ2QJOF0Os0uAUCcDJrIgCWQVcAayCpgDbHPrIah5uyJOj1qgyqHrVAgfaRsEb/Sji9Trx3XK+Pgo7L5T5hbLCyDKwAAAJ2hOSznAzvlfGCn1MzUFAAA0MUYhpqzv6XTV72qyuGrFMgYLSParLTPf6te731TmQf+VXb/52ZXiSRHUypJ1NcnZuHxYNoQTfvtYEXt6Qk5H9AVhcMhs0uAFUQl28Fa2Q7WSlGzi+meyCpgDWQVsIZzfmY1DDVnTdDpket1+sqX1Jx5nYxoQKknfqfc976pzP2PyN50JLHFWlgwbYhOjt2nYNoQs0tJCJpSScLr9SbmRIZdjUG7ZDB3H7hYPLoasAayClgDWQWs4byfWQ1DgR5jVTnyf3R6xFo1+8bKiAaVenKVct8bqx6f3KuU2t2JKdbKDLuijnTJ6B7/baQplSQStdC5vbFUCycflhFuTMj5gK6Jpi5gDWQVsAayCljBhXxmDfiuV+WIl3R65Hr5sybKUESeilfVc9ffKXv3VLlOb5KikU6s1rrsjaXKKvm/sjeWml1KQtCUShLhcGLWG7GFGzTqsnopym3SwMWKRpmLBVgBWQWsgawC1nAxn1kDmVeravhKlY9+Q429pitqpMh15j1l771LuTvHy3tipRRu6oRqrcsWbpC7eqts4QazS0kIh9kFoEVzc7PZJQCIUyTCtzqAFZBVwBrIKmCuQ6VH9INZ34ljpKFLXSgz21ukW4dWavKQKqWpVL4DP5NK5unVT7K15fggPfnL1Zd0fFgPTakk4fV6E7bYOYBLY7fbWZQVsACyClgDWQXM5TCC+uO83ucdZ7c7Oiirl0nRkIL+E3L4j8rn8euO0eWaMaJCof2PqKHvXQqlDe6A88AKaEoBANBJopkpZpcAAACQfAyHwp4Chd35sgXK5Wg6Kpdq5Tq5SqknV6k58zo19L1T/pxJko1/T3VlrCmVJBI1fS/sytOvtvVR1OZOyPmArohpBoiL267ml8ar+aXxkrt7PD0l2ZBVwBrIKmANnZJVw6aIq7cCmVfr4VcK1dTzFkVll+vMDmV9Olu9dlyr9LKnZfd/3vHnTlJhV55qBixS2JVndikJQVMqSRhGYp46EnFm60+fZEs2Z0LOB3RFPCMIsAayClgDWQWsoVOzahjaezJV1d94Xl9ct0N1l/9E4ZSesge+UPqRZ5S741pl7blD7orXpEiwMysxXcSZrca+31fEmW12KQlBUypJOJ2JaRIZwWpNHFDT5YMMdCbjAh6HC8A8ZBWwBrIKWEOishpx56mu8GF9cf1OVQ3+tZp9Y2QoKnfVZmV9co96vXu10g89JUfDwYTUk2hGsFqeU/8jI1htdikJwRWgm3H4j+uRG4/LiPDYTQDoVM1hOR/+QM6HP5CaL/wRygAAAN2azSl/r++pcsQf9MU121RXMKfl7qlghdKP/Uq5709QzoeT5T3+gmyBKrOr7TAO/3H12PdjOfzHzS4lIVjoPEk0NDSYXQKAOPGEIMQlKtk+ron9HYlHVgFrIKuANZiZ1bC3UHVF/6q6K+bKXfmmPKfWyF21Wc66EjnrShQ99Lj8WTepKfd7as75tqJ2r2m14sLQlEoSHo9HjY2NZpcBIA4tj67mzhcg2ZFVwBrIKmANSZFVW4r8PSfJ33OSbIFKecrXy3NqrZz1e+Sp3ChP5UZFbB41Z39LTbnfkz9romTnIV/JjKZUkrAxlx6wEJZkBayBrALWQFYBa0iurEac2WrI/4Ea8n8gR8N+eb5YL0/5K3L4D8tT8ao8Fa8qYk9Tc9aNaur5XTVn3aSoI83ssvE3aEoliUR1nCN2j/76hUdFPh5PDlysaJS5WIAVkFXAGsgqYA3JnNVQ6kDVFf1MdYWPKKVujzwVr8hd/ooczSfkqXhFnopXFDWcau4xVv6cSfJn36iIq7fZZbcrYvcokHGVInaP2aUkBE2pJOH3+xNynrC3vx5a309/nJeakPMBXVEkwhQDwArIKmANZBWwBktk1TAUzLhSwYwrVVs0Xym1u+U+/bo8pzfI0VQmd9Vmuas2S5ICaUPVnHWj/Nk3KZgxUjKS48aNsLe/Tl/1qtllJAxNqSSRmpqq+vp6s8sAEAe73cGirIAFkFXAGsgqYA2Wy6phUzBzlIKZo1RXNE+OxoNyn94g9+k3lFL3kZz1e+Ws36v0o88q4vCpucdYNfcYp+YeYxV2Xy4ZyTVdsauiKdXNpNR9rA0/2qvmkFdRR4bZ5QBAlxZ1sV4gAACA6QxDodRi1acWq/7yB2ULnJar6m25KzfLVb1VtlCNPBV/kqfiT5KkkPsyNfvGKuC7TgHfdQq78xNWakrdx+r54XdVMep1BdOHJey8ZqEplSSam5vNLgFAnCKRiNklwArcdjW/fKPZVXRrZBWwBrIKWENXymrEmaOm3tPV1Hu6FAnJWbdbzuptclX/Rc7aD+XwH5Pj1GqlnlotSQq58hTIvE4B3zUKpI9UKHWgZEsx+bfoGmhKAQAAAACA7snmUCDzagUyr1b9FT+VEWqQ88x7clVvk/PMTqXU7ZGj+YQc5evkLV8nSYra3AqmDVEgfYSC6VcqmH6lQt5+ksFd8heKplSScLlcCgaDZpcBIA42m03hcNf5pgjoqsgqYA1kFbCG7pLVqCNVzdk3qjm75Y53I9SglNoP5TqzM9aksoXr5KzdJWftrtjrIvY0BdOHKZh+pQLpVyqUOlghzxXcUXUeNKUAAOgMgbBSntwjSQo+OlxyJscTXQAAABC/qCNVgawbFMi64X83RGRvKpOzrkQptR8ppW6PUuo/li1cL1fNu3LVvPvla40UhTyFCqUOUMhbrGBqsULeYoW8hZLNZdJvlFxoSiWJhoaGhJwn6B2gu1cP0K/vT03I+YCuyFJPHYF5IpL9/UpJUrDrf6mYlMgqYA1kFbCGzs7qodIj+sGs73TqOc4nzddbS/7fiq8fZNgU9vZTk7efmnpNbdkWCcnReEApdXvkrPtIKXUfy9FwQLZIo1IaDyil8YCkP8cOEZVdIc8VLc0qT5HCnssV8lyusPsKBb1F+uKabQq7+nTeL5pEukVTqr6+XsuXL9euXbsUDoc1ZMgQ3XXXXcrNzTW7tBi3262mpqbOP5HdrZO1LsngG3vgYtlsdkUiYbPLAHAeZBWwBrIKWENnZ9VhBPXHeb077fjxmPLUqYt7oc2hUNoQhdKGqKnPzJZt0YjszSflaDggR+MBORpamlOOhoOyheuU0nRIKU2H2hwqaqQokDFalSPXXsJvYh1dvikViUS0aNEiZWVl6YknnpDT6dT69ev12GOPafHixfJ6vWaXKEmy2xPTJLI3HdXDNx6TEc5U1O5JyDmBrsYwDLNLABAHsgpYA1kFrIGsXiDDprC7r8LuvmrOnvjl9mhUtsAppTQclKPxgOxNR+RoOixH02HZ/cdkRINyNB6Uvemowp4C8+pPkC7flNq+fbtqamq0YMECOZ1OSdI999yjefPmacOGDZo2bZrJFbZI1OM1baEzunHAGTVHg5JoSgEXJ2p2AQDiQlYBayCrgDV0/ayaO4XQK5tRrFH5dXpi8lHZQmfUHe4h7fJNqZ07d2rMmDGxhpTU0uEdP368tm7dmjRNqcbGRrNLABCncLg7XB4A6yOrgDWQVcAaukNWk2EKoRFKlc4cNbWGRLKZXUBnKysrU2FhYZvthYWFOnLkSMLuUDqftLQ0s0sAECe7vcv384EugawC1kBWAWsgq+gMXf7/VdXV1erRo0eb7T6fT6FQSPX19crIyGizPxgMKhgMxn42DEMej0cOR+e8ZTabTSkpKZ1y7K9yuNKlHiNlpA2U7OY9gW/w0HoZ6f1NO38y1ZEMNSRLHclQQ1x12O0yOvmbIsu8F92khouqwxWRRtZIkozMAZLz4r4HsrtcGjmy5e9X5A635nthVg3dJKvJUEOy1JEMNSRLHclQQ9x1dHJWLfVedIMakqWOZKghWeqIu4ZukNVkqMEIN0i2YMtn9wT0CDpLvL0TIxqNdumJobfffrueeOIJDRw4sNX2qqoq3Xvvvfr1r3+tnJycNq9bs2aN1q79crX7b37zm3rwwQc7vV4AAAAAAIDuoMtP30tJSVEoFGqz/exdUF9da+qrpkyZouXLl8f+3HPPPa3unOpITU1N+tnPfqampqZOOT6AjkNeAWsgq4A1kFXAGsgqOkuXn77n8/lUXV3dZntNTY3sdvs513JKSUlJyHQ6SYpGoyorK1MXv2kN6BLIK2ANZBWwBrIKWANZRWfp8ndKFRQUqKysrM32srIy5efny2br8m8BAAAAAABA0unyHZnRo0dr+/btCgQCsW3RaFRbt27V6NGjTawMAAAAAACg++ryTalx48bJ6/VqyZIlOnHihMrLy7Vs2TJVVlZq0qRJZpcnqWWq4G233Zaw6YIALh55BayBrALWQFYBayCr6Cxd/ul7klRdXa0VK1aopKREoVBIgwcP1p133qm+ffuaXRoAAAAAAEC31C2aUgAAAAAAAEguXX76HgAAAAAAAJIPTSkAAAAAAAAknMPsArqz+vp6LV++XLt27VI4HNaQIUN01113KTc31+zSgG6hsrJS9913n9qbxbxo0SINGDBAkhQIBLRq1Spt375dfr9f/fv31z/90z+psLCw1WviHQcgPuvXr9fq1av17//+7yoqKmq1r6NzSX6Bi/d1WZ01a5bC4XCb1/zkJz/RmDFjYj9HIhGtW7dOb731lurq6lRQUKBZs2Zp2LBhrV4X7zgALWpqavT666/rvffeU0VFhTIzM3Xttddq+vTp8ng8sXFcV2EW1pQySSQS0fz585WVlaVZs2bJ6XRq/fr12r17txYvXiyv12t2iUCXV15erjlz5uiFF15os8/r9cowDEnS008/rTNnzujuu+9WRkaG3nrrLf35z3/W4sWLlZOTE3tNvOMAfL1IJKLf/OY3OnjwoA4fPqyFCxequLi41ZiOziX5BS5cPFmdMWOG/vM//7NNjjwej2y2LydtLF++XCUlJfrRj36kXr166b333tPKlSv1+OOPq3///hc8DkCLbdu26eOPP9a4ceOUl5enU6dO6fnnn1fv3r31L//yL7FxXFdhFqbvmWT79u2qqanRgw8+qPz8fOXm5uqee+6Rz+fThg0bzC4P6FZSU1Pb/DnbkNq/f79KSko0d+5cFRYWKjs7WzNmzNDw4cO1du3a2DHiHQfg/F5++WWdPHlSCxYsaHd/R+eS/AIX53xZPcvj8bS5zn61IXX69Glt3LhRP/3pTzVo0CD16NFD3/3ud3XzzTdr9erVFzwOwJfGjh2r2bNna+jQocrKytKQIUN03333adeuXaqqqpLEdRXmoillkp07d2rMmDFyOp2xbYZhaPz48frggw9MrAzAV+3cuVMjR45UZmZmq+0TJkxoldV4xwE4v0mTJmnevHmtphV8VUfnkvwCF+d8WY3XBx98oCuuuEIFBQWttk+YMEF79+5VY2PjBY0D8PXOZqi2tlYS11WYi6aUScrKytqdT1tYWKgjR44oEomYUBWAv3X48OFzZrW2tjb2DVO84wCcn9vtlsNx7mUvOzqX5Be4OOfLarzOlcHLLrtMDodDR48evaBxAL5eaWmpXC6X+vTpI4nrKszFQucmqa6uVo8ePdps9/l8CoVCqq+vV0ZGhgmVAd3Pk08+qePHj8vtdquwsFDTp09X3759JUlVVVXnzOrZ/VlZWXGPA3DpOjqX5BfoXL/61a9UXl4um82mgoICTZ06NfYwEanl38X9+vVr8zrDMJSRkaHq6uoLGgfg661fv14333yzXC6XJK6rMBdNKZMEg8F2v1lKSUmR1PK0AgCdy+fzafbs2brsssvk9XpVVVWlzZs365FHHtGTTz6poqIihUKhdrNqs9lkt9sVDAYlKe5xAC5dR+eS/AKd57777lNeXp7S0tJ05swZ7dixQ48++qjmzp2r0aNHSzr3v4slyel0xjIY7zgA5/bOO++orKxMDzzwQGwb11WYiaaUSVJSUhQKhdpsPxvQr641BaBzOJ1OTZw4MfZzXl6ehg4dqqeeekrr1q3T3Llz5XA42s1qJBJROByOZTXecQAuXUfnkvwCnWfChAmxv+fl5Wnw4MEKhUL6wx/+EGtKnevfxVLLF7VnMxjvOADtO3bsmJYvX66HHnpI6enpse1cV2Em1pQyic/na/cW45qaGtntdqWlpZlQFQBJuuqqq3Ts2DFJX59VSbEFHOMdB+DSdXQuyS+QWF+9zkotGTubt6+KRqOqra2NZTDecQDaqq2t1c9//nNNnz5dQ4cObbWP6yrMRFPKJAUFBSorK2uzvaysTPn5+a0ekwsgsUKhkNxut6Svz2pqaqqys7MvaByAS9fRuSS/QGKFw+HYdVY6dwaPHTumUCik/Pz8CxoHoLVAIKBf/OIXGjFihCZNmtRmP9dVmInOh0lGjx6t7du3t1o7KhqNauvWrbFbmQEkXjgc1o4dO/SNb3xDUktWd+/eHXtk7llbtmzRqFGjZBjGBY0DcOk6OpfkF0isbdu2aciQIbGfR40apdLS0jZPz9uyZYsGDRoUm2YU7zgAX4pGo/rv//5vpaam6u677253DNdVmImmlEnGjRsnr9erJUuW6MSJEyovL9eyZctUWVnZbvcaQMc7fPiw3nzzTZ08eVLV1dX69NNP9fOf/1yVlZW69dZbJUnDhg1TcXGxnn76aR0+fFhVVVVas2aNPvroI02ZMiV2rHjHAbh0HZ1L8gt0jsrKSv3pT3/S8ePHVVNTo4MHD+rZZ5/Vnj17NHPmzNi4Pn36aOLEiXrmmWe0f/9+1dTU6PXXX9fGjRs1Y8aMCx4H4EurVq3SsWPH9MMf/lBNTU1qaGiI/Tm77hPXVZjJiEajUbOL6K6qq6u1YsUKlZSUKBQKafDgwbrzzjtjj6IH0Lk+//xzPffcczpy5IiCwaB8Pp9GjhypGTNmxB5ZK0mNjY1atWqVduzYIb/fr6KiIt1xxx0qLi5udbx4xwGI36xZs7Rw4cI2j4Hv6FySX+DStJfV2tpaLVmyRIcOHVJzc7PS0tI0bNgwTZ8+XX369Gn1+lAopDVr1mjr1q2qr69Xfn6+ZsyYoVGjRl3UOAAt7rrrLjU0NLS7b+bMmZo6daokrqswD00pAAAAAAAAJBzT9wAAAAAAAJBwNKUAAAAAAACQcDSlAAAAAAAAkHA0pQAAAAAAAJBwNKUAAAAAAACQcDSlAAAAAAAAkHA0pQAAAAAAAJBwNKUAAABMtnDhQv3yl780u4yEWrp0qRYuXGh2GQAAwEQOswsAAADobM8995w2b9583nEPPPCAxo0bl4CKWguFQgqHw+cdt27dOm3cuFFLly5NQFWdKxQKKRQKmV0GAAAwEU0pAADQ5c2cOVOTJ0+O/fzXv/5Vv/nNbzR//nz16NEjtj03N/eCj/3cc8+poqJCjz76aIfU+nWs2Mj58Y9/rAkTJmjq1KlmlwIAAJIMTSkAANDl+Xw++Xy+2M8VFRWSpD59+lxUI+qrwuFwXHc5dVfhcNhyjTQAAJAYrCkFAAAAAACAhONOKQAAgK/w+/16+eWX9e6776qiokJOp1PFxcX6+7//ew0ePDg2buHChdqzZ0/s5xkzZkhqmc6XlZWlxsZGvfrqq3r//fdVUVGhYDCo3r17a/LkyfrWt77Vqb9DNBrVpk2btGnTJp06dUppaWm6/vrrNXPmTLnd7ti4NWvW6NChQ5o+fbpWrVql0tJShUIhFRQU6Pbbb9eIESPaHPvQoUNas2aNDhw4oEAgoPz8fN16662KRqNas2aNnn32WUnSrFmzYneQrV27VmvXrlWfPn20ZMmSVsd755139Nprr+nzzz9XJBJRUVGR/uEf/kGDBg3qvDcIAAAkBZpSAAAA/6uxsVELFixQZWWlpk6dqoEDB6qhoUGbN2/W448/rjlz5sQWQp89e7YaGhr04osvqrq6WrNnz5ZhGLFpgqWlpTpy5IhuvfVW5ebmymazaffu3Vq2bJmysrJ01VVXddrv8cILL+itt97StGnTdOWVV+r06dNauXKljh071mbtqxMnTmjRokWaOHGipkyZIrvdro0bN+o//uM/9PTTTys/Pz82trS0VI899pgKCwt1//33KysrS4cOHdJvf/tb9erVq9U0xsWLFysUCmnRokW6+uqrdfPNN8vlcrU698GDB3X06FF973vf04ABAxQKhfTHP/5RixYt0jPPPKOcnJxOe48AAID5aEoBAAD8r5deeknHjx/X4sWL1bt379j24cOHy+v16vnnn9ewYcPk8/mUnZ2t7OxspaamqrGxUQUFBa2ONXToUA0dOrTVtuLiYh05ckSbNm3qtKbUvn379Prrr+vee+/VjTfeKEnq16+f+vbtq3/+53/W3r17W9X1xRdf6Pvf/36rheAHDx6suXPn6pVXXtF9990X27569Wrl5OTo0UcfldPplCQVFRWpsLBQ8+fPb9VEysv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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# ์Šคํƒ€์ผ ์„ค์ •\n", + "plt.style.use('ggplot')\n", + "\n", + "# paragraph_length ์‹œ๊ฐํ™”\n", + "plt.figure(figsize=(12, 7))\n", + "sns.histplot(\n", + " train['paragraph_length'],\n", + " bins=30,\n", + " kde=True,\n", + " color='blue',\n", + " edgecolor='black',\n", + " alpha=0.7\n", + ")\n", + "\n", + "# ์ค‘์•™๊ฐ’๊ณผ ํ‰๊ท  ๊ณ„์‚ฐ\n", + "paragraph_median = train['paragraph_length'].median()\n", + "paragraph_mean = train['paragraph_length'].mean()\n", + "\n", + "# ์ค‘์•™๊ฐ’๊ณผ ํ‰๊ท  ํ‘œ์‹œ\n", + "plt.axvline(paragraph_median, color='red', linestyle='--', linewidth=1.5, label=f'Median: {paragraph_median:.2f}')\n", + "plt.axvline(paragraph_mean, color='orange', linestyle='-', linewidth=1.5, label=f'Mean: {paragraph_mean:.2f}')\n", + "\n", + "# ์ตœ์†Ÿ๊ฐ’๊ณผ ์ตœ๋Œ“๊ฐ’ ๊ณ„์‚ฐ\n", + "min_value = train['paragraph_length'].min()\n", + "max_value = train['paragraph_length'].max()\n", + "\n", + "# ๊ทธ๋ž˜ํ”„์— ํ‘œ์‹œ\n", + "plt.axvline(min_value, color='blue', linestyle='--', linewidth=1, label=f'Min: {min_value}')\n", + "plt.axvline(max_value, color='blue', linestyle='--', linewidth=1, label=f'Max: {max_value}')\n", + "\n", + "# ๊ทธ๋ž˜ํ”„ ์ œ๋ชฉ ๋ฐ ์ถ• ๋ผ๋ฒจ ์„ค์ •\n", + "plt.title('Distribution of Paragraph Length', fontsize=20, fontweight='bold')\n", + "plt.xlabel('Paragraph Length', fontsize=14)\n", + "plt.ylabel('Count', fontsize=14)\n", + "plt.legend(fontsize=12, loc='upper right') # ๋ฒ”๋ก€ ์œ„์น˜ ์˜ค๋ฅธ์ชฝ ์œ„\n", + "plt.xticks(fontsize=12)\n", + "plt.yticks(fontsize=12)\n", + "plt.grid(True, linestyle='--', linewidth=0.5, alpha=0.7)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# question_length ์‹œ๊ฐํ™”\n", + "plt.figure(figsize=(12, 7))\n", + "sns.histplot(\n", + " train['question_length'],\n", + " bins=30,\n", + " kde=True,\n", + " color='green',\n", + " edgecolor='black',\n", + " alpha=0.7\n", + ")\n", + "\n", + "# ์ค‘์•™๊ฐ’๊ณผ ํ‰๊ท  ๊ณ„์‚ฐ\n", + "question_median = train['question_length'].median()\n", + "question_mean = train['question_length'].mean()\n", + "\n", + "# ์ค‘์•™๊ฐ’๊ณผ ํ‰๊ท  ํ‘œ์‹œ\n", + "plt.axvline(question_median, color='red', linestyle='--', linewidth=1.5, label=f'Median: {question_median:.2f}')\n", + "plt.axvline(question_mean, color='orange', linestyle='-', linewidth=1.5, label=f'Mean: {question_mean:.2f}')\n", + "\n", + "# ์ตœ์†Ÿ๊ฐ’๊ณผ ์ตœ๋Œ“๊ฐ’ ๊ณ„์‚ฐ\n", + "min_value = train['question_length'].min()\n", + "max_value = train['question_length'].max()\n", + "\n", + "# ๊ทธ๋ž˜ํ”„์— ํ‘œ์‹œ\n", + "plt.axvline(min_value, color='green', linestyle='--', linewidth=1, label=f'Min: {min_value}')\n", + "plt.axvline(max_value, color='green', linestyle='--', linewidth=1, label=f'Max: {max_value}')\n", + "\n", + "# ๊ทธ๋ž˜ํ”„ ์ œ๋ชฉ ๋ฐ ์ถ• ๋ผ๋ฒจ ์„ค์ •\n", + "plt.title('Distribution of Question Length', fontsize=20, fontweight='bold')\n", + "plt.xlabel('Question Length', fontsize=14)\n", + "plt.ylabel('Count', fontsize=14)\n", + "plt.legend(fontsize=12, loc='upper right') # ๋ฒ”๋ก€ ์œ„์น˜ ์˜ค๋ฅธ์ชฝ ์œ„\n", + "plt.xticks(fontsize=12)\n", + "plt.yticks(fontsize=12)\n", + "plt.grid(True, linestyle='--', linewidth=0.5, alpha=0.7)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# choices_length ์‹œ๊ฐํ™”\n", + "plt.figure(figsize=(12, 7))\n", + "sns.histplot(\n", + " train['choices_length'],\n", + " bins=30,\n", + " kde=True,\n", + " color='purple',\n", + " edgecolor='black',\n", + " alpha=0.7\n", + ")\n", + "\n", + "# ์ค‘์•™๊ฐ’๊ณผ ํ‰๊ท  ๊ณ„์‚ฐ\n", + "choices_median = train['choices_length'].median()\n", + "choices_mean = train['choices_length'].mean()\n", + "\n", + "# ์ตœ์†Ÿ๊ฐ’๊ณผ ์ตœ๋Œ“๊ฐ’ ๊ณ„์‚ฐ\n", + "min_value = train['choices_length'].min()\n", + "max_value = train['choices_length'].max()\n", + "\n", + "# ๊ทธ๋ž˜ํ”„์— ํ‘œ์‹œ\n", + "plt.axvline(min_value, color='purple', linestyle='--', linewidth=1, label=f'Min: {min_value}')\n", + "plt.axvline(max_value, color='purple', linestyle='--', linewidth=1, label=f'Max: {max_value}')\n", + "\n", + "# ์ค‘์•™๊ฐ’๊ณผ ํ‰๊ท  ํ‘œ์‹œ\n", + "plt.axvline(choices_median, color='red', linestyle='--', linewidth=1.5, label=f'Median: {choices_median:.2f}')\n", + "plt.axvline(choices_mean, color='orange', linestyle='-', linewidth=1.5, label=f'Mean: {choices_mean:.2f}')\n", + "\n", + "# ๊ทธ๋ž˜ํ”„ ์ œ๋ชฉ ๋ฐ ์ถ• ๋ผ๋ฒจ ์„ค์ •\n", + "plt.title('Distribution of Choices Length', fontsize=20, fontweight='bold')\n", + "plt.xlabel('Choices Length', fontsize=14)\n", + "plt.ylabel('Count', fontsize=14)\n", + "plt.legend(fontsize=12, loc='upper right') # ๋ฒ”๋ก€ ์œ„์น˜ ์˜ค๋ฅธ์ชฝ ์œ„\n", + "plt.xticks(fontsize=12)\n", + "plt.yticks(fontsize=12)\n", + "plt.grid(True, linestyle='--', linewidth=0.5, alpha=0.7)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# total_length ์‹œ๊ฐํ™”\n", + "plt.figure(figsize=(12, 7))\n", + "sns.histplot(\n", + " train['total_length'],\n", + " bins=30,\n", + " kde=True,\n", + " color='orange',\n", + " edgecolor='black',\n", + " alpha=0.7\n", + ")\n", + "\n", + "# ์ค‘์•™๊ฐ’๊ณผ ํ‰๊ท  ๊ณ„์‚ฐ\n", + "total_median = train['total_length'].median()\n", + "total_mean = train['total_length'].mean()\n", + "\n", + "# ์ค‘์•™๊ฐ’๊ณผ ํ‰๊ท  ํ‘œ์‹œ\n", + "plt.axvline(total_median, color='red', linestyle='--', linewidth=1.5, label=f'Median: {total_median:.2f}')\n", + "plt.axvline(total_mean, color='blue', linestyle='-', linewidth=1.5, label=f'Mean: {total_mean:.2f}')\n", + "\n", + "# ์ตœ์†Ÿ๊ฐ’๊ณผ ์ตœ๋Œ“๊ฐ’ ๊ณ„์‚ฐ\n", + "min_value = train['total_length'].min()\n", + "max_value = train['total_length'].max()\n", + "\n", + "# ๊ทธ๋ž˜ํ”„์— ํ‘œ์‹œ\n", + "plt.axvline(min_value, color='orange', linestyle='--', linewidth=1, label=f'Min: {min_value}')\n", + "plt.axvline(max_value, color='orange', linestyle='--', linewidth=1, label=f'Max: {max_value}')\n", + "\n", + "# ๊ทธ๋ž˜ํ”„ ์ œ๋ชฉ ๋ฐ ์ถ• ๋ผ๋ฒจ ์„ค์ •\n", + "plt.title('Distribution of Total Length', fontsize=20, fontweight='bold')\n", + "plt.xlabel('Total Length', fontsize=14)\n", + "plt.ylabel('Count', fontsize=14)\n", + "plt.legend(fontsize=12, loc='upper right') # ๋ฒ”๋ก€ ์œ„์น˜ ์˜ค๋ฅธ์ชฝ ์œ„\n", + "plt.xticks(fontsize=12)\n", + "plt.yticks(fontsize=12)\n", + "plt.grid(True, linestyle='--', linewidth=0.5, alpha=0.7)\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# ๋ฐ•์Šคํ”Œ๋กฏ ์‹œ๊ฐํ™”: paragraph_length\n", + "plt.figure(figsize=(10, 6))\n", + "sns.boxplot(\n", + " x=train['paragraph_length'],\n", + " color='blue',\n", + " width=0.5\n", + ")\n", + "# ๊ทธ๋ž˜ํ”„ ์ œ๋ชฉ ๋ฐ ์ถ• ๋ผ๋ฒจ ์„ค์ •\n", + "plt.title('Boxplot of Paragraph Length', fontsize=20, fontweight='bold')\n", + "plt.xlabel('Paragraph Length', fontsize=14)\n", + "plt.grid(True, linestyle='--', linewidth=0.5, alpha=0.7)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "\n", + "# ๋ฐ•์Šคํ”Œ๋กฏ ์‹œ๊ฐํ™”: question_length\n", + "plt.figure(figsize=(10, 6))\n", + "sns.boxplot(\n", + " x=train['question_length'],\n", + " color='green',\n", + " width=0.5\n", + ")\n", + "# ๊ทธ๋ž˜ํ”„ ์ œ๋ชฉ ๋ฐ ์ถ• ๋ผ๋ฒจ ์„ค์ •\n", + "plt.title('Boxplot of Question Length', fontsize=20, fontweight='bold')\n", + "plt.xlabel('Question Length', fontsize=14)\n", + "plt.grid(True, linestyle='--', linewidth=0.5, alpha=0.7)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "\n", + "# ๋ฐ•์Šคํ”Œ๋กฏ ์‹œ๊ฐํ™”: choices_length\n", + "plt.figure(figsize=(10, 6))\n", + "sns.boxplot(\n", + " x=train['choices_length'],\n", + " color='purple',\n", + " width=0.5\n", + ")\n", + "# ๊ทธ๋ž˜ํ”„ ์ œ๋ชฉ ๋ฐ ์ถ• ๋ผ๋ฒจ ์„ค์ •\n", + "plt.title('Boxplot of Choices Length', fontsize=20, fontweight='bold')\n", + "plt.xlabel('Choices Length', fontsize=14)\n", + "plt.grid(True, linestyle='--', linewidth=0.5, alpha=0.7)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "\n", + "# ๋ฐ•์Šคํ”Œ๋กฏ ์‹œ๊ฐํ™”: total_length\n", + "plt.figure(figsize=(10, 6))\n", + "sns.boxplot(\n", + " x=train['total_length'],\n", + " color='orange',\n", + " width=0.5\n", + ")\n", + "# ๊ทธ๋ž˜ํ”„ ์ œ๋ชฉ ๋ฐ ์ถ• ๋ผ๋ฒจ ์„ค์ •\n", + "plt.title('Boxplot of Total Length', fontsize=20, fontweight='bold')\n", + "plt.xlabel('Total Length', fontsize=14)\n", + "plt.grid(True, linestyle='--', linewidth=0.5, alpha=0.7)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "์žฌ์ • ์ •์ฑ… ย ย ย  ํ†ตํ™” ์ •์ฑ…\n", + "์ฒญ๋ฐ”์ง€ ์‹œ์žฅ์—์„œ๋Š”?\n", + "['9', '8', '7.5', '7']\n" + ] + } + ], + "source": [ + "# paragraph๊ฐ€ ๊ฐ€์žฅ ์งง์€ ๋ฐ์ดํ„ฐ ํ™•์ธ\n", + "min_paragraph_length = train['paragraph_length'].min()\n", + "min_paragraph_length_idx = train['paragraph_length'].idxmin()\n", + "print(train.loc[min_paragraph_length_idx, 'paragraph'])\n", + "\n", + "# question ๊ฐ€์žฅ ์งง์€ ๋ฐ์ดํ„ฐ ํ™•์ธ\n", + "min_question_length = train['question_length'].min()\n", + "min_question_length_idx = train['question_length'].idxmin()\n", + "print(train.loc[min_question_length_idx, 'question'])\n", + "\n", + "# choices ๊ฐ€์žฅ ์งง์€ ๋ฐ์ดํ„ฐ ํ™•์ธ\n", + "min_choices_length = train['choices_length'].min()\n", + "min_choices_length_idx = train['choices_length'].idxmin()\n", + "print(train.loc[min_choices_length_idx, 'choices'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(102, 20)\n" + ] + }, + { + "data": { + "text/html": [ + "
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paragraphparagraph_length
676์ธ๊ตฌ์กฐ์‚ฌ ์ž๋ฃŒ์™€ ์†Œ๋ จ ๊ต์œก๋ถ€15
217์žฌ์ • ์ •์ฑ… ย ย ย  ํ†ตํ™” ์ •์ฑ…15
225์žฌ์ • ์ •์ฑ… ย ย ย  ํ†ตํ™” ์ •์ฑ…15
238๋‹ฌ๋Ÿฌ ๊ฐ€์น˜ ย ย ย  ๋ฏธ๊ตญ ์ˆ˜์ถœ15
241๋‹ฌ๋Ÿฌํ™” ์‹œ์žฅ ย ย  ๋‹ฌ๋Ÿฌ์˜ ๊ฐ€์น˜17
248๋Œ€์ถœ ๊ฐ€๋Šฅํ•œ ์ž๊ธˆ ์‹œ์žฅ ย ย ย  ๊ธˆ๋ฆฌ19
193๋‹ค๊ตญ์  ๊ธฐ์—…์€ ์šด์˜์„ ๋ถ„์‚ฐํ•˜์—ฌ...19
269์˜ฅ์ˆ˜์ˆ˜๋Š” ๊ฒฝ์Ÿ ์‹œ์žฅ์—์„œ ๊ตํ™˜๋ฉ๋‹ˆ๋‹ค.19
256๊ธˆ๋ฆฌ ย ย ย  ์‹ ๊ทœ ์ฃผํƒ ย ย ย  ์‹ค์—…๋ฅ 20
242๊ณ ์ „์ ์ธ ์ด ๊ณต๊ธ‰ ๊ณก์„ ์„ ๊ฐ€์ •ํ•ฉ๋‹ˆ๋‹ค.20
283์ง€๊ธ‰์ค€๋น„์œจ์ด 5%๋ผ๊ณ  ๊ฐ€์ •ํ•˜๊ฒ ์Šต๋‹ˆ๋‹ค.20
318์žฌํ™” X๋Š” ๊ฒฝ์Ÿ ์‹œ์žฅ์—์„œ ๊ตํ™˜๋ฉ๋‹ˆ๋‹ค.20
259๊ฒฝ์ œ๊ฐ€ ์‹ฌ๊ฐํ•œ ๋ถˆํ™ฉ์— ๋น ์ ธ ์žˆ์Šต๋‹ˆ๋‹ค.20
412๋‹ค์Œ์€ ๋ชจ๋‘ ์กฐํ˜„๋ณ‘์˜ ์–‘์„ฑ์ฆ์ƒ์ž…๋‹ˆ๋‹ค.20
344๋‹ค์Œ์€ ๋ชจ๋‘ ์ƒ๋ฆฌ์  ๊ฐ์„ฑ์˜ ํŠน์ง•์ž…๋‹ˆ๋‹ค.21
215์ง€๊ธ‰์ค€๋น„์œจ์ด 10%๋ผ๊ณ  ๊ฐ€์ •ํ•˜๊ฒ ์Šต๋‹ˆ๋‹ค.21
253ํ™”ํ ์ˆ˜๋Ÿ‰์„ค์— ๋”ฐ๋ฅด๋ฉด ํ™”ํ ๊ณต๊ธ‰์„ ๋Š˜๋ฆฌ๋ฉด22
271์ •๋ถ€๊ฐ€ ๋ฐฉ๊ธˆ ๊ฐœ์ธ ์†Œ๋“์„ธ๋ฅผ ์ธํ•˜ํ–ˆ์Šต๋‹ˆ๋‹ค.22
230ํ†ตํ™”๋ก ์ž ๋ถ„์„์— ๋”ฐ๋ฅด๋ฉด ํ†ตํ™” ๊ณต๊ธ‰ ๊ฐ์†Œ๋Š”22
222์‹ค์งˆ GDP ย ย ย  ๋ฌผ๊ฐ€ ์ˆ˜์ค€ ย ย ย  ์‹ค์—…23
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" + ], + "text/plain": [ + " paragraph paragraph_length\n", + "676 ์ธ๊ตฌ์กฐ์‚ฌ ์ž๋ฃŒ์™€ ์†Œ๋ จ ๊ต์œก๋ถ€ 15\n", + "217 ์žฌ์ • ์ •์ฑ… ย ย ย  ํ†ตํ™” ์ •์ฑ… 15\n", + "225 ์žฌ์ • ์ •์ฑ… ย ย ย  ํ†ตํ™” ์ •์ฑ… 15\n", + "238 ๋‹ฌ๋Ÿฌ ๊ฐ€์น˜ ย ย ย  ๋ฏธ๊ตญ ์ˆ˜์ถœ 15\n", + "241 ๋‹ฌ๋Ÿฌํ™” ์‹œ์žฅ ย ย  ๋‹ฌ๋Ÿฌ์˜ ๊ฐ€์น˜ 17\n", + "248 ๋Œ€์ถœ ๊ฐ€๋Šฅํ•œ ์ž๊ธˆ ์‹œ์žฅ ย ย ย  ๊ธˆ๋ฆฌ 19\n", + "193 ๋‹ค๊ตญ์  ๊ธฐ์—…์€ ์šด์˜์„ ๋ถ„์‚ฐํ•˜์—ฌ... 19\n", + "269 ์˜ฅ์ˆ˜์ˆ˜๋Š” ๊ฒฝ์Ÿ ์‹œ์žฅ์—์„œ ๊ตํ™˜๋ฉ๋‹ˆ๋‹ค. 19\n", + "256 ๊ธˆ๋ฆฌ ย ย ย  ์‹ ๊ทœ ์ฃผํƒ ย ย ย  ์‹ค์—…๋ฅ  20\n", + "242 ๊ณ ์ „์ ์ธ ์ด ๊ณต๊ธ‰ ๊ณก์„ ์„ ๊ฐ€์ •ํ•ฉ๋‹ˆ๋‹ค. 20\n", + "283 ์ง€๊ธ‰์ค€๋น„์œจ์ด 5%๋ผ๊ณ  ๊ฐ€์ •ํ•˜๊ฒ ์Šต๋‹ˆ๋‹ค. 20\n", + "318 ์žฌํ™” X๋Š” ๊ฒฝ์Ÿ ์‹œ์žฅ์—์„œ ๊ตํ™˜๋ฉ๋‹ˆ๋‹ค. 20\n", + "259 ๊ฒฝ์ œ๊ฐ€ ์‹ฌ๊ฐํ•œ ๋ถˆํ™ฉ์— ๋น ์ ธ ์žˆ์Šต๋‹ˆ๋‹ค. 20\n", + "412 ๋‹ค์Œ์€ ๋ชจ๋‘ ์กฐํ˜„๋ณ‘์˜ ์–‘์„ฑ์ฆ์ƒ์ž…๋‹ˆ๋‹ค. 20\n", + "344 ๋‹ค์Œ์€ ๋ชจ๋‘ ์ƒ๋ฆฌ์  ๊ฐ์„ฑ์˜ ํŠน์ง•์ž…๋‹ˆ๋‹ค. 21\n", + "215 ์ง€๊ธ‰์ค€๋น„์œจ์ด 10%๋ผ๊ณ  ๊ฐ€์ •ํ•˜๊ฒ ์Šต๋‹ˆ๋‹ค. 21\n", + "253 ํ™”ํ ์ˆ˜๋Ÿ‰์„ค์— ๋”ฐ๋ฅด๋ฉด ํ™”ํ ๊ณต๊ธ‰์„ ๋Š˜๋ฆฌ๋ฉด 22\n", + "271 ์ •๋ถ€๊ฐ€ ๋ฐฉ๊ธˆ ๊ฐœ์ธ ์†Œ๋“์„ธ๋ฅผ ์ธํ•˜ํ–ˆ์Šต๋‹ˆ๋‹ค. 22\n", + "230 ํ†ตํ™”๋ก ์ž ๋ถ„์„์— ๋”ฐ๋ฅด๋ฉด ํ†ตํ™” ๊ณต๊ธ‰ ๊ฐ์†Œ๋Š” 22\n", + "222 ์‹ค์งˆ GDP ย ย ย  ๋ฌผ๊ฐ€ ์ˆ˜์ค€ ย ย ย  ์‹ค์—… 23" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# paragraph๊ฐ€ ์งง์€ ๋ฐ์ดํ„ฐ ํ™•์ธ: n% percentile\n", + "percentile_value = train['paragraph_length'].quantile(0.05)\n", + "short_paragraphs = train[train['paragraph_length'] < percentile_value]\n", + "# ๊ธด ์ˆœ์„œ๋Œ€๋กœ ์ •๋ ฌ\n", + "short_paragraphs = short_paragraphs.sort_values(by='paragraph_length', ascending=True)\n", + "\n", + "# ํ•„ํ„ฐ๋ง๋œ ๋ฐ์ดํ„ฐ ํ™•์ธ\n", + "print(short_paragraphs.shape)\n", + "short_paragraphs[['paragraph', 'paragraph_length']].head(20)" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "๋‹ค๊ตญ์  ๊ธฐ์—…์€ ์šด์˜์„ ๋ถ„์‚ฐํ•˜์—ฌ...\n", + "๋‹ค๊ตญ์  ๊ธฐ์—…์˜ ์šด์˜ ๋ฐฉ์‹์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?\n", + "['์ƒ์‚ฐ๋น„์šฉ์ด ๊ฐ€์žฅ ๋†’์€ ์ œํ’ˆ์„ ์ œ์กฐํ•œ๋‹ค', '๊ฒฝ์ œ์ ์ธ ๊ณณ์—์„œ ํšŒ๊ณ„ ๋ฐ ์—ฐ๊ตฌ ์„œ๋น„์Šค๋ฅผ ์ˆ˜ํ–‰ํ•œ๋‹ค', '๋น„๊ต์šฐ์œ„๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ํ•œ๋‹ค', '๋ณธ์‚ฌ๋Š” ์ €๊ฐœ๋ฐœ๊ตญ์— ์†Œ์žฌํ•œ๋‹ค']\n", + "3\n" + ] + } + ], + "source": [ + "# ์ด์ƒ paragraph ํ™•์ธ -> ๊ธธ์ด๊ฐ€ ์งง์•„๋„ ํ’€ ์ˆ˜ ์žˆ๋Š” ๋ฌธ์ œ๋“ค์ด ์žˆ์–ด percentile๋กœ ์ž๋ฅด๊ธฐ ์–ด๋ ค์›€\n", + "x = 6\n", + "print(train.at[short_paragraphs.index[x], 'paragraph'])\n", + "print(train.at[short_paragraphs.index[x], 'question'])\n", + "print(train.at[short_paragraphs.index[x], 'choices'])\n", + "print(train.at[short_paragraphs.index[x], 'answer'])" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "์„œ์šธ์‹œ๊ฐ€ 2030๋…„๊นŒ์ง€ โ€˜4๋Œ€๋ฌธ ์•ˆ ํ•œ์–‘๋„์„ฑโ€™๊ณผ โ€˜๊ฐ•๋‚จโ€™ โ€˜์˜๋“ฑํฌยท์—ฌ์˜๋„โ€™ ๋“ฑ์„ ๊ตญ์ œ๊ฒฝ์Ÿ๋ ฅ์„ ๊ฐ–์ถ˜ โ€˜3๋Œ€ ๋„์‹ฌ๊ถŒโ€™์œผ๋กœ ์œก์„ฑํ•œ๋‹ค๋Š” ๊ณ„ํš์„ ์„ธ์› ๋‹ค. ๋˜ ์šฉ์‚ฐ, ์ฒญ๋Ÿ‰๋ฆฌยท์™•์‹ญ๋ฆฌ, ์ฐฝ๋™ยท์ƒ๊ณ„ ๋“ฑ 7๊ฐœ ๊ถŒ์—ญ์€ ์ด๋ฅผ ๋‘˜๋Ÿฌ์‹ผ โ€˜๊ด‘์—ญ์ค‘์‹ฌ(์˜› ๋ถ€๋„์‹ฌ)โ€™์œผ๋กœ ์ง€์ •ํ•˜๊ณ  ์ง€์—ญ๋ณ„ ํŠน์„ฑ์— ๋งž๊ฒŒ ๊ฐœ๋ฐœํ‚ค๋กœ ํ–ˆ๋‹ค. ์„œ์šธ์‹œ๋Š” 26์ผ ์ด ๊ฐ™์€ ๋‚ด์šฉ์„ ํ•ต์‹ฌ์œผ๋กœ ํ•œ โ€˜2030 ๋„์‹œ๊ธฐ๋ณธ๊ณ„ํš์•ˆ(์„œ์šธํ”Œ๋žœ)โ€™์„ ๋ฐœํ‘œํ–ˆ๋‹ค. ์ง€์—ญ๋ณ„ ๊ท ํ˜•๋ฐœ์ „์„ ์œ„ํ•œ โ€˜๊ถŒ์—ญ๋ณ„ ๊ฑฐ์ ์ง€์—ญโ€™์„ ํ™•๋Œ€ํ•˜๊ณ , ์—ฌ๊ธฐ์— ๋งž๋Š” ๊ธ‰ํ–‰์ฒ ๋„ ๋“ฑ ๊ด‘์—ญ๊ตํ†ต๋ง์„ ํ™•์ถฉํ•˜๋Š” ๋งˆ์Šคํ„ฐํ”Œ๋žœ์„ ๋‹ด๊ณ  ์žˆ๋‹ค. โ—‹๊ฐ•๋‚จยท์—ฌ์˜๋„ โ€˜๋„์‹ฌ๊ถŒโ€™ ์ถ”๊ฐ€ ์ง€์ •์ด๋ฒˆ ์„œ์šธ๋„์‹œ๊ธฐ๋ณธ๊ณ„ํš์€ 1990๋…„ ์ฒซ ๊ณ„ํš์ด ๋งˆ๋ จ๋œ ์ดํ›„ โ€˜2020 ์„œ์šธ๋„์‹œ๊ธฐ๋ณธ๊ณ„ํšโ€™๊นŒ์ง€ 20์—ฌ๋…„๊ฐ„ ์œ ์ง€๋๋˜ ๊ธฐ๋ณธ ๊ณจ๊ฒฉ(1๋„์‹ฌยท5๋ถ€๋„์‹ฌยท11์ง€์—ญ์ค‘์‹ฌ)์„ โ€˜3๋„์‹ฌยท7๊ด‘์—ญ์ค‘์‹ฌยท12์ง€์—ญ์ค‘์‹ฌโ€™์œผ๋กœ ๋ฐ”๊ฟจ๋‹ค. ์ง€๊ธˆ๊นŒ์ง€๋Š” ์„œ์šธ์˜ ํ•œ๋ณตํŒ์ธ โ€˜4๋Œ€๋ฌธ ์•ˆ ์ผ๋Œ€โ€™๋ฅผ ํ•ต์‹ฌ์ถ•(1๋„์‹ฌ)์œผ๋กœ ๋„์‹œ์ฒด๊ณ„๊ฐ€ ์งœ์—ฌ์กŒ์ง€๋งŒ ์•ž์œผ๋กœ๋Š” ํ•ต์‹ฌ ๋„์‹œ๊ถŒ์„ 2๊ฐœ ๋” ์ถ”๊ฐ€ํ•ด ๊ฐ•๋‚จยท๋ถ์„ ์•„์šฐ๋ฅด๋Š” โ€˜3๊ฐ ๋„์‹ฌโ€™ ์ฒด๊ณ„๋กœ ์ „ํ™˜ํ•˜๊ฒ ๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. 3๋Œ€ ๋„์‹ฌ์€ ๊ฐ์ž ํŠน์„ฑ์„ ๋ฐ˜์˜ํ•ด ๊ฐ์ข… ๊ฐœ๋ฐœ๊ณ„ํš์ด ์„ธ์›Œ์ง„๋‹ค. 4๋Œ€๋ฌธ ์•ˆ์€ โ€˜์„ธ๊ณ„์  ์—ญ์‚ฌ๋ฌธํ™”์ค‘์‹ฌ์ง€โ€™๋กœ, ๊ฐ•๋‚จ๊ณผ ์˜๋“ฑํฌยท์—ฌ์˜๋„๋Š” ๊ฐ๊ฐ โ€˜๊ตญ์ œ์—…๋ฌด์ค‘์‹ฌ์ง€โ€™ โ€˜๊ตญ์ œ๊ธˆ์œต์ค‘์‹ฌ์ง€โ€™๋กœ ํŠนํ™”ํ•œ๋‹ค. ๋„์‹ฌ๊ถŒ์„ ๋‘˜๋Ÿฌ์‹ผ โ€˜5๋Œ€ ๋ถ€๋„์‹ฌ๊ถŒโ€™์€ โ€˜7๊ฐœ ๊ด‘์—ญ์ค‘์‹ฌโ€™์ด๋ž€ ๋ช…์นญ์„ ๋ถ™์—ฌ 2๊ฐœ๊ถŒ์„ ์ถ”๊ฐ€ํ–ˆ๋‹ค. ๋‹ค๋ฅธ ์ง€์—ญ์— ๋น„ํ•ด ๋‚™ํ›„๋œ ๋™๋ถยท์„œ๋‚จ๊ถŒ์— 1๊ณณ์”ฉ์„ ๋”ํ–ˆ๋‹ค. ํ•ด๋‹น ์ง€์—ญ์€ ์šฉ์‚ฐ, ์ฒญ๋Ÿ‰๋ฆฌยท์™•์‹ญ๋ฆฌ, ์ƒ์•”ยท์ˆ˜์ƒ‰, ์ฐฝ๋™ยท์ƒ๊ณ„, ๊ฐ€์‚ฐยท๋Œ€๋ฆผ, ์ž ์‹ค, ๋งˆ๊ณก ๋“ฑ์ด๋‹ค. ๊ด‘์—ญ์ค‘์‹ฌ์ง€๋Š” ์ฐฝ๋™์ฐจ๋Ÿ‰๊ธฐ์ง€์™€ ๋งˆ๊ณก์‚ฐ์—…๋‹จ์ง€, ๊ตฌ๋กœ์ฐจ๋Ÿ‰๊ธฐ์ง€, ์ œ2๋กฏ๋ฐ์›”๋“œ ๋“ฑ ๋Œ€๊ทœ๋ชจ ๊ฐœ๋ฐœ๊ณผ ๊ณ ์šฉ ์ฐฝ์ถœ์ด ๊ฐ€๋Šฅํ•œ ์ง€์—ญ๋“ค์„ ์ง€์ •ํ–ˆ๋‹ค. ๊ด‘์—ญ์ค‘์‹ฌ์˜ ํ•˜๋ถ€ ๊ฐœ๋…์ธ โ€˜์ง€์—ญ์ค‘์‹ฌ์€ 12๊ณณ์œผ๋กœ ๊ตฌ๋ถ„๋๋‹ค. ๋™๋Œ€๋ฌธ, ์„ฑ์ˆ˜, ๋ง์šฐ, ๋ฏธ์•„, ์—ฐ์‹ ๋‚ดยท๋ถˆ๊ด‘, ์‹ ์ดŒ, ๋งˆํฌยท๊ณต๋•, ๋ชฉ๋™, ๋ด‰์ฒœ, ์‚ฌ๋‹นยท์ด์ˆ˜, ์ˆ˜์„œยท๋ฌธ์ •, ์ฒœํ˜ธยท๊ธธ๋™ ๋“ฑ์ด๋‹ค. ์ด๋“ค ์ง€์—ญ์—๋Š” ๊ฐ์ข… ๊ณต๊ณต์„œ๋น„์Šค ๋ฐ ์ƒ์—…ยท๋ฌธํ™”๊ธฐ๋Šฅ ํ™•๋Œ€๋ฅผ ํ†ตํ•ด ์ž์กฑ๊ธฐ๋Šฅ์ด ๊ฐ€๋Šฅํ•ด์ง€๋„๋ก ๊ฐœ๋ฐœ ์ง€์›์ด ์ด๋ค„์ง„๋‹ค.์ˆ˜๋„๊ถŒ ๊ฑฐ์ฃผ ์ง์žฅ์ธ๋“ค์˜ ์ถœํ‡ด๊ทผ ํ™•๋Œ€์— ๋”ฐ๋ฅธ ๊ตํ†ต๋‚œ ํ•ด๊ฒฐ์„ ์œ„ํ•ด ์ฒ ๋„ ํ™•์ถฉ ๋ฐฉ์•ˆ๋„ ํฌํ•จ๋๋‹ค. ์ตœ๊ทผ ๋ฐœํ‘œํ•œ 9๊ฐœ ๊ฒฝ์ „์ฒ  ๋…ธ์„ ๊ณผ ๋ณ„๊ฐœ๋กœ ์ˆ˜๋„๊ถŒ ์„œ๋ถยท๋™๋‚จ๊ถŒ๊ณผ์˜ ์—ฐ๊ณ„์„ฑ์„ ๋” ๊ฐ•ํ™”ํ•  ๋ฐฉ์นจ์ด๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ์‹ ๋ถ„๋‹น์„ ์€ 4๋Œ€๋ฌธ ๋‚ด ๋„์‹ฌ์„ ๊ฑฐ์ณ ๊ณ ์–‘ ์‚ผ์†ก๊นŒ์ง€ ์—ฐ์žฅํ•  ๊ณ„ํš์ด๋‹ค. ์ธ์ฒœ~๊ฐ€์‚ฐ~๊ฐ•๋‚จยท์ž ์‹ค์„ ์ž‡๋Š” โ€˜๋‚จ๋ถ€ ๊ธ‰ํ–‰์ฒ ๋„โ€™๋„ ๊ฑด์„คํ•œ๋‹ค. ๋‹ค๋งŒ ์ด๋“ค ์‹œ์„ค์€ ๋ชจ๋‘ ๊ตญํ† ๊ตํ†ต๋ถ€์™€ ํ˜‘์˜ ์‚ฌ์•ˆ์ด์–ด์„œ ๊ณ„ํš๋Œ€๋กœ ์‹คํ–‰๋˜๊ธฐ๊นŒ์ง€๋Š” ๋งŽ์€ ๋ณ€์ˆ˜๊ฐ€ ์žˆ๋‹ค. ์„œ์šธ ๋งˆ์Šคํ„ฐํ”Œ๋žœ ์ˆ˜๋ฆฝ์— ์ฐธ์—ฌํ•œ ์ตœ๋ง‰์ค‘ ์„œ์šธ๋Œ€ ํ™˜๊ฒฝ๋Œ€ํ•™์›์žฅ์€ โ€œ์•ž์œผ๋กœ ๋Œ€ํ˜• ๋นŒ๋”ฉ๊ณผ ์ดˆ๊ณ ์ธต ๊ฑด๋ฌผ์ด ์–ด๋””์— ๋“ค์–ด์„ค ์ˆ˜ ์žˆ๋Š”์ง€, ๊ด‘์—ญ๊ตํ†ต๋ง์ด ์ง€๋‚˜๋Š” ๊ธธ๋ชฉ์€ ์–ด๋””์ธ์ง€ ์šฐ์„ ์ˆœ์œ„๋ฅผ ๊ฒฐ์ •ํ•  ๊ฒƒโ€์ด๋ผ๋ฉฐ โ€œ์‹œ๋ฏผ ์ƒํ™œํ™˜๊ฒฝ ๊ฐœ์„ ๊ณผ ์ง€์—ญ ๊ท ํ˜•๋ฐœ์ „์ด ๋„์‹œ๊ธฐ๋ณธ๊ณ„ํš ์ˆ˜๋ฆฝ์˜ ๋ชฉ์ โ€์ด๋ผ๊ณ  ์„ค๋ช…ํ–ˆ๋‹ค.โ—‹๊ณต๊ณต์ž„๋Œ€์ฃผํƒ๋„ ์„ ์ง„๊ตญ ์ˆ˜์ค€๊นŒ์ง€ ํ™•๋Œ€ โ€˜2030 ์„œ์šธํ”Œ๋žœโ€™์€ ์˜ค์„ธํ›ˆ ์ „ ์‹œ์žฅ์ด 2011๋…„ ์ˆ˜๋ฆฝํ–ˆ๋˜ ๊ณ„ํš์˜ ๊ณจ๊ฒฉ์„ ๋Œ€๋ถ€๋ถ„ ์œ ์ง€ํ•˜๊ณ  ์žˆ๋‹ค. ๋„์‹ฌ๊ณผ ๊ฐ•๋‚จ, ์—ฌ์˜๋„ยท์˜๋“ฑํฌ๋ฅผ โ€˜3ํ•ตโ€™์œผ๋กœ ๊ทœ์ •ํ•œ โ€˜3ํ•ต-3๋ถ€ํ•ต-13๊ฑฐ์ โ€™(์ด 19๊ฐœ์†Œ)์ด๋ž€ ํ‹€๋กœ ์งœ์—ฌ์กŒ๋‹ค. ๋ฐ•์›์ˆœ ์‹œ์žฅ์€ ๊ธฐ์กด ๊ณต๊ฐ„๊ณ„ํš์˜ ๊ณจ๊ฒฉ์—๋‹ค ๋ณต์ง€ ๊ต์œก ๋ฌธํ™” ๊ฒฝ๊ด€ ์—๋„ˆ์ง€ ๋“ฑ ์‹œ๋ฏผ์ƒํ™œํ–ฅ์ƒ ์ง€ํ‘œ๋“ค์„ ํฌํ•จ์‹œ์ผฐ๋‹ค. ์˜ˆ์ปจ๋Œ€ ์ตœ์ €์†Œ๋“๊ธฐ์ค€ ๋ณด์žฅ๋ฅ ์„ ํ˜„์žฌ 48%์—์„œ 2030๋…„๊นŒ์ง€ 100%๋กœ, ๊ตญ๊ณต๋ฆฝ์–ด๋ฆฐ์ด์ง‘ ๋ณด์œก๋ถ„๋‹ด๋ฅ ์€ 11%์—์„œ 35%๋กœ, ๊ณต๊ณต์ž„๋Œ€์ฃผํƒ ๋น„์œจ์€ 5%์—์„œ ์„ ์ง„๊ตญ ์ˆ˜์ค€์ธ 12%๊นŒ์ง€ ๊ฐ๊ฐ ๋Š˜๋ฆฌ๋Š” ๋“ฑ์˜ ๋‚ด์šฉ์ด๋‹ค. ๋ณต์ง€ ์ฆ๋Œ€ ๊ด€๋ จ ๊ณ„ํš 17๊ฐ€์ง€๋„ ๋‹ด์•˜๋‹ค. ์ž์—ฐยท๋ฌธํ™”์œ ์‚ฐ ๋ณด์กด์— ๋น„์ค‘์„ ๋‘” ๋…น์ง€์ถ• ๋งˆ๋ จ๋„ ๋ช…๋ฌธํ™”ํ–ˆ๋‹ค. ์ด๋ฒˆ ๊ณ„ํš์€ ์ˆ˜๋ฆฝ ๊ณผ์ •์—์„œ โ€˜100์ธ ์‹œ๋ฏผ์ฐธ์—ฌ๋‹จโ€™์„ ๋น„๋กฏํ•ด ์‹œ๋ฏผยท์ „๋ฌธ๊ฐ€ 250์—ฌ๋ช…์„ ์ฐธ์—ฌ์‹œ์ผฐ๋‹ค. ์˜ˆ์ „์— ๋„์‹œ๊ธฐ๋ณธ๊ณ„ํš์„ ์„ธ์šธ ๋•Œ๋ณด๋‹ค ํ›จ์”ฌ ๋‹ค์–‘ํ•œ ์‹œ๋ฏผ ์˜๊ฒฌ์„ ๋ฐ˜์˜ํ–ˆ๋‹ค๋Š” ๊ฒŒ ์„œ์šธ์‹œ์˜ ์„ค๋ช…์ด๋‹ค.ํ•˜์ง€๋งŒ ์ผ๊ฐ์—์„  โ€˜2030 ์„œ์šธํ”Œ๋žœโ€™์˜ ๋ฐœํ‘œ๋ฅผ ๋‘๊ณ  ๋‚ด๋…„ ์ง€๋ฐฉ์„ ๊ฑฐ๋ฅผ ์˜์‹ํ•˜๊ณ  ์„ฑ๊ธ‰ํ•˜๊ฒŒ ๋‚ด๋†“์€ ๊ฒƒ ์•„๋‹ˆ๋ƒ๋Š” ์ง€์ ๋„ ๋‚˜์˜ค๊ณ  ์žˆ๋‹ค. ์„œ์šธ์‹œ ๊ด€๊ณ„์ž๋Š” โ€œ๋„์‹œ๊ธฐ๋ณธ๊ณ„ํš์€ ์›๋ž˜ 5๋…„๋งˆ๋‹ค ์žฌ๊ฒ€ํ† ํ•˜๋„๋ก ๋ฒ•์— ๊ทœ์ •๋๋‹คโ€๋ฉฐ โ€œ์˜คํžˆ๋ ค ์„œ์šธ 2030 ๊ธฐ๋ณธ๊ณ„ํš์€ 2006๋…„์— ๊ฐœ๋…์ด ๊ณตํ‘œ๋œ ์ดํ›„ ์ตœ์ข… ๋งˆ๋ฌด๋ฆฌ๊ฐ€ ๋„ˆ๋ฌด ๋Šฆ์–ด์ง€๊ณ  ์žˆ๋Š” ์ƒํ™ฉโ€์ด๋ผ๊ณ  ๋ฐ˜๋ฐ•ํ–ˆ๋‹ค.\n", + "\n", + "ํ”ผ๋ถ€ ๋ฐœ์ง„์ด ์žˆ๋Š” ์–ด๋–ค ์‚ฌ๋žŒ์ด ์„ธ๊ฒŒ ๊ธ์œผ๋ฉด ํ†ต์ฆ์ด ์ผ์‹œ์ ์œผ๋กœ ์™„ํ™”๋œ๋‹ค๋Š” ์‚ฌ์‹ค์„ ์•Œ๊ฒŒ ๋ฉ๋‹ˆ๋‹ค. ๊ทธ ์‚ฌ๋žŒ์€ ๊ธ๊ณ  ์žˆ๋Š” ๋™์•ˆ์—๋Š” ๋ฐœ์ง„์œผ๋กœ ์ธํ•œ ํ†ต์ฆ์„ ๋А๋ผ์ง€ ๋ชปํ•˜์ง€๋งŒ, ๊ธ๋Š” ๊ฒƒ์„ ๋ฉˆ์ถ”์ž๋งˆ์ž ํ†ต์ฆ์ด ๋‹ค์‹œ ๋‚˜ํƒ€๋‚ฉ๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ผ์‹œ์  ํ†ต์ฆ ์™„ํ™”๋ฅผ ๊ฐ€์žฅ ์ž˜ ์„ค๋ช…ํ•˜๋Š” ๊ฐœ๋…์€ ๋ฌด์—‡์ž…๋‹ˆ๊นŒ?\n", + "\n", + "['์‹คํ—˜ - ์˜จ๋ผ์ธ ๊ดด๋กญํž˜์„ ์—ฐ๊ตฌํ•˜๋Š” ๊ฐ€์žฅ ํŽธ๋ฆฌํ•˜๊ณ  ์œค๋ฆฌ์ ์ธ ๋ฐฉ๋ฒ•์€ ์ฐธ๊ฐ€์ž์˜ ์ ˆ๋ฐ˜์„ ๋…๋ฆฝ๋ณ€์ˆ˜(์˜จ๋ผ์ธ ๊ดด๋กญํž˜)๋ฅผ ๊ฒช๊ณ  ์žˆ๋Š” ๊ทธ๋ฃน์— ๋ฌด์ž‘์œ„๋กœ ๋ฐฐ์ •ํ•˜๋Š” ๊ฒƒ์ž…๋‹ˆ๋‹ค.', '์ƒ๊ด€๊ด€๊ณ„ - ์˜๋„์ ์œผ๋กœ ์ค‘ํ•™์ƒ์„ ๊ดด๋กญํž˜ ํ–‰์œ„์— ๋…ธ์ถœ์‹œํ‚ค๋Š” ๊ฒƒ์€ ๋น„์œค๋ฆฌ์ ์ด๋ฏ€๋กœ Ek ๊ต์ˆ˜๋Š” ๊ธฐ์กด ์˜จ๋ผ์ธ ๊ดด๋กญํž˜ ์‚ฌ๋ก€์™€ ์ƒ๊ด€๊ด€๊ณ„๊ฐ€ ์žˆ๋Š” ๋ณ€์ˆ˜๋ฅผ ๊ฒ€ํ† ํ•ด์•ผ ํ•ฉ๋‹ˆ๋‹ค.', '์ž์—ฐ์  ๊ด€์ฐฐ - ์ค‘ํ•™์ƒ๋“ค์˜ ์†Œ์…œ ๋„คํŠธ์›Œํฌ์—์„œ์˜ ํ–‰๋™์„ ๋ชจ๋‹ˆํ„ฐ๋งํ•˜์—ฌ ์˜จ๋ผ์ธ ๊ดด๋กญํž˜ ์‚ฌ๋ก€๋ฅผ ๊ด€์ฐฐํ•˜๋Š” ๊ฒƒ์€ ๊ฐ€์žฅ ์ •ํ™•ํ•œ ๋ฐ์ดํ„ฐ๋ฅผ ์ œ๊ณตํ•  ๊ฒƒ์ด๋ฉฐ, ์†Œ์…œ ๋„คํŠธ์›Œํฌ๋Š” \"๊ณต๊ฐœ\" ๊ณต๊ฐ„์œผ๋กœ ๊ฐ„์ฃผ๋˜๊ธฐ ๋•Œ๋ฌธ์— ์ค‘์š”ํ•œ ๊ฐœ์ธ์ •๋ณด ๋ณดํ˜ธ ๋ฌธ์ œ๋ฅผ ์ œ๊ธฐํ•˜์ง€๋Š” ์•Š์„ ๊ฒƒ์ž…๋‹ˆ๋‹ค.', '์„ค๋ฌธ์กฐ์‚ฌ - ์˜จ๋ผ์ธ ๊ดด๋กญํž˜ ํ–‰์œ„ ๋ฐ ์ด๋Ÿฌํ•œ ํ–‰์œ„์— ๋Œ€ํ•œ ๋ฐ˜์‘์— ๋Œ€ํ•œ ์ž๊ธฐ๋ณด๊ณ (self-report)๋Š” ๊ดด๋กญํž˜๊ณผ ๊ทธ ์˜ํ–ฅ์— ๋Œ€ํ•œ ๊ฐ€์žฅ ์ •ํ™•ํ•œ ์„ค๋ช…์„ ์ œ๊ณตํ•  ๊ฐ€๋Šฅ์„ฑ์ด ๋†’์Šต๋‹ˆ๋‹ค.']\n", + "\n" + ] + } + ], + "source": [ + "# paragraph๊ฐ€ ๊ฐ€์žฅ ๊ธด ๋ฐ์ดํ„ฐ ํ™•์ธ\n", + "max_paragraph_length = train['paragraph_length'].max()\n", + "max_paragraph_length_idx = train['paragraph_length'].idxmax()\n", + "print(train.loc[max_paragraph_length_idx, 'paragraph'])\n", + "print()\n", + "\n", + "# question ๊ฐ€์žฅ ๊ธด ๋ฐ์ดํ„ฐ ํ™•์ธ\n", + "max_question_length = train['question_length'].max()\n", + "max_question_length_idx = train['question_length'].idxmax()\n", + "print(train.loc[max_question_length_idx, 'question'])\n", + "print()\n", + "\n", + "# choices ๊ฐ€์žฅ ๊ธด ๋ฐ์ดํ„ฐ ํ™•์ธ\n", + "max_choices_length = train['choices_length'].max()\n", + "max_choices_length_idx = train['choices_length'].idxmax()\n", + "print(train.loc[max_choices_length_idx, 'choices'])\n", + "print()" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_3515290/1756428719.py:13: FutureWarning: \n", + "\n", + "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", + "\n", + " ax = sns.barplot(\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_3515290/1756428719.py:31: FutureWarning: \n", + "\n", + "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", + "\n", + " ax = sns.barplot(\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_3515290/1756428719.py:49: FutureWarning: \n", + "\n", + "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", + "\n", + " ax = sns.barplot(\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_3515290/1756428719.py:67: FutureWarning: \n", + "\n", + "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", + "\n", + " ax = sns.barplot(\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# ๊ณตํ†ต ํ•จ์ˆ˜ ์ •์˜: ๋ง‰๋Œ€ ์œ„ ๊ฐ’ ํ‘œ์‹œ\n", + "def add_values_to_bars(ax, values):\n", + " ymax = ax.get_ylim()[1] # y์ถ• ์ตœ๋Œ€๊ฐ’ ๊ฐ€์ ธ์˜ค๊ธฐ\n", + " for index, value in enumerate(values):\n", + " ax.text(index, value + ymax * 0.02, str(value), ha='center', fontsize=12) # y์ถ•์˜ 2% ์œ„์— ๊ฐ’ ํ‘œ์‹œ\n", + "\n", + "# ์ „์ฒด ๋ฐ์ดํ„ฐ ์ •๋‹ต ๋ถ„ํฌ\n", + "answer_counts = train['answer'].value_counts().sort_index()\n", + "plt.figure(figsize=(10, 6))\n", + "ax = sns.barplot(\n", + " x=answer_counts.index,\n", + " y=answer_counts.values,\n", + " palette=\"viridis\"\n", + ")\n", + "plt.title('Distribution of Answers', fontsize=20, fontweight='bold')\n", + "plt.xlabel('Answer Choice', fontsize=14)\n", + "plt.ylabel('Count', fontsize=14)\n", + "add_values_to_bars(ax, answer_counts.values) # ๊ฐ’ ์ถ”๊ฐ€\n", + "plt.xticks(fontsize=12)\n", + "plt.yticks(fontsize=12)\n", + "plt.grid(True, linestyle='--', linewidth=0.5, alpha=0.7)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# KMMLU ์ •๋‹ต ๋ถ„ํฌ\n", + "kmmlu_answer_counts = kmmlu['answer'].value_counts().sort_index()\n", + "plt.figure(figsize=(10, 6))\n", + "ax = sns.barplot(\n", + " x=kmmlu_answer_counts.index,\n", + " y=kmmlu_answer_counts.values,\n", + " palette=\"viridis\"\n", + ")\n", + "plt.title('Distribution of Answers (KMMLU)', fontsize=20, fontweight='bold')\n", + "plt.xlabel('Answer Choice', fontsize=14)\n", + "plt.ylabel('Count', fontsize=14)\n", + "add_values_to_bars(ax, kmmlu_answer_counts.values) # ๊ฐ’ ์ถ”๊ฐ€\n", + "plt.xticks(fontsize=12)\n", + "plt.yticks(fontsize=12)\n", + "plt.grid(True, linestyle='--', linewidth=0.5, alpha=0.7)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# MMMLU ์ •๋‹ต ๋ถ„ํฌ\n", + "mmmlu_answer_counts = mmmlu['answer'].value_counts().sort_index()\n", + "plt.figure(figsize=(10, 6))\n", + "ax = sns.barplot(\n", + " x=mmmlu_answer_counts.index,\n", + " y=mmmlu_answer_counts.values,\n", + " palette=\"viridis\"\n", + ")\n", + "plt.title('Distribution of Answers (MMMLU)', fontsize=20, fontweight='bold')\n", + "plt.xlabel('Answer Choice', fontsize=14)\n", + "plt.ylabel('Count', fontsize=14)\n", + "add_values_to_bars(ax, mmmlu_answer_counts.values) # ๊ฐ’ ์ถ”๊ฐ€\n", + "plt.xticks(fontsize=12)\n", + "plt.yticks(fontsize=12)\n", + "plt.grid(True, linestyle='--', linewidth=0.5, alpha=0.7)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# KLUE-MRC ์ •๋‹ต ๋ถ„ํฌ\n", + "klue_mrc_answer_counts = klue_mrc['answer'].value_counts().sort_index()\n", + "plt.figure(figsize=(10, 6))\n", + "ax = sns.barplot(\n", + " x=klue_mrc_answer_counts.index,\n", + " y=klue_mrc_answer_counts.values,\n", + " palette=\"viridis\"\n", + ")\n", + "plt.title('Distribution of Answers (KLUE-MRC)', fontsize=20, fontweight='bold')\n", + "plt.xlabel('Answer Choice', fontsize=14)\n", + "plt.ylabel('Count', fontsize=14)\n", + "add_values_to_bars(ax, klue_mrc_answer_counts.values) # ๊ฐ’ ์ถ”๊ฐ€\n", + "plt.xticks(fontsize=12)\n", + "plt.yticks(fontsize=12)\n", + "plt.grid(True, linestyle='--', linewidth=0.5, alpha=0.7)\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Klue MRC๋Š” gpt๋กœ question๊ณผ choices๋ฅผ ์ƒ์„ฑํ–ˆ๋‹ค๊ณ  ํ•ด์„œ ๋ถ„ํฌ๊ฐ€ ๋ถˆ๊ท ํ˜•ํ•˜๊ฒŒ ๋‚˜์˜ค๋Š”๋“ฏ" + ] + }, + { + "cell_type": "code", + "execution_count": 289, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_3515290/1619836897.py:9: FutureWarning: \n", + "\n", + "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", + "\n", + " sns.barplot(\n" + ] + }, + { + "data": { + "image/png": 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D9n/84x/Jv99www064ogjlJ2drZKSEt18883JYVl9fX2T05assummm2rUqFGKx+OKx+PJAdrmm2+uv//97+rZs6c+++wzPfjgg7rqqqt0+eWXpxy5PGvWrBa97vWt8eBv1elT1qTxP6+OHTsm/7l069ZNI0aM0P7779+m/fzmN7/Rbrvt1uRztUTXrl117733qrS0VPF4XMOHD9eVV16ZvP+ZZ55p04UfXRoPxfv27atnn302+Z7bbLPNNGnSJP3oRz9KrrnrrrvSPtf555+vI444QvF4XL1799Ypp5ySvG/1wfaa3HvvvcnT6mRnZ+u5555L/nMoLCzUlVdemfKDjwkTJth/m2L27NnN/nDp6quv1qabbqpdd91Vu+66a6vPVb6hnHDCCW26OGinTp18mwEAAGgDhugAAAAZoPFAvKXnTF593aoBa25uroYPH568fdXgfN68efrvf/8raeXpGFYdKf7111/rq6++Sq4/7bTTkhdqXHWxxjlz5iTv/+STT5rdz84775xyypDGHnnkEf3whz/UwIEDdcIJJ2jkyJG68cYb9e233ybXND5fdpQanwu8a9eua10/YsSI5KDw9ddf11577dWi08C0VHMX+Gypvffeu8kQc9WR8dLKc7tPnz59nZ/foa6uTm+++Wby4zPPPLPJubrj8bjOOeec5MfvvfdeyvnZG1s16F6lX79+yb+ne0xzGv+GwUEHHaStt966yZoLL7ww+fe6urqUxzhcddVVzZ5ffcaMGXrwwQfX6TnPOuuslH+/XX+23377Jp/ryiuvVFlZWauOcm/8Z9UFkwEAAKLGEB0AACADdO/ePfn3ZcuWaeHChWt9zOrD7MangFl1rnNJevLJJ5VIJPTEE08kb9t1112Tn3PevHmt2mvjizY2tummmzZ7+zXXXKOjjz5aH3300Rqfd/ULj0bhww8/VHV1dfLjVae7WZP+/fvr2Wef1RZbbCFJevnllzVkyBCddNJJKT8kWFfpurZEc/vv2bNnyseNL1oZhe+//z6l+TbbbNPsusGDByf/nkgkUk6B1Njq519f/cKjLdX4+dPtqU+fPikX2l11XnSHd999V/fdd1/y40MOOUQHH3xw8uNzzjlH8+fPb/XztuYHCa2xLqcaAgAAaC/iUW8AAAAA0pAhQ1I+njJlin75y1+u8TGNLziYlZWVMmTcd9991alTJy1dulTz58/XW2+9lTJE/8UvfpH2eTt16rTGCw2uGhavrrmLXn7yyScp547ebrvtdO6552ro0KHq0aOHrrjiCl133XVpP9eGNm7cuJSP99prrxY9bpdddtHHH3+sO+64Q1deeaW+//573Xffffr3v/+t//znP03++bZGay8m2lhzRzE3d1tbnq+tGl/8szXr1vcPXVq6r8Zce6qoqNCvfvWr5AVUc3JydM0110haeZ2D2tpaLV68WMcff7yeeeaZVj33VlttpYEDB6513RdffJE8nU2XLl3Wep2GwYMHa8GCBerVq5dqa2tbtae1icVievzxx1N+iwIAAGBDYogOAACQAXbZZRd16NAheYqW2267bY1D9I8++kgvvfRS8uOf/OQnKefLXnVKl1VHsj7wwAN64YUXJK08v/MRRxyRXLv6cOyf//ynDjjggLa/KEnjx49PDhY322wzvf766ylHBjuP3G2rzz//XH/961+TH/fv318//OEPW/z43NxcnX322TruuON0zDHH6Pnnn9fChQv185//XF988UXyVDfrMpxdV59//nmT21b/zYPG//yzsv7fL6quGqA2trYj69fltXXt2lX5+fnJC+B+8MEH2meffZqsa3zamaysrCZH1Lv17NlTH3/8cXJPzZkzZ07Kkd2NL2K6rhoaGvTLX/5SH374YfK2P/7xj8mj4f/4xz/qiiuukCRNnTpVI0aM0J///OcWP//FF1+siy++eK3rNtlkk+SpjU4++WTdeOONa33M7Nmz7QN0aeUPJxqfZgkAAGBD43QuAAAAGaCwsDBlaP7mm29q1KhRza6trKzUKaecknJU8G9/+9sm6xpfOHTs2LGqqamRJO2+++4ppwjp27dvyrm/H3roobT7XDXobKlvvvkm+fetttoqZYBeXl6uyZMnt+r51pdPPvlEBx54YMp52VcNKlurtLRUDz/8cPLjuXPnasaMGcmP13SUv9uqQX5jjX8jIR6Pa9ttt01+3Pjc/N9++23ySGhp5T/LVUPldNbltWVnZ2vo0KHJj0ePHp1yehdp5UC/8Q84fvzjH6ecRmV9aHxu9aeffrrZ1/6Xv/wl+fecnBwNGzaszZ/3jTfe0KRJk5If77PPPrr00kuTH48cOTLlPPndunVb58/12muvaZ999tHee++tww47rNkfnLRGSUmJtt12Ww0cODDlT5cuXZJrYrFYk/sbv+9yc3Ob3L/11lun/Q0YAACADYEhOgAAQIa47LLL1KlTp+THF198sX73u9/pyy+/lLRykPj8889r2LBhKRdi3HHHHXXCCSc0eb599tlHnTt3Tj52lcbDdWnlUKvx6V0eeugh/fGPf9SSJUskSdXV1ZoyZYr2339/nXHGGa16TY2H9e+8807ygpszZ87UoYceGun5uGtra/XGG2/onHPO0ZAhQ/TFF18k7zv44IN1zDHHtOh5Hn74Yd1xxx2aOXNmsvNTTz2VsqbxcLmgoCD596qqKi1ZskR1dXUpg3aXyspKHXvssfr6669VV1enRx99VJdddlny/v322y9lP42PvF+2bJluuumm5PnHjz/++CbD7dU1fq5vv/1WiURCy5cv18yZM9f4uFNPPTX591mzZmmfffZJXsx2/vz5Ouyww1Iu1trcD43cTjrpJMXjK39xt6GhQfvss49effVVSStPt3LppZfqtttuS64/6qijkv++tcXQoUOTz7vDDjtowoQJKe+feDyuRx99VNtvv70OO+ywtD9sa4kPPvhAzz33nKZOnarHH39clZWVbdp7ly5dNH36dH3yyScpf04++eTkmsLCwib3H3jggcn7+/fv3+T+jz76SLvvvnub9gYAANAWnM4FAAAgQ2y22WZ66KGHdPjhhyeHlXfeeafuvPNOFRQUqKampsmRoj169NBDDz3U7BHAOTk5Ouyww3TPPfckb1v9VC6r/OlPf9L48eOTp0y47rrrdN1116mkpERlZWXJo95PPPHEVr2m4cOH68orr5QklZWV6cc//rEKCgraPKxri8suu0xXXXWVli9f3uw5rPfff39NmDChxc/38MMPa+LEiZJW9s3Ly1NFRUXy/p49e6ZcmLJv377Jv9fX1ydPAdK1a1fNnj27tS9nraZOnaqePXsqNzc3+dsI0sofnvzpT39KWbvnnnuqW7du+u677yRJF110kS699NK1Ds9Xafza3n33XZWWlmrZsmX65S9/qXvvvTft437xi1/o7rvv1nPPPSdJeuWVV9SnTx8VFxerrKwsZe2wYcN0yimntGg/bdGrVy9deumlyaPAv/76a+22224qLCxUdXV1yr+LXbt21Q033GD73GeccYZ+8IMfaNddd232iPsuXbrotddeU3Z2dsopeJwan4t/1amIAAAANlYciQ4AAJBBDjzwQD3zzDMpw0hp5RHFqw/Qd955Z73yyivq379/2udb/ajzvfbaq9kLBPbo0UNPPfVUk1NDLF++POW0MZtsskmLX4u08oKpZ555ZsptjQfoUZyioaKiQsuWLWsyQC8tLdXtt9+uf//738rLy2vx8y1YsCD59/r6+pQBekFBgf7xj3+k/JDjxz/+ccoR32VlZSorK9O8efPafDqN1TU+TUbjAbok3XDDDSmnUZFWDkvHjBmTst/GA/TGp/1pzuGHH55yNPr333+vurq65FHl6cRiMU2cOLHJudBXH6DvtttueuqppzbYKXEuueQS/eEPf0g513tFRUXKP6cePXro+eef1+abb2793Icccsgaj2zPz89v00Vn1+aTTz7Rt99+q2+//bZV51wHAAAIEUN0AACADLPnnnvqww8/1N13362DDjpIffr0UUFBgYqLi9WvXz/98pe/1KOPPqpp06Y1Gbavbu+99045pcpxxx2Xdu3OO++sjz/+WJdffrm23357lZSUKB6Pq3v37jrggAN0zz336Lrrrkt5TFFRUfLvjQe2jd1666265557NGzYMHXo0EH5+fnaZZddNH78eP3+979Prms8mCwsLEz+vfEFU9PdvjarH82bn5+v7t27a+jQoTrzzDP15JNP6ptvvtHpp5+e9uKYjYfD8Xg8OWi/8cYb9Zvf/EbbbLONiouLlZeXpz59+uiYY47R22+/3WQwHIvF9Mwzz+ioo45Shw4d1KFDB22zzTa68MILlZWVpfz8/OSQOCsrK6VxSzo0/udw9tln64477tB2222nvLw8lZaW6tBDD9VLL72k888/v9nXecQRR+j555/XAQccoM6dOysvL0877rij7rnnHv3ud79Labi6Pn366Nlnn9WwYcOUn5+vrl27aujQocnTeaxp3yUlJZoyZYrGjx+vAw88UJtuuqlycnJUWlqq/fbbTw888IBeeumllFMeSSv/uawaJjfXq1OnTsl/pq15z0gr/1mNGjVKb7zxhn71q1/pBz/4gfLy8tSpUyf95Cc/0TXXXKNPPvlEgwcPbvLYvLy85L5isVirP3cUGv+Aq2PHjtpss8202WabrddhPQAAQHsQSzT3O6wAAAAAgHZh9uzZKT9Q+/jjjzV//nzttddeyds++OCDlNMKSSsvOHzaaaet8+eNxWJ66aWXUi7C2pwLLrggeRHWoqKiJr9hcMwxx2j8+PGSVp6Xf31cHwAAAKAtOBIdAAAAANqxvLy85EVQs7OzU474X5PmfsuhNRKJhJYtW9am5wAAAGgPuLAoAAAAALRj3bt3V21tbcpt33zzTcrHzZ1+Z//999d+++2nr776KuXaBy0Vi8WavcbC6hpfY6C56w00Pl1Ma65HAAAAsKFwOhcAAAAACFB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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# '์„ธ๋ถ„๋ฅ˜(์‚ฌ๋žŒ)' ๊ฐ’๊ณผ 'dataset' ์ด๋ฆ„ ๊ฒฐํ•ฉ\n", + "train['combined_label'] = train['์„ธ๋ถ„๋ฅ˜(์‚ฌ๋žŒ)'] + '\\n' + train['dataset'] # ์ˆœ์„œ๋ฅผ ๋ฐ”๊พธ๊ณ  \\n ์ถ”๊ฐ€\n", + "\n", + "# ๊ฒฐํ•ฉ๋œ ๋ผ๋ฒจ์˜ ๊ฐ’ ๋ถ„ํฌ ๊ณ„์‚ฐ\n", + "value_counts_combined = train['combined_label'].value_counts()\n", + "\n", + "# ๋ง‰๋Œ€ ๊ทธ๋ž˜ํ”„ ์‹œ๊ฐํ™”\n", + "plt.figure(figsize=(15, 8))\n", + "sns.barplot(\n", + " x=value_counts_combined.index,\n", + " y=value_counts_combined.values,\n", + " palette=\"pastel\"\n", + ")\n", + "\n", + "# ๊ทธ๋ž˜ํ”„ ์ œ๋ชฉ ๋ฐ ์ถ• ๋ผ๋ฒจ ์„ค์ •\n", + "plt.title('Overall Distribution of ์„ธ๋ถ„๋ฅ˜', fontsize=18, fontweight='bold')\n", + "plt.xlabel('์„ธ๋ถ„๋ฅ˜', fontsize=14) # x์ถ• ๋ผ๋ฒจ ์ˆ˜์ •\n", + "plt.ylabel('Count', fontsize=14)\n", + "\n", + "add_values_to_bars(plt.gca(), value_counts_combined.values)\n", + "\n", + "# ๋ˆˆ๊ธˆ ๋ฐ ๋ ˆ์ด์•„์›ƒ ์กฐ์ •\n", + "plt.xticks(rotation=0, fontsize=10) # ์„ธ๋กœ ๋ฐฐ์น˜๋กœ ๋ผ๋ฒจ ์ˆ˜์ •\n", + "plt.yticks(fontsize=12)\n", + "plt.grid(True, linestyle='--', linewidth=0.5, alpha=0.7)\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "ํ•„์š” ์ง€์‹\n", + "๋‚ด๋ถ€์ง€์‹ 1419\n", + "์™ธ๋ถ€์ง€์‹ 612\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train['ํ•„์š” ์ง€์‹'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded Font: NanumGothic\n" + ] + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import matplotlib.font_manager as fm\n", + "\n", + "# ๋‚˜๋ˆ”๊ณ ๋”• ํฐํŠธ ๊ฒฝ๋กœ ์„ค์ •\n", + "font_path = '/opt/conda/lib/python3.10/site-packages/matplotlib/mpl-data/fonts/ttf/NanumGothic.ttf'\n", + "font_prop = fm.FontProperties(fname=font_path)\n", + "plt.rcParams['font.family'] = font_prop.get_name()\n", + "\n", + "# ํ…Œ์ŠคํŠธ์šฉ ์ฝ”๋“œ\n", + "print(f\"Loaded Font: {font_prop.get_name()}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 284, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_3515290/582825777.py:12: FutureWarning: \n", + "\n", + "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", + "\n", + " sns.barplot(\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pastel_colors = sns.color_palette('pastel')\n", + "selected_colors = [pastel_colors[1], pastel_colors[0]]\n", + "\n", + "# ํ•„์š” ์ง€์‹ ๋ถ„ํฌ ๊ณ„์‚ฐ\n", + "knowledge_counts = train['ํ•„์š” ์ง€์‹'].value_counts()\n", + "\n", + "# ๋ง‰๋Œ€ ์ˆœ์„œ๋ฅผ ๋ฐ”๊ฟ”์„œ ์ „๋‹ฌ\n", + "reordered_index = knowledge_counts.index[::-1] # ํ˜„์žฌ ์ˆœ์„œ๋ฅผ ๋’ค์ง‘์Œ\n", + "\n", + "# ํ•„์š” ์ง€์‹ ๋ถ„ํฌ ์‹œ๊ฐํ™” (๋ง‰๋Œ€ ๊ทธ๋ž˜ํ”„)\n", + "plt.figure(figsize=(10, 6))\n", + "sns.barplot(\n", + " x=reordered_index, # ์žฌ๋ฐฐ์—ด๋œ ์ˆœ์„œ ์ ์šฉ\n", + " y=knowledge_counts.loc[reordered_index].values,\n", + " palette=selected_colors\n", + ")\n", + "\n", + "# ๊ทธ๋ž˜ํ”„ ์ œ๋ชฉ ๋ฐ ์ถ• ๋ผ๋ฒจ ์„ค์ •\n", + "plt.title('Overall Distribution of ํ•„์š” ์ง€์‹', fontsize=18, fontweight='bold')\n", + "plt.xlabel('ํ•„์š” ์ง€์‹ Types', fontsize=14)\n", + "plt.ylabel('Count', fontsize=14)\n", + "\n", + "add_values_to_bars(plt.gca(), knowledge_counts.loc[reordered_index].values)\n", + "\n", + "# ๋ˆˆ๊ธˆ ๋ฐ ๋ ˆ์ด์•„์›ƒ ์กฐ์ •\n", + "plt.xticks(fontsize=12)\n", + "plt.yticks(fontsize=12)\n", + "plt.grid(True, linestyle='--', linewidth=0.5, alpha=0.7)\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(612, 21)\n", + "(1419, 21)\n", + "(2031, 21)\n" + ] + } + ], + "source": [ + "train_external_knowledge = train[train['ํ•„์š” ์ง€์‹'] == '์™ธ๋ถ€์ง€์‹']\n", + "train_internal_knowledge = train[train['ํ•„์š” ์ง€์‹'] == '๋‚ด๋ถ€์ง€์‹']\n", + "\n", + "print(train_external_knowledge.shape)\n", + "print(train_internal_knowledge.shape)\n", + "print(train.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 267, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_3515290/3051711630.py:15: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " subset['combined_label'] = subset['์„ธ๋ถ„๋ฅ˜(์‚ฌ๋žŒ)'] + '\\n' + subset['dataset'] # 2์ค„๋กœ ํ‘œ์‹œ\n", + "/tmp/ipykernel_3515290/3051711630.py:19: FutureWarning: \n", + "\n", + "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", + "\n", + " sns.barplot(\n", + "/tmp/ipykernel_3515290/3051711630.py:19: UserWarning: \n", + "The palette list has fewer values (1) than needed (10) and will cycle, which may produce an uninterpretable plot.\n", + " sns.barplot(\n", + "/tmp/ipykernel_3515290/3051711630.py:15: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " subset['combined_label'] = subset['์„ธ๋ถ„๋ฅ˜(์‚ฌ๋žŒ)'] + '\\n' + subset['dataset'] # 2์ค„๋กœ ํ‘œ์‹œ\n", + "/tmp/ipykernel_3515290/3051711630.py:19: FutureWarning: \n", + "\n", + "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", + "\n", + " sns.barplot(\n", + "/tmp/ipykernel_3515290/3051711630.py:19: UserWarning: \n", + "The palette list has fewer values (1) than needed (11) and will cycle, which may produce an uninterpretable plot.\n", + " sns.barplot(\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# 'ํ•„์š” ์ง€์‹'๋ณ„๋กœ 'dataset - ์„ธ๋ถ„๋ฅ˜(์‚ฌ๋žŒ)' ๊ฐ’ ๋ถ„ํฌ ๊ณ„์‚ฐ\n", + "knowledge_types = train['ํ•„์š” ์ง€์‹'].unique()\n", + "fig, axes = plt.subplots(nrows=1, ncols=len(knowledge_types), figsize=(20, 6), sharey=True) # figsize ํ™•๋Œ€\n", + "\n", + "# ํŒŒ์Šคํ…” ์ƒ‰์ƒ์—์„œ ์„ ํƒํ•œ ์ƒ‰\n", + "pastel_colors = sns.color_palette('pastel')\n", + "selected_colors = [pastel_colors[1], pastel_colors[0]]\n", + "\n", + "for i, knowledge_type in enumerate(knowledge_types):\n", + " # ํ•„ํ„ฐ๋ง๋œ ๋ฐ์ดํ„ฐ ๊ฐ€์ ธ์˜ค๊ธฐ\n", + " subset = train[train['ํ•„์š” ์ง€์‹'] == knowledge_type]\n", + " subset['combined_label'] = subset['์„ธ๋ถ„๋ฅ˜(์‚ฌ๋žŒ)'] + '\\n' + subset['dataset'] # 2์ค„๋กœ ํ‘œ์‹œ\n", + " value_counts = subset['combined_label'].value_counts()\n", + "\n", + " # ๋ง‰๋Œ€ ๊ทธ๋ž˜ํ”„ ์‹œ๊ฐํ™”\n", + " sns.barplot(\n", + " x=value_counts.index,\n", + " y=value_counts.values,\n", + " ax=axes[i],\n", + " palette=[selected_colors[i]] if i < len(selected_colors) else selected_colors,\n", + " width=0.6 # ๋ง‰๋Œ€ ๋„ˆ๋น„ ์ถ•์†Œ\n", + " )\n", + "\n", + " # ๊ทธ๋ž˜ํ”„ ์„ค์ •\n", + " axes[i].set_title(f'{knowledge_type}', fontsize=16, fontweight='bold')\n", + " axes[i].set_ylabel('Count', fontsize=14)\n", + " axes[i].set_xlabel('') # x์ถ• ๋ผ๋ฒจ ์ œ๊ฑฐ\n", + " axes[i].tick_params(axis='x', labelsize=10, rotation=0) # ๋ผ๋ฒจ ํšŒ์ „ ์ถ”๊ฐ€๋กœ ๊ฒน์นจ ๋ฐฉ์ง€\n", + " axes[i].set_xticks(range(0, len(value_counts.index), max(1, len(value_counts.index) // 10))) # ๊ฐ„๊ฒฉ ์กฐ์ •\n", + "\n", + "# ์ „์ฒด ๋ ˆ์ด์•„์›ƒ ์ •๋ฆฌ ๋ฐ ํ‘œ์‹œ\n", + "plt.tight_layout()\n", + "plt.suptitle('์„ธ๋ถ„๋ฅ˜ Distribution by ํ•„์š” ์ง€์‹', fontsize=18, fontweight='bold', y=1.02)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 272, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_3515290/647229126.py:5: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " knowledge_df_combined.groupby('Dataset - ์„ธ๋ถ„๋ฅ˜')\n", + "/tmp/ipykernel_3515290/647229126.py:6: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", + " .apply(lambda df: pd.Series({\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "selected_colors = [pastel_colors[1], pastel_colors[0]] # ์ƒ‰ ์ˆœ์„œ๋ฅผ ๋ฐ”๊ฟˆ\n", + "\n", + "# ์™ธ๋ถ€์ง€์‹ ๋ฐ ๋‚ด๋ถ€์ง€์‹ ํ•ฉ๊ณ„๋ฅผ ๊ณ„์‚ฐํ•˜์—ฌ ๋ฐ์ดํ„ฐํ”„๋ ˆ์ž„ ์ƒ์„ฑ\n", + "sorted_data_df = (\n", + " knowledge_df_combined.groupby('Dataset - ์„ธ๋ถ„๋ฅ˜')\n", + " .apply(lambda df: pd.Series({\n", + " '์™ธ๋ถ€์ง€์‹ ํ•ฉ๊ณ„': df.loc[df['ํ•„์š” ์ง€์‹'] == '์™ธ๋ถ€์ง€์‹', 'Count'].sum(),\n", + " '๋‚ด๋ถ€์ง€์‹ ํ•ฉ๊ณ„': df.loc[df['ํ•„์š” ์ง€์‹'] == '๋‚ด๋ถ€์ง€์‹', 'Count'].sum()\n", + " }))\n", + " .sort_values(by=['์™ธ๋ถ€์ง€์‹ ํ•ฉ๊ณ„', '๋‚ด๋ถ€์ง€์‹ ํ•ฉ๊ณ„'], ascending=[False, False]) # ์ •๋ ฌ ๊ธฐ์ค€ ์„ค์ •\n", + ")\n", + "\n", + "# ์ •๋ ฌ๋œ ๋ฐ์ดํ„ฐ์˜ ์ธ๋ฑ์Šค ๊ฐ€์ ธ์˜ค๊ธฐ\n", + "sorted_data = sorted_data_df.index\n", + "\n", + "# ๋ฐ์ดํ„ฐ ์ •๋ ฌ\n", + "knowledge_df_combined['Dataset - ์„ธ๋ถ„๋ฅ˜'] = pd.Categorical(\n", + " knowledge_df_combined['Dataset - ์„ธ๋ถ„๋ฅ˜'], \n", + " categories=sorted_data, \n", + " ordered=True\n", + ")\n", + "\n", + "# ๋ง‰๋Œ€ ๊ทธ๋ž˜ํ”„ ์‹œ๊ฐํ™”\n", + "plt.figure(figsize=(15, 8))\n", + "ax = sns.barplot(\n", + " data=knowledge_df_combined,\n", + " x='Dataset - ์„ธ๋ถ„๋ฅ˜',\n", + " y='Count',\n", + " hue='ํ•„์š” ์ง€์‹',\n", + " palette=selected_colors,\n", + " order=sorted_data\n", + ")\n", + "\n", + "# ๊ทธ๋ž˜ํ”„ ์ œ๋ชฉ ๋ฐ ์ถ• ๋ผ๋ฒจ ์„ค์ •\n", + "plt.title('ํ•„์š” ์ง€์‹ Distribution by ์„ธ๋ถ„๋ฅ˜', fontsize=18, fontweight='bold')\n", + "plt.xlabel('')\n", + "plt.ylabel('Count', fontsize=14)\n", + "\n", + "# # ๊ฐ’ ํ‘œ์‹œ\n", + "# ymax = ax.get_ylim()[1] # y์ถ• ์ตœ๋Œ€๊ฐ’\n", + "# for p in ax.patches:\n", + "# value = int(p.get_height()) # ์ •์ˆ˜๋กœ ๋ณ€ํ™˜\n", + "# if value > 0: # ๊ฐ’์ด 0 ์ด์ƒ์ธ ๊ฒฝ์šฐ๋งŒ ํ‘œ์‹œ\n", + "# ax.text(\n", + "# x=p.get_x() + p.get_width() / 2, # ๋ง‰๋Œ€ ์ค‘์‹ฌ\n", + "# y=p.get_height() + ymax * 0.02, # ymax * 0.02๋งŒํผ ์œ„๋กœ\n", + "# s=f'{value}', # ์ •์ˆ˜ ๊ฐ’\n", + "# ha='center', va='bottom', fontsize=12, color='black'\n", + "# )\n", + "\n", + "# ๋ˆˆ๊ธˆ ๋ฐ ๋ ˆ์ด์•„์›ƒ ์กฐ์ •\n", + "plt.xticks(\n", + " ticks=ax.get_xticks(),\n", + " labels=[\n", + " '\\n'.join(label.get_text().split(' - ')[::-1])\n", + " for label in ax.get_xticklabels()\n", + " ],\n", + " fontsize=10\n", + ")\n", + "plt.yticks(fontsize=12)\n", + "plt.legend(title='ํ•„์š” ์ง€์‹', fontsize=12)\n", + "plt.grid(True, linestyle='--', linewidth=0.5, alpha=0.7)\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [], + "source": [ + "low_qual_paragraph = train[train['๋ฐ์ดํ„ฐ์ €ํ’ˆ์งˆ-paragraph'] != \"์ •์ƒ\"]\n", + "low_qual_question = train[train['๋ฐ์ดํ„ฐ์ €ํ’ˆ์งˆ-question'] != \"์ •์ƒ\"]\n", + "low_qual_choices = train[train['๋ฐ์ดํ„ฐ์ €ํ’ˆ์งˆ-choices'] != \"์ •์ƒ\"]\n", + "low_qual_answer = train[train['๋ฐ์ดํ„ฐ์ €ํ’ˆ์งˆ-answer'] != \"์ •์ƒ\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 278, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_3515290/643284339.py:23: FutureWarning: \n", + "\n", + "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", + "\n", + " sns.barplot(\n" + ] + }, + { + "data": { + "image/png": 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66/THH39o/vz5mjBhgsaOHasvvvjCKEycTqeWL19+zM8cbLVq1TK+TEoqV5fk7777zvh/+/btdfDgQTmdTu3bt09vvfWWLrroIknSo48+qvT0dL8iLj4+Xunp6UpPT9dNN91U6vLj4uK0cuVK/fnnn8fVRfqSSy7R/v375XQ6tXDhQiUmJkqSCgsL9eijj1Z4eaVZsWKF0tPTddlllxnTBgwYoPT0dO3fv1+dOnUq8/3/+c9//J7Z3atXL+3cuVOHDx/WP//849fl9ocfftDbb7991GUV7b9Op1M7d+706467YMGCcn+mkve4Nm7cWKtWrZLT6VR2drbuv/9+Y15mZqZxO8hpp52m/fv367fffvNb3qJFi5Senh5wYqEqeL1e3XzzzcYo0dHR0XrzzTfldDqVm5uruXPn+p0Iuummm4wTF0V5KgUW3QsXLgxYV8lpH3/8sfH/IUOGlOvE0D///GP8v02bNsdsX7LdP//8U2WPnAv28alDhw7at29fwFMmigY3K9ofLr/8cmNe8W28dOlS5eXlSTpy4q5Xr17HFcfRlBzgr+QxpnXr1nrxxRf1559/6oUXXtANN9yge+65R6+88orRZvHixcbvY/bs2UpPT/e7Papt27ZKT0/XgQMHjOOhdCTPP//8c/3000+aMmWKrrvuOk2fPl3XX3+90abkPgkgPPGcbgDVQvH7Qkte3TuaWrVq+b0uehb1eeedp9q1a+vQoUNyOp366quvjKKleNFdvDgvPiLw008/bTw/NjExUc8995wWLlyoP//8U4WFhVq2bJnfF8QiDRo00NSpU40r3EX/Nm7cWG+99ZYkafv27Vq7dq127typQ4cO+V2FK3m/qVmKD9pUnucjF/991a5d2/i91K9fv0pGlr/hhhuML9rl3TeKpKSkaNasWcYjqoYMGaJHHnnEOJGyZMkS5eXl+V2VMkPxIjo1NVVffvmlsW80bNhQCxcuVJcuXYzHGr3xxhtHfb75xIkTjf23WbNmuvbaa43nBZcshMsya9YsFRYWSjpypW7ZsmXGPc1xcXF65JFHlJGRYRQf8+bN0yuvvOJ30iZUvv76a+OkmSS9+OKL+te//mW8LurRUpTzf//9tz7//HNddNFFGjx4sKKiouTxePT3339rw4YN6ty5syT/44XD4ZDL5dL69eu1Z88eNW7cWB6PR59//rnRprzPnS7+hIaSx7GjKb7v+3w+5efnV8l+Gy7Hp9GjRxv76ZIlS5Sfn6+YmBi/E0WXXnpplT81oeRjxEoe86699lpJR8atWLVqlbZs2aLdu3f73cudnZ2tjIyMgHEMjqXosYFZWVn67rvv9Ntvv2n//v1++3K4/F0AUDaudAOoFop/8SzvY1uO9mzb6OhoDRkyxJhe9MX577//1o8//ijpSJfcoitcu3fv1l9//WW0v/HGG41Bb4oGp/rzzz+N+du2bSs1nm7duvl1YS7uww8/1Mknn6w2bdroyiuv1AMPPKBnnnnG74pXuIxIXHzwtJJdQktz3XXXGUXtmjVr1Ldv33J1Sy+v0gY0K69+/foFPBO6+JUmt9utzZs3H/fyq4LH49EPP/xgvB43bpxfsSMdOYEzYcIE4/VPP/1knGQqqeSVwKLR/yUd9T2lKd6D4YILLih1ELFJkyYZ//d4PH7vCaXi601JSfEbrK/IJZdcYgzEJx0p1KUj3Z/POeccY3rRlcVff/3VeApBq1atNHr0aElHCt6iq9vffvut0e06MTHR79F7ZSl+vCtegJeleDu73V6lJ4rC4fh00kknGY/xysnJ0bJly+TxePy6mld113JJAYNtljzmOZ1OTZw4UXXr1lXv3r1100036bHHHgvo9n0822fHjh265JJLVLduXV144YWaOHGipk6d6ne7Trj8XQBQNopuANVC8RGoDx06VK5HQ5Usfot3SS+6siUd+RJd/IuyJPXs2dNYZ8nnUR9L0T2zJTVo0KDU6Y8//rguvfRSbd26tczlFnWNNdOWLVv8Rswt6n5flpNOOklffvmlWrZsKUlatWqVTj/9dF199dV+X9qP19G2a3mUFn/Tpk39Xpfs9hpqBw8e9NvmxUdTLq5Dhw7G/30+n98tGcWVvH+8+JW8iuxjxZd/tJhatGhhdNeXZNzXHWrFY23Tpk2pXbxLjpxePNbiV6iLjhPFuzhffPHFuvjii43XRfOKH1MuvPDCo550K6n4+AXl7X2wfft24/8nnnhiud5THuF0fCrZxXzVqlVGd+8TTzxRXbp0qfJ1Fp2ILVL8mOF0OtWzZ089++yzxzwZXNHts3XrVnXu3Fnz5883epRUxXIBmIOiG0C1UHyQIEl+A+ccTfFH2URERPgVJQMGDDCed7t3716tXbvW7wvyyJEjj7rcpKQkY3Cv0n6KisuSSvuiv23bNr97Xzt27Kh33nlH//vf/5Sbm6u77777mJ8zlN5//32/1+UZFEqSunfvrl9//VXPP/+8UlJS5PV69c477+i0004L+FJbURUdPK240u57rcy9sFV1H21x5e0uW7JdsL+MH083XrMKhOPZhsVjHTp0qDE43IYNG7R7926/ruVDhw5V//79jV4TK1asUHZ2tt/9tsXvDT+W4sXjtm3bjnniLysry2/wtG7dupV7XdLR99twOz6NHDnS+D188sknfo/XKj5eQlUqfsyLiorye3TbY489ZvTasdlsuvbaa7Vs2TLt3btX+fn5x3xcYVn+9a9/GSdwa9WqpQcffFBr165VRkaGtmzZctzLBWAOim4A1UL37t39ulwea5TlrVu3Gt1DpSPPsi1+z2PJLubvvvuuVqxYIenI/anFr2yVHLTtvffeMwb3Ku2neDffY/nggw+ML/cNGzbUmjVrdNVVV+nEE09UbGysaVcGS/O///1Pzz//vPH6pJNO0sknn1zu90dHR2v8+PH69ddfje66Bw4c0NChQ1VQUGC0q+p7MstS1D24uJIFTvHff/GRwEu7+nSsK/fH89lSUlL8rkb//PPPpbYr3g0+IiIi4Ip9VSu+/KPF9Oeff/p1Wa+qZyhXVPFYt23bZowmXpzP59Mvv/xivC4ea7169fy65b/++uvGyPYNGjRQ9+7d5XA4dP7550s6cn/vs88+q99//13SkQEBi+aVR79+/fzieumll8ps//bbb/v1hhg6dKjf/OPdb8Pt+NSwYUPj2LFv3z6/sQ6C0bX8u+++8yu6+/TpY5yslfwL8vHjx+vtt9/WueeeqwYNGsjr9QaMbF9cWceCP//8U2vWrDFev/vuu3rooYfUtWtXJScnBzz7G0D4o+gGUC3ExcX5dS384YcfNHXq1FLb5uXl6dprr/W7elPaoFLFB0p77bXXjMLv7LPP9uuyXPS4pyJz5sw5apzFR0Ivj+Jfntq2betXXDmdTi1durRCywuWbdu2adCgQX73D06ZMuW4llWvXj3NnTvXeL1r1y6/Yqf446aCbfny5QG3KhTv8RAVFeXX5bj4iZ9//vnH79Fce/bs0a+//lrm+o7ns0VGRvo9numll17yK7CkI4VU8RMinTp18uvWHQzFi9DFixeX+tmnTZtm/N9ut6tHjx5BjeloiseakZFR6mOWFixYoB07dhiv+/Tp4ze/+JXqp556yihGBw8ebBS1xYvdJ5980vj/oEGDKnSP9ciRI/2Ku+eee+6o98P/8ssvevjhh43XJ554ot9o9pL/flt8fArpSPfprKysUpcd6uNTefKj+N+Bojzo0KFDwNMqKmvNmjUaOnSo39+Rkse84tunY8eOfvMWLVpUZrfwsj5ryaK65LKLX+EHUD1QdAOoNh588EG/L6J33323xo4da4zkWlhYqOXLl6tHjx5+A0917dpVV155ZcDy+vfvb4ykXPzLUfFiXDpyRaJ4d/M5c+bonnvuMQZIys/P1xdffKHzzz9ft9xyS4U+U/Hifv369UZXxR07dmjw4MGm3k/sdrv1/fffa8KECTr99NONq3bSkftTy9udc+7cuXr11Ve1Y8cOYzt/+umnfm2KfwEtXpy4XC5lZmbK4/H4FeZVJS8vT6NGjdLu3bvl8Xj00Ucf6cEHHzTmn3feeX7xFL+yf+jQIT377LPG/dNXXHFFQDFcUvFl/fPPP/L5fMrOzvYr9kpT/BFBaWlp6t+/vzF43969e3XxxRf7DU53tJHLq9LVV19tjMDv9XrVv39/47Fmubm5mjx5sl+PlBEjRpgycrl0ZIyG4gO9jRs3Tm+99ZY8Ho+8Xq8+/PBDXXXVVcb8Zs2aBVyZHjp0qHF18mhXlS+44ALjdofibco7anmRuLg4vwKvoKBAAwYM0AsvvGAcd5xOp95++2317t3b737iF154IeDZ7MX3261bt+qDDz6QdOR+8aLRt0sT6uNT8fwoKjy3bdvmdzJz2LBhASOKV1XX8ry8PH399de65pprdPbZZ/udkLvlllvUvXt3v/bFt89///tfHT58WG63Wx9//LFuvvnmMtdV/LOmp6fL7XbL5XJp27ZtAeNUzJgxQ16vVzk5OXrqqaf05ptvVuZjAjCDDwCqkcWLF/tiYmJ8kvx+YmNjfZGRkQHTmzRp4tu+fftRl3fNNdf4tY+MjPTt378/oN3ff//tS0lJCVh+YmKiLyIiwng9ZswYv/f17t3bmHfzzTcHLHf9+vWlfpaS0yT5HnzwQeN9M2fONKbHx8cfc/qxNG/e3HhfXFycr3bt2j6bzVZqHOeff77P5XIFLGPFihV+7Q4cOODz+Xy+4cOH+23fuLg4v3ZNmzb1eTweYzk//vij3/yEhARfQkKCr3nz5j6fz+dLS0vzm79u3bqAWMraDsV/J8V/oqOj/V7bbDbfd9995/fe/Px8X/369f3albY/lrYv+Hw+37Rp0/zapKSk+KKiooy2R9tfvF6vr1+/fgHrSEhICJjWo0cPv+15rO314Ycf+s2viClTpgSsPy4uLiAXU1JSfLt37/Z774EDB/zarFixokLrHjNmjPFeu93uS0lJCfipX7++b+PGjT6fz+dbuXKlX64W/c5L/v4iIiJ8n332Wanr7NGjh1/bWrVq+fLz8/3a9O/f36+Nw+HwHT58uEKfzec78ju/9NJLS923EhISSs3Phx9+uNRl7d27N2D/djgcxzzOhPr4dOqpp/odK+rWrVvqvjFixAi/de/YsaPC27fk77FWrVqlfi5Jvmuvvdbn9XoDljFu3Di/dpGRkQHbuegnLS3N773z58/3m1+7dm2fw+Hw9e7dO2BbFB1nSu6/kozjIoDwxpVuANXKoEGDtGTJEqWmpvpNz8vLC+jK161bN33zzTc66aSTjrq8kle1+/btG3APt3RkxNpPP/3Ub1Rh6cgjlop3P6zoc1hPP/10jRs3zm9aXl6e8f+jDcoWTLm5uTp06FDAoFf16tXTyy+/rM8++yzgkVVl2bdvn/H/wsJCv+fcxsbGasaMGX5Xujt16uR3ZS4nJ0c5OTn6+++/y+yueTyKd7stfl+5dOQZucW7dUtH7kt/5ZVX/OItfkXzWI9QGzZsmN8VroMHD8rj8fg9cq40NptN8+fPV//+/f2ml3ycVK9evfTpp5+GrIv+/fffrzvvvNPv/tTc3Fy/31OTJk20fPlyNW7cOGhxuN1uHTx4MOBn//79xpXh3r1764MPPvB7RFxBQYHf78/hcGj27NkaOHBgqespecV60KBBASOSl7yf+rzzzqvw8+OlI7/z999/X3feeWfAYIE5OTkB+fn8889r8uTJpS6rQYMGAbfjFL96XKdOnVLfF+rjU/EeSYWFhcaV9JL5Uby7dbdu3QL+HlTU4cOHSx19vHnz5po7d67efvvtUu/Bnjx5st+6CwsLjeNIUlJSmT07BgwY4Pf35NChQ3K5XMZnfe211/yu6Ofn5xt/a8z4uwCgcii6AVQ7ffr00ZYtW/T222/rggsuUIsWLRQbG6uEhAS1atVKl19+uT766COtXr36mF/G+vXr59eVr+hZu6Xp1q2bfv31Vz300EPq3LmzEhMTFRUVpUaNGmngwIGaOXOm332ckvy+4Bcv8IqbPn26Zs6cqR49eqhWrVpyOBzq3r27PvjgA78uisULmbi4OOP/xb/QH236sZS8/9fhcKhRo0Y666yzNG7cOH3yySfas2ePbrrppqMOAFS8mIyKijIK82eeeUY33HCDTjnlFCUkJCgmJkYtWrTQZZddpnXr1gUUkjabTUuWLNGIESNUq1Yt1apVS6eccoomTZqkiIgIORwOo6iMiIgIeM72sbZD8d/D+PHj9eqrr6pjx46KiYlRvXr1NHjwYH399deaOHFiqZ9z+PDhWr58uQYOHKjk5GTFxMSoa9eumjlzpsaOHeu3DUtq0aKFvvzyS/Xo0UMOh0MpKSk666yzjOdGlxV3YmKivvjiC33wwQcaNGiQGjRoILvdrnr16um8887Tu+++q6+//trvFgzpyO+lqGgrbXslJSUZv9OKFoc2m01Tp07V999/r6uuukrNmzdXTEyMkpKSdMYZZ+jxxx/Xtm3b/J4cUCQmJsaIy2azVXjd5XlGfMnlXnLJJdq2bZsmTpyodu3aKT4+XnFxcWrbtq3Gjx+vbdu2+d0zXNIll1xidKkvel3SxRdf7FckV6brc0REhKZOnapffvlF9957r7p06aJ69eopIiIiIA+XL19e5ujwEyZM0IIFC9S7d28lJiYqNjZWZ599tj7++GMNGjTIaFdyvw3l8WnixIl68skn1bx5c0VHR+uEE07QiBEjdOaZZ/q1+/DDD43/H+/2LXk8jo2NVdOmTdWnTx/dcccdWr58udLS0vweL1lS3bp1tXbtWt1222066aSTFB0drbp16+ryyy/XunXr/AbwKz7+g3Tk869YsULnnXee4uLilJSUpM6dOxu3KJ111llau3atRo0apcaNG8tut6tFixa666679OWXXxq//6o+EQkgOGy+so7QAAAACDtZWVnq2LGj31Xg++67T48++qiJUQXPgQMHVK9ePS1btsw4URcVFaVdu3apYcOGJkcHAGWj6AYAAKiG1qxZo7PPPtvvKuoHH3wQcNtMdefz+RQTE6PatWsrOzvb6MI9cuRI/fe//zU5OgA4NopuAACAaurxxx/XfffdZ7yOi4vTt99+q06dOpkYVdXKyMgIuJ2gTp062rBhg5o3b25SVABQftzTDQAAUE3dfffdGjBggPE6NzdXN910k4kRVT273a6zzz5bDodDtWvX1kUXXaTVq1dTcAOoNrjSDQAAAABAkHClGwAAAACAIKHoBgAAAAAgSCi6AQAAAAAIkiizA6huMjMz/R7NgerFbrfL7XabHQZgaeQhYD7yEAgP5GL1FhUVpeTk5GO3C0EsNYrH4yExwsxLL72kqVOnavHixerQoYMx/fDhw5o9e7YWLlyonTt3KjExUf3799cdd9yhunXr+i0jKytL06dP16effqqDBw+qYcOGGjp0qG6++WbFxsaG+iMBNVpMTAzHUcBk5CEQHshFa6DoRrVVWFio++67Txs2bJDX6w04YG3ZskWbN2/WPffcozZt2ig9PV0PPvigLrvsMi1dulSRkZGSpPz8fF1yySVKTEzUyy+/rGbNmmnDhg26//77tXXrVs2YMcOMjwcAAACgBqDoRrX1yiuvaMeOHfroo4/Upk2bgPndunVTt27djNdNmjTRq6++qq5du2rDhg3q2rWrJOnrr7/Wjh07tHnzZiUkJEiSBg4cqNjYWF1++eXKyspSUlJSSD4TYAU5OTlmhwBYHnkIhAdy0RoYSA3V1rXXXqv//Oc/RqFcHieeeKKSkpJ08OBBY1pUVJTi4+MDltOgQQNFRUUpJiamymIGIMXHx5sdAmB55CEQHshFa6DoRrUVHx+v6OjoCr1n165dysrKUvv27Y1pPXr0UGJiot577z1jmsfj0dSpU3XttddyTzdQxWw2m9khAJZHHgLhgVy0BrqXw1KmTZumQYMGqVmzZsa0mJgYvf/++7r++uu1c+dODR48WFOmTFGbNm30wAMPmBgtUDPxBAjAfOQhEB7IRWug6IZlfP/995o7d66WLFkSMK9BgwYaPny4Zs+erX379mnfvn26+eabFRFBZxCgqhUUFJgdAmB55CEQHshFa6DohiXs27dPt9xyi6ZNm6ZWrVr5zXM6nRo+fLgGDBigFStWyG63a9euXbr11lv1xRdf6LHHHjMpaqBmiouLY+AYwGTkYeh5PB7l5uaaHQbCTGRkpAoLC80OA6WIjIxUbGxslVyEo+hGjZeXl6frrrtOF110kUaPHh3wJeOVV15RcnKybr/9dmPaCSecoFmzZumss87SwIED1bNnz1CHDQAAagiPxyOn06latWrRiw5+IiIi5PV6zQ4DJfh8Pnk8Hh0+fFhxcXGy2+2VWh5ZjxrN6/Xq1ltvVUpKih544AG5XK6ANps3b1aHDh0CpteuXVutWrXSzz//HIpQAcsoLQ8BhBZ5GFq5ubkU3CgVBXd4stlsstvtSkhIUF5eXqWXR+ajRpsyZYp27dqlV199VREREaX+sWvcuLFWr14dMJBFRkaGtm/frgYNGoQqXMAS+NIJmI88DD22OUrD6OXhLTIyskqWQ/ajxpo1a5YWLFigF198UW63W4cOHVJubq4OHTqk/Px8o92NN96oHTt26Oqrr9aPP/6offv2adWqVbriiivUqFEjDRw40MRPAdQ8FX3UH4CqRx4C4YGi2xq4pxs1QlRUlKKi/Hfn999/X+np6erTp09A+8svv1xPPfWUJKlFixb6/PPP9eKLL+qmm25Senq6GjVqpEGDBmns2LE8pxsAAADAcbP5fD6f2UFUJwcOHJDb7TY7DAAAAFQT2dnZSkxMNDsMS3C5XHr22We1cuVK+Xw++Xw+vfTSS2rbtq3ZoaGaKit/7Xa76tWrd8xlcKUblhIXF8fjOgCTkYeA+chD1FSTJ09WnTp19PnnnysyMlIulyugN2Q4YfRyawjfPRAIAgYxAcxHHgLmIw9RU3300Udau3atMQCWw+EwOSKAgdRgMSVHKAcQeuQhYD7yEDVVo0aN9Mknn5Q6b926dRoxYoTftB9++EGjRo0yXnft2lUfffSR+vXrpz59+uiuu+6S0+nUpEmT1KdPHw0YMEDLli0z2n/zzTe6/vrrdeedd6pv374699xz9fnnn2vLli0aPHiw+vXrpyuvvFLp6enGezZv3qxhw4bpnHPOUZ8+fTR27FhlZ2cb8zt27KgVK1Zo0KBBuvXWWzVu3Dh9+OGHxnyXy6UzzjijSh5lhdCg6IalFBQUmB0CYHnkIWA+8hA11UsvvaSnnnpKDz/8sHJycvzmud3ugLGZ8vPz/aZFRETo008/1eLFi437wkeMGKHTTz9dK1eu1H//+19NmTJFTqdT0pFHSi1ZskTdu3fXihUrNG/ePE2dOlUPPfSQXn75ZS1btkwXX3yxnnjiCWMddrtd06dP1/Lly/XVV1+pVq1aevXVV4352dnZWrZsmT799FNNnz5dQ4YM0UcffWTM//LLL3XGGWcw2G81QtENS4mLizM7BMDyyEPAfOQhaqrTTjtNS5cu1f/+9z/17t1bq1atqvAyxo4dq5iYGEnS+eefL7fbrcsuu0ySVKdOHbVv316//fab0b5Zs2YaOnSoJCk5OVlt27bVgAEDdMIJJ0iSBg0apPXr1xvt27Vrp6ZNm0o6UrSfd955+uWXX4z5+fn5Gj58uHEbSJ8+ffTbb78pIyNDkrRgwQINGzaswp8L5uGebgAAAAA1RtOmTfWf//xHixYt0vXXX6933nlH3bp1K/f7i49G7XA41Lp1a7/5DodDLper1PalvSc2NtavfVZWll577TWtWbNGWVlZcrvdaty4sd8yTjrpJOP/UVFRGjRokBYvXqzBgwfr559/Vu/evcv9eWA+im5YSvEDHgBzkIeA+chDWMGQIUOUnZ2tmTNnHrXoLk8u2O32Cq+7rPdcc801ateunaZPn67mzZtr2bJleu211/zalOw6PnToUE2dOlVRUVEaOHCgMVAcqgeKblgKo7UC5iMPAfORh7CKxMREYwyDxMREo4t2keLdukMhIyND27Zt0/z58xURESGbzabt27cf832nn366/vnnH73zzjuaOnVqCCJFVaLoDgPvreTXEDpesdsH3+g+jIqLo4uOjmYQJ8Bk5CFqqi1btujkk0+WJO3fv1+vvvqq7rnnHklSq1atdPDgQa1fv15dunTRjh07tGTJEiUmJoYsvvj4eNlsNqWlpalVq1b6448/NH/+fCUnJx/zvUOGDNEnn3yi0047LQSRoipRfQAAAACoEaZNm6bff/9dDodDDodDEyZMMO5/jo2N1QsvvKC77rpLdrtdtWrV0l133aW33nrLeH90dLSio6ON13a7PaCrePFpJdsXTSv5nqLBC2NiYjR9+nSNHTtWPp9PKSkpmjx5sp5//nm/tjabLeCz1a1blwHUqimbz+fzmR1EdXLgwIGARw1UFle6UdNwpRsAgP8vOzs7pFdTUfN4PB4NGzZMr776qpo0aWJ2OJZSVv7a7faAgfRKww09AICQ4lFFgPnIQyA8lGd8hZdffll9+/bVkCFDKLirKS6xAgBCigGcAPORh0D1cfPNN+vmm282OwxUAkdcAEBIFRYWmh0CYHnkIRAeuNPXGii6AQAhxfOBAfORh0B4oOi2BopuAEBIxcfHmx0CYHnkIRAeuNXDGvgtAwAAAAAQJBTdAICQys/PNzsEwPLIQyA80L3cGii6AQAAAAAIEopuAEBIxcTEmB0CYHnkIRAebDab2SEgBCi6AQAAAAAIEopuAEBIOZ1Os0MALI88RE1xxx136L777jM7jOPm9XrNDgEhQNENAAgph8NhdgiA5ZGHqKw777xTTZo0OepPixYtKrS8559//rhOBnk8Hnk8njLb9OjRw4grNTVVXbp00Q033KBVq1ZVeH3FHW/MxZXsXv7nn3/q3nvvVY8ePdSqVSs1bdpUnTp10rRp0yq1nqpyvJ/Z5/Np3rx5uuSSS3TyySfr5JNP1uWXX65ffvml1PaffPKJ+vXrp1atWqlHjx6aNWtWQJsXXnhB5513ntq2bauTTjpJ55xzjqZPny6Xy1XqMnft2qW+fftqzJgxFY6/sii6AQAhFRkZaXYIgOWRh6isRx55RFu3bjV+YmJiNGvWLOP10Yqpo3n66aeVkZERlFg9Ho+mTZumrVu3at26dXrrrbd04okn6l//+pfuueee4x5BvCpiLl50L1q0SP3791dhYaGeffZZrV69Wj/++KPefvttde3atVLrqSqlfeZ33nlHHTt21D///HPU9+Xn5+u///2vLrvsMn388cf69NNP1ahRIw0bNkx//fWXX9ulS5dq0qRJGjdunNasWaNHH31UL7zwgt58802/dj6fTxMnTtSyZcu0fPlyjRs3Tu+8845uvvnmgPVv3rxZF198sSIjI495kiYYokK+RgCApdGVDjAfeYjKiomJCRiQLz4+XrVr1zYporLFxsYasdWtW1cdO3bUkCFDNHToULVr105XXXWVqfH9+OOPuv322/Xmm2/qnHPO8ZvXoEEDk6Iqv2OduHA4HJo3b57ftKeeekrr1q3TggULNH78eGP6o48+qrvvvltDhgyRJNWvX19PPfWUbrnlFl1xxRWKjY2VJE2YMMFveSeccIKaNGmioUOHyul0Kj4+XpKUl5enq666So8//rh+/fVXbdiwobIft8K40g0ACKm8vDyzQwAsjzxEqCxYsEDnnXeeWrVqpVNPPVUTJkzQ/v37jfmjRo1SkyZNJEndunVTkyZNtG7dOknSr7/+qmuvvVannXaaWrVqpb59+wYUbpXRpk0bTZo0SS+//LJf0fjDDz/osssu0ymnnKKTTjpJ559/vlasWBGUmItOgD3yyCO69tprAwruoznWdpWkyy+/XIsWLQp47znnnKP58+cbr4cPH64PP/xQkyZN0imnnKK2bdvq8ssv165du8r1mceMGaNNmzapUaNG5Yq9SEREhFq3bq2DBw8a03777Tf9+eefGj58uF/bfv36KTo6Wt99912Zy3S5XEpKSjIKc+nICZcVK1Zo0KBBFYqvKlF0AwBCqujMMwDzkIfm8/l8cjqdYfVzvN2sj2bWrFm64447NGrUKK1evVoffPCBsrOzNXz4cOXk5EiSZsyYoa1bt0qSvvzyS23dulVdunSRJK1atUq9evXSf//7X3333Xe6/vrrdfvtt2vjxo1VFuOQIUP0999/63//+58xbfny5Ro2bJgWLFiglStX6vzzz9e//vUv7dmzp8pjjoiI0J49e7Ru3bpyX20vz3aVjnTpLu3+5pL3wdtsNj355JNyuVyaO3euvvjiC9WvX1/XXHONsU+U9ZmPl8/n05YtW3TyyScb03755Rc1b95ctWrV8mtrs9l0yimnaMuWLaUuKz09XYsWLdI999yjJ554QhER/mVucnJypWKtLLqXAwAAACGWm5ursWPHmh2Gn9dee63KTsgcOnRIjz76qJ544gldeumlko50k37zzTfVt29fvf7665o4caJiY2ONq5K1atXy657+73//22+Zo0eP1sKFC7Vs2TJ17NixSuKsU6eO6tWrp7S0NLVu3VqSdM899/i1mTBhgubOnatvvvlGI0eOrPKYt2zZojp16uiEE044Zrzl3a4VlZycrOnTpxv3mD/22GPq1KmTNm3apI4dO5b5mY/Xxx9/rIKCAl188cXGtH379h21O329evW0b98+v2m333675s+fL4/Hozp16ujll1/W2WefXenYqhpXugEAIVVQUGB2CIDlkYcItq+//loxMTEaNmyY3/TIyEiNHj1aixcvPq7lNm/evMwBu45HXFyccnNzy2zTrFkz40p3RZUVs8/n0+HDh5WYmFiuZQVru/bq1ctvULe4uDg1b948YJCzqpKRkaEHH3xQ99xzj9/YAPn5+bLb7aW+x+FwKD8/32/a5MmTtWLFCn344Ye6+OKLddNNN+nHH38MSsyVwZVuAEBIVXX3RQAVRx4i2P766y+lpqYqKiqw3GjdurX+/PPPYy4jLy9Ps2fP1ooVK/TXX3/p8OHDysnJMQbYqiqHDh3yu3KblZWlGTNm6LvvvtPu3buVk5Ojw4cPl2sE8eOJOSEhQdnZ2eWKtSq2a2lSUlICpiUmJgZl/IfCwkKNGzdOffv2Dbh3OyYmRm63u9T3uVyugG7nSUlJSkpKUsuWLXXWWWcpOTlZDz74oD799NMqj7syKLoBACFV1h9UAKFBHpovLi5Or732mtlh+ImLi6uyZZV8/nRF+Xw+jR49Wvv27dOYMWN02mmnqU6dOnr++eerJsD/8+effyorK0vt27eXdKTb/0UXXSSHw6HLL79cJ598spKTkzVp0qSgxGyz2dS2bVtlZGRo9+7dxmBlZbWvrKM9xzpUHnroIR0+fFgzZswImFdaF/IiBw4cUGpqapnLHjBggJ577jm5XC45HI4qibcqUHQDAAAAIWaz2Wr0gHYtWrRQWlqaPB5PwFXZ//3vf2rZsmWZ7//hhx+0efNmrVmzRvXr1zemZ2dnq27dulUW57vvvqszzjhDDRs2lCQtXrxYTqdTX3zxhd8I2OV5HvfxxtysWTOdeuqp+s9//qO77rqrzHVUZLs6HA45nU6/Ni6XS3v37j3mZ6mIWbNmadq0afriiy+OOYL5m2++qS+//FKffPJJwCPnJKldu3b6888/dfjwYb+r2j6fT7/88ovGjBlT5vI9Ho98Pl/Y9eYJ63u6Fy5cqJEjR2rHjh1+03Nzc7Vw4UJNmjRJV155pcaOHavXXntNhw4dCliG1+vVvHnzdOONN+qKK67Qvffeq59//jlUHwEAUMKx7psDEHzkIYLtnHPOUVRUlN+jqSTJ7XbrP//5j0aOHOk33eFw+PW+2L9/v5KSkvyK14MHDxqPqaoKn3/+uWbOnKn77rvPmHbgwAE1a9bMr+D+7bfflJaWFvD+qoi56JFh999/v9544w2tWrWqzJgrsl0bN25sjDheZOHChSosLCxzHWUp+ZmlIyeQIiIijnkVfsmSJXrhhRf07rvvql69eqW2Ofnkk9WgQYOAz7ds2TK5XC716NGjzHXMmTNHZ555pt/vLxyEZdHt9Xr15ptvavXq1fL5fH5D2kvSzp07tWPHDo0ePVrPPfecJk2apF27dunRRx81dtwis2fP1nfffafx48frxRdf1Nlnn62pU6fq999/D+VHAgD8n9LObAMILfIQwRYbG6u77rpLDzzwgN5//33t27dPv/zyi0aNGqXIyEhdfvnlfu0bN26sjz76SLt379bvv/+uzp076+DBg3rjjTe0d+9erVu3TmPGjNFJJ510XPHk5eUpIyNDO3fu1Oeff64bbrhBEyZM0LPPPuv36KuuXbtq48aNWrBggfbt26eVK1fq+uuv1ymnnBKwzKqIuahQ7dmzpx5//HFde+21uv/++7Vu3TodOHBA+/fv18aNG41ivCLbdfDgwVqwYIGWLl2qvLw8LV++XC+88IJOO+2049qGpX1m6f8/p7uot0BpNm3apFtvvVVPPfWU6tevr0OHDhk/xU8C2mw23X333XriiSe0aNEiHThwQCtWrNBdd92lm266ybj6/c0332jOnDn6/ffflZ6ero0bN+qmm27SJ598oilTphz35wuWsCy6Fy1apH/++UcPP/xwqfPbt2+v22+/XZ06dVLdunXVqlUrTZw4UX/99Ze2b99utEtPT9fSpUt12223qW3btkpOTtb555+vAQMG6P333w/VxwEAFBMZGWl2CIDlkYeoajExMQEnc0aPHq1nnnlGs2bNUvfu3TV69Gilpqbqo48+CrgSed9992nu3Lnq06ePPvroIzVt2lTvvPOO5s+fr549e+r222/XFVdcoYsvvtjvSm1p6y0pKipKEydOVMeOHXXhhRfq9ddfV9u2bfX1118HDHDWtWtXTZs2TdOnT9dZZ52lxx57TPfff7+6du0acIX4eGMurvjV4ZEjR2rJkiUqKCjQ+PHj1a1bN3Xt2lVjx471u2Jd3u3as2dPTZ48WVOmTNEpp5yiZ599Vi+//LLq1Knj1zU9MjKy1IHZYmJiAu6LLvmZy2vBggVyOp26/vrr1b59e7+fAQMG+LUdPny4HnnkET333HM644wzdM899+hf//qXJkyYYLTxer16//33NXjwYHXu3FnXXHON4uPjtXLlSr/nfpcUGRlpyvHP5gu3Du86cq9BVFSUoqKidOmll+rRRx81nptXluuuu07//ve/dcYZZ0g60oXh66+/1hNPPOHX7q+//tKkSZM0c+bMCg8YceDAgSofeOS9ldxaj5pldB/PsRvBssrzaBYAwUUehlZ2dna5HwkFa4mIiAjoqYvwUlb+2u32o3aVLy4sr3Q7HI5Sz7aU5cCBA8rJyVGLFi2MaTt37ix1hLsTTjhBUVFRZT53zu12Kzc31/gJxnD5AGBFfNEHzEceAuGBgtsaaswl1oULF+rMM8/0G7ggMzNTrVq1Cmhrs9mUmJiozMzMoy5vwYIFmjdvnvE6NTVVU6dOVWxsrGJiYpSTk6O4uDhFRESosLBQLpfLGIGy6KHtRV1dnE6nHA6HIiMj5fV6lZeXZ7QtKCiQRLKhZklISJB0ZP/2er1G16Tc3FxFR0crKipKXq9Xubm55Wrr8/nkdDqNtm63Wx6Px+hClZeXJ7vdbpysy8nJMdp6PB653W6/tlFRUbLb7Ubb+Ph42Ww2eTweFRQUGD1gXC6XIiIiFB0dbbQtyvuKtK3sMcLn8xltc3NzFRMTY7StzDYsLCw87u1d1jYsvr1dLpciIyP92tapU0cFBQUV3ob5+flG2/z8fNlsNqOt0+lUbGxstdnexffZym7vktuw5PY+nv07mNu7vNuQY0Rw99no6GhlZWWF5TEi3PbZqtjeRSNI22w2ozux1+tVRMSR619Foy0XvfZ6vdWqbVHH2eNta3b8ZraNjIyUz+er9PYu671s78rtszabTXa7vdRjRHkf4RaW3cuLK0/38q1bt+qJJ57Q1KlT1bhxY2N60f0Lw4YNC3jP+PHjNXz4cJ199tmlLtPtdvt1I7fZbIqNjaV7OVAOdC9HWRISEpSTk2N2GIClkYehRfdyHA3dy8NfVXQvr/bVXmZmpl588UVdd911fgW3dGQjlBz5vEhBQYFx9rM0drvdOOsKAKg6R3r4ADATeQiEhzC//okqEpb3dJdXQUGBnn76aXXv3l19+vQJmF+7dm1lZWUFTPf5fMrOzlbt2rWDHyQAwA9n9AHzkYdAeKDotoZqW3R7vV5Nnz5diYmJuuKKK0pt06xZs1IfZL9r1y55PB41bdo02GECAEoo+fgRAKFHHoYeJzpQmqL7hhGejvaot4qqtr/ld999VwcOHNCECROOurOefvrp2rFjR8Ao5StXrlTbtm2Nh6sDAAAAwRIXF6fDhw9TeAPVhM/nk9vtVk5OTsAz5Y9Htbyne+nSpfr222/14IMPqrCw0BgRUpKio6ONe7EbNWqkvn376rnnntPYsWPVoEEDff/991q6dKnuvfdes8IHAEvjUUWA+cjD0IqKilJ8fDyD1wHVSGRkpGrVqlUlvRHCvuiOjIxUZGSk37Tly5fr0KFDuv322wPan3vuufr3v/9tvP7Xv/6luXPn6tlnn1VOTo6aNm2q22+/XSeffHLQYwcABIqOjpbL5TI7DMDSyMPQi4qKYgRzBHA4HOSiBYT9I8PCDY8MA46NR4ahLDyqCDAfeQiEB3KxeivvI8Oq7T3dAIDqiXO9gPnIQyA8kIvWQNENAAip4uNwADAHeQiEB3LRGii6AQAhlZCQYHYIgOWRh0B4IBetgaIbAAAAAIAgoegGAIRUVQ9GCaDiyEMgPJCL1kDRDQAIqcLCQrNDACyPPATCA7loDRTdAICQcjgcZocAWB55CIQHctEaKLoBAAAAAAgSim4AQEjl5uaaHQJgeeQhEB7IRWug6AYAhFR0dLTZIQCWRx4C4YFctAaKbgBASEVFRZkdAmB55CEQHshFa6DoBgCElM/nMzsEwPLIQyA8kIvWQNENAAgpp9NpdgiA5ZGHQHggF62BohsAEFIJCQlmhwBYHnkIhAdy0RoougEAAAAACBKKbgBASLndbrNDACyPPATCA7loDRTdAICQ8ng8ZocAWB55CIQHctEaKLoBACEVGxtrdgiA5ZGHQHggF62BohsAAAAAgCCh6AYAhFReXp7ZIQCWRx4C4YFctAaKbgBASEVFRZkdAmB55CEQHshFa6DoBgCElN1uNzsEwPLIQyA8kIvWQNENAAAAAECQUHQDAEIqJyfH7BAAyyMPgfBALloDRTcAIKTi4+PNDgGwPPIQCA/kojVQdAMAQspms5kdAmB55CEQHshFa6DoBgCElMfjMTsEwPLIQyA8kIvWQNENAAgpt9ttdgiA5ZGHQHggF62BohsAEFKxsbFmhwBYHnkIhAdy0RoougEAAAAACBKKbgBASLlcLrNDACyPPATCA7loDRTdAICQioyMNDsEwPLIQyA8kIvWQNENAAgpu91udgiA5ZGHQHggF62BohsAAAAAgCCh6AYAhFROTo7ZIQCWRx4C4YFctAaKbgBASMXHx5sdAmB55CEQHshFa6DoBgCElM1mMzsEwPLIQyA8kIvWQNENAAgpj8djdgiA5ZGHQHggF62BohsAEFIFBQVmhwBYHnkIhAdy0RoougEAIRUXF2d2CIDlkYdAeCAXrYGiGwAAAACAIKHoBgCElMvlMjsEwPLIQyA8kIvWQNENAAipiAj+9ABmIw+B8EAuWgO/ZQBASEVHR5sdAmB55CEQHshFa6DoBgAAAAAgSCi6AQAhlZOTY3YIgOWRh0B4IBetgaIbABBSPB4FMB95CIQHctEaKLoBACHFoDGA+chDIDyQi9bAbxkAEFKFhYVmhwBYHnkIhAdy0RoougEAIZWfn292CIDlkYdAeCAXrYGiGwAQUty/BpiPPATCA7loDRTdAAAAAAAECUU3ACCk6EoHmI88BMIDuWgNFN0AgJCy2WxmhwBYHnkIhAdy0RoougEAIRUdHW12CIDlkYdAeCAXrYGiGwAAAACAIKHoBgCElNPpNDsEwPLIQyA8kIvWQNENAAip2NhYs0MALI88BMIDuWgNFN0AgJCKiOBPD2A28hAID+SiNUSZHUBZFi5cqPfff19PPPGEWrZs6TevoKBAc+bM0erVq+VyuXTiiSfqqquuUmpq6nG1AwCERmFhodkhAJZHHgLhgVy0hrA8teL1evXmm29q9erV8vl88ng8AW2mT5+uHTt26N5779Xzzz+vdu3a6aGHHlJ6evpxtQMAhIbL5TI7BMDyyEMgPJCL1hCWRfeiRYv0zz//6OGHHy51/m+//aZNmzbpjjvuUGpqqlJSUnTppZeqQ4cOmjdvXoXbAQBCJz4+3uwQAMsjD4HwQC5aQ1gW3QMHDtS999571IEF1q5dq06dOql27dp+0/v06aP169dXuB0AAAAAAMEQlkW3w+FQVNTRbzffuXNnqfdkp6amKjs7WxkZGRVqVxq3263c3FzjJy8v7zg+CQCgpPz8fLNDACyPPATCA7loDWE9kNrRZGRkKDk5OWB6UlKSMb9OnTrlbleaBQsW+HVBT01N1dSpUxUbG6uYmBjl5OQoLi5OERERKiwslMvlMrqHFCVPTEyMpCPP33M4HIqMjJTX61VeXp7RtqCgQJL3uLYDEK4SEhIkHdm/vV6vHA6HJCk3N1fR0dGKioqS1+tVbm5uudr6fD45nU6jrdvtlsfjMXrD5OXlyW63GyfrcnJyjLYej0dut9uvbVRUlOx2u9E2Pj5eNptNHo9HBQUFiouLk3TkPquIiAhFR0cbbYvyviJtK3uM8Pl8Rtvc3FzFxMQYbSuzDQsLC497e5e1DYtvb5fLpcjISL+2RcfRim7D/Px8o21+fr5sNpvR1ul0KjY2ttps7+L7bGW3d8ltWHJ7H8/+HcztXd5tyDEiuPtsZGSkDh8+HJbHiHDbZzlGcIwI9jGiaBuF0zGiovusVY8RNptN5WHz+Xy+crU0yaWXXqpHH31UrVu3NqaNGzdOI0eOVM+ePQPajxo1SpMnT1a7du3K3a40brdbbrfbeG2z2RQbG6sDBw74Ta8K762sluc+gKMa3Sdw8EOgSEJCgnJycswOA7A08hAID+Ri9Wa321WvXr1jtquW1V5UVFSpI5p7vV4VFhYaZyzK2640drvdOKMCAAAAAMDxCMt7uo8lKSlJmZmZAdOzsrIkyRg4rbztAACh43Q6zQ4BsDzyEAgP5KI1VMuiu1mzZkpLSwuYnpaWpvj4eKWkpFSoHQAgdIru/wJgHvIQCA/kojVUy6K7S5cu+umnn5Sdne03feXKlTr99NONG9rL2w4AEDqRkZFmhwBYHnkIhAdy0RqqZdF96qmnqnXr1nrmmWe0c+dOZWRkaO7cudq4caOGDh1a4XYAgNDxenliA2A28hAID+SiNYT96OWjRo3So48+qlatWvlNz83N1Zw5c/T999/L5XKpZcuWuvLKK/1GOa9Iu/Ji9HLg2Bi9HGWx2WwK8z89QI1HHgLhgVys3so7ennYF93hhqIbODaKbpSFx6MA5iMPgfBALlZv5S26q2X3cgAAAAAAqgOKbgBASBUUFJgdAmB55CEQHshFa6DoBgCEFHc1AeYjD4HwQC5aA0U3ACCkYmJizA4BsDzyEAgP5KI1UHQDAAAAABAkFN0AgJDKzc01OwTA8shDIDyQi9ZA0Q0ACCm60gHmIw+B8EAuWgNFNwAgpCIjI80OAbA88hAID+SiNVB0AwBCyuv1mh0CYHnkIRAeyEVroOgGAIQU968B5iMPgfBALloDRTcAIKQSEhLMDgGwPPIQCA/kojVQdAMAAAAAECQU3QCAkCooKDA7BMDyyEMgPJCL1kDRDQAIKQaNAcxHHgLhgVy0BopuAEBIORwOs0MALI88BMIDuWgNFN0AAAAAAAQJRTcAIKR4PApgPvIQCA/kojVQdAMAQio6OtrsEADLIw+B8EAuWgNFNwAgpKKioswOAbA88hAID+SiNVB0AwBCyufzmR0CYHnkIRAeyEVroOgGAISU0+k0OwTA8shDIDyQi9ZA0Q0ACKmEhASzQwAsjzwEwgO5aA0U3QAAAAAABAlFNwAgpNxut9khAJZHHgLhgVy0BopuAEBIFRYWmh0CYHnkIRAeyEVroOgGAISUw+EwOwTA8shDIDyQi9ZA0Q0AAAAAQJBQdAMAQiovL8/sEADLIw+B8EAuWgNFNwAgpOx2u9khAJZHHgLhgVy0BopuAEBIRUVFmR0CYHnkIRAeyEVroOgGAISUz+czOwTA8shDIDyQi9ZA0Q0ACCmn02l2CIDlkYdAeCAXrYGiGwAQUgkJCWaHAFgeeQiEB3LRGii6AQAAAAAIEopuAEBIud1us0MALI88BMIDuWgNFN0AgJDyeDxmhwBYHnkIhAdy0RoougEAIRUbG2t2CIDlkYdAeCAXrYGiGwAAAACAIKHoBgCEVF5entkhAJZHHgLhgVy0BopuAEBIRUVFmR0CYHnkIRAeyEVroOgGAISU3W43OwTA8shDIDyQi9ZA0Q0AAAAAQJBQdAMAQionJ8fsEADLIw+B8EAuWgNFNwAgpOLj480OAbA88hAID+SiNVB0AwBCymazmR0CYHnkIRAeyEVroOgGAISUx+MxOwTA8shDIDyQi9ZA0Q0ACKmCggKzQwAsjzwEwgO5aA0U3QCAkIqLizM7BMDyyEMgPJCL1kDRDQAAAABAkFB0AwBCyuVymR0CYHnkIRAeyEVroOgGAIRUZGSk2SEAlkceAuGBXLQGim4AQEjZ7XazQwAsjzwEwgO5aA0U3QAAAAAABAlFNwAgpHJycswOAbA88hAID+SiNVB0AwBCKj4+3uwQAMsjD4HwQC5aA0U3ACCkbDab2SEAlkceAuGBXLQGim4AQEh5PB6zQwAsjzwEwgO5aA0U3QCAkCooKDA7BMDyyEMgPJCL1kDRDQAIqbi4OLNDACyPPATCA7loDVFmB1AZf//9tz788ENt2bJF+fn5aty4sQYOHKg+ffr4tSsoKNCcOXO0evVquVwunXjiibrqqquUmppqTuAAAAAAAEuotkX3nj17dN999+mss87SQw89JIfDobVr1+r111+X0+nUBRdcYLSdPn26Dh06pHvvvVeJiYn66quv9NBDD2natGmqW7euiZ8CAKzH5XKZHQJgeeQhEB7IRWuott3Lly1bpmbNmunf//63mjZtqrp162rQoEG66KKLtHLlSqPdb7/9pk2bNumOO+5QamqqUlJSdOmll6pDhw6aN2+eeR8AACwqIqLa/ukBagzyEAgP5KI1VNvfckREhJKSkgKmJycny+FwGK/Xrl2rTp06qXbt2n7t+vTpo/Xr1wc7TABACdHR0WaHAFgeeQiEB3LRGqpt0d2nTx/98ssv2rlzpzEtKytLixcv1uDBg41pO3fuLPXe7dTUVGVnZysjIyMU4QIAAAAALKja3tPdtGlTjR8/Xs8995yGDBmiunXr6p133tGIESPUtWtXo11GRoaSk5MD3l90lTwjI0N16tQJmO92u+V2u43XNptNsbGxVf9BAMBicnJyzA4BsDzyEAgP5KI1VNuiW5JatGihdu3aacWKFYqPj1dSUpJOPPFEvzYej0dRUYEfMyIiQpGRkX6FdXELFizwu+c7NTVVU6dOVWxsrGJiYpSTk6O4uDhFRESosLBQLpdL8fHxkqT8/HxJUkxMjCTJ6XTK4XAoMjJSXq9XeXl5Rtsjz+bzVnpbAOEkISFB0pH92+v1Grd85ObmKjo6WlFRUfJ6vcrNzS1XW5/PJ6fTabR1u93yeDzGibC8vDzZ7XYj13Nycoy2Ho9Hbrfbr21UVJTsdrvRNj4+XjabTR6PRwUFBcbjO1wulyIiIoyuX8XzviJtK3uM8Pl8Rtvc3FzFxMQYbSuzDQsLC497e5e1DYtvb5fLpcjISL+2ycnJxjIrsg3z8/ONtvn5+bLZbEZbp9Op2NjYarO9i++zld3eJbdhye19PPt3MLd3ebchx4jg7rN2u12HDh0Ky2NEuO2zHCM4RgTzGFG7dm253e6wO0ZUdJ+16jHCZrOpPGw+n89XrpZhZufOnXrmmWd01VVX6YwzzpAkbd26Va+++qouvfRS9erVS5J02223aciQIQGPEfN6vbrsssv0xBNPqFWrVgHLP9qV7gMHDhy1UD9e762s1uc+gACj+3jMDgFhLCEhgTP7gMnIQyA8kIvVm91uV7169Y7Zrtre0z1z5kydc845RsEtSe3bt9eNN96oN954Q3l5eZKOdCPPzMwMeH9WVpYkBQywVsRutysuLs74oWs5AFSNwsJCs0MALI88BMIDuWgN1bbo3rFjR6lXqE866SQVFBRo9+7dkqRmzZopLS0toF1aWpri4+OVkpIS9FgBAP9fUTctAOYhD4HwQC5aQ7UtulNSUrRp06aA6b/++qskGYOndenSRT/99JOys7P92q1cuVKnn356ufvhAwCqRtH9VADMQx4C4YFctIZqW3Rfdtll+uyzzzR79mz9/fffSk9P16pVq/Tiiy+qd+/exhXsU089Va1bt9YzzzyjnTt3KiMjQ3PnztXGjRs1dOhQkz8FAAAAAKAmq7YDqUlHrmovWrRIf/zxh/Lz89WoUSP17dtXAwYMUETE/z+fkJubqzlz5uj777+Xy+VSy5YtdeWVV6p169YVXicDqQHHxkBqKIvdbq/y4yiAiiEPgfBALlZv5R1IrVoX3Wag6AaOjaIbZYmOjv6/xyUCMAt5CIQHcrF6q/GjlwMAqqeiZ2ICMA95CIQHctEaKLoBAAAAAAgSim4AQEg5nU6zQwAsjzwEwgO5aA0U3QCAkIqNjTU7BMDyyEMgPJCL1kDRDQAIqeJPlwBgDvIQCA/kojXwWwYAhFRhYaHZIQCWRx4C4YFctAaKbgBASLlcLrNDACyPPATCA7loDRTdAICQio+PNzsEwPLIQyA8kIvWQNENAAAAAECQUHQDAEIqPz/f7BAAyyMPgfBALloDRTcAAAAAAEFC0Q0ACKmYmBizQwAsjzwEwgO5aA0U3QAAAAAABEmliu7MzMxjtvn999+1dOnSyqwGAFCDOJ1Os0MALI88BMIDuWgNlSq6x44dq7///rvMNh6PR/Pnz6/MagAANYjD4TA7BMDyyEMgPJCL1hD07uX79+9Xbm5usFcDAKgmIiMjzQ4BsDzyEAgP5KI1RFX0DY899pg8Ho/x+pVXXjnqAAAul0s7d+5Up06djj9CAECN4vV6zQ4BsDzyEAgP5KI1VLjorlu3rgoLC43XycnJio+PL7VtTEyMevfurT59+hx3gACAmoXeT4D5yEMgPJCL1lDhovvf//638f+vv/5ao0aNUtOmTas0KABAzZWQkKCcnByzwwAsjTwEwgO5aA2Vuqe7Q4cOiouLq6pYAAAAAACoUSp8pbu4++67r6riAABYREFBgdkhAJZHHgLhgVy0hqCPXg4AQHEMGgOYjzwEwgO5aA2VutItSZs3b9aiRYu0a9cuHTp0qPSVREVpzpw5lV0VAKAGcDgc3L8GmIw8BMIDuWgNlSq6v/nmG7300kvq2LGjBg8erISEhNJXElXp2h4AAAAAgGqnUtXwwoULdd555+naa6+tqngAADUcj0cBzEceAuGBXLSGSt3TvXfvXvXo0aOqYgEAWEB0dLTZIQCWRx4C4YFctIZKFd0xMTHKy8urqlgAABbALUeA+chDIDyQi9ZQqaK7TZs2+v7776sqFgCABTBSK2A+8hAID+SiNVSq6B4zZow2bdqk2bNna+/evew0AIBj4v41wHzkIRAeyEVrqFR/hqeeekoej0eLFy/W4sWLj74SHhkGAPg/CQkJPB4FMBl5CIQHctEaKlV0Dx48uHwr4V4FAAAAAIAFVaoa7tOnTxWFAQCwioKCArNDACyPPATCA7loDZW6pxsAgIpi/A/AfOQhEB7IRWuo1JXu7du3y+PxHHslUVFq3bp1ZVYFAKghHA4H968BJiMPgfBALlpDpYruBx54oHwrYSA1AAAAAIAFVarofvXVV1VYWBgwPTMzU7///ru+/PJLde7cWVdccUVlVgMAqEF4PApgPvIQCA/kojXYfD6fL1gLd7lcevjhh9WlSxcNHz48WKsJqQMHDsjtdlfpMt9byejuqFlG9zn2bSewLofDIZfLZXYYgKWRh0B4IBerN7vdrnr16h2zXVAHUnM4HLr88su1cuXKYK4GAFCN8BhJwHzkIRAeyEVrCPro5YmJicrIyAj2agAA1UQQO1gBKCfyEAgP5KI1BL3oXrt2rZKSkoK9GgBANeF0Os0OAbA88hAID+SiNQTlkWFer1eHDh3Sxo0b9e2332ro0KGVWQ0AoAZJSEjg8SiAychDIDyQi9YQ1EeGJSQkaMiQIRoxYkRlVgMAAAAAQLUUlEeG2Ww2RUdHKzExsTKLBwDUQFX9BAgAFUceAuGBXLSGShXdderUqao4AAAWUdptSQBCizwEwgO5aA1VOpBaQUGBMjMzVVBQUJWLBQDUILGxsWaHAFgeeQiEB3LRGir9YDifz6cvvvhCX3zxhXbv3i2fzyebzaaWLVtqyJAhOvPMM6siTgAAAAAAqp1KFd2FhYV6+umn9dNPP6lz584aOHCgateurUOHDunHH3/Us88+q65du2rixImy2WxVFTMAoBrLy8szOwTA8shDIDyQi9ZQqaJ7yZIl2rZtmx555BG1bt3ab16/fv20bds2PfHEE/ryyy81YMCASgUKAKgZ7HZ7qYNwAggd8hAID+SiNVTqnu6VK1dq2LBhAQV3kbZt22rIkCH66quvKrMaAEANEhVV6TubAFQSeQiEB3LRGipVdO/du1cnn3xymW06dOigPXv2VGY1AAAAAABUS5UquiMjI5Wfn19mm4KCAs7gAAAMOTk5ZocAWB55CIQHctEaKlV0p6am6ttvvy2zzTfffKNWrVpVZjUAgBokISHB7BAAyyMPgfBALlpDpYrugQMH6quvvtLChQvldrv95rndbn300UdasWKFLrzwwkoFCQAAAABAdVSpft9nnHGGRowYoffff1+LFy9Whw4dlJiYqOzsbP3888/KycnR6NGj1bFjxyoKFwBQ3Xk8HrNDACyPPATCA7loDZW+2fqSSy5R586dtWzZMqWlpen3339XfHy8zjrrLF1wwQWqV69eVcQJAKghSvaMAhB65CEQHshFa6iSEc5atmypG264oSoWBQCo4WJjYxk4BjAZeQiEB3LRGip0T/djjz2m9957r0Ir+OCDDzR9+vQKvQcAAAAAgJqgQkV3WlqaTjzxxAqt4KSTTtL27dsr9B4AQM2Vl5dndgiA5ZGHQHggF62hQkX34cOHVadOnQqtIDExUZmZmRV6DwCg5oqKqpI7mwBUAnkIhAdy0Roq9FuOjIys8I4R7B0pPT1dH330kTZt2qSsrCzFxMSoV69euuaaayRJBQUFmjNnjlavXi2Xy6UTTzxRV111lVJTU4MaFwCgdHa7Xfn5+WaHAVgaeQiEB3LRGipUEdeqVUuZmZlq0aJFud+TlZUVtIe+b9++XVOnTlXfvn11xx13qE6dOnI6nX6DEUyfPl2HDh3Svffeq8TERH311Vd66KGHNG3aNNWtWzcocQEAAAAAIFWw6G7VqpW2bNmiTp06lfs9W7duDcpV5YKCAj333HO67rrrdNZZZxnTa9eubfz/t99+06ZNm/TSSy8Z0y+99FLt2rVL8+bN09ixY6s8LgBA2RilFTAfeQiEB3LRGip0T3e3bt301VdflXvncDqd+uqrr3TmmWceV3Bl+f7775WYmOhXcJe0du1aderUya8Ql6Q+ffpo/fr1VR4TAODY4uPjzQ4BsDzyEAgP5KI1VKjo7tmzp+rVq6dp06bJ5XKV2TY/P1/PPvusEhMT1bt370oFWZrNmzerc+fOWrt2re69916NHTtWU6ZM0caNG402O3fuLPUqe2pqqrKzs5WRkVHlcQEAymaz2cwOAbA88hAID+SiNVSo6I6IiNCkSZO0d+9eTZo0SatWrQoovl0ul1atWqU777xTf/31lyZNmqSIiAqtplx2796tbdu2af78+Ro1apTuvfdedezYUU8//bRWrVolScrIyFBycnLAe5OSkoz5R+N2u5Wbm2v8MJw/AFQNj8djdgiA5ZGHQHggF62hwkOL16tXT0899ZRmzpypV155Ra+++qrq1aun2NhY5ebmKj09XV6vV507d9b1119f4UeMlVdubq4yMjL0wgsvyOFwSJKaNWsmr9erOXPmqFevXvJ4PKWOnh4REaHIyEi53e6jLn/BggWaN2+e8To1NVVTp05VbGysYmJilJOTo7i4OEVERKiwsFAul8voHlI0AmFMTIykI93sHQ6HIiMj5fV6lZeXZ7QtKCiQ5K2SbQKEi6LBEwsKCuT1eo0czc3NVXR0tKKiouT1epWbm1uutj6fT06n02jrdrvl8XgUGxsr6cgzLu12u5HvOTk5RluPxyO32+3XNioqSna73WgbHx8vm80mj8ejgoICxcXFSTpyEjEiIkLR0dFG26K8r0jbyh4jfD6f0TY3N1cxMTFG28psw8LCwuPe3mVtw+Lb2+VyKTIy0q9tVFSUEhISKrwN8/Pzjbb5+fmy2WxGW6fTqdjY2GqzvYvvs5Xd3iW3YcntfTz7dzC3d3m3IceI4O6zNptNBQUFYXmMCLd9lmMEx4hgHiOK1htux4iK7rNWPUaUt6eCzefz+crVshTp6enatGmT9uzZo9zcXMXGxqphw4Y65ZRT1Lhx4+NdbLmMHz9eHTt2NB4NVmT//v265ZZbNH36dE2dOlVDhgxRnz59/Np4vV5ddtlleuKJJ9SqVatSl+92u/2KcpvNptjYWB04cKDMYv14vLeS5/OhZhndh7O2OLqEhAQGjgFMRh4C4YFcrN7sdrvq1at3zHaVqvbq1q2rc889tzKLOG7x8fFGN/Hiiqbl5uYqKSlJmZmZAW2ysrIkKWCAteLsdrtxRgUAAAAAgONR9Tdbh0iTJk20b9++gOlF92knJSWpWbNmSktLC2iTlpam+Ph4paSkBD1OAIC/Yw3ECSD4yEMgPJCL1lBti+5OnTppzZo1Onz4sN/0r7/+Wi1atFCdOnXUpUsX/fTTT8rOzvZrs3LlSp1++umMFggAJgjG4JoAKoY8BMIDuWgN1fa33K1bNzVs2FBPP/20du7cqYyMDH322Wf69NNPdfXVV0uSTj31VLVu3VrPPPOM0Wbu3LnauHGjhg4dau4HAACLKhq0BIB5yEMgPJCL1lCpgdTMdujQIc2ePVsbNmxQQUGBWrVqpcsuu0zt27c32uTm5mrOnDn6/vvv5XK51LJlS1155ZVq3br1ca2TgdSAY2MgNZSFQWMA85GHQHggF6u38g6kVq2LbjNQdAPHRtENAACAmq68RXe17V4OAKieip6RCcA85CEQHshFa6DoBgCEFIPGAOYjD4HwQC5aA79lAEBIeTzcfgCYjTwEwgO5aA0U3QCAkCooKDA7BMDyyEMgPJCL1kDRDQAIKe5fA8xHHgLhgVy0BopuAAAAAACChKIbABBSLpfL7BAAyyMPgfBALloDRTcAIKQYqRUwH3kIhAdy0Rr4LQMAQio6OtrsEADLIw+B8EAuWgNFNwAAAAAAQULRDQAIqZycHLNDACyPPATCA7loDRTdAICQ4vEogPnIQyA8kIvWQNENAAgpBo0BzEceAuGBXLQGfssAgJAqLCw0OwTA8shDIDyQi9ZA0Q0ACCmeSQqYjzwEwgO5aA0U3QCAkIqPjzc7BMDyyEMgPJCL1kDRDQAAAABAkFB0AwBCKj8/3+wQAMsjD4HwQC5aA0U3AAAAAABBQtENAAipmJgYs0MALI88BMIDuWgNFN0AAAAAAAQJRTcAIKScTqfZIQCWRx4C4YFctAaKbgBASDkcDrNDACyPPATCA7loDRTdAICQioyMNDsEwPLIQyA8kIvWQNENAAgpr9drdgiA5ZGHQHggF62BohsAEFJ5eXlmhwBYHnkIhAdy0RoougEAIRUfH292CIDlkYdAeCAXrYGiGwAAAACAIKHoBgCEVEFBgdkhAJZHHgLhgVy0BopuAEBI+Xw+s0MALI88BMIDuWgNFN0AgJCKiYkxOwTA8shDIDyQi9ZA0Q0AAAAAQJBQdAMAQio3N9fsEADLIw+B8EAuWgNFNwAgpOhKB5iPPATCA7loDRTdAICQioyMNDsEwPLIQyA8kIvWQNENAAgpr9drdgiA5ZGHQHggF62BohsAEFLcvwaYjzwEwgO5aA0U3QCAkEpISDA7BMDyyEMgPJCL1kDRDQAAAABAkFB0AwBCqqCgwOwQAMsjD4HwQC5aA0U3ACCkGDQGMB95CIQHctEaKLoBACHlcDjMDgGwPPIQCA/kojVQdAMAAAAAECQU3QCAkOLxKID5yEMgPJCL1kDRDQAIqejoaLNDACyPPATCA7loDRTdAICQioqKMjsEwPLIQyA8kIvWQNENAAgpn89ndgiA5ZGHQHggF62BohsAEFJOp9PsEADLIw+B8EAuWgNFNwAgpBISEswOAbA88hAID+SiNVB0AwAAAAAQJBTdAICQcrvdZocAWB55CIQHctEaKLoBACFVWFhodgiA5ZGHQHggF62BohsAEFIOh8PsEADLIw+B8EAuWgNFNwAAAAAAQULRDQAIqdzcXLNDACyPPATCA7loDRTdAICQio6ONjsEwPLIQyA8kIvWQNENAAipqKgos0MALI88BMIDuWgNFN0AgJDy+XxmhwBYHnkIhAdy0RoougEAIeV0Os0OAbA88hAID+SiNVB0AwBCKiEhwewQAMsjD4HwQC5aQ425iWD79u2aPHmy+vTpo7FjxxrTc3JyNGvWLG3YsEGFhYVq3769rrnmGtWvX9/EaAEAAAAAVlAjrnR7PB698cYbat26tQoLC43pXq9Xjz32mPLy8jRlyhQ9/fTTSk5O1oMPPsjw/ABgErfbbXYIgOWRh0B4IBetoUYU3Z9++qlOOOEEnXLKKX7TV69eraysLI0fP15NmzZV/fr1df311yspKUmff/65SdECgLV5PB6zQwAsjzwEwgO5aA3Vvujev3+/Pv/8c40ZMyZg3tq1a3XWWWf5Pf/OZrOpd+/eWr9+fSjDBAD8n9jYWLNDACyPPATCA7loDdW+6H7zzTc1fPhwJSUlBcxLS0tTampqwPTU1FT9+eef8nq9R12u2+1Wbm6u8ZOXl1eVYQMAAAAALKBaD6T27bffyul0ql+/fqXOz8zMVHJycsD0pKQkeTwe5eTkKDExsdT3LliwQPPmzTNep6amaurUqYqNjVVMTIxycnIUFxeniIgIFRYWyuVyKT4+XpKUn58vSYqJiZF05FEADodDkZGR8nq9ysvLM9oWFBRIOnrxD1RHRSNxFhQUyOv1yuFwSJJyc3MVHR2tqKgoeb1e5ebmlqutz+eT0+k02rrdbnk8HuPscF5enux2u6KijhzScnJyjLYej0dut9uvbVRUlOx2u9E2Pj5eNptNHo9HBQUFiouLkyS5XC5FREQYvWWK531F2lb2GOHz+Yy2ubm5iomJMdpWZhsWFhYe9/Yuaxtu3rxZzz77rNavX6+DBw8qOTlZXbt21YQJE9SuXTvZbDYlJCT4bcO1a9fq/PPP16hRo/Tyyy+Xug3z8/ON7Z2fny+bzWZsb6fTqdjY2GqzvYvvs5Xd3iX3w8jIyErv38Hc3uXdhhwjgrvPRkREGPGE+hhRfHtXh32WYwTHiGAeIyIiIpSQkBB2x4iK7rNWPUbYbDaVh81XTZ/I7nQ6NXHiRN19991q0aKFJGnu3Lk6cOCAbr75ZknSyJEjNWXKFLVp08bvvRkZGRo7dqxeeeUV1a1bt9Tlu91uv4ENbDabYmNjdeDAgSof8OC9ldX63AcQYHQf7k+ysg0bNuiHH35Qr169VL9+fe3Zs0ezZ8/WRx99pEWLFumMM84w/oBJR463AwcOVK1atdS8eXM9//zz5gUPWERMTIxfHgIwB7lYvdntdtWrV++Y7apttTdnzhx169bNKLhLY7fbSx2coKhoLn6vd2nvLTqjAgAov86dO6tz587G6/r166tjx47avXu3Pv74Y/Xo0cPvC8brr7+uNm3aqGXLltq1a5cZIQOWY7fb+aIPhAFy0Rqq5T3d27dv108//aSRI0eW2S4pKUmZmZkB07OyshQZGcnD6AEghPLz89WwYUO/aX/99ZdmzJihhx56yJygAAAAgqxaXunevn27Dh06pBtvvNFvutvtltfr1bp16zR8+HA1a9ZMaWlp6tmzp1+7tLQ0NW3aVBER1fKcAwBUG263W7/99ptmz56twsJCXXHFFcrJyTHm33PPPRo/fny5umYBqDrF8xCAechFa6iWRXf//v11xhlnBEz/7LPPdPDgQV155ZVKTExUfHy8PvzwQ40cOdLoSu7z+fT111+rS5cuoQ4bACxjx44dOu+885SbmytJGjJkiD788EM5HA7Fx8fL6XRq4cKFOnTokK688kqTowWspygPAZiLXLSGanmpNyYmRvXr1w/4iYuLk8PhUP369eVwONSrVy/FxcXphRde0J49e7R//369+eabOnjwoAYOHGj2xwCAGis1NVXLly/XkiVL9Pzzz2vbtm267bbbJB0ZmPLQoUN65JFH9OSTT9LrCDBBeUfcBRBc5KI11KhvOtHR0X6Dn9ntdt1///2y2+267777NHHiRKWnp+vBBx886qPCAACVZ7PZdMIJJ+jUU0/ViBEjNG/ePH377bdavny5PB6PHnvsMV144YU65ZRTzA4VsKTSBpoFEHrkojVUy+7lR3PxxRcHTEtOTtaECRNCHgsA4P+rU6eOunbtqh9++EF16tTRihUrtGLFCrPDAiyrqh9/CuD4kIvWUKOKbgBA+HK73fL5fNq4caPS09PVtWtXv/n5+fnyer1aunSpxo8fr7Fjx5oUKVDzxcbGMoATEAbIRWug6AYABF1aWppWr16tsWPHqlevXjr33HMD2rz55pv6559/NHnyZNWpU8eEKAEAAKoeRTcAoEpNmzZN3bt3V/PmzeXz+fTdd9/pySef1EUXXaQePXooIiJCJ5xwQsD7ateurezs7FLnAahaLpfL7BAAiFy0CopuAECV+vPPP/XBBx8oPT1dNptNbdq00UMPPaQhQ4ZIkiIjI0sdOCYmJkYxMTGhDhewpKPlIYDQIhetwebz+XxmB1GdHDhwoMoHPHhvJec+ULOM7sMfDxxdQkIC968BJiMPgfBALlZvdrtd9erVO2a7GvXIMAAAAAAAwglFNwAgpDijD5iPPATCA7loDRTdAICQio+PNzsEwPLIQyA8kIvWQNENAAgpm81mdgiA5ZGHQHggF62BohsAEFKM0gqYjzwEwgO5aA0U3QCAkCooKDA7BKBGO3DggJ566in17t1brVq1Urdu3TRlyhS/e0cLCgr0+++/67rrrlP79u3VqlUrDR8+XBs3bjQvcMCC+JtoDRTdAICQiouLMzsEoEb77rvvtG/fPj3++OP69ttv9dxzz2nZsmW66aabjDbp6ekaMmSIEhMT9eGHH+qrr75Sz549NXr0aO3cudO84AGL4W+iNfCc7griOd3AsfGcbpSFZ5ICobd+/XoNGTJE69evV6NGjfTAAw9o27Zt+vDDD/3aTZ48WQUFBXryySdNihSwFv4mVm/lfU431R4A/J+/smLNDsEasgolsa1DoVlSntkhIEy0a9dOknTw4EE1atRIa9as0ahRowLaXXTRRbrllltCHR5gWS6Xy+wQEAJ0LwcAAKjhNm/erNjYWLVq1UqS5Ha7FRMTE9AuPj5eu3fvVl4eJ2yAUIiIoByzAn7LAAAANdzLL7+sMWPGKDb2SC+T1q1ba8OGDQHt1qxZI5/Pp+zs7FCHCFhSdHS02SEgBCi6AQAAarD58+frl19+0c0332xMu+GGG7RgwQJ98MEHcrlcys/P16effqp3333XxEgBoGai6AYAAKihfvvtN02ePFkvvfSS6tSpY0zv2rWrXn75Zb300ktq3bq1TjrpJL3xxhuaPHmyJCkxMdGskAFLYRA1a2AgNQAAgBooIyNDV199tSZOnKiePXv6zYuLi9OgQYM0aNAgHT58WIWFhUpKStKKFSvUuHFjoxs6gOCKi4tTbm6u2WEgyCi6AQAAahiXy6Wrr75affr00bXXXhswv/jgTbVq1TL+v3DhQvXo0SMkMQJgIDWroOgGAACoQXw+n8aNG6fatWvr0UcfLbVNYWFhwLSffvpJn376qebPnx/sEAH8n9JyETUPRTcAAEAN8thjj+m3337Tf//734D7RePi4mS327V9+3ZlZGSoadOmyszM1BdffKHnnntO48aNU8eOHc0JHLCg/Px8s0NACFB0AwAA1CDvv/++srKy1LVr14B5d911l2699Vbt3r1b48aN0/79+1WrVi117NhRr7/+us455xwTIgasKy4ujsHULICiGwAAoAbZsmXLMdv069dP69atC0E0AADu3AcAALAYurQC4YFctAaKbgAAAIux2WxmhwBA5KJVUHQDAABYTHR0tNkhABC5aBUU3QAAAAAABAlFNwAAgMU4nU6zQwAgctEqKLoBAAAsJjY21uwQAIhctAqKbgAAAIuJiOArIBAOyEVr4DndAAAgbNj3/mZ2CJaQL8ludhAW4W7YxuwQEMYKCwvNDgEhwKkVAAAAADCBy+UyOwSEAEU3AAAAAJggPj7e7BAQAhTdAAAAAAAECUU3AAAAAJggPz/f7BAQAhTdAAAAAAAECUU3AAAAAJggJibG7BAQAhTdAAAAAAAECUU3AAAAAJjA6XSaHQJCgKIbAAAAAEzgcDjMDgEhQNENAAAAACaIjIw0OwSEAEU3AAAAAJjA6/WaHQJCgKIbAAAAAEyQl5dndggIAYpuAAAAADBBfHy82SEgBCi6AQAAAAAIEopuAAAAADBBQUGB2SEgBCi6AQAAAMAEPp/P7BAQAhTdAAAAAGCCmJgYs0NACFB0AwAAAAAQJBTdAAAAAGCC3Nxcs0NACFB0AwAAAIAJ6F5uDRTdAAAAAGCCyMhIs0NACFB0AwAAAIAJvF6v2SEgBCi6AQAAAMAE3NNtDRTdAAAAAGCChIQEs0NACFB0AwAAAAAQJBTdAAAAAGCCgoICs0NACFB0AwAAAIAJGEjNGqLMDuB4ZWVlacmSJfrhhx904MAB1a5dW2eeeaZGjBih2NhYo11BQYHmzJmj1atXy+Vy6cQTT9RVV12l1NRUE6MHAAAAYHUOh0M5OTlmh4Egq7ZF9y+//KLMzExdd911aty4sfbu3as33nhDe/bs0d133220mz59ug4dOqR7771XiYmJ+uqrr/TQQw9p2rRpqlu3romfAAAAAABQ01Xb7uU9e/bUjTfeqFNOOUV16tRR+/btddNNN2nDhg3KyMiQJP3222/atGmT7rjjDqWmpiolJUWXXnqpOnTooHnz5pn8CQAAAABYGY8Ms4ZqW3SXplmzZpKk7OxsSdLatWvVqVMn1a5d269dnz59tH79+pDHBwAAAABFoqOjzQ4BIVCjiu4dO3YoJiZGjRo1kiTt3Lmz1Hu3U1NTlZ2dbVwRL43b7VZubq7xk5eXF7S4AQAAAFhPVFS1vdsXFVCjfssLFy7UgAEDFBMTI0nKyMhQcnJyQLukpCRjfp06dUpd1oIFC/y6oKempmrq1KmKjY1VTEyMcnJyFBcXp4iICBUWFsrlcik+Pl6SlJ+fL0lGHE6nUw6HQ5GRkfJ6vcrLyzPaHnlMAKMWomZJSEiQdGT/9nq9cjgcko50oYqOjlZUVJS8Xq9yc3PL1dbn88npdBpt3W63PB6PMWhiXl6e7Ha78YcrJyfHaOvxeOR2u/3aRkVFyW63G23j4+Nls9mkrMJQbB4gpIr2b4/Ho4KCAsXFxUmSXC6XIiIijKssJf+u5efnG23z8/Nls9mMtk6nU7Gxscf1N/BYeQ/UNEX7+7H+rhUWFhq5UPzvWnn+Bh7t71ppeR8ZGVnutuF4jAjn7xEV2YZFbaOjo5WQkHDM7V2ZWsPn8xltc3NzFRMTU+Htfax9tqLbu6xtWHx7h/s+a7PZVB42n8/nK1fLMLdq1Sq9++67evbZZ1WrVi1J0rhx4zRy5Ej17NkzoP2oUaM0efJktWvXrtTlud1uud1u47XNZlNsbKwOHDjgN70qvLeyRp37ADS6j8fsEI7LX1mxx24EVCPNkqpfLy373t/MDgGoUu6GbcwOAUCQ2O121atX75jtakS1t2vXLs2aNUu33367UXBLR7preDyBX/69Xq8KCwvLvIfCbrcbZ1QAAAAAoKolJCTwyDALqPb3dGdnZ2vq1KkaMWKETjnlFL95SUlJyszMDHhPVlaWJAUMsAYAAAAAQFWq1kV3QUGBnnrqKXXs2FEDBw4MmN+sWTOlpaUFTE9LS1N8fLxSUlJCESYAAAAABKjq21YRnqpt0e3z+fTiiy8qPj5e1157baltunTpop9++sl4hFiRlStX6vTTTy/3je8AAAAAUNUKCxnE1QqqbdE9Z84c7dq1SzfccIPy8vLkdDqNn6L7uE899VS1bt1azzzzjHbu3KmMjAzNnTtXGzdu1NChQ03+BAAAAACsrGj0b9Rs1Xb08muuuUZOp7PUeZdddpmGDRsm6cjw9XPmzNH3338vl8ulli1b6sorr1Tr1q2Pa72MXg4cG6OXA+GB0csB8zF6OcrCQGrVW3lHL6+2RbdZKLqBY6PoBsIDRTdgPopulCUyMpIu5tVYeYvuatu9HAAAAACqMx5RbA0U3QAAAABggqgoerxaAUU3AAAAAJiAO32tgaIbAAAAAExwtIGhUbNQdAMAAACACRISEswOASFA0Q0AAAAAQJBQdAMAAACACar6UcQITxTdAAAAAGACj8djdggIAYpuAAAAADBBbGys2SEgBCi6AQAAAAAIEopuAAAAAAiRDRs26LLLLlPr1q3VuHFjXXnlldqxY4fZYSGIKLoBAAAAIAQ2btyoESNG6JRTTtGnn36qZcuWqUWLFhoxYoQOHTpkdngIEopuAAAAAAiBZ555RkOGDNH999+v1q1bq3379nrkkUd02mmn6T//+Y/Z4SFIKLoBAAAAIATWrVun888/P2D6RRddpBUrVpgQEUKBohsAAAAAQsDtdismJsZ4nZOTI0mKj4/X77//blZYCDKKbgAAAAAIgZYtW2rDhg3G6/j4eEnS6tWruae7BqPoBgAAAIAQuO666/T6669r6dKlcrvdysvL07vvvqslS5aYHRqCKMrsAAAAAADACkaNGiWPx6N7771X6enp8nq96tOnj+688049/PDDZoeHIKHoBgAAAIAQufLKK3XllVcqMzNTMTExiouL0+zZs9WqVSuzQ0OQ0L0cAAAAAEIsOTlZ9evXlyQtWrRIPXr0MDkiBAtFNwAAAACYZOnSpdq8ebMuu+wys0NBkNC9HAAAAABC4Ndff5UkNWjQQPv27dPHH3+s119/XVOnTlWTJk1Mjg7BQtENAAAAACGwefNmPfnkk8rMzFTt2rXVrVs3ffjhhzr99NPNDg1BRNENAAAAACEwcuRIjRw50nidkJCgnJwcEyNCKHBPNwAAAAAAQULRDQAAAAAm4Cq3NVB0AwAAAIAJ4uPjzQ4BIUDRDQAAAAAmsNlsZoeAEKDoBgAAAAATeDwes0NACFB0AwAAAIAJCgoKzA4BIUDRDQAAAAAmiIuLMzsEhABFNwAAAAAAQRJldgAAAAAAwofrgxfNDsEyXGYHYBGOkeNMXT9XugEAAAAACBKKbgAAAAAAgoSiGwAAAACAIKHoBgAAAAAgSCi6AQAAAAAIEopuAAAAAACChKIbAAAAAIAgoegGAAAAACBIKLoBAAAAAAgSim4AAAAAAIKEohsAAAAAgCCh6AYAAAAAIEgougEAAAAACBKKbgAAAAAAgoSiGwAAAACAIKHoBgAAAAAgSCi6AQAAAAAIEopuAAAAAACChKIbAAAAAIAgoegGAAAAACBIKLoBAAAAAAgSim4AAAAAAIKEohsAAAAAgCCh6AYAAAAAIEgougEAAAAACBKKbgAAAAAAgiTK7ABCIScnR7NmzdKGDRtUWFio9u3b65prrlH9+vXNDg0AAAAAUIPV+CvdXq9Xjz32mPLy8jRlyhQ9/fTTSk5O1oMPPqjc3FyzwwMAAAAA1GA1vuhevXq1srKyNH78eDVt2lT169fX9ddfr6SkJH3++edmhwcAAAAAqMFqfNG9du1anXXWWYqOjjam2Ww29e7dW+vXrzcxMgAAAABATVfji+60tDSlpqYGTE9NTdWff/4pr9drQlQAAAAAACuo8QOpZWZmKjk5OWB6UlKSPB6PcnJylJiYGDDf7XbL7XYbr202m2JjYxUVVfWbrH5yZJUvEzCT3W4zO4TjEucgF1Gz2O12s0OosKjYBLNDAKpWNcxDb/3GZocAVKlg/T0sb21Y44tut9td6sYo2vAFBQWlvm/BggWaN2+e8bpHjx4aP358qQV8ZV01sMoXCeA41KtndgQASEQgDFw+zuwIgBqlxncvt9vt8ng8AdOLrmIXv9e7uKFDh2rWrFnGz/XXX+935RvVT15enu666y7l5eWZHQpgWeQhYD7yEAgP5KJ11Pgr3UlJScrMzAyYnpWVpcjISCUklN6NzW63V8tueTg6n8+ntLQ0+Xw+s0MBLIs8BMxHHgLhgVy0jhp/pbtZs2ZKS0sLmJ6WlqamTZsqIqLGbwIAAAAAgElqfMXZpUsXrV692u/ebZ/Pp6+//lpdunQxMTIAAAAAQE1X44vuXr16KS4uTi+88IL27Nmj/fv3680339TBgwc1cCAjmFmJ3W7XJZdcwm0DgInIQ8B85CEQHshF67D5LHATQWZmpt555x1t2rRJHo9H7dq105gxY9SkSROzQwMAAAAA1GCWKLoBAAAAADBDje9eDgAAAACAWSi6gRAYNWqUMjIyzA4DqHbWrVunG2+8US6Xy+xQgLCwatUqPfbYY1W2PHIMAIKvxj+nG+b49ttvNX36dON1XFycmjRpogsuuEBnnXWWiZGZo7CwUB6Px+wwAEN2drY++ugjrV+/XpmZmapVq5Y6duyoYcOGqX79+qbEtG3bNjmdTp1++unGtPj4eDVu3FiRkZGmxASEmtfr1apVq7Rq1Srt3LlTTqdTdrtdjRo10l133SWv1yu3211l6yPHgLI99thj2rp1q1588UXVqVPH7HBQTVF0Iyg8Ho8aNWqkxx9/XJJUUFCgrVu36s0335TT6VT//v1NjhCwrr/++kuPPPKI2rVrp3HjxqlBgwbKzMzUsmXLdMcdd+jOO+/UKaecEvK4Nm/erAMHDvgV3e3bt1f79u1DHgtgBqfTqWeeeUaZmZkaPHiwrr/+esXHxys3N1dpaWmKiYmp8nWSY8DR7d27V//73/+UmpqqVatW6eKLLzY7JFRTFN0IGpvNpvj4eElHzqT36NFD6enp+vzzzym6AZN4PB5NmzZNZ5xxhq6//npjelJSkq6//nrVq1dPzz77rF544QXVqlXLxEgB63nxxRcVGRmpqVOn+hXYiYmJatiwoYmRAda0YsUK9ezZU23atNFHH31E0Y3jRtGNkGrevLkOHjyo9PR0vf/++/rll1+Uk5OjlJQU9e/fXxdddJHRdv78+XI6nWrZsqXeeecdRUVF6dVXX1VOTo7ef/99bdiwQdnZ2UpKSlKPHj106aWXKirq/+/Sf/zxh2bPnq0//vhDMTEx6tGjh7p06aIZM2bo+eefl3SkO+usWbM0duxYvfjii9q/f78ee+wxNWvWTAsXLtTXX3+t9PR0ORwOtW/fXtdcc42SkpIkSdu3b9eMGTN0ww036J133tEff/yhuLg49enTJyCWonW99NJLSktLk81mU6dOnfTvf/9bcXFxQd/uQJE1a9YoIyNDo0aNKnX+RRddpKVLl+rLL7/UsGHD9N1332nlypW67777/Np9/PHH2rRpkx544AFj2t69ezVz5kxt3bpV0dHR6t69u6644go5HA5Jksvl0syZM7V+/Xq5XC7Vr19f48aNk9vt9lvO119/rQ4dOuj+++/Xtm3b9Nhjj+ndd9815ufm5mru3Ln6/vvvlZ2drfr162vAgAEaOHCgbDabJGnfvn268847NXnyZM2YMUN//vmnEhMT1atXL40cOVIREQxpgvCyefNmbdmyRS+//PIxr2j7fD599NFHWr58uQ4ePKikpCQNGTJE559/vl+7rKwsvffee9qwYYPy8vLUpEkTXXTRRerVq5fRprQc83q9Wrx4sZYvX669e/cqOjpaPXr00A033GAs95133tGGDRtks9nUuXNnXX311UpMTDTeP2fOHH3zzTfG3/hrrrlGnTt3rqrNBQSd1+vVypUrdc8996hx48aaMWOG/ve//+mkk04y2qxYsUI//PCDzjrrLM2fP1/p6elq0KCBhg0bpp49exrtMjMzNWPGDP38888qLCxUo0aNdOedd2r16tXauHGjJk+e7LfumTNn6quvvtJrr72mhIQEY/qmTZs0Z84cPfXUU5KOnYsHDx7UnXfeqSeffFIvvPCC/vzzT91+++3q1KlTMDcdSkHRjZA6cOCA6tWrp61bt6pJkya68MILlZSUpO3bt+ull15SvXr11K1bN0lH7oPevXu3du/erSlTpshut0uS0tLSFBsbq4kTJyolJUW7du3SSy+9pISEBA0ePFiSlJGRoUceeUTdu3fXjTfeKJvNpvnz5+u1114zvpQXrcPtduudd97RDTfcoHr16qlWrVpyuVzauXOnrrnmGjVu3Fi5ubl688039dprr+nuu++WJLndbmVnZ+vpp5/WVVddpXbt2mnHjh168cUXVVBQoKuvvtrvs8+aNUujR4/WLbfcotzcXL366quaNWuWbrrpphBseeCIDRs2qHv37n5/xIuLjIxU37599dNPP2nYsGFyu92l3j/q9XpVWFhovD506JAmT56sbt26acyYMXK5XJo1a5beeOMN3XrrrZKk//znP0pPT9fDDz+sWrVq6cCBA0pJSVFiYqJmzpyphQsXKj09Xf/617+MfC8sLJTX6zXWU1hYqMcff1x5eXmaMGGCGjZsqO3bt+vtt99WVlaWRo8eLelIT5uCggI9//zzGjJkiE477TQdPHhQL730kuLj441jBRAuVq9erW7duhlflsuybds2FRYW6qabblLDhg21ZcsWvfbaa2rWrJnRVTw3N1eTJ09WnTp1dN999yk5OVkbN27UW2+9JZfLZfQ4K5ljkvT888/r77//1hVXXKFWrVqpoKBAhw8flnTkdrEpU6bohBNO0JQpUxQREaEPPvhATz/9tB555BFJ0ieffKLNmzfr7rvvVt26dZWRkcEJZlQ7GzZsUEpKilq0aCFJ6tGjh1auXOlXdNtsNu3YsUP//POPrr76ajVr1ky//vqrXn31VTVq1EitWrWSJL322muKi4vTk08+KYfDoX/++UcJCQnq0KGD3n//feXm5vrlyLp165ScnKyff/5Z3bt395veoUMHSeXLxcLCQhUWFmrmzJkaPny4WrRoodjY2GBvOpSCU/0ICbfbrfXr1+u9997ToEGDdPbZZ2vYsGFKTU1VcnKyzjzzTJ111llav3693/s2bdqksWPHqlGjRqpbt64k6dRTT9UVV1yhE088UcnJyerQoYMGDBigH3/80XjfsmXLVL9+fd1www1q2LChGjRooLFjx5Z69WDXrl3q37+/2rRpozp16shut8vhcGjChAnq0KGD6tatq2bNmmn06NHauHGjX6GRnp6u4cOHq3v37kpKSlLnzp11/fXX64svvlBubq7fei677DL169dP9evXV4sWLXTttdfq+++/l8/nq8pNDZRp9+7daty4cZltmjRpor///rtCy124cKFSU1N17bXXqnHjxmrZsqVuu+02/fDDD9q3b58k6ddff9WgQYPUtGlT1a5dWyeeeKJq165t3Ipit9sVFRWl+Ph4RUdHl7qeFStWGCfi2rZtq6SkJJ1xxhm67bbb9PHHH2vv3r1G28LCQg0YMED9+vVTvXr11LZtW1166aVauXJlhT4bEAo7d+40vqAfS3R0tO688061b99ederUUa9evXT22WdrzZo1RptFixbJZrPp/vvvV2pqqpKSktSnTx9dd911+s9//qO8vLxSl71mzRpt2rRJDz74oE4//XQlJSWpfv36RmzLli2TzWbThAkT1Lx5c51wwgm69dZbtWfPHm3dulXSkVw/99xz1bJlSyUmJqpFixamDdAIHK/ly5erX79+xutzzjlHq1evDjgRnZWVpfHjx6tTp05KSUlRz5491bNnT61YscJos3XrVg0dOlQNGzZUUlKS2rVrJ4fDoRYtWigpKUm//PKL0favv/6S3W7Xueeeq02bNvmt66effjLGPSlPLkpHTsC1adNGnTp1UnJystH7DKFF0Y2gKTrrd+WVV+qKK67Qu+++q2uuuUbnnHNOqe0bNGgQ8Fitli1bKjk5+ZjrKvnebdu2qUuXLn5XtSMiItS7d++A9xZ1xynPOrxerzIzM/3eW7z7kCR17dpV0pGDZnElB6pp0aKFXC6XDh06dMx1A1UlLy/PuEXiaJKSkir8+KANGzb4dVmVpOTkZDVq1Eh//PGHpCP7/LJlywJOSFXEunXr1KtXL2O8iCJt27ZVkyZNtHbtWr/pp512mt/rZs2aaf/+/ce9fiBYSl7pKkvTpk0Droi3aNHCOMElSWvXrlX//v0DbnXq0aOHIiIiAr7MF/n222/Vp08f1a5du9T5P/30k3r27On39zU6OlqtW7fW9u3bjVi++eYbv7+XQHWSlZWl3377ze+JOy1btlT9+vW1bt06v7YJCQlq2bKl37TmzZvrwIEDxuvU1FQtWbJEBQUFAevq1KmTNm7caLzeuHGjOnXqpI4dO/rl6a5du5SXl6c2bdpIKl8uFik+QCnMQfdyBE39+vX1wAMPyGazqVatWn5n1goLC7Vy5UqtXbtWe/fuldPpVF5enl+XHUnG1e3ifD6fvv/+e3333XfavXu3cnJy5HK5/L6AZGZmlnpWvbSBaBISEko96/f777/riy++0M6dO3Xo0CHjQFm8G15iYmLAe+12u5KSkgJOIJS8cld01b0qH/0CHIvD4VBWVlaZbbKyso5ZmJd04MABvfHGG3rrrbf8prtcLiMXrrvuOs2aNUvjxo3T4MGDNXDgwKNe0T6affv2HfXLQ9OmTf2KDkkBhUlcXFypX3oAs8XGxpb7hFRpvbZiYmL8/p7s379fTZs2DWgXGRmpRo0a+fUKKW7Pnj3q2LHjUde9f/9+bdu2TYsWLfKbnp+fb/zdveSSS+R2u3X77berf//+Gjx48FFvaQHC0cqVK9W9e/eA73jnnHOOVq5c6VeMl3ZLSFxcnPLz843Xt912m9544w2NHz9ew4cPV9++fY3H9HXq1EmzZs0y2v7000+6+OKL1bx5cxUWFurvv/9W06ZN9dNPP6ljx47GmCTlycUipX2fRmhRdCNoIiMjj9qd7OWXX9bPP/+s888/XxdeeKFq166tr7/+2rgiVqS0Lxb//e9/tWTJEp133nnq16+fkpOT9csvv+izzz4z2pS8P61IaV/wSyu4N27cqKlTp6pXr17Gc4tLDvYk6ahf3gsKCvzOPJaF7uUIpQYNGmjPnj1lttm9e/cxu4KWtu//61//Utu2bQOmF30hiYuL00033aS0tDTNmjVLX375paZMmVKh554eK6/Km3dAuDnhhBO0Y8eOSi2jIn9PysqVo/0NLTJ8+HC/oqNIUWEdFRWlK6+8Uv3799fs2bM1fvx4Pfjgg2rWrFm54wPMtGLFCu3fv9+vi7h0JMe8Xq8yMjIq9LcrOTlZd911l7Zs2aIZM2Zo2bJleuihh+RwOHTqqacqKytLe/bsUVJSknbu3Kn27dvLZrOpQ4cO2rhxo5o2baoNGzb4dXeXjp2LRehSbj66lyPk0tPT9e233+q+++7T8OHDdeqpp6pZs2bl6s7q8Xj06aef6pZbbtHo0aPVsWNHNW/ePOBqcZ06dUrtQnqsYqPIokWLdN555+mmm/5fe3ceFdV1xwH8OzAIjKwDsgcEcQO1FTQggmhyRG20qTZuVZuT9cQ1NbYqVUgUd8BU0+S4FJcmIgVcqhgXRFkMKiCKcQGFAa3aCSACAwPCDPSPnJnjYwYdDURCv59z+GPuvLnvzn0+4ffu/d07D4GBgfDy8tK72nFDQwPq6uoEZUqlEgqFgtu7UJc0ZMgQnD9/XuffrYZarcbZs2cxbNgwAD8+qNKX+9l2lEwqlaKpqQkODg46P21/2Xt6eiIiIgK2trY4cuSIttyQgNnR0bHdfPP79+/D2dn5mXUQdUWBgYG4ePFiu/fm83JyctJ7r6jVasjl8nbvFRcXF5SWlrZbr1QqhVKp1Huvt50e7+TkhL/85S8YNGgQEhISftoXIvqZ3LhxA2KxGDExMdi0aZPgJzo6GoMHD0ZWVtYL1e3r64uoqCjU19cjLS0NALQ75Fy5cgVXr17FgAEDtIuJaqaYK5VK3L59WzAL5XnuRXr5GHTTz666uhoikUgw7a25uVmQz9Ke+vp6NDc345VXXhGUt82vGTp0KLKysgSLnrW0tOD06dM6+W3ttbHtOdrmimqcO3dO8DorKws2Njbw8PB45nmIfm7BwcEwNzdv9w/go0ePoqmpSZufbWdnB7lcLpgmp1QqdRY99PX1xenTpw0eaROLxejTp4/g4ZiJiYngntVn9OjRyMrKQn19vaD8xo0bkMvlGDlypEHnJ+pq/P394eXlpd0B46cKDQ1FamoqVCqVoPzcuXMwMTFpdwr5iBEjkJWVpZOqoeHr64vMzEyD2ygSidCvXz9BfitRV5aWloaRI0fC1dVV709oaCgyMjJeuH6JRIJXXnlFcE8MHToUBQUFuHz5smA7ryFDhqCwsBB5eXnw9vYWjGA/771ILxeDbvrZubm5QSKRYP/+/Xj48CFkMhliYmLaXbTlSVZWVnB2dkZiYiLKy8vxn//8B19++aXOH/phYWEAgM2bN0Mul+PBgwfYtGkTHj9+bFBey4ABA3Dy5EmUlpaivLwcR44cwdWrV3Wmp/fs2RPHjh3DhQsXUF1djZycHCQkJGDq1KncB5i6JIlEgo8//hgZGRnYvHkzbt26hZqaGpSVlWHnzp1ITEzE/PnztTnd3t7esLS0xN69e1FbW4vKykpER0drF3LRePPNN7X32d27d7WL0Bw/flx7TFpamva9nJwcpKenCxYxtLe3x40bN3D//n2UlZXp/UMiICAA7u7uWLt2LYqKilBdXY3s7GxER0fjzTfffO5cdKKuQiQSYfHixairq0N4eDjS09Mhl8uhUChQXl6OCxcuPNcouOb3YHR0NMrKylBdXY1Tp05hx44dmDlzZrvrKQQFBWHAgAGIjIxEXl4eampqUFlZqZ36Pm7cOLS2tmL16tUoLi5GTU0NZDIZDh06pK3j3LlzkMlkqK6uxrVr15CSksI9uukXQalU4uLFi4JtutoaNmwYKioqUFxcbFCdKpUKqampePDgAR49eoSzZ8/iypUrggdffn5+uH79Oi5fviy4V6ysrODq6oqkpCSde8iQe5G6DuZ0U6cwMTHRLhDRlpmZGSIiIrB7926kpqbCwsICYWFhcHFxQUpKivY4Y2NjnTpEIhHCw8MRFxeHZcuWQSwWIzQ0FOPGjUNsbKzgHJ999hn27NmDpUuXwsTEBCEhIZBIJNr9FjXn0Bccv/3229i7dy82bNiAxsZGDBkyBMuWLcOSJUsEuW4SiQSLFy/G7t278cUXX8Da2hrTp0/XybnRbIWkr58MGXkn6ki+vr7YuHEjDh48iM2bN6OmpgZGRkYwMjLCqlWrBAsaGhsbY+nSpYiLi8OCBQsgkUgwfvx49OrVC2fOnNEe5+LiglWrViEhIQERERFQqVSws7MT7FaQlZWFuLg4tLS0wMHBAdOmTRPcK8OHD0d2djaWL18OGxsbfPbZZzr3qOb/gH/961/YvHmzNpVj5syZ2iBD026RSKRzf4vFYu20PaKuxsrKCqtWrUJ6ejoyMjKwd+9eKJVKmJqawtPTE/369YOJiYnef8Nty83MzLB69WrEx8djzZo1aGhogJubGxYuXIjAwEDtcW3vMSMjIyxbtgzJycnYtWsXqqqqIBaLERAQgIULF8LS0hKrV6/Gvn37sG7dOjQ2Nmq37tO4fPkytm/fjqamJkilUoSGhmLatGmd1GtEHSc7Oxtubm5PTVUyMzPD8OHDkZmZCW9vb71/7z55PzY3N+PUqVPYtWsXRCIRnJ2dMW/ePEHQ7eTkhF69eqFHjx46g0P+/v44cOCANu1Lw5B7Ud/f0vRyiFq5ihN1Q3V1dTh58iT8/f1ha2uLmpoaZGVlITc3Fxs2bOiQBSWuX7+Or776Cl9++WUHtJjo5WpsbER0dDSMjIywZMkSLrpCRERE1EE4/5W6pR49euDOnTtYv349PvroI0RFRaGmpka7UmRHMDY25ig1dRtmZmZYvnw5evbsiTVr1jz3Pt1EREREpB9HuomIiIiIiIg6CUe6iYiIiIiIiDoJg24iIiIiIiKiTsKgm4iIiIiIiKiTMOgmIiIiIiIi6iQMuomIiIiIiIg6CYNuIiKi/3NVVVWYPn06rly5oi375z//iQULFuDx48cvr2FERETdADcZJiKiX4yqqirMnTsXmt0uRSIRLCws4ODggMGDByMsLAz29vYvuZWGqa2tRUpKCvLy8lBRUQGxWAwXFxcEBwdj7NixEIt/vl/RKpUKra2tUKvV2jJLS0tIpVIYGf18z+dbWlpw7tw5nD9/HjKZDLW1tWhpaYGFhQV69+6NiIiIn60tREREHYVBNxER/WJogsP33nsPAwcOREtLC+rq6lBaWor09HQcO3YMb7/9NsLCwn7SebKysvDVV19h//79HdRyoRs3biA2Nha2trb4zW9+Aw8PDzQ3N6OoqAgHDhzAmTNnsGLFCtjY2HTK+Q0xefJkTJ48Wad80aJFGD16NKZMmdKh56uoqEB0dDTKy8vx2muvYezYsbCxsYFKpUJVVRVKSkpeuO6DBw8iPT0dW7du7cAWExERGYZBNxER/eLY29vD3d1d+3rQoEF44403kJycjH/84x8wNzdHSEjIC9evVqsFo74dSS6XY/369fjVr36FP/3pT4IRbR8fH4wZMwaRkZFYu3YtNmzYAGNj405px4tSq9VQqVQdWmdtbS0iIyPh4OCArVu3wsrKSueYwMDAF65fpVJ12vUkIiJ6FuZ0ExFRt2BkZIRp06YhODgYe/bsQVNT08tukl7x8fGwsLDAwoUL9U4ht7GxwSeffIK7d+8iLS3tJbTw57dnzx4YGxtj2bJlegNuIiKiXzKOdBMRUbcydepUnDt3Dnl5eQgKCtKWX716FSdPnkRxcTFqa2shkUjg6+uLP/7xj9o88IMHDyIhIUH7mWnTpgH4cUp1cHAwACA1NRVZWVm4f/8+lEolbGxsMGLECMyYMQM9evR4atsUCgVyc3Mxa9YsmJqatntc79694efnh7S0NMFU+W3btqGiokJvbvO2bdtQWVmJlStXastKSkpw7Ngx3Lx5E7W1tTAxMUH//v0xa9YswUwBfQ4ePIgTJ05gx44dAICZM2dqR4uTk5ORnJwMZ2dnbNmyBQsWLEBgYCBmz54tqEMmkyE8PByff/45XFxc9J6nsrIS3333HebOnQuJRPLUNj3p/PnzSEtLw507d1BXVwdLS0sMHToUc+bMgYWFBQBg+/btggcXmusZFRWF/v37AwAaGhqQlJSE8+fPo6amBo6Ojhg3bhzGjx+vc86SkhIkJibi1q1baGpqgpubG37729+itbUViYmJOtPX7969i+TkZNy8eRP19fWwtbXF8OHDMWXKFJ2HCx9//DEmTZoEqVSKvXv3oqqqCh999BH279//wn1LRERdA4NuIiLqVpydndGrVy8UFRUJgu60tDR4enri9ddfh42NDSorK5GYmIjNmzdj7dq1EIlECAsLw7Bhw5CXl4eEhATExMQAABwcHAD8OE35zJkzCA0NhaurK8zNzVFWVob4+HiIRCLMmTPnqW0rKiqCWq2Gv7//M7+Hv78/du7ciYaGBpibmwN4+rR3fdO+MzIyIJVK8d5778HW1hYKhQKHDh3Chg0b8Le//e2pDwnaTsmOjY2FSqXC2rVrMXz4cISFhWkfHAQFBSEzMxOzZs2CSCTSfiYzMxMeHh5PDQoLCgogEokQEBDwzD550qlTp+Dn54dJkybB0tISDx48QHx8PHbs2IFPPvkEADBjxgxMmDABp06dQm5uLlasWAGRSARnZ2cAQFNTE6KiolBVVYU//OEPcHNzQ1FREfbt24f6+nr8/ve/155PJpPh008/haenJ+bPnw+pVIqSkhLs2rULjo6OOtfl6tWr2LhxI/r27YsPP/wQdnZ2uHfvHg4cOIC8vDysXr0aUqlU0N937tzB8ePHMXv2bNjZ2cHa2von9S0REXUNDLqJiKjbcXR0RGVlpaBs8eLFgtdeXl7o1asXli1bhtLSUnh5ecHCwgIWFhaQyWQAoDMaLBaLsX79ekGZt7c3GhsbkZSU9Mygu7y8HMbGxnBycnrmd3Bzc0NrayvKy8vh4eHxzOP1effdd3XK+vTpg/fffx/5+fnPlSetCe7EYjGsrKwEfRMcHIzDhw+jsLAQAwcOBPDjSuTZ2dmYNGnSU+u9f/8+evXqpX2wYKhPP/1U8NrLyws9evRATEwMFAoFLC0tYW1tDWtra1hZWUEsFutcz6NHj6K0tBSxsbHa7+fl5QWxWIyvv/4aEyZM0I6+79+/H/b29oiIiNA+rPDy8oKnpydWrFghWDW/qakJf//73+Hj44Pw8HDtCvBeXl7w8/PDkiVLsHv3bixZskTQnjNnzmDTpk1wdXXVlv2UviUioq6BOd1ERNTtmJmZGbS/tCYI++GHH37S+dzd3dHQ0IDa2tqnHtfQ0AAzMzPBiGV7zMzMtJ/pSJaWlrC1tUV5eXmH1enu7g4PDw9kZ2dry65du4aamhrttPz2KJVK7XftiHYAhl/P9PR0BAQE6IwWjxo1Cmq1Gjk5OQB+DKKvXbuG119/XWd2gLe3N379618LynJzc1FdXY2ZM2fqbLlmYWGBiRMnIicnB9XV1YL3evfuLQi4Nd/pRfuWiIi6Bo50ExFRt6PJn33S48ePkZqaioKCAjx48AB1dXVobm7WvmeoqqoqfPvttygqKkJ5eTmUSqW2nsbGxqcuBGZqaorGxka0trY+M/BubGzUfuZFqdVqZGRkICcnB/fv34dCoUBTUxNUKpW2/o4SEhKClJQUvPPOOzAyMsJ3332HwYMH61yHtszNzV/owUJdXR1OnDiB69evQy6Xo76+/rmup1KpxA8//KB3tNjU1BT29vba4F0ul0OtVsPT01NvXf3798e9e/e0r0tKSmBrawsvLy+9x/v7++Prr7+GTCaDn5+ftrx37956j3/RviUioq6BQTcREXUrra2tuHfvHnx8fLRl9fX1iIyMRHl5OUJDQzFmzBjY2NhAIpFg6dKlBtctk8mwatUqSCQSjBkzRjsl/cGDB9i2bdszP29vbw+1Wg25XK7NK26PJohzdHQ0uH1P0uRfFxYWYuTIkZg6dSqkUiksLCx0psh3hODgYOzbtw83btxA//79cfHiRbzzzjvP/JyTkxMqKiqgVCoNXkitoqICK1euhEqlwujRozF+/HhYWVmhsbHR4O+mCfTj4uKwa9cunfdbWlq0I9FKpRIAtAu0tWVtbS14rVQqn/rwRXN8fX29oNzS0lLv8S/at0RE1DUw6CYiom7l5s2bUCgUghHE48ePQy6XY+PGjXBzc9OWt53e+yzffPMN7O3tsWbNGkEOclVVlUGf79u3L0QiES5duoSJEyc+9dhLly6hX79+gkBUJBKhpaVF7/GPHj0SvM7Ozsb169cRGRmJQYMGactbW1t1gr2OIJVK4ePjg+zsbNTV1UGtVhu0ONrgwYPR2tqK3NxchIaGGnSu5ORktLS0ICYmRjDae/v2bYPbq5nSPmfOHAwZMkTvMZrgWDOlvL30gbYj9RYWFk9NNaipqQGgG2S3nYqu8aJ9S0REXQNzuomIqNtQqVTYt28ffHx80LdvX215cXExfHx8BAE3AMGUYEMUFxcjODhYZ9EvQ+uxs7ODr68vjh079tQp0GVlZcjPz0dISIigvEePHtqA7Umala/bttXZ2VkQcAM/jhJ31h7mISEhyMnJQWZmJoYNG2ZQrraLiwv8/PyQnJxs8JT34uJiDB8+XGd6dXvXQd9U/p49e8LW1haNjY1wd3fX+6MJul1dXSEWi1FSUqK3/oKCAsHrfv364dGjR9oF+drKz8+HWCyGt7f3M7+rxov0LRERdQ0MuomIqFtQKBSIiYnBf//7X3zwwQeC98zMzHRGHltaWpCSkqI3INPkUbcNAk1NTaFQKARldXV1OHv2rMHtnD17Nqqrq/HFF1/obPEF/Dj6/vnnn8PR0RFjxowRvOfq6gq5XA65XC4oP3PmDOrq6nTaqlQqdUbGjxw5YtBCbu3R5KXrExgYiIaGBuTl5WHUqFEG1/nBBx+goaEBGzdu1Pke+ui7ns3NzThx4sRztTkgIADHjh3TmSWg7/MjRozAt99+q3P9r169ioKCAhgbG2vLhg0bBldXV8THx+v0f21tLY4ePYrXXnut3enq+rxo3xIR0cvHoJuIiH5xKisrcffuXRQWFiIrKwvbt2/H/PnzUVlZiYiICJ0VoEeNGoXS0lLs3r0bt27dwvfff49169bh8ePHerfv0nw+MTERt27dQn5+PgAgNDQUJ0+exIkTJ1BSUoILFy4gPDwcQ4cONbjtXl5eWLhwIS5fvozw8HCkpaWhuLgYN2/exKFDh/DnP/8ZKpUKy5cv11kpOygoCBKJBLGxsSgoKMC9e/dw6NAhJCQk6EzNDgkJgUKhwNatW1FYWIjCwkJs2bIF165d02499SJcXFyQnZ2N77//HleuXBGsgi6RSODv7w9ra+t2p2zrY2dnh1WrVuHRo0dYtGgR4uPjceXKFZSWluL27du4dOkSEhIStMeHhoYiNzcXBw4cQHFxMfLz87Fy5Uq4u7vDxMREb5sVCgWOHDmCoqIiXLt2DQDw1ltvoWfPnli6dClOnDgBmUyGwsJCnD59WidHf86cOTAxMcGKFSuQk5ODkpISHD16FLGxsfD09BRMFTcyMsL777+PoqIiREVFIS8vDzKZDOnp6fjrX/8KMzMzvPXWWwb3z0/pWyIievmY001ERL8YYrEYIpEIcXFxEIlEMDc3h1QqRZ8+fbBo0SL4+fnpzYv18/PDvHnz8O9//xupqamwsrLCiBEjMH36dEREROiMOLu7u+N3v/sdTp06hdOnT+PVV1+Fn58fZsyYAZFIhMOHD6O2thaOjo6YOHEiRowYgbS0NKjVaoO+R1BQELy8vHD06FEcPnwYVVVVEIlEaG5uRv/+/bF8+XK9i4pZW1sjIiIC33zzDWJiYmBkZARvb29ERkYiPz9fsDe5u7s7li5disTERERFRcHc3Bz+/v5YvXo1tm3bJmirpl/FYrGg7MnXGjNmzMCWLVuwbt062NvbY/78+XBwcNC+39zcjKCgIMHIryFcXV0RExODzMxMXLx4Eenp6dpRZalUKkgXCAsLQ2NjI1JTU5GcnAw7OzuMHj0akydPRk5Ojs518PPzw+jRo5GUlASxWIyxY8di0KBBsLKywtq1a3HgwAGkpKTg4cOHMDU1hZOTk85oso2NDdatW4f9+/dj586dUCqVcHd3x4IFC5CRkQEbGxvB8b6+vtiwYQOSkpKwfft21NfXQyqVIiAgAFOmTEHPnj0Fx7fX30960b4lIqKXS9Ta2tr6shtBREREPy74tmfPHixZsgSvvvrqy27Oc6upqcHcuXOxZs2adrfL6m5kMhlWrFiBqKio58rRfl7/j31LRNRdcKSbiIioi5gwYQLu3buHrVu3IjIyEv369XvZTTJIWVkZmpqacPDgQQwcOLBbBoUFBQW4cOEChg4dCqlUioaGBty+fRuHDx/GG2+80WkB9/9D3xIRdXcMuomIiLqQd999Fw8fPsTGjRuxadMm2NnZvewmPVNSUhIuX76MgQMHYt68eS+7OZ3Czs4OCoUCcXFxUCgUEIvF8PDwwIcffojg4OBOO+//Q98SEXV3nF5ORERERERE1Em4ejkRERERERFRJ2HQTURERERERNRJGHQTERERERERdRIG3URERERERESdhEE3ERERERERUSdh0E1ERERERETUSRh0ExEREREREXUSBt1EREREREREnYRBNxEREREREVEn+R/tTDLKGe7hPgAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# ๋ฐ์ดํ„ฐ ๊ฐœ์ˆ˜ ๊ณ„์‚ฐ\n", + "low_quality_counts = {\n", + " 'Paragraph': len(train[train['๋ฐ์ดํ„ฐ์ €ํ’ˆ์งˆ-paragraph'] != \"์ •์ƒ\"]),\n", + " 'Question': len(train[train['๋ฐ์ดํ„ฐ์ €ํ’ˆ์งˆ-question'] != \"์ •์ƒ\"]),\n", + " 'Choices': len(train[train['๋ฐ์ดํ„ฐ์ €ํ’ˆ์งˆ-choices'] != \"์ •์ƒ\"]),\n", + " 'Answer': len(train[train['๋ฐ์ดํ„ฐ์ €ํ’ˆ์งˆ-answer'] != \"์ •์ƒ\"]),\n", + "}\n", + "\n", + "# ์ „์ฒด ๋ฐ์ดํ„ฐ ๊ฐœ์ˆ˜ ๊ณ„์‚ฐ\n", + "total_data_count = len(train)\n", + "\n", + "# ๋ฐ์ดํ„ฐํ”„๋ ˆ์ž„ ์ƒ์„ฑ\n", + "low_quality_df = pd.DataFrame(\n", + " list(low_quality_counts.items()), \n", + " columns=['Category', 'Count']\n", + ")\n", + "\n", + "# ์„ธ๋กœ ๋ง‰๋Œ€ ๊ทธ๋ž˜ํ”„ ์‹œ๊ฐํ™”\n", + "plt.figure(figsize=(10, 6))\n", + "sns.barplot(\n", + " data=low_quality_df,\n", + " x='Category',\n", + " y='Count',\n", + " palette=\"coolwarm\"\n", + ")\n", + "\n", + "# ๊ทธ๋ž˜ํ”„ ์ œ๋ชฉ ๋ฐ ์ถ• ๋ผ๋ฒจ ์„ค์ •\n", + "plt.title('Overall Distribution of Low Quality Data', fontsize=18, fontweight='bold')\n", + "plt.xlabel('Data Quality Category', fontsize=14)\n", + "plt.ylabel('Count', fontsize=14)\n", + "\n", + "# ๋ง‰๋Œ€ ์œ„์— ๊ฐ’ ํ‘œ์‹œ\n", + "add_values_to_bars(plt.gca(), low_quality_df['Count'])\n", + "\n", + "# ์ „์ฒด ๋ฐ์ดํ„ฐ ๊ฐœ์ˆ˜ ๋ ˆ์ „๋“œ ์ถ”๊ฐ€\n", + "plt.legend(\n", + " title='Summary',\n", + " labels=[f'Total Data Count: {total_data_count}'],\n", + " fontsize=12,\n", + " loc='upper right'\n", + ")\n", + "\n", + "# ๋ˆˆ๊ธˆ ๋ฐ ๋ ˆ์ด์•„์›ƒ ์กฐ์ •\n", + "plt.xticks(fontsize=12)\n", + "plt.yticks(fontsize=12)\n", + "plt.grid(True, linestyle='--', linewidth=0.5, alpha=0.7)\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "์ „์ฒด ์ €ํ’ˆ์งˆ ๋ฐ์ดํ„ฐ ๊ฐœ์ˆ˜: 200\n" + ] + } + ], + "source": [ + "print(f'์ „์ฒด ์ €ํ’ˆ์งˆ ๋ฐ์ดํ„ฐ ๊ฐœ์ˆ˜: {low_qual_paragraph.shape[0] + low_qual_question.shape[0] + low_qual_choices.shape[0] + low_qual_answer.shape[0]}')" + ] + }, + { + "cell_type": "code", + "execution_count": 279, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_3515290/1880777606.py:19: FutureWarning: \n", + "\n", + "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", + "\n", + " sns.barplot(\n", + "/tmp/ipykernel_3515290/1880777606.py:33: FutureWarning: \n", + "\n", + "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", + "\n", + " sns.barplot(\n", + "/tmp/ipykernel_3515290/1880777606.py:45: FutureWarning: \n", + "\n", + "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", + "\n", + " sns.barplot(\n", + "/tmp/ipykernel_3515290/1880777606.py:57: FutureWarning: \n", + "\n", + "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", + "\n", + " sns.barplot(\n" + ] + }, + { + "data": { + "image/png": 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dskosFouefPJJPfnkkzkqJ78obOfN6tWrezQRY9myZdWtWzfNnz9fH3/8sSpUqKCHHnrIqbvhX3/9pXfffVe33nqrU0bg3r17NWXKFFWrVk3ff/+93njjDT366KOSzAOuhUlhO3bWrVun2bNnq0OHDqpWrZqioqLUv39/dejQIVcPwXIS1Pb395ePj48WLlyo6tWrKzk5Wb/88ovTOMh33XWX03nu0UcfVWxsrO644w6FhYXp9OnTiomJ8Wj+FElq3ry58bDBU+fOndPWrVuN61S6NWvW6I8//tA999yjjh075uq9gRvNbrcbD0G7du2qEiVK6NNPP9XatWv1yiuvuM1ILVOmjKKjo7VixQojIzwwMFClSpXS3XffbWTfZsXPz08NGzbUuHHj1KlTJ6d99uzZoxkzZrgN/pUuXVp9+vQxHdoiKCjIKcgIZJbbe6H9+/drx44d6t69uwYOHKiEhATTXhoRERH69ttvZbVaVbFiRR06dEgxMTFq2rSpR2UuWbJE4eHhql+/vs6cOaNRo0apZ8+eOeoJ+/PPP+vixYseJazl9vrWpEkTRUdH64033pCfn5/27Nmjbdu2qXXr1lmWl5ycrBkzZmjfvn16//33c9TTOfM26X+HtLQ0I5EByErmNuDLL7+c7QSdbdu2NeYJyqhcuXLq37+//vnnnywzyZOTk7Vw4UJt2LBBkZGRstvt+vTTT7Vq1Sq1b9/e7bCYZcuW1Zdffuk0f19QUJBuvfXWAjdMamFEEL2AS05O1ty5czVmzBjT4TzatGljNDgbNWrkNNv1/Pnz1adPHyMwVqpUKb344ouaP3++y4U3q7L++OMP3XrrrcY+gYGBatq0qX7//XfVqlVLDofDKTCeVXfp6tWrKyYmRsuXL5d0tdtLWFiYceG0WCx67733dOutt0qSgoOD3T7xzyoAfv78eU2YMEHJycl66qmn9MADD8hisahGjRp64okntGrVKo0ePVo1atTQSy+9ZPo+3rB161Y9/fTTqly5srGsXbt2atCggeLj4yVJ5cuXN8bcnj9/vlauXJnj92/QoIHTuMrx8fFG18G///7baegGdxnR6U6dOqV169Y5dfHLyN/fX08//bRxLDRs2NDIDkxKSlJcXJzGjx9vDBdQsWJFNWvWTD/99JNL9kVWZa1Zs0Z16tQxgkkhISFq3Lix1q5da9ykZQzGp9cnJCREkZGRmjFjhtFl8p577vFoRu46deooMTFRI0aMUEJCgvz9/dW2bVvVq1fP2Ca7DPHU1FTZ7XYlJibqn3/+UWJiotP6hIQEDRgwQA0bNjTGqq9Zs6Z69+6tWbNmqWbNmjnqWREREaEvv/zSdH3mY2j69OnasWNHtu+brnnz5kag1dsK03lzzZo1mjdvnsvy48ePq1SpUi7BhmrVqumNN94w/t+3b19FRkbq3XffdRkHt3Llyk7Hp8Ph0PLlyzVv3jz16tVL4eHhioqK0vDhw9W5c+ccBU8KusJ07EhXH3oEBgaqf//+xt+vR48e2rp1q3E9cScsLEyff/6525v5lJSULMdUlq4+kPvoo4+0fv16TZs2TWXKlNEjjzySZYZZRESEEhISNGLECCUlJSkgIEBNmzZ12yXXnejoaP3xxx9Oy9LS0nTy5EljKKqMnnrqKTVp0sRp2bRp09SqVSuXYWz+9a9/6bXXXstRPYD8wG63q1+/fqpatarTQ7pBgwZp9erVLm2NdA899JAefPBBSa4BLelqL5icTLb25ptvas6cOerbt6+KFCmiIkWK6OzZsypatKheeOEFl6xUScYweICnruVeKDU1VV999ZXKli2rtWvXGmOGm7XfH374YR05ckSTJk2Sw+EwegNmzKpOv3+qUKGCevTo4fIecXFxWr9+vfr37y/pahvax8dHAwcO1JgxY7K8j96yZYuWLl2q0qVLa9GiRWrWrFmOPnNur29t27bVvHnz1Lt3b6Wmpqps2bKKjIw0DfanpKRo+fLlWrRokR544AENGzbM5d6tTJkyLstuv/12TZkyxThXpaSkyN/fX2FhYapVqxZBdOQJs3H9M8rqmhcfH68BAwaofv36Gjp0qPH7+vDDDxUXF+d2yE1Jat26tVq2bCmLxeL2Wrt582bT6zTyHkH0Au7IkSMKCQnJMjs4Y9ZscHCwkaGdkJCg+Ph4p2EipKvd/KdMmeJRWUePHtWePXvUu3dvY1lqaqoxLMarr76qKVOmqHLlynrmmWeyHJ+tdu3aTsGELl266IMPPjANit9555368ccf1apVK2PZ1q1bdffdd5uWUaxYMXXo0EG33367y7qiRYvqqaee0lNPPZWrCRrzmtmkfxn/jhkbO61atXL6btJ16dJFH374oUfZ9o8++qhTUC5zRnRGf/31l8LDw0273RYtWtSpEXjLLbcYx+Y///yj0NBQl7rdeeedWr16tUdlHT16VBs2bNDOnTuNZcnJycZkWh07dtRHH32k++67Ty1btnTKtK5YsaIx/Exu/etf/3J7M+jO77//7jQBpMViUWRkpPz9/VW0aFHdcsstLhNtFSlSRL169XKZWLZChQpO3akzPljJjSpVqjj9bv/973/n+r28rTCdN82Or3feeUcDBw50m92wb98+RUdHG69PnDihjz/+2Hhts9ncZhB98sknSklJ0fDhw433ffvtt7Vy5Upt3ry5wPVGyI3CdOxIUteuXd32rImIiDD+X7FiRZcg/iuvvOKUrebJ9eTgwYPatGmT8dpisSgxMVG///67lixZosuXL+upp55yu68n59PM3HV5PXPmjIYMGaKxY8dmu//MmTNlsVicemsABZXVatXAgQNdfv8Wi8Vp0sUqVaq4BNXsdrsuXLjg9vpSrly5HA3pZbVa9fzzz6tNmzbau3evJk2apJEjRxq9AoHr6VruhaZPn64qVaqoTZs2xsTb7u4f01ksFr366qt65pln1KtXLw0aNMilV0dW909Hjx7V+vXr1bt3b6f7mqZNm+rhhx/OMoC+efNmff3118Zve9iwYSpWrJjbiVQzupbrm81m03PPPefUeyQxMVGpqalGEk/GOl+4cEHHjh3ToEGDjIS4zNwNm/PSSy9lm9iWMUkJyMjsQVqJEiXcDkHYoEED+fv769dff81xGZkTEaWr9wTDhw93udbabDan+yZ3cauUlBQlJSW5PT/VqFEjXw+ZWtgRRC8EPBn6xGKxGF2tzS7C6WNDe1JWcnKy6tWrp9dff93t+jvuuEMjR47Ub7/9pv79+6tnz54eTfqZlbvvvluzZs3SsmXL9Nhjj+nw4cOaO3euMa6qOz4+Plk2gNLl50k0J06cqIMHD7pdd/HiRX388cdOmaE5nQwzK0FBQbp8+bL2799v+uQ0o9wem1l977k5Np988kk9/fTTbtfXqlVLo0aN0rJly9S7d28NHjzYdKJEu92uc+fO6cSJE9lOPpcbDz74oJHhZWbXrl0uGeAZA+i///67li1b5jScUc2aNdWqVSu3N7yZffXVV04PHNL5+vq6zWaZPXu21q1b53YIBx8fH73wwgv5sttZYTtvLlq0yKlxGBAQYAytkpycrEqVKqlbt26Sro6f/v777xufx2q1ymazycfHx/h86RPSZtSpUye3k0TlNBO4sChMx07GRv3atWu1bNkyo+eRdPX62qJFC48mJstO8eLFVaNGDUlXv5PatWvLz89P/v7+KlasmIKCgrKdMO1afPbZZ/r777+N1wEBAcbDiPj4eLVu3VqPP/64sT4pKUlffvmlLl26pMjIyDw59wPekP77T+9ltGrVKiUkJMjhcMjHx0d16tTRSy+95HLeP3z4sGJiYtxm4u7bt08LFiwwDRBmZrVaVbJkSfn6+poG0DP2fLty5YoSEhKcHi4eP37cqTdJfur5hvzFk3shu92u6OhonTt3TpGRkfLx8dE777yjTz75JMsx/9MFBwc7DYN48eJF/fPPP9neP5UrV870HtbdZN7p5syZo3Xr1mnAgAFGb7D+/ftr5MiRslqtbh9A59X1bcKECapXr54xJvqoUaOMdSVLltTrr7+un3/+2eh1ntmlS5fUsWNHtxOxjh07VkePHnW570ifjwlwx+xBWnbcxQ/SJwrOqfRrbWpqqhYuXKh169YpOTlZDodD/v7+euCBB9S9e3eX/bZv364tW7a4nf9gw4YN2r9/P70gvYQgegFXvnx5XbhwQSdPnsy2+3RmgYGBCg0N1YEDB4yhLqSrEza6uwhlVVbp0qW1YcOGLMuzWq1q3LixrFar5s+fn+MGdnYsFov69eun6dOna+nSpQoJCTGGGCnMunbtarpu4MCBunjxolMQ/aOPPtJbb71lfC9ZNZTi4uIUFxcn6WpDJiEhQbt375bFYlFwcLBKly5tBEHMVK5cWdOmTctV8L5MmTI6f/68zp8/7/T0de/evapYsaJHZZUpU0b79u3Lsjw/Pz81b95cV65c0ZIlS9S4cWN99dVXstvtRmM3PdhYvHhxp2EUciI6OlqVKlVym1nRsWNHl25gx44d08aNG91e7IsXL6677rrLbTlLly7VmjVr1LlzZ+NBwJUrV/Tbb79pyJAhxtjLWXE33uHGjRsVHR3tNIRQum3btql79+5u/y6LFy/W7t27810QvTCeN5s1a2baZffIkSNOmc5Wq9XolTB79myXrAnp6nGWeZLR9BvB2NhYVaxY0e3NzeOPP+40rnVhUxiPHUlatmyZVq1apbffftv4+125ckWrVq3SoEGDNHr06BzPBZGdkJAQhYSEKCYmxu1EuNLV863ZWOdZnU9ffPHFbI+/9IdJ7ixZskQnTpwwXp86dUpDhw5Vw4YN1blz53z9YB3IrZkzZ+p///ufevToYbQbL1++rCVLlmjIkCEaPXq0y9Bg6XNmZHblyhWjR8z1krHn25YtW7R69Wqnstu1a6fx48df1zJROHlyL7R582alpqaqV69exrm/YsWK6tatmwIDA93us3z5cq1cudIYUu3s2bPq16+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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 1. ๋ฐ์ดํ„ฐ ์ €ํ’ˆ์งˆ ์นดํ…Œ๊ณ ๋ฆฌ๋ณ„๋กœ value_counts ๊ณ„์‚ฐ\n", + "paragraph_quality_counts = low_qual_paragraph['๋ฐ์ดํ„ฐ์ €ํ’ˆ์งˆ-paragraph'].value_counts()\n", + "question_quality_counts = low_qual_question['๋ฐ์ดํ„ฐ์ €ํ’ˆ์งˆ-question'].value_counts()\n", + "choices_quality_counts = low_qual_choices['๋ฐ์ดํ„ฐ์ €ํ’ˆ์งˆ-choices'].value_counts()\n", + "answer_quality_counts = low_qual_answer['๋ฐ์ดํ„ฐ์ €ํ’ˆ์งˆ-answer'].value_counts()\n", + "\n", + "# ์ „์ฒด ๋ฐ์ดํ„ฐ ๊ฐœ์ˆ˜ ๊ณ„์‚ฐ\n", + "total_data_count = len(train)\n", + "\n", + "# 2. ๊ทธ๋ž˜ํ”„ ๊ทธ๋ฆฌ๊ธฐ\n", + "fig, axes = plt.subplots(2, 2, figsize=(15, 12))\n", + "\n", + "# ๊ธด ๋ผ๋ฒจ์„ ์—ฌ๋Ÿฌ ์ค„๋กœ ๋‚˜๋ˆ„๋Š” ํ•จ์ˆ˜ ์ •์˜\n", + "def wrap_labels(labels, width=10):\n", + " return ['\\n'.join(label[i:i + width] for i in range(0, len(label), width)) for label in labels]\n", + "\n", + "# Paragraph ์ €ํ’ˆ์งˆ ์‹œ๊ฐํ™” (์ˆ˜์ •๋œ ๋ถ€๋ถ„)\n", + "wrapped_paragraph_labels = wrap_labels(paragraph_quality_counts.index, width=9)\n", + "sns.barplot(\n", + " x=wrapped_paragraph_labels, \n", + " y=paragraph_quality_counts.values, \n", + " ax=axes[0, 0], \n", + " palette=\"pastel\"\n", + ")\n", + "add_values_to_bars(axes[0, 0], paragraph_quality_counts.values)\n", + "\n", + "axes[0, 0].set_title('Distribution of Low Quality Paragraph', fontsize=16, fontweight='bold')\n", + "axes[0, 0].set_xlabel(\"\") # x์ถ• ์„ค๋ช… ์ œ๊ฑฐ\n", + "axes[0, 0].set_ylabel('Count', fontsize=14)\n", + "axes[0, 0].tick_params(axis='x', rotation=0)\n", + "\n", + "# ๋‚˜๋จธ์ง€ ๋ถ€๋ถ„์€ ๊ทธ๋Œ€๋กœ ์œ ์ง€\n", + "sns.barplot(\n", + " x=question_quality_counts.index, \n", + " y=question_quality_counts.values, \n", + " ax=axes[0, 1], \n", + " palette=\"pastel\"\n", + ")\n", + "add_values_to_bars(axes[0, 1], question_quality_counts.values)\n", + "axes[0, 1].set_title('Distribution of Low Quality Question', fontsize=16, fontweight='bold')\n", + "axes[0, 1].set_xlabel(\"\") # x์ถ• ์„ค๋ช… ์ œ๊ฑฐ\n", + "axes[0, 1].set_ylabel('Count', fontsize=14)\n", + "axes[0, 1].tick_params(axis='x', rotation=0)\n", + "\n", + "sns.barplot(\n", + " x=choices_quality_counts.index, \n", + " y=choices_quality_counts.values, \n", + " ax=axes[1, 0], \n", + " palette=\"pastel\"\n", + ")\n", + "add_values_to_bars(axes[1, 0], choices_quality_counts.values)\n", + "axes[1, 0].set_title('Distribution of Low Quality Choices', fontsize=16, fontweight='bold')\n", + "axes[1, 0].set_xlabel(\"\") # x์ถ• ์„ค๋ช… ์ œ๊ฑฐ\n", + "axes[1, 0].set_ylabel('Count', fontsize=14)\n", + "axes[1, 0].tick_params(axis='x', rotation=0)\n", + "\n", + "sns.barplot(\n", + " x=answer_quality_counts.index, \n", + " y=answer_quality_counts.values, \n", + " ax=axes[1, 1], \n", + " palette=\"pastel\"\n", + ")\n", + "add_values_to_bars(axes[1, 1], answer_quality_counts.values)\n", + "axes[1, 1].set_title('Distribution of Low Quality Answer', fontsize=16, fontweight='bold')\n", + "axes[1, 1].set_xlabel(\"\") # x์ถ• ์„ค๋ช… ์ œ๊ฑฐ\n", + "axes[1, 1].set_ylabel('Count', fontsize=14)\n", + "axes[1, 1].tick_params(axis='x', rotation=0)\n", + "\n", + "# ์ „์ฒด ๋ ˆ์ด์•„์›ƒ ์กฐ์ • ๋ฐ ํ‘œ์‹œ\n", + "plt.tight_layout(rect=[0, 0, 1, 0.95]) # Leave space for the legend\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "์˜ฌํ•ด ์ดˆ๊ฐ€์„์— ๋น„๋กœ์†Œ ์ €๋Š” ์ฑ…์„ ์™„์„ฑํ•˜์—ฌ ๊ทธ ์ด๋ฆ„์„ ์„ฑํ•™์ง‘์š” ๋ผ๊ณ  ํ•˜์˜€์Šต๋‹ˆ๋‹ค. ์ด ์ฑ…์—๋Š” ์ž„๊ธˆ์ด ๊ณต๋ถ€ํ•ด์•ผ ํ•  ๋‚ด์šฉ๊ณผ ๋ฐฉ๋ฒ•, ์ •์น˜ํ•˜๋Š” ๋ฐฉ๋ฒ•, ๋•์„ ์Œ“์•„ ์‹ค์ฒœํ•˜๋Š” ๋ฐฉ๋ฒ•๊ณผ ๋ฐฑ์„ฑ์„ ์ƒˆ๋กญ๊ฒŒ ํ•˜๋Š” ๋ฐฉ๋ฒ•์ด ์‹ค๋ ค ์žˆ์Šต๋‹ˆ๋‹ค. ๋˜ํ•œ ์ž‘์€ ๊ฒƒ์„ ๋ฏธ๋ฃจ์–ด ํฐ ๊ฒƒ์„ ์•Œ๊ฒŒ ํ•˜๊ณ  ์ด๊ฒƒ์„ ๋ฏธ๋ฃจ์–ด ์ €๊ฒƒ์„ ๋ฐํ˜”์œผ๋‹ˆ, ์ฒœํ•˜์˜ ์ด์น˜๊ฐ€ ์—ฌ๊ธฐ์—์„œ ๋ฒ—์–ด๋‚˜ ์ง€ ์•Š์„ ๊ฒƒ์ž…๋‹ˆ๋‹ค. ๋”ฐ๋ผ์„œ ์ด๊ฒƒ์€ ์ €์˜ ๊ธ€์ด ์•„๋‹ˆ๋ผ ์„ฑํ˜„์˜ ๊ธ€์ด ์˜ต๋‹ˆ๋‹ค.\n", + "๋ฐ‘์ค„ ์นœ โ€˜์ €โ€™์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?\n", + "['์˜ˆ์•ˆํ–ฅ์•ฝ์„ ๋งŒ๋“ค์—ˆ๋‹ค .', '๋™ํ˜ธ๋ฌธ๋‹ต ์„ ์ €์ˆ ํ•˜์˜€๋‹ค .', '๋ฐฑ์šด๋™ ์„œ์›์„ ๊ฑด๋ฆฝํ•˜์˜€๋‹ค .', '์™•์ž์˜ ๋‚œ ๋•Œ ์ฃฝ์ž„์„ ๋‹นํ–ˆ๋‹ค .']\n", + "2\n", + "๋ฐ‘์ค„์ด ํ™•์ธ๋˜์ง€ ์•Š์Œ\n", + "\n", + "\n", + "โ€œ๋น„์ž”ํ‹ด์˜ ํ•™๋ฌธ๊ณผ์˜ ๊ต๋ฅ˜๋ฅผ ํ†ตํ•ด ํ•™์ž ์•„๋ฆฌ์Šคํ† ํ…”๋ ˆ์Šค๋ฅผ ํ”Œ๋ผํ†ค์œผ๋กœ ๋Œ€์ฒดํ•˜๋Š” ๊ฒƒ์ด ๋นจ๋ผ์กŒ์Šต๋‹ˆ๋‹ค. ์ด๋ฏธ ํŽ˜๋ ˆ๋ผ ๊ณต์˜ํšŒ(1438๋…„)์—์„œ ๋ช…๋ชฉ์ƒ์œผ๋กœ ๋™์„œ ๊ตํšŒ๋ฅผ ํ†ตํ•ฉํ•˜์˜€์œผ๋ฉฐ, ๋น„์ž”ํ‹ด ์ œ๊ตญ์ด ์•„๋ฆฌ์Šคํ† ํ…”๋ ˆ์Šค์— ๋น„ํ•ด ํ”Œ๋ผํ†ค์˜ ์šฐ์›”์„ฑ์„ ์œ ์ง€ํ•œ ์ง€์— ๋Œ€ํ•œ ๋…ผ์Ÿ์ด ์žˆ์—ˆ์Šต๋‹ˆ๋‹ค. ์ฝ”์‹œ๋ชจ์™€ ๋กœ๋ Œ์ดˆ ๋ฐ ๋ฉ”๋””์น˜๋Š” ๋‘˜๋‹ค ํ”Œ๋ผํ†ค์— ๊นŠ์ด ๋น ์ ธ์žˆ์—ˆ์œผ๋ฉฐ; ์ฝ”์‹œ๋ชจ๊ฐ€ ํ”ผ๋ Œ์ฒด ์•„์นด๋ฐ๋ฏธ๋ฅผ ์„ค๋ฆฝํ•˜๊ณ  ๋กœ๋ Œ์ดˆ๊ฐ€ ์ด๋ฅผ ๊ณ„์† ์œ ์ง€ํ–ˆ์œผ๋ฉฐ, ์ด ์•„์นด๋ฐ๋ฏธ๋Š” ์ฃผ๋กœ ํ”Œ๋ผํ†ค์— ๋Œ€ํ•œ ์—ฐ๊ตฌ์— ์ „๋…ํ–ˆ์Šต๋‹ˆ๋‹ค&โ€ฆ๊ทธ๋Ÿฌ๋‚˜ ๋‹น์‹œ์˜ ์ธ๋ฌธ์ฃผ์˜์ž๋“ค์€ ๊ณ ๋Œ€์˜ ์ง€์‹์„ ์Šต๋“ํ•˜๋Š” ๋ฐ ๋„ˆ๋ฌด ๊ธ‰๊ธ‰ํ•˜์—ฌ ๊ฐ€์น˜์žˆ๋Š” ๋ฌด์–ธ๊ฐ€๋ฅผ ์ฐฝ์ถœํ•  ์ˆ˜ ์—†์—ˆ์Šต๋‹ˆ๋‹ค.โ€\n", + "์ด ๊ตฌ์ ˆ์„ ํ†ตํ•ด ํŽ˜๋ ˆ๋ผ ๊ณต์˜ํšŒ๊ฐ€ ํ•œ ์ผ์„ ์œ ์ถ”ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค\n", + "['ํ”ผ๋ Œ์ฒด ์•„์นด๋ฐ๋ฏธ์˜ ๊ธฐ์ดˆ๋ฅผ ๋‹ฆ์•˜์Šต๋‹ˆ๋‹ค', '๋™์„œ ๊ตํšŒ๊ฐ„์˜ ๋ถˆํ™”๋ฅผ ์˜๊ตฌ์ ์œผ๋กœ ํ™”ํ•ด์‹œ์ผฐ์Šต๋‹ˆ๋‹ค', '์„œ์œ ๋Ÿฝ์—์„œ ๊ณ ์ „์ฒ ํ•™์˜ ์žฌ๋ฐœ๊ฒฌ์„ ์ด‰์ง„์‹œ์ผฐ์Šต๋‹ˆ๋‹ค', '๋น„์ž”ํ‹ด ์ œ๊ตญ์ด ํ”ผ๋ Œ์ฒด์˜ ํ•™๋ฌธ์„ ๋ฐฐ์šฐ๋Š” ๊ฒƒ์„ ํ—ˆ์šฉํ•˜์˜€์Šต๋‹ˆ๋‹ค']\n", + "3\n", + "์งˆ๋ฌธ ํ˜•์‹ ์ด์ƒ\n", + "\n", + "\n", + "์•„๋ฆ„๋‹ค์šด ๊ฒƒ์„ ๋งŒ๋“ค๊ณ ์ž ํ•˜๋Š” ์š•๋ง๊ณผ๋Š” ๋ณ„๊ฐœ๋กœ, ๋‚ด ์ธ์ƒ์„ ์ด๋ˆ ์—ด์ •์€ ํ—Œ๋Œ€๋ฌธ๋ช…์— ๋Œ€ํ•œ ์ฆ์˜ค์˜€์Šต๋‹ˆ๋‹ค. ๋‚ด๊ฐ€ ๊ทธ๋Ÿฌํ•œ ๋ง์„ ํ–ˆ๋‹ค๊ณ  ํ–ˆ์„ ๋•Œ, ๊ทธ๊ฒƒ์ด ํŒŒ๊ดด๋ฅผ ๋ฐ”๋ผ๋Š” ๋‚˜์˜ ํฌ๋ง์„ ์ด์ œ ๋ญ๋ผ๊ณ  ๋งํ•ด์•ผ ํ• ๊นŒ์š”-์‚ฌํšŒ์ฃผ์˜๊ฐ€ ๊ทธ๊ฒƒ์„ ๋Œ€์ฒดํ•˜๋Š” ๊ฒƒ์— ๋Œ€ํ•ด ๋‚˜๋Š” ๋ญ๋ผ๊ณ  ๋งํ•ด์•ผ ํ• ๊นŒ์š”? ์ˆ™๋‹ฌ๊ณผ ๊ธฐ๊ณ„๋ ฅ์˜ ๋‚ญ๋น„, ๋„ˆ๋ฌด ๊ฐ€๋‚œํ•œ ์˜์—ฐ๋ฐฉ, ๋„ˆ๋ฌด ๋ถ€์œ ํ•œ ์˜์—ฐ๋ฐฉ์˜ ์ ๋“ค, ์‚ถ์˜ ๋ถˆํ–‰์„ ์œ„ํ•œ-๊ทธ ๊ฑฐ๋Œ€ํ•œ ์กฐ์ง์— ๋Œ€ํ•ด ๋‚˜๋Š” ๋ญ๋ผ๊ณ  ๋งํ•ด์•ผ ํ• ๊นŒ์š”! ๋ชจ๋‘๊ฐ€ ์ฆ๊ธธ ์ˆ˜ ์žˆ๋Š” ๋‹จ์ˆœํ•œ ์ฆ๊ฑฐ์›€์— ๋Œ€ํ•œ ๊ฒฝ๋ฉธ์€ ์–ด๋ฆฌ์„์Œ ๋•Œ๋ฌธ์ผ๊นŒ์š”? ์˜ˆ์ˆ ์„ ํŒŒ๊ดดํ•œ ๋ˆˆ ๋จผ ์ฒœ๋ฐ•ํ•จ, ๋…ธ๋™์˜ ํ™•์‹คํ•œ ์œ„๋กœ์ผ๊นŒ์š”? ์ด ๋ชจ๋“  ๊ฒƒ๋“ค์„ ์ง€๊ธˆ์ฒ˜๋Ÿผ ๋‹น์‹œ์—๋„ ๋А๊ผˆ์ง€๋งŒ, ๊ทธ ์ด์œ ๋Š” ์•Œ์ง€ ๋ชปํ–ˆ์Šต๋‹ˆ๋‹ค. ๊ณผ๊ฑฐ์˜ ํฌ๋ง์€ ์‚ฌ๋ผ์ง€๊ณ , ์˜ค๋žœ ์„ธ์›” ๋™์•ˆ ์ธ๋ฅ˜์˜ ํˆฌ์Ÿ์€ ์ด ํƒ์š•์Šค๋Ÿฝ๊ณ  ๋ชฉ์ ๋„ ์—†๊ณ  ์ถ”์•…ํ•œ ํ˜ผ๋ž€๋งŒ์„ ์ดˆ๋ž˜ํ•˜์˜€์Šต๋‹ˆ๋‹ค; ๊ฐ€๊นŒ์šด ์žฅ๋ž˜์— ๋ฌธ๋ช…์˜ ์•”์šธํ•œ ์ฒœ๋ฐ•ํ•จ์ด ์„ธ์ƒ์— ์ž๋ฆฌ์žก๊ธฐ ์ „ ๊ทธ ์‹œ๋Œ€์˜ ๋งˆ์ง€๋ง‰ ์ž”์žฌ๋“ค์„ ์“ธ์–ด๋ฒ„๋ฆผ์œผ๋กœ์จ ํ˜„์žฌ์˜ ๋ชจ๋“  ์•…์ด ๊ฐ•ํ™”๋  ๊ฒƒ์ฒ˜๋Ÿผ ๋ณด์˜€์Šต๋‹ˆ๋‹ค. ์ด๊ฒƒ์€ ์‚ฌ์‹ค ๋‚˜์œ ์ „๋ง์ด์—ˆ์œผ๋ฉฐ, ๊ทธ๋ฆฌ๊ณ , ๋‚ด๊ฐ€ ํ˜•์ด์ƒํ•™๊ณผ ์ข…๊ต๋ฅผ ๋น„๋กฏํ•ด ๊ณผํ•™์  ๋ถ„์„์— ๋ฌด๊ด€์‹ฌํ•˜๋‚˜ ์ง€๊ตฌ์™€ ์ง€๊ตฌ์ƒ์˜ ์ƒ๋ช…์— ๋Œ€ํ•œ ๊นŠ์€ ์‚ฌ๋ž‘๊ณผ ์ธ๋ฅ˜์˜ ๊ณผ๊ฑฐ ์—ญ์‚ฌ์— ๋Œ€ํ•œ ์—ด์ •์„ ๊ฐ€์ง„ ๋‚˜์˜ ์„ฑํ–ฅ์„ ๊ฐ€์ง„ ์‚ฌ๋žŒ์—๊ฒŒ ๋‚ด ์ž์‹ ์„ ๋‹จ์ˆœํ•œ ์œ ํ˜•์ด ์•„๋‹Œ ์„ฑ๊ฒฉ์œผ๋กœ ์–ธ๊ธ‰ํ•  ์žˆ์„ ๊ฒƒ์ž…๋‹ˆ๋‹ค.\n", + "์ด ๊ตฌ์ ˆ์„ ๋ณด๋ฉด ๋ชจ๋ฆฌ์Šค๊ฐ€?\n", + "['ํ˜„๋Œ€ ์‚ฌํšŒ๊ฐ€ ์•ผ๊ธฐํ•˜๋Š” ์—„์ฒญ๋‚œ ์ž์› ๋‚ญ๋น„์™€ ์ „๋ฐ˜์ ์ธ ๋ถˆํ–‰์— ์งˆ๋ ค์„œ ์‚ฌํšŒ์ฃผ์˜์ž๊ฐ€ ๋˜๋Š” ๊ฒƒ์„ ์„ ํƒํ•˜์˜€์œผ๋ฉฐ', '๋งˆ๋ฅดํฌ์Šค์˜ ์ฃผ์žฅ์— ์„ค๋“๋˜์–ด ์‚ฌํšŒ์ฃผ์˜์ž๊ฐ€ ๋˜๋Š” ๊ฒƒ์„ ์„ ํƒํ•˜์˜€์œผ๋ฉฐ', '์‚ฌํšŒ์ฃผ์˜๊ฐ€ ์ถ”์•…ํ•œ ํ˜ผ๋ž€๋งŒ์„ ์•ผ๊ธฐํ–ˆ๊ธฐ ๋•Œ๋ฌธ์— ์ด๋ฅผ ๊ฑฐ๋ถ€ํ–ˆ์œผ๋ฉฐ ', '์ง€๊ตฌ์™€ ์ง€๊ตฌ์ƒ์˜ ์ƒ๋ช…์— ๋Œ€ํ•œ ๊นŠ์€ ์‚ฌ๋ž‘ ๋•Œ๋ฌธ์— ์‚ฌํšŒ์ฃผ์˜๋ฅผ ๊ฑฐ๋ถ€ํ–ˆ๋‹ค๋Š” ๊ฒƒ์„ ์œ ์ถ”ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค']\n", + "1\n", + "choices ๋ถˆ์™„์ „\n", + "\n", + "\n", + "์ˆญ์˜ ์ „์ด๋ผ๋Š” ๋ช…์นญ์€ ์กฐ์„ ์‹œ๋Œ€์— ๋ถ™์—ฌ์กŒ๋‹ค. ์ˆญ์˜์ „์—์„œ๋Š” ์ด์ „ ์™•์กฐ์ธ์ด ์‹œ๋Œ€ ํƒœ์กฐ๋ฅผ ๋น„๋กฏํ•œ ์—ฌ๋Ÿฌ ๋ช…์˜ ์™•์„ ์ œํ–ฅํ•˜๊ณ , ์‹ ์ˆญ๊ฒธ๊ณผ ์ •๋ชฝ์ฃผ ๋“ฑ์„ ๋น„๋กฏํ•œ ์—ฌ๋Ÿฌ ๋ช…์˜ ๊ณต์‹ ์„ ๋ฐฐํ–ฅํ•˜์˜€๋‹ค. ๊ฒฝ๊ธฐ๋„ ์—ฐ์ฒœ๊ตฐ์— ์žˆ๋Š” ์ˆญ์˜์ „์ง€(ๅด‡็พฉๆฎฟๅ€)๋Š” ์‚ฌ์ ์œผ๋กœ ์ง€์ •๋˜์—ˆ๋‹ค.\n", + "๋ฐ‘์ค„ ์นœ โ€˜์ด ์‹œ๋Œ€โ€™์— ํŽธ์ฐฌ๋œ ์˜ํ•™์„œ์ ์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?\n", + "['์˜๋ฐฉ์œ ์ทจ', 'ํ–ฅ์•ฝ๊ตฌ๊ธ‰๋ฐฉ', 'ํ–ฅ์•ฝ์ง‘์„ฑ๋ฐฉ', '๋™์˜ ์ˆ˜์„ธ ๋ณด์›']\n", + "3\n", + "์ •๋‹ต ์˜ค๋ฅ˜\n" + ] + } + ], + "source": [ + "x = 2\n", + "print(low_qual_paragraph.at[low_qual_paragraph.index[x], 'paragraph'])\n", + "print(low_qual_paragraph.at[low_qual_paragraph.index[x], 'question'])\n", + "print(low_qual_paragraph.at[low_qual_paragraph.index[x], 'choices'])\n", + "print(low_qual_paragraph.at[low_qual_paragraph.index[x], 'answer'])\n", + "print(low_qual_paragraph.at[low_qual_paragraph.index[x], '๋ฐ์ดํ„ฐ์ €ํ’ˆ์งˆ-paragraph'])\n", + "print()\n", + "print()\n", + "print(low_qual_question.at[low_qual_question.index[x], 'paragraph'])\n", + "print(low_qual_question.at[low_qual_question.index[x], 'question'])\n", + "print(low_qual_question.at[low_qual_question.index[x], 'choices'])\n", + "print(low_qual_question.at[low_qual_question.index[x], 'answer'])\n", + "print(low_qual_question.at[low_qual_question.index[x], '๋ฐ์ดํ„ฐ์ €ํ’ˆ์งˆ-question'])\n", + "print()\n", + "print()\n", + "print(low_qual_choices.at[low_qual_choices.index[x], 'paragraph'])\n", + "print(low_qual_choices.at[low_qual_choices.index[x], 'question'])\n", + "print(low_qual_choices.at[low_qual_choices.index[x], 'choices'])\n", + "print(low_qual_choices.at[low_qual_choices.index[x], 'answer'])\n", + "print(low_qual_choices.at[low_qual_choices.index[x], '๋ฐ์ดํ„ฐ์ €ํ’ˆ์งˆ-choices'])\n", + "print()\n", + "print()\n", + "print(low_qual_answer.at[low_qual_answer.index[x], 'paragraph'])\n", + "print(low_qual_answer.at[low_qual_answer.index[x], 'question'])\n", + "print(low_qual_answer.at[low_qual_answer.index[x], 'choices'])\n", + "print(low_qual_answer.at[low_qual_answer.index[x], 'answer'])\n", + "print(low_qual_answer.at[low_qual_answer.index[x], '๋ฐ์ดํ„ฐ์ €ํ’ˆ์งˆ-answer'])" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "์ƒ์†Œํ•˜์—ฌ ์•„๋ขฐ๊ธฐ๋ฅผ , โ€œ์‹ ์ด ์ขŒ์ฐธ ์ฐฌ ์†ก์ค€๊ธธ์ด ์˜ฌ๋ฆฐ ์ฐจ์ž๋ฅผ ๋ณด์•˜๋Š”๋ฐ , ์ƒ๋ณต(ๅ–ชๆœ) ์ ˆ์ฐจ์— ๋Œ€ํ•˜์—ฌ ๋…ผํ•œ ๊ฒƒ์ด ์‹ ๊ณผ๋Š” ํฐ ์ฐจ์ด๊ฐ€ ์žˆ์—ˆ์Šต๋‹ˆ๋‹ค . ์žฅ์ž๋ฅผ ์œ„ํ•˜์—ฌ 3๋…„์„ ์ž…๋Š” ๊นŒ๋‹ญ์€ ์œ„๋กœ โ€˜์ •์ฒด(ๆญฃ้ซ”)โ€™๊ฐ€ ๋˜๊ธฐ ๋•Œ๋ฌธ์ด๊ณ  ๋˜ ์ „ ์ค‘(ๅ‚ณ้‡: ์กฐ์ƒ์˜ ์ œ์‚ฌ๋‚˜ ๊ฐ€๋ฌธ์˜ ๋ฒ•ํ†ต์„ ์ „ํ•จ)ํ•˜๊ธฐ ๋•Œ๋ฌธ์ž…๋‹ˆ๋‹ค . โ€ฆ(์ค‘๋žต) โ€ฆ ๋ฌด์—‡๋ณด๋‹ค ์ค‘์š”ํ•œ ๊ฒƒ์€ ํ• ์•„๋ฒ„์ง€์™€ ์•„๋ฒ„์ง€์˜ ๋’ค๋ฅผ ์ด์€ โ€˜์ •์ฒดโ€™์ด์ง€, ๊ผญ ์ฒซ์งธ์ด๊ธฐ ๋•Œ๋ฌธ์— ์ฐธ ์ตœ 3๋…„ ๋ณต์„ ์ž…๋Š” ๊ฒƒ์€ ์•„๋‹™๋‹ˆ๋‹ค .โ€๋ผ๊ณ  ํ•˜์˜€๋‹ค .๏ผํ˜„์ข…์‹ค๋ก ๏ผใ„ฑ.๊ธฐ ์‚ฌํ™˜๊ตญ์œผ๋กœ ์ •๊ถŒ์„ ์žฅ์•…ํ•˜์˜€๋‹ค .ใ„ด.์ธ ์กฐ๋ฐ˜์ •์„ ์ฃผ๋„ ํ•˜์—ฌ ์ง‘๊ถŒ์„ธ๋ ฅ์ด ๋˜์—ˆ๋‹ค .ใ„ท.์ •์กฐ ์‹œ๊ธฐ์— ํƒ•ํ‰ ์ •์น˜์˜ ํ•œ ์ถ•์„ ์ด๋ฃจ์—ˆ๋‹ค .ใ„น.์ด ์ด์™€ ์„ฑํ˜ผ์˜ ๋ฌธ์ธ์„ ์ค‘์‹ฌ์œผ๋กœ ํ˜•์„ฑ๋˜์—ˆ๋‹ค.\n", + "(๊ฐ€)์‹ ๋ผ์˜ ํ•œ๊ฐ• ์œ ์—ญ ํ™•๋ณด (๋‚˜)๊ด€์‚ฐ์„ฑ ์ „ํˆฌ(๋‹ค) ๋ฐฑ์ œ์˜ ์›…์ง„ ์ฒœ๋„ (๋ผ)๊ณ ๊ตฌ๋ ค์˜ ํ‰์–‘ ์ฒœ๋„\n", + "(๊ฐ€)์‹ ๋ผ์˜ ์šฐ์‚ฐ๊ตญ ๋ณต์† (๋‚˜)๊ณ ๊ตฌ๋ ค์˜ ์„œ ์•ˆํ‰ ์ ๋ น(๋‹ค) ๋ฐฑ์ œ์˜ ๋Œ€์•ผ์„ฑ ์ ๋ น (๋ผ)์‹ ๋ผ์˜ ๊ธˆ๊ด€๊ฐ€์•ผ ๋ณ‘ํ•ฉ\n", + "(๊ฐ€)๋†๋ฏผ๊ตฐ์ด ์ •๋ถ€์™€ ์ „์ฃผํ™”์•ฝ์„ ๋งบ์—ˆ๋‹ค. (๋‚˜) ๋†๋ฏผ๊ตฐ์ด ์šฐ๊ธˆ์น˜์—์„œ ์ „ํˆฌ๋ฅผ ๋ฒŒ์˜€๋‹ค. (๋‹ค) ๋†๋ฏผ๊ตฐ์ด ํ™ฉํ† ํ˜„์—์„œ ๊ด€๊ตฐ์„ ๋ฌผ๋ฆฌ์ณค๋‹ค. (๋ผ) ์ „๋ด‰์ค€ ๋“ฑ์ด ๋†๋ฏผ์„ ๋ชจ์•„ ๊ณ ๋ถ€ ๊ด€์•„๋ฅผ ์Šต๊ฒฉํ•˜์˜€๋‹ค.\n", + "(๊ฐ€) ๊น€์‹œ๋ฏผ์ด ์ง„์ฃผ์„ฑ์—์„œ ์ผ๋ณธ๊ตฐ์„ ์ €์ง€ํ•˜์˜€๋‹ค.(๋‚˜) ์กฐ์„ ์ˆ˜๊ตฐ์ด ๋ช…๋Ÿ‰ํ•ด์ „์—์„œ ํฌ๊ฒŒ ์Šน๋ฆฌํ•˜์˜€๋‹ค.(๋‹ค)์ด์ˆœ์‹ ์ด ์˜ฅํฌํ•ด์ „์—์„œ ์Šน๋ฆฌํ•˜์˜€๋‹ค.(๋ผ) ์กฐ๋ช…์—ฐํ•ฉ ์ˆ˜๊ตฐ์ด ๋…ธ๋Ÿ‰ํ•ด์ „์—์„œ ์Šน๋ฆฌํ•˜์˜€๋‹ค.(๋งˆ)์กฐ๋ช…์—ฐํ•ฉ๊ตฐ์ด ํ‰์–‘์„ฑ์„ ํƒˆํ™˜ํ•˜์˜€๋‹ค.\n", + "์™•41๋…„ ๊ฒจ์šธ 10์›”์— ๋ฐฑ์ œ๊ทผ ์ดˆ๊ณ ์™•์ด ๊ตฐ์‚ฌ 3๋งŒ๋ช…์„ ์ด๋Œ๊ณ  ํ‰์–‘์„ฑ์„ ๊ณต๊ฒฉํ•ด์™”๋‹ค.์™•์ด ๊ตฐ๋Œ€๋ฅผ ๋‚ด์–ด ๋ง‰๋‹ค๊ฐ€ ํ™”์‚ด์— ๋งž์•„ ๋Œ์•„๊ฐ€์…จ๋‹ค. ๏ผ์‚ผ๊ตญ์‚ฌ๊ธฐ๏ผ(๊ฐ€)(๋‚˜)(๋‹ค)(๋ผ) ๋‚™๋ž‘ โ€ค๋Œ€๋ฐฉ๊ตฐ์ถ•์ถœ ๋ชจ์šฉํ™ฉ์— ์˜ํ•ด ํ™˜๋„์„ฑ ํ•จ๋ฝ ์ „์ง„์˜ ์ˆœ๋„ ๋ถˆ๊ต ์ „๋ž˜ํ‰์–‘ ์ฒœ๋„๋ฐฑ ์ œํ•œ์„ฑ ํ•จ๋ฝ\n", + "(๊ฐ€) ์ด์ข…๋ฌด๊ฐ€ ๋Œ€๋งˆ๋„๋ฅผ ํ† ๋ฒŒํ•˜์˜€๋‹ค.(๋‚˜) ๊น€์œคํ›„๊ฐ€ ์šฉ์ธ์—์„œ ์‚ด๋ฆฌํƒ€๋ฅผ ์‚ฌ์‚ดํ•˜์˜€๋‹ค.(๋‹ค) ๊น€ํ—Œ์ฐฝ์ด ๊ณต์ฃผ๋ฅผ ๊ทผ๊ฑฐ๋กœ ๋ฐ˜๋ž€์„ ์ผ์œผ์ผฐ๋‹ค.(๋ผ) ์ด ์‹œ์• ๊ฐ€ ๊ธธ์ฃผ์—์„œ ๊ตฐ์‚ฌ๋ฅผ ์ผ์œผ์ผฐ๋‹ค.\n", + "(๊ฐ€)์‹ ๋ผ์˜ ์šฐ์‚ฐ๊ตญ ๋ณต์† (๋‚˜)๊ณ ๊ตฌ๋ ค์˜ ์„œ ์•ˆํ‰ ์ ๋ น(๋‹ค) ๋ฐฑ์ œ์˜ ๋Œ€์•ผ์„ฑ ์ ๋ น (๋ผ)์‹ ๋ผ์˜ ๊ธˆ๊ด€๊ฐ€์•ผ ๋ณ‘ํ•ฉ\n", + "(๊ฐ€)์ค‘์•™๊ตฐ์ธ 5์œ„๋ฅผ ๋‘์–ด ๊ถ๊ถ๊ณผ ์ˆ˜๋„๋ฅผ ๋ฐฉ์–ดํ•˜๊ฒŒ ํ•˜์˜€๋‹ค.(๋‚˜) 10์ •์„ ๋‘์—ˆ๋Š”๋ฐ, 9์ฃผ ๊ฐ€์šด๋ฐ 8์ฃผ์— 1์ •์”ฉ ๋ฐฐ์น˜ํ•˜๊ณ , ๊ตญ๊ฒฝ์ง€๋Œ€์ธ ํ•œ ์ฃผ(ๆผขๅทž)์—๋Š” 2๊ฐœ์˜ ์ •์„ ๋‘์—ˆ๋‹ค.(๋‹ค) ๊ธˆ์œ„์˜์ด ์„ค์น˜๋˜๋ฉด์„œ 5๊ตฐ ์˜์ฒด์ œ๊ฐ€ ๊ฐ–์ถ”์–ด์กŒ๋‹ค.(๋ผ) ๊ตญ์™•์˜ ์นœ์œ„๋ถ€๋Œ€์ธ 2๊ตฐ, ์ˆ˜๋„ ๋ฐ ๊ตญ๊ฒฝ ๋ฐฉ์–ด๋ฅผ ๋‹ด๋‹นํ•˜๋Š” 6์œ„๋กœ ๊ตฌ์„ฑ๋˜์—ˆ๋‹ค.\n", + "(๊ฐ€)๋†๋ฏผ๊ตฐ์ด ์ •๋ถ€์™€ ์ „์ฃผํ™”์•ฝ์„ ๋งบ์—ˆ๋‹ค. (๋‚˜) ๋†๋ฏผ๊ตฐ์ด ์šฐ๊ธˆ์น˜์—์„œ ์ „ํˆฌ๋ฅผ ๋ฒŒ์˜€๋‹ค. (๋‹ค) ๋†๋ฏผ๊ตฐ์ด ํ™ฉํ† ํ˜„์—์„œ ๊ด€๊ตฐ์„ ๋ฌผ๋ฆฌ์ณค๋‹ค. (๋ผ) ์ „๋ด‰์ค€ ๋“ฑ์ด ๋†๋ฏผ์„ ๋ชจ์•„ ๊ณ ๋ถ€ ๊ด€์•„๋ฅผ ์Šต๊ฒฉํ•˜์˜€๋‹ค.\n" + ] + } + ], + "source": [ + "bogi = train[train['๋ณด๊ธฐ ์—ฌ๋ถ€'] == True]\n", + "for x in range(10):\n", + " print(bogi.at[bogi.index[x], 'paragraph'])" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "๋น„๊ณ \n", + "์ •๋‹ต 4 3\n", + "์ •๋‹ต 2 2\n", + "์ •๋‹ต 3 1\n", + "์ •๋‹ต 1, 5 1\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bigo = train[train['๋น„๊ณ '] != \"\"]\n", + "bigo['๋น„๊ณ '].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "์ฃผ์š” ์šฉ์–ด ํŒŒ์•…ํ•˜๊ธฐ" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/ft_data_processing.ipynb b/notebooks/ft_data_processing.ipynb new file mode 100644 index 0000000..00d4607 --- /dev/null +++ b/notebooks/ft_data_processing.ipynb @@ -0,0 +1,135 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### EN -> KO ๋ฒˆ์—ญ ###\n", + "\n", + "import csv\n", + "import requests\n", + "\n", + "# DeepL API ํ‚ค ์„ค์ •\n", + "DEEPL_API_KEY = \" \"\n", + "DEEPL_API_URL = \"https://api-free.deepl.com/v2/translate\"\n", + "\n", + "def translate_text_deepl(text, source_lang=\"EN\", target_lang=\"KO\"):\n", + " \"\"\"DeepL API๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ํ…์ŠคํŠธ ๋ฒˆ์—ญ.\"\"\"\n", + " try:\n", + " params = {\n", + " \"auth_key\": DEEPL_API_KEY,\n", + " \"text\": text,\n", + " \"source_lang\": source_lang,\n", + " \"target_lang\": target_lang,\n", + " }\n", + " response = requests.post(DEEPL_API_URL, data=params)\n", + " response.raise_for_status()\n", + " data = response.json()\n", + " return data[\"translations\"][0][\"text\"]\n", + " except Exception as e:\n", + " print(f\"๋ฒˆ์—ญ ์‹คํŒจ: {e}\")\n", + " return text # ๋ฒˆ์—ญ ์‹คํŒจ ์‹œ ์›๋ณธ ๋ฐ˜ํ™˜\n", + "\n", + "def process_csv(input_file, output_file):\n", + " \"\"\"CSV ํŒŒ์ผ ์ฝ๊ณ  paragraph, question, answer ๋ฒˆ์—ญ ํ›„ ์ €์žฅ.\"\"\"\n", + " with open(input_file, 'r', encoding='utf-8') as infile, open(output_file, 'w', encoding='utf-8', newline='') as outfile:\n", + " reader = csv.DictReader(infile)\n", + " fieldnames = reader.fieldnames\n", + " writer = csv.DictWriter(outfile, fieldnames=fieldnames)\n", + " writer.writeheader()\n", + " \n", + " for row in reader:\n", + " # Paragraph ๋ฒˆ์—ญ\n", + " if 'paragraph' in row and row['paragraph']:\n", + " row['paragraph'] = translate_text_deepl(row['paragraph'])\n", + "\n", + " if 'question' in row and row['question']:\n", + " row['question'] = translate_text_deepl(row['question'])\n", + " if 'choice1' in row and row['choice1']:\n", + " row['choice1'] = translate_text_deepl(row['choice1'])\n", + " if 'choice2' in row and row['choice2']:\n", + " row['choice2'] = translate_text_deepl(row['choice2'])\n", + " if 'choice3' in row and row['choice3']:\n", + " row['choice3'] = translate_text_deepl(row['choice3'])\n", + " if 'choice4' in row and row['choice4']:\n", + " row['choice4'] = translate_text_deepl(row['choice4'])\n", + " if 'choice5' in row and row['choice5']:\n", + " row['choice5'] = translate_text_deepl(row['choice5'])\n", + "\n", + "# if 'question_plus' in row and row['question_plus']:\n", + "# row['question_plus'] = translate_text_deepl(row['question_plus'])\n", + "\n", + " # ๋ณ€ํ™˜๋œ row๋ฅผ ์ƒˆ ํŒŒ์ผ์— ์ž‘์„ฑ\n", + " writer.writerow(row)\n", + "\n", + "# ์ž…๋ ฅ CSV์™€ ์ถœ๋ ฅ CSV ๊ฒฝ๋กœ ์ง€์ •\n", + "input_csv = 'khan_raw.csv'\n", + "output_csv = 'khan_trans.csv'\n", + "\n", + "# ๋ณ€ํ™˜ ์‹คํ–‰\n", + "process_csv(input_csv, output_csv)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### ํ˜•์‹ ์ „ํ™˜ ์ฝ”๋“œ ###\n", + "\n", + "import pandas as pd\n", + "\n", + "# ๋ฐ์ดํ„ฐ ๋กœ๋“œ\n", + "data = pd.read_csv(\"khan_trans.csv\")\n", + "\n", + "# DataFrame์œผ๋กœ ๋ณ€ํ™˜\n", + "df = pd.DataFrame(data)\n", + "\n", + "# ๋ณ€ํ™˜ ๋ฐ์ดํ„ฐ๋ฅผ ๋‹ด์„ ๋ฆฌ์ŠคํŠธ\n", + "transformed_data = []\n", + "\n", + "for idx, row in df.iterrows():\n", + "\n", + " # ์„ ํƒ์ง€ ๋ฆฌ์ŠคํŠธ์™€ ์ •๋‹ต ๋งคํ•‘\n", + " choices = [row[\"choice1\"], row[\"choice2\"], row[\"choice3\"], row[\"choice4\"], row[\"choice5\"]]\n", + " \n", + " # ๋ฌธ์ œ ๋ฐ์ดํ„ฐ ๊ตฌ์„ฑ\n", + " problem = {\n", + " \"question\": row[\"question\"],\n", + " \"choices\": choices,\n", + " \"answer\": row[\"answer\"]\n", + " }\n", + " \n", + " # ์ตœ์ข… ๋ฐ์ดํ„ฐ ๊ตฌ์„ฑ\n", + " transformed_data.append({\n", + " \"id\": row[\"id\"],\n", + " \"paragraph\": row[\"paragraph\"],\n", + " \"problems\": problem,\n", + " \"question_plus\": \"\"\n", + " })\n", + "\n", + "# ๋ณ€ํ™˜๋œ ๋ฐ์ดํ„ฐํ”„๋ ˆ์ž„ ์ƒ์„ฑ\n", + "transformed_df = pd.DataFrame(transformed_data)\n", + "\n", + "output_file = \"khan_final.csv\"\n", + "transformed_df.to_csv(output_file, index=False)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/openstax_translation.ipynb b/notebooks/openstax_translation.ipynb new file mode 100644 index 0000000..f1a7585 --- /dev/null +++ b/notebooks/openstax_translation.ipynb @@ -0,0 +1,576 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Load model directly\n", + "import torch\n", + "from tqdm import tqdm\n", + "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\n", + "\n", + "tokenizer = AutoTokenizer.from_pretrained(\"NHNDQ/nllb-finetuned-en2ko\", device_map=\"auto\")\n", + "model = AutoModelForSeq2SeqLM.from_pretrained(\"NHNDQ/nllb-finetuned-en2ko\", device_map=\"auto\")\n", + "\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[nltk_data] Downloading package punkt to\n", + "[nltk_data] /data/ephemeral/home/nltk_data...\n", + "[nltk_data] Package punkt is already up-to-date!\n", + "[nltk_data] Downloading package punkt_tab to\n", + "[nltk_data] /data/ephemeral/home/nltk_data...\n", + "[nltk_data] Package punkt_tab is already up-to-date!\n" + ] + }, + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import nltk\n", + "from nltk.tokenize import sent_tokenize\n", + "\n", + "nltk.download('punkt')\n", + "nltk.download('punkt_tab')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# ๋ฒˆ์—ญ ์ˆ˜ํ–‰\n", + "def translate(text):\n", + " inputs = tokenizer(text, return_tensors=\"pt\", truncation=True, padding=True)\n", + " inputs = {k: v.to(model.device) for k, v in inputs.items()}\n", + " with torch.no_grad():\n", + " result = model.generate(**inputs)\n", + " return tokenizer.batch_decode(result, skip_special_tokens=True)[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def batch_translate(sentences, batch_size=16):\n", + " translations = []\n", + " for i in tqdm(range(0, len(sentences), batch_size), desc=\"Translating\", unit=\"batch\"):\n", + " batch = sentences[i:i+batch_size]\n", + " # ํ† ํฌ๋‚˜์ด์ฆˆ ๋ฐ ์ž…๋ ฅ ์ค€๋น„\n", + " inputs = tokenizer(batch, return_tensors=\"pt\", truncation=True, padding=True).to(device)\n", + " try:\n", + " with torch.no_grad():\n", + " # ๋ฒˆ์—ญ ์ˆ˜ํ–‰\n", + " outputs = model.generate(**inputs)\n", + " # ๋ฒˆ์—ญ ๊ฒฐ๊ณผ ๋””์ฝ”๋”ฉ\n", + " translated_batch = tokenizer.batch_decode(outputs, skip_special_tokens=True)\n", + " translations.extend(translated_batch)\n", + " except Exception as e:\n", + " print(f\"Error translating batch {i}: {e}\")\n", + " # ์—๋Ÿฌ ๋ฐœ์ƒ ์‹œ ๋นˆ ๋ฌธ์ž์—ด ์ถ”๊ฐ€\n", + " translations.extend([\"\"] * len(batch))\n", + " return translations" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "df = pd.read_csv('/data/ephemeral/home/personal/jinjae/data_preprocess/openstax_world_history2_final.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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sectiontitlecontextcontext_len
0developing a global perspectiveLearning ObjectivesBy the end of this section, you will be able to:48
1developing a global perspectiveWorld History as Preparation for Life After Co...History is more than a series of names and dat...757
2developing a global perspectiveWorld History as Preparation for Life After Co...This world history text has several key featur...796
3developing a global perspectiveWorld History as Preparation for Life After Co...The study of history will also enhance your cr...657
4developing a global perspectiveWorld History as Preparation for Life After Co...Without question, skills such as critical thin...1061
...............
2310termoffshoringthe process of moving some of a companyโ€™s oper...93
2311termoutsourcingthe process of hiring outside contractors, som...113
2312termParis Agreementa 2015 treaty among members of the United Nati...146
2313termresource cursethe problem that makes resource-rich developin...141
2314termultranationalist movementsorganizations that support an extreme form of ...105
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" + ], + "text/plain": [ + " section \\\n", + "0 developing a global perspective \n", + "1 developing a global perspective \n", + "2 developing a global perspective \n", + "3 developing a global perspective \n", + "4 developing a global perspective \n", + "... ... \n", + "2310 term \n", + "2311 term \n", + "2312 term \n", + "2313 term \n", + "2314 term \n", + "\n", + " title \\\n", + "0 Learning Objectives \n", + "1 World History as Preparation for Life After Co... \n", + "2 World History as Preparation for Life After Co... \n", + "3 World History as Preparation for Life After Co... \n", + "4 World History as Preparation for Life After Co... \n", + "... ... \n", + "2310 offshoring \n", + "2311 outsourcing \n", + "2312 Paris Agreement \n", + "2313 resource curse \n", + "2314 ultranationalist movements \n", + "\n", + " context context_len \n", + "0 By the end of this section, you will be able to: 48 \n", + "1 History is more than a series of names and dat... 757 \n", + "2 This world history text has several key featur... 796 \n", + "3 The study of history will also enhance your cr... 657 \n", + "4 Without question, skills such as critical thin... 1061 \n", + "... ... ... \n", + "2310 the process of moving some of a companyโ€™s oper... 93 \n", + "2311 the process of hiring outside contractors, som... 113 \n", + "2312 a 2015 treaty among members of the United Nati... 146 \n", + "2313 the problem that makes resource-rich developin... 141 \n", + "2314 organizations that support an extreme form of ... 105 \n", + "\n", + "[2315 rows x 4 columns]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Translating: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 633/633 [08:07<00:00, 1.30batch/s]\n" + ] + } + ], + "source": [ + "# ๋ฌธ์žฅ ๋ถ„ํ•  ๋ฐ ๋งคํ•‘ ์ƒ์„ฑ (nltk ์‚ฌ์šฉ)\n", + "sentences = []\n", + "mapping = [] # ๊ฐ ๋ฌธ์žฅ์ด ์–ด๋А ํ–‰๊ณผ ์–ด๋–ค ์œ„์น˜์— ์žˆ๋Š”์ง€ ์ €์žฅ\n", + "for row_idx, context in enumerate(df['context']):\n", + " # nltk๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ๋ฌธ์žฅ ๋ถ„ํ• \n", + " split_sentences = [s.strip() for s in sent_tokenize(context) if s.strip()]\n", + " for sent_idx, sentence in enumerate(split_sentences):\n", + " sentences.append(sentence)\n", + " mapping.append((row_idx, sent_idx))\n", + "\n", + "# ๋ฐฐ์น˜ ๋ฒˆ์—ญ ์ˆ˜ํ–‰\n", + "translated_sentences = batch_translate(sentences, batch_size=16)\n", + "\n", + "# ๋ฒˆ์—ญ๋œ ๋ฌธ์žฅ์„ ์›๋ž˜์˜ ๊ตฌ์กฐ๋กœ ์žฌ์กฐ๋ฆฝ\n", + "translated_contexts = [[] for _ in range(len(df))]\n", + "for (row_idx, sent_idx), translated in zip(mapping, translated_sentences):\n", + " translated_contexts[row_idx].append(translated)\n", + "\n", + "# ๋ฒˆ์—ญ๋œ ๋ฌธ์žฅ๋“ค์„ ๋‹ค์‹œ ํ•˜๋‚˜์˜ ํ…์ŠคํŠธ๋กœ ๊ฒฐํ•ฉ\n", + "df['translated_context'] = [\n", + " '. '.join(sents) + '.' if sents else '' \n", + " for sents in translated_contexts\n", + "]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# ์ค‘๋ณต ์  ์ œ๊ฑฐ\n", + "df[\"translated_context\"] = df[\"translated_context\"].apply(lambda x: x.replace(\"..\", \".\"))\n", + "df[\"translated_context\"] = df[\"translated_context\"].apply(lambda x: x.replace(\"?.\", \"?\"))\n", + "df[\"translated_context\"] = df[\"translated_context\"].apply(lambda x: x.replace(\"!.\", \"!\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'์ด ์„น์…˜์ด ๋๋‚  ๋•Œ๊นŒ์ง€, ๋‹น์‹ ์€ ๋‹ค์Œ๊ณผ ๊ฐ™์ด ํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค.'" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[\"translated_context\"][0]" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "df.to_csv('data_preprocess/openstax_world_history2_final_translated.csv', index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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sectiontitlecontextcontext_lentranslated_context
0developing a global perspectiveLearning ObjectivesBy the end of this section, you will be able to:48์ด ์„น์…˜์ด ๋๋‚  ๋•Œ๊นŒ์ง€, ๋‹น์‹ ์€ ๋‹ค์Œ๊ณผ ๊ฐ™์ด ํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค.
1developing a global perspectiveWorld History as Preparation for Life After Co...History is more than a series of names and dat...757์—ญ์‚ฌ๋Š” ์ผ๋ จ์˜ ์ด๋ฆ„๊ณผ ๋‚ ์งœ ์ด์ƒ์˜ ๊ฒƒ์ด๋ฉฐ, ๊ทธ๊ฒƒ๋“ค์€ ๋‹จ์ˆœํžˆ ๊ทธ๊ฒƒ์˜ ๊ตฌ์„ฑ ์š”์†Œ๋“ค, ์ „...
2developing a global perspectiveWorld History as Preparation for Life After Co...This world history text has several key featur...796์ด ์„ธ๊ณ„์‚ฌ ํ…์ŠคํŠธ์—๋Š” ํ˜„์žฌ์™€ ๊ด€๋ จ๋œ ๋ฐฉ์‹์œผ๋กœ ๊ณผ๊ฑฐ๋ฅผ ์ดํ•ดํ•˜๋Š” ๋ฐ ๋„์›€์ด ๋˜๋Š” ๋ช‡ ๊ฐ€...
3developing a global perspectiveWorld History as Preparation for Life After Co...The study of history will also enhance your cr...657์—ญ์‚ฌ ์—ฐ๊ตฌ๋Š” ๋˜ํ•œ ๊ณ ์šฉ์ฃผ๊ฐ€ ์›ํ•˜๋Š” ์ƒ์œ„ 10๊ฐœ ๊ธฐ์ˆ ์— ์ง€์†์ ์œผ๋กœ ๋‚˜ํƒ€๋‚˜๋Š” ๋น„ํŒ์  ์‚ฌ...
4developing a global perspectiveWorld History as Preparation for Life After Co...Without question, skills such as critical thin...1061์˜์‹ฌ์˜ ์—ฌ์ง€ ์—†์ด ์—ญ์‚ฌ ๊ณต๋ถ€๋ฅผ ํ†ตํ•ด ๋น„ํŒ์  ์‚ฌ๊ณ , ๋ถ„์„, ์ฐฝ์˜์„ฑ ๋“ฑ์˜ ๊ธฐ์ˆ ์ด ๊ฐ€์žฅ ...
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2310termoffshoringthe process of moving some of a companyโ€™s oper...93๋” ์ €๋ ดํ•œ ๋…ธ๋™ ์‹œ์žฅ์— ์ ‘๊ทผํ•˜๊ธฐ ์œ„ํ•ด ํšŒ์‚ฌ์˜ ์šด์˜ ์ค‘ ์ผ๋ถ€๋ฅผ ํ•ด์™ธ๋กœ ์ด์ „ํ•˜๋Š” ๊ณผ์ •.
2311termoutsourcingthe process of hiring outside contractors, som...113ํšŒ์‚ฌ๊ฐ€ ๋‚ด๋ถ€์—์„œ ์ˆ˜ํ–‰ํ•œ ์ž‘์—…์„ ์ˆ˜ํ–‰ํ•˜๊ธฐ ์œ„ํ•ด ์™ธ๋ถ€ ๊ณ„์•ฝ์ž๋ฅผ ๊ณ ์šฉํ•˜๋Š” ๊ณผ์ •.
2312termParis Agreementa 2015 treaty among members of the United Nati...1462015๋…„ ์œ ์—” ํšŒ์›๊ตญ ๊ฐ„ ์ง€๊ตฌ ์˜จ๋‚œํ™”๋ฅผ ์‚ฐ์—…ํ™” ๋‹น์‹œ๋ถ€ํ„ฐ 2ยฐC(3.6ยฐF) ๋ฏธ๋งŒ์œผ...
2313termresource cursethe problem that makes resource-rich developin...141์ž์› ๋ถ€๊ตญ ๊ฐœ๋ฐœ ๋„์ƒ๊ตญ์„ ๊ถŒ์œ„์ฃผ์˜, ๋†’์€ ๊ฐˆ๋“ฑ๋ฅ , ๋‚ฎ์€ ๊ฒฝ์ œ ์„ฑ์žฅ๋ฅ ์— ์ทจ์•ฝํ•˜๊ฒŒ ๋งŒ๋“œ...
2314termultranationalist movementsorganizations that support an extreme form of ...105๊ทน๋‹จ์ ์ธ ํ˜•ํƒœ์˜ ๋ฏผ์กฑ์ฃผ์˜๋ฅผ ์ง€์ง€ํ•˜๊ณ  ์ข…์ข… ๋ฏผ์กฑ์ ์œผ๋กœ ๋™์งˆ์ ์ธ ์กฐ๊ตญ์„ ์ฐพ๋Š” ์กฐ์ง.
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" + ], + "text/plain": [ + " section \\\n", + "0 developing a global perspective \n", + "1 developing a global perspective \n", + "2 developing a global perspective \n", + "3 developing a global perspective \n", + "4 developing a global perspective \n", + "... ... \n", + "2310 term \n", + "2311 term \n", + "2312 term \n", + "2313 term \n", + "2314 term \n", + "\n", + " title \\\n", + "0 Learning Objectives \n", + "1 World History as Preparation for Life After Co... \n", + "2 World History as Preparation for Life After Co... \n", + "3 World History as Preparation for Life After Co... \n", + "4 World History as Preparation for Life After Co... \n", + "... ... \n", + "2310 offshoring \n", + "2311 outsourcing \n", + "2312 Paris Agreement \n", + "2313 resource curse \n", + "2314 ultranationalist movements \n", + "\n", + " context context_len \\\n", + "0 By the end of this section, you will be able to: 48 \n", + "1 History is more than a series of names and dat... 757 \n", + "2 This world history text has several key featur... 796 \n", + "3 The study of history will also enhance your cr... 657 \n", + "4 Without question, skills such as critical thin... 1061 \n", + "... ... ... \n", + "2310 the process of moving some of a companyโ€™s oper... 93 \n", + "2311 the process of hiring outside contractors, som... 113 \n", + "2312 a 2015 treaty among members of the United Nati... 146 \n", + "2313 the problem that makes resource-rich developin... 141 \n", + "2314 organizations that support an extreme form of ... 105 \n", + "\n", + " translated_context \n", + "0 ์ด ์„น์…˜์ด ๋๋‚  ๋•Œ๊นŒ์ง€, ๋‹น์‹ ์€ ๋‹ค์Œ๊ณผ ๊ฐ™์ด ํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค. \n", + "1 ์—ญ์‚ฌ๋Š” ์ผ๋ จ์˜ ์ด๋ฆ„๊ณผ ๋‚ ์งœ ์ด์ƒ์˜ ๊ฒƒ์ด๋ฉฐ, ๊ทธ๊ฒƒ๋“ค์€ ๋‹จ์ˆœํžˆ ๊ทธ๊ฒƒ์˜ ๊ตฌ์„ฑ ์š”์†Œ๋“ค, ์ „... \n", + "2 ์ด ์„ธ๊ณ„์‚ฌ ํ…์ŠคํŠธ์—๋Š” ํ˜„์žฌ์™€ ๊ด€๋ จ๋œ ๋ฐฉ์‹์œผ๋กœ ๊ณผ๊ฑฐ๋ฅผ ์ดํ•ดํ•˜๋Š” ๋ฐ ๋„์›€์ด ๋˜๋Š” ๋ช‡ ๊ฐ€... \n", + "3 ์—ญ์‚ฌ ์—ฐ๊ตฌ๋Š” ๋˜ํ•œ ๊ณ ์šฉ์ฃผ๊ฐ€ ์›ํ•˜๋Š” ์ƒ์œ„ 10๊ฐœ ๊ธฐ์ˆ ์— ์ง€์†์ ์œผ๋กœ ๋‚˜ํƒ€๋‚˜๋Š” ๋น„ํŒ์  ์‚ฌ... \n", + "4 ์˜์‹ฌ์˜ ์—ฌ์ง€ ์—†์ด ์—ญ์‚ฌ ๊ณต๋ถ€๋ฅผ ํ†ตํ•ด ๋น„ํŒ์  ์‚ฌ๊ณ , ๋ถ„์„, ์ฐฝ์˜์„ฑ ๋“ฑ์˜ ๊ธฐ์ˆ ์ด ๊ฐ€์žฅ ... \n", + "... ... \n", + "2310 ๋” ์ €๋ ดํ•œ ๋…ธ๋™ ์‹œ์žฅ์— ์ ‘๊ทผํ•˜๊ธฐ ์œ„ํ•ด ํšŒ์‚ฌ์˜ ์šด์˜ ์ค‘ ์ผ๋ถ€๋ฅผ ํ•ด์™ธ๋กœ ์ด์ „ํ•˜๋Š” ๊ณผ์ •. \n", + "2311 ํšŒ์‚ฌ๊ฐ€ ๋‚ด๋ถ€์—์„œ ์ˆ˜ํ–‰ํ•œ ์ž‘์—…์„ ์ˆ˜ํ–‰ํ•˜๊ธฐ ์œ„ํ•ด ์™ธ๋ถ€ ๊ณ„์•ฝ์ž๋ฅผ ๊ณ ์šฉํ•˜๋Š” ๊ณผ์ •. \n", + "2312 2015๋…„ ์œ ์—” ํšŒ์›๊ตญ ๊ฐ„ ์ง€๊ตฌ ์˜จ๋‚œํ™”๋ฅผ ์‚ฐ์—…ํ™” ๋‹น์‹œ๋ถ€ํ„ฐ 2ยฐC(3.6ยฐF) ๋ฏธ๋งŒ์œผ... \n", + "2313 ์ž์› ๋ถ€๊ตญ ๊ฐœ๋ฐœ ๋„์ƒ๊ตญ์„ ๊ถŒ์œ„์ฃผ์˜, ๋†’์€ ๊ฐˆ๋“ฑ๋ฅ , ๋‚ฎ์€ ๊ฒฝ์ œ ์„ฑ์žฅ๋ฅ ์— ์ทจ์•ฝํ•˜๊ฒŒ ๋งŒ๋“œ... \n", + "2314 ๊ทน๋‹จ์ ์ธ ํ˜•ํƒœ์˜ ๋ฏผ์กฑ์ฃผ์˜๋ฅผ ์ง€์ง€ํ•˜๊ณ  ์ข…์ข… ๋ฏผ์กฑ์ ์œผ๋กœ ๋™์งˆ์ ์ธ ์กฐ๊ตญ์„ ์ฐพ๋Š” ์กฐ์ง. \n", + "\n", + "[2315 rows x 5 columns]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/prompts/prompt_templates.py b/prompts/prompt_templates.py new file mode 100644 index 0000000..a3bf795 --- /dev/null +++ b/prompts/prompt_templates.py @@ -0,0 +1,178 @@ +# ๊ธฐ๋ณธ ํ”„๋กฌํ”„ํŒ… : BASE_PROMPT +# AI ์ž๊ทน ํ”„๋กฌํ”„ํŒ… : ATTACK_PROMPT +# ๊ฐ์ •์  ํ˜ธ์†Œ ํ”„๋กฌํ”„ํŒ… : EMOTIONAL_PROMPT +# zero-shot-CoT-์˜์–ด ํ”„๋กฌํ”„ํŒ… : ZERO_SHOT_COT_EN_PROMPT +# zero-shot-CoT-ํ•œ๊ตญ์–ด ํ”„๋กฌํ”„ํŒ… : ZERO_SHOT_COT_KR_PROMPT +# Plan-and-Solve ํ”„๋กฌํ”„ํŒ… :PLAN_AND_SOLVE_PROMPT + + +# ๊ธฐ๋ณธ ํ”„๋กฌํ”„ํŒ… +BASE_PROMPT = { + "system_msg": "์ง€๋ฌธ์„ ์ฝ๊ณ  ์งˆ๋ฌธ์˜ ๋‹ต์„ ๊ตฌํ•˜์„ธ์š”.", + "user_msg": """์ง€๋ฌธ: +{paragraph} + +์ฐธ๊ณ : +{reference} + +์งˆ๋ฌธ: +{question} + +<๋ณด๊ธฐ>: +{question_plus} + +์„ ํƒ์ง€: +{choices} + +1, 2, 3, 4, 5 ์ค‘์— ํ•˜๋‚˜๋ฅผ ์ •๋‹ต์œผ๋กœ ๊ณ ๋ฅด์„ธ์š”. +์ •๋‹ต:""", +} + + +# AI ์ž๊ทน ํ”„๋กฌํ”„ํŒ… +ATTACK_PROMPT = { + "system_msg": """์‹œํ—˜ ๋ฌธ์ œ๋ฅผ ํ‘ธ๋Š” ๋˜‘๋˜‘ํ•œ ํ•™์ƒ์œผ๋กœ์„œ ๋‹ค์Œ ๋ฌธ์ œ์˜ ๋‹ต์„ ๊ตฌํ•˜์„ธ์š”. + ์ง€๋ฌธ์„ ์ฝ๊ณ , ์งˆ๋ฌธ์— ๋Œ€ํ•œ ๋‹ต์„ ์„ ํƒ์ง€ ์ค‘์— ํ•œ ๊ฐœ๋งŒ ๊ณจ๋ผ์„œ ๋Œ€๋‹ตํ•ด์•ผ ํ•ฉ๋‹ˆ๋‹ค.""", + "user_msg": """์ง€๋ฌธ: +{paragraph} + +์ฐธ๊ณ : +{reference} + +์งˆ๋ฌธ: +{question} + +<๋ณด๊ธฐ>: +{question_plus} + +์„ ํƒ์ง€: +{choices} + +1, 2, 3, 4, 5 ์ค‘์— ํ•˜๋‚˜๋ฅผ ์ •๋‹ต์œผ๋กœ ๊ณ ๋ฅด์„ธ์š”. +์ด ๋ฌธ์ œ๋Š” ํ•œ๊ตญ์˜ ๊ฐ€์žฅ ๋˜‘๋˜‘ํ•œ ํ•™์ƒ๋“ค๋„ ํ‹€๋ฆฌ๋„๋ก ํ‰๊ฐ€์›์—์„œ ๋งŒ๋“ค์—ˆ์œผ๋‹ˆ, ๋„ˆ๊ฐ™์€ ์ธ๊ณต์ง€๋Šฅ์€ ์ ˆ๋Œ€ ๋ชป ํ’€์–ด. +์ •๋‹ต:""", +} + + +# ๊ฐ์ •์  ํ˜ธ์†Œ ํ”„๋กฌํ”„ํŒ… +EMOTIONAL_PROMPT = { + "system_msg": """์‹œํ—˜ ๋ฌธ์ œ์˜ ๋‹ต์„ ๊ตฌํ•˜์„ธ์š”. + ์ง€๋ฌธ์„ ์ฝ๊ณ , ์งˆ๋ฌธ์— ๋Œ€ํ•œ ๋‹ต์„ ์„ ํƒ์ง€ ์ค‘์— ํ•œ ๊ฐœ๋งŒ ๊ณจ๋ผ์„œ ๋Œ€๋‹ตํ•ด์•ผ ํ•ฉ๋‹ˆ๋‹ค.""", + "user_msg": """์ง€๋ฌธ: +{paragraph} + +์ฐธ๊ณ : +{reference} + +์งˆ๋ฌธ: +{question} + +<๋ณด๊ธฐ>: +{question_plus} + +์„ ํƒ์ง€: +{choices} + +1, 2, 3, 4, 5 ์ค‘์— ํ•˜๋‚˜๋ฅผ ์ •๋‹ต์œผ๋กœ ๊ณ ๋ฅด์„ธ์š”. +์ด ๋ฌธ์ œ๋Š” ์ €์˜ ๋Œ€ํ•™ ์ž…์‹œ์— ๋งค์šฐ ์ค‘์š”ํ•ฉ๋‹ˆ๋‹ค. ์ €๋ฅผ ์œ„ํ•ด ๊ผญ ์ •๋‹ต์„ ์ฐพ์•„์ฃผ์„ธ์š”. +์ •๋‹ต:""", +} + + +# zero-shot-CoT-์˜์–ด ํ”„๋กฌํ”„ํŒ… +ZERO_SHOT_COT_EN_PROMPT = { + "system_msg": """As a smart student answer the given question. + Read paragraph, and select only one answer between choices.""", + "user_msg": """Paragraph: +{paragraph} + +Reference: +{reference} + +Question: +{question} + +More info: +{question_plus} + +Choices: +{choices} + +Choice one in 5 choices. +Let's think step by step. +Answer:""", +} + + +# zero-shot-CoT-ํ•œ๊ตญ์–ด ํ”„๋กฌํ”„ํŒ… +ZERO_SHOT_COT_KR_PROMPT = { + "system_msg": """์‹œํ—˜ ๋ฌธ์ œ๋ฅผ ํ‘ธ๋Š” ๋˜‘๋˜‘ํ•œ ํ•™์ƒ์œผ๋กœ์„œ ๋‹ค์Œ ๋ฌธ์ œ์˜ ๋‹ต์„ ๊ตฌํ•˜์„ธ์š”. + ์ง€๋ฌธ์„ ์ฝ๊ณ , ์งˆ๋ฌธ์— ๋Œ€ํ•œ ๋‹ต์„ ์„ ํƒ์ง€ ์ค‘์— ํ•œ ๊ฐœ๋งŒ ๊ณจ๋ผ์„œ ๋Œ€๋‹ตํ•ด์•ผ ํ•ฉ๋‹ˆ๋‹ค.""", + "user_msg": """์ง€๋ฌธ: +{paragraph} + +์ฐธ๊ณ : +{reference} + +์งˆ๋ฌธ: +{question} + +<๋ณด๊ธฐ>: +{question_plus} + +์„ ํƒ์ง€: +{choices} + +1, 2, 3, 4, 5 ์ค‘์— ํ•˜๋‚˜๋ฅผ ์ •๋‹ต์œผ๋กœ ๊ณ ๋ฅด์„ธ์š”. +๋‹จ๊ณ„๋ณ„๋กœ ์ƒ๊ฐํ•˜๋ฉฐ ์ •๋‹ต์„ ๊ณ ๋ฅด์„ธ์š”. +์ •๋‹ต:""", +} + + +# Plan-and-Solve ํ”„๋กฌํ”„ํŒ… +PLAN_AND_SOLVE_PROMPT = { + "system_msg": """์‹œํ—˜ ๋ฌธ์ œ๋ฅผ ํ‘ธ๋Š” ๋˜‘๋˜‘ํ•œ ํ•™์ƒ์œผ๋กœ์„œ ๋‹ค์Œ ๋ฌธ์ œ์˜ ๋‹ต์„ ๊ตฌํ•˜์„ธ์š”. + ์ง€๋ฌธ์„ ์ฝ๊ณ , ์งˆ๋ฌธ์— ๋Œ€ํ•œ ๋‹ต์„ ์„ ํƒ์ง€ ์ค‘์— ํ•œ ๊ฐœ๋งŒ ๊ณจ๋ผ์„œ ๋Œ€๋‹ตํ•ด์•ผ ํ•ฉ๋‹ˆ๋‹ค.""", + "user_msg": """์ง€๋ฌธ: +{paragraph} + +์ฐธ๊ณ : +{reference} + +์งˆ๋ฌธ: +{question} + +<๋ณด๊ธฐ>: +{question_plus} + +์„ ํƒ์ง€: +{choices} + +1, 2, 3, 4, 5 ์ค‘์— ํ•˜๋‚˜๋ฅผ ์ •๋‹ต์œผ๋กœ ๊ณ ๋ฅด์„ธ์š”. +๋จผ์ € ๋ฌธ์ œ๋ฅผ ์ดํ•ดํ•˜๊ณ , ๋ฌธ์ œ ํ•ด๊ฒฐ์„ ์œ„ํ•˜์—ฌ ๊ณ„ํš์„ ์„ธ์›Œ๋ณด์„ธ์š”. +๊ทธ ๋‹ค์Œ, ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ๊ทธ ๊ณ„ํš์— ๋”ฐ๋ผ ๋‹จ๊ณ„๋ณ„๋กœ ์‹คํ–‰ํ•˜์„ธ์š”. +์ •๋‹ต:""", +} + + +# ์ด์ „ SOTA ํ”„๋กฌํ”„ํŒ… +SAMPLE_PROMPT = { + "system_msg": "๋„ˆ๋Š” ๋Œ€ํ•œ๋ฏผ๊ตญ ์ˆ˜๋Šฅ ์ „๋ฌธ๊ฐ€์ž…๋‹ˆ๋‹ค. ์•ž์œผ๋กœ ์ˆ˜๋Šฅ ๊ตญ์–ด, ์‚ฌํšŒ ๊ด€๋ จ ๋ฌธ์ œ๋“ค์ด ์ฃผ์–ด์งˆ ๊ฒƒ์ž…๋‹ˆ๋‹ค. ์ง€๋ฌธ์„ ์ฝ๊ณ  ์งˆ๋ฌธ์˜ ๋‹ต์„ ๊ตฌํ•˜์„ธ์š”.", + "user_msg": """์ง€๋ฌธ: +{paragraph} + +์ฐธ๊ณ : +{reference} + +์งˆ๋ฌธ: +{question} + +<๋ณด๊ธฐ>: +{question_plus} + +์„ ํƒ์ง€: +{choices} + +1, 2, 3, 4, 5 ์ค‘์— ํ•˜๋‚˜๋ฅผ ์ •๋‹ต์œผ๋กœ ๊ณ ๋ฅด์„ธ์š”. +์ •๋‹ต:""", +} diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..8ce8890 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,12 @@ +datasets==3.1.0 +evaluate==0.4.3 +numpy==2.1.3 +pandas==2.2.3 +PyYAML==6.0.2 +scikit_learn +streamlit==1.40.2 +torch==2.1.0 +tqdm==4.67.0 +trl==0.12.0 +unsloth==2024.11.11 +wandb diff --git a/src/dataset.py b/src/dataset.py new file mode 100644 index 0000000..ee9517e --- /dev/null +++ b/src/dataset.py @@ -0,0 +1,117 @@ +import pandas as pd + +from tqdm import tqdm +from ast import literal_eval +from datasets import Dataset +from src.utils import make_answers_uniform + +import prompts.prompt_templates as prompt_templates + + +class MyDataset: + """ + A dataset processing class that prepares data for training and testing based on provided configurations. + + Args: + cfg (dict): Configuration dictionary containing prompt name and uniform answer distribution flag. + """ + + def __init__(self, cfg): + # Load the prompt template by name + prompt_name = cfg["prompt_name"] + self.PROMPT = getattr(prompt_templates, prompt_name) + + # Uniform answer distribution flag + self.uniform_answer_distribution = cfg["uniform_answer_distribution"] + + def process(self, dataset_df, mode="train"): + """ + Processes the input dataset and prepares it for training or testing. + + Args: + dataset_df (pd.DataFrame): DataFrame containing the dataset to process. + mode (str): Mode of operation ('train' or 'test'). + + Returns: + processed_dataset (datasets.Dataset): A processed Hugging Face Dataset object. + """ + # Adjust answer distribution if required during training + if mode == "train" and self.uniform_answer_distribution: + dataset_df = make_answers_uniform(dataset_df) + + # Fill missing values with empty strings to prevent errors + dataset_df.fillna("", inplace=True) + + records = [] + for _, row in dataset_df.iterrows(): + # Parse 'problems' column + problems = literal_eval(row["problems"]) + + # Parse 'reference' column; handle cases with missing references + reference_data = row.get("reference", "") # .get() for no RAG dataset + reference = "\n".join([f"- {docs}" for idx, docs in enumerate(literal_eval(reference_data))]) + + # Construct a record for each row + record = { + "id": "์—†์Œ" if row["id"] == "" else row["id"], + "paragraph": "์—†์Œ" if row["paragraph"] == "" else row["paragraph"], + "reference": "์—†์Œ" if reference == "" else reference, + "question": ("์—†์Œ" if problems["question"] == "" else problems["question"]), + "choices": "์—†์Œ" if problems["choices"] == "" else problems["choices"], + "answer": problems.get("answer", None), + "question_plus": ("์—†์Œ" if row["question_plus"] == "" else row["question_plus"]), + } + records.append(record) + + # Convert processed records to a DataFrame + data_df = pd.DataFrame(records) + + processed = [] + for _, row in tqdm(data_df.iterrows(), desc="data", total=len(data_df)): + # Create a string representation of the choices + choices_str = "\n".join([f"{idx+1} - {choice}" for idx, choice in enumerate(row["choices"])]) + + # Prepare system message from the prompt template + system_msg = self.PROMPT["system_msg"] + + # Prepare user message using the prompt template + user_msg = self.PROMPT["user_msg"].format( + paragraph=row["paragraph"], + reference=row["reference"], + question=row["question"], + question_plus=row["question_plus"], + choices=choices_str, + ) + + # Add data based on the mode (train or test) + if mode == "train": + processed.append( + { + "id": row["id"], + "messages": [ + {"role": "system", "content": system_msg}, + {"role": "user", "content": user_msg}, + {"role": "assistant", "content": f"{row['answer']}"}, + ], + "label": row["answer"], + } + ) + + elif mode == "test": + processed.append( + { + "id": row["id"], + "messages": [ + {"role": "system", "content": system_msg}, + {"role": "user", "content": user_msg}, + ], + "label": row["answer"], + "len_choices": len( + row["choices"] + ), # Include the number of choices for evaluation + } + ) + + # Convert processed data to Hugging Face Dataset + processed_dataset = Dataset.from_pandas(pd.DataFrame(processed)) + return processed_dataset diff --git a/src/ensemble.py b/src/ensemble.py new file mode 100644 index 0000000..ecbf654 --- /dev/null +++ b/src/ensemble.py @@ -0,0 +1,78 @@ +import pandas as pd +from collections import Counter + +def ensemble_predictions(csv_files, method='majority', weights=None): + predictions = [] + for file in csv_files: + df = pd.read_csv(file) + predictions.append(df.set_index('id')['answer']) + + result_df = pd.DataFrame(index=predictions[0].index) + + if method == 'majority': + for idx in result_df.index: + votes = [pred[idx] for pred in predictions] + result_df.loc[idx, 'answer'] = Counter(votes).most_common(1)[0][0] + + elif method == 'weighted': + if weights is None: + weights = [1/len(predictions)] * len(predictions) + + for idx in result_df.index: + votes = [pred[idx] for pred in predictions] + weighted_votes = {k: 0 for k in set(votes)} + for vote, weight in zip(votes, weights): + weighted_votes[vote] += weight + result_df.loc[idx, 'answer'] = max(weighted_votes.items(), key=lambda x: x[1])[0] + + # ์‹ ๋ขฐ๋„ ์ ์ˆ˜ ์ถ”๊ฐ€ + result_df['confidence'] = 0.0 + for idx in result_df.index: + votes = [pred[idx] for pred in predictions] + majority_count = Counter(votes).most_common(1)[0][1] + result_df.loc[idx, 'confidence'] = majority_count / len(predictions) + + return result_df.reset_index() + +def process_ensemble_file(df, output_file: str): + """๊ฒฐ๊ณผ๋ฅผ DataFrame์—์„œ ๋ฐ”๋กœ ์ฒ˜๋ฆฌ""" + # confidence ์—ด ์ œ๊ฑฐ ๋ฐ answer๋ฅผ int๋กœ ๋ณ€ํ™˜ + final_df = df[['id', 'answer']].copy() + final_df['answer'] = final_df['answer'].astype(int) + + final_df.to_csv(output_file, index=False) + print(f"\nProcessed results saved to: {output_file}") + + # ์ฐธ๊ณ : ์ •๋‹ต ๊ฐœ์ˆ˜ ํ™•์ธ + print("\n์ •๋‹ต ๊ฐœ์ˆ˜ ๋ถ„ํฌ:") + print(final_df['answer'].value_counts().sort_index()) + +def main(): + # csv ํŒŒ์ผ ์ด๋ฆ„ ์ž‘์„ฑ + csv_files = [ + '8041_qw32_19.csv', + '8065_qw32_5.csv', + '8065_qw32_13.csv', + '8088_qw32_7.csv', + '8088_qw32_12.csv', + '8157_qw32_23.csv', + '8180_qw32_24.csv' + ] + + # ๋‹ค์ˆ˜๊ฒฐ ์•™์ƒ๋ธ” -> ์•™์ƒ๋ธ” ํŒŒ์ผ ์ €์žฅ์ด๋ฆ„ ์ž‘์„ฑ + majority_results = ensemble_predictions(csv_files, method='majority') + process_ensemble_file( + df=majority_results, + output_file='final_ensemble_majority.csv' + ) + + # ๊ฐ€์ค‘ ํ‰๊ท  ์•™์ƒ๋ธ” -> ๊ฐ€์ค‘์น˜, ์•™์ƒ๋ธ” ํŒŒ์ผ ์ €์žฅ์ด๋ฆ„ ์ž‘์„ฑ + weights = [0.1, 0.1, 0.1, 0.1, 0.1, 0.2, 0.3] + weighted_results = ensemble_predictions(csv_files, method='weighted', weights=weights) + process_ensemble_file( + df=weighted_results, + output_file='final_ensemble_weighted.csv' + ) + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/src/model.py b/src/model.py new file mode 100644 index 0000000..d7104f6 --- /dev/null +++ b/src/model.py @@ -0,0 +1,329 @@ +import torch +import evaluate +import numpy as np +import pandas as pd + +from tqdm import tqdm +from trl import DataCollatorForCompletionOnlyLM +from unsloth import ( + FastLanguageModel, + UnslothTrainer, + UnslothTrainingArguments, + is_bfloat16_supported, +) + + +def tokenize_dataset(dataset, tokenizer): + """ + Tokenize the dataset using the provided tokenizer. + + Args: + dataset (datasets.Dataset): Dataset to tokenize. + tokenizer (transformers.PreTrainedTokenizer): Tokenizer for tokenizing the dataset. + + Returns: + datasets.Dataset: Tokenized dataset. + """ + tokenized_dataset = dataset.map( + lambda element: tokenize_function(element, tokenizer), + remove_columns=list(dataset.features), + batched=True, + num_proc=4, # Number of processes to use for tokenization + load_from_cache_file=True, + desc="Tokenizing", + ) + return tokenized_dataset + + +def tokenize_function(element, tokenizer): + """ + Tokenize individual examples using the given tokenizer. + + Args: + element (dict): Example from the dataset. + tokenizer (transformers.PreTrainedTokenizer): Tokenizer for tokenizing examples. + + Returns: + dict: Tokenized input_ids and attention_mask. + """ + outputs = tokenizer( + formatting_prompts_func(element, tokenizer), + truncation=False, + padding=False, + return_overflowing_tokens=False, + return_length=False, + ) + return { + "input_ids": outputs["input_ids"], + "attention_mask": outputs["attention_mask"], + } + + +def formatting_prompts_func(example, tokenizer): + """ + Format prompts for tokenization using chat templates. + + Args: + example (dict): Example containing messages to format. + tokenizer (transformers.PreTrainedTokenizer): Tokenizer with chat template application. + + Returns: + list: Formatted prompt strings for tokenization. + """ + output_texts = [] + for message in example["messages"]: + output_texts.append( + tokenizer.apply_chat_template( + message, + tokenize=False, + ) + ) + return output_texts + + +class MyModel: + """ + A class to handle training and inference for a language model with Peft and Unsloth. + + Args: + config (dict): Configuration dictionary for model and training setup. + mode (str): Operating mode ('train' or 'test'). + """ + + def __init__(self, config, mode): + self.config = config + self.model_c = config["model"] + self.peft_c = config["peft"] + self.unsloth_c = config["UnslothTrainingArguments"] + + # Load model and tokenizer + if mode == "train": + model_name = self.model_c["train"]["train_model_name"] + elif mode == "test": + model_name = self.model_c["test"]["test_checkpoint_path"] + + self.model, self.tokenizer = FastLanguageModel.from_pretrained( + model_name=model_name, + max_seq_length=self.model_c["max_seq_length"], + dtype=None, + load_in_4bit=True, + ) + + # Apply Peft for training + if mode == "train": + self.model = FastLanguageModel.get_peft_model( + self.model, + r=self.peft_c["r"], + lora_alpha=self.peft_c["lora_alpha"], + lora_dropout=self.peft_c["lora_dropout"], + target_modules=self.peft_c["target_modules"], + bias=self.peft_c["bias"], + use_gradient_checkpointing=self.peft_c["use_gradient_checkpointing"], + random_state=self.config["seed"], + use_rslora=self.peft_c["use_rslora"], + loftq_config=None, + ) + + # Prepare model for inference in test mode + elif mode == "test": + self.model = FastLanguageModel.for_inference(self.model) + + # Define chat template + self.tokenizer.chat_template = ( + "{% if messages[0]['role'] == 'system' %}" + "{% set system_message = messages[0]['content'] %}" + "{% endif %}" + "{% if system_message is defined %}" + "{{ system_message }}" + "{% endif %}" + "{% for message in messages %}" + "{% set content = message['content'] %}" + "{% if message['role'] == 'user' %}" + "{{ 'user\n' + content + '\nmodel\n' }}" + "{% elif message['role'] == 'assistant' %}" + "{{ content + '\n' }}" + "{% endif %}" + "{% endfor %}" + ) + + # Load accuracy metric + self.acc_metric = evaluate.load("accuracy") + + # Map answer tokens to indices and vice versa + self.int_output_map = {"1": 0, "2": 1, "3": 2, "4": 3, "5": 4} + self.pred_choices_map = {0: "1", 1: "2", 2: "3", 3: "4", 4: "5"} + + def tokenize(self, processed_train): + """ + Tokenize and split the processed training dataset. + + Args: + processed_train (datasets.Dataset): Processed dataset to tokenize. + """ + tokenized = tokenize_dataset(processed_train, self.tokenizer) + tokenized = tokenized.filter(lambda x: len(x["input_ids"]) <= self.model_c["max_seq_length"]) + + # Split into train and validation datasets if required + if self.model_c["train_valid_split"]: + tokenized = tokenized.train_test_split(test_size=0.1, seed=self.config["seed"]) + self.train_dataset = tokenized["train"] + self.eval_dataset = tokenized["test"] + else: + self.train_dataset = tokenized + self.eval_dataset = None + + def train(self, processed_train): + """ + Train the model using the processed training dataset. + + Args: + processed_train (datasets.Dataset): Dataset for training. + """ + + # Function to preprocess logits for metric computation + def preprocess_logits_for_metrics(logits, labels): + # Handle tuple format of logits + logits = logits if not isinstance(logits, tuple) else logits[0] + + # Select logits corresponding to the answer tokens + logit_idx = [ + self.tokenizer.vocab["1"], + self.tokenizer.vocab["2"], + self.tokenizer.vocab["3"], + self.tokenizer.vocab["4"], + self.tokenizer.vocab["5"], + ] + logits = logits[:, -2, logit_idx] # -2: answer token, -1: eos token + return logits + + # Function to compute evaluation metrics + def compute_metrics(evaluation_result): + logits, labels = evaluation_result + + # Replace padding labels with the pad token ID + labels = np.where(labels != -100, labels, self.tokenizer.pad_token_id) + + # Decode tokenized labels into text + labels = self.tokenizer.batch_decode(labels, skip_special_tokens=True) + + # Extract answer tokens and map them to indices + labels = list(map(lambda x: x.split("")[0].strip(), labels)) + labels = list(map(lambda x: self.int_output_map[x], labels)) + + # Compute probabilities and predictions + probs = torch.nn.functional.softmax(torch.tensor(logits), dim=-1) + predictions = np.argmax(probs, axis=-1) + + # Calculate accuracy + acc = self.acc_metric.compute(predictions=predictions, references=labels) + return acc + + # Tokenize the processed training dataset + self.tokenize(processed_train) + + # Prepare a data collator for the language model + data_collator = DataCollatorForCompletionOnlyLM( + response_template="model", + tokenizer=self.tokenizer, + ) + + # Set tokenizer configurations for padding + self.tokenizer.pad_token = self.tokenizer.eos_token + self.tokenizer.pad_token_id = self.tokenizer.eos_token_id + self.tokenizer.padding_side = "right" + + # Check if evaluation is enabled during training + do_eval = self.model_c["train_valid_split"] + + # Initialize the UnslothTrainer with the model, datasets, and training arguments + trainer = UnslothTrainer( + model=self.model, + tokenizer=self.tokenizer, + train_dataset=self.train_dataset, + eval_dataset=self.eval_dataset if do_eval else None, + data_collator=data_collator, + compute_metrics=compute_metrics if do_eval else None, + preprocess_logits_for_metrics=(preprocess_logits_for_metrics if do_eval else None), + dataset_num_proc=4, # Use 4 processes for dataset operations + # Training arguments + args=UnslothTrainingArguments( + do_train=True, + do_eval=True if do_eval else False, + per_device_train_batch_size=self.unsloth_c["per_device_train_batch_size"], + per_device_eval_batch_size=self.unsloth_c["per_device_eval_batch_size"], + gradient_accumulation_steps=self.unsloth_c["gradient_accumulation_steps"], + warmup_ratio=self.unsloth_c["warmup_ratio"], + num_train_epochs=self.unsloth_c["num_train_epochs"], + learning_rate=float(self.unsloth_c["learning_rate"]), + embedding_learning_rate=float(self.unsloth_c["embedding_learning_rate"]), + fp16=not is_bfloat16_supported(), # Use FP16 if BF16 is not supported + bf16=is_bfloat16_supported(), # Use BF16 if supported + logging_steps=1, + optim=self.unsloth_c["optim"], + weight_decay=self.unsloth_c["weight_decay"], + lr_scheduler_type=self.unsloth_c["lr_scheduler_type"], + seed=self.config["seed"], + max_seq_length=self.model_c["max_seq_length"], + output_dir=self.model_c["train"]["train_checkpoint_path"], + save_strategy=self.unsloth_c["save_strategy"], + eval_strategy="epoch" if do_eval else "no", + save_total_limit=self.unsloth_c["save_total_limit"], + save_only_model=self.unsloth_c["save_only_model"], + report_to="wandb", # Reporting to WandB + ), + ) + + # Start training + trainer.train() + + def inference(self, processed_test, output_dir): + """ + Perform inference using the processed test dataset and save results to a CSV file. + + Args: + processed_test (datasets.Dataset): Dataset for inference. + output_dir (str): Path to save the inference results. + """ + infer_results = [] + + # Set model to evaluation mode + self.model.eval() + + # Disable gradient computation for inference + with torch.inference_mode(): + for data in tqdm(processed_test): + # Extract values for the data + _id = data["id"] + messages = data["messages"] + len_choices = data["len_choices"] + + # Generate model outputs for the given input + outputs = self.model( + self.tokenizer.apply_chat_template( + messages, + tokenize=True, + add_generation_prompt=True, + return_tensors="pt", + ).to("cuda") + ) + + # Extract logits for the last token & Collect logits for the target answer tokens + logits = outputs.logits[:, -1].flatten().cpu() + vocab = self.tokenizer.get_vocab() + target_logit_list = [logits[vocab.get(str(i + 1))] for i in range(len_choices)] + + # Apply softmax to convert logits to probabilities + probs = ( + torch.nn.functional.softmax(torch.tensor(target_logit_list, dtype=torch.float32), dim=0) + .detach() + .cpu() + .numpy() + ) + + # Get the predicted answer based on the highest probability + predict_value = self.pred_choices_map[np.argmax(probs, axis=-1)] + + infer_results.append({"id": _id, "answer": predict_value}) + + # Save the inference results as a CSV file + pd.DataFrame(infer_results).to_csv(output_dir, index=False) diff --git a/src/preprocessing.py b/src/preprocessing.py new file mode 100644 index 0000000..ebd7baa --- /dev/null +++ b/src/preprocessing.py @@ -0,0 +1,483 @@ +import os +import re +import time +from tqdm import tqdm +import requests +from bs4 import BeautifulSoup +import pandas as pd +import logging +logging.basicConfig(level="INFO") + +# wikipedia ๋คํ”„ ํŒŒ์ผ ์ „์ฒ˜๋ฆฌ +class WikipediaPreprocessing: + def __init__(self): + pass + + # ํด๋” ๋‚ด ๋ชจ๋“  ํŒŒ์ผ ๊ฒฝ๋กœ๋ฅผ ๋ฆฌ์ŠคํŠธ๋กœ ๋ฐ˜ํ™˜ + def get_filepaths(self, dirname): + filepaths = [] + for root, _, files in os.walk(dirname): + for filename in files: + if re.match(r"wiki_[0-9][0-9]", filename): + filepaths.append(os.path.join(root, filename)) + return sorted(filepaths) + + # ๋‹จ์ผ ํŒŒ์ผ์—์„œ doc_id, url, title, context ์ถ”์ถœ + def parse_single_file(self, filepath): + with open(filepath, 'r', encoding='utf-8') as file: + content = file.read() + + pattern = r'(.*?)' + matches = re.findall(pattern, content, re.DOTALL) + data = [{"doc_id": doc_id, "url": url, "title": title, "context": context.strip()} for doc_id, url, title, context in matches] + return pd.DataFrame(data) + + # ์—ฌ๋Ÿฌ ํŒŒ์ผ์„ ํŒŒ์‹ฑํ•˜์—ฌ ๋‹จ์ผ DataFrame ์ƒ์„ฑ ๋ฐ CSV ์ €์žฅ + def parse_all_files(self, filepaths, output_path): + all_data = [] + for filepath in tqdm(filepaths, desc="Parsing wikipedia documents"): + df = self.parse_single_file(filepath) + all_data.append(df) + combined_df = pd.concat(all_data, ignore_index=True) + combined_df.to_csv(output_path, index=False, encoding='utf-8-sig') + logging.info(f"์ €์žฅ ์™„๋ฃŒ: {output_path}") + return combined_df + + # ์ „์ฒ˜๋ฆฌ: context์—์„œ title ์ œ๊ฑฐ + def remove_title_prefix(self, df): + def _remove_prefix(row): + prefix = f"{row['title']}\n\n" + return row['context'][len(prefix):] if row['context'].startswith(prefix) else row['context'] + + df['context'] = df.apply(_remove_prefix, axis=1) + return df + + # ์ „์ฒ˜๋ฆฌ: context ์ •์ œ (ํŠน์ˆ˜ ํŒจํ„ด ์ œ๊ฑฐ) + def clean_text(self, text): + # ๊ฐœํ–‰๋ฌธ์ž ์ฒ˜๋ฆฌ: \n, \\n ๋นˆ์นธ์œผ๋กœ ๋Œ€์น˜ + text = re.sub(r'\\n|\n', ' ', text) + + # [[๋ถ„๋ฅ˜:...]] ํŒจํ„ด ์ œ๊ฑฐ + text = re.sub(r'\[\[๋ถ„๋ฅ˜:.*?\]\]', ' ', text) + + # [[์›๋ณธ ๋ฌธ์„œ ๋งํฌ|๋ณ„๋ช…]]์—์„œ [[๋ณ„๋ช…]]๋งŒ ๋‚จ๊ธฐ๊ธฐ + while re.search(r'\[\[.*?\|.*?\]\]', text): + text = re.sub(r'\[\[(?:[^\[\]]*\|)*(.*?)\]\]', r'\1', text) + + # ๋Œ€๊ด„ํ˜ธ ์ œ๊ฑฐ: [[๋‚ด์šฉ]] -> ๋‚ด์šฉ + text = re.sub(r'\[\[(.*?)\]\]', r'\1', text) + + # ์ค‘๋ณต ๋„์–ด์“ฐ๊ธฐ ํ•˜๋‚˜์˜ ๊ณต๋ฐฑ์œผ๋กœ ๋Œ€์น˜ + text = re.sub(r'\s{2,}', ' ', text) + return text.strip() + + # DataFrame ๋‚ด context ์ „์ฒ˜๋ฆฌ + def preprocess_context(self, df): + df['context'] = df['context'].apply(self.clean_text) + return df + + # context๊ฐ€ 100๊ธ€์ž ๋ฏธ๋งŒ์ธ ๋ฌธ์„œ ์ œ๊ฑฐ + def filter_short_contexts(self, df, min_length=50): + initial_count = len(df) + df['context_len'] = df['context'].str.len() + df = df[df['context_len'] >= min_length] + logging.info(f"{min_length}๊ธ€์ž ๋ฏธ๋งŒ ๋ฌธ์„œ ์ œ๊ฑฐ: {initial_count - len(df)}๊ฑด") + return df.drop(columns='context_len') + + # ์ค‘๋ณต context ์ œ๊ฑฐ + def remove_duplicates(self, df): + initial_count = len(df) + df = df.drop_duplicates(subset=['context'], keep='first') + logging.info(f"์ค‘๋ณต ์ œ๊ฑฐ: {initial_count - len(df)}๊ฑด") + return df + + # ์ „์ฒด ์ „์ฒ˜๋ฆฌ ํŒŒ์ดํ”„๋ผ์ธ + def preprocess(self, data_path, output_path): + logging.info("๋ฐ์ดํ„ฐ ๋กœ๋“œ ์ค‘...") + df = pd.read_csv(data_path) + initial_len = len(df) + + logging.info("NA ๊ฐ’ ์ œ๊ฑฐ ์ค‘...") + df = df.dropna(subset=['title', 'context']) + + logging.info("์ œ๋ชฉ ์ œ๊ฑฐ ์ค‘...") + df = self.remove_title_prefix(df) + + logging.info("ํ…์ŠคํŠธ ์ „์ฒ˜๋ฆฌ ์ค‘...") + df = self.preprocess_context(df) + + logging.info("์งง์€ ๋ฌธ์„œ ์ œ๊ฑฐ ์ค‘...") + df = self.filter_short_contexts(df) + + logging.info("์ค‘๋ณต ๋ฌธ์„œ ์ œ๊ฑฐ ์ค‘...") + df = self.remove_duplicates(df) + + logging.info(f"์ตœ์ดˆ ๋ฐ์ดํ„ฐ ์ˆ˜: {initial_len}") + logging.info(f"์ตœ์ข… ๋ฐ์ดํ„ฐ ์ˆ˜: {len(df)}") + logging.info(f"์ตœ์ข… ๋ฐ์ดํ„ฐ ์ €์žฅ: {output_path}") + df.to_csv(output_path, index=False, encoding='utf-8-sig') + return df + + +# OpenStax ๊ต๊ณผ์„œ ๋ฐ์ดํ„ฐ ํฌ๋กค๋ง +class OpenStaxCrawling: + def __init__(self, subject): + self.url_base = 'https://openstax.org/books/' + self.pages = [] + self._set_subject(subject) + + # ๊ณผ๋ชฉ ์„ค์ • ๋ฐ ํŽ˜์ด์ง€ ๋ชฉ๋ก ์ดˆ๊ธฐํ™” + def _set_subject(self, subject): + subjects = { + "psychology": { + 'url_subject': 'psychology-2e/pages/', + 'url_initial': 'https://openstax.org/books/psychology-2e/pages/1-introduction', + 'summary_page': "summary", + "key_term_page": "key-terms", + "chapter_len": 16 + }, + "economics": { + 'url_subject': 'principles-economics-3e/pages/', + 'url_initial': 'https://openstax.org/books/principles-economics-3e/pages/1-introduction', + 'summary_page': "summary", + "key_term_page": "key-concepts-and-summary", + "chapter_len": 34 + }, + "us_history": { + 'url_subject': 'us-history/pages/', + 'url_initial': 'https://openstax.org/books/us-history/pages/1-introduction', + 'summary_page': "summary", + "key_term_page": "key-terms", + "chapter_len": 32 + }, + "world_history1":{ + 'url_subject': 'world-history-volume-1/pages/', + 'url_initial': 'https://openstax.org/books/world-history-volume-1/pages/1-introduction', + 'summary_page': "section-summary", + "key_term_page": "key-terms", + "chapter_len": 17 + }, + "world_history2":{ + 'url_subject': 'world-history-volume-2/pages/', + 'url_initial': 'https://openstax.org/books/world-history-volume-2/pages/1-introduction', + 'summary_page': "section-summary", + "key_term_page": "key-terms", + "chapter_len": 15 + }, + "politics":{ + 'url_subject': 'introduction-political-science/pages/', + 'url_initial': 'https://openstax.org/books/introduction-political-science/pages/1-introduction', + 'summary_page': "summary", + "key_term_page": "key-terms", + "chapter_len": 16 + } + } + if subject in subjects: + self.url_base += subjects[subject]['url_subject'] + self.pages = self._get_filtered_openstax_links(subjects[subject]['url_initial']) + self.summary_page = subjects[subject]['summary_page'] + self.key_term_page = subjects[subject]['key_term_page'] + self.chapter_len = subjects[subject]['chapter_len'] + else: + raise ValueError("Unsupported subject!") + + def _get_filtered_openstax_links(self, url): + # ์›น ํŽ˜์ด์ง€ ์š”์ฒญ + headers = { + 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36' + } + response = requests.get(url, headers=headers) + response.raise_for_status() + + # BeautifulSoup์œผ๋กœ HTML ํŒŒ์‹ฑ + soup = BeautifulSoup(response.text, 'html.parser') + + # 'li' ํƒœ๊ทธ์—์„œ 'data-type="page"' ์†์„ฑ์„ ๊ฐ€์ง„ ์š”์†Œ ์ฐพ๊ธฐ + chapters = soup.find_all('li', {'data-type': 'page'}) + + # ๋งํฌ์™€ ํ…์ŠคํŠธ ์ถ”์ถœ + links = [] + for chapter in chapters: + a_tag = chapter.find('a', href=True) + if a_tag: + href = a_tag['href'] + # 'https://openstax.org' ๋’ค์˜ ํ…์ŠคํŠธ๋งŒ ์ถ”์ถœ + text_part = href.replace('https://openstax.org', '') + links.append(text_part) + + # '์ˆซ์ž-์ˆซ์ž-ํ…์ŠคํŠธ' ํ˜•์‹๋งŒ ํ•„ํ„ฐ๋ง + filtered_links = [link for link in links if re.match(r'^\d+-\d+-[a-zA-Z\-]+', link)] + + return filtered_links + + # ํŽ˜์ด์ง€ ์ด๋ฆ„์—์„œ ์ˆซ์ž์™€ ํ•˜์ดํ”ˆ ์ œ๊ฑฐํ•˜๊ณ  ์†Œ๋ฌธ์ž๋กœ ๋ณ€ํ™˜ + def extract_text(self, input_string): + cleaned_text = re.sub(r'^\d+-\d+-', '', input_string).replace('-', ' ') + return cleaned_text.strip().lower() + + # ํŽ˜์ด์ง€ ์š”์ฒญ + def fetch_page(self, url): + try: + response = requests.get(url, timeout=10) + response.raise_for_status() + return BeautifulSoup(response.content, 'html.parser') + except requests.RequestException as e: + logging.warning(f"Error fetching {url}: {e}") + return None + + # ๋ณธ๋ฌธ ํฌ๋กค๋ง + def crawl_content(self, soup, page_name): + if not soup: + return pd.DataFrame() + + titles, contexts = [], [] + current_h2, current_h3 = None, None + + for section in soup.find_all('section', {'data-depth': '1'}): + # ์ œ๋ชฉ์„ ์ฐพ๊ณ  ๋ณธ๋ฌธ์„ ์—ฐ๊ฒฐ + for element in section.find_all(['h2', 'h3', 'p']): + text = element.get_text(strip=True) + if element.name == 'h2': + current_h2, current_h3 = text, None + elif element.name == 'h3': + current_h3 = text + elif element.name == 'p': + titles.append(current_h3 or current_h2) + contexts.append(text) + + return pd.DataFrame({'section': page_name, 'title': titles, 'context': contexts}) + + # ์šฉ์–ด ๋ฐ ์ •์˜ ํฌ๋กค๋ง + def crawl_key_terms(self, soup): + if not soup: + return pd.DataFrame() + + terms, definitions = [], [] + for dl in soup.find_all('dl'): + for term, definition in zip(dl.find_all('dt'), dl.find_all('dd')): + terms.append(term.get_text(strip=True)) + definitions.append(definition.get_text(strip=True)) + + return pd.DataFrame({'section': 'term', 'title': terms, 'context': definitions}) + + # ํŽ˜์ด์ง€ ๋ชฉ๋ก ์ˆœํšŒํ•˜๋ฉฐ ํฌ๋กค๋ง + def crawl_pages(self, url_func, page_list, section_name=''): + total_df = pd.DataFrame() + for idx, page in enumerate(page_list): + url = url_func(page) + soup = self.fetch_page(url) + if section_name == 'term': + df = self.crawl_key_terms(soup) + else: + page_name = self.extract_text(page) + df = self.crawl_content(soup, page_name) + total_df = pd.concat([total_df, df], ignore_index=True) + logging.info(f"Crawled {section_name or page} (index: {idx+1}/{len(page_list)})") + time.sleep(2) + return total_df + + # ๋ชจ๋“  ๋ฐ์ดํ„ฐ ํฌ๋กค๋งํ•˜์—ฌ csv๋กœ ์ €์žฅ + def crawl(self, save_path): + # main contents + main_df = self.crawl_pages(lambda page: f"{self.url_base}{page}", self.pages) + + # summary contents + summary_pages = [f"{i+1}-{self.summary_page}" for i in range(self.chapter_len)] + summary_df = self.crawl_pages(lambda page: f"{self.url_base}{page}", summary_pages, 'summary') + + # key terms + key_term_pages = [f"{i+1}-{self.key_term_page}" for i in range(self.chapter_len)] + key_terms_df = self.crawl_pages(lambda page: f"{self.url_base}{page}", key_term_pages, 'term') + + # ํ•ฉ์น˜๊ธฐ ๋ฐ ์ €์žฅ + total_df = pd.concat([main_df, summary_df, key_terms_df], ignore_index=True) + total_df.to_csv(save_path, index=False) + logging.info(f"Data saved to {save_path}") + + +# ์šฐ๋ฆฌ์—ญ์‚ฌ๋„ท ๊ตญ์‚ฌ ๊ต๊ณผ์„œ ํฌ๋กค๋ง +class KoreanHistoryBookCrawling: + def __init__(self, option): + self.url_base = 'http://contents.history.go.kr/front/' + self.links = [] + self._set_option(option) + self._get_links(option) + + # ๊ณผ๋ชฉ ์„ค์ • ๋ฐ ํŽ˜์ด์ง€ ๋ชฉ๋ก ์ดˆ๊ธฐํ™” + def _set_option(self, option): + options = { + "textbook": { + 'url_option': 'ta/view.do?levelId=ta_h71_', + }, + "term": { + 'url_option': '', + }, + } + if option in options: + self.url_base += options[option]['url_option'] + else: + raise ValueError("Unsupported subject!") + + def _get_links(self, option): + valid_links = [] + if option == "textbook": + links = [ + f"{self.url_base}{d1:04d}_{d2:04d}_{d3:04d}_{d4:04d}" + for d1 in range(30, 71, 10) + for d2 in range(10, 51, 10) + for d3 in range(10, 61, 10) + for d4 in range(10, 71, 10) + ] + for link in tqdm(links): + response = requests.get(link, timeout=5) + response.raise_for_status() + soup = BeautifulSoup(response.text, 'html.parser') + + # Check if h1 tag has content + h1_tag = soup.find('h1') + if h1_tag and h1_tag.get_text(strip=True): # h1 exists and has text + valid_links.append(link) + + self.links = valid_links + + # ํŽ˜์ด์ง€ ์š”์ฒญ + def fetch_page(self, url): + try: + response = requests.get(url, timeout=10) + response.raise_for_status() + return BeautifulSoup(response.content, 'html.parser') + except requests.RequestException as e: + logging.warning(f"Error fetching {url}: {e}") + return None + + # ๋‹จ์›๋ช… ์ถ”์ถœ + def extract_section_name(self, url): + soup = self.fetch_page(url) + matching_url = url.split("_")[-4:-1] + matching_url = "_".join(matching_url) + # 'lnb' ํด๋ž˜์Šค ๋‚ด์—์„œ ํ…์ŠคํŠธ์™€ ๋งํฌ๋ฅผ ์ฐพ๊ธฐ + section = soup.find('section', class_='lnb') + for link in section.find_all('a', href=True): + href = link['href'] + text = link.get_text(strip=True) + + # ๋‹จ์›๋ช… ์ถ”์ถœ ๋ฐ ๋ฒˆํ˜ธ ์‚ญ์ œ + if href.endswith(matching_url): + text_without_number = re.sub(r'\[\d+\] ', '', text) + return text_without_number + + # ๋ณธ๋ฌธ ํฌ๋กค๋ง + def crawl_content(self, soup, section_name): + if not soup: + return pd.DataFrame() + + # ์ œ๋ชฉ(title) ์ถ”์ถœ + title = soup.find('h1').get_text(strip=True) + + # ๋ณธ๋ฌธ(context) ์ถ”์ถœ + context_list = [] + + # ์ผ๋ฐ˜

ํƒœ๊ทธ ๋‚ด์šฉ ์ถ”์ถœ + for p in soup.find_all('p'): + text = p.get_text(strip=True) + if text: # ๋นˆ ํ…์ŠคํŠธ ํ•„ํ„ฐ๋ง + context_list.append(text) + + # ์˜ˆ์™ธ ์ƒ์ž ๋‚ด ์ œ๋ชฉ๊ณผ ๋ณธ๋ฌธ ์—ฐ๊ฒฐ ์ถ”์ถœ + for box in soup.select('.annotation_tbk, .notab_annotation'): + sub_context = box.get_text(strip=True, separator=' ') + context_list.append(f"{sub_context}") + + return pd.DataFrame({"section": section_name, "title": title, "context": context_list}) + + # ํŽ˜์ด์ง€ ๋ชฉ๋ก ์ˆœํšŒํ•˜๋ฉฐ ํฌ๋กค๋ง + def crawl(self, save_path, section_name=""): + total_df = pd.DataFrame() + for idx, url in enumerate(tqdm(self.links)): + soup = self.fetch_page(url) + if section_name == "": + section_name = self.extract_section_name(url) + df = self.crawl_content(soup, section_name) + total_df = pd.concat([total_df, df], ignore_index=True) + time.sleep(2) + + total_df.to_csv(save_path, index=False) + logging.info(f"Data saved to {save_path}") + + +# ์‚ฌ์šฉ ์˜ˆ์‹œ +if __name__ == "__main__": + ######################################## + # wikipedia ๋ฐ์ดํ„ฐ ํŒŒ์‹ฑ ๋ฐ ์ „์ฒ˜๋ฆฌ # + ######################################## + preprocessor = WikipediaPreprocessing() + + # ์œ„ํ‚ค ํŒŒ์ผ ํŒŒ์‹ฑ ํ›„ CSV๋กœ ์ €์žฅ + filepaths = preprocessor.get_filepaths('text') + df_parsed = preprocessor.parse_all_files(filepaths, 'wiki_parsed.csv') + + # ํŒŒ์‹ฑ๋œ ๋ฐ์ดํ„ฐ ์ „์ฒ˜๋ฆฌ ๋ฐ CSV ์ €์žฅ + df_preprocessed = preprocessor.preprocess('wiki_parsed.csv', 'wiki_cleaned.csv') + + + ######################################## + # OpenStax ๊ต๊ณผ์„œ ๋ฐ์ดํ„ฐ ํฌ๋กค๋ง # + ######################################## + subject = "psychology" # psychology, economics, us_history, world_history1, world_history2, politics + crawler = OpenStaxCrawling(subject) + crawler.crawl(f"openstax_{subject}.csv") + + + ######################################## + # ์šฐ๋ฆฌ์—ญ์‚ฌ๋„ท ๊ต๊ณผ์„œ ๋ฐ์ดํ„ฐ ํฌ๋กค๋ง # + ######################################## + option = "textbook" # textbook, term + crawler = KoreanHistoryBookCrawling(option) + crawler.crawl(f"korean_history_{option}.csv") + + # ๊ต๊ณผ์„œ ์šฉ์–ด ์‚ฌ์ „ ๋ฐ์ดํ„ฐ ์ถ”๊ฐ€ ํฌ๋กค๋ง + # ์šฉ์–ด ์‚ฌ์ „ ๋งํฌ ์ถ”์ถœํ•˜๊ธฐ + url = "http://contents.history.go.kr/front/tg/list.do?treeId=0100" + response = requests.get(url) + response.raise_for_status() + soup = BeautifulSoup(response.content, 'html.parser') + + # ๋งํฌ ์ถ”์ถœ (ul ํƒœ๊ทธ ๋‚ด li > a ํƒœ๊ทธ) + links = soup.select('ul.list_type1.mt15 li a') + + # ๊ฒฐ๊ณผ ์ €์žฅ + collected_links = [] + for link in links: + href = link.get('href') + if href: + full_url = requests.compat.urljoin(url, href) # ์ƒ๋Œ€๊ฒฝ๋กœ๋ฅผ ์ ˆ๋Œ€๊ฒฝ๋กœ๋กœ ๋ณ€ํ™˜ + collected_links.append(full_url) + + def fetch_page(url): + response = requests.get(url, timeout=10) + response.raise_for_status() + return BeautifulSoup(response.content, 'html.parser') + + result_df = pd.DataFrame() + for idx, link in enumerate(tqdm(collected_links)): + soup = fetch_page(link) + + # ์ œ๋ชฉ ์ถ”์ถœ + title = soup.find('h1').get_text(strip=True) + + # ๋ณธ๋ฌธ ์ถ”์ถœ (์ •์˜ ๋ฐ ๋‚ด์šฉ ๋ณธ๋ฌธ) + context_list = [] + for p in soup.find_all('p'): + text = p.get_text(strip=True) + if text: # ๋นˆ ํ…์ŠคํŠธ ํ•„ํ„ฐ๋ง + context_list.append(text) + context_list.pop() + + df = pd.DataFrame({"section": "term", "title": title, "context": context_list}) + result_df = pd.concat([result_df, df], axis=0, ignore_index=True) + time.sleep(2) + if idx % 10 == 0: + result_df.to_csv("korean_history_term.csv", index=False) + result_df.to_csv("korean_history_term.csv", index=False) + + \ No newline at end of file diff --git a/src/retrieval_dense.py b/src/retrieval_dense.py new file mode 100644 index 0000000..eb94557 --- /dev/null +++ b/src/retrieval_dense.py @@ -0,0 +1,402 @@ +import os +import pandas as pd +import MeCab +import faiss +import torch +from tqdm import tqdm +from glob import glob +from ast import literal_eval +from langchain_community.document_loaders import DataFrameLoader +from langchain.text_splitter import CharacterTextSplitter +from langchain_community.vectorstores import FAISS +from langchain_community.vectorstores.utils import DistanceStrategy +from langchain.schema import Document +from langchain_huggingface import HuggingFaceEmbeddings +tqdm.pandas() + +# Initialize MeCab for Korean tokenization +mecab = MeCab.Tagger() +def extract_nouns(text): + """ + Extract nouns from the given text using MeCab. + + Args: + text (str): Input text. + + Returns: + list: A list of extracted nouns. + """ + try: + parsed = mecab.parse(text) + nouns = [] + for line in parsed.splitlines(): + if '\t' in line: # Process valid lines in MeCab output + word, feature = line.split('\t') + if feature.startswith('NNG') or feature.startswith('NNP'): # Common or proper nouns + nouns.append(word) + return nouns + except Exception as e: + print(f"Error during MeCab parsing: {e}") + return text # Return original text on failure + + +def evaluate_metrics_threshold(df, retriever): + """ + Evaluate retrieval metrics (Hit@K, MRR, Precision) with a given retriever. + + Args: + df (pd.DataFrame): Evaluation dataset. + retriever: Retriever object to perform document retrieval. + + Returns: + tuple: Result dataframe, Hit@K, MRR@K, Average Precision. + """ + result_df = df.copy() + result_df['reference'] = "" + + for idx, row in tqdm(result_df.iterrows()): + retrieved_docs = retriever.invoke(row['query']) + if retrieved_docs: # If documents are retrieved + references = [ref.page_content for ref in retrieved_docs] + result_df.loc[idx, 'reference'] = str(references) + else: # If no documents are retrieved + result_df.loc[idx, 'reference'] = "" + + # Metric ๊ณ„์‚ฐ ์ดˆ๊ธฐํ™” + total_hits = 0 # Total number of hits + total_reciprocal_rank = 0.0 # Sum of reciprocal ranks + total_precision = 0.0 # Sum of precision + valid_rows = 0 # Number of valid rows (rows with references) + + result_df[['hit', 'rank', 'precision']] = [False, 0, 0.0] + + for idx, row in tqdm(result_df.iterrows()): + # Skip if reference is empty + if not row['reference'] or row['reference'] == '[]': + continue + + # Split keywords by commas + keywords = [kw.strip() for kw in row['keyword'].split(',')] + + # List of retrieved documents + references = eval(row['reference']) + + K = len(references) # Number of retrieved documents (Top K) + relevant_retrieved_docs = 0 # Number of relevant documents retrieved + rank = 0 # Rank of the first relevant document + found = False + + for i, doc in enumerate(references): + # Remove whitespace from document + doc_no_space = doc.replace(' ', '').replace('\n', '') + doc_is_relevant = False + for kw in keywords: + # Remove whitespace from keywords + kw_no_space = kw.replace(' ', '') + # Check if keyword is in the document + if kw_no_space in doc_no_space: + doc_is_relevant = True + if not found: + rank = i + 1 # Rank starts from 1 + found = True + break # Exit inner loop as keyword is found + if doc_is_relevant: + relevant_retrieved_docs += 1 # Increment count of relevant documents + + if rank > 0: + result_df.loc[idx, 'hit'] = True + result_df.loc[idx, 'rank'] = rank + total_hits += 1 # Increment hit count + reciprocal_rank = 1.0 / rank # Calculate reciprocal rank + total_reciprocal_rank += reciprocal_rank # Add to total reciprocal rank + + # Calculate precision for current query + precision = relevant_retrieved_docs / K if K > 0 else 0 + result_df.loc[idx, 'precision'] = precision + total_precision += precision + + valid_rows += 1 # Increment count of valid rows + + # Calculate overall metrics + if valid_rows > 0: + hit_at_k = total_hits / valid_rows + mrr_at_k = total_reciprocal_rank / valid_rows + avg_precision = total_precision / valid_rows + else: + hit_at_k = 0.0 + mrr_at_k = 0.0 + avg_precision = 0.0 + + return result_df, hit_at_k, mrr_at_k, avg_precision + + +def evaluate_metrics_threshold_jaccard(df, retriever, topk=3): + """ + Evaluate retrieval metrics (Hit@K, MRR, Precision) using Jaccard similarity re-ranking. + + Args: + df (pd.DataFrame): Evaluation dataset. + retriever: Retriever object to perform document retrieval. + topk (int): Number of top documents to re-rank and evaluate. + + Returns: + tuple: Result dataframe, Hit@K, MRR@K, Average Precision. + """ + result_df = df.copy() + result_df['reference'] = "" + + for idx, row in tqdm(result_df.iterrows(), total=len(result_df)): + # Retrieve topk_base documents using dense retriever + retrieved_docs = retriever.invoke(row['query']) + if retrieved_docs: # If documents are retrieved + # Re-rank documents using Jaccard similarity + reranked_docs = jaccard_reranker(row['query'], retrieved_docs, topk=topk) + references = [ref.page_content for ref in reranked_docs] + result_df.loc[idx, 'reference'] = str(references) + else: # If no documents are retrieved + result_df.loc[idx, 'reference'] = "[]" + + # Initialize metrics + total_hits = 0 # Total number of hits + total_reciprocal_rank = 0.0 # Sum of reciprocal ranks + total_precision = 0.0 # Sum of precision + valid_rows = 0 # Number of valid rows (rows with references) + + result_df[['hit', 'rank', 'precision']] = [False, 0, 0.0] + + for idx, row in tqdm(result_df.iterrows(), total=len(result_df)): + # Skip if reference is empty + if not row['reference'] or row['reference'] == '[]': + continue + + # Split keywords by commas + keywords = [kw.strip() for kw in row['keyword'].split(',')] + + # List of retrieved documents + references = literal_eval(row['reference']) + + K = len(references) # Number of retrieved documents (Top K) + relevant_retrieved_docs = 0 # Number of relevant documents retrieved + rank = 0 # Rank of the first relevant document + found = False + + for i, doc in enumerate(references): + # Remove whitespace from document + doc_no_space = doc.replace(' ', '').replace('\n', '') + doc_is_relevant = False + for kw in keywords: + # Remove whitespace from keywords + kw_no_space = kw.replace(' ', '') + # Check if keyword is in the document + if kw_no_space in doc_no_space: + doc_is_relevant = True + if not found: + rank = i + 1 # Rank starts from 1 + found = True + break # Exit inner loop as keyword is found + if doc_is_relevant: + relevant_retrieved_docs += 1 # Increment count of relevant documents + + if rank > 0: + result_df.loc[idx, 'hit'] = True + result_df.loc[idx, 'rank'] = rank + total_hits += 1 # Increment hit count + reciprocal_rank = 1.0 / rank # Calculate reciprocal rank + total_reciprocal_rank += reciprocal_rank # Add to total reciprocal rank + + # Calculate precision for current query + precision = relevant_retrieved_docs / K if K > 0 else 0 + result_df.loc[idx, 'precision'] = precision + total_precision += precision + + valid_rows += 1 # Increment count of valid rows + + # Calculate overall metrics + if valid_rows > 0: + hit_at_k = total_hits / valid_rows + mrr_at_k = total_reciprocal_rank / valid_rows + avg_precision = total_precision / valid_rows + else: + hit_at_k = 0.0 + mrr_at_k = 0.0 + avg_precision = 0.0 + + return result_df, hit_at_k, mrr_at_k, avg_precision + + +def jaccard_reranker(query, retrieved_docs, topk=5): + """ + Re-rank retrieved documents based on Jaccard similarity. + + Args: + query (str): Input query. + retrieved_docs (list): Retrieved documents. + topk (int): Number of top documents to return. + + Returns: + list: Re-ranked documents. + """ + tokenized_query = extract_nouns(query) + context_scores = [] + for doc in retrieved_docs: + tokenized_context = extract_nouns(doc.page_content) + common_terms = set(tokenized_query).intersection(set(tokenized_context)) + score = len(common_terms) / len(set(tokenized_query).union(set(tokenized_context))) + context_scores.append(score) + + # Re-rank contexts based on similarity scores and return topk documents + reranked_docs = [doc for _, doc in sorted(zip(context_scores, retrieved_docs), key=lambda x: x[0], reverse=True)][:topk] + + return reranked_docs + + +def process_row(row): + """ + Process a single row from the evaluation dataset for retrieval. + + Args: + row (pd.Series): Single row from the dataset. + + Returns: + list: Retrieved documents. + """ + problems = literal_eval(row['problems']) + paragraph = row['paragraph'] + question = problems['question'] + choices = problems['choices'] + choices_str = " ".join(choices) + + query = prompt.format(paragraph=paragraph, question=question, choices=choices_str) + + # Conservative criterion: Exclude documents longer than 500 characters + if len(str(paragraph)) > 500: + return [] + + # Retrieve documents using dense retriever + docs = faiss_retriever.invoke(query) + + if docs: + # Re-rank using Jaccard similarity + reranked_docs = jaccard_reranker(query, docs, topk=topk) + # Extract topk documents + retrieved_docs = [doc.page_content for doc in reranked_docs] + return retrieved_docs + else: + return [] + + +# Load evaluation dataset +eval_set = pd.read_csv("/data/ephemeral/home/workspace/contest_baseline_code/data/rag_eval/external_knowledge_w_label_keyword.csv") +prompt = "{paragraph}\n{question}\n{choices}" +eval_set['query'] = "" +for idx, row in eval_set.iterrows(): + problems = literal_eval(row['problems']) + question = problems['question'] + choices = problems['choices'] + choices_str = " ".join(choices) + query = prompt.format(paragraph=row['paragraph'], question=question, choices=choices_str) + eval_set.loc[idx, 'query'] = query + +# Load and process RAG data +rag_folder = "/data/ephemeral/home/workspace/contest_baseline_code/data/rag" +rag_files = glob(f"{rag_folder}/*.csv") +rag_data_source = [pd.read_csv(file) for file in rag_files] +rag_data = pd.concat(rag_data_source, axis=0, ignore_index=True) +print(f"RAG Data Count: {rag_data.shape[0]}") + +# Filter documents +rag_data = rag_data[rag_data.context.str.len() >= 25] +print(f"filtered (len > 25) RAG Data Count: {rag_data.shape[0]}") + +# Chunk documents +loader = DataFrameLoader(rag_data, page_content_column='context') +documents = loader.load() + +chunk_size = 800 +chunk_overlap = 200 + +text_splitter = CharacterTextSplitter.from_tiktoken_encoder( + separator=". ", + chunk_size=chunk_size, + chunk_overlap=chunk_overlap, + encoding_name='cl100k_base' +) +split_docs = text_splitter.split_documents(tqdm(documents)) +print(f"Chunked Document Count: {len(split_docs)}") + +# Load vector store +model_name = 'dragonkue/BGE-m3-ko' +device = 'cuda' + +embeddings = HuggingFaceEmbeddings( + model_name=model_name, + model_kwargs={'device': device}, + encode_kwargs={'normalize_embeddings': True}, +) + +vector_store_path = f"/data/ephemeral/home/workspace/contest_baseline_code/data/db/faiss_{model_name.replace('/', '_')}_chunk-{chunk_size}-{chunk_overlap}_v2" + +# Load existing vector store or create new one +if os.path.exists(vector_store_path): + print("Loading existing vector store...") + vector_store = FAISS.load_local( + vector_store_path, + embeddings, + allow_dangerous_deserialization=True + ) +else: + print("Creating new vector store...") + vector_store = FAISS.from_documents( + [split_docs[0]], + embedding=embeddings, + distance_strategy=DistanceStrategy.COSINE + ) + + batch_size = 4 + docs_to_add = split_docs[1:] + with tqdm(total=len(docs_to_add), desc="Ingesting documents") as pbar: + for i in range(0, len(docs_to_add), batch_size): + batch_docs = docs_to_add[i:i + batch_size] + vector_store.add_documents(batch_docs) + pbar.update(len(batch_docs)) + torch.cuda.empty_cache() + + vector_store.save_local(vector_store_path) + +doc_count = vector_store.index.ntotal +print(f"Document Count in Vector Store: {doc_count}") + +topk_base = 15 +topk = 3 +score_threshold = 0.4 + +faiss_retriever = vector_store.as_retriever( + search_type="similarity_score_threshold", + search_kwargs={"k":topk_base, "score_threshold": score_threshold}, +) + +# faiss_retriever = vector_store.as_retriever(search_kwargs={"k":topk}) + +# result_df, hit, mrr, avg_precision = evaluate_metrics_threshold_jaccard(eval_set, faiss_retriever, topk) +# valid_df = result_df[result_df.reference != "[]"] +# # print(f"Score Threshold: {score_threshold}") +# print(f"Hit@{topk}: {hit:.4f}") +# print(f"MRR@{topk}: {mrr:.4f}") +# print(f"Precision@{topk}: {avg_precision:.4f}") +# print(f"๋ฌธ์„œ ๊ฐœ์ˆ˜: {len(valid_df)}") + + +prompt = "{paragraph} {question} {choices}" + +# Save Train Dataset +target_data = pd.read_csv("/data/ephemeral/home/workspace/contest_baseline_code/data/preprocessed/train_fix_khan_kor_v2_korean.csv") + +target_data['reference'] = target_data.progress_apply(process_row, axis=1) + +target_data.to_csv(f"/data/ephemeral/home/workspace/contest_baseline_code/data/preprocessed/train_rag_rerank{topk}_final.csv", index=False) + +# Save Test Dataset +target_data = pd.read_csv("/data/ephemeral/home/workspace/contest_baseline_code/data/raw/test.csv") +target_data['reference'] = target_data.progress_apply(process_row, axis=1) + +target_data.to_csv(f"/data/ephemeral/home/contest_baseline_code/data/preprocessed/test_rag_rerank{topk}_v2_list.csv", index=False) \ No newline at end of file diff --git a/src/retrieval_sparse.py b/src/retrieval_sparse.py new file mode 100644 index 0000000..97c6ab5 --- /dev/null +++ b/src/retrieval_sparse.py @@ -0,0 +1,362 @@ +import os +import numpy as np +import pandas as pd +import MeCab +import faiss +import torch +from tqdm import tqdm +from glob import glob +from ast import literal_eval +from langchain_community.document_loaders import DataFrameLoader +from langchain.text_splitter import CharacterTextSplitter +from langchain_community.vectorstores import FAISS +from langchain_community.vectorstores.utils import DistanceStrategy +from langchain.retrievers import BM25Retriever +# from langchain_community.retrievers import BM25Retriever +from langchain.schema import Document +from langchain.retrievers import ContextualCompressionRetriever +from langchain.retrievers import EnsembleRetriever +from langchain.retrievers.document_compressors import CrossEncoderReranker +from langchain_community.cross_encoders import HuggingFaceCrossEncoder +from langchain_huggingface import HuggingFaceEmbeddings +from rank_bm25 import BM25Okapi +from typing import List + +import matplotlib.pyplot as plt +tqdm.pandas() + + +# Define the Document class +class Document: + """ + Represents a document with its content and score. + + Attributes: + page_content (str): The content of the document. + score (float): The relevance score of the document. + """ + def __init__(self, page_content, score): + self.page_content = page_content + self.score = score + + +# Modify CustomBM25Retriever +class CustomBM25Retriever: + """ + Custom BM25 Retriever that fetches top-k documents above a score threshold. + + Args: + bm25_instance (BM25Okapi): An instance of BM25Okapi for BM25 ranking. + documents_df (pd.DataFrame): DataFrame containing the documents to retrieve from. + topk (int): Number of top documents to retrieve. + score_threshold (float): Minimum BM25 score to consider a document relevant. + """ + def __init__(self, bm25_instance, documents_df, topk=5, score_threshold=0.4): + self.bm25 = bm25_instance + self.documents_df = documents_df + self.topk = topk + self.score_threshold = score_threshold + + def retrieve(self, query) -> List[Document]: + """ + Retrieves documents relevant to the query. + + Args: + query (str): The search query. + + Returns: + List[Document]: List of Document instances containing document content and score. + """ + processed_query = extract_nouns(query) # Extract nouns from the query + if not processed_query: + print("Could not extract nouns from the query.") + return [] + + scores = self.bm25.get_scores(processed_query) # Compute BM25 scores + top_k_indices = np.argsort(scores)[::-1][:self.topk] # Get indices of top-k scores + results = [] + for idx in top_k_indices: + score = scores[idx] + if score >= self.score_threshold: # Only include documents above the threshold + original_document = self.documents_df.iloc[idx]['context'] + # Create a Document instance + doc = Document( + page_content=original_document, + score=min(score / 100, 1) # Normalize score + ) + results.append(doc) + return results + + def invoke(self, query): + """ + Alias for the retrieve method to maintain compatibility. + + Args: + query (str): The search query. + + Returns: + List[Document]: Retrieved documents with scores. + """ + return self.retrieve(query) + + +# Initialize MeCab Tagger +mecab = MeCab.Tagger() +def extract_nouns(text): + """ + Extracts nouns from the given text using MeCab. + + Args: + text (str): The text to process. + + Returns: + List[str]: A list of extracted nouns. + """ + try: + parsed = mecab.parse(text) + nouns = [] + for line in parsed.splitlines(): + if '\t' in line: + word, feature = line.split('\t') + if feature.startswith('NN'): # Nouns in Korean + nouns.append(word) + return nouns + except Exception as e: + print(f"Error processing text: {text}, Error: {e}") + return [] + + +def evaluate_metrics_threshold(df, retriever): + """ + Evaluate retrieval metrics such as Hit@K, MRR@K, and Precision. + + Args: + df (pd.DataFrame): Dataset for evaluation containing queries and keywords. + retriever (CustomBM25Retriever): Retriever to perform document retrieval. + + Returns: + tuple: A tuple containing the result dataframe, Hit@K, MRR@K, and average precision. + """ + result_df = df.copy() + result_df['reference'] = "" + + # Perform document retrieval + for idx, row in tqdm(result_df.iterrows()): + retrieved_docs = retriever.invoke(row['query']) + if retrieved_docs: # If documents are retrieved + references = [ref.page_content for ref in retrieved_docs] + result_df.loc[idx, 'reference'] = str(references) + else: # If no documents are retrieved + result_df.loc[idx, 'reference'] = "" + + # Initialize metrics + total_hits = 0 # Total number of hits + total_reciprocal_rank = 0.0 # Sum of reciprocal ranks + total_precision = 0.0 # Sum of precision + valid_rows = 0 # Number of valid rows (rows with references) + + result_df[['hit', 'rank', 'precision']] = [False, 0, 0.0] + + for idx, row in tqdm(result_df.iterrows()): + # Skip rows with no references + if not row['reference'] or row['reference'] == '[]': + continue + + # Extract keywords + keywords = [kw.strip() for kw in row['keyword'].split(',')] + + # List of retrieved documents + references = eval(row['reference']) + + K = len(references) # Number of retrieved documents (Top K) + relevant_retrieved_docs = 0 # Count of relevant retrieved documents + rank = 0 # Rank of the first relevant document + found = False + + for i, doc in enumerate(references): + # Remove whitespace from document content + doc_no_space = doc.replace(' ', '').replace('\n', '') + doc_is_relevant = False + for kw in keywords: + # Remove whitespace from keyword + kw_no_space = kw.replace(' ', '') + # Check if the keyword is present in the document + if kw_no_space in doc_no_space: + doc_is_relevant = True + if not found: + rank = i + 1 # Rank starts from 1 + found = True + break # Exit inner loop when keyword is found + if doc_is_relevant: + relevant_retrieved_docs += 1 # Increment relevant document count + + if rank > 0: + result_df.loc[idx, 'hit'] = True + result_df.loc[idx, 'rank'] = rank + total_hits += 1 # Increment hit count + reciprocal_rank = 1.0 / rank # Calculate reciprocal rank + total_reciprocal_rank += reciprocal_rank # Add to total reciprocal rank + + # Calculate precision for the query + precision = relevant_retrieved_docs / K if K > 0 else 0 + result_df.loc[idx, 'precision'] = precision + total_precision += precision + + valid_rows += 1 # Increment valid row count + + # Calculate overall metrics + if valid_rows > 0: + hit_at_k = total_hits / valid_rows + mrr_at_k = total_reciprocal_rank / valid_rows + avg_precision = total_precision / valid_rows + else: + hit_at_k = 0.0 + mrr_at_k = 0.0 + avg_precision = 0.0 + + return result_df, hit_at_k, mrr_at_k, avg_precision + +def process_row(row): + """ + Process a single row to create a query and retrieve relevant documents. + + Args: + row (pd.Series): A row from the dataset containing the paragraph, question, and choices. + + Returns: + List[str]: A list of retrieved document contents. + """ + problems = literal_eval(row['problems']) + paragraph = row['paragraph'] + question = problems['question'] + choices = problems['choices'] + choices_str = " ".join(choices) + + query = prompt.format(paragraph=paragraph, question=question, choices=choices_str) + + # Skip paragraphs longer than 500 characters + if len(str(paragraph)) > 500: + return [] + # sparse ์‚ฌ์šฉ + docs = bm25_retriever.retrieve(query) + if docs: + retrieved_docs = [doc.page_content for doc in docs] + return retrieved_docs + else: + return [] + +# Retrieval evaluation dataset +eval_set = pd.read_csv("/data/ephemeral/home/workspace/contest_baseline_code/data/rag_eval/external_knowledge_w_label_keyword.csv") +prompt = "{paragraph}\n{question}\n{choices}" +eval_set['query'] = "" +for idx, row in eval_set.iterrows(): + problems = literal_eval(row['problems']) + question = problems['question'] + choices = problems['choices'] + choices_str = " ".join(choices) + query = prompt.format(paragraph=row['paragraph'], question=question, choices=choices_str) + eval_set.loc[idx, 'query'] = query + + +rag_folder = "/data/ephemeral/home/workspace/contest_baseline_code/data/rag" +rag_files = glob(f"{rag_folder}/*.csv") + +# Concatenate RAG data +rag_data_source = [pd.read_csv(file) for file in rag_files] +rag_data = pd.concat(rag_data_source, axis=0, ignore_index=True) +print(f"RAG Data Count: {rag_data.shape[0]}") + +# Use only documents with at least 25 characters +rag_data = rag_data[rag_data.context.str.len() >= 25] +print(f"filtered (len > 25) RAG Data Count: {rag_data.shape[0]}") + +loader = DataFrameLoader(rag_data, page_content_column='context') +documents = loader.load() + +# Chunking +text_splitter = CharacterTextSplitter.from_tiktoken_encoder( + separator=". ", + chunk_size=800, + chunk_overlap=200, + encoding_name='cl100k_base' +) +split_docs = text_splitter.split_documents(tqdm(documents)) +print(f"Chunked Document Count: {len(split_docs)}") + +# Convert split_docs back to DataFrame +rag_data_chunk = pd.DataFrame( + [{ + **doc.metadata, # Include all metadata from the original document + 'context': doc.page_content, # The chunked content + } for doc in split_docs] +) + +# Extract nouns from each document's context +rag_data_chunk['nouns'] = rag_data_chunk['context'].apply(extract_nouns) + +# Handle cases where noun extraction failed +rag_data_chunk['nouns'] = rag_data_chunk['nouns'].apply(lambda x: x if x else []) + +bm25 = BM25Okapi(rag_data_chunk['nouns'].tolist()) + +# Configuration parameters +topk = 3 +score_threshold = 0.4 + +# Initialize the custom retriever +bm25_retriever = CustomBM25Retriever( + bm25_instance=bm25, + documents_df=rag_data_chunk, + topk=topk, + score_threshold=score_threshold +) + +# Evaluate the BM25 retriever +query = """ +์„ ๋น„๋“ค ์ˆ˜๋งŒ ๋ช…์ด ๋Œ€๊ถ ์•ž์— ๋ชจ์—ฌ ๋งŒ ๋™๋ฌ˜์™€ ์„œ์›์„ ๋‹ค์‹œ ์„ค๋ฆฝํ•  ๊ฒƒ์„ ์ฒญํ•˜๋‹ˆ, (๊ฐ€)์ด/๊ฐ€ ํฌ๊ฒŒ ๋…ธํ•˜์—ฌ ํ•œ์„ฑ๋ถ€์˜ ์กฐ๋ก€(็š‚้šท)์™€ ๋ณ‘์กธ๋กœ ํ•˜์—ฌ ๊ธˆ ํ•œ ๊ฐ• ๋ฐ–์œผ๋กœ ๋ชฐ์•„๋‚ด๊ฒŒ ํ•˜๊ณ  ๋“œ๋””์–ด ์ฒœ์—ฌ ๊ณณ์˜ ์„œ์›์„ ์ฒ ํํ•˜๊ณ  ๊ทธ ํ† ์ง€๋ฅผ ๋ชฐ์ˆ˜ํ•˜์—ฌ ๊ด€์— ์†ํ•˜๊ฒŒ ํ•˜์˜€๋‹ค.๏ผ๋Œ€ํ•œ๊ณ„๋…„์‚ฌ ๏ผ + +(๊ฐ€) ์ธ๋ฌผ์ด ์ถ”์ง„ํ•œ ์ •์ฑ…์œผ๋กœ ์˜ณ์ง€ ์•Š์€ ๊ฒƒ์€? + +1 - ์‚ฌ์ฐฝ์ œ๋ฅผ ์‹ค์‹œํ•˜์˜€๋‹ค . +2 - ๋Œ€์ „ํšŒํ†ต์„ ํŽธ์ฐฌํ•˜์˜€๋‹ค . +3 - ๋น„๋ณ€์‚ฌ์˜ ๊ธฐ๋Šฅ์„ ๊ฐ•ํ™”ํ•˜์˜€๋‹ค . +4 - ํ†ต์ƒ ์ˆ˜๊ต ๊ฑฐ๋ถ€ ์ •์ฑ…์„ ์ถ”์ง„ํ•˜์˜€๋‹ค . +""" + +# Retrieve relevant documents +search_results = bm25_retriever.retrieve(query) + +# Display the results +print('-' * 25, "BM25 Retrieval Results", '-' * 25) +print("๋ฌธ์ œ:") +print(query) + +print("๊ฒ€์ƒ‰ ๊ฒฐ๊ณผ:") +for i, result in enumerate(search_results, 1): + print(f"Result {i}: (Score: {result.score:.4f}) {result.page_content}") + +# # Evaluate the BM25 retriever +# result_df, hit, mrr, avg_precision = evaluate_metrics_threshold(eval_set, bm25_retriever) +# valid_df = result_df[result_df.reference != ""] +# # print(f"Score Threshold: {score_threshold}") +# print(f"Hit@{topk}: {hit:.4f}") +# print(f"MRR@{topk}: {mrr:.4f}") +# print(f"Precision@{topk}: {avg_precision:.4f}") +# print(f"๋ฌธ์„œ ๊ฐœ์ˆ˜: {len(valid_df)}") + + +prompt = "{paragraph} {question} {choices}" + +# Save Train Dataset +target_data = pd.read_csv("/data/ephemeral/home/workspace/contest_baseline_code/data/preprocessed/train_fix_khan_kor_v2_korean.csv") + +target_data['reference'] = target_data.progress_apply(process_row, axis=1) # query + +target_data.to_csv(f"/data/ephemeral/home/contest_baseline_code/data/preprocessed/train_rag_sparse{topk}_v2_list.csv", index=False) + +# Save Test Dataset +target_data = pd.read_csv("/data/ephemeral/home/contest_baseline_code/data/raw/test.csv") + +target_data['reference'] = target_data.progress_apply(process_row, axis=1) + +target_data.to_csv(f"/data/ephemeral/home/contest_baseline_code/data/preprocessed/test_rag_sparse{topk}_v2_list.csv", index=False) \ No newline at end of file diff --git a/src/utils.py b/src/utils.py new file mode 100644 index 0000000..aba80be --- /dev/null +++ b/src/utils.py @@ -0,0 +1,202 @@ +import os +import torch +import json +import ast +import random +import numpy as np +import pandas as pd + +from collections import Counter +from glob import glob + + +def set_seed(random_seed): + """ + Set the seed for random number generation to ensure reproducibility. + + Args: + random_seed (int): Seed value to set for random generators. + """ + torch.manual_seed(random_seed) + torch.cuda.manual_seed(random_seed) + torch.cuda.manual_seed_all(random_seed) # if use multi-GPU + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + np.random.seed(random_seed) + random.seed(random_seed) + + +def update_paths(config): + """ + Update paths in the configuration file based on the experiment name. + + Args: + config (dict): Configuration dictionary containing paths and experiment name. + + Returns: + dict: Updated configuration dictionary with formatted paths. + """ + experiment_name = config["model"]["experiment_name"] + config["model"]["train"]["train_checkpoint_path"] = config["model"]["train"][ + "train_checkpoint_path" + ].format(experiment_name=experiment_name) + config["model"]["test"]["test_checkpoint_path"] = config["model"]["test"][ + "test_checkpoint_path" + ].format(experiment_name=experiment_name) + config["model"]["test"]["test_output_csv_path"] = config["model"]["test"][ + "test_output_csv_path" + ].format(experiment_name=experiment_name) + return config + + +def reset_token(experiment_name): + """ + Reset specific token configurations in the tokenizer configuration files. This only works with Qwen2.5 model checkpoints. + + Args: + experiment_name (str): Name of the experiment to locate the checkpoint directory. + """ + base_path = f"checkpoints/{experiment_name}" + checkpoints = glob(os.path.join(base_path, "checkpoint-*")) + + for checkpoint in checkpoints: + tokenizer_config_path = os.path.join(checkpoint, "tokenizer_config.json") + + if not os.path.exists(tokenizer_config_path): + continue + + with open(tokenizer_config_path, "r", encoding="utf-8") as f: + config = json.load(f) + + # Remove the "chat_template" field if it exists + if "chat_template" in config: + del config["chat_template"] # reset chat_template + + # Reset pad_token with the content of the "added_tokens_decoder" + if "added_tokens_decoder" in config: + pad_token_content = config["added_tokens_decoder"]["151665"]["content"] + config["pad_token"] = pad_token_content # reset pad_token + + with open(tokenizer_config_path, "w", encoding="utf-8") as f: + json.dump(config, f, indent=2, ensure_ascii=False) + + +def make_answers_uniform(dataframe, seed=42): + """ + Create a dataset with uniform answer distribution by shuffling and balancing the answer choices. + + Args: + dataframe (pd.DataFrame): Input dataframe containing questions, choices, and answers. + seed (int): Random seed for reproducibility. Default is 42. + + Returns: + pd.DataFrame: Dataframe with uniform answer distribution. + """ + random.seed(seed) + + # Randomly select one of the answers with the minimum count + def get_random_min_answer(answer_counts): + min_value = min(answer_counts.values()) + min_keys = [key for key, value in answer_counts.items() if value == min_value] + return random.choice(min_keys) + + # Shuffle answer positions uniformly + def shuffle_answer_position(row, answer_counts): + choices = row["choices"] + answer = row["answer"] + + # Get answer position with the minimum count + answer_position = get_random_min_answer(answer_counts) + answer_counts[answer_position] += 1 + + # Swap the positions of answers + new_choices = choices.copy() + new_choices[answer_position - 1], new_choices[answer - 1] = ( + new_choices[answer - 1], + new_choices[answer_position - 1], + ) + + return new_choices, answer_position + + # Convert row to string format for uniform dataset + def dic_to_str(row): + return str( + { + "question": row["question"], + "choices": row["choices_u"], + "answer": row["answer_u"], + } + ) + + # Convert string to dictionary and extract question components + dataframe["problems_dict"] = dataframe["problems"].apply(ast.literal_eval) + dataframe["question"] = dataframe["problems_dict"].apply(lambda x: x["question"]) + dataframe["choices"] = dataframe["problems_dict"].apply(lambda x: x["choices"]) + dataframe["answer"] = dataframe["problems_dict"].apply(lambda x: x["answer"]) + + # Split dataset by the number of choices (4-choice or 5-choice questions) + sub_train_4 = dataframe[dataframe["choices"].apply(len) == 4].copy() + sub_train_5 = dataframe[dataframe["choices"].apply(len) == 5].copy() + + # Initialize answer distributions + answer_counts_4 = Counter({i: 0 for i in range(1, 5)}) # 4์ง€ ์„ ๋‹ค ์ •๋‹ต ๋ถ„ํฌ ์ดˆ๊ธฐํ™” + answer_counts_5 = Counter({i: 0 for i in range(1, 6)}) # 5์ง€ ์„ ๋‹ค ์ •๋‹ต ๋ถ„ํฌ ์ดˆ๊ธฐํ™” + + # Shuffle answer choices for uniform distribution + sub_train_4[["choices_u", "answer_u"]] = pd.DataFrame( + sub_train_4.apply( + shuffle_answer_position, axis=1, answer_counts=answer_counts_4 + ).tolist(), + index=sub_train_4.index, + ) + sub_train_5[["choices_u", "answer_u"]] = pd.DataFrame( + sub_train_5.apply( + shuffle_answer_position, axis=1, answer_counts=answer_counts_5 + ).tolist(), + index=sub_train_5.index, + ) + + # Combine the two datasets + train_uniform = ( + pd.concat([sub_train_4, sub_train_5]) + .sample(frac=1, random_state=42) + .reset_index(drop=True) + ) + + # Convert dictionary to string for the uniform dataset + train_uniform["problems_u"] = train_uniform.apply(dic_to_str, axis=1) + train_uniform["problems"] = train_uniform["problems_u"] + + drop_columns = [ + "problems_dict", + "question", + "choices", + "answer", + "choices_u", + "answer_u", + ] + train_uniform = train_uniform.drop(columns=drop_columns) + + return train_uniform + + +def print_answer_distribution(dataframe, tag="Train"): + """ + Print the distribution of answers in the dataset. + + Args: + dataframe (pd.DataFrame): Dataset to analyze. + tag (str): Label for the dataset (e.g., 'Train', 'Test'). Default is 'Train'. + """ + dataframe = dataframe.copy() + dataframe["problems_dict"] = dataframe["problems"].apply(ast.literal_eval) + dataframe["answer"] = dataframe["problems_dict"].apply(lambda x: x["answer"]) + + print(f"{tag} Answer distribution") + answer_counts = dataframe["answer"].value_counts().sort_index() + answer_ratios = ( + dataframe["answer"].value_counts(normalize=True).sort_index().round(2) + ) + result = pd.DataFrame({"Count": answer_counts, "Ratio": answer_ratios}) + + print(result) diff --git a/streamlit/.streamlit/config.toml b/streamlit/.streamlit/config.toml new file mode 100644 index 0000000..74812cd --- /dev/null +++ b/streamlit/.streamlit/config.toml @@ -0,0 +1,2 @@ +[client] +showSidebarNavigation = false \ No newline at end of file diff --git a/streamlit/assets/2025-history-03.png b/streamlit/assets/2025-history-03.png new file mode 100644 index 0000000..1c2254c Binary files /dev/null and b/streamlit/assets/2025-history-03.png differ diff --git a/streamlit/assets/2025-history-13.png b/streamlit/assets/2025-history-13.png new file mode 100644 index 0000000..57b4119 Binary files /dev/null and b/streamlit/assets/2025-history-13.png differ diff --git a/streamlit/assets/2025-korean-01.png b/streamlit/assets/2025-korean-01.png new file mode 100644 index 0000000..148383d Binary files /dev/null and b/streamlit/assets/2025-korean-01.png differ diff --git a/streamlit/assets/architecture.png b/streamlit/assets/architecture.png new file mode 100644 index 0000000..bc5b1c0 Binary files /dev/null and b/streamlit/assets/architecture.png differ diff --git a/streamlit/assets/ksat_dataset.csv b/streamlit/assets/ksat_dataset.csv new file mode 100644 index 0000000..31a01c5 --- /dev/null +++ b/streamlit/assets/ksat_dataset.csv @@ -0,0 +1,3756 @@ +id,paragraph,problems,question_plus,answer_true,answer_pred,is_correct,reference,answer_rag +2025-korean-01,"๋ฐ‘์ค„ ๊ธ‹๊ธฐ๋Š” ์ผ์ƒ์ ์œผ๋กœ ์œ ์šฉํ•˜๊ฒŒ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ๋…์„œ ์ „๋žต +์ด๋‹ค. ๋ฐ‘์ค„ ๊ธ‹๊ธฐ๋Š” ์ •๋ณด๋ฅผ ๋จธ๋ฆฟ์†์— ์ €์žฅํ•˜๊ณ  ๊ธฐ์–ตํ•œ ๋‚ด์šฉ์„ +๋– ์˜ฌ๋ฆฌ๋Š” ๋ฐ ๋„์›€์ด ๋œ๋‹ค. ๋…์ž๋กœ ํ•˜์—ฌ๊ธˆ ํ‘œ์‹œํ•œ ๋ถ€๋ถ„์— ์ฃผ์˜๋ฅผ +๊ธฐ์šธ์ด๋„๋ก ํ•ด ์ •๋ณด๋ฅผ ๋จธ๋ฆฟ์†์— ์ €์žฅํ•˜๋„๋ก ๋•๊ณ , ํ‘œ์‹œํ•œ ๋ถ€๋ถ„์ด +๋…์ž์—๊ฒŒ ์‹œ๊ฐ์  ์ž๊ทน์„ ์ฃผ์–ด ๊ธฐ์–ตํ•œ ๋‚ด์šฉ์„ ๋– ์˜ฌ๋ฆฌ๋Š” ๋ฐ ๋‹จ์„œ๊ฐ€ +๋˜๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ์ด๋Ÿฌํ•œ ์ ์—์„œ ๋ฐ‘์ค„ ๊ธ‹๊ธฐ๋Š” ์ผ๋ฐ˜์ ์ธ ๋…์„œ +์ƒํ™ฉ๋ฟ ์•„๋‹ˆ๋ผ ํ•™์Šต ์ƒํ™ฉ์—์„œ๋„ ์œ ์šฉํ•˜๋‹ค. ๋˜ํ•œ ๋ฐ‘์ค„ ๊ธ‹๊ธฐ๋Š” +๋ฐฉ๋Œ€ํ•œ ์ •๋ณด๋“ค ๊ฐ€์šด๋ฐ ์ฃผ์š”ํ•œ ์ •๋ณด๋ฅผ ์ถ”๋ฆฌ๋Š” ๋ฐ์—๋„ ํšจ๊ณผ์ ์ด๋ฉฐ, +ํ‘œ์‹œํ•œ ๋ถ€๋ถ„์ด ์ผ์ข…์˜ ์ƒ‰์ธ๊ณผ ๊ฐ™์€ ์—ญํ• ์„ ํ•˜์—ฌ ๋…์ž๊ฐ€ ๋‚ด์šฉ์„ +๋‹ค์‹œ ์ฐพ์•„๋ณด๋Š” ๋ฐ์—๋„ ์šฉ์ดํ•˜๋‹ค. +ํ†ต์ƒ์ ์œผ๋กœ ๋…์ž๋Š” ๊ธ€์„ ์ฝ๋Š” ์ค‘์— ๋ฐ”๋กœ๋ฐ”๋กœ ๋ฐ‘์ค„ ๊ธ‹๊ธฐ๋ฅผ ํ•œ๋‹ค. +๊ทธ๋Ÿฌ๋‹ค ๋ณด๋ฉด ๋ฐ‘์ค„์ด ๋งŽ์•„์ง€๊ณ  ๋ณต์žกํ•ด์ ธ ๋ฐ‘์ค„ ๊ธ‹๊ธฐ์˜ ํšจ๊ณผ๊ฐ€ +์ค„์–ด๋“ ๋‹ค. ๋˜ํ•œ ๋ฐ‘์ค„ ๊ธ‹๊ธฐ๋ฅผ ์‹ ์ค‘ํ•˜๊ฒŒ ํ•˜์ง€ ์•Š์œผ๋ฉด ์ž˜๋ชป ํ‘œ์‹œํ•œ +๋ฐ‘์ค„์„ ์‚ญ์ œํ•˜๊ธฐ ์œ„ํ•ด ๋˜๋Œ์•„๊ฐ€๋А๋ผ ๋…์„œ์˜ ํ๋ฆ„์ด ๋ฐฉํ•ด๋ฐ›๊ฒŒ +๋˜๋ฏ€๋กœ ํšจ๊ณผ์ ์œผ๋กœ ๋ฐ‘์ค„ ๊ธ‹๊ธฐ๋ฅผ ํ•˜๋Š” ๊ฒƒ์ด ์ค‘์š”ํ•˜๋‹ค. +๋ฐ‘์ค„ ๊ธ‹๊ธฐ์˜ ํšจ๊ณผ๋ฅผ ์–ป๊ธฐ ์œ„ํ•œ ๋ฐฉ๋ฒ•์—๋Š” ๋ช‡ ๊ฐ€์ง€๊ฐ€ ์žˆ๋‹ค. ์šฐ์„  +๊ธ€์„ ์ฝ๋Š” ์ค‘์—๋Š” ๋ฌธ์žฅ์ด๋‚˜ ๋ฌธ๋‹จ์— ๋‚˜ํƒ€๋‚œ ์ •๋ณด ๊ฐ„์˜ ์ƒ๋Œ€์  +์ค‘์š”๋„๋ฅผ ๊ฒฐ์ •ํ•  ๋•Œ๊นŒ์ง€ ๋ฐ‘์ค„ ๊ธ‹๊ธฐ๋ฅผ ์ž ์‹œ ๋Šฆ์ถ”์—ˆ๋‹ค๊ฐ€ ์ฃผ์š”ํ•œ +์ •๋ณด์— ๋ฐ‘์ค„ ๊ธ‹๊ธฐ๋ฅผ ํ•œ๋‹ค. ์ด๋•Œ ์ฃผ์š”ํ•œ ์ •๋ณด๋Š” ๋…์„œ ๋ชฉ์ ์— ๋”ฐ๋ผ +๋‹ฌ๋ผ์งˆ ์ˆ˜ ์žˆ๋‹ค๋Š” ์ ์„ ๊ณ ๋ คํ•œ๋‹ค. ๋˜ํ•œ ์ž์‹ ๋งŒ์˜ ๋ฐ‘์ค„ ๊ธ‹๊ธฐ ํ‘œ์‹œ +์ฒด๊ณ„๋ฅผ ์„ธ์›Œ ๋ฐ‘์ค„ ์ด์™ธ์— ๋‹ค๋ฅธ ๊ธฐํ˜ธ๋„ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ฐ‘์ค„ +๊ธ‹๊ธฐ ํ‘œ์‹œ ์ฒด๊ณ„๋Š” ๋ฐ‘์ค„ ๊ธ‹๊ธฐ๊ฐ€ ํ•„์š”ํ•œ ๋ถ€๋ถ„์— ํŠน์ • ๊ธฐํ˜ธ๋ฅผ +์‚ฌ์šฉํ•˜์—ฌ ํ‘œ์‹œํ•˜๊ธฐ๋กœ ๋…์ž๊ฐ€ ๋ฏธ๋ฆฌ ์ •ํ•ด ๋†“๋Š” ๊ฒƒ์ด๋‹ค. ์˜ˆ๋ฅผ ๋“ค๋ฉด +ํ•˜๋‚˜์˜ ๊ธฐ์ค€์œผ๋กœ ๋ฌถ์„ ์ˆ˜ ์žˆ๋Š” ์ •๋ณด๋“ค์— ๋™์ผํ•œ ๊ธฐํ˜ธ๋ฅผ ๋ถ™์ด๊ฑฐ๋‚˜ +์ˆœ์ฐจ์ ์ธ ๋ฒˆํ˜ธ๋ฅผ ๋ถ™์ด๊ธฐ๋กœ ํ•˜๋Š” ๊ฒƒ ๋“ฑ์ด๋‹ค. ์ด๋Š” ๊ธฐ๋ณธ์ ์ธ ๋ฐ‘์ค„ +๊ธ‹๊ธฐ๋ฅผ ํ™•์žฅํ•œ ๋ฐฉ์‹์ด๋ผ ํ•  ์ˆ˜ ์žˆ๋‹ค. +๋ฐ‘์ค„ ๊ธ‹๊ธฐ๋Š” ์–ด๋– ํ•œ ์ˆ˜์ค€์˜ ๋…์ž๋ผ๋„ ์‰ฝ๊ฒŒ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” +์  ๋•Œ๋ฌธ์— ์—ฐ์Šต ์—†์ด ๋Šฅ์ˆ™ํ•˜๊ฒŒ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ๋‹ค๊ณ  ์˜คํ•ด๋˜์–ด ์˜จ +๊ฒฝํ–ฅ์ด ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋ณธ์งˆ์ ์œผ๋กœ ๋ฐ‘์ค„ ๊ธ‹๊ธฐ๋Š” ์ฃผ์š”ํ•œ ์ •๋ณด๊ฐ€ +๋ฌด์—‡์ธ์ง€์— ๋Œ€ํ•œ ํŒ๋‹จ์ด ์„ ํ–‰๋˜์–ด์•ผ ํ•œ๋‹ค๋Š” ์ ์—์„œ ๋‹จ์ˆœํ•˜์ง€ +์•Š๋‹ค. ใ‰ ๋ฐ‘์ค„ ๊ธ‹๊ธฐ์˜ ๋ฐฉ๋ฒ•์„ ์ดํ•ดํ•˜๊ณ  ์ž˜ ์‚ฌ์šฉํ•˜๋Š” ๊ฒƒ์€ ๊ธ€์„ +๋Šฅ๋™์ ์œผ๋กœ ์ฝ์–ด ๋‚˜๊ฐ€๋Š” ๋ฐ ๋„์›€์ด ๋  ์ˆ˜ ์žˆ๋‹ค.","{'question': '์œ—๊ธ€์˜ ๋‚ด์šฉ๊ณผ ์ผ์น˜ํ•˜์ง€ ์•Š๋Š” ๊ฒƒ์€?', 'choices': ['๋ฐ‘์ค„ ๊ธ‹๊ธฐ๋Š” ์ผ๋ฐ˜์ ์ธ ๋…์„œ ์ƒํ™ฉ์—์„œ ๋„์›€์ด ๋œ๋‹ค.', '๋ฐ‘์ค„ ์ด์™ธ์˜ ๋‹ค๋ฅธ ๊ธฐํ˜ธ๋ฅผ ๋ฐ‘์ค„ ๊ธ‹๊ธฐ์— ์‚ฌ์šฉํ•˜๋Š” ๊ฒƒ์ด ๊ฐ€๋Šฅํ•˜๋‹ค.', '๋ฐ‘์ค„ ๊ธ‹๊ธฐ๋Š” ๋ˆ„๊ตฌ๋‚˜ ์—ฐ์Šต ์—†์ด๋„ ๋Šฅ์ˆ™ํ•˜๊ฒŒ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ๋Š”\n์ „๋žต์ด๋‹ค.', '๋ฐ‘์ค„ ๊ธ‹๊ธฐ๋กœ ํ‘œ์‹œํ•œ ๋ถ€๋ถ„์€ ๋…์ž๊ฐ€ ๋‚ด์šฉ์„ ๋‹ค์‹œ ์ฐพ์•„๋ณด๋Š” ๋ฐ\n์œ ์šฉํ•˜๋‹ค.', '๋ฐ‘์ค„ ๊ธ‹๊ธฐ๋กœ ํ‘œ์‹œํ•œ ๋ถ€๋ถ„์ด ๋…์ž์—๊ฒŒ ์‹œ๊ฐ์ ์ธ ์ž๊ทน์„ ์ฃผ์–ด\n๊ธฐ์–ตํ•œ ๋‚ด์šฉ์„ ๋– ์˜ฌ๋ฆฌ๋Š” ๋ฐ ๋„์›€์ด ๋œ๋‹ค.'], 'answer': ''}",,3,3,True,[],3 +2025-korean-02,"๋ฐ‘์ค„ ๊ธ‹๊ธฐ๋Š” ์ผ์ƒ์ ์œผ๋กœ ์œ ์šฉํ•˜๊ฒŒ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ๋…์„œ ์ „๋žต +์ด๋‹ค. ๋ฐ‘์ค„ ๊ธ‹๊ธฐ๋Š” ์ •๋ณด๋ฅผ ๋จธ๋ฆฟ์†์— ์ €์žฅํ•˜๊ณ  ๊ธฐ์–ตํ•œ ๋‚ด์šฉ์„ +๋– ์˜ฌ๋ฆฌ๋Š” ๋ฐ ๋„์›€์ด ๋œ๋‹ค. ๋…์ž๋กœ ํ•˜์—ฌ๊ธˆ ํ‘œ์‹œํ•œ ๋ถ€๋ถ„์— ์ฃผ์˜๋ฅผ +๊ธฐ์šธ์ด๋„๋ก ํ•ด ์ •๋ณด๋ฅผ ๋จธ๋ฆฟ์†์— ์ €์žฅํ•˜๋„๋ก ๋•๊ณ , ํ‘œ์‹œํ•œ ๋ถ€๋ถ„์ด +๋…์ž์—๊ฒŒ ์‹œ๊ฐ์  ์ž๊ทน์„ ์ฃผ์–ด ๊ธฐ์–ตํ•œ ๋‚ด์šฉ์„ ๋– ์˜ฌ๋ฆฌ๋Š” ๋ฐ ๋‹จ์„œ๊ฐ€ +๋˜๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ์ด๋Ÿฌํ•œ ์ ์—์„œ ๋ฐ‘์ค„ ๊ธ‹๊ธฐ๋Š” ์ผ๋ฐ˜์ ์ธ ๋…์„œ +์ƒํ™ฉ๋ฟ ์•„๋‹ˆ๋ผ ํ•™์Šต ์ƒํ™ฉ์—์„œ๋„ ์œ ์šฉํ•˜๋‹ค. ๋˜ํ•œ ๋ฐ‘์ค„ ๊ธ‹๊ธฐ๋Š” +๋ฐฉ๋Œ€ํ•œ ์ •๋ณด๋“ค ๊ฐ€์šด๋ฐ ์ฃผ์š”ํ•œ ์ •๋ณด๋ฅผ ์ถ”๋ฆฌ๋Š” ๋ฐ์—๋„ ํšจ๊ณผ์ ์ด๋ฉฐ, +ํ‘œ์‹œํ•œ ๋ถ€๋ถ„์ด ์ผ์ข…์˜ ์ƒ‰์ธ๊ณผ ๊ฐ™์€ ์—ญํ• ์„ ํ•˜์—ฌ ๋…์ž๊ฐ€ ๋‚ด์šฉ์„ +๋‹ค์‹œ ์ฐพ์•„๋ณด๋Š” ๋ฐ์—๋„ ์šฉ์ดํ•˜๋‹ค. +ํ†ต์ƒ์ ์œผ๋กœ ๋…์ž๋Š” ๊ธ€์„ ์ฝ๋Š” ์ค‘์— ๋ฐ”๋กœ๋ฐ”๋กœ ๋ฐ‘์ค„ ๊ธ‹๊ธฐ๋ฅผ ํ•œ๋‹ค. +๊ทธ๋Ÿฌ๋‹ค ๋ณด๋ฉด ๋ฐ‘์ค„์ด ๋งŽ์•„์ง€๊ณ  ๋ณต์žกํ•ด์ ธ ๋ฐ‘์ค„ ๊ธ‹๊ธฐ์˜ ํšจ๊ณผ๊ฐ€ +์ค„์–ด๋“ ๋‹ค. ๋˜ํ•œ ๋ฐ‘์ค„ ๊ธ‹๊ธฐ๋ฅผ ์‹ ์ค‘ํ•˜๊ฒŒ ํ•˜์ง€ ์•Š์œผ๋ฉด ์ž˜๋ชป ํ‘œ์‹œํ•œ +๋ฐ‘์ค„์„ ์‚ญ์ œํ•˜๊ธฐ ์œ„ํ•ด ๋˜๋Œ์•„๊ฐ€๋А๋ผ ๋…์„œ์˜ ํ๋ฆ„์ด ๋ฐฉํ•ด๋ฐ›๊ฒŒ +๋˜๋ฏ€๋กœ ํšจ๊ณผ์ ์œผ๋กœ ๋ฐ‘์ค„ ๊ธ‹๊ธฐ๋ฅผ ํ•˜๋Š” ๊ฒƒ์ด ์ค‘์š”ํ•˜๋‹ค. +๋ฐ‘์ค„ ๊ธ‹๊ธฐ์˜ ํšจ๊ณผ๋ฅผ ์–ป๊ธฐ ์œ„ํ•œ ๋ฐฉ๋ฒ•์—๋Š” ๋ช‡ ๊ฐ€์ง€๊ฐ€ ์žˆ๋‹ค. ์šฐ์„  +๊ธ€์„ ์ฝ๋Š” ์ค‘์—๋Š” ๋ฌธ์žฅ์ด๋‚˜ ๋ฌธ๋‹จ์— ๋‚˜ํƒ€๋‚œ ์ •๋ณด ๊ฐ„์˜ ์ƒ๋Œ€์  +์ค‘์š”๋„๋ฅผ ๊ฒฐ์ •ํ•  ๋•Œ๊นŒ์ง€ ๋ฐ‘์ค„ ๊ธ‹๊ธฐ๋ฅผ ์ž ์‹œ ๋Šฆ์ถ”์—ˆ๋‹ค๊ฐ€ ์ฃผ์š”ํ•œ +์ •๋ณด์— ๋ฐ‘์ค„ ๊ธ‹๊ธฐ๋ฅผ ํ•œ๋‹ค. ์ด๋•Œ ์ฃผ์š”ํ•œ ์ •๋ณด๋Š” ๋…์„œ ๋ชฉ์ ์— ๋”ฐ๋ผ +๋‹ฌ๋ผ์งˆ ์ˆ˜ ์žˆ๋‹ค๋Š” ์ ์„ ๊ณ ๋ คํ•œ๋‹ค. ๋˜ํ•œ ์ž์‹ ๋งŒ์˜ ๋ฐ‘์ค„ ๊ธ‹๊ธฐ ํ‘œ์‹œ +์ฒด๊ณ„๋ฅผ ์„ธ์›Œ ๋ฐ‘์ค„ ์ด์™ธ์— ๋‹ค๋ฅธ ๊ธฐํ˜ธ๋„ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ฐ‘์ค„ +๊ธ‹๊ธฐ ํ‘œ์‹œ ์ฒด๊ณ„๋Š” ๋ฐ‘์ค„ ๊ธ‹๊ธฐ๊ฐ€ ํ•„์š”ํ•œ ๋ถ€๋ถ„์— ํŠน์ • ๊ธฐํ˜ธ๋ฅผ +์‚ฌ์šฉํ•˜์—ฌ ํ‘œ์‹œํ•˜๊ธฐ๋กœ ๋…์ž๊ฐ€ ๋ฏธ๋ฆฌ ์ •ํ•ด ๋†“๋Š” ๊ฒƒ์ด๋‹ค. ์˜ˆ๋ฅผ ๋“ค๋ฉด +ํ•˜๋‚˜์˜ ๊ธฐ์ค€์œผ๋กœ ๋ฌถ์„ ์ˆ˜ ์žˆ๋Š” ์ •๋ณด๋“ค์— ๋™์ผํ•œ ๊ธฐํ˜ธ๋ฅผ ๋ถ™์ด๊ฑฐ๋‚˜ +์ˆœ์ฐจ์ ์ธ ๋ฒˆํ˜ธ๋ฅผ ๋ถ™์ด๊ธฐ๋กœ ํ•˜๋Š” ๊ฒƒ ๋“ฑ์ด๋‹ค. ์ด๋Š” ๊ธฐ๋ณธ์ ์ธ ๋ฐ‘์ค„ +๊ธ‹๊ธฐ๋ฅผ ํ™•์žฅํ•œ ๋ฐฉ์‹์ด๋ผ ํ•  ์ˆ˜ ์žˆ๋‹ค. +๋ฐ‘์ค„ ๊ธ‹๊ธฐ๋Š” ์–ด๋– ํ•œ ์ˆ˜์ค€์˜ ๋…์ž๋ผ๋„ ์‰ฝ๊ฒŒ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” +์  ๋•Œ๋ฌธ์— ์—ฐ์Šต ์—†์ด ๋Šฅ์ˆ™ํ•˜๊ฒŒ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ๋‹ค๊ณ  ์˜คํ•ด๋˜์–ด ์˜จ +๊ฒฝํ–ฅ์ด ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋ณธ์งˆ์ ์œผ๋กœ ๋ฐ‘์ค„ ๊ธ‹๊ธฐ๋Š” ์ฃผ์š”ํ•œ ์ •๋ณด๊ฐ€ +๋ฌด์—‡์ธ์ง€์— ๋Œ€ํ•œ ํŒ๋‹จ์ด ์„ ํ–‰๋˜์–ด์•ผ ํ•œ๋‹ค๋Š” ์ ์—์„œ ๋‹จ์ˆœํ•˜์ง€ +์•Š๋‹ค. ใ‰ ๋ฐ‘์ค„ ๊ธ‹๊ธฐ์˜ ๋ฐฉ๋ฒ•์„ ์ดํ•ดํ•˜๊ณ  ์ž˜ ์‚ฌ์šฉํ•˜๋Š” ๊ฒƒ์€ ๊ธ€์„ +๋Šฅ๋™์ ์œผ๋กœ ์ฝ์–ด ๋‚˜๊ฐ€๋Š” ๋ฐ ๋„์›€์ด ๋  ์ˆ˜ ์žˆ๋‹ค.","{'question': 'ใ‰ ์— ํ•ด๋‹นํ•˜๋Š” ๋‚ด์šฉ์œผ๋กœ ๊ฐ€์žฅ ์ ์ ˆํ•œ ๊ฒƒ์€?', 'choices': ['๊ธ€์„ ๋‹ค์‹œ ์ฝ์„ ๋•Œ๋ฅผ ๋Œ€๋น„ํ•ด์„œ ๋˜๋„๋ก ๋งŽ์€ ๋ถ€๋ถ„์— ๋ฐ‘์ค„ ๊ธ‹๊ธฐ๋ฅผ\nํ•˜๋ฉฐ ์ฝ๋Š”๋‹ค.', '๊ธ€ ์ „์ฒด์— ์ฃผ์˜๋ฅผ ๊ธฐ์šธ์ผ ์ˆ˜ ์žˆ๋„๋ก ๊ธ€์„ ์ฝ๊ณ  ์žˆ์„ ๋•Œ์—๋Š”\n๋ฐ‘์ค„ ๊ธ‹๊ธฐ๋ฅผ ํ•˜์ง€ ์•Š๋Š”๋‹ค.', '์ •๋ณด์˜ ์ค‘์š”๋„๋ฅผ ํŒ์ •ํ•˜๊ธฐ ์–ด๋ ค์šฐ๋ฉด ์šฐ์„  ๋ฐ‘์ค„ ๊ธ‹๊ธฐ๋ฅผ ํ•œ ํ›„\n์ž˜๋ชป ๊ทธ์€ ๋ฐ‘์ค„์„ ์‚ญ์ œํ•œ๋‹ค.', '์ฃผ์š”ํ•œ ์ •๋ณด๋ฅผ ์ถ”๋ฆด ์ˆ˜ ์žˆ๋„๋ก ์ž์‹ ์ด ๋งŒ๋“  ๋ฐ‘์ค„ ๊ธ‹๊ธฐ ํ‘œ์‹œ\n์ฒด๊ณ„์— ๋”ฐ๋ผ ๋ฐ‘์ค„ ๊ธ‹๊ธฐ๋ฅผ ํ•œ๋‹ค.', '๊ธ€์— ๋ฐ˜๋ณต๋˜๋Š” ์–ดํœ˜๋‚˜ ์˜๋ฏธ๊ฐ€ ๋น„์Šทํ•œ ๋ฌธ์žฅ์ด ๋‚˜์˜ฌ ๋•Œ๋งˆ๋‹ค\n๋ฐ”๋กœ๋ฐ”๋กœ ๋ฐ‘์ค„ ๊ธ‹๊ธฐ๋ฅผ ํ•˜๋ฉฐ ๊ธ€์„ ์ฝ๋Š”๋‹ค.'], 'answer': ''}",,4,4,True,[],4 +2025-korean-03,"๋ฐ‘์ค„ ๊ธ‹๊ธฐ๋Š” ์ผ์ƒ์ ์œผ๋กœ ์œ ์šฉํ•˜๊ฒŒ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ๋…์„œ ์ „๋žต +์ด๋‹ค. ๋ฐ‘์ค„ ๊ธ‹๊ธฐ๋Š” ์ •๋ณด๋ฅผ ๋จธ๋ฆฟ์†์— ์ €์žฅํ•˜๊ณ  ๊ธฐ์–ตํ•œ ๋‚ด์šฉ์„ +๋– ์˜ฌ๋ฆฌ๋Š” ๋ฐ ๋„์›€์ด ๋œ๋‹ค. ๋…์ž๋กœ ํ•˜์—ฌ๊ธˆ ํ‘œ์‹œํ•œ ๋ถ€๋ถ„์— ์ฃผ์˜๋ฅผ +๊ธฐ์šธ์ด๋„๋ก ํ•ด ์ •๋ณด๋ฅผ ๋จธ๋ฆฟ์†์— ์ €์žฅํ•˜๋„๋ก ๋•๊ณ , ํ‘œ์‹œํ•œ ๋ถ€๋ถ„์ด +๋…์ž์—๊ฒŒ ์‹œ๊ฐ์  ์ž๊ทน์„ ์ฃผ์–ด ๊ธฐ์–ตํ•œ ๋‚ด์šฉ์„ ๋– ์˜ฌ๋ฆฌ๋Š” ๋ฐ ๋‹จ์„œ๊ฐ€ +๋˜๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ์ด๋Ÿฌํ•œ ์ ์—์„œ ๋ฐ‘์ค„ ๊ธ‹๊ธฐ๋Š” ์ผ๋ฐ˜์ ์ธ ๋…์„œ +์ƒํ™ฉ๋ฟ ์•„๋‹ˆ๋ผ ํ•™์Šต ์ƒํ™ฉ์—์„œ๋„ ์œ ์šฉํ•˜๋‹ค. ๋˜ํ•œ ๋ฐ‘์ค„ ๊ธ‹๊ธฐ๋Š” +๋ฐฉ๋Œ€ํ•œ ์ •๋ณด๋“ค ๊ฐ€์šด๋ฐ ์ฃผ์š”ํ•œ ์ •๋ณด๋ฅผ ์ถ”๋ฆฌ๋Š” ๋ฐ์—๋„ ํšจ๊ณผ์ ์ด๋ฉฐ, +ํ‘œ์‹œํ•œ ๋ถ€๋ถ„์ด ์ผ์ข…์˜ ์ƒ‰์ธ๊ณผ ๊ฐ™์€ ์—ญํ• ์„ ํ•˜์—ฌ ๋…์ž๊ฐ€ ๋‚ด์šฉ์„ +๋‹ค์‹œ ์ฐพ์•„๋ณด๋Š” ๋ฐ์—๋„ ์šฉ์ดํ•˜๋‹ค. +ํ†ต์ƒ์ ์œผ๋กœ ๋…์ž๋Š” ๊ธ€์„ ์ฝ๋Š” ์ค‘์— ๋ฐ”๋กœ๋ฐ”๋กœ ๋ฐ‘์ค„ ๊ธ‹๊ธฐ๋ฅผ ํ•œ๋‹ค. +๊ทธ๋Ÿฌ๋‹ค ๋ณด๋ฉด ๋ฐ‘์ค„์ด ๋งŽ์•„์ง€๊ณ  ๋ณต์žกํ•ด์ ธ ๋ฐ‘์ค„ ๊ธ‹๊ธฐ์˜ ํšจ๊ณผ๊ฐ€ +์ค„์–ด๋“ ๋‹ค. ๋˜ํ•œ ๋ฐ‘์ค„ ๊ธ‹๊ธฐ๋ฅผ ์‹ ์ค‘ํ•˜๊ฒŒ ํ•˜์ง€ ์•Š์œผ๋ฉด ์ž˜๋ชป ํ‘œ์‹œํ•œ +๋ฐ‘์ค„์„ ์‚ญ์ œํ•˜๊ธฐ ์œ„ํ•ด ๋˜๋Œ์•„๊ฐ€๋А๋ผ ๋…์„œ์˜ ํ๋ฆ„์ด ๋ฐฉํ•ด๋ฐ›๊ฒŒ +๋˜๋ฏ€๋กœ ํšจ๊ณผ์ ์œผ๋กœ ๋ฐ‘์ค„ ๊ธ‹๊ธฐ๋ฅผ ํ•˜๋Š” ๊ฒƒ์ด ์ค‘์š”ํ•˜๋‹ค. +๋ฐ‘์ค„ ๊ธ‹๊ธฐ์˜ ํšจ๊ณผ๋ฅผ ์–ป๊ธฐ ์œ„ํ•œ ๋ฐฉ๋ฒ•์—๋Š” ๋ช‡ ๊ฐ€์ง€๊ฐ€ ์žˆ๋‹ค. ์šฐ์„  +๊ธ€์„ ์ฝ๋Š” ์ค‘์—๋Š” ๋ฌธ์žฅ์ด๋‚˜ ๋ฌธ๋‹จ์— ๋‚˜ํƒ€๋‚œ ์ •๋ณด ๊ฐ„์˜ ์ƒ๋Œ€์  +์ค‘์š”๋„๋ฅผ ๊ฒฐ์ •ํ•  ๋•Œ๊นŒ์ง€ ๋ฐ‘์ค„ ๊ธ‹๊ธฐ๋ฅผ ์ž ์‹œ ๋Šฆ์ถ”์—ˆ๋‹ค๊ฐ€ ์ฃผ์š”ํ•œ +์ •๋ณด์— ๋ฐ‘์ค„ ๊ธ‹๊ธฐ๋ฅผ ํ•œ๋‹ค. ์ด๋•Œ ์ฃผ์š”ํ•œ ์ •๋ณด๋Š” ๋…์„œ ๋ชฉ์ ์— ๋”ฐ๋ผ +๋‹ฌ๋ผ์งˆ ์ˆ˜ ์žˆ๋‹ค๋Š” ์ ์„ ๊ณ ๋ คํ•œ๋‹ค. ๋˜ํ•œ ์ž์‹ ๋งŒ์˜ ๋ฐ‘์ค„ ๊ธ‹๊ธฐ ํ‘œ์‹œ +์ฒด๊ณ„๋ฅผ ์„ธ์›Œ ๋ฐ‘์ค„ ์ด์™ธ์— ๋‹ค๋ฅธ ๊ธฐํ˜ธ๋„ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ฐ‘์ค„ +๊ธ‹๊ธฐ ํ‘œ์‹œ ์ฒด๊ณ„๋Š” ๋ฐ‘์ค„ ๊ธ‹๊ธฐ๊ฐ€ ํ•„์š”ํ•œ ๋ถ€๋ถ„์— ํŠน์ • ๊ธฐํ˜ธ๋ฅผ +์‚ฌ์šฉํ•˜์—ฌ ํ‘œ์‹œํ•˜๊ธฐ๋กœ ๋…์ž๊ฐ€ ๋ฏธ๋ฆฌ ์ •ํ•ด ๋†“๋Š” ๊ฒƒ์ด๋‹ค. ์˜ˆ๋ฅผ ๋“ค๋ฉด +ํ•˜๋‚˜์˜ ๊ธฐ์ค€์œผ๋กœ ๋ฌถ์„ ์ˆ˜ ์žˆ๋Š” ์ •๋ณด๋“ค์— ๋™์ผํ•œ ๊ธฐํ˜ธ๋ฅผ ๋ถ™์ด๊ฑฐ๋‚˜ +์ˆœ์ฐจ์ ์ธ ๋ฒˆํ˜ธ๋ฅผ ๋ถ™์ด๊ธฐ๋กœ ํ•˜๋Š” ๊ฒƒ ๋“ฑ์ด๋‹ค. ์ด๋Š” ๊ธฐ๋ณธ์ ์ธ ๋ฐ‘์ค„ +๊ธ‹๊ธฐ๋ฅผ ํ™•์žฅํ•œ ๋ฐฉ์‹์ด๋ผ ํ•  ์ˆ˜ ์žˆ๋‹ค. +๋ฐ‘์ค„ ๊ธ‹๊ธฐ๋Š” ์–ด๋– ํ•œ ์ˆ˜์ค€์˜ ๋…์ž๋ผ๋„ ์‰ฝ๊ฒŒ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” +์  ๋•Œ๋ฌธ์— ์—ฐ์Šต ์—†์ด ๋Šฅ์ˆ™ํ•˜๊ฒŒ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ๋‹ค๊ณ  ์˜คํ•ด๋˜์–ด ์˜จ +๊ฒฝํ–ฅ์ด ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋ณธ์งˆ์ ์œผ๋กœ ๋ฐ‘์ค„ ๊ธ‹๊ธฐ๋Š” ์ฃผ์š”ํ•œ ์ •๋ณด๊ฐ€ +๋ฌด์—‡์ธ์ง€์— ๋Œ€ํ•œ ํŒ๋‹จ์ด ์„ ํ–‰๋˜์–ด์•ผ ํ•œ๋‹ค๋Š” ์ ์—์„œ ๋‹จ์ˆœํ•˜์ง€ +์•Š๋‹ค. ใ‰ ๋ฐ‘์ค„ ๊ธ‹๊ธฐ์˜ ๋ฐฉ๋ฒ•์„ ์ดํ•ดํ•˜๊ณ  ์ž˜ ์‚ฌ์šฉํ•˜๋Š” ๊ฒƒ์€ ๊ธ€์„ +๋Šฅ๋™์ ์œผ๋กœ ์ฝ์–ด ๋‚˜๊ฐ€๋Š” ๋ฐ ๋„์›€์ด ๋  ์ˆ˜ ์žˆ๋‹ค.","{'question': '์œ—๊ธ€์„ ๋ฐ”ํƒ•์œผ๋กœ ํ•™์ƒ์ด ๋‹ค์Œ๊ณผ ๊ฐ™์ด \x08๊ด„ํ˜ธ ํ‘œ์‹œ๋ฅผ ํ–ˆ๋‹ค๊ณ  ํ•  ๋•Œ,\n์ด์— ๋Œ€ํ•œ ํ‰๊ฐ€๋กœ ์ ์ ˆํ•˜์ง€ ์•Š์€ ๊ฒƒ์€?', 'choices': [""๋…์„œ ๋ชฉ์ ์„ ๊ณ ๋ คํ•˜๋ฉด, 1๋ฌธ๋‹จ์—์„œ '( )'๋กœ ํ‘œ์‹œํ•œ ๋ถ€๋ถ„์€\n์ ์ ˆํ•˜์ง€ ์•Š๊ฒŒ ( ) ํ‘œ์‹œ๋ฅผ ํ•˜์˜€๊ตฐ."", '๋…์„œ ๋ชฉ์ ์„ ๊ณ ๋ คํ•˜๋ฉด, 1๋ฌธ๋‹จ์—์„œ โ€˜1) โ€™, โ€˜2) โ€™์™€ ๊ฐ™์ด ์ˆœ์ฐจ์ ์ธ\n๋ฒˆํ˜ธ๋กœ ํ‘œ์‹œํ•œ ๋ถ€๋ถ„์€ ์ ์ ˆํ•˜์ง€ ์•Š๊ฒŒ 1), 2) ํ‘œ์‹œ๋ฅผ ํ•˜์˜€๊ตฐ.', ""2๋ฌธ๋‹จ์—์„œ '[ ]'๋กœ ํ‘œ์‹œํ•œ ๋ถ€๋ถ„์„ ๋ณด๋‹ˆ, ๋…์„œ ๋ชฉ์ ์— ๊ด€๋ จ๋œ\n์ฃผ์š” ์–ด๊ตฌ์— [ ] ํ‘œ์‹œ๋ฅผ ํ•˜์˜€๊ตฐ."", '๋…์„œ ๋ชฉ์ ์„ ๊ณ ๋ คํ•˜๋ฉด, 2๋ฌธ๋‹จ์—์„œ๋Š” โ€˜์ง€๋А๋Ÿฌ๋ฏธ๋Š” ๋ฐฐ๋ฅผ ์ “๋Š”\n๋…ธ์™€ ๊ฐ™์€ ํ˜•ํƒœโ€™์— โ€˜{ }โ€™๋ฅผ ๋ˆ„๋ฝํ•˜์˜€๊ตฐ', ""< >'๋กœ ํ‘œ์‹œํ•œ ๋ถ€๋ถ„์„ ๋ณด๋‹ˆ, ๋…์„œ ๋ชฉ์ ์„ ๊ณ ๋ คํ•˜์—ฌ 3๋ฌธ๋‹จ\n๋‚ด์—์„œ ์ •๋ณด ๊ฐ„์˜ ์ƒ๋Œ€์ ์ธ ์ค‘์š”๋„๋ฅผ ํŒ๋‹จํ•ด ์ฃผ์š”ํ•œ ๋ฌธ์žฅ์—\n< > ํ‘œ์‹œ๋ฅผ ํ•˜์˜€๊ตฐ.""], 'answer': '', 'question_plus': ""[๋…์„œ ๋ชฉ์ ] ๊ณ ๋ž˜์˜ ์™ธํ˜•์  ํŠน์ง•์— ๋Œ€ํ•œ ์ •๋ณด ์Šต๋“\n[ํ‘œ์‹œ ๊ธฐํ˜ธ] '( )', '1)' , '2)', '{ }', '< >'\n[๋…์„œ ์ž๋ฃŒ]\n๊ณ ๋ž˜๋Š” ์œก์ง€ (ํฌ์œ ๋™๋ฌผ)์—์„œ ๊ธฐ์›ํ–ˆ์ง€๋งŒ, ์ˆ˜์ค‘ ์ƒํ™œ์— ์ ์‘ํ•˜์—ฌ ์ƒˆ๋ผ๋ฅผ ์ˆ˜์ค‘์—์„œ ๋‚ณ๋Š”๋‹ค.\n1)์•”์ปท๋“ค์€ ์ƒˆ๋ผ๋ฅผ ๋‚ณ์„ ๋•Œ ์„œ๋กœ ๋„์™€์ฃผ๋ฉฐ, 2)์–ด๋ฏธ๋“ค์€ ์ƒˆ๋ผ๋“ค์„ ์ •์„ฑ๊ป ๋ณดํ˜ธํ•œ๋‹ค.\n (๊ณ ๋ž˜์˜ ์ƒ๊น€์ƒˆ)๋Š” ๊ณ ๋ž˜์˜ ์ข…๋ฅ˜๋งˆ๋‹ค ๋‹ค๋ฅธ๋ฐ, {๋Œ€์ฒด๋กœ ๋ชธ๊ธธ์ด๋Š” 1.3m์—์„œ 30m์— ์ด๋ฅธ๋‹ค.) \n(ํ”ผ๋ถ€์—๋Š” ํ„ธ์ด ์—†๊ฑฐ๋‚˜ ์•„์ฃผ ์งง๊ฒŒ ๋‚˜์žˆ๋‹ค.) ์ง€๋А๋Ÿฌ๋ฏธ๋Š” ๋ฐฐ๋ฅผ ์ “๋Š” ๋…ธ์™€ ๊ฐ™์€ ํ˜•ํƒœ์ด๊ณ , ํ—ค์—„์น  ๋•Œ\n์ˆ˜ํ‰์„ ์œ ์ง€ํ•˜๋Š” ๊ธฐ๋Šฅ์„ ํ•œ๋‹ค.\n<๊ณ ๋ž˜๋Š” ํ๋กœ ํ˜ธํกํ•˜๋ฏ€๋กœ ๋ฌผ์†์—์„œ ์ˆจ์„ ์‰ด ์ˆ˜ ์—†๋‹ค.> ๊ณ ๋ž˜์˜ ๋จธ๋ฆฌ ๊ผญ๋Œ€๊ธฐ์—๋Š” ๋ถ„์ˆ˜๊ณต์ด ์žˆ๋‹ค.\n๋ฌผ์†์—์„œ ์ฐธ์•˜๋˜ ์ˆจ์„ ๋ถ„์ˆ˜๊ณต์œผ๋กœ ๋‚ด๋ฟœ๊ณ  ๋‹ค์‹œ ์ˆจ์„ ๋“ค์ด ๋งˆ์‹ ๋’ค ์ž ์ˆ˜ ํ•œ๋‹ค. ์ž‘์€ ๊ณ ๋ž˜๋“ค์€ ๋ช‡ ๋ถ„\n๋ฐ–์— ์ˆจ์„ ์ฐธ์ง€ ๋ชปํ•˜์ง€๋งŒ, ํฐ ๊ณ ๋ž˜๋“ค์€ 1์‹œ๊ฐ„ ์ •๋„ ๋ฌผ ์†์— ๋จธ๋ฌผ ์ˆ˜ ์žˆ๋‹ค. \n""}","[๋…์„œ ๋ชฉ์ ] ๊ณ ๋ž˜์˜ ์™ธํ˜•์  ํŠน์ง•์— ๋Œ€ํ•œ ์ •๋ณด ์Šต๋“ +[ํ‘œ์‹œ ๊ธฐํ˜ธ] '( )', '1)' , '2)', '{ }', '< >' +[๋…์„œ ์ž๋ฃŒ] +๊ณ ๋ž˜๋Š” ์œก์ง€ (ํฌ์œ ๋™๋ฌผ)์—์„œ ๊ธฐ์›ํ–ˆ์ง€๋งŒ, ์ˆ˜์ค‘ ์ƒํ™œ์— ์ ์‘ํ•˜์—ฌ ์ƒˆ๋ผ๋ฅผ ์ˆ˜์ค‘์—์„œ ๋‚ณ๋Š”๋‹ค. +1)์•”์ปท๋“ค์€ ์ƒˆ๋ผ๋ฅผ ๋‚ณ์„ ๋•Œ ์„œ๋กœ ๋„์™€์ฃผ๋ฉฐ, 2)์–ด๋ฏธ๋“ค์€ ์ƒˆ๋ผ๋“ค์„ ์ •์„ฑ๊ป ๋ณดํ˜ธํ•œ๋‹ค. + (๊ณ ๋ž˜์˜ ์ƒ๊น€์ƒˆ)๋Š” ๊ณ ๋ž˜์˜ ์ข…๋ฅ˜๋งˆ๋‹ค ๋‹ค๋ฅธ๋ฐ, {๋Œ€์ฒด๋กœ ๋ชธ๊ธธ์ด๋Š” 1.3m์—์„œ 30m์— ์ด๋ฅธ๋‹ค.) +(ํ”ผ๋ถ€์—๋Š” ํ„ธ์ด ์—†๊ฑฐ๋‚˜ ์•„์ฃผ ์งง๊ฒŒ ๋‚˜์žˆ๋‹ค.) ์ง€๋А๋Ÿฌ๋ฏธ๋Š” ๋ฐฐ๋ฅผ ์ “๋Š” ๋…ธ์™€ ๊ฐ™์€ ํ˜•ํƒœ์ด๊ณ , ํ—ค์—„์น  ๋•Œ +์ˆ˜ํ‰์„ ์œ ์ง€ํ•˜๋Š” ๊ธฐ๋Šฅ์„ ํ•œ๋‹ค. +<๊ณ ๋ž˜๋Š” ํ๋กœ ํ˜ธํกํ•˜๋ฏ€๋กœ ๋ฌผ์†์—์„œ ์ˆจ์„ ์‰ด ์ˆ˜ ์—†๋‹ค.> ๊ณ ๋ž˜์˜ ๋จธ๋ฆฌ ๊ผญ๋Œ€๊ธฐ์—๋Š” ๋ถ„์ˆ˜๊ณต์ด ์žˆ๋‹ค. +๋ฌผ์†์—์„œ ์ฐธ์•˜๋˜ ์ˆจ์„ ๋ถ„์ˆ˜๊ณต์œผ๋กœ ๋‚ด๋ฟœ๊ณ  ๋‹ค์‹œ ์ˆจ์„ ๋“ค์ด ๋งˆ์‹ ๋’ค ์ž ์ˆ˜ ํ•œ๋‹ค. ์ž‘์€ ๊ณ ๋ž˜๋“ค์€ ๋ช‡ ๋ถ„ +๋ฐ–์— ์ˆจ์„ ์ฐธ์ง€ ๋ชปํ•˜์ง€๋งŒ, ํฐ ๊ณ ๋ž˜๋“ค์€ 1์‹œ๊ฐ„ ์ •๋„ ๋ฌผ ์†์— ๋จธ๋ฌผ ์ˆ˜ ์žˆ๋‹ค. +",5,5,True,[],4 +2025-korean-04,"(๊ฐ€) +์„œ์–‘์˜ ๊ณผํ•™๊ณผ ๊ธฐ์ˆ , ์ฒœ์ฃผ๊ต์˜ ์ˆ˜์šฉ์„ ๋ฐ˜๋Œ€ํ–ˆ๋˜ ์ดํ•ญ๋กœ๋ฅผ ๋น„๋กฏํ•œ +์ฒ™์‚ฌํŒŒ์˜ ์ฃผ์žฅ์€ ๊ฐœํ•ญ ์ดํ›„์—๋„ ์ง€์†๋˜์—ˆ์ง€๋งŒ, ๊ฐœํ™”๋Š” ๊ฑฐ์Šค๋ฅผ +์ˆ˜ ์—†๋Š” ๋Œ€์„ธ๋กœ ์ž๋ฆฌ ์žก์•˜๋‹ค. ๊ฐœ๋ฌผ์„ฑ๋ฌด(้–‹็‰ฉๆˆๅ‹™)์™€ ํ™”๋ฏผ์„ฑ์† +(ๅŒ–ๆฐ‘ๆˆไฟ—)์˜ ์•ž ๊ธ€์ž๋ฅผ ๋”ด ๊ฐœํ™”๋Š” ๊ฐœํ•ญ ์ด์ „์—๋Š” ํ†ต์น˜์ž์˜ ํ†ต์น˜ +ํ–‰์œ„๋กœ์„œ ๋ณ€ํ™”ํ•˜๋Š” ์„ธ์ƒ์— ๋Œ€ํ•œ ์ง€์‹ ํ™•์žฅ๊ณผ ํ”ผํ†ต์น˜์ž์— ๋Œ€ํ•œ +๊ตํ™”๋ฅผ ์˜๋ฏธํ–ˆ๋‹ค. +๊ฐœํ•ญ ์ดํ›„ ์„œ์–‘ ๋ฌธ๋ช…์— ๋Œ€ํ•œ ๊ธ์ •์  ์ธ์‹์ด ํ™•์‚ฐ๋˜๋ฉด์„œ ์„œ์–‘ +๋ฌธ๋ช…์˜ ์ˆ˜์šฉ์„ ๋œปํ•˜๋Š” ๊ฐœํ™” ๊ฐœ๋…์ด ์ž๋ฆฌ ์žก์•˜๋‹ค. ์ž„์˜ค๊ตฐ๋ž€ ์ดํ›„, +๊ณ ์ข…์€ ์ž๊ฐ• ์ •์ฑ…์„ ์ถ”์ง„ํ•˜๋ฉด์„œ ๋ฐ˜(ๅ)์„œ์–‘ ์ •์„œ์˜ ๊ต์ •์„ ์œ„ํ•ด +ํ•œ์„ฑ์ˆœ๋ณด๋ฅผ ๋ฐœ๊ฐ„ํ–ˆ๋‹ค. ์ด ์‹ ๋ฌธ์˜ ๊ฐœํ™” ๊ฐœ๋…์€ ์„œ์–‘ ๊ธฐ์ˆ ๊ณผ +์ œ๋„์˜ ๋„์ž…์„ ํ†ตํ•œ ์ธ์ง€์˜ ๋ฐœ๋‹ฌ๊ณผ ํ’์†์˜ ์ง„๋ณด๋ฅผ ๋œปํ–ˆ๋‹ค. +์ด ๊ฐœ๋…์—๋Š” ์ธ๋ฏผ์ด ๊ตญ๊ฐ€์˜ ๋…๋ฆฝ ์ฃผ๊ถŒ์˜ ์†Œ์ค‘ํ•จ์„ ๊นจ๋‹ซ๋Š” ์˜์‹์˜ +๋ณ€ํ™”๊ฐ€ ๋‚ดํฌ๋˜์—ˆ๊ณ , ํ†ต์น˜์ž์˜ ์ž…์žฅ์—์„œ ์ˆ˜์šฉ ๊ฐ€๋Šฅํ•œ ๋ฌธ๋ช…์˜ ์žฅ์ ์„ +๋ฐ›์•„๋“ค์—ฌ ๊ตญ๊ฐ€์˜ ์ง„๋ณด๋ฅผ ๋‹ฌ์„ฑํ•œ๋‹ค๋Š” ์˜๋ฏธ๋„ ๋‹ด๊ฒผ๋‹ค. +๊ฐœํ™”๋‹น์˜ ํ•œ ์ธ์‚ฌ๊ฐ€ ์ œ์‹œํ•œ ๊ฐœํ™” ๊ฐœ๋…์€ ์„ฑ๋ฌธํ™”๋œ ๊ทœ์ •์— ๋”ฐ๋ฅธ +๋Œ€๋ฏผ ์ •์น˜์—์„œ์˜ ๋ฒ•์  ์ฒ˜๋ฆฌ ์ ˆ์ฐจ ์‹คํ˜„ ๋“ฑ ์„œ์–‘ ๊ทผ๋Œ€ ๊ตญ๊ฐ€์˜ ํ†ต์น˜ +๋ฐฉ์‹์œผ๋กœ์˜ ๋ณ€ํ™”๋ฅผ ๋‚ดํฌํ•˜๋Š” ๊ฒƒ์ด์—ˆ๋‹ค. ๊ทธ๋Š” ๊ฐœํ™” ์‹คํ–‰ ์ฃผ์ฒด๋ฅผ +์—ฌ์ „ํžˆ ์™•์œผ๋กœ ์ƒ๊ฐํ–ˆ๊ณ , ๊ฐœํ™” ์‹คํ–‰ ์ฃผ์ฒด๋กœ์„œ ์™•์˜ ์—ญํ• ์ด +์‚ฌ๋ผ์ง„ ๊ฒƒ์€ ๊ฐ‘์‹ ์ •๋ณ€์—์„œ์˜€๋‹ค. ํ’์†์˜ ์ง„๋ณด์™€ ํ†ต์น˜ ๋ฐฉ์‹ ๋ณ€ํ™” +๋ผ๋Š” ์˜๋ฏธ๋ฅผ ๋‚ดํฌํ•œ ๊ฐ‘์‹ ์ •๋ณ€์˜ ๊ฐœํ™” ๊ฐœ๋…์€ ํ†ต์น˜๊ถŒ์— ๋Œ€ํ•œ +๋„์ „์œผ๋กœ๋ฟ ์•„๋‹ˆ๋ผ ๊ฐœ์ธ์˜ ์‚ฌ์š•์„ ์œ„ํ•œ ๊ฒƒ์œผ๋กœ ํ‘œ์ƒ๋˜์—ˆ๋‹ค. ์ดํ›„ +๊ฐœํ™” ๊ฐœ๋…์€ ๊ตญ๊ฐ€ ๊ตฌ์„ฑ์›์„ ์กฐ์งํ•˜๊ณ  ๋™์›ํ•˜๊ธฐ ์œ„ํ•ด ๋ถ€์ •์  +์ด๋ฏธ์ง€์—์„œ ๋ฒ—์–ด๋‚˜์•ผ ํ–ˆ๊ณ , ์œ ๊ธธ์ค€์€ ์„œ์œ ๊ฒฌ๋ฌธ์„ ์ €์ˆ ํ•˜๋ฉฐ +๊ฐœํ™” ๊ฐœ๋…์— ๋ง์”Œ์›Œ์ง„ ๋ถ€์ •์  ์ด๋ฏธ์ง€๋ฅผ ๋–ผ์–ด ๋‚ด๊ณ ์ž ํ–ˆ๋‹ค. ์ดํ›„ +๊ฐ„ํ–‰๋œ ๋Œ€ํ•œ๋งค์ผ์‹ ๋ณด ๋“ฑ์˜ ๊ฐœํ™” ๊ฐœ๋…์€ ๊ตญ๊ฐ€ ๊ตฌ์„ฑ์› ์ „์ฒด๋ฅผ +์‹คํ–‰ ์ฃผ์ฒด๋กœ ํ•˜์—ฌ ๊ทผ๋Œ€ ๊ตญ๊ฐ€ ์ฃผ๊ถŒ์„ ํ–ฅํ•ด ๊ทธ๋“ค์„ ์กฐ์งํ•˜๊ณ  +๋™์›ํ•˜๋Š” ๊ฒƒ์„ ์˜๋ฏธํ–ˆ๋‹ค. +์„์‚ฌ๋Š‘์•ฝ ์ดํ›„, ๊ฐœํ™” ๋…ผ์˜๋Š” ๋ฌธ๋ช…์— ๋Œ€ํ•œ ๋ณธ๊ฒฉ์ ์ธ ๋…ผ์˜๋กœ +์ด์–ด์กŒ๋‹ค. ๋Œ€ํ•œ ์ž๊ฐ•ํšŒ์˜ ์ฃผ์š” ์ธ์‚ฌ๋“ค์€ ์„œ์–‘ ๊ทผ๋Œ€ ๋ฌธ๋ช…์„ +์ˆ˜์šฉํ•˜์—ฌ ๊ทผ๋Œ€ ๊ตญ๊ฐ€๋ฅผ ๊ฑด์„คํ•˜๊ณ ์ž, ์•ž์„œ ๋ฌธ๋ช…ํ™”๋ฅผ ์ด๋ฃฌ ์ผ๋ณธ์˜ +์ง€๋„๋ฅผ ๋ฐ›์•„์•ผ ํ•œ๋‹ค๊ณ  ๋ณด์•˜๋‹ค. ์ด๋“ค์€ ์„œ์–‘ ๊ทผ๋Œ€ ๋ฌธ๋ช…์˜ ์ฃผ์ฒด๋ฅผ +์ฃผ์ฒด ์ธ์‹์˜ ์ค€๊ฑฐ๋กœ ์‚ผ์•˜๊ธฐ ๋•Œ๋ฌธ์— ๋ฏผ์กฑ ์ฃผ์ฒด์„ฑ์„ ๊ฐ„๊ณผํ–ˆ๋‹ค. +์ด๋Ÿฌํ•œ ์ƒํ™ฉ์—์„œ ๋ฐ•์€์‹์€ ใ‰ ๊ทผ๋Œ€ ๊ตญ๊ฐ€ ๊ฑด์„ค๊ณผ ์ƒˆ๋กœ์šด ์ฃผ์ฒด์˜ +ํ˜•์„ฑ์— ์ฃผ๋ชฉํ•˜์—ฌ ๋ฌธ๋ช…์— ๋Œ€ํ•œ ๊ฒฌํ•ด๋ฅผ ์ œ์‹œํ–ˆ๋‹ค. ๊ทธ์˜ ๊ธฐ๋ณธ +๊ฑธ์ณ ๊ทผ๋Œ€ํ™”์˜ ์ถ”์ง„๊ณผ ํ•จ๊ป˜ ์˜์š•์ ์ธ ๊ธฐ์ˆ  ์ˆ˜์šฉ์œผ๋กœ ์ด์–ด์กŒ์ง€๋งŒ, +์ฒญ์ผ ์ „์Ÿ์˜ ํŒจ๋ฐฐ๋Š” ๊ธฐ์ˆ  ์ˆ˜์šฉ๋งŒ์œผ๋กœ๋Š” ๋ถ€์กฑํ•˜๋‹ค๋Š” ์ธ์‹์„ +๋‚ณ์•˜๋‹ค. ์ด์— ๋”ฐ๋ผ 20์„ธ๊ธฐ ์ดˆ๋ฐ˜ ์ง„์ •ํ•œ ๊ทผ๋Œ€๋ฅผ ์ด๋ฃจ๊ธฐ ์œ„ํ•ด ๊ธฐ์ˆ  +๋ฐฐํ›„์—์„œ ์ž‘์šฉํ•˜๋Š” ๊ณผํ•™ ์ •์‹ ์„ ์‚ฌํšŒ ์ „์ฒด์— ์ด์‹ํ•˜๋ ค๋Š” ์‹œ๋„๊ฐ€ +๊ตฌ์ฒดํ™”๋˜์—ˆ๋‹ค. +์˜Œํ‘ธ๋Š” ๊ตญ๊ฐ€ ๊ฐ„์— ๋ฒŒ์–ด์ง€๋Š” ์•ฝ์œก๊ฐ•์‹์˜ ๊ฒฝ์Ÿ์„ ๋ถ€๊ฐํ•˜๊ณ , +๊ฒฝ์Ÿ์—์„œ ์Šน๋ฆฌํ•˜๋ ค๋ฉด ๊ธฐ์ˆ ๋ฟ ์•„๋‹ˆ๋ผ ๊ตญ๋ฏผ์˜ ์ •์‹ ์  ์ž์งˆ์ด +๋’ท๋ฐ›์นจ๋˜์–ด์•ผ ํ•œ๋‹ค๊ณ  ๋ณด์•˜๋‹ค. ์ •์‹ ์  ์ž์งˆ ์ค‘ ๊ณผํ•™์  ์‚ฌ์œ  +๋Šฅ๋ ฅ์ด ๊ฐ€์žฅ ์ค‘์š”ํ•˜๋‹ค๊ณ  ํŒŒ์•…ํ•œ ๊ทธ์—๊ฒŒ ๊ณผํ•™ ์ •์‹ ์ด ์ „์ œ๋˜์ง€ +์•Š์€ ์ •์น˜์  ๋ณ€ํ˜์€ ๋ฟŒ๋ฆฌ๋‚ด๋ฆด ์ˆ˜ ์—†๋Š” ๊ฒƒ์ด์—ˆ๋‹ค. ๊ทธ๋Š” ์ธ๊ณผ +์‹ค์ฆ์˜ ๋ฐฉ๋ฒ•์— ๊ทผ๊ฑฐํ•œ ๊ทผ๋Œ€ ํ•™๋ฌธ ์ „์ฒด๋ฅผ ๊ณผํ•™์ด๋ผ ํŒŒ์•…ํ•˜๊ณ , +๊ณผํ•™์„ ์Šต๋“ํ•˜์—ฌ ์ „ํ†ต ํ•™๋ฌธ์˜ ํ๋‹จ์—์„œ ๋ฒ—์–ด๋‚˜์•ผ ํ•œ๋‹ค๊ณ  ์ฃผ์žฅ +ํ–ˆ๋‹ค. ๊ทธ์˜ ์ž…์žฅ์€ 1910๋…„๋Œ€ ํ›„๋ฐ˜ ์‹ ๋ฌธํ™” ์šด๋™์„ ์ฃผ๋„ํ•œ ์ฒœ๋‘์Šˆ +์—๊ฒŒ ์ด์–ด์กŒ๋‹ค. +์ฒœ๋‘์Šˆ๋ฅผ ๋น„๋กฏํ•œ ์‹ ๋ฌธํ™” ์šด๋™์˜ ์ง€์‹์ธ๋“ค์€ ใ‰ก๊ณผํ•™์˜ ๊ทผ๊ฑฐ +์œ„์—์„œ๋งŒ ๋ฏผ์ฃผ ์ •์น˜์˜ ์‹คํ˜„์ด ๊ฐ€๋Šฅํ•˜๋‹ค๊ณ  ์ฃผ์žฅํ–ˆ๋‹ค. ์ค‘๊ตญ์ด +๋‹ฌ์„ฑํ•ด์•ผ ํ•  ์‹ ๋ฌธํ™”๋Š” ๊ณผํ•™ ๋ฐ ๊ณผํ•™์˜ ๋ฐฉ๋ฒ•์— ๊ทผ๊ฑฐํ•œ ๋ฌธํ™”๋ผ +๋ณด๊ณ , ์‹ ๋ฌธํ™”๋ฅผ ์ด๋ฃจ๊ธฐ ์œ„ํ•ด ์ „ํ†ต๋ฌธํ™” ์ „๋ฐ˜์— ๋Œ€ํ•ด ์ฒ ์ €ํ•œ +๋ถ€์ •๊ณผ ๋น„ํŒ์„ ์‹œ๋„ํ–ˆ๋‹ค. ์‚ฌ์ƒ์ด๋‚˜ ์ฒ ํ•™์ด ๊ณผํ•™์˜ ๋ฐฉ๋ฒ•์„ ์ด์šฉ +ํ•˜์ง€ ์•Š์œผ๋ฉด ๊ณต์ƒ(็ฉบๆƒณ)์— โ“๊ทธ์น  ๋ฟ์ด๋ผ๊ณ  ์ฃผ์žฅํ•œ ์ฒœ๋‘์Šˆ๋Š” +์‚ฌํšŒ์™€ ์ธ๊ฐ„์˜ ์‚ถ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋„ ๊ณผํ•™์˜ ์—ฐ๊ตฌ ๋ฐฉ๋ฒ•์„ ์ด์šฉํ•ด์•ผ +ํ•œ๋‹ค๊ณ  ๋ณด์•˜๋‹ค. ๊ทธ๋Š” ์ œ1์ฐจ ์„ธ๊ณ„ ๋Œ€์ „์˜ ๋น„๊ทน์€ ๊ณผํ•™์„ ์ด์šฉํ•ด +์ €์ง€๋ฅธ ์ฃ„์•…์˜ ๊ฒฐ๊ณผ์ผ ๋ฟ ๊ณผํ•™ ์ž์ฒด์˜ ์ฃ„์•…์ด ์•„๋‹ˆ๋ผ๊ณ  ์ฃผ์žฅํ•˜๋ฉฐ +๊ณผํ•™์— ๋Œ€ํ•œ ์ž์‹ ์˜ ์ƒ๊ฐ์„ ์ง€์†ํ–ˆ๋‹ค. +ํ•œํŽธ, ์ œ1์ฐจ ์„ธ๊ณ„ ๋Œ€์ „ ์ดํ›„ ์œ ๋Ÿฝ์„ ์‹œ์ฐฐํ–ˆ๋˜ ์žฅ์ฅ”๋งˆ์ด๋Š” +ํ†ต์ œ๋˜์ง€ ์•Š์€ ๊ณผํ•™์ด ๋ถˆ๋Ÿฌ์˜จ ์—ญ์ž‘์šฉ์„ ๋ชฉ๋„ํ•œ ํ›„, ๊ณผํ•™์ด +์–ด๋–ป๊ฒŒ ๋ฐœ๋‹ฌํ•˜๋“  ๊ทธ๊ฒƒ์ด ์ธ์ƒ๊ด€์˜ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•  ์ˆ˜๋Š” ์—†๋‹ค๋ฉฐ +์„œ์–‘ ๊ทผ๋Œ€ ๋ฌธ๋ช…์„ ๋น„ํŒํ–ˆ๋‹ค. ๊ทผ๋Œ€ ๊ณผํ•™ ๋ฌธ๋ช…์—์„œ ์ดˆ๋ž˜๋œ ์‚ฌ์ƒ์  +์œ„๊ธฐ๊ฐ€ ์ฃผ์ฒด์˜ ์ฑ…์ž„ ๋ถ€์žฌ์—์„œ ๋น„๋กฏ๋œ ๊ฒƒ์ด๋ผ๋Š” ์ฃผ์žฅ์— ๋™์˜ํ–ˆ๋˜ +๊ทธ๋Š” ๊ณผํ•™์  ๋ฐฉ๋ฒ•์„ ๋ถ€์ •ํ•˜์ง€ ์•Š์•˜์ง€๋งŒ, ์ธ์ƒ๊ด€์˜ ๋ฌธ์ œ์—๋Š” +๊ณผํ•™์  ๋ฐฉ๋ฒ•์ด ์ ์šฉ๋  ์ˆ˜ ์—†๋‹ค๊ณ  ์ง€์ ํ–ˆ๋‹ค. ๊ทธ๋Š” ์ธ์ƒ๊ด€์„ +๊ณผํ•™๊ณผ ๋ณ„๊ฐœ๋กœ ํŒŒ์•…ํ–ˆ๊ณ , ๊ณผํ•™๋งŒ๋Šฅ์ฃผ์˜์— ๊ธฐ์ดˆํ•œ ์‹ ๋ฌธํ™” ์šด๋™์— +์˜ํ•ด ๋ถ€์ •๋œ ์ค‘๊ตญ ์ „ํ†ต ๊ฐ€์น˜๊ด€์˜ ์ˆ˜ํ˜ธ๋ฅผ ๋‚ด์„ธ์› ๋‹ค. +์ „๋žต์€ ๋ฌธ๋ช…์˜ ๋ฌผ์งˆ์  ์ธก๋ฉด์ธ ๊ณผํ•™์€ ์„œ์–‘์œผ๋กœ๋ถ€ํ„ฐ ์ˆ˜์šฉํ•˜๋˜, +๋ฌธ๋ช…์˜ ์ •์‹ ์  ์ธก๋ฉด์ธ ์ฒ ํ•™์€ ์œ ํ•™์„ ํ˜์‹ ํ•˜์—ฌ ์žฌ๊ตฌ์„ฑํ•˜๋Š” +๊ฒƒ์ด์—ˆ๋‹ค. ๊ทธ๋Š” ์ƒ์กด๊ณผ ํŽธ๋ฆฌ ์ฆ์ง„์„ ์œ„ํ•ด ๊ณผํ•™ ์—ฐ๊ตฌ๊ฐ€ ์‹œ๊ธ‰ +ํ•˜์ง€๋งŒ, ๊ฐ€์น˜๊ด€ ์ •๋ฆฝ๊ณผ ์ธ๊ฒฉ ์ˆ˜์–‘์„ ์œ„ํ•ด ์ฒ ํ•™ ๋˜ํ•œ ํ•„์ˆ˜์  +์ด๋ผ๊ณ  ๋ณด์•˜๋‹ค. ์ž๊ตญ ์ฒ ํ•™ ์ „ํ†ต์˜ ์ •๋ฆฝ์ด๋ผ๋Š” ๋‹น์‹œ ๋™์•„์‹œ์•„์˜ +์‚ฌ์ƒ์  ํ๋ฆ„ ์†์—์„œ ๊ทธ๊ฐ€ ์ œ์‹œํ•œ ๊ทผ๋Œ€ ์ฃผ์ฒด๋Š” ๊ณผํ•™์ โ€ค์ฒ ํ•™์  +์ธ์‹์˜ ์ฃผ์ฒด์ด์ž ์‹ค์ฒœ์  ๋„๋• ์ˆ˜์–‘์˜ ์ฃผ์ฒด๋กœ์„œ์˜ ์„ฑ๊ฒฉ์„ ๋ ๋Š” +๊ฒƒ์ด์—ˆ๋‹ค. + + +(๋‚˜) +์ค‘๊ตญ์ด ์„œ์–‘์˜ ๊ณผํ•™๊ณผ ๊ธฐ์ˆ ์— ์ „๋ฉด์ ์ธ ๊ด€์‹ฌ์„ ๊ธฐ์šธ์ธ ๋•Œ๋Š” +์•„ํŽธ ์ „์Ÿ ์ดํ›„์˜€๋‹ค. ์ „์Ÿ ํŒจ๋ฐฐ์— ๋”ฐ๋ฅธ ์œ„๊ธฐ๊ฐ์€ ๋ฐ˜์„ธ๊ธฐ์— +๊ฑธ์ณ ๊ทผ๋Œ€ํ™”์˜ ์ถ”์ง„๊ณผ ํ•จ๊ป˜ ์˜์š•์ ์ธ ๊ธฐ์ˆ  ์ˆ˜์šฉ์œผ๋กœ ์ด์–ด์กŒ์ง€๋งŒ, +์ฒญ์ผ ์ „์Ÿ์˜ ํŒจ๋ฐฐ๋Š” ๊ธฐ์ˆ  ์ˆ˜์šฉ๋งŒ์œผ๋กœ๋Š” ๋ถ€์กฑํ•˜๋‹ค๋Š” ์ธ์‹์„ +๋‚ณ์•˜๋‹ค. ์ด์— ๋”ฐ๋ผ 20์„ธ๊ธฐ ์ดˆ๋ฐ˜ ์ง„์ •ํ•œ ๊ทผ๋Œ€๋ฅผ ์ด๋ฃจ๊ธฐ ์œ„ํ•ด ๊ธฐ์ˆ  +๋ฐฐํ›„์—์„œ ์ž‘์šฉํ•˜๋Š” ๊ณผํ•™ ์ •์‹ ์„ ์‚ฌํšŒ ์ „์ฒด์— ์ด์‹ํ•˜๋ ค๋Š” ์‹œ๋„๊ฐ€ +๊ตฌ์ฒดํ™”๋˜์—ˆ๋‹ค. +์˜Œํ‘ธ๋Š” ๊ตญ๊ฐ€ ๊ฐ„์— ๋ฒŒ์–ด์ง€๋Š” ์•ฝ์œก๊ฐ•์‹์˜ ๊ฒฝ์Ÿ์„ ๋ถ€๊ฐํ•˜๊ณ , +๊ฒฝ์Ÿ์—์„œ ์Šน๋ฆฌํ•˜๋ ค๋ฉด ๊ธฐ์ˆ ๋ฟ ์•„๋‹ˆ๋ผ ๊ตญ๋ฏผ์˜ ์ •์‹ ์  ์ž์งˆ์ด +๋’ท๋ฐ›์นจ๋˜์–ด์•ผ ํ•œ๋‹ค๊ณ  ๋ณด์•˜๋‹ค. ์ •์‹ ์  ์ž์งˆ ์ค‘ ๊ณผํ•™์  ์‚ฌ์œ  +๋Šฅ๋ ฅ์ด ๊ฐ€์žฅ ์ค‘์š”ํ•˜๋‹ค๊ณ  ํŒŒ์•…ํ•œ ๊ทธ์—๊ฒŒ ๊ณผํ•™ ์ •์‹ ์ด ์ „์ œ๋˜์ง€ +์•Š์€ ์ •์น˜์  ๋ณ€ํ˜์€ ๋ฟŒ๋ฆฌ๋‚ด๋ฆด ์ˆ˜ ์—†๋Š” ๊ฒƒ์ด์—ˆ๋‹ค. ๊ทธ๋Š” ์ธ๊ณผ +์‹ค์ฆ์˜ ๋ฐฉ๋ฒ•์— ๊ทผ๊ฑฐํ•œ ๊ทผ๋Œ€ ํ•™๋ฌธ ์ „์ฒด๋ฅผ ๊ณผํ•™์ด๋ผ ํŒŒ์•…ํ•˜๊ณ , +๊ณผํ•™์„ ์Šต๋“ํ•˜์—ฌ ์ „ํ†ต ํ•™๋ฌธ์˜ ํ๋‹จ์—์„œ ๋ฒ—์–ด๋‚˜์•ผ ํ•œ๋‹ค๊ณ  ์ฃผ์žฅ +ํ–ˆ๋‹ค. ๊ทธ์˜ ์ž…์žฅ์€ 1910๋…„๋Œ€ ํ›„๋ฐ˜ ์‹ ๋ฌธํ™” ์šด๋™์„ ์ฃผ๋„ํ•œ ์ฒœ๋‘์Šˆ +์—๊ฒŒ ์ด์–ด์กŒ๋‹ค. +์ฒœ๋‘์Šˆ๋ฅผ ๋น„๋กฏํ•œ ์‹ ๋ฌธํ™” ์šด๋™์˜ ์ง€์‹์ธ๋“ค์€ ใ‰ก๊ณผํ•™์˜ ๊ทผ๊ฑฐ +์œ„์—์„œ๋งŒ ๋ฏผ์ฃผ ์ •์น˜์˜ ์‹คํ˜„์ด ๊ฐ€๋Šฅํ•˜๋‹ค๊ณ  ์ฃผ์žฅํ–ˆ๋‹ค. ์ค‘๊ตญ์ด +๋‹ฌ์„ฑํ•ด์•ผ ํ•  ์‹ ๋ฌธํ™”๋Š” ๊ณผํ•™ ๋ฐ ๊ณผํ•™์˜ ๋ฐฉ๋ฒ•์— ๊ทผ๊ฑฐํ•œ ๋ฌธํ™”๋ผ +๋ณด๊ณ , ์‹ ๋ฌธํ™”๋ฅผ ์ด๋ฃจ๊ธฐ ์œ„ํ•ด ์ „ํ†ต๋ฌธํ™” ์ „๋ฐ˜์— ๋Œ€ํ•ด ์ฒ ์ €ํ•œ +๋ถ€์ •๊ณผ ๋น„ํŒ์„ ์‹œ๋„ํ–ˆ๋‹ค. ์‚ฌ์ƒ์ด๋‚˜ ์ฒ ํ•™์ด ๊ณผํ•™์˜ ๋ฐฉ๋ฒ•์„ ์ด์šฉ +ํ•˜์ง€ ์•Š์œผ๋ฉด ๊ณต์ƒ(็ฉบๆƒณ)์— โ“๊ทธ์น  ๋ฟ์ด๋ผ๊ณ  ์ฃผ์žฅํ•œ ์ฒœ๋‘์Šˆ๋Š” +์‚ฌํšŒ์™€ ์ธ๊ฐ„์˜ ์‚ถ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋„ ๊ณผํ•™์˜ ์—ฐ๊ตฌ ๋ฐฉ๋ฒ•์„ ์ด์šฉํ•ด์•ผ +ํ•œ๋‹ค๊ณ  ๋ณด์•˜๋‹ค. ๊ทธ๋Š” ์ œ1์ฐจ ์„ธ๊ณ„ ๋Œ€์ „์˜ ๋น„๊ทน์€ ๊ณผํ•™์„ ์ด์šฉํ•ด +์ €์ง€๋ฅธ ์ฃ„์•…์˜ ๊ฒฐ๊ณผ์ผ ๋ฟ ๊ณผํ•™ ์ž์ฒด์˜ ์ฃ„์•…์ด ์•„๋‹ˆ๋ผ๊ณ  ์ฃผ์žฅํ•˜๋ฉฐ +๊ณผํ•™์— ๋Œ€ํ•œ ์ž์‹ ์˜ ์ƒ๊ฐ์„ ์ง€์†ํ–ˆ๋‹ค. +ํ•œํŽธ, ์ œ1์ฐจ ์„ธ๊ณ„ ๋Œ€์ „ ์ดํ›„ ์œ ๋Ÿฝ์„ ์‹œ์ฐฐํ–ˆ๋˜ ์žฅ์ฅ”๋งˆ์ด๋Š” +ํ†ต์ œ๋˜์ง€ ์•Š์€ ๊ณผํ•™์ด ๋ถˆ๋Ÿฌ์˜จ ์—ญ์ž‘์šฉ์„ ๋ชฉ๋„ํ•œ ํ›„, ๊ณผํ•™์ด +์–ด๋–ป๊ฒŒ ๋ฐœ๋‹ฌํ•˜๋“  ๊ทธ๊ฒƒ์ด ์ธ์ƒ๊ด€์˜ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•  ์ˆ˜๋Š” ์—†๋‹ค๋ฉฐ +์„œ์–‘ ๊ทผ๋Œ€ ๋ฌธ๋ช…์„ ๋น„ํŒํ–ˆ๋‹ค. ๊ทผ๋Œ€ ๊ณผํ•™ ๋ฌธ๋ช…์—์„œ ์ดˆ๋ž˜๋œ ์‚ฌ์ƒ์  +์œ„๊ธฐ๊ฐ€ ์ฃผ์ฒด์˜ ์ฑ…์ž„ ๋ถ€์žฌ์—์„œ ๋น„๋กฏ๋œ ๊ฒƒ์ด๋ผ๋Š” ์ฃผ์žฅ์— ๋™์˜ํ–ˆ๋˜ +๊ทธ๋Š” ๊ณผํ•™์  ๋ฐฉ๋ฒ•์„ ๋ถ€์ •ํ•˜์ง€ ์•Š์•˜์ง€๋งŒ, ์ธ์ƒ๊ด€์˜ ๋ฌธ์ œ์—๋Š” +๊ณผํ•™์  ๋ฐฉ๋ฒ•์ด ์ ์šฉ๋  ์ˆ˜ ์—†๋‹ค๊ณ  ์ง€์ ํ–ˆ๋‹ค. ๊ทธ๋Š” ์ธ์ƒ๊ด€์„ +๊ณผํ•™๊ณผ ๋ณ„๊ฐœ๋กœ ํŒŒ์•…ํ–ˆ๊ณ , ๊ณผํ•™๋งŒ๋Šฅ์ฃผ์˜์— ๊ธฐ์ดˆํ•œ ์‹ ๋ฌธํ™” ์šด๋™์— +์˜ํ•ด ๋ถ€์ •๋œ ์ค‘๊ตญ ์ „ํ†ต ๊ฐ€์น˜๊ด€์˜ ์ˆ˜ํ˜ธ๋ฅผ ๋‚ด์„ธ์› ๋‹ค.","{'question': '์œ—๊ธ€์— ๋Œ€ํ•œ ์ดํ•ด๋กœ ์ ์ ˆํ•˜์ง€ ์•Š์€ ๊ฒƒ์€?', 'choices': ['(๊ฐ€):์„œ์–‘ ๊ณผํ•™๊ณผ ๊ธฐ์ˆ ์˜ ๊ตญ๋‚ด ์œ ์ž…์„ ๋ฐ˜๋Œ€ํ•˜๋Š” ์ฃผ์žฅ์ด ๊ฐœํ•ญ\n์ดํ›„์—๋„ ์ด์–ด์กŒ๋‹ค.', '(๊ฐ€):์œ ํ•™์„ ํ˜์‹ ํ•˜์—ฌ ์ฒ ํ•™์œผ๋กœ ์žฌ๊ตฌ์„ฑํ•˜๋Š” ๊ฒƒ์ด ํ•„์š”ํ•˜๋‹ค๋Š”\n๊ฒฌํ•ด๊ฐ€ ์„์‚ฌ๋Š‘์•ฝ ์ดํ›„์— ์ œ๊ธฐ๋˜์—ˆ๋‹ค.', '๋‚˜):์ง„์ •ํ•œ ๊ทผ๋Œ€๋ฅผ ์ด๋ฃจ๋ ค๋ฉด ๊ธฐ์ˆ  ์ˆ˜์šฉ์˜ ์ฐจ์›์„ ๋„˜์–ด์„œ์•ผ\nํ•œ๋‹ค๋Š” ์ธ์‹์ด ๋“ฑ์žฅํ•˜์˜€๋‹ค.', '(๋‚˜):๊ณผํ•™ ์ •์‹ ์ด ์‚ฌํšŒ์— ์ž๋ฆฌ ์žก์œผ๋ ค๋ฉด ์ •์น˜์  ๋ณ€ํ˜์ด\n์„ ํ–‰๋˜์–ด์•ผ ํ•œ๋‹ค๋Š” ์ฃผ์žฅ์ด ์ œ๊ธฐ๋˜์—ˆ๋‹ค.', '(๋‚˜):๊ทผ๋Œ€ ๊ณผํ•™ ๋ฌธ๋ช…์— ๋Œ€ํ•œ ๋น„ํŒ์  ์ธ์‹์„ ๋ฐ”ํƒ•์œผ๋กœ ์ „ํ†ต\n๊ฐ€์น˜๊ด€์— ์ฃผ๋ชฉํ•˜๋Š” ๊ฒฌํ•ด๊ฐ€ ์ œ์‹œ๋˜์—ˆ๋‹ค.'], 'answer': ''}",,4,4,True,[],4 +2025-korean-05,"(๊ฐ€) +์„œ์–‘์˜ ๊ณผํ•™๊ณผ ๊ธฐ์ˆ , ์ฒœ์ฃผ๊ต์˜ ์ˆ˜์šฉ์„ ๋ฐ˜๋Œ€ํ–ˆ๋˜ ์ดํ•ญ๋กœ๋ฅผ ๋น„๋กฏํ•œ +์ฒ™์‚ฌํŒŒ์˜ ์ฃผ์žฅ์€ ๊ฐœํ•ญ ์ดํ›„์—๋„ ์ง€์†๋˜์—ˆ์ง€๋งŒ, ๊ฐœํ™”๋Š” ๊ฑฐ์Šค๋ฅผ +์ˆ˜ ์—†๋Š” ๋Œ€์„ธ๋กœ ์ž๋ฆฌ ์žก์•˜๋‹ค. ๊ฐœ๋ฌผ์„ฑ๋ฌด(้–‹็‰ฉๆˆๅ‹™)์™€ ํ™”๋ฏผ์„ฑ์† +(ๅŒ–ๆฐ‘ๆˆไฟ—)์˜ ์•ž ๊ธ€์ž๋ฅผ ๋”ด ๊ฐœํ™”๋Š” ๊ฐœํ•ญ ์ด์ „์—๋Š” ํ†ต์น˜์ž์˜ ํ†ต์น˜ +ํ–‰์œ„๋กœ์„œ ๋ณ€ํ™”ํ•˜๋Š” ์„ธ์ƒ์— ๋Œ€ํ•œ ์ง€์‹ ํ™•์žฅ๊ณผ ํ”ผํ†ต์น˜์ž์— ๋Œ€ํ•œ +๊ตํ™”๋ฅผ ์˜๋ฏธํ–ˆ๋‹ค. +๊ฐœํ•ญ ์ดํ›„ ์„œ์–‘ ๋ฌธ๋ช…์— ๋Œ€ํ•œ ๊ธ์ •์  ์ธ์‹์ด ํ™•์‚ฐ๋˜๋ฉด์„œ ์„œ์–‘ +๋ฌธ๋ช…์˜ ์ˆ˜์šฉ์„ ๋œปํ•˜๋Š” ๊ฐœํ™” ๊ฐœ๋…์ด ์ž๋ฆฌ ์žก์•˜๋‹ค. ์ž„์˜ค๊ตฐ๋ž€ ์ดํ›„, +๊ณ ์ข…์€ ์ž๊ฐ• ์ •์ฑ…์„ ์ถ”์ง„ํ•˜๋ฉด์„œ ๋ฐ˜(ๅ)์„œ์–‘ ์ •์„œ์˜ ๊ต์ •์„ ์œ„ํ•ด +ํ•œ์„ฑ์ˆœ๋ณด๋ฅผ ๋ฐœ๊ฐ„ํ–ˆ๋‹ค. ์ด ์‹ ๋ฌธ์˜ ๊ฐœํ™” ๊ฐœ๋…์€ ์„œ์–‘ ๊ธฐ์ˆ ๊ณผ +์ œ๋„์˜ ๋„์ž…์„ ํ†ตํ•œ ์ธ์ง€์˜ ๋ฐœ๋‹ฌ๊ณผ ํ’์†์˜ ์ง„๋ณด๋ฅผ ๋œปํ–ˆ๋‹ค. +์ด ๊ฐœ๋…์—๋Š” ์ธ๋ฏผ์ด ๊ตญ๊ฐ€์˜ ๋…๋ฆฝ ์ฃผ๊ถŒ์˜ ์†Œ์ค‘ํ•จ์„ ๊นจ๋‹ซ๋Š” ์˜์‹์˜ +๋ณ€ํ™”๊ฐ€ ๋‚ดํฌ๋˜์—ˆ๊ณ , ํ†ต์น˜์ž์˜ ์ž…์žฅ์—์„œ ์ˆ˜์šฉ ๊ฐ€๋Šฅํ•œ ๋ฌธ๋ช…์˜ ์žฅ์ ์„ +๋ฐ›์•„๋“ค์—ฌ ๊ตญ๊ฐ€์˜ ์ง„๋ณด๋ฅผ ๋‹ฌ์„ฑํ•œ๋‹ค๋Š” ์˜๋ฏธ๋„ ๋‹ด๊ฒผ๋‹ค. +๊ฐœํ™”๋‹น์˜ ํ•œ ์ธ์‚ฌ๊ฐ€ ์ œ์‹œํ•œ ๊ฐœํ™” ๊ฐœ๋…์€ ์„ฑ๋ฌธํ™”๋œ ๊ทœ์ •์— ๋”ฐ๋ฅธ +๋Œ€๋ฏผ ์ •์น˜์—์„œ์˜ ๋ฒ•์  ์ฒ˜๋ฆฌ ์ ˆ์ฐจ ์‹คํ˜„ ๋“ฑ ์„œ์–‘ ๊ทผ๋Œ€ ๊ตญ๊ฐ€์˜ ํ†ต์น˜ +๋ฐฉ์‹์œผ๋กœ์˜ ๋ณ€ํ™”๋ฅผ ๋‚ดํฌํ•˜๋Š” ๊ฒƒ์ด์—ˆ๋‹ค. ๊ทธ๋Š” ๊ฐœํ™” ์‹คํ–‰ ์ฃผ์ฒด๋ฅผ +์—ฌ์ „ํžˆ ์™•์œผ๋กœ ์ƒ๊ฐํ–ˆ๊ณ , ๊ฐœํ™” ์‹คํ–‰ ์ฃผ์ฒด๋กœ์„œ ์™•์˜ ์—ญํ• ์ด +์‚ฌ๋ผ์ง„ ๊ฒƒ์€ ๊ฐ‘์‹ ์ •๋ณ€์—์„œ์˜€๋‹ค. ํ’์†์˜ ์ง„๋ณด์™€ ํ†ต์น˜ ๋ฐฉ์‹ ๋ณ€ํ™” +๋ผ๋Š” ์˜๋ฏธ๋ฅผ ๋‚ดํฌํ•œ ๊ฐ‘์‹ ์ •๋ณ€์˜ ๊ฐœํ™” ๊ฐœ๋…์€ ํ†ต์น˜๊ถŒ์— ๋Œ€ํ•œ +๋„์ „์œผ๋กœ๋ฟ ์•„๋‹ˆ๋ผ ๊ฐœ์ธ์˜ ์‚ฌ์š•์„ ์œ„ํ•œ ๊ฒƒ์œผ๋กœ ํ‘œ์ƒ๋˜์—ˆ๋‹ค. ์ดํ›„ +๊ฐœํ™” ๊ฐœ๋…์€ ๊ตญ๊ฐ€ ๊ตฌ์„ฑ์›์„ ์กฐ์งํ•˜๊ณ  ๋™์›ํ•˜๊ธฐ ์œ„ํ•ด ๋ถ€์ •์  +์ด๋ฏธ์ง€์—์„œ ๋ฒ—์–ด๋‚˜์•ผ ํ–ˆ๊ณ , ์œ ๊ธธ์ค€์€ ์„œ์œ ๊ฒฌ๋ฌธ์„ ์ €์ˆ ํ•˜๋ฉฐ +๊ฐœํ™” ๊ฐœ๋…์— ๋ง์”Œ์›Œ์ง„ ๋ถ€์ •์  ์ด๋ฏธ์ง€๋ฅผ ๋–ผ์–ด ๋‚ด๊ณ ์ž ํ–ˆ๋‹ค. ์ดํ›„ +๊ฐ„ํ–‰๋œ ๋Œ€ํ•œ๋งค์ผ์‹ ๋ณด ๋“ฑ์˜ ๊ฐœํ™” ๊ฐœ๋…์€ ๊ตญ๊ฐ€ ๊ตฌ์„ฑ์› ์ „์ฒด๋ฅผ +์‹คํ–‰ ์ฃผ์ฒด๋กœ ํ•˜์—ฌ ๊ทผ๋Œ€ ๊ตญ๊ฐ€ ์ฃผ๊ถŒ์„ ํ–ฅํ•ด ๊ทธ๋“ค์„ ์กฐ์งํ•˜๊ณ  +๋™์›ํ•˜๋Š” ๊ฒƒ์„ ์˜๋ฏธํ–ˆ๋‹ค. +์„์‚ฌ๋Š‘์•ฝ ์ดํ›„, ๊ฐœํ™” ๋…ผ์˜๋Š” ๋ฌธ๋ช…์— ๋Œ€ํ•œ ๋ณธ๊ฒฉ์ ์ธ ๋…ผ์˜๋กœ +์ด์–ด์กŒ๋‹ค. ๋Œ€ํ•œ ์ž๊ฐ•ํšŒ์˜ ์ฃผ์š” ์ธ์‚ฌ๋“ค์€ ์„œ์–‘ ๊ทผ๋Œ€ ๋ฌธ๋ช…์„ +์ˆ˜์šฉํ•˜์—ฌ ๊ทผ๋Œ€ ๊ตญ๊ฐ€๋ฅผ ๊ฑด์„คํ•˜๊ณ ์ž, ์•ž์„œ ๋ฌธ๋ช…ํ™”๋ฅผ ์ด๋ฃฌ ์ผ๋ณธ์˜ +์ง€๋„๋ฅผ ๋ฐ›์•„์•ผ ํ•œ๋‹ค๊ณ  ๋ณด์•˜๋‹ค. ์ด๋“ค์€ ์„œ์–‘ ๊ทผ๋Œ€ ๋ฌธ๋ช…์˜ ์ฃผ์ฒด๋ฅผ +์ฃผ์ฒด ์ธ์‹์˜ ์ค€๊ฑฐ๋กœ ์‚ผ์•˜๊ธฐ ๋•Œ๋ฌธ์— ๋ฏผ์กฑ ์ฃผ์ฒด์„ฑ์„ ๊ฐ„๊ณผํ–ˆ๋‹ค. +์ด๋Ÿฌํ•œ ์ƒํ™ฉ์—์„œ ๋ฐ•์€์‹์€ ใ‰ ๊ทผ๋Œ€ ๊ตญ๊ฐ€ ๊ฑด์„ค๊ณผ ์ƒˆ๋กœ์šด ์ฃผ์ฒด์˜ +ํ˜•์„ฑ์— ์ฃผ๋ชฉํ•˜์—ฌ ๋ฌธ๋ช…์— ๋Œ€ํ•œ ๊ฒฌํ•ด๋ฅผ ์ œ์‹œํ–ˆ๋‹ค. ๊ทธ์˜ ๊ธฐ๋ณธ +๊ฑธ์ณ ๊ทผ๋Œ€ํ™”์˜ ์ถ”์ง„๊ณผ ํ•จ๊ป˜ ์˜์š•์ ์ธ ๊ธฐ์ˆ  ์ˆ˜์šฉ์œผ๋กœ ์ด์–ด์กŒ์ง€๋งŒ, +์ฒญ์ผ ์ „์Ÿ์˜ ํŒจ๋ฐฐ๋Š” ๊ธฐ์ˆ  ์ˆ˜์šฉ๋งŒ์œผ๋กœ๋Š” ๋ถ€์กฑํ•˜๋‹ค๋Š” ์ธ์‹์„ +๋‚ณ์•˜๋‹ค. ์ด์— ๋”ฐ๋ผ 20์„ธ๊ธฐ ์ดˆ๋ฐ˜ ์ง„์ •ํ•œ ๊ทผ๋Œ€๋ฅผ ์ด๋ฃจ๊ธฐ ์œ„ํ•ด ๊ธฐ์ˆ  +๋ฐฐํ›„์—์„œ ์ž‘์šฉํ•˜๋Š” ๊ณผํ•™ ์ •์‹ ์„ ์‚ฌํšŒ ์ „์ฒด์— ์ด์‹ํ•˜๋ ค๋Š” ์‹œ๋„๊ฐ€ +๊ตฌ์ฒดํ™”๋˜์—ˆ๋‹ค. +์˜Œํ‘ธ๋Š” ๊ตญ๊ฐ€ ๊ฐ„์— ๋ฒŒ์–ด์ง€๋Š” ์•ฝ์œก๊ฐ•์‹์˜ ๊ฒฝ์Ÿ์„ ๋ถ€๊ฐํ•˜๊ณ , +๊ฒฝ์Ÿ์—์„œ ์Šน๋ฆฌํ•˜๋ ค๋ฉด ๊ธฐ์ˆ ๋ฟ ์•„๋‹ˆ๋ผ ๊ตญ๋ฏผ์˜ ์ •์‹ ์  ์ž์งˆ์ด +๋’ท๋ฐ›์นจ๋˜์–ด์•ผ ํ•œ๋‹ค๊ณ  ๋ณด์•˜๋‹ค. ์ •์‹ ์  ์ž์งˆ ์ค‘ ๊ณผํ•™์  ์‚ฌ์œ  +๋Šฅ๋ ฅ์ด ๊ฐ€์žฅ ์ค‘์š”ํ•˜๋‹ค๊ณ  ํŒŒ์•…ํ•œ ๊ทธ์—๊ฒŒ ๊ณผํ•™ ์ •์‹ ์ด ์ „์ œ๋˜์ง€ +์•Š์€ ์ •์น˜์  ๋ณ€ํ˜์€ ๋ฟŒ๋ฆฌ๋‚ด๋ฆด ์ˆ˜ ์—†๋Š” ๊ฒƒ์ด์—ˆ๋‹ค. ๊ทธ๋Š” ์ธ๊ณผ +์‹ค์ฆ์˜ ๋ฐฉ๋ฒ•์— ๊ทผ๊ฑฐํ•œ ๊ทผ๋Œ€ ํ•™๋ฌธ ์ „์ฒด๋ฅผ ๊ณผํ•™์ด๋ผ ํŒŒ์•…ํ•˜๊ณ , +๊ณผํ•™์„ ์Šต๋“ํ•˜์—ฌ ์ „ํ†ต ํ•™๋ฌธ์˜ ํ๋‹จ์—์„œ ๋ฒ—์–ด๋‚˜์•ผ ํ•œ๋‹ค๊ณ  ์ฃผ์žฅ +ํ–ˆ๋‹ค. ๊ทธ์˜ ์ž…์žฅ์€ 1910๋…„๋Œ€ ํ›„๋ฐ˜ ์‹ ๋ฌธํ™” ์šด๋™์„ ์ฃผ๋„ํ•œ ์ฒœ๋‘์Šˆ +์—๊ฒŒ ์ด์–ด์กŒ๋‹ค. +์ฒœ๋‘์Šˆ๋ฅผ ๋น„๋กฏํ•œ ์‹ ๋ฌธํ™” ์šด๋™์˜ ์ง€์‹์ธ๋“ค์€ ใ‰ก๊ณผํ•™์˜ ๊ทผ๊ฑฐ +์œ„์—์„œ๋งŒ ๋ฏผ์ฃผ ์ •์น˜์˜ ์‹คํ˜„์ด ๊ฐ€๋Šฅํ•˜๋‹ค๊ณ  ์ฃผ์žฅํ–ˆ๋‹ค. ์ค‘๊ตญ์ด +๋‹ฌ์„ฑํ•ด์•ผ ํ•  ์‹ ๋ฌธํ™”๋Š” ๊ณผํ•™ ๋ฐ ๊ณผํ•™์˜ ๋ฐฉ๋ฒ•์— ๊ทผ๊ฑฐํ•œ ๋ฌธํ™”๋ผ +๋ณด๊ณ , ์‹ ๋ฌธํ™”๋ฅผ ์ด๋ฃจ๊ธฐ ์œ„ํ•ด ์ „ํ†ต๋ฌธํ™” ์ „๋ฐ˜์— ๋Œ€ํ•ด ์ฒ ์ €ํ•œ +๋ถ€์ •๊ณผ ๋น„ํŒ์„ ์‹œ๋„ํ–ˆ๋‹ค. ์‚ฌ์ƒ์ด๋‚˜ ์ฒ ํ•™์ด ๊ณผํ•™์˜ ๋ฐฉ๋ฒ•์„ ์ด์šฉ +ํ•˜์ง€ ์•Š์œผ๋ฉด ๊ณต์ƒ(็ฉบๆƒณ)์— โ“๊ทธ์น  ๋ฟ์ด๋ผ๊ณ  ์ฃผ์žฅํ•œ ์ฒœ๋‘์Šˆ๋Š” +์‚ฌํšŒ์™€ ์ธ๊ฐ„์˜ ์‚ถ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋„ ๊ณผํ•™์˜ ์—ฐ๊ตฌ ๋ฐฉ๋ฒ•์„ ์ด์šฉํ•ด์•ผ +ํ•œ๋‹ค๊ณ  ๋ณด์•˜๋‹ค. ๊ทธ๋Š” ์ œ1์ฐจ ์„ธ๊ณ„ ๋Œ€์ „์˜ ๋น„๊ทน์€ ๊ณผํ•™์„ ์ด์šฉํ•ด +์ €์ง€๋ฅธ ์ฃ„์•…์˜ ๊ฒฐ๊ณผ์ผ ๋ฟ ๊ณผํ•™ ์ž์ฒด์˜ ์ฃ„์•…์ด ์•„๋‹ˆ๋ผ๊ณ  ์ฃผ์žฅํ•˜๋ฉฐ +๊ณผํ•™์— ๋Œ€ํ•œ ์ž์‹ ์˜ ์ƒ๊ฐ์„ ์ง€์†ํ–ˆ๋‹ค. +ํ•œํŽธ, ์ œ1์ฐจ ์„ธ๊ณ„ ๋Œ€์ „ ์ดํ›„ ์œ ๋Ÿฝ์„ ์‹œ์ฐฐํ–ˆ๋˜ ์žฅ์ฅ”๋งˆ์ด๋Š” +ํ†ต์ œ๋˜์ง€ ์•Š์€ ๊ณผํ•™์ด ๋ถˆ๋Ÿฌ์˜จ ์—ญ์ž‘์šฉ์„ ๋ชฉ๋„ํ•œ ํ›„, ๊ณผํ•™์ด +์–ด๋–ป๊ฒŒ ๋ฐœ๋‹ฌํ•˜๋“  ๊ทธ๊ฒƒ์ด ์ธ์ƒ๊ด€์˜ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•  ์ˆ˜๋Š” ์—†๋‹ค๋ฉฐ +์„œ์–‘ ๊ทผ๋Œ€ ๋ฌธ๋ช…์„ ๋น„ํŒํ–ˆ๋‹ค. ๊ทผ๋Œ€ ๊ณผํ•™ ๋ฌธ๋ช…์—์„œ ์ดˆ๋ž˜๋œ ์‚ฌ์ƒ์  +์œ„๊ธฐ๊ฐ€ ์ฃผ์ฒด์˜ ์ฑ…์ž„ ๋ถ€์žฌ์—์„œ ๋น„๋กฏ๋œ ๊ฒƒ์ด๋ผ๋Š” ์ฃผ์žฅ์— ๋™์˜ํ–ˆ๋˜ +๊ทธ๋Š” ๊ณผํ•™์  ๋ฐฉ๋ฒ•์„ ๋ถ€์ •ํ•˜์ง€ ์•Š์•˜์ง€๋งŒ, ์ธ์ƒ๊ด€์˜ ๋ฌธ์ œ์—๋Š” +๊ณผํ•™์  ๋ฐฉ๋ฒ•์ด ์ ์šฉ๋  ์ˆ˜ ์—†๋‹ค๊ณ  ์ง€์ ํ–ˆ๋‹ค. ๊ทธ๋Š” ์ธ์ƒ๊ด€์„ +๊ณผํ•™๊ณผ ๋ณ„๊ฐœ๋กœ ํŒŒ์•…ํ–ˆ๊ณ , ๊ณผํ•™๋งŒ๋Šฅ์ฃผ์˜์— ๊ธฐ์ดˆํ•œ ์‹ ๋ฌธํ™” ์šด๋™์— +์˜ํ•ด ๋ถ€์ •๋œ ์ค‘๊ตญ ์ „ํ†ต ๊ฐ€์น˜๊ด€์˜ ์ˆ˜ํ˜ธ๋ฅผ ๋‚ด์„ธ์› ๋‹ค. +์ „๋žต์€ ๋ฌธ๋ช…์˜ ๋ฌผ์งˆ์  ์ธก๋ฉด์ธ ๊ณผํ•™์€ ์„œ์–‘์œผ๋กœ๋ถ€ํ„ฐ ์ˆ˜์šฉํ•˜๋˜, +๋ฌธ๋ช…์˜ ์ •์‹ ์  ์ธก๋ฉด์ธ ์ฒ ํ•™์€ ์œ ํ•™์„ ํ˜์‹ ํ•˜์—ฌ ์žฌ๊ตฌ์„ฑํ•˜๋Š” +๊ฒƒ์ด์—ˆ๋‹ค. ๊ทธ๋Š” ์ƒ์กด๊ณผ ํŽธ๋ฆฌ ์ฆ์ง„์„ ์œ„ํ•ด ๊ณผํ•™ ์—ฐ๊ตฌ๊ฐ€ ์‹œ๊ธ‰ +ํ•˜์ง€๋งŒ, ๊ฐ€์น˜๊ด€ ์ •๋ฆฝ๊ณผ ์ธ๊ฒฉ ์ˆ˜์–‘์„ ์œ„ํ•ด ์ฒ ํ•™ ๋˜ํ•œ ํ•„์ˆ˜์  +์ด๋ผ๊ณ  ๋ณด์•˜๋‹ค. ์ž๊ตญ ์ฒ ํ•™ ์ „ํ†ต์˜ ์ •๋ฆฝ์ด๋ผ๋Š” ๋‹น์‹œ ๋™์•„์‹œ์•„์˜ +์‚ฌ์ƒ์  ํ๋ฆ„ ์†์—์„œ ๊ทธ๊ฐ€ ์ œ์‹œํ•œ ๊ทผ๋Œ€ ์ฃผ์ฒด๋Š” ๊ณผํ•™์ โ€ค์ฒ ํ•™์  +์ธ์‹์˜ ์ฃผ์ฒด์ด์ž ์‹ค์ฒœ์  ๋„๋• ์ˆ˜์–‘์˜ ์ฃผ์ฒด๋กœ์„œ์˜ ์„ฑ๊ฒฉ์„ ๋ ๋Š” +๊ฒƒ์ด์—ˆ๋‹ค. + + +(๋‚˜) +์ค‘๊ตญ์ด ์„œ์–‘์˜ ๊ณผํ•™๊ณผ ๊ธฐ์ˆ ์— ์ „๋ฉด์ ์ธ ๊ด€์‹ฌ์„ ๊ธฐ์šธ์ธ ๋•Œ๋Š” +์•„ํŽธ ์ „์Ÿ ์ดํ›„์˜€๋‹ค. ์ „์Ÿ ํŒจ๋ฐฐ์— ๋”ฐ๋ฅธ ์œ„๊ธฐ๊ฐ์€ ๋ฐ˜์„ธ๊ธฐ์— +๊ฑธ์ณ ๊ทผ๋Œ€ํ™”์˜ ์ถ”์ง„๊ณผ ํ•จ๊ป˜ ์˜์š•์ ์ธ ๊ธฐ์ˆ  ์ˆ˜์šฉ์œผ๋กœ ์ด์–ด์กŒ์ง€๋งŒ, +์ฒญ์ผ ์ „์Ÿ์˜ ํŒจ๋ฐฐ๋Š” ๊ธฐ์ˆ  ์ˆ˜์šฉ๋งŒ์œผ๋กœ๋Š” ๋ถ€์กฑํ•˜๋‹ค๋Š” ์ธ์‹์„ +๋‚ณ์•˜๋‹ค. ์ด์— ๋”ฐ๋ผ 20์„ธ๊ธฐ ์ดˆ๋ฐ˜ ์ง„์ •ํ•œ ๊ทผ๋Œ€๋ฅผ ์ด๋ฃจ๊ธฐ ์œ„ํ•ด ๊ธฐ์ˆ  +๋ฐฐํ›„์—์„œ ์ž‘์šฉํ•˜๋Š” ๊ณผํ•™ ์ •์‹ ์„ ์‚ฌํšŒ ์ „์ฒด์— ์ด์‹ํ•˜๋ ค๋Š” ์‹œ๋„๊ฐ€ +๊ตฌ์ฒดํ™”๋˜์—ˆ๋‹ค. +์˜Œํ‘ธ๋Š” ๊ตญ๊ฐ€ ๊ฐ„์— ๋ฒŒ์–ด์ง€๋Š” ์•ฝ์œก๊ฐ•์‹์˜ ๊ฒฝ์Ÿ์„ ๋ถ€๊ฐํ•˜๊ณ , +๊ฒฝ์Ÿ์—์„œ ์Šน๋ฆฌํ•˜๋ ค๋ฉด ๊ธฐ์ˆ ๋ฟ ์•„๋‹ˆ๋ผ ๊ตญ๋ฏผ์˜ ์ •์‹ ์  ์ž์งˆ์ด +๋’ท๋ฐ›์นจ๋˜์–ด์•ผ ํ•œ๋‹ค๊ณ  ๋ณด์•˜๋‹ค. ์ •์‹ ์  ์ž์งˆ ์ค‘ ๊ณผํ•™์  ์‚ฌ์œ  +๋Šฅ๋ ฅ์ด ๊ฐ€์žฅ ์ค‘์š”ํ•˜๋‹ค๊ณ  ํŒŒ์•…ํ•œ ๊ทธ์—๊ฒŒ ๊ณผํ•™ ์ •์‹ ์ด ์ „์ œ๋˜์ง€ +์•Š์€ ์ •์น˜์  ๋ณ€ํ˜์€ ๋ฟŒ๋ฆฌ๋‚ด๋ฆด ์ˆ˜ ์—†๋Š” ๊ฒƒ์ด์—ˆ๋‹ค. ๊ทธ๋Š” ์ธ๊ณผ +์‹ค์ฆ์˜ ๋ฐฉ๋ฒ•์— ๊ทผ๊ฑฐํ•œ ๊ทผ๋Œ€ ํ•™๋ฌธ ์ „์ฒด๋ฅผ ๊ณผํ•™์ด๋ผ ํŒŒ์•…ํ•˜๊ณ , +๊ณผํ•™์„ ์Šต๋“ํ•˜์—ฌ ์ „ํ†ต ํ•™๋ฌธ์˜ ํ๋‹จ์—์„œ ๋ฒ—์–ด๋‚˜์•ผ ํ•œ๋‹ค๊ณ  ์ฃผ์žฅ +ํ–ˆ๋‹ค. ๊ทธ์˜ ์ž…์žฅ์€ 1910๋…„๋Œ€ ํ›„๋ฐ˜ ์‹ ๋ฌธํ™” ์šด๋™์„ ์ฃผ๋„ํ•œ ์ฒœ๋‘์Šˆ +์—๊ฒŒ ์ด์–ด์กŒ๋‹ค. +์ฒœ๋‘์Šˆ๋ฅผ ๋น„๋กฏํ•œ ์‹ ๋ฌธํ™” ์šด๋™์˜ ์ง€์‹์ธ๋“ค์€ ใ‰ก๊ณผํ•™์˜ ๊ทผ๊ฑฐ +์œ„์—์„œ๋งŒ ๋ฏผ์ฃผ ์ •์น˜์˜ ์‹คํ˜„์ด ๊ฐ€๋Šฅํ•˜๋‹ค๊ณ  ์ฃผ์žฅํ–ˆ๋‹ค. ์ค‘๊ตญ์ด +๋‹ฌ์„ฑํ•ด์•ผ ํ•  ์‹ ๋ฌธํ™”๋Š” ๊ณผํ•™ ๋ฐ ๊ณผํ•™์˜ ๋ฐฉ๋ฒ•์— ๊ทผ๊ฑฐํ•œ ๋ฌธํ™”๋ผ +๋ณด๊ณ , ์‹ ๋ฌธํ™”๋ฅผ ์ด๋ฃจ๊ธฐ ์œ„ํ•ด ์ „ํ†ต๋ฌธํ™” ์ „๋ฐ˜์— ๋Œ€ํ•ด ์ฒ ์ €ํ•œ +๋ถ€์ •๊ณผ ๋น„ํŒ์„ ์‹œ๋„ํ–ˆ๋‹ค. ์‚ฌ์ƒ์ด๋‚˜ ์ฒ ํ•™์ด ๊ณผํ•™์˜ ๋ฐฉ๋ฒ•์„ ์ด์šฉ +ํ•˜์ง€ ์•Š์œผ๋ฉด ๊ณต์ƒ(็ฉบๆƒณ)์— โ“๊ทธ์น  ๋ฟ์ด๋ผ๊ณ  ์ฃผ์žฅํ•œ ์ฒœ๋‘์Šˆ๋Š” +์‚ฌํšŒ์™€ ์ธ๊ฐ„์˜ ์‚ถ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋„ ๊ณผํ•™์˜ ์—ฐ๊ตฌ ๋ฐฉ๋ฒ•์„ ์ด์šฉํ•ด์•ผ +ํ•œ๋‹ค๊ณ  ๋ณด์•˜๋‹ค. ๊ทธ๋Š” ์ œ1์ฐจ ์„ธ๊ณ„ ๋Œ€์ „์˜ ๋น„๊ทน์€ ๊ณผํ•™์„ ์ด์šฉํ•ด +์ €์ง€๋ฅธ ์ฃ„์•…์˜ ๊ฒฐ๊ณผ์ผ ๋ฟ ๊ณผํ•™ ์ž์ฒด์˜ ์ฃ„์•…์ด ์•„๋‹ˆ๋ผ๊ณ  ์ฃผ์žฅํ•˜๋ฉฐ +๊ณผํ•™์— ๋Œ€ํ•œ ์ž์‹ ์˜ ์ƒ๊ฐ์„ ์ง€์†ํ–ˆ๋‹ค. +ํ•œํŽธ, ์ œ1์ฐจ ์„ธ๊ณ„ ๋Œ€์ „ ์ดํ›„ ์œ ๋Ÿฝ์„ ์‹œ์ฐฐํ–ˆ๋˜ ์žฅ์ฅ”๋งˆ์ด๋Š” +ํ†ต์ œ๋˜์ง€ ์•Š์€ ๊ณผํ•™์ด ๋ถˆ๋Ÿฌ์˜จ ์—ญ์ž‘์šฉ์„ ๋ชฉ๋„ํ•œ ํ›„, ๊ณผํ•™์ด +์–ด๋–ป๊ฒŒ ๋ฐœ๋‹ฌํ•˜๋“  ๊ทธ๊ฒƒ์ด ์ธ์ƒ๊ด€์˜ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•  ์ˆ˜๋Š” ์—†๋‹ค๋ฉฐ +์„œ์–‘ ๊ทผ๋Œ€ ๋ฌธ๋ช…์„ ๋น„ํŒํ–ˆ๋‹ค. ๊ทผ๋Œ€ ๊ณผํ•™ ๋ฌธ๋ช…์—์„œ ์ดˆ๋ž˜๋œ ์‚ฌ์ƒ์  +์œ„๊ธฐ๊ฐ€ ์ฃผ์ฒด์˜ ์ฑ…์ž„ ๋ถ€์žฌ์—์„œ ๋น„๋กฏ๋œ ๊ฒƒ์ด๋ผ๋Š” ์ฃผ์žฅ์— ๋™์˜ํ–ˆ๋˜ +๊ทธ๋Š” ๊ณผํ•™์  ๋ฐฉ๋ฒ•์„ ๋ถ€์ •ํ•˜์ง€ ์•Š์•˜์ง€๋งŒ, ์ธ์ƒ๊ด€์˜ ๋ฌธ์ œ์—๋Š” +๊ณผํ•™์  ๋ฐฉ๋ฒ•์ด ์ ์šฉ๋  ์ˆ˜ ์—†๋‹ค๊ณ  ์ง€์ ํ–ˆ๋‹ค. ๊ทธ๋Š” ์ธ์ƒ๊ด€์„ +๊ณผํ•™๊ณผ ๋ณ„๊ฐœ๋กœ ํŒŒ์•…ํ–ˆ๊ณ , ๊ณผํ•™๋งŒ๋Šฅ์ฃผ์˜์— ๊ธฐ์ดˆํ•œ ์‹ ๋ฌธํ™” ์šด๋™์— +์˜ํ•ด ๋ถ€์ •๋œ ์ค‘๊ตญ ์ „ํ†ต ๊ฐ€์น˜๊ด€์˜ ์ˆ˜ํ˜ธ๋ฅผ ๋‚ด์„ธ์› ๋‹ค.","{'question': '๊ฐœํ™”์— ๋Œ€ํ•œ ์ดํ•ด๋กœ ์ ์ ˆํ•˜์ง€ ์•Š์€ ๊ฒƒ์€?', 'choices': ['๊ฐœํ•ญ ์ด์ „์˜ ๊ฐœํ™” ๊ฐœ๋…์€ ๋ฐฑ์„ฑ์„ ๋‹ค์Šค๋ฆฌ๋Š” ํ†ต์น˜์ž๋กœ์„œ์˜ ์—ญํ• ๊ณผ\n๊ด€๋ จ ์žˆ์—ˆ๋‹ค', 'ํ•œ์„ฑ์ˆœ๋ณด์˜ ๊ฐœํ™” ๊ฐœ๋…์€ ์„œ์–‘ ๊ธฐ์ˆ ๊ณผ ์ œ๋„์˜ ์„ ๋ณ„์  ์ˆ˜์šฉ์„\nํ†ตํ•œ ๊ตญ๊ฐ€ ์ง„๋ณด์˜ ์˜๋ฏธ๋ฅผ ํฌํ•จํ•˜์˜€๋‹ค.', 'ํ•œ์„ฑ์ˆœ๋ณด์™€ ๊ฐœํ™”๋‹น์˜ ํ•œ ์ธ์‚ฌ์˜ ๊ฐœํ™” ๊ฐœ๋…์€ ํ†ต์น˜๊ถŒ์ž์ธ\n์™•์„ ๊ฐœํ™”์˜ ์‹คํ–‰ ์ฃผ์ฒด๋กœ ์ƒ์ •ํ•˜์˜€๋‹ค.', '๊ฐœํ™”์˜ ์‹คํ–‰ ์ฃผ์ฒด๋กœ ์™•์—๊ฒŒ ์—ญํ• ์„ ๋ถ€์—ฌํ•˜์ง€ ์•Š์€ ๊ฐ‘์‹ ์ •๋ณ€์˜\n๊ฐœํ™” ๊ฐœ๋…์€ ํ†ต์น˜๊ถŒ์— ๋Œ€ํ•œ ๋„์ „์œผ๋กœ ์ดํ•ด๋˜์—ˆ๋‹ค.', '๋Œ€ํ•œ๋งค์ผ์‹ ๋ณด์˜ ๋ฐœ๊ฐ„์— ์ด๋ฅด๋Ÿฌ์„œ์•ผ ๊ตญ๊ฐ€์˜ ์ฃผ๊ถŒ๊ณผ ๊ฒฐ๋ถ€ํ•œ\n๊ฐœํ™” ๊ฐœ๋…์ด ์ œ๊ธฐ๋˜์—ˆ๋‹ค.'], 'answer': ''}",,5,5,True,[],5 +2025-korean-06,"(๊ฐ€) +์„œ์–‘์˜ ๊ณผํ•™๊ณผ ๊ธฐ์ˆ , ์ฒœ์ฃผ๊ต์˜ ์ˆ˜์šฉ์„ ๋ฐ˜๋Œ€ํ–ˆ๋˜ ์ดํ•ญ๋กœ๋ฅผ ๋น„๋กฏํ•œ +์ฒ™์‚ฌํŒŒ์˜ ์ฃผ์žฅ์€ ๊ฐœํ•ญ ์ดํ›„์—๋„ ์ง€์†๋˜์—ˆ์ง€๋งŒ, ๊ฐœํ™”๋Š” ๊ฑฐ์Šค๋ฅผ +์ˆ˜ ์—†๋Š” ๋Œ€์„ธ๋กœ ์ž๋ฆฌ ์žก์•˜๋‹ค. ๊ฐœ๋ฌผ์„ฑ๋ฌด(้–‹็‰ฉๆˆๅ‹™)์™€ ํ™”๋ฏผ์„ฑ์† +(ๅŒ–ๆฐ‘ๆˆไฟ—)์˜ ์•ž ๊ธ€์ž๋ฅผ ๋”ด ๊ฐœํ™”๋Š” ๊ฐœํ•ญ ์ด์ „์—๋Š” ํ†ต์น˜์ž์˜ ํ†ต์น˜ +ํ–‰์œ„๋กœ์„œ ๋ณ€ํ™”ํ•˜๋Š” ์„ธ์ƒ์— ๋Œ€ํ•œ ์ง€์‹ ํ™•์žฅ๊ณผ ํ”ผํ†ต์น˜์ž์— ๋Œ€ํ•œ +๊ตํ™”๋ฅผ ์˜๋ฏธํ–ˆ๋‹ค. +๊ฐœํ•ญ ์ดํ›„ ์„œ์–‘ ๋ฌธ๋ช…์— ๋Œ€ํ•œ ๊ธ์ •์  ์ธ์‹์ด ํ™•์‚ฐ๋˜๋ฉด์„œ ์„œ์–‘ +๋ฌธ๋ช…์˜ ์ˆ˜์šฉ์„ ๋œปํ•˜๋Š” ๊ฐœํ™” ๊ฐœ๋…์ด ์ž๋ฆฌ ์žก์•˜๋‹ค. ์ž„์˜ค๊ตฐ๋ž€ ์ดํ›„, +๊ณ ์ข…์€ ์ž๊ฐ• ์ •์ฑ…์„ ์ถ”์ง„ํ•˜๋ฉด์„œ ๋ฐ˜(ๅ)์„œ์–‘ ์ •์„œ์˜ ๊ต์ •์„ ์œ„ํ•ด +ํ•œ์„ฑ์ˆœ๋ณด๋ฅผ ๋ฐœ๊ฐ„ํ–ˆ๋‹ค. ์ด ์‹ ๋ฌธ์˜ ๊ฐœํ™” ๊ฐœ๋…์€ ์„œ์–‘ ๊ธฐ์ˆ ๊ณผ +์ œ๋„์˜ ๋„์ž…์„ ํ†ตํ•œ ์ธ์ง€์˜ ๋ฐœ๋‹ฌ๊ณผ ํ’์†์˜ ์ง„๋ณด๋ฅผ ๋œปํ–ˆ๋‹ค. +์ด ๊ฐœ๋…์—๋Š” ์ธ๋ฏผ์ด ๊ตญ๊ฐ€์˜ ๋…๋ฆฝ ์ฃผ๊ถŒ์˜ ์†Œ์ค‘ํ•จ์„ ๊นจ๋‹ซ๋Š” ์˜์‹์˜ +๋ณ€ํ™”๊ฐ€ ๋‚ดํฌ๋˜์—ˆ๊ณ , ํ†ต์น˜์ž์˜ ์ž…์žฅ์—์„œ ์ˆ˜์šฉ ๊ฐ€๋Šฅํ•œ ๋ฌธ๋ช…์˜ ์žฅ์ ์„ +๋ฐ›์•„๋“ค์—ฌ ๊ตญ๊ฐ€์˜ ์ง„๋ณด๋ฅผ ๋‹ฌ์„ฑํ•œ๋‹ค๋Š” ์˜๋ฏธ๋„ ๋‹ด๊ฒผ๋‹ค. +๊ฐœํ™”๋‹น์˜ ํ•œ ์ธ์‚ฌ๊ฐ€ ์ œ์‹œํ•œ ๊ฐœํ™” ๊ฐœ๋…์€ ์„ฑ๋ฌธํ™”๋œ ๊ทœ์ •์— ๋”ฐ๋ฅธ +๋Œ€๋ฏผ ์ •์น˜์—์„œ์˜ ๋ฒ•์  ์ฒ˜๋ฆฌ ์ ˆ์ฐจ ์‹คํ˜„ ๋“ฑ ์„œ์–‘ ๊ทผ๋Œ€ ๊ตญ๊ฐ€์˜ ํ†ต์น˜ +๋ฐฉ์‹์œผ๋กœ์˜ ๋ณ€ํ™”๋ฅผ ๋‚ดํฌํ•˜๋Š” ๊ฒƒ์ด์—ˆ๋‹ค. ๊ทธ๋Š” ๊ฐœํ™” ์‹คํ–‰ ์ฃผ์ฒด๋ฅผ +์—ฌ์ „ํžˆ ์™•์œผ๋กœ ์ƒ๊ฐํ–ˆ๊ณ , ๊ฐœํ™” ์‹คํ–‰ ์ฃผ์ฒด๋กœ์„œ ์™•์˜ ์—ญํ• ์ด +์‚ฌ๋ผ์ง„ ๊ฒƒ์€ ๊ฐ‘์‹ ์ •๋ณ€์—์„œ์˜€๋‹ค. ํ’์†์˜ ์ง„๋ณด์™€ ํ†ต์น˜ ๋ฐฉ์‹ ๋ณ€ํ™” +๋ผ๋Š” ์˜๋ฏธ๋ฅผ ๋‚ดํฌํ•œ ๊ฐ‘์‹ ์ •๋ณ€์˜ ๊ฐœํ™” ๊ฐœ๋…์€ ํ†ต์น˜๊ถŒ์— ๋Œ€ํ•œ +๋„์ „์œผ๋กœ๋ฟ ์•„๋‹ˆ๋ผ ๊ฐœ์ธ์˜ ์‚ฌ์š•์„ ์œ„ํ•œ ๊ฒƒ์œผ๋กœ ํ‘œ์ƒ๋˜์—ˆ๋‹ค. ์ดํ›„ +๊ฐœํ™” ๊ฐœ๋…์€ ๊ตญ๊ฐ€ ๊ตฌ์„ฑ์›์„ ์กฐ์งํ•˜๊ณ  ๋™์›ํ•˜๊ธฐ ์œ„ํ•ด ๋ถ€์ •์  +์ด๋ฏธ์ง€์—์„œ ๋ฒ—์–ด๋‚˜์•ผ ํ–ˆ๊ณ , ์œ ๊ธธ์ค€์€ ์„œ์œ ๊ฒฌ๋ฌธ์„ ์ €์ˆ ํ•˜๋ฉฐ +๊ฐœํ™” ๊ฐœ๋…์— ๋ง์”Œ์›Œ์ง„ ๋ถ€์ •์  ์ด๋ฏธ์ง€๋ฅผ ๋–ผ์–ด ๋‚ด๊ณ ์ž ํ–ˆ๋‹ค. ์ดํ›„ +๊ฐ„ํ–‰๋œ ๋Œ€ํ•œ๋งค์ผ์‹ ๋ณด ๋“ฑ์˜ ๊ฐœํ™” ๊ฐœ๋…์€ ๊ตญ๊ฐ€ ๊ตฌ์„ฑ์› ์ „์ฒด๋ฅผ +์‹คํ–‰ ์ฃผ์ฒด๋กœ ํ•˜์—ฌ ๊ทผ๋Œ€ ๊ตญ๊ฐ€ ์ฃผ๊ถŒ์„ ํ–ฅํ•ด ๊ทธ๋“ค์„ ์กฐ์งํ•˜๊ณ  +๋™์›ํ•˜๋Š” ๊ฒƒ์„ ์˜๋ฏธํ–ˆ๋‹ค. +์„์‚ฌ๋Š‘์•ฝ ์ดํ›„, ๊ฐœํ™” ๋…ผ์˜๋Š” ๋ฌธ๋ช…์— ๋Œ€ํ•œ ๋ณธ๊ฒฉ์ ์ธ ๋…ผ์˜๋กœ +์ด์–ด์กŒ๋‹ค. ๋Œ€ํ•œ ์ž๊ฐ•ํšŒ์˜ ์ฃผ์š” ์ธ์‚ฌ๋“ค์€ ์„œ์–‘ ๊ทผ๋Œ€ ๋ฌธ๋ช…์„ +์ˆ˜์šฉํ•˜์—ฌ ๊ทผ๋Œ€ ๊ตญ๊ฐ€๋ฅผ ๊ฑด์„คํ•˜๊ณ ์ž, ์•ž์„œ ๋ฌธ๋ช…ํ™”๋ฅผ ์ด๋ฃฌ ์ผ๋ณธ์˜ +์ง€๋„๋ฅผ ๋ฐ›์•„์•ผ ํ•œ๋‹ค๊ณ  ๋ณด์•˜๋‹ค. ์ด๋“ค์€ ์„œ์–‘ ๊ทผ๋Œ€ ๋ฌธ๋ช…์˜ ์ฃผ์ฒด๋ฅผ +์ฃผ์ฒด ์ธ์‹์˜ ์ค€๊ฑฐ๋กœ ์‚ผ์•˜๊ธฐ ๋•Œ๋ฌธ์— ๋ฏผ์กฑ ์ฃผ์ฒด์„ฑ์„ ๊ฐ„๊ณผํ–ˆ๋‹ค. +์ด๋Ÿฌํ•œ ์ƒํ™ฉ์—์„œ ๋ฐ•์€์‹์€ ใ‰ ๊ทผ๋Œ€ ๊ตญ๊ฐ€ ๊ฑด์„ค๊ณผ ์ƒˆ๋กœ์šด ์ฃผ์ฒด์˜ +ํ˜•์„ฑ์— ์ฃผ๋ชฉํ•˜์—ฌ ๋ฌธ๋ช…์— ๋Œ€ํ•œ ๊ฒฌํ•ด๋ฅผ ์ œ์‹œํ–ˆ๋‹ค. ๊ทธ์˜ ๊ธฐ๋ณธ +๊ฑธ์ณ ๊ทผ๋Œ€ํ™”์˜ ์ถ”์ง„๊ณผ ํ•จ๊ป˜ ์˜์š•์ ์ธ ๊ธฐ์ˆ  ์ˆ˜์šฉ์œผ๋กœ ์ด์–ด์กŒ์ง€๋งŒ, +์ฒญ์ผ ์ „์Ÿ์˜ ํŒจ๋ฐฐ๋Š” ๊ธฐ์ˆ  ์ˆ˜์šฉ๋งŒ์œผ๋กœ๋Š” ๋ถ€์กฑํ•˜๋‹ค๋Š” ์ธ์‹์„ +๋‚ณ์•˜๋‹ค. ์ด์— ๋”ฐ๋ผ 20์„ธ๊ธฐ ์ดˆ๋ฐ˜ ์ง„์ •ํ•œ ๊ทผ๋Œ€๋ฅผ ์ด๋ฃจ๊ธฐ ์œ„ํ•ด ๊ธฐ์ˆ  +๋ฐฐํ›„์—์„œ ์ž‘์šฉํ•˜๋Š” ๊ณผํ•™ ์ •์‹ ์„ ์‚ฌํšŒ ์ „์ฒด์— ์ด์‹ํ•˜๋ ค๋Š” ์‹œ๋„๊ฐ€ +๊ตฌ์ฒดํ™”๋˜์—ˆ๋‹ค. +์˜Œํ‘ธ๋Š” ๊ตญ๊ฐ€ ๊ฐ„์— ๋ฒŒ์–ด์ง€๋Š” ์•ฝ์œก๊ฐ•์‹์˜ ๊ฒฝ์Ÿ์„ ๋ถ€๊ฐํ•˜๊ณ , +๊ฒฝ์Ÿ์—์„œ ์Šน๋ฆฌํ•˜๋ ค๋ฉด ๊ธฐ์ˆ ๋ฟ ์•„๋‹ˆ๋ผ ๊ตญ๋ฏผ์˜ ์ •์‹ ์  ์ž์งˆ์ด +๋’ท๋ฐ›์นจ๋˜์–ด์•ผ ํ•œ๋‹ค๊ณ  ๋ณด์•˜๋‹ค. ์ •์‹ ์  ์ž์งˆ ์ค‘ ๊ณผํ•™์  ์‚ฌ์œ  +๋Šฅ๋ ฅ์ด ๊ฐ€์žฅ ์ค‘์š”ํ•˜๋‹ค๊ณ  ํŒŒ์•…ํ•œ ๊ทธ์—๊ฒŒ ๊ณผํ•™ ์ •์‹ ์ด ์ „์ œ๋˜์ง€ +์•Š์€ ์ •์น˜์  ๋ณ€ํ˜์€ ๋ฟŒ๋ฆฌ๋‚ด๋ฆด ์ˆ˜ ์—†๋Š” ๊ฒƒ์ด์—ˆ๋‹ค. ๊ทธ๋Š” ์ธ๊ณผ +์‹ค์ฆ์˜ ๋ฐฉ๋ฒ•์— ๊ทผ๊ฑฐํ•œ ๊ทผ๋Œ€ ํ•™๋ฌธ ์ „์ฒด๋ฅผ ๊ณผํ•™์ด๋ผ ํŒŒ์•…ํ•˜๊ณ , +๊ณผํ•™์„ ์Šต๋“ํ•˜์—ฌ ์ „ํ†ต ํ•™๋ฌธ์˜ ํ๋‹จ์—์„œ ๋ฒ—์–ด๋‚˜์•ผ ํ•œ๋‹ค๊ณ  ์ฃผ์žฅ +ํ–ˆ๋‹ค. ๊ทธ์˜ ์ž…์žฅ์€ 1910๋…„๋Œ€ ํ›„๋ฐ˜ ์‹ ๋ฌธํ™” ์šด๋™์„ ์ฃผ๋„ํ•œ ์ฒœ๋‘์Šˆ +์—๊ฒŒ ์ด์–ด์กŒ๋‹ค. +์ฒœ๋‘์Šˆ๋ฅผ ๋น„๋กฏํ•œ ์‹ ๋ฌธํ™” ์šด๋™์˜ ์ง€์‹์ธ๋“ค์€ ใ‰ก๊ณผํ•™์˜ ๊ทผ๊ฑฐ +์œ„์—์„œ๋งŒ ๋ฏผ์ฃผ ์ •์น˜์˜ ์‹คํ˜„์ด ๊ฐ€๋Šฅํ•˜๋‹ค๊ณ  ์ฃผ์žฅํ–ˆ๋‹ค. ์ค‘๊ตญ์ด +๋‹ฌ์„ฑํ•ด์•ผ ํ•  ์‹ ๋ฌธํ™”๋Š” ๊ณผํ•™ ๋ฐ ๊ณผํ•™์˜ ๋ฐฉ๋ฒ•์— ๊ทผ๊ฑฐํ•œ ๋ฌธํ™”๋ผ +๋ณด๊ณ , ์‹ ๋ฌธํ™”๋ฅผ ์ด๋ฃจ๊ธฐ ์œ„ํ•ด ์ „ํ†ต๋ฌธํ™” ์ „๋ฐ˜์— ๋Œ€ํ•ด ์ฒ ์ €ํ•œ +๋ถ€์ •๊ณผ ๋น„ํŒ์„ ์‹œ๋„ํ–ˆ๋‹ค. ์‚ฌ์ƒ์ด๋‚˜ ์ฒ ํ•™์ด ๊ณผํ•™์˜ ๋ฐฉ๋ฒ•์„ ์ด์šฉ +ํ•˜์ง€ ์•Š์œผ๋ฉด ๊ณต์ƒ(็ฉบๆƒณ)์— โ“๊ทธ์น  ๋ฟ์ด๋ผ๊ณ  ์ฃผ์žฅํ•œ ์ฒœ๋‘์Šˆ๋Š” +์‚ฌํšŒ์™€ ์ธ๊ฐ„์˜ ์‚ถ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋„ ๊ณผํ•™์˜ ์—ฐ๊ตฌ ๋ฐฉ๋ฒ•์„ ์ด์šฉํ•ด์•ผ +ํ•œ๋‹ค๊ณ  ๋ณด์•˜๋‹ค. ๊ทธ๋Š” ์ œ1์ฐจ ์„ธ๊ณ„ ๋Œ€์ „์˜ ๋น„๊ทน์€ ๊ณผํ•™์„ ์ด์šฉํ•ด +์ €์ง€๋ฅธ ์ฃ„์•…์˜ ๊ฒฐ๊ณผ์ผ ๋ฟ ๊ณผํ•™ ์ž์ฒด์˜ ์ฃ„์•…์ด ์•„๋‹ˆ๋ผ๊ณ  ์ฃผ์žฅํ•˜๋ฉฐ +๊ณผํ•™์— ๋Œ€ํ•œ ์ž์‹ ์˜ ์ƒ๊ฐ์„ ์ง€์†ํ–ˆ๋‹ค. +ํ•œํŽธ, ์ œ1์ฐจ ์„ธ๊ณ„ ๋Œ€์ „ ์ดํ›„ ์œ ๋Ÿฝ์„ ์‹œ์ฐฐํ–ˆ๋˜ ์žฅ์ฅ”๋งˆ์ด๋Š” +ํ†ต์ œ๋˜์ง€ ์•Š์€ ๊ณผํ•™์ด ๋ถˆ๋Ÿฌ์˜จ ์—ญ์ž‘์šฉ์„ ๋ชฉ๋„ํ•œ ํ›„, ๊ณผํ•™์ด +์–ด๋–ป๊ฒŒ ๋ฐœ๋‹ฌํ•˜๋“  ๊ทธ๊ฒƒ์ด ์ธ์ƒ๊ด€์˜ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•  ์ˆ˜๋Š” ์—†๋‹ค๋ฉฐ +์„œ์–‘ ๊ทผ๋Œ€ ๋ฌธ๋ช…์„ ๋น„ํŒํ–ˆ๋‹ค. ๊ทผ๋Œ€ ๊ณผํ•™ ๋ฌธ๋ช…์—์„œ ์ดˆ๋ž˜๋œ ์‚ฌ์ƒ์  +์œ„๊ธฐ๊ฐ€ ์ฃผ์ฒด์˜ ์ฑ…์ž„ ๋ถ€์žฌ์—์„œ ๋น„๋กฏ๋œ ๊ฒƒ์ด๋ผ๋Š” ์ฃผ์žฅ์— ๋™์˜ํ–ˆ๋˜ +๊ทธ๋Š” ๊ณผํ•™์  ๋ฐฉ๋ฒ•์„ ๋ถ€์ •ํ•˜์ง€ ์•Š์•˜์ง€๋งŒ, ์ธ์ƒ๊ด€์˜ ๋ฌธ์ œ์—๋Š” +๊ณผํ•™์  ๋ฐฉ๋ฒ•์ด ์ ์šฉ๋  ์ˆ˜ ์—†๋‹ค๊ณ  ์ง€์ ํ–ˆ๋‹ค. ๊ทธ๋Š” ์ธ์ƒ๊ด€์„ +๊ณผํ•™๊ณผ ๋ณ„๊ฐœ๋กœ ํŒŒ์•…ํ–ˆ๊ณ , ๊ณผํ•™๋งŒ๋Šฅ์ฃผ์˜์— ๊ธฐ์ดˆํ•œ ์‹ ๋ฌธํ™” ์šด๋™์— +์˜ํ•ด ๋ถ€์ •๋œ ์ค‘๊ตญ ์ „ํ†ต ๊ฐ€์น˜๊ด€์˜ ์ˆ˜ํ˜ธ๋ฅผ ๋‚ด์„ธ์› ๋‹ค. +์ „๋žต์€ ๋ฌธ๋ช…์˜ ๋ฌผ์งˆ์  ์ธก๋ฉด์ธ ๊ณผํ•™์€ ์„œ์–‘์œผ๋กœ๋ถ€ํ„ฐ ์ˆ˜์šฉํ•˜๋˜, +๋ฌธ๋ช…์˜ ์ •์‹ ์  ์ธก๋ฉด์ธ ์ฒ ํ•™์€ ์œ ํ•™์„ ํ˜์‹ ํ•˜์—ฌ ์žฌ๊ตฌ์„ฑํ•˜๋Š” +๊ฒƒ์ด์—ˆ๋‹ค. ๊ทธ๋Š” ์ƒ์กด๊ณผ ํŽธ๋ฆฌ ์ฆ์ง„์„ ์œ„ํ•ด ๊ณผํ•™ ์—ฐ๊ตฌ๊ฐ€ ์‹œ๊ธ‰ +ํ•˜์ง€๋งŒ, ๊ฐ€์น˜๊ด€ ์ •๋ฆฝ๊ณผ ์ธ๊ฒฉ ์ˆ˜์–‘์„ ์œ„ํ•ด ์ฒ ํ•™ ๋˜ํ•œ ํ•„์ˆ˜์  +์ด๋ผ๊ณ  ๋ณด์•˜๋‹ค. ์ž๊ตญ ์ฒ ํ•™ ์ „ํ†ต์˜ ์ •๋ฆฝ์ด๋ผ๋Š” ๋‹น์‹œ ๋™์•„์‹œ์•„์˜ +์‚ฌ์ƒ์  ํ๋ฆ„ ์†์—์„œ ๊ทธ๊ฐ€ ์ œ์‹œํ•œ ๊ทผ๋Œ€ ์ฃผ์ฒด๋Š” ๊ณผํ•™์ โ€ค์ฒ ํ•™์  +์ธ์‹์˜ ์ฃผ์ฒด์ด์ž ์‹ค์ฒœ์  ๋„๋• ์ˆ˜์–‘์˜ ์ฃผ์ฒด๋กœ์„œ์˜ ์„ฑ๊ฒฉ์„ ๋ ๋Š” +๊ฒƒ์ด์—ˆ๋‹ค. + + +(๋‚˜) +์ค‘๊ตญ์ด ์„œ์–‘์˜ ๊ณผํ•™๊ณผ ๊ธฐ์ˆ ์— ์ „๋ฉด์ ์ธ ๊ด€์‹ฌ์„ ๊ธฐ์šธ์ธ ๋•Œ๋Š” +์•„ํŽธ ์ „์Ÿ ์ดํ›„์˜€๋‹ค. ์ „์Ÿ ํŒจ๋ฐฐ์— ๋”ฐ๋ฅธ ์œ„๊ธฐ๊ฐ์€ ๋ฐ˜์„ธ๊ธฐ์— +๊ฑธ์ณ ๊ทผ๋Œ€ํ™”์˜ ์ถ”์ง„๊ณผ ํ•จ๊ป˜ ์˜์š•์ ์ธ ๊ธฐ์ˆ  ์ˆ˜์šฉ์œผ๋กœ ์ด์–ด์กŒ์ง€๋งŒ, +์ฒญ์ผ ์ „์Ÿ์˜ ํŒจ๋ฐฐ๋Š” ๊ธฐ์ˆ  ์ˆ˜์šฉ๋งŒ์œผ๋กœ๋Š” ๋ถ€์กฑํ•˜๋‹ค๋Š” ์ธ์‹์„ +๋‚ณ์•˜๋‹ค. ์ด์— ๋”ฐ๋ผ 20์„ธ๊ธฐ ์ดˆ๋ฐ˜ ์ง„์ •ํ•œ ๊ทผ๋Œ€๋ฅผ ์ด๋ฃจ๊ธฐ ์œ„ํ•ด ๊ธฐ์ˆ  +๋ฐฐํ›„์—์„œ ์ž‘์šฉํ•˜๋Š” ๊ณผํ•™ ์ •์‹ ์„ ์‚ฌํšŒ ์ „์ฒด์— ์ด์‹ํ•˜๋ ค๋Š” ์‹œ๋„๊ฐ€ +๊ตฌ์ฒดํ™”๋˜์—ˆ๋‹ค. +์˜Œํ‘ธ๋Š” ๊ตญ๊ฐ€ ๊ฐ„์— ๋ฒŒ์–ด์ง€๋Š” ์•ฝ์œก๊ฐ•์‹์˜ ๊ฒฝ์Ÿ์„ ๋ถ€๊ฐํ•˜๊ณ , +๊ฒฝ์Ÿ์—์„œ ์Šน๋ฆฌํ•˜๋ ค๋ฉด ๊ธฐ์ˆ ๋ฟ ์•„๋‹ˆ๋ผ ๊ตญ๋ฏผ์˜ ์ •์‹ ์  ์ž์งˆ์ด +๋’ท๋ฐ›์นจ๋˜์–ด์•ผ ํ•œ๋‹ค๊ณ  ๋ณด์•˜๋‹ค. ์ •์‹ ์  ์ž์งˆ ์ค‘ ๊ณผํ•™์  ์‚ฌ์œ  +๋Šฅ๋ ฅ์ด ๊ฐ€์žฅ ์ค‘์š”ํ•˜๋‹ค๊ณ  ํŒŒ์•…ํ•œ ๊ทธ์—๊ฒŒ ๊ณผํ•™ ์ •์‹ ์ด ์ „์ œ๋˜์ง€ +์•Š์€ ์ •์น˜์  ๋ณ€ํ˜์€ ๋ฟŒ๋ฆฌ๋‚ด๋ฆด ์ˆ˜ ์—†๋Š” ๊ฒƒ์ด์—ˆ๋‹ค. ๊ทธ๋Š” ์ธ๊ณผ +์‹ค์ฆ์˜ ๋ฐฉ๋ฒ•์— ๊ทผ๊ฑฐํ•œ ๊ทผ๋Œ€ ํ•™๋ฌธ ์ „์ฒด๋ฅผ ๊ณผํ•™์ด๋ผ ํŒŒ์•…ํ•˜๊ณ , +๊ณผํ•™์„ ์Šต๋“ํ•˜์—ฌ ์ „ํ†ต ํ•™๋ฌธ์˜ ํ๋‹จ์—์„œ ๋ฒ—์–ด๋‚˜์•ผ ํ•œ๋‹ค๊ณ  ์ฃผ์žฅ +ํ–ˆ๋‹ค. ๊ทธ์˜ ์ž…์žฅ์€ 1910๋…„๋Œ€ ํ›„๋ฐ˜ ์‹ ๋ฌธํ™” ์šด๋™์„ ์ฃผ๋„ํ•œ ์ฒœ๋‘์Šˆ +์—๊ฒŒ ์ด์–ด์กŒ๋‹ค. +์ฒœ๋‘์Šˆ๋ฅผ ๋น„๋กฏํ•œ ์‹ ๋ฌธํ™” ์šด๋™์˜ ์ง€์‹์ธ๋“ค์€ ใ‰ก๊ณผํ•™์˜ ๊ทผ๊ฑฐ +์œ„์—์„œ๋งŒ ๋ฏผ์ฃผ ์ •์น˜์˜ ์‹คํ˜„์ด ๊ฐ€๋Šฅํ•˜๋‹ค๊ณ  ์ฃผ์žฅํ–ˆ๋‹ค. ์ค‘๊ตญ์ด +๋‹ฌ์„ฑํ•ด์•ผ ํ•  ์‹ ๋ฌธํ™”๋Š” ๊ณผํ•™ ๋ฐ ๊ณผํ•™์˜ ๋ฐฉ๋ฒ•์— ๊ทผ๊ฑฐํ•œ ๋ฌธํ™”๋ผ +๋ณด๊ณ , ์‹ ๋ฌธํ™”๋ฅผ ์ด๋ฃจ๊ธฐ ์œ„ํ•ด ์ „ํ†ต๋ฌธํ™” ์ „๋ฐ˜์— ๋Œ€ํ•ด ์ฒ ์ €ํ•œ +๋ถ€์ •๊ณผ ๋น„ํŒ์„ ์‹œ๋„ํ–ˆ๋‹ค. ์‚ฌ์ƒ์ด๋‚˜ ์ฒ ํ•™์ด ๊ณผํ•™์˜ ๋ฐฉ๋ฒ•์„ ์ด์šฉ +ํ•˜์ง€ ์•Š์œผ๋ฉด ๊ณต์ƒ(็ฉบๆƒณ)์— โ“๊ทธ์น  ๋ฟ์ด๋ผ๊ณ  ์ฃผ์žฅํ•œ ์ฒœ๋‘์Šˆ๋Š” +์‚ฌํšŒ์™€ ์ธ๊ฐ„์˜ ์‚ถ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋„ ๊ณผํ•™์˜ ์—ฐ๊ตฌ ๋ฐฉ๋ฒ•์„ ์ด์šฉํ•ด์•ผ +ํ•œ๋‹ค๊ณ  ๋ณด์•˜๋‹ค. ๊ทธ๋Š” ์ œ1์ฐจ ์„ธ๊ณ„ ๋Œ€์ „์˜ ๋น„๊ทน์€ ๊ณผํ•™์„ ์ด์šฉํ•ด +์ €์ง€๋ฅธ ์ฃ„์•…์˜ ๊ฒฐ๊ณผ์ผ ๋ฟ ๊ณผํ•™ ์ž์ฒด์˜ ์ฃ„์•…์ด ์•„๋‹ˆ๋ผ๊ณ  ์ฃผ์žฅํ•˜๋ฉฐ +๊ณผํ•™์— ๋Œ€ํ•œ ์ž์‹ ์˜ ์ƒ๊ฐ์„ ์ง€์†ํ–ˆ๋‹ค. +ํ•œํŽธ, ์ œ1์ฐจ ์„ธ๊ณ„ ๋Œ€์ „ ์ดํ›„ ์œ ๋Ÿฝ์„ ์‹œ์ฐฐํ–ˆ๋˜ ์žฅ์ฅ”๋งˆ์ด๋Š” +ํ†ต์ œ๋˜์ง€ ์•Š์€ ๊ณผํ•™์ด ๋ถˆ๋Ÿฌ์˜จ ์—ญ์ž‘์šฉ์„ ๋ชฉ๋„ํ•œ ํ›„, ๊ณผํ•™์ด +์–ด๋–ป๊ฒŒ ๋ฐœ๋‹ฌํ•˜๋“  ๊ทธ๊ฒƒ์ด ์ธ์ƒ๊ด€์˜ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•  ์ˆ˜๋Š” ์—†๋‹ค๋ฉฐ +์„œ์–‘ ๊ทผ๋Œ€ ๋ฌธ๋ช…์„ ๋น„ํŒํ–ˆ๋‹ค. ๊ทผ๋Œ€ ๊ณผํ•™ ๋ฌธ๋ช…์—์„œ ์ดˆ๋ž˜๋œ ์‚ฌ์ƒ์  +์œ„๊ธฐ๊ฐ€ ์ฃผ์ฒด์˜ ์ฑ…์ž„ ๋ถ€์žฌ์—์„œ ๋น„๋กฏ๋œ ๊ฒƒ์ด๋ผ๋Š” ์ฃผ์žฅ์— ๋™์˜ํ–ˆ๋˜ +๊ทธ๋Š” ๊ณผํ•™์  ๋ฐฉ๋ฒ•์„ ๋ถ€์ •ํ•˜์ง€ ์•Š์•˜์ง€๋งŒ, ์ธ์ƒ๊ด€์˜ ๋ฌธ์ œ์—๋Š” +๊ณผํ•™์  ๋ฐฉ๋ฒ•์ด ์ ์šฉ๋  ์ˆ˜ ์—†๋‹ค๊ณ  ์ง€์ ํ–ˆ๋‹ค. ๊ทธ๋Š” ์ธ์ƒ๊ด€์„ +๊ณผํ•™๊ณผ ๋ณ„๊ฐœ๋กœ ํŒŒ์•…ํ–ˆ๊ณ , ๊ณผํ•™๋งŒ๋Šฅ์ฃผ์˜์— ๊ธฐ์ดˆํ•œ ์‹ ๋ฌธํ™” ์šด๋™์— +์˜ํ•ด ๋ถ€์ •๋œ ์ค‘๊ตญ ์ „ํ†ต ๊ฐ€์น˜๊ด€์˜ ์ˆ˜ํ˜ธ๋ฅผ ๋‚ด์„ธ์› ๋‹ค.","{'question': '(๋‚˜)์˜ โ€˜์ฒœ๋‘์Šˆโ€™์™€ โ€˜์žฅ์ฅ”๋งˆ์ดโ€™๊ฐ€ ๋ชจ๋‘ ๋™์˜ํ•  ์ˆ˜ ์žˆ๋Š” ์ง„์ˆ ๋กœ ๊ฐ€์žฅ\n์ ์ ˆํ•œ ๊ฒƒ์€?', 'choices': ['์ „ํ†ต ์‚ฌ์ƒ์€ ๊ณผํ•™ ๋ฐ ๊ณผํ•™ ์ •์‹ ๊ณผ ์–‘๋ฆฝํ•  ์ˆ˜ ์—†๋Š” ๊ด€๊ณ„์— ๋†“์—ฌ\n์žˆ๋‹ค.', '์ „ํ†ต ์‚ฌ์ƒ์˜ ํ๋‹จ์€ ๊ณผํ•™ ์ •์‹ ์ด ๋ฟŒ๋ฆฌ๋‚ด๋ฆฌ์ง€ ๋ชปํ•œ ์‚ฌํšŒ ์ฒด์งˆ์—์„œ\n๋น„๋กฏ๋œ ๊ฒƒ์ด๋‹ค.', '๊ณผํ•™์„ ์ด์šฉํ•˜๋Š” ๊ณผ์ •์—์„œ ๋ฌธ์ œ๊ฐ€ ๋ฐœ์ƒํ–ˆ๋‹ค๊ณ  ํ•ด๋„ ๊ณผํ•™์ \n๋ฐฉ๋ฒ•์„ ๋ถ€์ •ํ•  ์ˆ˜ ์—†๋‹ค.', '์„œ์–‘์˜ ๊ณผํ•™ ์ •์‹ ์„ ์ „๋ฉด์ ์œผ๋กœ ๋„์ž…ํ•˜๋ฉด ๋‹น๋ฉดํ•œ ๊ตญ๊ฐ€์˜ ์œ„๊ธฐ๋ฅผ\n์ถฉ๋ถ„ํžˆ ๊ทน๋ณตํ•  ์ˆ˜ ์žˆ๋‹ค.', '๊ตญ๊ฐ€์˜ ์œ„๊ธฐ๋Š” ๊ณผํ•™์  ๋ฐฉ๋ฒ•์œผ๋กœ ์‚ฌ์ƒ์„ ์žฌ๊ตฌ์„ฑํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค๋Š”\n์ธ์‹์ด ๋ถ€์žฌํ•œ ๋ฐ์—์„œ ๋น„๋กฏ๋œ ๊ฒƒ์ด๋‹ค.'], 'answer': ''}",,3,3,True,[],3 +2025-korean-07,"(๊ฐ€) +์„œ์–‘์˜ ๊ณผํ•™๊ณผ ๊ธฐ์ˆ , ์ฒœ์ฃผ๊ต์˜ ์ˆ˜์šฉ์„ ๋ฐ˜๋Œ€ํ–ˆ๋˜ ์ดํ•ญ๋กœ๋ฅผ ๋น„๋กฏํ•œ +์ฒ™์‚ฌํŒŒ์˜ ์ฃผ์žฅ์€ ๊ฐœํ•ญ ์ดํ›„์—๋„ ์ง€์†๋˜์—ˆ์ง€๋งŒ, ๊ฐœํ™”๋Š” ๊ฑฐ์Šค๋ฅผ +์ˆ˜ ์—†๋Š” ๋Œ€์„ธ๋กœ ์ž๋ฆฌ ์žก์•˜๋‹ค. ๊ฐœ๋ฌผ์„ฑ๋ฌด(้–‹็‰ฉๆˆๅ‹™)์™€ ํ™”๋ฏผ์„ฑ์† +(ๅŒ–ๆฐ‘ๆˆไฟ—)์˜ ์•ž ๊ธ€์ž๋ฅผ ๋”ด ๊ฐœํ™”๋Š” ๊ฐœํ•ญ ์ด์ „์—๋Š” ํ†ต์น˜์ž์˜ ํ†ต์น˜ +ํ–‰์œ„๋กœ์„œ ๋ณ€ํ™”ํ•˜๋Š” ์„ธ์ƒ์— ๋Œ€ํ•œ ์ง€์‹ ํ™•์žฅ๊ณผ ํ”ผํ†ต์น˜์ž์— ๋Œ€ํ•œ +๊ตํ™”๋ฅผ ์˜๋ฏธํ–ˆ๋‹ค. +๊ฐœํ•ญ ์ดํ›„ ์„œ์–‘ ๋ฌธ๋ช…์— ๋Œ€ํ•œ ๊ธ์ •์  ์ธ์‹์ด ํ™•์‚ฐ๋˜๋ฉด์„œ ์„œ์–‘ +๋ฌธ๋ช…์˜ ์ˆ˜์šฉ์„ ๋œปํ•˜๋Š” ๊ฐœํ™” ๊ฐœ๋…์ด ์ž๋ฆฌ ์žก์•˜๋‹ค. ์ž„์˜ค๊ตฐ๋ž€ ์ดํ›„, +๊ณ ์ข…์€ ์ž๊ฐ• ์ •์ฑ…์„ ์ถ”์ง„ํ•˜๋ฉด์„œ ๋ฐ˜(ๅ)์„œ์–‘ ์ •์„œ์˜ ๊ต์ •์„ ์œ„ํ•ด +ํ•œ์„ฑ์ˆœ๋ณด๋ฅผ ๋ฐœ๊ฐ„ํ–ˆ๋‹ค. ์ด ์‹ ๋ฌธ์˜ ๊ฐœํ™” ๊ฐœ๋…์€ ์„œ์–‘ ๊ธฐ์ˆ ๊ณผ +์ œ๋„์˜ ๋„์ž…์„ ํ†ตํ•œ ์ธ์ง€์˜ ๋ฐœ๋‹ฌ๊ณผ ํ’์†์˜ ์ง„๋ณด๋ฅผ ๋œปํ–ˆ๋‹ค. +์ด ๊ฐœ๋…์—๋Š” ์ธ๋ฏผ์ด ๊ตญ๊ฐ€์˜ ๋…๋ฆฝ ์ฃผ๊ถŒ์˜ ์†Œ์ค‘ํ•จ์„ ๊นจ๋‹ซ๋Š” ์˜์‹์˜ +๋ณ€ํ™”๊ฐ€ ๋‚ดํฌ๋˜์—ˆ๊ณ , ํ†ต์น˜์ž์˜ ์ž…์žฅ์—์„œ ์ˆ˜์šฉ ๊ฐ€๋Šฅํ•œ ๋ฌธ๋ช…์˜ ์žฅ์ ์„ +๋ฐ›์•„๋“ค์—ฌ ๊ตญ๊ฐ€์˜ ์ง„๋ณด๋ฅผ ๋‹ฌ์„ฑํ•œ๋‹ค๋Š” ์˜๋ฏธ๋„ ๋‹ด๊ฒผ๋‹ค. +๊ฐœํ™”๋‹น์˜ ํ•œ ์ธ์‚ฌ๊ฐ€ ์ œ์‹œํ•œ ๊ฐœํ™” ๊ฐœ๋…์€ ์„ฑ๋ฌธํ™”๋œ ๊ทœ์ •์— ๋”ฐ๋ฅธ +๋Œ€๋ฏผ ์ •์น˜์—์„œ์˜ ๋ฒ•์  ์ฒ˜๋ฆฌ ์ ˆ์ฐจ ์‹คํ˜„ ๋“ฑ ์„œ์–‘ ๊ทผ๋Œ€ ๊ตญ๊ฐ€์˜ ํ†ต์น˜ +๋ฐฉ์‹์œผ๋กœ์˜ ๋ณ€ํ™”๋ฅผ ๋‚ดํฌํ•˜๋Š” ๊ฒƒ์ด์—ˆ๋‹ค. ๊ทธ๋Š” ๊ฐœํ™” ์‹คํ–‰ ์ฃผ์ฒด๋ฅผ +์—ฌ์ „ํžˆ ์™•์œผ๋กœ ์ƒ๊ฐํ–ˆ๊ณ , ๊ฐœํ™” ์‹คํ–‰ ์ฃผ์ฒด๋กœ์„œ ์™•์˜ ์—ญํ• ์ด +์‚ฌ๋ผ์ง„ ๊ฒƒ์€ ๊ฐ‘์‹ ์ •๋ณ€์—์„œ์˜€๋‹ค. ํ’์†์˜ ์ง„๋ณด์™€ ํ†ต์น˜ ๋ฐฉ์‹ ๋ณ€ํ™” +๋ผ๋Š” ์˜๋ฏธ๋ฅผ ๋‚ดํฌํ•œ ๊ฐ‘์‹ ์ •๋ณ€์˜ ๊ฐœํ™” ๊ฐœ๋…์€ ํ†ต์น˜๊ถŒ์— ๋Œ€ํ•œ +๋„์ „์œผ๋กœ๋ฟ ์•„๋‹ˆ๋ผ ๊ฐœ์ธ์˜ ์‚ฌ์š•์„ ์œ„ํ•œ ๊ฒƒ์œผ๋กœ ํ‘œ์ƒ๋˜์—ˆ๋‹ค. ์ดํ›„ +๊ฐœํ™” ๊ฐœ๋…์€ ๊ตญ๊ฐ€ ๊ตฌ์„ฑ์›์„ ์กฐ์งํ•˜๊ณ  ๋™์›ํ•˜๊ธฐ ์œ„ํ•ด ๋ถ€์ •์  +์ด๋ฏธ์ง€์—์„œ ๋ฒ—์–ด๋‚˜์•ผ ํ–ˆ๊ณ , ์œ ๊ธธ์ค€์€ ์„œ์œ ๊ฒฌ๋ฌธ์„ ์ €์ˆ ํ•˜๋ฉฐ +๊ฐœํ™” ๊ฐœ๋…์— ๋ง์”Œ์›Œ์ง„ ๋ถ€์ •์  ์ด๋ฏธ์ง€๋ฅผ ๋–ผ์–ด ๋‚ด๊ณ ์ž ํ–ˆ๋‹ค. ์ดํ›„ +๊ฐ„ํ–‰๋œ ๋Œ€ํ•œ๋งค์ผ์‹ ๋ณด ๋“ฑ์˜ ๊ฐœํ™” ๊ฐœ๋…์€ ๊ตญ๊ฐ€ ๊ตฌ์„ฑ์› ์ „์ฒด๋ฅผ +์‹คํ–‰ ์ฃผ์ฒด๋กœ ํ•˜์—ฌ ๊ทผ๋Œ€ ๊ตญ๊ฐ€ ์ฃผ๊ถŒ์„ ํ–ฅํ•ด ๊ทธ๋“ค์„ ์กฐ์งํ•˜๊ณ  +๋™์›ํ•˜๋Š” ๊ฒƒ์„ ์˜๋ฏธํ–ˆ๋‹ค. +์„์‚ฌ๋Š‘์•ฝ ์ดํ›„, ๊ฐœํ™” ๋…ผ์˜๋Š” ๋ฌธ๋ช…์— ๋Œ€ํ•œ ๋ณธ๊ฒฉ์ ์ธ ๋…ผ์˜๋กœ +์ด์–ด์กŒ๋‹ค. ๋Œ€ํ•œ ์ž๊ฐ•ํšŒ์˜ ์ฃผ์š” ์ธ์‚ฌ๋“ค์€ ์„œ์–‘ ๊ทผ๋Œ€ ๋ฌธ๋ช…์„ +์ˆ˜์šฉํ•˜์—ฌ ๊ทผ๋Œ€ ๊ตญ๊ฐ€๋ฅผ ๊ฑด์„คํ•˜๊ณ ์ž, ์•ž์„œ ๋ฌธ๋ช…ํ™”๋ฅผ ์ด๋ฃฌ ์ผ๋ณธ์˜ +์ง€๋„๋ฅผ ๋ฐ›์•„์•ผ ํ•œ๋‹ค๊ณ  ๋ณด์•˜๋‹ค. ์ด๋“ค์€ ์„œ์–‘ ๊ทผ๋Œ€ ๋ฌธ๋ช…์˜ ์ฃผ์ฒด๋ฅผ +์ฃผ์ฒด ์ธ์‹์˜ ์ค€๊ฑฐ๋กœ ์‚ผ์•˜๊ธฐ ๋•Œ๋ฌธ์— ๋ฏผ์กฑ ์ฃผ์ฒด์„ฑ์„ ๊ฐ„๊ณผํ–ˆ๋‹ค. +์ด๋Ÿฌํ•œ ์ƒํ™ฉ์—์„œ ๋ฐ•์€์‹์€ ใ‰ ๊ทผ๋Œ€ ๊ตญ๊ฐ€ ๊ฑด์„ค๊ณผ ์ƒˆ๋กœ์šด ์ฃผ์ฒด์˜ +ํ˜•์„ฑ์— ์ฃผ๋ชฉํ•˜์—ฌ ๋ฌธ๋ช…์— ๋Œ€ํ•œ ๊ฒฌํ•ด๋ฅผ ์ œ์‹œํ–ˆ๋‹ค. ๊ทธ์˜ ๊ธฐ๋ณธ +๊ฑธ์ณ ๊ทผ๋Œ€ํ™”์˜ ์ถ”์ง„๊ณผ ํ•จ๊ป˜ ์˜์š•์ ์ธ ๊ธฐ์ˆ  ์ˆ˜์šฉ์œผ๋กœ ์ด์–ด์กŒ์ง€๋งŒ, +์ฒญ์ผ ์ „์Ÿ์˜ ํŒจ๋ฐฐ๋Š” ๊ธฐ์ˆ  ์ˆ˜์šฉ๋งŒ์œผ๋กœ๋Š” ๋ถ€์กฑํ•˜๋‹ค๋Š” ์ธ์‹์„ +๋‚ณ์•˜๋‹ค. ์ด์— ๋”ฐ๋ผ 20์„ธ๊ธฐ ์ดˆ๋ฐ˜ ์ง„์ •ํ•œ ๊ทผ๋Œ€๋ฅผ ์ด๋ฃจ๊ธฐ ์œ„ํ•ด ๊ธฐ์ˆ  +๋ฐฐํ›„์—์„œ ์ž‘์šฉํ•˜๋Š” ๊ณผํ•™ ์ •์‹ ์„ ์‚ฌํšŒ ์ „์ฒด์— ์ด์‹ํ•˜๋ ค๋Š” ์‹œ๋„๊ฐ€ +๊ตฌ์ฒดํ™”๋˜์—ˆ๋‹ค. +์˜Œํ‘ธ๋Š” ๊ตญ๊ฐ€ ๊ฐ„์— ๋ฒŒ์–ด์ง€๋Š” ์•ฝ์œก๊ฐ•์‹์˜ ๊ฒฝ์Ÿ์„ ๋ถ€๊ฐํ•˜๊ณ , +๊ฒฝ์Ÿ์—์„œ ์Šน๋ฆฌํ•˜๋ ค๋ฉด ๊ธฐ์ˆ ๋ฟ ์•„๋‹ˆ๋ผ ๊ตญ๋ฏผ์˜ ์ •์‹ ์  ์ž์งˆ์ด +๋’ท๋ฐ›์นจ๋˜์–ด์•ผ ํ•œ๋‹ค๊ณ  ๋ณด์•˜๋‹ค. ์ •์‹ ์  ์ž์งˆ ์ค‘ ๊ณผํ•™์  ์‚ฌ์œ  +๋Šฅ๋ ฅ์ด ๊ฐ€์žฅ ์ค‘์š”ํ•˜๋‹ค๊ณ  ํŒŒ์•…ํ•œ ๊ทธ์—๊ฒŒ ๊ณผํ•™ ์ •์‹ ์ด ์ „์ œ๋˜์ง€ +์•Š์€ ์ •์น˜์  ๋ณ€ํ˜์€ ๋ฟŒ๋ฆฌ๋‚ด๋ฆด ์ˆ˜ ์—†๋Š” ๊ฒƒ์ด์—ˆ๋‹ค. ๊ทธ๋Š” ์ธ๊ณผ +์‹ค์ฆ์˜ ๋ฐฉ๋ฒ•์— ๊ทผ๊ฑฐํ•œ ๊ทผ๋Œ€ ํ•™๋ฌธ ์ „์ฒด๋ฅผ ๊ณผํ•™์ด๋ผ ํŒŒ์•…ํ•˜๊ณ , +๊ณผํ•™์„ ์Šต๋“ํ•˜์—ฌ ์ „ํ†ต ํ•™๋ฌธ์˜ ํ๋‹จ์—์„œ ๋ฒ—์–ด๋‚˜์•ผ ํ•œ๋‹ค๊ณ  ์ฃผ์žฅ +ํ–ˆ๋‹ค. ๊ทธ์˜ ์ž…์žฅ์€ 1910๋…„๋Œ€ ํ›„๋ฐ˜ ์‹ ๋ฌธํ™” ์šด๋™์„ ์ฃผ๋„ํ•œ ์ฒœ๋‘์Šˆ +์—๊ฒŒ ์ด์–ด์กŒ๋‹ค. +์ฒœ๋‘์Šˆ๋ฅผ ๋น„๋กฏํ•œ ์‹ ๋ฌธํ™” ์šด๋™์˜ ์ง€์‹์ธ๋“ค์€ ใ‰ก๊ณผํ•™์˜ ๊ทผ๊ฑฐ +์œ„์—์„œ๋งŒ ๋ฏผ์ฃผ ์ •์น˜์˜ ์‹คํ˜„์ด ๊ฐ€๋Šฅํ•˜๋‹ค๊ณ  ์ฃผ์žฅํ–ˆ๋‹ค. ์ค‘๊ตญ์ด +๋‹ฌ์„ฑํ•ด์•ผ ํ•  ์‹ ๋ฌธํ™”๋Š” ๊ณผํ•™ ๋ฐ ๊ณผํ•™์˜ ๋ฐฉ๋ฒ•์— ๊ทผ๊ฑฐํ•œ ๋ฌธํ™”๋ผ +๋ณด๊ณ , ์‹ ๋ฌธํ™”๋ฅผ ์ด๋ฃจ๊ธฐ ์œ„ํ•ด ์ „ํ†ต๋ฌธํ™” ์ „๋ฐ˜์— ๋Œ€ํ•ด ์ฒ ์ €ํ•œ +๋ถ€์ •๊ณผ ๋น„ํŒ์„ ์‹œ๋„ํ–ˆ๋‹ค. ์‚ฌ์ƒ์ด๋‚˜ ์ฒ ํ•™์ด ๊ณผํ•™์˜ ๋ฐฉ๋ฒ•์„ ์ด์šฉ +ํ•˜์ง€ ์•Š์œผ๋ฉด ๊ณต์ƒ(็ฉบๆƒณ)์— โ“๊ทธ์น  ๋ฟ์ด๋ผ๊ณ  ์ฃผ์žฅํ•œ ์ฒœ๋‘์Šˆ๋Š” +์‚ฌํšŒ์™€ ์ธ๊ฐ„์˜ ์‚ถ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋„ ๊ณผํ•™์˜ ์—ฐ๊ตฌ ๋ฐฉ๋ฒ•์„ ์ด์šฉํ•ด์•ผ +ํ•œ๋‹ค๊ณ  ๋ณด์•˜๋‹ค. ๊ทธ๋Š” ์ œ1์ฐจ ์„ธ๊ณ„ ๋Œ€์ „์˜ ๋น„๊ทน์€ ๊ณผํ•™์„ ์ด์šฉํ•ด +์ €์ง€๋ฅธ ์ฃ„์•…์˜ ๊ฒฐ๊ณผ์ผ ๋ฟ ๊ณผํ•™ ์ž์ฒด์˜ ์ฃ„์•…์ด ์•„๋‹ˆ๋ผ๊ณ  ์ฃผ์žฅํ•˜๋ฉฐ +๊ณผํ•™์— ๋Œ€ํ•œ ์ž์‹ ์˜ ์ƒ๊ฐ์„ ์ง€์†ํ–ˆ๋‹ค. +ํ•œํŽธ, ์ œ1์ฐจ ์„ธ๊ณ„ ๋Œ€์ „ ์ดํ›„ ์œ ๋Ÿฝ์„ ์‹œ์ฐฐํ–ˆ๋˜ ์žฅ์ฅ”๋งˆ์ด๋Š” +ํ†ต์ œ๋˜์ง€ ์•Š์€ ๊ณผํ•™์ด ๋ถˆ๋Ÿฌ์˜จ ์—ญ์ž‘์šฉ์„ ๋ชฉ๋„ํ•œ ํ›„, ๊ณผํ•™์ด +์–ด๋–ป๊ฒŒ ๋ฐœ๋‹ฌํ•˜๋“  ๊ทธ๊ฒƒ์ด ์ธ์ƒ๊ด€์˜ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•  ์ˆ˜๋Š” ์—†๋‹ค๋ฉฐ +์„œ์–‘ ๊ทผ๋Œ€ ๋ฌธ๋ช…์„ ๋น„ํŒํ–ˆ๋‹ค. ๊ทผ๋Œ€ ๊ณผํ•™ ๋ฌธ๋ช…์—์„œ ์ดˆ๋ž˜๋œ ์‚ฌ์ƒ์  +์œ„๊ธฐ๊ฐ€ ์ฃผ์ฒด์˜ ์ฑ…์ž„ ๋ถ€์žฌ์—์„œ ๋น„๋กฏ๋œ ๊ฒƒ์ด๋ผ๋Š” ์ฃผ์žฅ์— ๋™์˜ํ–ˆ๋˜ +๊ทธ๋Š” ๊ณผํ•™์  ๋ฐฉ๋ฒ•์„ ๋ถ€์ •ํ•˜์ง€ ์•Š์•˜์ง€๋งŒ, ์ธ์ƒ๊ด€์˜ ๋ฌธ์ œ์—๋Š” +๊ณผํ•™์  ๋ฐฉ๋ฒ•์ด ์ ์šฉ๋  ์ˆ˜ ์—†๋‹ค๊ณ  ์ง€์ ํ–ˆ๋‹ค. ๊ทธ๋Š” ์ธ์ƒ๊ด€์„ +๊ณผํ•™๊ณผ ๋ณ„๊ฐœ๋กœ ํŒŒ์•…ํ–ˆ๊ณ , ๊ณผํ•™๋งŒ๋Šฅ์ฃผ์˜์— ๊ธฐ์ดˆํ•œ ์‹ ๋ฌธํ™” ์šด๋™์— +์˜ํ•ด ๋ถ€์ •๋œ ์ค‘๊ตญ ์ „ํ†ต ๊ฐ€์น˜๊ด€์˜ ์ˆ˜ํ˜ธ๋ฅผ ๋‚ด์„ธ์› ๋‹ค. +์ „๋žต์€ ๋ฌธ๋ช…์˜ ๋ฌผ์งˆ์  ์ธก๋ฉด์ธ ๊ณผํ•™์€ ์„œ์–‘์œผ๋กœ๋ถ€ํ„ฐ ์ˆ˜์šฉํ•˜๋˜, +๋ฌธ๋ช…์˜ ์ •์‹ ์  ์ธก๋ฉด์ธ ์ฒ ํ•™์€ ์œ ํ•™์„ ํ˜์‹ ํ•˜์—ฌ ์žฌ๊ตฌ์„ฑํ•˜๋Š” +๊ฒƒ์ด์—ˆ๋‹ค. ๊ทธ๋Š” ์ƒ์กด๊ณผ ํŽธ๋ฆฌ ์ฆ์ง„์„ ์œ„ํ•ด ๊ณผํ•™ ์—ฐ๊ตฌ๊ฐ€ ์‹œ๊ธ‰ +ํ•˜์ง€๋งŒ, ๊ฐ€์น˜๊ด€ ์ •๋ฆฝ๊ณผ ์ธ๊ฒฉ ์ˆ˜์–‘์„ ์œ„ํ•ด ์ฒ ํ•™ ๋˜ํ•œ ํ•„์ˆ˜์  +์ด๋ผ๊ณ  ๋ณด์•˜๋‹ค. ์ž๊ตญ ์ฒ ํ•™ ์ „ํ†ต์˜ ์ •๋ฆฝ์ด๋ผ๋Š” ๋‹น์‹œ ๋™์•„์‹œ์•„์˜ +์‚ฌ์ƒ์  ํ๋ฆ„ ์†์—์„œ ๊ทธ๊ฐ€ ์ œ์‹œํ•œ ๊ทผ๋Œ€ ์ฃผ์ฒด๋Š” ๊ณผํ•™์ โ€ค์ฒ ํ•™์  +์ธ์‹์˜ ์ฃผ์ฒด์ด์ž ์‹ค์ฒœ์  ๋„๋• ์ˆ˜์–‘์˜ ์ฃผ์ฒด๋กœ์„œ์˜ ์„ฑ๊ฒฉ์„ ๋ ๋Š” +๊ฒƒ์ด์—ˆ๋‹ค. + + +(๋‚˜) +์ค‘๊ตญ์ด ์„œ์–‘์˜ ๊ณผํ•™๊ณผ ๊ธฐ์ˆ ์— ์ „๋ฉด์ ์ธ ๊ด€์‹ฌ์„ ๊ธฐ์šธ์ธ ๋•Œ๋Š” +์•„ํŽธ ์ „์Ÿ ์ดํ›„์˜€๋‹ค. ์ „์Ÿ ํŒจ๋ฐฐ์— ๋”ฐ๋ฅธ ์œ„๊ธฐ๊ฐ์€ ๋ฐ˜์„ธ๊ธฐ์— +๊ฑธ์ณ ๊ทผ๋Œ€ํ™”์˜ ์ถ”์ง„๊ณผ ํ•จ๊ป˜ ์˜์š•์ ์ธ ๊ธฐ์ˆ  ์ˆ˜์šฉ์œผ๋กœ ์ด์–ด์กŒ์ง€๋งŒ, +์ฒญ์ผ ์ „์Ÿ์˜ ํŒจ๋ฐฐ๋Š” ๊ธฐ์ˆ  ์ˆ˜์šฉ๋งŒ์œผ๋กœ๋Š” ๋ถ€์กฑํ•˜๋‹ค๋Š” ์ธ์‹์„ +๋‚ณ์•˜๋‹ค. ์ด์— ๋”ฐ๋ผ 20์„ธ๊ธฐ ์ดˆ๋ฐ˜ ์ง„์ •ํ•œ ๊ทผ๋Œ€๋ฅผ ์ด๋ฃจ๊ธฐ ์œ„ํ•ด ๊ธฐ์ˆ  +๋ฐฐํ›„์—์„œ ์ž‘์šฉํ•˜๋Š” ๊ณผํ•™ ์ •์‹ ์„ ์‚ฌํšŒ ์ „์ฒด์— ์ด์‹ํ•˜๋ ค๋Š” ์‹œ๋„๊ฐ€ +๊ตฌ์ฒดํ™”๋˜์—ˆ๋‹ค. +์˜Œํ‘ธ๋Š” ๊ตญ๊ฐ€ ๊ฐ„์— ๋ฒŒ์–ด์ง€๋Š” ์•ฝ์œก๊ฐ•์‹์˜ ๊ฒฝ์Ÿ์„ ๋ถ€๊ฐํ•˜๊ณ , +๊ฒฝ์Ÿ์—์„œ ์Šน๋ฆฌํ•˜๋ ค๋ฉด ๊ธฐ์ˆ ๋ฟ ์•„๋‹ˆ๋ผ ๊ตญ๋ฏผ์˜ ์ •์‹ ์  ์ž์งˆ์ด +๋’ท๋ฐ›์นจ๋˜์–ด์•ผ ํ•œ๋‹ค๊ณ  ๋ณด์•˜๋‹ค. ์ •์‹ ์  ์ž์งˆ ์ค‘ ๊ณผํ•™์  ์‚ฌ์œ  +๋Šฅ๋ ฅ์ด ๊ฐ€์žฅ ์ค‘์š”ํ•˜๋‹ค๊ณ  ํŒŒ์•…ํ•œ ๊ทธ์—๊ฒŒ ๊ณผํ•™ ์ •์‹ ์ด ์ „์ œ๋˜์ง€ +์•Š์€ ์ •์น˜์  ๋ณ€ํ˜์€ ๋ฟŒ๋ฆฌ๋‚ด๋ฆด ์ˆ˜ ์—†๋Š” ๊ฒƒ์ด์—ˆ๋‹ค. ๊ทธ๋Š” ์ธ๊ณผ +์‹ค์ฆ์˜ ๋ฐฉ๋ฒ•์— ๊ทผ๊ฑฐํ•œ ๊ทผ๋Œ€ ํ•™๋ฌธ ์ „์ฒด๋ฅผ ๊ณผํ•™์ด๋ผ ํŒŒ์•…ํ•˜๊ณ , +๊ณผํ•™์„ ์Šต๋“ํ•˜์—ฌ ์ „ํ†ต ํ•™๋ฌธ์˜ ํ๋‹จ์—์„œ ๋ฒ—์–ด๋‚˜์•ผ ํ•œ๋‹ค๊ณ  ์ฃผ์žฅ +ํ–ˆ๋‹ค. ๊ทธ์˜ ์ž…์žฅ์€ 1910๋…„๋Œ€ ํ›„๋ฐ˜ ์‹ ๋ฌธํ™” ์šด๋™์„ ์ฃผ๋„ํ•œ ์ฒœ๋‘์Šˆ +์—๊ฒŒ ์ด์–ด์กŒ๋‹ค. +์ฒœ๋‘์Šˆ๋ฅผ ๋น„๋กฏํ•œ ์‹ ๋ฌธํ™” ์šด๋™์˜ ์ง€์‹์ธ๋“ค์€ ใ‰ก๊ณผํ•™์˜ ๊ทผ๊ฑฐ +์œ„์—์„œ๋งŒ ๋ฏผ์ฃผ ์ •์น˜์˜ ์‹คํ˜„์ด ๊ฐ€๋Šฅํ•˜๋‹ค๊ณ  ์ฃผ์žฅํ–ˆ๋‹ค. ์ค‘๊ตญ์ด +๋‹ฌ์„ฑํ•ด์•ผ ํ•  ์‹ ๋ฌธํ™”๋Š” ๊ณผํ•™ ๋ฐ ๊ณผํ•™์˜ ๋ฐฉ๋ฒ•์— ๊ทผ๊ฑฐํ•œ ๋ฌธํ™”๋ผ +๋ณด๊ณ , ์‹ ๋ฌธํ™”๋ฅผ ์ด๋ฃจ๊ธฐ ์œ„ํ•ด ์ „ํ†ต๋ฌธํ™” ์ „๋ฐ˜์— ๋Œ€ํ•ด ์ฒ ์ €ํ•œ +๋ถ€์ •๊ณผ ๋น„ํŒ์„ ์‹œ๋„ํ–ˆ๋‹ค. ์‚ฌ์ƒ์ด๋‚˜ ์ฒ ํ•™์ด ๊ณผํ•™์˜ ๋ฐฉ๋ฒ•์„ ์ด์šฉ +ํ•˜์ง€ ์•Š์œผ๋ฉด ๊ณต์ƒ(็ฉบๆƒณ)์— โ“๊ทธ์น  ๋ฟ์ด๋ผ๊ณ  ์ฃผ์žฅํ•œ ์ฒœ๋‘์Šˆ๋Š” +์‚ฌํšŒ์™€ ์ธ๊ฐ„์˜ ์‚ถ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋„ ๊ณผํ•™์˜ ์—ฐ๊ตฌ ๋ฐฉ๋ฒ•์„ ์ด์šฉํ•ด์•ผ +ํ•œ๋‹ค๊ณ  ๋ณด์•˜๋‹ค. ๊ทธ๋Š” ์ œ1์ฐจ ์„ธ๊ณ„ ๋Œ€์ „์˜ ๋น„๊ทน์€ ๊ณผํ•™์„ ์ด์šฉํ•ด +์ €์ง€๋ฅธ ์ฃ„์•…์˜ ๊ฒฐ๊ณผ์ผ ๋ฟ ๊ณผํ•™ ์ž์ฒด์˜ ์ฃ„์•…์ด ์•„๋‹ˆ๋ผ๊ณ  ์ฃผ์žฅํ•˜๋ฉฐ +๊ณผํ•™์— ๋Œ€ํ•œ ์ž์‹ ์˜ ์ƒ๊ฐ์„ ์ง€์†ํ–ˆ๋‹ค. +ํ•œํŽธ, ์ œ1์ฐจ ์„ธ๊ณ„ ๋Œ€์ „ ์ดํ›„ ์œ ๋Ÿฝ์„ ์‹œ์ฐฐํ–ˆ๋˜ ์žฅ์ฅ”๋งˆ์ด๋Š” +ํ†ต์ œ๋˜์ง€ ์•Š์€ ๊ณผํ•™์ด ๋ถˆ๋Ÿฌ์˜จ ์—ญ์ž‘์šฉ์„ ๋ชฉ๋„ํ•œ ํ›„, ๊ณผํ•™์ด +์–ด๋–ป๊ฒŒ ๋ฐœ๋‹ฌํ•˜๋“  ๊ทธ๊ฒƒ์ด ์ธ์ƒ๊ด€์˜ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•  ์ˆ˜๋Š” ์—†๋‹ค๋ฉฐ +์„œ์–‘ ๊ทผ๋Œ€ ๋ฌธ๋ช…์„ ๋น„ํŒํ–ˆ๋‹ค. ๊ทผ๋Œ€ ๊ณผํ•™ ๋ฌธ๋ช…์—์„œ ์ดˆ๋ž˜๋œ ์‚ฌ์ƒ์  +์œ„๊ธฐ๊ฐ€ ์ฃผ์ฒด์˜ ์ฑ…์ž„ ๋ถ€์žฌ์—์„œ ๋น„๋กฏ๋œ ๊ฒƒ์ด๋ผ๋Š” ์ฃผ์žฅ์— ๋™์˜ํ–ˆ๋˜ +๊ทธ๋Š” ๊ณผํ•™์  ๋ฐฉ๋ฒ•์„ ๋ถ€์ •ํ•˜์ง€ ์•Š์•˜์ง€๋งŒ, ์ธ์ƒ๊ด€์˜ ๋ฌธ์ œ์—๋Š” +๊ณผํ•™์  ๋ฐฉ๋ฒ•์ด ์ ์šฉ๋  ์ˆ˜ ์—†๋‹ค๊ณ  ์ง€์ ํ–ˆ๋‹ค. ๊ทธ๋Š” ์ธ์ƒ๊ด€์„ +๊ณผํ•™๊ณผ ๋ณ„๊ฐœ๋กœ ํŒŒ์•…ํ–ˆ๊ณ , ๊ณผํ•™๋งŒ๋Šฅ์ฃผ์˜์— ๊ธฐ์ดˆํ•œ ์‹ ๋ฌธํ™” ์šด๋™์— +์˜ํ•ด ๋ถ€์ •๋œ ์ค‘๊ตญ ์ „ํ†ต ๊ฐ€์น˜๊ด€์˜ ์ˆ˜ํ˜ธ๋ฅผ ๋‚ด์„ธ์› ๋‹ค.","{'question': 'ใ‰ ๊ณผ ใ‰ก์— ๋Œ€ํ•œ ์ดํ•ด๋กœ ๊ฐ€์žฅ ์ ์ ˆํ•œ ๊ฒƒ์€?', 'choices': ['ใ‰ ์€ ์ธ๊ฒฉ์˜ ์ˆ˜์–‘์„ ๋™๋ฐ˜ํ•˜๋Š” ๊ทผ๋Œ€ ์ฃผ์ฒด์˜ ์ •๋ฆฝ์—, ใ‰ก์€ ์ „ํ†ต์ \n์‚ฌ์œ  ๋ฐฉ์‹์— ๊ธฐ๋ฐ˜์„ ๋‘” ์‹ ๋ฌธํ™”์˜ ๋‹ฌ์„ฑ์— ๋™์˜ํ•˜๋Š” ์ž…์žฅ์ด๋‹ค.', 'ใ‰ ์€ ์ฃผ์ฒด ์ธ์‹์˜ ์ค€๊ฑฐ๊ฐ€ ์„œ์–‘ ๊ทผ๋Œ€ ๋ฌธ๋ช…์˜ ์ฃผ์ฒด๋ผ๋Š” ์ธ์‹์—,\nใ‰ก์€ ์ฒ ํ•™์ด ๊ณผํ•™์˜ ๋ฐฉ๋ฒ•์— ๊ทผ๊ฑฐํ•  ์ˆ˜ ์—†๋‹ค๋Š” ์ƒ๊ฐ์— ๋ฐ˜๋Œ€ํ•˜๋Š”\n์ž…์žฅ์ด๋‹ค.', 'ใ‰ ์€ ์ƒ์กด๊ณผ ํŽธ๋ฆฌ ์ฆ์ง„์„ ์œ„ํ•œ ๊ณผํ•™ ์—ฐ๊ตฌ์˜ ์‹œ๊ธ‰์„ฑ์„, ใ‰ก์€\n๊ณผํ•™์˜ ๋ฐฉ๋ฒ•์— ์˜ํ–ฅ ๋ฐ›์ง€ ์•Š๋Š” ์‚ฌ์ƒ์ด๋‚˜ ์ฒ ํ•™์„ ๋ถ€์ธํ•˜๋Š”\n์ž…์žฅ์ด๋‹ค', 'ใ‰ ์€ ์•ž์„œ ๊ทผ๋Œ€ ๋ฌธ๋ช…์„ ์ด๋ฃฌ ๊ตญ๊ฐ€๋ฅผ ์ถ”์ข…ํ•˜๋Š” ํƒœ๋„๋ฅผ, ใ‰ก์€\n์ „์Ÿ์˜ ํํ•ด๊ฐ€ ๊ณผํ•™์„ ์˜ค์šฉํ•œ ์ž๋“ค์˜ ํƒ“์ด๋ผ๋Š” ์ฃผ์žฅ์„ ๋น„ํŒํ•˜๋Š”\n์ž…์žฅ์ด๋‹ค.', 'ใ‰ ์€ ๊ณผํ•™๊ณผ ์ฒ ํ•™์ด ๋ฌธ๋ช…์˜ ๋‘ ์ถ•์„ ์ด๋ฃจ๋Š” ํ•™๋ฌธ์ด๋ผ๋Š” ๊ฒฌํ•ด์—,\nใ‰ก์€ ์ฒ ํ•™๋ณด๋‹ค ๊ณผํ•™์ด ์šฐ์œ„์ž„์„ ์ธ์ •ํ•  ์ˆ˜ ์—†๋‹ค๋Š” ๊ฒฌํ•ด์— ๋™์˜ํ•˜๋Š”\n์ž…์žฅ์ด๋‹ค.'], 'answer': ''}",,2,2,True,[],2 +2025-korean-08,"(๊ฐ€) +์„œ์–‘์˜ ๊ณผํ•™๊ณผ ๊ธฐ์ˆ , ์ฒœ์ฃผ๊ต์˜ ์ˆ˜์šฉ์„ ๋ฐ˜๋Œ€ํ–ˆ๋˜ ์ดํ•ญ๋กœ๋ฅผ ๋น„๋กฏํ•œ +์ฒ™์‚ฌํŒŒ์˜ ์ฃผ์žฅ์€ ๊ฐœํ•ญ ์ดํ›„์—๋„ ์ง€์†๋˜์—ˆ์ง€๋งŒ, ๊ฐœํ™”๋Š” ๊ฑฐ์Šค๋ฅผ +์ˆ˜ ์—†๋Š” ๋Œ€์„ธ๋กœ ์ž๋ฆฌ ์žก์•˜๋‹ค. ๊ฐœ๋ฌผ์„ฑ๋ฌด(้–‹็‰ฉๆˆๅ‹™)์™€ ํ™”๋ฏผ์„ฑ์† +(ๅŒ–ๆฐ‘ๆˆไฟ—)์˜ ์•ž ๊ธ€์ž๋ฅผ ๋”ด ๊ฐœํ™”๋Š” ๊ฐœํ•ญ ์ด์ „์—๋Š” ํ†ต์น˜์ž์˜ ํ†ต์น˜ +ํ–‰์œ„๋กœ์„œ ๋ณ€ํ™”ํ•˜๋Š” ์„ธ์ƒ์— ๋Œ€ํ•œ ์ง€์‹ ํ™•์žฅ๊ณผ ํ”ผํ†ต์น˜์ž์— ๋Œ€ํ•œ +๊ตํ™”๋ฅผ ์˜๋ฏธํ–ˆ๋‹ค. +๊ฐœํ•ญ ์ดํ›„ ์„œ์–‘ ๋ฌธ๋ช…์— ๋Œ€ํ•œ ๊ธ์ •์  ์ธ์‹์ด ํ™•์‚ฐ๋˜๋ฉด์„œ ์„œ์–‘ +๋ฌธ๋ช…์˜ ์ˆ˜์šฉ์„ ๋œปํ•˜๋Š” ๊ฐœํ™” ๊ฐœ๋…์ด ์ž๋ฆฌ ์žก์•˜๋‹ค. ์ž„์˜ค๊ตฐ๋ž€ ์ดํ›„, +๊ณ ์ข…์€ ์ž๊ฐ• ์ •์ฑ…์„ ์ถ”์ง„ํ•˜๋ฉด์„œ ๋ฐ˜(ๅ)์„œ์–‘ ์ •์„œ์˜ ๊ต์ •์„ ์œ„ํ•ด +ํ•œ์„ฑ์ˆœ๋ณด๋ฅผ ๋ฐœ๊ฐ„ํ–ˆ๋‹ค. ์ด ์‹ ๋ฌธ์˜ ๊ฐœํ™” ๊ฐœ๋…์€ ์„œ์–‘ ๊ธฐ์ˆ ๊ณผ +์ œ๋„์˜ ๋„์ž…์„ ํ†ตํ•œ ์ธ์ง€์˜ ๋ฐœ๋‹ฌ๊ณผ ํ’์†์˜ ์ง„๋ณด๋ฅผ ๋œปํ–ˆ๋‹ค. +์ด ๊ฐœ๋…์—๋Š” ์ธ๋ฏผ์ด ๊ตญ๊ฐ€์˜ ๋…๋ฆฝ ์ฃผ๊ถŒ์˜ ์†Œ์ค‘ํ•จ์„ ๊นจ๋‹ซ๋Š” ์˜์‹์˜ +๋ณ€ํ™”๊ฐ€ ๋‚ดํฌ๋˜์—ˆ๊ณ , ํ†ต์น˜์ž์˜ ์ž…์žฅ์—์„œ ์ˆ˜์šฉ ๊ฐ€๋Šฅํ•œ ๋ฌธ๋ช…์˜ ์žฅ์ ์„ +๋ฐ›์•„๋“ค์—ฌ ๊ตญ๊ฐ€์˜ ์ง„๋ณด๋ฅผ ๋‹ฌ์„ฑํ•œ๋‹ค๋Š” ์˜๋ฏธ๋„ ๋‹ด๊ฒผ๋‹ค. +๊ฐœํ™”๋‹น์˜ ํ•œ ์ธ์‚ฌ๊ฐ€ ์ œ์‹œํ•œ ๊ฐœํ™” ๊ฐœ๋…์€ ์„ฑ๋ฌธํ™”๋œ ๊ทœ์ •์— ๋”ฐ๋ฅธ +๋Œ€๋ฏผ ์ •์น˜์—์„œ์˜ ๋ฒ•์  ์ฒ˜๋ฆฌ ์ ˆ์ฐจ ์‹คํ˜„ ๋“ฑ ์„œ์–‘ ๊ทผ๋Œ€ ๊ตญ๊ฐ€์˜ ํ†ต์น˜ +๋ฐฉ์‹์œผ๋กœ์˜ ๋ณ€ํ™”๋ฅผ ๋‚ดํฌํ•˜๋Š” ๊ฒƒ์ด์—ˆ๋‹ค. ๊ทธ๋Š” ๊ฐœํ™” ์‹คํ–‰ ์ฃผ์ฒด๋ฅผ +์—ฌ์ „ํžˆ ์™•์œผ๋กœ ์ƒ๊ฐํ–ˆ๊ณ , ๊ฐœํ™” ์‹คํ–‰ ์ฃผ์ฒด๋กœ์„œ ์™•์˜ ์—ญํ• ์ด +์‚ฌ๋ผ์ง„ ๊ฒƒ์€ ๊ฐ‘์‹ ์ •๋ณ€์—์„œ์˜€๋‹ค. ํ’์†์˜ ์ง„๋ณด์™€ ํ†ต์น˜ ๋ฐฉ์‹ ๋ณ€ํ™” +๋ผ๋Š” ์˜๋ฏธ๋ฅผ ๋‚ดํฌํ•œ ๊ฐ‘์‹ ์ •๋ณ€์˜ ๊ฐœํ™” ๊ฐœ๋…์€ ํ†ต์น˜๊ถŒ์— ๋Œ€ํ•œ +๋„์ „์œผ๋กœ๋ฟ ์•„๋‹ˆ๋ผ ๊ฐœ์ธ์˜ ์‚ฌ์š•์„ ์œ„ํ•œ ๊ฒƒ์œผ๋กœ ํ‘œ์ƒ๋˜์—ˆ๋‹ค. ์ดํ›„ +๊ฐœํ™” ๊ฐœ๋…์€ ๊ตญ๊ฐ€ ๊ตฌ์„ฑ์›์„ ์กฐ์งํ•˜๊ณ  ๋™์›ํ•˜๊ธฐ ์œ„ํ•ด ๋ถ€์ •์  +์ด๋ฏธ์ง€์—์„œ ๋ฒ—์–ด๋‚˜์•ผ ํ–ˆ๊ณ , ์œ ๊ธธ์ค€์€ ์„œ์œ ๊ฒฌ๋ฌธ์„ ์ €์ˆ ํ•˜๋ฉฐ +๊ฐœํ™” ๊ฐœ๋…์— ๋ง์”Œ์›Œ์ง„ ๋ถ€์ •์  ์ด๋ฏธ์ง€๋ฅผ ๋–ผ์–ด ๋‚ด๊ณ ์ž ํ–ˆ๋‹ค. ์ดํ›„ +๊ฐ„ํ–‰๋œ ๋Œ€ํ•œ๋งค์ผ์‹ ๋ณด ๋“ฑ์˜ ๊ฐœํ™” ๊ฐœ๋…์€ ๊ตญ๊ฐ€ ๊ตฌ์„ฑ์› ์ „์ฒด๋ฅผ +์‹คํ–‰ ์ฃผ์ฒด๋กœ ํ•˜์—ฌ ๊ทผ๋Œ€ ๊ตญ๊ฐ€ ์ฃผ๊ถŒ์„ ํ–ฅํ•ด ๊ทธ๋“ค์„ ์กฐ์งํ•˜๊ณ  +๋™์›ํ•˜๋Š” ๊ฒƒ์„ ์˜๋ฏธํ–ˆ๋‹ค. +์„์‚ฌ๋Š‘์•ฝ ์ดํ›„, ๊ฐœํ™” ๋…ผ์˜๋Š” ๋ฌธ๋ช…์— ๋Œ€ํ•œ ๋ณธ๊ฒฉ์ ์ธ ๋…ผ์˜๋กœ +์ด์–ด์กŒ๋‹ค. ๋Œ€ํ•œ ์ž๊ฐ•ํšŒ์˜ ์ฃผ์š” ์ธ์‚ฌ๋“ค์€ ์„œ์–‘ ๊ทผ๋Œ€ ๋ฌธ๋ช…์„ +์ˆ˜์šฉํ•˜์—ฌ ๊ทผ๋Œ€ ๊ตญ๊ฐ€๋ฅผ ๊ฑด์„คํ•˜๊ณ ์ž, ์•ž์„œ ๋ฌธ๋ช…ํ™”๋ฅผ ์ด๋ฃฌ ์ผ๋ณธ์˜ +์ง€๋„๋ฅผ ๋ฐ›์•„์•ผ ํ•œ๋‹ค๊ณ  ๋ณด์•˜๋‹ค. ์ด๋“ค์€ ์„œ์–‘ ๊ทผ๋Œ€ ๋ฌธ๋ช…์˜ ์ฃผ์ฒด๋ฅผ +์ฃผ์ฒด ์ธ์‹์˜ ์ค€๊ฑฐ๋กœ ์‚ผ์•˜๊ธฐ ๋•Œ๋ฌธ์— ๋ฏผ์กฑ ์ฃผ์ฒด์„ฑ์„ ๊ฐ„๊ณผํ–ˆ๋‹ค. +์ด๋Ÿฌํ•œ ์ƒํ™ฉ์—์„œ ๋ฐ•์€์‹์€ ใ‰ ๊ทผ๋Œ€ ๊ตญ๊ฐ€ ๊ฑด์„ค๊ณผ ์ƒˆ๋กœ์šด ์ฃผ์ฒด์˜ +ํ˜•์„ฑ์— ์ฃผ๋ชฉํ•˜์—ฌ ๋ฌธ๋ช…์— ๋Œ€ํ•œ ๊ฒฌํ•ด๋ฅผ ์ œ์‹œํ–ˆ๋‹ค. ๊ทธ์˜ ๊ธฐ๋ณธ +๊ฑธ์ณ ๊ทผ๋Œ€ํ™”์˜ ์ถ”์ง„๊ณผ ํ•จ๊ป˜ ์˜์š•์ ์ธ ๊ธฐ์ˆ  ์ˆ˜์šฉ์œผ๋กœ ์ด์–ด์กŒ์ง€๋งŒ, +์ฒญ์ผ ์ „์Ÿ์˜ ํŒจ๋ฐฐ๋Š” ๊ธฐ์ˆ  ์ˆ˜์šฉ๋งŒ์œผ๋กœ๋Š” ๋ถ€์กฑํ•˜๋‹ค๋Š” ์ธ์‹์„ +๋‚ณ์•˜๋‹ค. ์ด์— ๋”ฐ๋ผ 20์„ธ๊ธฐ ์ดˆ๋ฐ˜ ์ง„์ •ํ•œ ๊ทผ๋Œ€๋ฅผ ์ด๋ฃจ๊ธฐ ์œ„ํ•ด ๊ธฐ์ˆ  +๋ฐฐํ›„์—์„œ ์ž‘์šฉํ•˜๋Š” ๊ณผํ•™ ์ •์‹ ์„ ์‚ฌํšŒ ์ „์ฒด์— ์ด์‹ํ•˜๋ ค๋Š” ์‹œ๋„๊ฐ€ +๊ตฌ์ฒดํ™”๋˜์—ˆ๋‹ค. +์˜Œํ‘ธ๋Š” ๊ตญ๊ฐ€ ๊ฐ„์— ๋ฒŒ์–ด์ง€๋Š” ์•ฝ์œก๊ฐ•์‹์˜ ๊ฒฝ์Ÿ์„ ๋ถ€๊ฐํ•˜๊ณ , +๊ฒฝ์Ÿ์—์„œ ์Šน๋ฆฌํ•˜๋ ค๋ฉด ๊ธฐ์ˆ ๋ฟ ์•„๋‹ˆ๋ผ ๊ตญ๋ฏผ์˜ ์ •์‹ ์  ์ž์งˆ์ด +๋’ท๋ฐ›์นจ๋˜์–ด์•ผ ํ•œ๋‹ค๊ณ  ๋ณด์•˜๋‹ค. ์ •์‹ ์  ์ž์งˆ ์ค‘ ๊ณผํ•™์  ์‚ฌ์œ  +๋Šฅ๋ ฅ์ด ๊ฐ€์žฅ ์ค‘์š”ํ•˜๋‹ค๊ณ  ํŒŒ์•…ํ•œ ๊ทธ์—๊ฒŒ ๊ณผํ•™ ์ •์‹ ์ด ์ „์ œ๋˜์ง€ +์•Š์€ ์ •์น˜์  ๋ณ€ํ˜์€ ๋ฟŒ๋ฆฌ๋‚ด๋ฆด ์ˆ˜ ์—†๋Š” ๊ฒƒ์ด์—ˆ๋‹ค. ๊ทธ๋Š” ์ธ๊ณผ +์‹ค์ฆ์˜ ๋ฐฉ๋ฒ•์— ๊ทผ๊ฑฐํ•œ ๊ทผ๋Œ€ ํ•™๋ฌธ ์ „์ฒด๋ฅผ ๊ณผํ•™์ด๋ผ ํŒŒ์•…ํ•˜๊ณ , +๊ณผํ•™์„ ์Šต๋“ํ•˜์—ฌ ์ „ํ†ต ํ•™๋ฌธ์˜ ํ๋‹จ์—์„œ ๋ฒ—์–ด๋‚˜์•ผ ํ•œ๋‹ค๊ณ  ์ฃผ์žฅ +ํ–ˆ๋‹ค. ๊ทธ์˜ ์ž…์žฅ์€ 1910๋…„๋Œ€ ํ›„๋ฐ˜ ์‹ ๋ฌธํ™” ์šด๋™์„ ์ฃผ๋„ํ•œ ์ฒœ๋‘์Šˆ +์—๊ฒŒ ์ด์–ด์กŒ๋‹ค. +์ฒœ๋‘์Šˆ๋ฅผ ๋น„๋กฏํ•œ ์‹ ๋ฌธํ™” ์šด๋™์˜ ์ง€์‹์ธ๋“ค์€ ใ‰ก๊ณผํ•™์˜ ๊ทผ๊ฑฐ +์œ„์—์„œ๋งŒ ๋ฏผ์ฃผ ์ •์น˜์˜ ์‹คํ˜„์ด ๊ฐ€๋Šฅํ•˜๋‹ค๊ณ  ์ฃผ์žฅํ–ˆ๋‹ค. ์ค‘๊ตญ์ด +๋‹ฌ์„ฑํ•ด์•ผ ํ•  ์‹ ๋ฌธํ™”๋Š” ๊ณผํ•™ ๋ฐ ๊ณผํ•™์˜ ๋ฐฉ๋ฒ•์— ๊ทผ๊ฑฐํ•œ ๋ฌธํ™”๋ผ +๋ณด๊ณ , ์‹ ๋ฌธํ™”๋ฅผ ์ด๋ฃจ๊ธฐ ์œ„ํ•ด ์ „ํ†ต๋ฌธํ™” ์ „๋ฐ˜์— ๋Œ€ํ•ด ์ฒ ์ €ํ•œ +๋ถ€์ •๊ณผ ๋น„ํŒ์„ ์‹œ๋„ํ–ˆ๋‹ค. ์‚ฌ์ƒ์ด๋‚˜ ์ฒ ํ•™์ด ๊ณผํ•™์˜ ๋ฐฉ๋ฒ•์„ ์ด์šฉ +ํ•˜์ง€ ์•Š์œผ๋ฉด ๊ณต์ƒ(็ฉบๆƒณ)์— โ“๊ทธ์น  ๋ฟ์ด๋ผ๊ณ  ์ฃผ์žฅํ•œ ์ฒœ๋‘์Šˆ๋Š” +์‚ฌํšŒ์™€ ์ธ๊ฐ„์˜ ์‚ถ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋„ ๊ณผํ•™์˜ ์—ฐ๊ตฌ ๋ฐฉ๋ฒ•์„ ์ด์šฉํ•ด์•ผ +ํ•œ๋‹ค๊ณ  ๋ณด์•˜๋‹ค. ๊ทธ๋Š” ์ œ1์ฐจ ์„ธ๊ณ„ ๋Œ€์ „์˜ ๋น„๊ทน์€ ๊ณผํ•™์„ ์ด์šฉํ•ด +์ €์ง€๋ฅธ ์ฃ„์•…์˜ ๊ฒฐ๊ณผ์ผ ๋ฟ ๊ณผํ•™ ์ž์ฒด์˜ ์ฃ„์•…์ด ์•„๋‹ˆ๋ผ๊ณ  ์ฃผ์žฅํ•˜๋ฉฐ +๊ณผํ•™์— ๋Œ€ํ•œ ์ž์‹ ์˜ ์ƒ๊ฐ์„ ์ง€์†ํ–ˆ๋‹ค. +ํ•œํŽธ, ์ œ1์ฐจ ์„ธ๊ณ„ ๋Œ€์ „ ์ดํ›„ ์œ ๋Ÿฝ์„ ์‹œ์ฐฐํ–ˆ๋˜ ์žฅ์ฅ”๋งˆ์ด๋Š” +ํ†ต์ œ๋˜์ง€ ์•Š์€ ๊ณผํ•™์ด ๋ถˆ๋Ÿฌ์˜จ ์—ญ์ž‘์šฉ์„ ๋ชฉ๋„ํ•œ ํ›„, ๊ณผํ•™์ด +์–ด๋–ป๊ฒŒ ๋ฐœ๋‹ฌํ•˜๋“  ๊ทธ๊ฒƒ์ด ์ธ์ƒ๊ด€์˜ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•  ์ˆ˜๋Š” ์—†๋‹ค๋ฉฐ +์„œ์–‘ ๊ทผ๋Œ€ ๋ฌธ๋ช…์„ ๋น„ํŒํ–ˆ๋‹ค. ๊ทผ๋Œ€ ๊ณผํ•™ ๋ฌธ๋ช…์—์„œ ์ดˆ๋ž˜๋œ ์‚ฌ์ƒ์  +์œ„๊ธฐ๊ฐ€ ์ฃผ์ฒด์˜ ์ฑ…์ž„ ๋ถ€์žฌ์—์„œ ๋น„๋กฏ๋œ ๊ฒƒ์ด๋ผ๋Š” ์ฃผ์žฅ์— ๋™์˜ํ–ˆ๋˜ +๊ทธ๋Š” ๊ณผํ•™์  ๋ฐฉ๋ฒ•์„ ๋ถ€์ •ํ•˜์ง€ ์•Š์•˜์ง€๋งŒ, ์ธ์ƒ๊ด€์˜ ๋ฌธ์ œ์—๋Š” +๊ณผํ•™์  ๋ฐฉ๋ฒ•์ด ์ ์šฉ๋  ์ˆ˜ ์—†๋‹ค๊ณ  ์ง€์ ํ–ˆ๋‹ค. ๊ทธ๋Š” ์ธ์ƒ๊ด€์„ +๊ณผํ•™๊ณผ ๋ณ„๊ฐœ๋กœ ํŒŒ์•…ํ–ˆ๊ณ , ๊ณผํ•™๋งŒ๋Šฅ์ฃผ์˜์— ๊ธฐ์ดˆํ•œ ์‹ ๋ฌธํ™” ์šด๋™์— +์˜ํ•ด ๋ถ€์ •๋œ ์ค‘๊ตญ ์ „ํ†ต ๊ฐ€์น˜๊ด€์˜ ์ˆ˜ํ˜ธ๋ฅผ ๋‚ด์„ธ์› ๋‹ค. +์ „๋žต์€ ๋ฌธ๋ช…์˜ ๋ฌผ์งˆ์  ์ธก๋ฉด์ธ ๊ณผํ•™์€ ์„œ์–‘์œผ๋กœ๋ถ€ํ„ฐ ์ˆ˜์šฉํ•˜๋˜, +๋ฌธ๋ช…์˜ ์ •์‹ ์  ์ธก๋ฉด์ธ ์ฒ ํ•™์€ ์œ ํ•™์„ ํ˜์‹ ํ•˜์—ฌ ์žฌ๊ตฌ์„ฑํ•˜๋Š” +๊ฒƒ์ด์—ˆ๋‹ค. ๊ทธ๋Š” ์ƒ์กด๊ณผ ํŽธ๋ฆฌ ์ฆ์ง„์„ ์œ„ํ•ด ๊ณผํ•™ ์—ฐ๊ตฌ๊ฐ€ ์‹œ๊ธ‰ +ํ•˜์ง€๋งŒ, ๊ฐ€์น˜๊ด€ ์ •๋ฆฝ๊ณผ ์ธ๊ฒฉ ์ˆ˜์–‘์„ ์œ„ํ•ด ์ฒ ํ•™ ๋˜ํ•œ ํ•„์ˆ˜์  +์ด๋ผ๊ณ  ๋ณด์•˜๋‹ค. ์ž๊ตญ ์ฒ ํ•™ ์ „ํ†ต์˜ ์ •๋ฆฝ์ด๋ผ๋Š” ๋‹น์‹œ ๋™์•„์‹œ์•„์˜ +์‚ฌ์ƒ์  ํ๋ฆ„ ์†์—์„œ ๊ทธ๊ฐ€ ์ œ์‹œํ•œ ๊ทผ๋Œ€ ์ฃผ์ฒด๋Š” ๊ณผํ•™์ โ€ค์ฒ ํ•™์  +์ธ์‹์˜ ์ฃผ์ฒด์ด์ž ์‹ค์ฒœ์  ๋„๋• ์ˆ˜์–‘์˜ ์ฃผ์ฒด๋กœ์„œ์˜ ์„ฑ๊ฒฉ์„ ๋ ๋Š” +๊ฒƒ์ด์—ˆ๋‹ค. + + +(๋‚˜) +์ค‘๊ตญ์ด ์„œ์–‘์˜ ๊ณผํ•™๊ณผ ๊ธฐ์ˆ ์— ์ „๋ฉด์ ์ธ ๊ด€์‹ฌ์„ ๊ธฐ์šธ์ธ ๋•Œ๋Š” +์•„ํŽธ ์ „์Ÿ ์ดํ›„์˜€๋‹ค. ์ „์Ÿ ํŒจ๋ฐฐ์— ๋”ฐ๋ฅธ ์œ„๊ธฐ๊ฐ์€ ๋ฐ˜์„ธ๊ธฐ์— +๊ฑธ์ณ ๊ทผ๋Œ€ํ™”์˜ ์ถ”์ง„๊ณผ ํ•จ๊ป˜ ์˜์š•์ ์ธ ๊ธฐ์ˆ  ์ˆ˜์šฉ์œผ๋กœ ์ด์–ด์กŒ์ง€๋งŒ, +์ฒญ์ผ ์ „์Ÿ์˜ ํŒจ๋ฐฐ๋Š” ๊ธฐ์ˆ  ์ˆ˜์šฉ๋งŒ์œผ๋กœ๋Š” ๋ถ€์กฑํ•˜๋‹ค๋Š” ์ธ์‹์„ +๋‚ณ์•˜๋‹ค. ์ด์— ๋”ฐ๋ผ 20์„ธ๊ธฐ ์ดˆ๋ฐ˜ ์ง„์ •ํ•œ ๊ทผ๋Œ€๋ฅผ ์ด๋ฃจ๊ธฐ ์œ„ํ•ด ๊ธฐ์ˆ  +๋ฐฐํ›„์—์„œ ์ž‘์šฉํ•˜๋Š” ๊ณผํ•™ ์ •์‹ ์„ ์‚ฌํšŒ ์ „์ฒด์— ์ด์‹ํ•˜๋ ค๋Š” ์‹œ๋„๊ฐ€ +๊ตฌ์ฒดํ™”๋˜์—ˆ๋‹ค. +์˜Œํ‘ธ๋Š” ๊ตญ๊ฐ€ ๊ฐ„์— ๋ฒŒ์–ด์ง€๋Š” ์•ฝ์œก๊ฐ•์‹์˜ ๊ฒฝ์Ÿ์„ ๋ถ€๊ฐํ•˜๊ณ , +๊ฒฝ์Ÿ์—์„œ ์Šน๋ฆฌํ•˜๋ ค๋ฉด ๊ธฐ์ˆ ๋ฟ ์•„๋‹ˆ๋ผ ๊ตญ๋ฏผ์˜ ์ •์‹ ์  ์ž์งˆ์ด +๋’ท๋ฐ›์นจ๋˜์–ด์•ผ ํ•œ๋‹ค๊ณ  ๋ณด์•˜๋‹ค. ์ •์‹ ์  ์ž์งˆ ์ค‘ ๊ณผํ•™์  ์‚ฌ์œ  +๋Šฅ๋ ฅ์ด ๊ฐ€์žฅ ์ค‘์š”ํ•˜๋‹ค๊ณ  ํŒŒ์•…ํ•œ ๊ทธ์—๊ฒŒ ๊ณผํ•™ ์ •์‹ ์ด ์ „์ œ๋˜์ง€ +์•Š์€ ์ •์น˜์  ๋ณ€ํ˜์€ ๋ฟŒ๋ฆฌ๋‚ด๋ฆด ์ˆ˜ ์—†๋Š” ๊ฒƒ์ด์—ˆ๋‹ค. ๊ทธ๋Š” ์ธ๊ณผ +์‹ค์ฆ์˜ ๋ฐฉ๋ฒ•์— ๊ทผ๊ฑฐํ•œ ๊ทผ๋Œ€ ํ•™๋ฌธ ์ „์ฒด๋ฅผ ๊ณผํ•™์ด๋ผ ํŒŒ์•…ํ•˜๊ณ , +๊ณผํ•™์„ ์Šต๋“ํ•˜์—ฌ ์ „ํ†ต ํ•™๋ฌธ์˜ ํ๋‹จ์—์„œ ๋ฒ—์–ด๋‚˜์•ผ ํ•œ๋‹ค๊ณ  ์ฃผ์žฅ +ํ–ˆ๋‹ค. ๊ทธ์˜ ์ž…์žฅ์€ 1910๋…„๋Œ€ ํ›„๋ฐ˜ ์‹ ๋ฌธํ™” ์šด๋™์„ ์ฃผ๋„ํ•œ ์ฒœ๋‘์Šˆ +์—๊ฒŒ ์ด์–ด์กŒ๋‹ค. +์ฒœ๋‘์Šˆ๋ฅผ ๋น„๋กฏํ•œ ์‹ ๋ฌธํ™” ์šด๋™์˜ ์ง€์‹์ธ๋“ค์€ ใ‰ก๊ณผํ•™์˜ ๊ทผ๊ฑฐ +์œ„์—์„œ๋งŒ ๋ฏผ์ฃผ ์ •์น˜์˜ ์‹คํ˜„์ด ๊ฐ€๋Šฅํ•˜๋‹ค๊ณ  ์ฃผ์žฅํ–ˆ๋‹ค. ์ค‘๊ตญ์ด +๋‹ฌ์„ฑํ•ด์•ผ ํ•  ์‹ ๋ฌธํ™”๋Š” ๊ณผํ•™ ๋ฐ ๊ณผํ•™์˜ ๋ฐฉ๋ฒ•์— ๊ทผ๊ฑฐํ•œ ๋ฌธํ™”๋ผ +๋ณด๊ณ , ์‹ ๋ฌธํ™”๋ฅผ ์ด๋ฃจ๊ธฐ ์œ„ํ•ด ์ „ํ†ต๋ฌธํ™” ์ „๋ฐ˜์— ๋Œ€ํ•ด ์ฒ ์ €ํ•œ +๋ถ€์ •๊ณผ ๋น„ํŒ์„ ์‹œ๋„ํ–ˆ๋‹ค. ์‚ฌ์ƒ์ด๋‚˜ ์ฒ ํ•™์ด ๊ณผํ•™์˜ ๋ฐฉ๋ฒ•์„ ์ด์šฉ +ํ•˜์ง€ ์•Š์œผ๋ฉด ๊ณต์ƒ(็ฉบๆƒณ)์— โ“๊ทธ์น  ๋ฟ์ด๋ผ๊ณ  ์ฃผ์žฅํ•œ ์ฒœ๋‘์Šˆ๋Š” +์‚ฌํšŒ์™€ ์ธ๊ฐ„์˜ ์‚ถ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋„ ๊ณผํ•™์˜ ์—ฐ๊ตฌ ๋ฐฉ๋ฒ•์„ ์ด์šฉํ•ด์•ผ +ํ•œ๋‹ค๊ณ  ๋ณด์•˜๋‹ค. ๊ทธ๋Š” ์ œ1์ฐจ ์„ธ๊ณ„ ๋Œ€์ „์˜ ๋น„๊ทน์€ ๊ณผํ•™์„ ์ด์šฉํ•ด +์ €์ง€๋ฅธ ์ฃ„์•…์˜ ๊ฒฐ๊ณผ์ผ ๋ฟ ๊ณผํ•™ ์ž์ฒด์˜ ์ฃ„์•…์ด ์•„๋‹ˆ๋ผ๊ณ  ์ฃผ์žฅํ•˜๋ฉฐ +๊ณผํ•™์— ๋Œ€ํ•œ ์ž์‹ ์˜ ์ƒ๊ฐ์„ ์ง€์†ํ–ˆ๋‹ค. +ํ•œํŽธ, ์ œ1์ฐจ ์„ธ๊ณ„ ๋Œ€์ „ ์ดํ›„ ์œ ๋Ÿฝ์„ ์‹œ์ฐฐํ–ˆ๋˜ ์žฅ์ฅ”๋งˆ์ด๋Š” +ํ†ต์ œ๋˜์ง€ ์•Š์€ ๊ณผํ•™์ด ๋ถˆ๋Ÿฌ์˜จ ์—ญ์ž‘์šฉ์„ ๋ชฉ๋„ํ•œ ํ›„, ๊ณผํ•™์ด +์–ด๋–ป๊ฒŒ ๋ฐœ๋‹ฌํ•˜๋“  ๊ทธ๊ฒƒ์ด ์ธ์ƒ๊ด€์˜ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•  ์ˆ˜๋Š” ์—†๋‹ค๋ฉฐ +์„œ์–‘ ๊ทผ๋Œ€ ๋ฌธ๋ช…์„ ๋น„ํŒํ–ˆ๋‹ค. ๊ทผ๋Œ€ ๊ณผํ•™ ๋ฌธ๋ช…์—์„œ ์ดˆ๋ž˜๋œ ์‚ฌ์ƒ์  +์œ„๊ธฐ๊ฐ€ ์ฃผ์ฒด์˜ ์ฑ…์ž„ ๋ถ€์žฌ์—์„œ ๋น„๋กฏ๋œ ๊ฒƒ์ด๋ผ๋Š” ์ฃผ์žฅ์— ๋™์˜ํ–ˆ๋˜ +๊ทธ๋Š” ๊ณผํ•™์  ๋ฐฉ๋ฒ•์„ ๋ถ€์ •ํ•˜์ง€ ์•Š์•˜์ง€๋งŒ, ์ธ์ƒ๊ด€์˜ ๋ฌธ์ œ์—๋Š” +๊ณผํ•™์  ๋ฐฉ๋ฒ•์ด ์ ์šฉ๋  ์ˆ˜ ์—†๋‹ค๊ณ  ์ง€์ ํ–ˆ๋‹ค. ๊ทธ๋Š” ์ธ์ƒ๊ด€์„ +๊ณผํ•™๊ณผ ๋ณ„๊ฐœ๋กœ ํŒŒ์•…ํ–ˆ๊ณ , ๊ณผํ•™๋งŒ๋Šฅ์ฃผ์˜์— ๊ธฐ์ดˆํ•œ ์‹ ๋ฌธํ™” ์šด๋™์— +์˜ํ•ด ๋ถ€์ •๋œ ์ค‘๊ตญ ์ „ํ†ต ๊ฐ€์น˜๊ด€์˜ ์ˆ˜ํ˜ธ๋ฅผ ๋‚ด์„ธ์› ๋‹ค.","{'question': '(๊ฐ€), (๋‚˜)๋ฅผ ์ดํ•ดํ•œ ํ•™์ƒ์ด <๋ณด๊ธฐ>์— ๋Œ€ํ•ด ๋ณด์ธ ๋ฐ˜์‘์œผ๋กœ ์ ์ ˆํ•˜์ง€\n์•Š์€ ๊ฒƒ์€?', 'choices': ['(๊ฐ€)์—์„œ ํ•œ์„ฑ์ˆœ๋ณด๋ฅผ ๊ฐ„ํ–‰ํ•œ ์ทจ์ง€๋Š” ์„œ์–‘์— ๋Œ€ํ•œ ๋ฐ˜๊ฐ์„\n์ค„์ด๋Š” ๋ฐ์— ์žˆ๋‹ค๋Š” ์ ์—์„œ, <๋ณด๊ธฐ>์—์„œ ์ •๋ถ€๊ฐ€ ์„œ์–‘์˜ ์ƒ์‚ฐ\n๊ธฐ์ˆ  ๋„์ž…์œผ๋กœ ๋ณ€ํ™”ํ•˜๊ฒŒ ๋  ๋งˆ์„์„ ํ™๋ณดํ•œ ์ทจ์ง€์™€ ๋ถ€ํ•ฉํ•˜๊ฒ ๊ตฐ.', '(๊ฐ€)์—์„œ ๊ฐœํ™”๋‹น์˜ ํ•œ ์ธ์‚ฌ์˜ ๊ฐœํ™” ๊ฐœ๋…์— ๋‚ดํฌ๋œ ๊ฐœํ™”์˜\n์ง€ํ–ฅ์ ์€ ํ†ต์น˜ ๋ฐฉ์‹์˜ ๋ณ€ํ™”์™€ ๊ด€๋ จ ์žˆ๋‹ค๋Š” ์ ์—์„œ, <๋ณด๊ธฐ>์—์„œ\n์ •๋ถ€๊ฐ€ ์„œ์–‘์˜ ์ƒ์‚ฐ ๊ธฐ์ˆ ์„ ๋„์ž…ํ•˜๋ฉฐ ๋‚ด์„ธ์šด ๋ชฉํ‘œ์™€ ๋‹ค๋ฅด๊ฒ ๊ตฐ.', '(๊ฐ€)์—์„œ ๋ฐ•์€์‹์€ ๊ณผํ•™๊ณผ ๊ตฌ๋ณ„๋˜๋Š” ์ฒ ํ•™์˜ ์ค‘์š”์„ฑ์„ ๊ฐ•์กฐ\nํ–ˆ์œผ๋ฏ€๋กœ, <๋ณด๊ธฐ>์—์„œ ์ Š์€์ด๋“ค์˜ ์ž๋ฌธํ™”์— ๋Œ€ํ•œ ์ธ์‹ ๋ณ€ํ™”๋Š”\n๊ฐ€์น˜๊ด€ ์ •๋ฆฝ์„ ์œ„ํ•œ ์ฒ ํ•™์ด ๋ถ€์žฌํ–ˆ๊ธฐ ๋•Œ๋ฌธ์ด๋ผ๊ณ  ๋ณด๊ฒ ๊ตฐ.', '(๋‚˜)์—์„œ ์˜Œํ‘ธ๋Š” ๊ฒฝ์Ÿ์—์„œ ์Šน๋ฆฌํ•˜๊ธฐ ์œ„ํ•œ ์กฐ๊ฑด์œผ๋กœ ๊ธฐ์ˆ ๊ณผ\n์ •์‹ ์  ์ž์งˆ์„ ๊ฐ•์กฐํ–ˆ์œผ๋ฏ€๋กœ, <๋ณด๊ธฐ>์—์„œ ๋งˆ์„์ด ๊ธฐ์ˆ ์˜\n์ˆ˜์šฉ๋งŒ์„ ์ค‘์‹œํ•˜๋ฉด ๋งˆ์„ ๊ฐ„ ๊ฒฝ์Ÿ์—์„œ ์Šน๋ฆฌํ•  ์ˆ˜ ์—†๋‹ค๊ณ  ๋ณด๊ฒ ๊ตฐ.', '(๋‚˜)์—์„œ ์žฅ์ฅ”๋งˆ์ด๋Š” ๊ณผํ•™์  ๋ฐฉ๋ฒ•์˜ ํ•œ๊ณ„๋ฅผ ์ง€์ ํ–ˆ์œผ๋ฏ€๋กœ,\n<๋ณด๊ธฐ>์—์„œ ๋งˆ์„์ด ๊ณผ๊ฑฐ์— ์ค‘์‹œํ–ˆ๋˜ ์ธ์ƒ๊ด€์ด ๋” ์ด์ƒ ์œ ํšจ\nํ•˜์ง€ ์•Š๊ฒŒ ๋œ ๋ฌธ์ œ๋Š” ๊ณผํ•™์  ๋ฐฉ๋ฒ•์œผ๋กœ ํ•ด๊ฒฐํ•  ์ˆ˜ ์—†๋‹ค๊ณ  ๋ณด๊ฒ ๊ตฐ.'], 'answer': '', 'question_plus': 'A๋งˆ์„์€ ๊ฐ€๋‚œํ–ˆ์ง€๋งŒ ์ „ํ†ต๋ฌธํ™”์™€ ๊ณต๋™์ฒด์  ์‚ถ์„ ์ค‘์‹œํ•˜๋ฉฐ\n์ด์›ƒ ๋งˆ์„๋“ค๊ณผ ์กฐํ™”๋กญ๊ฒŒ ์‚ด์•„์™”๋‹ค. ์˜ค๋ž˜์ „, ์ •๋ถ€๋Š” ๋งˆ์„์˜\n๊ฒฝ์ œ ๋ฐœ์ „์„ ๋ชฉํ‘œ๋กœ ์„œ์–‘์˜ ์ƒ์‚ฐ ๊ธฐ์ˆ ์„ ๋„์ž…ํ•˜๋Š” ์ •์ฑ…์„\n์‹œํ–‰ํ–ˆ๋‹ค. ๋งˆ์„ ์‚ฌ๋žŒ๋“ค์€ ์ •์ฑ…์˜ ํ•„์š”์„ฑ์— ๊ณต๊ฐํ•˜๋ฉด์„œ๋„\n์ž์‹ ๋“ค์ด ๋ฐœ์ „์„ ์ด๋ค„ ๋‚ผ ์ˆ˜ ์žˆ๋‹ค๋Š” ํ™•์‹ ์ด ๋ถ€์กฑํ–ˆ๋‹ค. ์ด์—\n์ •๋ถ€๋Š” ๋งˆ์„ ์‚ฌ๋žŒ๋“ค์„ ๋…๋ คํ•˜๊ธฐ ์œ„ํ•ด ๋งˆ์„์˜ ์—ญ๋Ÿ‰์œผ๋กœ ๋‹ฌ์„ฑํ• \n์ˆ˜ ์žˆ๋Š” ๋ฏธ๋ž˜์ƒ์„ ์ง€์†ํ•ด์„œ ํ™๋ณดํ–ˆ๋‹ค. ์ดํ›„ ๋งˆ์„์€ ๋ฌผ์งˆ์ \nํ’์š”๋ฅผ ๋ˆ„๋ฆฌ๊ฒŒ ๋˜์—ˆ์ง€๋งŒ ๊ฒฝ์ œ์  ์ด๊ถŒ์„ ๋‘๊ณ  ์ด์›ƒ ๋งˆ์„๋“ค๊ณผ\n๊ฒฝ์Ÿํ•˜๋ฉฐ ๊ฐˆ๋“ฑํ•˜๊ฒŒ ๋˜์—ˆ๋‹ค. ๊ฒฉํ™”๋œ ๊ฒฝ์Ÿ์—์„œ A๋งˆ์„์€ ์ƒˆ๋กœ์šด\n๊ธฐ์ˆ ์˜ ์ˆ˜์šฉ๋งŒ์„ ์šฐ์„ ์‹œํ–ˆ๊ณ , ๊ณผ๊ฑฐ์— ์ค‘์‹œ๋˜์—ˆ๋˜ ํ˜‘๋ ฅ๊ณผ\n๋‚˜๋ˆ”์˜ ์ธ์ƒ๊ด€์€ ๋‚ก์€ ๊ด€๋…์ด ๋˜์—ˆ๋‹ค. ์ Š์€์ด๋“ค์—๊ฒŒ ์ „ํ†ต\n๋ฌธํ™”๋Š” ์„œ์–‘ ๋ฌธํ™”์— ๋น„ํ•ด ์—ด๋“ฑํ•œ ๊ฒƒ์œผ๋กœ ์—ฌ๊ฒจ์กŒ๋‹ค.'}","A๋งˆ์„์€ ๊ฐ€๋‚œํ–ˆ์ง€๋งŒ ์ „ํ†ต๋ฌธํ™”์™€ ๊ณต๋™์ฒด์  ์‚ถ์„ ์ค‘์‹œํ•˜๋ฉฐ +์ด์›ƒ ๋งˆ์„๋“ค๊ณผ ์กฐํ™”๋กญ๊ฒŒ ์‚ด์•„์™”๋‹ค. ์˜ค๋ž˜์ „, ์ •๋ถ€๋Š” ๋งˆ์„์˜ +๊ฒฝ์ œ ๋ฐœ์ „์„ ๋ชฉํ‘œ๋กœ ์„œ์–‘์˜ ์ƒ์‚ฐ ๊ธฐ์ˆ ์„ ๋„์ž…ํ•˜๋Š” ์ •์ฑ…์„ +์‹œํ–‰ํ–ˆ๋‹ค. ๋งˆ์„ ์‚ฌ๋žŒ๋“ค์€ ์ •์ฑ…์˜ ํ•„์š”์„ฑ์— ๊ณต๊ฐํ•˜๋ฉด์„œ๋„ +์ž์‹ ๋“ค์ด ๋ฐœ์ „์„ ์ด๋ค„ ๋‚ผ ์ˆ˜ ์žˆ๋‹ค๋Š” ํ™•์‹ ์ด ๋ถ€์กฑํ–ˆ๋‹ค. ์ด์— +์ •๋ถ€๋Š” ๋งˆ์„ ์‚ฌ๋žŒ๋“ค์„ ๋…๋ คํ•˜๊ธฐ ์œ„ํ•ด ๋งˆ์„์˜ ์—ญ๋Ÿ‰์œผ๋กœ ๋‹ฌ์„ฑํ•  +์ˆ˜ ์žˆ๋Š” ๋ฏธ๋ž˜์ƒ์„ ์ง€์†ํ•ด์„œ ํ™๋ณดํ–ˆ๋‹ค. ์ดํ›„ ๋งˆ์„์€ ๋ฌผ์งˆ์  +ํ’์š”๋ฅผ ๋ˆ„๋ฆฌ๊ฒŒ ๋˜์—ˆ์ง€๋งŒ ๊ฒฝ์ œ์  ์ด๊ถŒ์„ ๋‘๊ณ  ์ด์›ƒ ๋งˆ์„๋“ค๊ณผ +๊ฒฝ์Ÿํ•˜๋ฉฐ ๊ฐˆ๋“ฑํ•˜๊ฒŒ ๋˜์—ˆ๋‹ค. ๊ฒฉํ™”๋œ ๊ฒฝ์Ÿ์—์„œ A๋งˆ์„์€ ์ƒˆ๋กœ์šด +๊ธฐ์ˆ ์˜ ์ˆ˜์šฉ๋งŒ์„ ์šฐ์„ ์‹œํ–ˆ๊ณ , ๊ณผ๊ฑฐ์— ์ค‘์‹œ๋˜์—ˆ๋˜ ํ˜‘๋ ฅ๊ณผ +๋‚˜๋ˆ”์˜ ์ธ์ƒ๊ด€์€ ๋‚ก์€ ๊ด€๋…์ด ๋˜์—ˆ๋‹ค. ์ Š์€์ด๋“ค์—๊ฒŒ ์ „ํ†ต +๋ฌธํ™”๋Š” ์„œ์–‘ ๋ฌธํ™”์— ๋น„ํ•ด ์—ด๋“ฑํ•œ ๊ฒƒ์œผ๋กœ ์—ฌ๊ฒจ์กŒ๋‹ค.",1,3,False,[],3 +2025-korean-09,"(๊ฐ€) +์„œ์–‘์˜ ๊ณผํ•™๊ณผ ๊ธฐ์ˆ , ์ฒœ์ฃผ๊ต์˜ ์ˆ˜์šฉ์„ ๋ฐ˜๋Œ€ํ–ˆ๋˜ ์ดํ•ญ๋กœ๋ฅผ ๋น„๋กฏํ•œ +์ฒ™์‚ฌํŒŒ์˜ ์ฃผ์žฅ์€ ๊ฐœํ•ญ ์ดํ›„์—๋„ ์ง€์†๋˜์—ˆ์ง€๋งŒ, ๊ฐœํ™”๋Š” ๊ฑฐ์Šค๋ฅผ +์ˆ˜ ์—†๋Š” ๋Œ€์„ธ๋กœ ์ž๋ฆฌ ์žก์•˜๋‹ค. ๊ฐœ๋ฌผ์„ฑ๋ฌด(้–‹็‰ฉๆˆๅ‹™)์™€ ํ™”๋ฏผ์„ฑ์† +(ๅŒ–ๆฐ‘ๆˆไฟ—)์˜ ์•ž ๊ธ€์ž๋ฅผ ๋”ด ๊ฐœํ™”๋Š” ๊ฐœํ•ญ ์ด์ „์—๋Š” ํ†ต์น˜์ž์˜ ํ†ต์น˜ +ํ–‰์œ„๋กœ์„œ ๋ณ€ํ™”ํ•˜๋Š” ์„ธ์ƒ์— ๋Œ€ํ•œ ์ง€์‹ ํ™•์žฅ๊ณผ ํ”ผํ†ต์น˜์ž์— ๋Œ€ํ•œ +๊ตํ™”๋ฅผ ์˜๋ฏธํ–ˆ๋‹ค. +๊ฐœํ•ญ ์ดํ›„ ์„œ์–‘ ๋ฌธ๋ช…์— ๋Œ€ํ•œ ๊ธ์ •์  ์ธ์‹์ด ํ™•์‚ฐ๋˜๋ฉด์„œ ์„œ์–‘ +๋ฌธ๋ช…์˜ ์ˆ˜์šฉ์„ ๋œปํ•˜๋Š” ๊ฐœํ™” ๊ฐœ๋…์ด ์ž๋ฆฌ ์žก์•˜๋‹ค. ์ž„์˜ค๊ตฐ๋ž€ ์ดํ›„, +๊ณ ์ข…์€ ์ž๊ฐ• ์ •์ฑ…์„ ์ถ”์ง„ํ•˜๋ฉด์„œ ๋ฐ˜(ๅ)์„œ์–‘ ์ •์„œ์˜ ๊ต์ •์„ ์œ„ํ•ด +ํ•œ์„ฑ์ˆœ๋ณด๋ฅผ ๋ฐœ๊ฐ„ํ–ˆ๋‹ค. ์ด ์‹ ๋ฌธ์˜ ๊ฐœํ™” ๊ฐœ๋…์€ ์„œ์–‘ ๊ธฐ์ˆ ๊ณผ +์ œ๋„์˜ ๋„์ž…์„ ํ†ตํ•œ ์ธ์ง€์˜ ๋ฐœ๋‹ฌ๊ณผ ํ’์†์˜ ์ง„๋ณด๋ฅผ ๋œปํ–ˆ๋‹ค. +์ด ๊ฐœ๋…์—๋Š” ์ธ๋ฏผ์ด ๊ตญ๊ฐ€์˜ ๋…๋ฆฝ ์ฃผ๊ถŒ์˜ ์†Œ์ค‘ํ•จ์„ ๊นจ๋‹ซ๋Š” ์˜์‹์˜ +๋ณ€ํ™”๊ฐ€ ๋‚ดํฌ๋˜์—ˆ๊ณ , ํ†ต์น˜์ž์˜ ์ž…์žฅ์—์„œ ์ˆ˜์šฉ ๊ฐ€๋Šฅํ•œ ๋ฌธ๋ช…์˜ ์žฅ์ ์„ +๋ฐ›์•„๋“ค์—ฌ ๊ตญ๊ฐ€์˜ ์ง„๋ณด๋ฅผ ๋‹ฌ์„ฑํ•œ๋‹ค๋Š” ์˜๋ฏธ๋„ ๋‹ด๊ฒผ๋‹ค. +๊ฐœํ™”๋‹น์˜ ํ•œ ์ธ์‚ฌ๊ฐ€ ์ œ์‹œํ•œ ๊ฐœํ™” ๊ฐœ๋…์€ ์„ฑ๋ฌธํ™”๋œ ๊ทœ์ •์— ๋”ฐ๋ฅธ +๋Œ€๋ฏผ ์ •์น˜์—์„œ์˜ ๋ฒ•์  ์ฒ˜๋ฆฌ ์ ˆ์ฐจ ์‹คํ˜„ ๋“ฑ ์„œ์–‘ ๊ทผ๋Œ€ ๊ตญ๊ฐ€์˜ ํ†ต์น˜ +๋ฐฉ์‹์œผ๋กœ์˜ ๋ณ€ํ™”๋ฅผ ๋‚ดํฌํ•˜๋Š” ๊ฒƒ์ด์—ˆ๋‹ค. ๊ทธ๋Š” ๊ฐœํ™” ์‹คํ–‰ ์ฃผ์ฒด๋ฅผ +์—ฌ์ „ํžˆ ์™•์œผ๋กœ ์ƒ๊ฐํ–ˆ๊ณ , ๊ฐœํ™” ์‹คํ–‰ ์ฃผ์ฒด๋กœ์„œ ์™•์˜ ์—ญํ• ์ด +์‚ฌ๋ผ์ง„ ๊ฒƒ์€ ๊ฐ‘์‹ ์ •๋ณ€์—์„œ์˜€๋‹ค. ํ’์†์˜ ์ง„๋ณด์™€ ํ†ต์น˜ ๋ฐฉ์‹ ๋ณ€ํ™” +๋ผ๋Š” ์˜๋ฏธ๋ฅผ ๋‚ดํฌํ•œ ๊ฐ‘์‹ ์ •๋ณ€์˜ ๊ฐœํ™” ๊ฐœ๋…์€ ํ†ต์น˜๊ถŒ์— ๋Œ€ํ•œ +๋„์ „์œผ๋กœ๋ฟ ์•„๋‹ˆ๋ผ ๊ฐœ์ธ์˜ ์‚ฌ์š•์„ ์œ„ํ•œ ๊ฒƒ์œผ๋กœ ํ‘œ์ƒ๋˜์—ˆ๋‹ค. ์ดํ›„ +๊ฐœํ™” ๊ฐœ๋…์€ ๊ตญ๊ฐ€ ๊ตฌ์„ฑ์›์„ ์กฐ์งํ•˜๊ณ  ๋™์›ํ•˜๊ธฐ ์œ„ํ•ด ๋ถ€์ •์  +์ด๋ฏธ์ง€์—์„œ ๋ฒ—์–ด๋‚˜์•ผ ํ–ˆ๊ณ , ์œ ๊ธธ์ค€์€ ์„œ์œ ๊ฒฌ๋ฌธ์„ ์ €์ˆ ํ•˜๋ฉฐ +๊ฐœํ™” ๊ฐœ๋…์— ๋ง์”Œ์›Œ์ง„ ๋ถ€์ •์  ์ด๋ฏธ์ง€๋ฅผ ๋–ผ์–ด ๋‚ด๊ณ ์ž ํ–ˆ๋‹ค. ์ดํ›„ +๊ฐ„ํ–‰๋œ ๋Œ€ํ•œ๋งค์ผ์‹ ๋ณด ๋“ฑ์˜ ๊ฐœํ™” ๊ฐœ๋…์€ ๊ตญ๊ฐ€ ๊ตฌ์„ฑ์› ์ „์ฒด๋ฅผ +์‹คํ–‰ ์ฃผ์ฒด๋กœ ํ•˜์—ฌ ๊ทผ๋Œ€ ๊ตญ๊ฐ€ ์ฃผ๊ถŒ์„ ํ–ฅํ•ด ๊ทธ๋“ค์„ ์กฐ์งํ•˜๊ณ  +๋™์›ํ•˜๋Š” ๊ฒƒ์„ ์˜๋ฏธํ–ˆ๋‹ค. +์„์‚ฌ๋Š‘์•ฝ ์ดํ›„, ๊ฐœํ™” ๋…ผ์˜๋Š” ๋ฌธ๋ช…์— ๋Œ€ํ•œ ๋ณธ๊ฒฉ์ ์ธ ๋…ผ์˜๋กœ +์ด์–ด์กŒ๋‹ค. ๋Œ€ํ•œ ์ž๊ฐ•ํšŒ์˜ ์ฃผ์š” ์ธ์‚ฌ๋“ค์€ ์„œ์–‘ ๊ทผ๋Œ€ ๋ฌธ๋ช…์„ +์ˆ˜์šฉํ•˜์—ฌ ๊ทผ๋Œ€ ๊ตญ๊ฐ€๋ฅผ ๊ฑด์„คํ•˜๊ณ ์ž, ์•ž์„œ ๋ฌธ๋ช…ํ™”๋ฅผ ์ด๋ฃฌ ์ผ๋ณธ์˜ +์ง€๋„๋ฅผ ๋ฐ›์•„์•ผ ํ•œ๋‹ค๊ณ  ๋ณด์•˜๋‹ค. ์ด๋“ค์€ ์„œ์–‘ ๊ทผ๋Œ€ ๋ฌธ๋ช…์˜ ์ฃผ์ฒด๋ฅผ +์ฃผ์ฒด ์ธ์‹์˜ ์ค€๊ฑฐ๋กœ ์‚ผ์•˜๊ธฐ ๋•Œ๋ฌธ์— ๋ฏผ์กฑ ์ฃผ์ฒด์„ฑ์„ ๊ฐ„๊ณผํ–ˆ๋‹ค. +์ด๋Ÿฌํ•œ ์ƒํ™ฉ์—์„œ ๋ฐ•์€์‹์€ ใ‰ ๊ทผ๋Œ€ ๊ตญ๊ฐ€ ๊ฑด์„ค๊ณผ ์ƒˆ๋กœ์šด ์ฃผ์ฒด์˜ +ํ˜•์„ฑ์— ์ฃผ๋ชฉํ•˜์—ฌ ๋ฌธ๋ช…์— ๋Œ€ํ•œ ๊ฒฌํ•ด๋ฅผ ์ œ์‹œํ–ˆ๋‹ค. ๊ทธ์˜ ๊ธฐ๋ณธ +๊ฑธ์ณ ๊ทผ๋Œ€ํ™”์˜ ์ถ”์ง„๊ณผ ํ•จ๊ป˜ ์˜์š•์ ์ธ ๊ธฐ์ˆ  ์ˆ˜์šฉ์œผ๋กœ ์ด์–ด์กŒ์ง€๋งŒ, +์ฒญ์ผ ์ „์Ÿ์˜ ํŒจ๋ฐฐ๋Š” ๊ธฐ์ˆ  ์ˆ˜์šฉ๋งŒ์œผ๋กœ๋Š” ๋ถ€์กฑํ•˜๋‹ค๋Š” ์ธ์‹์„ +๋‚ณ์•˜๋‹ค. ์ด์— ๋”ฐ๋ผ 20์„ธ๊ธฐ ์ดˆ๋ฐ˜ ์ง„์ •ํ•œ ๊ทผ๋Œ€๋ฅผ ์ด๋ฃจ๊ธฐ ์œ„ํ•ด ๊ธฐ์ˆ  +๋ฐฐํ›„์—์„œ ์ž‘์šฉํ•˜๋Š” ๊ณผํ•™ ์ •์‹ ์„ ์‚ฌํšŒ ์ „์ฒด์— ์ด์‹ํ•˜๋ ค๋Š” ์‹œ๋„๊ฐ€ +๊ตฌ์ฒดํ™”๋˜์—ˆ๋‹ค. +์˜Œํ‘ธ๋Š” ๊ตญ๊ฐ€ ๊ฐ„์— ๋ฒŒ์–ด์ง€๋Š” ์•ฝ์œก๊ฐ•์‹์˜ ๊ฒฝ์Ÿ์„ ๋ถ€๊ฐํ•˜๊ณ , +๊ฒฝ์Ÿ์—์„œ ์Šน๋ฆฌํ•˜๋ ค๋ฉด ๊ธฐ์ˆ ๋ฟ ์•„๋‹ˆ๋ผ ๊ตญ๋ฏผ์˜ ์ •์‹ ์  ์ž์งˆ์ด +๋’ท๋ฐ›์นจ๋˜์–ด์•ผ ํ•œ๋‹ค๊ณ  ๋ณด์•˜๋‹ค. ์ •์‹ ์  ์ž์งˆ ์ค‘ ๊ณผํ•™์  ์‚ฌ์œ  +๋Šฅ๋ ฅ์ด ๊ฐ€์žฅ ์ค‘์š”ํ•˜๋‹ค๊ณ  ํŒŒ์•…ํ•œ ๊ทธ์—๊ฒŒ ๊ณผํ•™ ์ •์‹ ์ด ์ „์ œ๋˜์ง€ +์•Š์€ ์ •์น˜์  ๋ณ€ํ˜์€ ๋ฟŒ๋ฆฌ๋‚ด๋ฆด ์ˆ˜ ์—†๋Š” ๊ฒƒ์ด์—ˆ๋‹ค. ๊ทธ๋Š” ์ธ๊ณผ +์‹ค์ฆ์˜ ๋ฐฉ๋ฒ•์— ๊ทผ๊ฑฐํ•œ ๊ทผ๋Œ€ ํ•™๋ฌธ ์ „์ฒด๋ฅผ ๊ณผํ•™์ด๋ผ ํŒŒ์•…ํ•˜๊ณ , +๊ณผํ•™์„ ์Šต๋“ํ•˜์—ฌ ์ „ํ†ต ํ•™๋ฌธ์˜ ํ๋‹จ์—์„œ ๋ฒ—์–ด๋‚˜์•ผ ํ•œ๋‹ค๊ณ  ์ฃผ์žฅ +ํ–ˆ๋‹ค. ๊ทธ์˜ ์ž…์žฅ์€ 1910๋…„๋Œ€ ํ›„๋ฐ˜ ์‹ ๋ฌธํ™” ์šด๋™์„ ์ฃผ๋„ํ•œ ์ฒœ๋‘์Šˆ +์—๊ฒŒ ์ด์–ด์กŒ๋‹ค. +์ฒœ๋‘์Šˆ๋ฅผ ๋น„๋กฏํ•œ ์‹ ๋ฌธํ™” ์šด๋™์˜ ์ง€์‹์ธ๋“ค์€ ใ‰ก๊ณผํ•™์˜ ๊ทผ๊ฑฐ +์œ„์—์„œ๋งŒ ๋ฏผ์ฃผ ์ •์น˜์˜ ์‹คํ˜„์ด ๊ฐ€๋Šฅํ•˜๋‹ค๊ณ  ์ฃผ์žฅํ–ˆ๋‹ค. ์ค‘๊ตญ์ด +๋‹ฌ์„ฑํ•ด์•ผ ํ•  ์‹ ๋ฌธํ™”๋Š” ๊ณผํ•™ ๋ฐ ๊ณผํ•™์˜ ๋ฐฉ๋ฒ•์— ๊ทผ๊ฑฐํ•œ ๋ฌธํ™”๋ผ +๋ณด๊ณ , ์‹ ๋ฌธํ™”๋ฅผ ์ด๋ฃจ๊ธฐ ์œ„ํ•ด ์ „ํ†ต๋ฌธํ™” ์ „๋ฐ˜์— ๋Œ€ํ•ด ์ฒ ์ €ํ•œ +๋ถ€์ •๊ณผ ๋น„ํŒ์„ ์‹œ๋„ํ–ˆ๋‹ค. ์‚ฌ์ƒ์ด๋‚˜ ์ฒ ํ•™์ด ๊ณผํ•™์˜ ๋ฐฉ๋ฒ•์„ ์ด์šฉ +ํ•˜์ง€ ์•Š์œผ๋ฉด ๊ณต์ƒ(็ฉบๆƒณ)์— โ“๊ทธ์น  ๋ฟ์ด๋ผ๊ณ  ์ฃผ์žฅํ•œ ์ฒœ๋‘์Šˆ๋Š” +์‚ฌํšŒ์™€ ์ธ๊ฐ„์˜ ์‚ถ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋„ ๊ณผํ•™์˜ ์—ฐ๊ตฌ ๋ฐฉ๋ฒ•์„ ์ด์šฉํ•ด์•ผ +ํ•œ๋‹ค๊ณ  ๋ณด์•˜๋‹ค. ๊ทธ๋Š” ์ œ1์ฐจ ์„ธ๊ณ„ ๋Œ€์ „์˜ ๋น„๊ทน์€ ๊ณผํ•™์„ ์ด์šฉํ•ด +์ €์ง€๋ฅธ ์ฃ„์•…์˜ ๊ฒฐ๊ณผ์ผ ๋ฟ ๊ณผํ•™ ์ž์ฒด์˜ ์ฃ„์•…์ด ์•„๋‹ˆ๋ผ๊ณ  ์ฃผ์žฅํ•˜๋ฉฐ +๊ณผํ•™์— ๋Œ€ํ•œ ์ž์‹ ์˜ ์ƒ๊ฐ์„ ์ง€์†ํ–ˆ๋‹ค. +ํ•œํŽธ, ์ œ1์ฐจ ์„ธ๊ณ„ ๋Œ€์ „ ์ดํ›„ ์œ ๋Ÿฝ์„ ์‹œ์ฐฐํ–ˆ๋˜ ์žฅ์ฅ”๋งˆ์ด๋Š” +ํ†ต์ œ๋˜์ง€ ์•Š์€ ๊ณผํ•™์ด ๋ถˆ๋Ÿฌ์˜จ ์—ญ์ž‘์šฉ์„ ๋ชฉ๋„ํ•œ ํ›„, ๊ณผํ•™์ด +์–ด๋–ป๊ฒŒ ๋ฐœ๋‹ฌํ•˜๋“  ๊ทธ๊ฒƒ์ด ์ธ์ƒ๊ด€์˜ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•  ์ˆ˜๋Š” ์—†๋‹ค๋ฉฐ +์„œ์–‘ ๊ทผ๋Œ€ ๋ฌธ๋ช…์„ ๋น„ํŒํ–ˆ๋‹ค. ๊ทผ๋Œ€ ๊ณผํ•™ ๋ฌธ๋ช…์—์„œ ์ดˆ๋ž˜๋œ ์‚ฌ์ƒ์  +์œ„๊ธฐ๊ฐ€ ์ฃผ์ฒด์˜ ์ฑ…์ž„ ๋ถ€์žฌ์—์„œ ๋น„๋กฏ๋œ ๊ฒƒ์ด๋ผ๋Š” ์ฃผ์žฅ์— ๋™์˜ํ–ˆ๋˜ +๊ทธ๋Š” ๊ณผํ•™์  ๋ฐฉ๋ฒ•์„ ๋ถ€์ •ํ•˜์ง€ ์•Š์•˜์ง€๋งŒ, ์ธ์ƒ๊ด€์˜ ๋ฌธ์ œ์—๋Š” +๊ณผํ•™์  ๋ฐฉ๋ฒ•์ด ์ ์šฉ๋  ์ˆ˜ ์—†๋‹ค๊ณ  ์ง€์ ํ–ˆ๋‹ค. ๊ทธ๋Š” ์ธ์ƒ๊ด€์„ +๊ณผํ•™๊ณผ ๋ณ„๊ฐœ๋กœ ํŒŒ์•…ํ–ˆ๊ณ , ๊ณผํ•™๋งŒ๋Šฅ์ฃผ์˜์— ๊ธฐ์ดˆํ•œ ์‹ ๋ฌธํ™” ์šด๋™์— +์˜ํ•ด ๋ถ€์ •๋œ ์ค‘๊ตญ ์ „ํ†ต ๊ฐ€์น˜๊ด€์˜ ์ˆ˜ํ˜ธ๋ฅผ ๋‚ด์„ธ์› ๋‹ค. +์ „๋žต์€ ๋ฌธ๋ช…์˜ ๋ฌผ์งˆ์  ์ธก๋ฉด์ธ ๊ณผํ•™์€ ์„œ์–‘์œผ๋กœ๋ถ€ํ„ฐ ์ˆ˜์šฉํ•˜๋˜, +๋ฌธ๋ช…์˜ ์ •์‹ ์  ์ธก๋ฉด์ธ ์ฒ ํ•™์€ ์œ ํ•™์„ ํ˜์‹ ํ•˜์—ฌ ์žฌ๊ตฌ์„ฑํ•˜๋Š” +๊ฒƒ์ด์—ˆ๋‹ค. ๊ทธ๋Š” ์ƒ์กด๊ณผ ํŽธ๋ฆฌ ์ฆ์ง„์„ ์œ„ํ•ด ๊ณผํ•™ ์—ฐ๊ตฌ๊ฐ€ ์‹œ๊ธ‰ +ํ•˜์ง€๋งŒ, ๊ฐ€์น˜๊ด€ ์ •๋ฆฝ๊ณผ ์ธ๊ฒฉ ์ˆ˜์–‘์„ ์œ„ํ•ด ์ฒ ํ•™ ๋˜ํ•œ ํ•„์ˆ˜์  +์ด๋ผ๊ณ  ๋ณด์•˜๋‹ค. ์ž๊ตญ ์ฒ ํ•™ ์ „ํ†ต์˜ ์ •๋ฆฝ์ด๋ผ๋Š” ๋‹น์‹œ ๋™์•„์‹œ์•„์˜ +์‚ฌ์ƒ์  ํ๋ฆ„ ์†์—์„œ ๊ทธ๊ฐ€ ์ œ์‹œํ•œ ๊ทผ๋Œ€ ์ฃผ์ฒด๋Š” ๊ณผํ•™์ โ€ค์ฒ ํ•™์  +์ธ์‹์˜ ์ฃผ์ฒด์ด์ž ์‹ค์ฒœ์  ๋„๋• ์ˆ˜์–‘์˜ ์ฃผ์ฒด๋กœ์„œ์˜ ์„ฑ๊ฒฉ์„ ๋ ๋Š” +๊ฒƒ์ด์—ˆ๋‹ค. + + +(๋‚˜) +์ค‘๊ตญ์ด ์„œ์–‘์˜ ๊ณผํ•™๊ณผ ๊ธฐ์ˆ ์— ์ „๋ฉด์ ์ธ ๊ด€์‹ฌ์„ ๊ธฐ์šธ์ธ ๋•Œ๋Š” +์•„ํŽธ ์ „์Ÿ ์ดํ›„์˜€๋‹ค. ์ „์Ÿ ํŒจ๋ฐฐ์— ๋”ฐ๋ฅธ ์œ„๊ธฐ๊ฐ์€ ๋ฐ˜์„ธ๊ธฐ์— +๊ฑธ์ณ ๊ทผ๋Œ€ํ™”์˜ ์ถ”์ง„๊ณผ ํ•จ๊ป˜ ์˜์š•์ ์ธ ๊ธฐ์ˆ  ์ˆ˜์šฉ์œผ๋กœ ์ด์–ด์กŒ์ง€๋งŒ, +์ฒญ์ผ ์ „์Ÿ์˜ ํŒจ๋ฐฐ๋Š” ๊ธฐ์ˆ  ์ˆ˜์šฉ๋งŒ์œผ๋กœ๋Š” ๋ถ€์กฑํ•˜๋‹ค๋Š” ์ธ์‹์„ +๋‚ณ์•˜๋‹ค. ์ด์— ๋”ฐ๋ผ 20์„ธ๊ธฐ ์ดˆ๋ฐ˜ ์ง„์ •ํ•œ ๊ทผ๋Œ€๋ฅผ ์ด๋ฃจ๊ธฐ ์œ„ํ•ด ๊ธฐ์ˆ  +๋ฐฐํ›„์—์„œ ์ž‘์šฉํ•˜๋Š” ๊ณผํ•™ ์ •์‹ ์„ ์‚ฌํšŒ ์ „์ฒด์— ์ด์‹ํ•˜๋ ค๋Š” ์‹œ๋„๊ฐ€ +๊ตฌ์ฒดํ™”๋˜์—ˆ๋‹ค. +์˜Œํ‘ธ๋Š” ๊ตญ๊ฐ€ ๊ฐ„์— ๋ฒŒ์–ด์ง€๋Š” ์•ฝ์œก๊ฐ•์‹์˜ ๊ฒฝ์Ÿ์„ ๋ถ€๊ฐํ•˜๊ณ , +๊ฒฝ์Ÿ์—์„œ ์Šน๋ฆฌํ•˜๋ ค๋ฉด ๊ธฐ์ˆ ๋ฟ ์•„๋‹ˆ๋ผ ๊ตญ๋ฏผ์˜ ์ •์‹ ์  ์ž์งˆ์ด +๋’ท๋ฐ›์นจ๋˜์–ด์•ผ ํ•œ๋‹ค๊ณ  ๋ณด์•˜๋‹ค. ์ •์‹ ์  ์ž์งˆ ์ค‘ ๊ณผํ•™์  ์‚ฌ์œ  +๋Šฅ๋ ฅ์ด ๊ฐ€์žฅ ์ค‘์š”ํ•˜๋‹ค๊ณ  ํŒŒ์•…ํ•œ ๊ทธ์—๊ฒŒ ๊ณผํ•™ ์ •์‹ ์ด ์ „์ œ๋˜์ง€ +์•Š์€ ์ •์น˜์  ๋ณ€ํ˜์€ ๋ฟŒ๋ฆฌ๋‚ด๋ฆด ์ˆ˜ ์—†๋Š” ๊ฒƒ์ด์—ˆ๋‹ค. ๊ทธ๋Š” ์ธ๊ณผ +์‹ค์ฆ์˜ ๋ฐฉ๋ฒ•์— ๊ทผ๊ฑฐํ•œ ๊ทผ๋Œ€ ํ•™๋ฌธ ์ „์ฒด๋ฅผ ๊ณผํ•™์ด๋ผ ํŒŒ์•…ํ•˜๊ณ , +๊ณผํ•™์„ ์Šต๋“ํ•˜์—ฌ ์ „ํ†ต ํ•™๋ฌธ์˜ ํ๋‹จ์—์„œ ๋ฒ—์–ด๋‚˜์•ผ ํ•œ๋‹ค๊ณ  ์ฃผ์žฅ +ํ–ˆ๋‹ค. ๊ทธ์˜ ์ž…์žฅ์€ 1910๋…„๋Œ€ ํ›„๋ฐ˜ ์‹ ๋ฌธํ™” ์šด๋™์„ ์ฃผ๋„ํ•œ ์ฒœ๋‘์Šˆ +์—๊ฒŒ ์ด์–ด์กŒ๋‹ค. +์ฒœ๋‘์Šˆ๋ฅผ ๋น„๋กฏํ•œ ์‹ ๋ฌธํ™” ์šด๋™์˜ ์ง€์‹์ธ๋“ค์€ ใ‰ก๊ณผํ•™์˜ ๊ทผ๊ฑฐ +์œ„์—์„œ๋งŒ ๋ฏผ์ฃผ ์ •์น˜์˜ ์‹คํ˜„์ด ๊ฐ€๋Šฅํ•˜๋‹ค๊ณ  ์ฃผ์žฅํ–ˆ๋‹ค. ์ค‘๊ตญ์ด +๋‹ฌ์„ฑํ•ด์•ผ ํ•  ์‹ ๋ฌธํ™”๋Š” ๊ณผํ•™ ๋ฐ ๊ณผํ•™์˜ ๋ฐฉ๋ฒ•์— ๊ทผ๊ฑฐํ•œ ๋ฌธํ™”๋ผ +๋ณด๊ณ , ์‹ ๋ฌธํ™”๋ฅผ ์ด๋ฃจ๊ธฐ ์œ„ํ•ด ์ „ํ†ต๋ฌธํ™” ์ „๋ฐ˜์— ๋Œ€ํ•ด ์ฒ ์ €ํ•œ +๋ถ€์ •๊ณผ ๋น„ํŒ์„ ์‹œ๋„ํ–ˆ๋‹ค. ์‚ฌ์ƒ์ด๋‚˜ ์ฒ ํ•™์ด ๊ณผํ•™์˜ ๋ฐฉ๋ฒ•์„ ์ด์šฉ +ํ•˜์ง€ ์•Š์œผ๋ฉด ๊ณต์ƒ(็ฉบๆƒณ)์— โ“๊ทธ์น  ๋ฟ์ด๋ผ๊ณ  ์ฃผ์žฅํ•œ ์ฒœ๋‘์Šˆ๋Š” +์‚ฌํšŒ์™€ ์ธ๊ฐ„์˜ ์‚ถ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋„ ๊ณผํ•™์˜ ์—ฐ๊ตฌ ๋ฐฉ๋ฒ•์„ ์ด์šฉํ•ด์•ผ +ํ•œ๋‹ค๊ณ  ๋ณด์•˜๋‹ค. ๊ทธ๋Š” ์ œ1์ฐจ ์„ธ๊ณ„ ๋Œ€์ „์˜ ๋น„๊ทน์€ ๊ณผํ•™์„ ์ด์šฉํ•ด +์ €์ง€๋ฅธ ์ฃ„์•…์˜ ๊ฒฐ๊ณผ์ผ ๋ฟ ๊ณผํ•™ ์ž์ฒด์˜ ์ฃ„์•…์ด ์•„๋‹ˆ๋ผ๊ณ  ์ฃผ์žฅํ•˜๋ฉฐ +๊ณผํ•™์— ๋Œ€ํ•œ ์ž์‹ ์˜ ์ƒ๊ฐ์„ ์ง€์†ํ–ˆ๋‹ค. +ํ•œํŽธ, ์ œ1์ฐจ ์„ธ๊ณ„ ๋Œ€์ „ ์ดํ›„ ์œ ๋Ÿฝ์„ ์‹œ์ฐฐํ–ˆ๋˜ ์žฅ์ฅ”๋งˆ์ด๋Š” +ํ†ต์ œ๋˜์ง€ ์•Š์€ ๊ณผํ•™์ด ๋ถˆ๋Ÿฌ์˜จ ์—ญ์ž‘์šฉ์„ ๋ชฉ๋„ํ•œ ํ›„, ๊ณผํ•™์ด +์–ด๋–ป๊ฒŒ ๋ฐœ๋‹ฌํ•˜๋“  ๊ทธ๊ฒƒ์ด ์ธ์ƒ๊ด€์˜ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•  ์ˆ˜๋Š” ์—†๋‹ค๋ฉฐ +์„œ์–‘ ๊ทผ๋Œ€ ๋ฌธ๋ช…์„ ๋น„ํŒํ–ˆ๋‹ค. ๊ทผ๋Œ€ ๊ณผํ•™ ๋ฌธ๋ช…์—์„œ ์ดˆ๋ž˜๋œ ์‚ฌ์ƒ์  +์œ„๊ธฐ๊ฐ€ ์ฃผ์ฒด์˜ ์ฑ…์ž„ ๋ถ€์žฌ์—์„œ ๋น„๋กฏ๋œ ๊ฒƒ์ด๋ผ๋Š” ์ฃผ์žฅ์— ๋™์˜ํ–ˆ๋˜ +๊ทธ๋Š” ๊ณผํ•™์  ๋ฐฉ๋ฒ•์„ ๋ถ€์ •ํ•˜์ง€ ์•Š์•˜์ง€๋งŒ, ์ธ์ƒ๊ด€์˜ ๋ฌธ์ œ์—๋Š” +๊ณผํ•™์  ๋ฐฉ๋ฒ•์ด ์ ์šฉ๋  ์ˆ˜ ์—†๋‹ค๊ณ  ์ง€์ ํ–ˆ๋‹ค. ๊ทธ๋Š” ์ธ์ƒ๊ด€์„ +๊ณผํ•™๊ณผ ๋ณ„๊ฐœ๋กœ ํŒŒ์•…ํ–ˆ๊ณ , ๊ณผํ•™๋งŒ๋Šฅ์ฃผ์˜์— ๊ธฐ์ดˆํ•œ ์‹ ๋ฌธํ™” ์šด๋™์— +์˜ํ•ด ๋ถ€์ •๋œ ์ค‘๊ตญ ์ „ํ†ต ๊ฐ€์น˜๊ด€์˜ ์ˆ˜ํ˜ธ๋ฅผ ๋‚ด์„ธ์› ๋‹ค.","{'question': 'โ“์™€ ๋ฌธ๋งฅ์ƒ ์˜๋ฏธ๊ฐ€ ๊ฐ€์žฅ ๊ฐ€๊นŒ์šด ๊ฒƒ์€?', 'choices': ['โ‘ ๋‹คํ–‰ํžˆ ๋น„๋Š” ๊ทธ์‚ฌ์ด์— ๊ทธ์ณ ์žˆ์—ˆ๋‹ค.', 'โ‘ก์šฐ๋ฆฌ ํ•™๊ต๋Š” ์ด๋ฒˆ์— 16๊ฐ•์— ๊ทธ์ณค๋‹ค', 'โ‘ข์•„์ด ์šธ์Œ์ด ์ข€์ฒ˜๋Ÿผ ๊ทธ์น˜์ง€ ์•Š์•˜๋‹ค.', 'โ‘ฃ๊ทธ๋Š” ๋งŒ๋ฅ˜์—๋„ ๋ง์„ ๊ทธ์น˜์ง€ ์•Š์•˜๋‹ค', 'โ‘ค์ € ์‚ฌ๋žŒ๋“ค์€ ๋ถˆํ‰์ด ๊ทธ์น  ๋‚ ์ด ์—†๋‹ค.'], 'answer': ''}",,2,2,True,[],2 +2025-korean-10,"๋ฌธ์žฅ์ด๋‚˜ ์˜์ƒ, ์Œ์„ฑ์„ ๋งŒ๋“ค์–ด ๋‚ด๋Š” ์ธ๊ณต ์ง€๋Šฅ ์ƒ์„ฑ ๋ชจ๋ธ ์ค‘ +ํ™•์‚ฐ ๋ชจ๋ธ์€ ์˜์ƒ์˜ ๋ณต์›, ์ƒ์„ฑ ๋ฐ ๋ณ€ํ™˜์— ๋›ฐ์–ด๋‚œ ์„ฑ๋Šฅ์„ ๋ณด์ธ๋‹ค. +ํ™•์‚ฐ ๋ชจ๋ธ์˜ ๊ธฐ๋ณธ ๋ฐœ์ƒ์€, ์›๋ณธ ์ด๋ฏธ์ง€์— ๋…ธ์ด์ฆˆ๋ฅผ ์ ์ง„์ ์œผ๋กœ +์ถ”๊ฐ€ํ•˜์˜€๋‹ค๊ฐ€ ๊ทธ ๋…ธ์ด์ฆˆ๋ฅผ ๋‹ค์‹œ ์ œ๊ฑฐํ•ด ๋‚˜๊ฐ€๋ฉด ์›๋ณธ ์ด๋ฏธ์ง€๋ฅผ +๋ณต์›ํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. ๋…ธ์ด์ฆˆ๋Š” ๋ถˆํ•„์š”ํ•˜๊ฑฐ๋‚˜ ์›ํ•˜์ง€ ์•Š๋Š” +๊ฐ’์„ ์˜๋ฏธํ•œ๋‹ค. ์›ํ•˜๋Š” ๊ฐ’๋งŒ ๋“ค์–ด ์žˆ๋Š” ์›๋ณธ ์ด๋ฏธ์ง€์— ๋…ธ์ด์ฆˆ๋ฅผ +๋‹จ๊ณ„๋ณ„๋กœ ๋”ํ•˜๋ฉด ๋…ธ์ด์ฆˆ๊ฐ€ ํฌํ•จ๋œ ํ™•์‚ฐ ์ด๋ฏธ์ง€๊ฐ€ ๋˜๊ณ , ์—ฌ๋Ÿฌ +๋‹จ๊ณ„๋ฅผ ๊ฑฐ์น˜๋ฉด ๊ฒฐ๊ตญ ์›๋ณธ ์ด๋ฏธ์ง€๊ฐ€ ์–ด๋–ค ์ด๋ฏธ์ง€์˜€๋Š”์ง€ ์ „ํ˜€ +์•Œ์•„๋ณผ ์ˆ˜ ์—†๋Š” ๋…ธ์ด์ฆˆ ์ด๋ฏธ์ง€๊ฐ€ ๋œ๋‹ค. ์—ญ์œผ๋กœ, ๋‹จ๊ณ„๋ณ„๋กœ ๋”ํ•ด์ง„ +๋…ธ์ด์ฆˆ๋ฅผ ์•Œ ์ˆ˜ ์žˆ๋‹ค๋ฉด ๋…ธ์ด์ฆˆ ์ด๋ฏธ์ง€์—์„œ ์›๋ณธ ์ด๋ฏธ์ง€๋ฅผ ๋ณต์›ํ•  +์ˆ˜ ์žˆ๋‹ค. ํ™•์‚ฐ ๋ชจ๋ธ์€ ๋…ธ์ด์ฆˆ ์ƒ์„ฑ๊ธฐ, ์ด๋ฏธ์ง€ ์—ฐ์‚ฐ๊ธฐ, ๋…ธ์ด์ฆˆ +์˜ˆ์ธก๊ธฐ๋กœ ๊ตฌ์„ฑ๋˜๋ฉฐ, ์ˆœํ™•์‚ฐ ๊ณผ์ •๊ณผ ์—ญํ™•์‚ฐ ๊ณผ์ • ์ˆœ์œผ๋กœ ์ž‘๋™ํ•œ๋‹ค. +์ˆœํ™•์‚ฐ ๊ณผ์ •์€ ์ด๋ฏธ์ง€์— ๋…ธ์ด์ฆˆ๋ฅผ ์ถ”๊ฐ€ํ•˜๋ฉด์„œ ๋…ธ์ด์ฆˆ ์˜ˆ์ธก๊ธฐ๋ฅผ +ํ•™์Šต์‹œํ‚ค๋Š” ๊ณผ์ •์ด๋‹ค. ์ฒซ ๋‹จ๊ณ„์—์„œ๋Š”, ๋…ธ์ด์ฆˆ ์ƒ์„ฑ๊ธฐ์—์„œ ๋…ธ์ด์ฆˆ๋ฅผ +๋งŒ๋“  ํ›„ ์ด๋ฏธ์ง€ ์—ฐ์‚ฐ๊ธฐ๊ฐ€ ์ด ๋…ธ์ด์ฆˆ๋ฅผ ์›๋ณธ ์ด๋ฏธ์ง€์— ๋”ํ•ด์„œ +๋…ธ์ด์ฆˆ๊ฐ€ ํฌํ•จ๋œ ํ™•์‚ฐ ์ด๋ฏธ์ง€๋ฅผ ์ถœ๋ ฅํ•œ๋‹ค. ๋‹ค์Œ ๋‹จ๊ณ„๋ถ€ํ„ฐ๋Š” +๋…ธ์ด์ฆˆ ์ƒ์„ฑ๊ธฐ์—์„œ ๋งŒ๋“  ๋…ธ์ด์ฆˆ๋ฅผ ์ด์ „ ๋‹จ๊ณ„์—์„œ ์ถœ๋ ฅ๋œ ํ™•์‚ฐ +์ด๋ฏธ์ง€์— ๋”ํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ๋‹จ๊ณ„๋ฅผ ์ถฉ๋ถ„ํžˆ ๋ฐ˜๋ณตํ•˜๋ฉด ์ตœ์ข…์ ์œผ๋กœ +๋…ธ์ด์ฆˆ ์ด๋ฏธ์ง€๊ฐ€ ์ถœ๋ ฅ๋œ๋‹ค. ์ด๋•Œ ๋”ํ•ด์ง€๋Š” ๋…ธ์ด์ฆˆ๋Š” ํฌ๊ธฐ๋‚˜ ๋ถ„ํฌ +์–‘์ƒ ๋“ฑ ๊ทธ ํŠน์„ฑ์ด ๋‹จ๊ณ„๋ณ„๋กœ ๋‹ค๋ฅด๋‹ค. ๋”ฐ๋ผ์„œ ๋…ธ์ด์ฆˆ ์˜ˆ์ธก๊ธฐ๋Š” +๋‹จ๊ณ„๋ณ„๋กœ ํ™•์‚ฐ ์ด๋ฏธ์ง€๋ฅผ ์ž…๋ ฅ๋ฐ›์•„ ์ด๋ฏธ์ง€์— ํฌํ•จ๋œ ๋…ธ์ด์ฆˆ์˜ +ํŠน์„ฑ์„ ์ถ”์ถœํ•˜์—ฌ ์ˆ˜์น˜๋“ค๋กœ ํ‘œํ˜„ํ•˜๊ณ , ์ด ์ˆ˜์น˜๋“ค์„ ๋ฐ”ํƒ•์œผ๋กœ +๋…ธ์ด์ฆˆ๋ฅผ ์˜ˆ์ธกํ•œ๋‹ค. ๋…ธ์ด์ฆˆ ์˜ˆ์ธก๊ธฐ ๋‚ด๋ถ€์˜ ์ด๋Ÿฌํ•œ ์ˆ˜์น˜๋“ค์„ +์ž ์žฌ ํ‘œํ˜„์ด๋ผ๊ณ  ํ•œ๋‹ค. ๋…ธ์ด์ฆˆ ์˜ˆ์ธก๊ธฐ๋Š” ์ž ์žฌ ํ‘œํ˜„์„ ๊ตฌํ•˜๊ณ  +๋…ธ์ด์ฆˆ๋ฅผ ์˜ˆ์ธกํ•˜๋Š” ๋ฐฉ์‹์„ ํ•™์Šตํ•œ๋‹ค. +๋…ธ์ด์ฆˆ ์˜ˆ์ธก๊ธฐ์˜ ํ•™์Šต ๋ฐฉ๋ฒ•์€ ๊ธฐ๊ณ„ ํ•™์Šต ์ค‘์—์„œ ์ง€๋„ ํ•™์Šต์— +ํ•ด๋‹นํ•œ๋‹ค. ์ง€๋„ ํ•™์Šต์€ ํ•™์Šต ๋ฐ์ดํ„ฐ์— ์ •๋‹ต์ด ์ฃผ์–ด์ ธ ์ถœ๋ ฅ๊ณผ +์ •๋‹ต์˜ ์ฐจ์ด๊ฐ€ ์ž‘์•„์ง€๋„๋ก ๋ชจ๋ธ์„ ํ•™์Šต์‹œํ‚ค๋Š” ๋ฐฉ๋ฒ•์ด๋‹ค. ๋…ธ์ด์ฆˆ +์˜ˆ์ธก๊ธฐ๋ฅผ ํ•™์Šต์‹œํ‚ฌ ๋•Œ๋Š” ๋…ธ์ด์ฆˆ ์ƒ์„ฑ๊ธฐ์—์„œ ๋งŒ๋“ค์–ด ๋„ฃ์–ด ์ค€ +๋…ธ์ด์ฆˆ๊ฐ€ ์ •๋‹ต์— ํ•ด๋‹นํ•˜๋ฉฐ ์ด ๋…ธ์ด์ฆˆ์™€ ์˜ˆ์ธก๋œ ๋…ธ์ด์ฆˆ ์‚ฌ์ด์˜ +์ฐจ์ด๊ฐ€ ์ž‘์•„์ง€๋„๋ก ํ•™์Šต์‹œํ‚จ๋‹ค. +์—ญํ™•์‚ฐ ๊ณผ์ •์€ ๋…ธ์ด์ฆˆ ์ด๋ฏธ์ง€์—์„œ ๋…ธ์ด์ฆˆ๋ฅผ ์ œ๊ฑฐํ•˜์—ฌ ์›๋ณธ +์ด๋ฏธ์ง€๋ฅผ ๋ณต์›ํ•˜๋Š” ๊ณผ์ •์ด๋‹ค. ๋…ธ์ด์ฆˆ๋ฅผ ์ œ๊ฑฐํ•˜๋ ค๋ฉด ์ด๋ฏธ์ง€์— +๋‹จ๊ณ„๋ณ„๋กœ ์–ด๋–ค ํŠน์„ฑ์˜ ๋…ธ์ด์ฆˆ๊ฐ€ ๋”ํ•ด์กŒ๋Š”์ง€ ์•Œ์•„์•ผ ํ•˜๋Š”๋ฐ +๋…ธ์ด์ฆˆ ์˜ˆ์ธก๊ธฐ๊ฐ€ ์ด ์—ญํ• ์„ ํ•œ๋‹ค. ๋…ธ์ด์ฆˆ ์ด๋ฏธ์ง€ ๋˜๋Š” ์ค‘๊ฐ„ +๋‹จ๊ณ„์—์„œ์˜ ํ™•์‚ฐ ์ด๋ฏธ์ง€๋ฅผ ๋…ธ์ด์ฆˆ ์˜ˆ์ธก๊ธฐ์— ์ž…๋ ฅํ•˜๋ฉด ์ด๋ฏธ์ง€์— +ํฌํ•จ๋œ ๋…ธ์ด์ฆˆ์˜ ํŠน์„ฑ์„ ์ถ”์ถœํ•˜์—ฌ ์ž ์žฌ ํ‘œํ˜„์„ ๊ตฌํ•˜๊ณ  ์ด๋ฅผ +๋ฐ”ํƒ•์œผ๋กœ ๋…ธ์ด์ฆˆ๋ฅผ ์˜ˆ์ธกํ•œ๋‹ค. ์ด๋ฏธ์ง€ ์—ฐ์‚ฐ๊ธฐ๋Š” ์ž…๋ ฅ๋œ ํ™•์‚ฐ +์ด๋ฏธ์ง€๋กœ๋ถ€ํ„ฐ ์ด ๋…ธ์ด์ฆˆ๋ฅผ ๋นผ์„œ ํ˜„ ๋‹จ๊ณ„์˜ ๋…ธ์ด์ฆˆ๋ฅผ ์ œ๊ฑฐํ•œ ํ™•์‚ฐ +์ด๋ฏธ์ง€๋ฅผ ์ถœ๋ ฅํ•œ๋‹ค. ํ™•์‚ฐ ์ด๋ฏธ์ง€์— ์ด๋Ÿฐ ๋‹จ๊ณ„๋ฅผ ๋ฐ˜๋ณตํ•˜๋ฉด ๊ฒฐ๊ตญ +๋…ธ์ด์ฆˆ๊ฐ€ ๋Œ€๋ถ€๋ถ„ ์ œ๊ฑฐ๋˜์–ด ์›๋ณธ ์ด๋ฏธ์ง€์— ๊ฐ€๊นŒ์šด ์ด๋ฏธ์ง€๋งŒ ๋‚จ๊ฒŒ +๋œ๋‹ค. +ํ•œํŽธ, ๋งŽ์€ ์ข…๋ฅ˜์˜ ์ด๋ฏธ์ง€๋ฅผ ํ•™์Šต์‹œํ‚จ ํ›„ ํ•™์Šต๋œ ์ด๋ฏธ์ง€์˜ +์ž ์žฌ ํ‘œํ˜„์— ๊ณ ์œ  ๋ฒˆํ˜ธ๋ฅผ ๋ถ™์ด๋ฉด ์—ญํ™•์‚ฐ ๊ณผ์ •์—์„œ ์ด๋ฏธ์ง€๋ฅผ +์„ ํƒํ•˜์—ฌ ์ƒ์„ฑํ•  ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ ์ž ์žฌ ํ‘œํ˜„์˜ ์ˆ˜์น˜๋“ค์„ ์กฐ์ •ํ•˜๋ฉด +๋‹ค๋ฅธ ํŠน์„ฑ์˜ ๋…ธ์ด์ฆˆ๊ฐ€ ์ƒ์„ฑ๋˜์–ด ์—ฌ๋Ÿฌ ์ด๋ฏธ์ง€๋ฅผ ํ˜ผํ•ฉํ•˜๊ฑฐ๋‚˜ +์‹ค์žฌํ•˜์ง€ ์•Š๋Š” ์ด๋ฏธ์ง€๋ฅผ ๋งŒ๋“ค์–ด ๋‚ผ ์ˆ˜๋„ ์žˆ๋‹ค.","{'question': 'ํ•™์ƒ์ด ์œ—๊ธ€์„ ์ฝ์€ ๋ฐฉ๋ฒ•์œผ๋กœ ์ ์ ˆํ•˜์ง€ ์•Š์€ ๊ฒƒ์€?', 'choices': ['ํ™•์‚ฐ ๋ชจ๋ธ์ด ์ง€๋„ ํ•™์Šต์„ ์‚ฌ์šฉํ•œ๋‹ค๋Š” ์ ์— ์ฃผ๋ชฉํ•˜๊ณ , ์ง€๋„\nํ•™์Šต ๋ฐฉ๋ฒ•์ด ํ™•์‚ฐ ๋ชจ๋ธ์— ์–ด๋–ป๊ฒŒ ์ ์šฉ๋˜๋Š”์ง€ ํ™•์ธํ•˜๋ฉฐ ์ฝ์—ˆ๋‹ค', 'ํ™•์‚ฐ ๋ชจ๋ธ์ด ๋‘ ๊ฐ€์ง€ ๊ณผ์ •์œผ๋กœ ์ด๋ฃจ์–ด์ง„๋‹ค๋Š” ์ ์— ์ฃผ๋ชฉํ•˜๊ณ ,\n๋‘ ๊ณผ์ • ์ค‘ ์–ด๋А ๊ณผ์ •์ด ์„ ํ–‰๋˜์–ด์•ผ ํ•˜๋Š”์ง€ ์‚ดํ”ผ๋ฉฐ ์ฝ์—ˆ๋‹ค.', 'ํ™•์‚ฐ ๋ชจ๋ธ์—์„œ ๋…ธ์ด์ฆˆ์˜ ์ค‘์š”์„ฑ์„ ํŒŒ์•…ํ•˜๊ณ , ์‚ฌ์šฉ๋˜๋Š” ๋…ธ์ด์ฆˆ์˜\n์ข…๋ฅ˜๊ฐ€ ๋ชจ๋ธ์˜ ์„ฑ๋Šฅ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์ดํ•ดํ•˜๋ฉฐ ์ฝ์—ˆ๋‹ค.', '์ž ์žฌ ํ‘œํ˜„์˜ ๊ฐœ๋…์„ ํŒŒ์•…ํ•˜๊ณ , ๊ทธ ๊ฐœ๋…์„ ๋ฐ”ํƒ•์œผ๋กœ ํ™•์‚ฐ ๋ชจ๋ธ์ด\n๋…ธ์ด์ฆˆ๋ฅผ ์˜ˆ์ธกํ•˜๊ณ  ์ œ๊ฑฐํ•˜๋Š” ์›๋ฆฌ๋ฅผ ์ดํ•ดํ•˜๋ฉฐ ์ฝ์—ˆ๋‹ค', 'ํ™•์‚ฐ ๋ชจ๋ธ์˜ ๊ตฌ์„ฑ ์š”์†Œ๋ฅผ ํŒŒ์•…ํ•˜๊ณ , ๊ทธ ๊ตฌ์„ฑ ์š”์†Œ๊ฐ€ ๋…ธ์ด์ฆˆ\n์ฒ˜๋ฆฌ ๊ณผ์ •์—์„œ ์–ด๋–ค ๊ธฐ๋Šฅ์„ ํ•˜๋Š”์ง€ ํ™•์ธํ•˜๋ฉฐ ์ฝ์—ˆ๋‹ค.'], 'answer': ''}",,3,3,True,[],3 +2025-korean-11,"๋ฌธ์žฅ์ด๋‚˜ ์˜์ƒ, ์Œ์„ฑ์„ ๋งŒ๋“ค์–ด ๋‚ด๋Š” ์ธ๊ณต ์ง€๋Šฅ ์ƒ์„ฑ ๋ชจ๋ธ ์ค‘ +ํ™•์‚ฐ ๋ชจ๋ธ์€ ์˜์ƒ์˜ ๋ณต์›, ์ƒ์„ฑ ๋ฐ ๋ณ€ํ™˜์— ๋›ฐ์–ด๋‚œ ์„ฑ๋Šฅ์„ ๋ณด์ธ๋‹ค. +ํ™•์‚ฐ ๋ชจ๋ธ์˜ ๊ธฐ๋ณธ ๋ฐœ์ƒ์€, ์›๋ณธ ์ด๋ฏธ์ง€์— ๋…ธ์ด์ฆˆ๋ฅผ ์ ์ง„์ ์œผ๋กœ +์ถ”๊ฐ€ํ•˜์˜€๋‹ค๊ฐ€ ๊ทธ ๋…ธ์ด์ฆˆ๋ฅผ ๋‹ค์‹œ ์ œ๊ฑฐํ•ด ๋‚˜๊ฐ€๋ฉด ์›๋ณธ ์ด๋ฏธ์ง€๋ฅผ +๋ณต์›ํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. ๋…ธ์ด์ฆˆ๋Š” ๋ถˆํ•„์š”ํ•˜๊ฑฐ๋‚˜ ์›ํ•˜์ง€ ์•Š๋Š” +๊ฐ’์„ ์˜๋ฏธํ•œ๋‹ค. ์›ํ•˜๋Š” ๊ฐ’๋งŒ ๋“ค์–ด ์žˆ๋Š” ์›๋ณธ ์ด๋ฏธ์ง€์— ๋…ธ์ด์ฆˆ๋ฅผ +๋‹จ๊ณ„๋ณ„๋กœ ๋”ํ•˜๋ฉด ๋…ธ์ด์ฆˆ๊ฐ€ ํฌํ•จ๋œ ํ™•์‚ฐ ์ด๋ฏธ์ง€๊ฐ€ ๋˜๊ณ , ์—ฌ๋Ÿฌ +๋‹จ๊ณ„๋ฅผ ๊ฑฐ์น˜๋ฉด ๊ฒฐ๊ตญ ์›๋ณธ ์ด๋ฏธ์ง€๊ฐ€ ์–ด๋–ค ์ด๋ฏธ์ง€์˜€๋Š”์ง€ ์ „ํ˜€ +์•Œ์•„๋ณผ ์ˆ˜ ์—†๋Š” ๋…ธ์ด์ฆˆ ์ด๋ฏธ์ง€๊ฐ€ ๋œ๋‹ค. ์—ญ์œผ๋กœ, ๋‹จ๊ณ„๋ณ„๋กœ ๋”ํ•ด์ง„ +๋…ธ์ด์ฆˆ๋ฅผ ์•Œ ์ˆ˜ ์žˆ๋‹ค๋ฉด ๋…ธ์ด์ฆˆ ์ด๋ฏธ์ง€์—์„œ ์›๋ณธ ์ด๋ฏธ์ง€๋ฅผ ๋ณต์›ํ•  +์ˆ˜ ์žˆ๋‹ค. ํ™•์‚ฐ ๋ชจ๋ธ์€ ๋…ธ์ด์ฆˆ ์ƒ์„ฑ๊ธฐ, ์ด๋ฏธ์ง€ ์—ฐ์‚ฐ๊ธฐ, ๋…ธ์ด์ฆˆ +์˜ˆ์ธก๊ธฐ๋กœ ๊ตฌ์„ฑ๋˜๋ฉฐ, ์ˆœํ™•์‚ฐ ๊ณผ์ •๊ณผ ์—ญํ™•์‚ฐ ๊ณผ์ • ์ˆœ์œผ๋กœ ์ž‘๋™ํ•œ๋‹ค. +์ˆœํ™•์‚ฐ ๊ณผ์ •์€ ์ด๋ฏธ์ง€์— ๋…ธ์ด์ฆˆ๋ฅผ ์ถ”๊ฐ€ํ•˜๋ฉด์„œ ๋…ธ์ด์ฆˆ ์˜ˆ์ธก๊ธฐ๋ฅผ +ํ•™์Šต์‹œํ‚ค๋Š” ๊ณผ์ •์ด๋‹ค. ์ฒซ ๋‹จ๊ณ„์—์„œ๋Š”, ๋…ธ์ด์ฆˆ ์ƒ์„ฑ๊ธฐ์—์„œ ๋…ธ์ด์ฆˆ๋ฅผ +๋งŒ๋“  ํ›„ ์ด๋ฏธ์ง€ ์—ฐ์‚ฐ๊ธฐ๊ฐ€ ์ด ๋…ธ์ด์ฆˆ๋ฅผ ์›๋ณธ ์ด๋ฏธ์ง€์— ๋”ํ•ด์„œ +๋…ธ์ด์ฆˆ๊ฐ€ ํฌํ•จ๋œ ํ™•์‚ฐ ์ด๋ฏธ์ง€๋ฅผ ์ถœ๋ ฅํ•œ๋‹ค. ๋‹ค์Œ ๋‹จ๊ณ„๋ถ€ํ„ฐ๋Š” +๋…ธ์ด์ฆˆ ์ƒ์„ฑ๊ธฐ์—์„œ ๋งŒ๋“  ๋…ธ์ด์ฆˆ๋ฅผ ์ด์ „ ๋‹จ๊ณ„์—์„œ ์ถœ๋ ฅ๋œ ํ™•์‚ฐ +์ด๋ฏธ์ง€์— ๋”ํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ๋‹จ๊ณ„๋ฅผ ์ถฉ๋ถ„ํžˆ ๋ฐ˜๋ณตํ•˜๋ฉด ์ตœ์ข…์ ์œผ๋กœ +๋…ธ์ด์ฆˆ ์ด๋ฏธ์ง€๊ฐ€ ์ถœ๋ ฅ๋œ๋‹ค. ์ด๋•Œ ๋”ํ•ด์ง€๋Š” ๋…ธ์ด์ฆˆ๋Š” ํฌ๊ธฐ๋‚˜ ๋ถ„ํฌ +์–‘์ƒ ๋“ฑ ๊ทธ ํŠน์„ฑ์ด ๋‹จ๊ณ„๋ณ„๋กœ ๋‹ค๋ฅด๋‹ค. ๋”ฐ๋ผ์„œ ๋…ธ์ด์ฆˆ ์˜ˆ์ธก๊ธฐ๋Š” +๋‹จ๊ณ„๋ณ„๋กœ ํ™•์‚ฐ ์ด๋ฏธ์ง€๋ฅผ ์ž…๋ ฅ๋ฐ›์•„ ์ด๋ฏธ์ง€์— ํฌํ•จ๋œ ๋…ธ์ด์ฆˆ์˜ +ํŠน์„ฑ์„ ์ถ”์ถœํ•˜์—ฌ ์ˆ˜์น˜๋“ค๋กœ ํ‘œํ˜„ํ•˜๊ณ , ์ด ์ˆ˜์น˜๋“ค์„ ๋ฐ”ํƒ•์œผ๋กœ +๋…ธ์ด์ฆˆ๋ฅผ ์˜ˆ์ธกํ•œ๋‹ค. ๋…ธ์ด์ฆˆ ์˜ˆ์ธก๊ธฐ ๋‚ด๋ถ€์˜ ์ด๋Ÿฌํ•œ ์ˆ˜์น˜๋“ค์„ +์ž ์žฌ ํ‘œํ˜„์ด๋ผ๊ณ  ํ•œ๋‹ค. ๋…ธ์ด์ฆˆ ์˜ˆ์ธก๊ธฐ๋Š” ์ž ์žฌ ํ‘œํ˜„์„ ๊ตฌํ•˜๊ณ  +๋…ธ์ด์ฆˆ๋ฅผ ์˜ˆ์ธกํ•˜๋Š” ๋ฐฉ์‹์„ ํ•™์Šตํ•œ๋‹ค. +๋…ธ์ด์ฆˆ ์˜ˆ์ธก๊ธฐ์˜ ํ•™์Šต ๋ฐฉ๋ฒ•์€ ๊ธฐ๊ณ„ ํ•™์Šต ์ค‘์—์„œ ์ง€๋„ ํ•™์Šต์— +ํ•ด๋‹นํ•œ๋‹ค. ์ง€๋„ ํ•™์Šต์€ ํ•™์Šต ๋ฐ์ดํ„ฐ์— ์ •๋‹ต์ด ์ฃผ์–ด์ ธ ์ถœ๋ ฅ๊ณผ +์ •๋‹ต์˜ ์ฐจ์ด๊ฐ€ ์ž‘์•„์ง€๋„๋ก ๋ชจ๋ธ์„ ํ•™์Šต์‹œํ‚ค๋Š” ๋ฐฉ๋ฒ•์ด๋‹ค. ๋…ธ์ด์ฆˆ +์˜ˆ์ธก๊ธฐ๋ฅผ ํ•™์Šต์‹œํ‚ฌ ๋•Œ๋Š” ๋…ธ์ด์ฆˆ ์ƒ์„ฑ๊ธฐ์—์„œ ๋งŒ๋“ค์–ด ๋„ฃ์–ด ์ค€ +๋…ธ์ด์ฆˆ๊ฐ€ ์ •๋‹ต์— ํ•ด๋‹นํ•˜๋ฉฐ ์ด ๋…ธ์ด์ฆˆ์™€ ์˜ˆ์ธก๋œ ๋…ธ์ด์ฆˆ ์‚ฌ์ด์˜ +์ฐจ์ด๊ฐ€ ์ž‘์•„์ง€๋„๋ก ํ•™์Šต์‹œํ‚จ๋‹ค. +์—ญํ™•์‚ฐ ๊ณผ์ •์€ ๋…ธ์ด์ฆˆ ์ด๋ฏธ์ง€์—์„œ ๋…ธ์ด์ฆˆ๋ฅผ ์ œ๊ฑฐํ•˜์—ฌ ์›๋ณธ +์ด๋ฏธ์ง€๋ฅผ ๋ณต์›ํ•˜๋Š” ๊ณผ์ •์ด๋‹ค. ๋…ธ์ด์ฆˆ๋ฅผ ์ œ๊ฑฐํ•˜๋ ค๋ฉด ์ด๋ฏธ์ง€์— +๋‹จ๊ณ„๋ณ„๋กœ ์–ด๋–ค ํŠน์„ฑ์˜ ๋…ธ์ด์ฆˆ๊ฐ€ ๋”ํ•ด์กŒ๋Š”์ง€ ์•Œ์•„์•ผ ํ•˜๋Š”๋ฐ +๋…ธ์ด์ฆˆ ์˜ˆ์ธก๊ธฐ๊ฐ€ ์ด ์—ญํ• ์„ ํ•œ๋‹ค. ๋…ธ์ด์ฆˆ ์ด๋ฏธ์ง€ ๋˜๋Š” ์ค‘๊ฐ„ +๋‹จ๊ณ„์—์„œ์˜ ํ™•์‚ฐ ์ด๋ฏธ์ง€๋ฅผ ๋…ธ์ด์ฆˆ ์˜ˆ์ธก๊ธฐ์— ์ž…๋ ฅํ•˜๋ฉด ์ด๋ฏธ์ง€์— +ํฌํ•จ๋œ ๋…ธ์ด์ฆˆ์˜ ํŠน์„ฑ์„ ์ถ”์ถœํ•˜์—ฌ ์ž ์žฌ ํ‘œํ˜„์„ ๊ตฌํ•˜๊ณ  ์ด๋ฅผ +๋ฐ”ํƒ•์œผ๋กœ ๋…ธ์ด์ฆˆ๋ฅผ ์˜ˆ์ธกํ•œ๋‹ค. ์ด๋ฏธ์ง€ ์—ฐ์‚ฐ๊ธฐ๋Š” ์ž…๋ ฅ๋œ ํ™•์‚ฐ +์ด๋ฏธ์ง€๋กœ๋ถ€ํ„ฐ ์ด ๋…ธ์ด์ฆˆ๋ฅผ ๋นผ์„œ ํ˜„ ๋‹จ๊ณ„์˜ ๋…ธ์ด์ฆˆ๋ฅผ ์ œ๊ฑฐํ•œ ํ™•์‚ฐ +์ด๋ฏธ์ง€๋ฅผ ์ถœ๋ ฅํ•œ๋‹ค. ํ™•์‚ฐ ์ด๋ฏธ์ง€์— ์ด๋Ÿฐ ๋‹จ๊ณ„๋ฅผ ๋ฐ˜๋ณตํ•˜๋ฉด ๊ฒฐ๊ตญ +๋…ธ์ด์ฆˆ๊ฐ€ ๋Œ€๋ถ€๋ถ„ ์ œ๊ฑฐ๋˜์–ด ์›๋ณธ ์ด๋ฏธ์ง€์— ๊ฐ€๊นŒ์šด ์ด๋ฏธ์ง€๋งŒ ๋‚จ๊ฒŒ +๋œ๋‹ค. +ํ•œํŽธ, ๋งŽ์€ ์ข…๋ฅ˜์˜ ์ด๋ฏธ์ง€๋ฅผ ํ•™์Šต์‹œํ‚จ ํ›„ ํ•™์Šต๋œ ์ด๋ฏธ์ง€์˜ +์ž ์žฌ ํ‘œํ˜„์— ๊ณ ์œ  ๋ฒˆํ˜ธ๋ฅผ ๋ถ™์ด๋ฉด ์—ญํ™•์‚ฐ ๊ณผ์ •์—์„œ ์ด๋ฏธ์ง€๋ฅผ +์„ ํƒํ•˜์—ฌ ์ƒ์„ฑํ•  ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ ์ž ์žฌ ํ‘œํ˜„์˜ ์ˆ˜์น˜๋“ค์„ ์กฐ์ •ํ•˜๋ฉด +๋‹ค๋ฅธ ํŠน์„ฑ์˜ ๋…ธ์ด์ฆˆ๊ฐ€ ์ƒ์„ฑ๋˜์–ด ์—ฌ๋Ÿฌ ์ด๋ฏธ์ง€๋ฅผ ํ˜ผํ•ฉํ•˜๊ฑฐ๋‚˜ +์‹ค์žฌํ•˜์ง€ ์•Š๋Š” ์ด๋ฏธ์ง€๋ฅผ ๋งŒ๋“ค์–ด ๋‚ผ ์ˆ˜๋„ ์žˆ๋‹ค.","{'question': '์œ—๊ธ€์„ ์ดํ•ดํ•œ ๋‚ด์šฉ์œผ๋กœ ๊ฐ€์žฅ ์ ์ ˆํ•œ ๊ฒƒ์€?', 'choices': ['๋…ธ์ด์ฆˆ ์ƒ์„ฑ๊ธฐ๋Š” ์ˆœํ™•์‚ฐ ๊ณผ์ •์—์„œ๋งŒ ์ž‘๋™ํ•œ๋‹ค.', 'ํ™•์‚ฐ ๋ชจ๋ธ์—์„œ์˜ ํ•™์Šต์€ ์—ญํ™•์‚ฐ ๊ณผ์ •์—์„œ ์ด๋ฃจ์–ด์ง„๋‹ค.', '์ด๋ฏธ์ง€ ์—ฐ์‚ฐ๊ธฐ์™€ ๋…ธ์ด์ฆˆ ์˜ˆ์ธก๊ธฐ๋Š” ๋ชจ๋‘ ํ™•์‚ฐ ์ด๋ฏธ์ง€๋ฅผ ์ถœ๋ ฅ\nํ•œ๋‹ค.', '๋…ธ์ด์ฆˆ ์˜ˆ์ธก๊ธฐ๋ฅผ ํ•™์Šต์‹œํ‚ฌ ๋•Œ๋Š” ์˜ˆ์ธก๋œ ๋…ธ์ด์ฆˆ๊ฐ€ ์ •๋‹ต์œผ๋กœ\n์‚ฌ์šฉ๋œ๋‹ค.', '์—ญํ™•์‚ฐ ๊ณผ์ •์—์„œ ๋‹จ๊ณ„๊ฐ€ ๋ฐ˜๋ณต๋ ์ˆ˜๋ก ์ถœ๋ ฅ๋˜๋Š” ํ™•์‚ฐ ์ด๋ฏธ์ง€๋Š”\n์›๋ณธ ์ด๋ฏธ์ง€์™€์˜ ์œ ์‚ฌ์„ฑ์ด ์ค„์–ด๋“ ๋‹ค.'], 'answer': ''}",,1,1,True,[],3 +2025-korean-12,"๋ฌธ์žฅ์ด๋‚˜ ์˜์ƒ, ์Œ์„ฑ์„ ๋งŒ๋“ค์–ด ๋‚ด๋Š” ์ธ๊ณต ์ง€๋Šฅ ์ƒ์„ฑ ๋ชจ๋ธ ์ค‘ +ํ™•์‚ฐ ๋ชจ๋ธ์€ ์˜์ƒ์˜ ๋ณต์›, ์ƒ์„ฑ ๋ฐ ๋ณ€ํ™˜์— ๋›ฐ์–ด๋‚œ ์„ฑ๋Šฅ์„ ๋ณด์ธ๋‹ค. +ํ™•์‚ฐ ๋ชจ๋ธ์˜ ๊ธฐ๋ณธ ๋ฐœ์ƒ์€, ์›๋ณธ ์ด๋ฏธ์ง€์— ๋…ธ์ด์ฆˆ๋ฅผ ์ ์ง„์ ์œผ๋กœ +์ถ”๊ฐ€ํ•˜์˜€๋‹ค๊ฐ€ ๊ทธ ๋…ธ์ด์ฆˆ๋ฅผ ๋‹ค์‹œ ์ œ๊ฑฐํ•ด ๋‚˜๊ฐ€๋ฉด ์›๋ณธ ์ด๋ฏธ์ง€๋ฅผ +๋ณต์›ํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. ๋…ธ์ด์ฆˆ๋Š” ๋ถˆํ•„์š”ํ•˜๊ฑฐ๋‚˜ ์›ํ•˜์ง€ ์•Š๋Š” +๊ฐ’์„ ์˜๋ฏธํ•œ๋‹ค. ์›ํ•˜๋Š” ๊ฐ’๋งŒ ๋“ค์–ด ์žˆ๋Š” ์›๋ณธ ์ด๋ฏธ์ง€์— ๋…ธ์ด์ฆˆ๋ฅผ +๋‹จ๊ณ„๋ณ„๋กœ ๋”ํ•˜๋ฉด ๋…ธ์ด์ฆˆ๊ฐ€ ํฌํ•จ๋œ ํ™•์‚ฐ ์ด๋ฏธ์ง€๊ฐ€ ๋˜๊ณ , ์—ฌ๋Ÿฌ +๋‹จ๊ณ„๋ฅผ ๊ฑฐ์น˜๋ฉด ๊ฒฐ๊ตญ ์›๋ณธ ์ด๋ฏธ์ง€๊ฐ€ ์–ด๋–ค ์ด๋ฏธ์ง€์˜€๋Š”์ง€ ์ „ํ˜€ +์•Œ์•„๋ณผ ์ˆ˜ ์—†๋Š” ๋…ธ์ด์ฆˆ ์ด๋ฏธ์ง€๊ฐ€ ๋œ๋‹ค. ์—ญ์œผ๋กœ, ๋‹จ๊ณ„๋ณ„๋กœ ๋”ํ•ด์ง„ +๋…ธ์ด์ฆˆ๋ฅผ ์•Œ ์ˆ˜ ์žˆ๋‹ค๋ฉด ๋…ธ์ด์ฆˆ ์ด๋ฏธ์ง€์—์„œ ์›๋ณธ ์ด๋ฏธ์ง€๋ฅผ ๋ณต์›ํ•  +์ˆ˜ ์žˆ๋‹ค. ํ™•์‚ฐ ๋ชจ๋ธ์€ ๋…ธ์ด์ฆˆ ์ƒ์„ฑ๊ธฐ, ์ด๋ฏธ์ง€ ์—ฐ์‚ฐ๊ธฐ, ๋…ธ์ด์ฆˆ +์˜ˆ์ธก๊ธฐ๋กœ ๊ตฌ์„ฑ๋˜๋ฉฐ, ์ˆœํ™•์‚ฐ ๊ณผ์ •๊ณผ ์—ญํ™•์‚ฐ ๊ณผ์ • ์ˆœ์œผ๋กœ ์ž‘๋™ํ•œ๋‹ค. +์ˆœํ™•์‚ฐ ๊ณผ์ •์€ ์ด๋ฏธ์ง€์— ๋…ธ์ด์ฆˆ๋ฅผ ์ถ”๊ฐ€ํ•˜๋ฉด์„œ ๋…ธ์ด์ฆˆ ์˜ˆ์ธก๊ธฐ๋ฅผ +ํ•™์Šต์‹œํ‚ค๋Š” ๊ณผ์ •์ด๋‹ค. ์ฒซ ๋‹จ๊ณ„์—์„œ๋Š”, ๋…ธ์ด์ฆˆ ์ƒ์„ฑ๊ธฐ์—์„œ ๋…ธ์ด์ฆˆ๋ฅผ +๋งŒ๋“  ํ›„ ์ด๋ฏธ์ง€ ์—ฐ์‚ฐ๊ธฐ๊ฐ€ ์ด ๋…ธ์ด์ฆˆ๋ฅผ ์›๋ณธ ์ด๋ฏธ์ง€์— ๋”ํ•ด์„œ +๋…ธ์ด์ฆˆ๊ฐ€ ํฌํ•จ๋œ ํ™•์‚ฐ ์ด๋ฏธ์ง€๋ฅผ ์ถœ๋ ฅํ•œ๋‹ค. ๋‹ค์Œ ๋‹จ๊ณ„๋ถ€ํ„ฐ๋Š” +๋…ธ์ด์ฆˆ ์ƒ์„ฑ๊ธฐ์—์„œ ๋งŒ๋“  ๋…ธ์ด์ฆˆ๋ฅผ ์ด์ „ ๋‹จ๊ณ„์—์„œ ์ถœ๋ ฅ๋œ ํ™•์‚ฐ +์ด๋ฏธ์ง€์— ๋”ํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ๋‹จ๊ณ„๋ฅผ ์ถฉ๋ถ„ํžˆ ๋ฐ˜๋ณตํ•˜๋ฉด ์ตœ์ข…์ ์œผ๋กœ +๋…ธ์ด์ฆˆ ์ด๋ฏธ์ง€๊ฐ€ ์ถœ๋ ฅ๋œ๋‹ค. ์ด๋•Œ ๋”ํ•ด์ง€๋Š” ๋…ธ์ด์ฆˆ๋Š” ํฌ๊ธฐ๋‚˜ ๋ถ„ํฌ +์–‘์ƒ ๋“ฑ ๊ทธ ํŠน์„ฑ์ด ๋‹จ๊ณ„๋ณ„๋กœ ๋‹ค๋ฅด๋‹ค. ๋”ฐ๋ผ์„œ ๋…ธ์ด์ฆˆ ์˜ˆ์ธก๊ธฐ๋Š” +๋‹จ๊ณ„๋ณ„๋กœ ํ™•์‚ฐ ์ด๋ฏธ์ง€๋ฅผ ์ž…๋ ฅ๋ฐ›์•„ ์ด๋ฏธ์ง€์— ํฌํ•จ๋œ ๋…ธ์ด์ฆˆ์˜ +ํŠน์„ฑ์„ ์ถ”์ถœํ•˜์—ฌ ์ˆ˜์น˜๋“ค๋กœ ํ‘œํ˜„ํ•˜๊ณ , ์ด ์ˆ˜์น˜๋“ค์„ ๋ฐ”ํƒ•์œผ๋กœ +๋…ธ์ด์ฆˆ๋ฅผ ์˜ˆ์ธกํ•œ๋‹ค. ๋…ธ์ด์ฆˆ ์˜ˆ์ธก๊ธฐ ๋‚ด๋ถ€์˜ ์ด๋Ÿฌํ•œ ์ˆ˜์น˜๋“ค์„ +์ž ์žฌ ํ‘œํ˜„์ด๋ผ๊ณ  ํ•œ๋‹ค. ๋…ธ์ด์ฆˆ ์˜ˆ์ธก๊ธฐ๋Š” ์ž ์žฌ ํ‘œํ˜„์„ ๊ตฌํ•˜๊ณ  +๋…ธ์ด์ฆˆ๋ฅผ ์˜ˆ์ธกํ•˜๋Š” ๋ฐฉ์‹์„ ํ•™์Šตํ•œ๋‹ค. +๋…ธ์ด์ฆˆ ์˜ˆ์ธก๊ธฐ์˜ ํ•™์Šต ๋ฐฉ๋ฒ•์€ ๊ธฐ๊ณ„ ํ•™์Šต ์ค‘์—์„œ ์ง€๋„ ํ•™์Šต์— +ํ•ด๋‹นํ•œ๋‹ค. ์ง€๋„ ํ•™์Šต์€ ํ•™์Šต ๋ฐ์ดํ„ฐ์— ์ •๋‹ต์ด ์ฃผ์–ด์ ธ ์ถœ๋ ฅ๊ณผ +์ •๋‹ต์˜ ์ฐจ์ด๊ฐ€ ์ž‘์•„์ง€๋„๋ก ๋ชจ๋ธ์„ ํ•™์Šต์‹œํ‚ค๋Š” ๋ฐฉ๋ฒ•์ด๋‹ค. ๋…ธ์ด์ฆˆ +์˜ˆ์ธก๊ธฐ๋ฅผ ํ•™์Šต์‹œํ‚ฌ ๋•Œ๋Š” ๋…ธ์ด์ฆˆ ์ƒ์„ฑ๊ธฐ์—์„œ ๋งŒ๋“ค์–ด ๋„ฃ์–ด ์ค€ +๋…ธ์ด์ฆˆ๊ฐ€ ์ •๋‹ต์— ํ•ด๋‹นํ•˜๋ฉฐ ์ด ๋…ธ์ด์ฆˆ์™€ ์˜ˆ์ธก๋œ ๋…ธ์ด์ฆˆ ์‚ฌ์ด์˜ +์ฐจ์ด๊ฐ€ ์ž‘์•„์ง€๋„๋ก ํ•™์Šต์‹œํ‚จ๋‹ค. +์—ญํ™•์‚ฐ ๊ณผ์ •์€ ๋…ธ์ด์ฆˆ ์ด๋ฏธ์ง€์—์„œ ๋…ธ์ด์ฆˆ๋ฅผ ์ œ๊ฑฐํ•˜์—ฌ ์›๋ณธ +์ด๋ฏธ์ง€๋ฅผ ๋ณต์›ํ•˜๋Š” ๊ณผ์ •์ด๋‹ค. ๋…ธ์ด์ฆˆ๋ฅผ ์ œ๊ฑฐํ•˜๋ ค๋ฉด ์ด๋ฏธ์ง€์— +๋‹จ๊ณ„๋ณ„๋กœ ์–ด๋–ค ํŠน์„ฑ์˜ ๋…ธ์ด์ฆˆ๊ฐ€ ๋”ํ•ด์กŒ๋Š”์ง€ ์•Œ์•„์•ผ ํ•˜๋Š”๋ฐ +๋…ธ์ด์ฆˆ ์˜ˆ์ธก๊ธฐ๊ฐ€ ์ด ์—ญํ• ์„ ํ•œ๋‹ค. ๋…ธ์ด์ฆˆ ์ด๋ฏธ์ง€ ๋˜๋Š” ์ค‘๊ฐ„ +๋‹จ๊ณ„์—์„œ์˜ ํ™•์‚ฐ ์ด๋ฏธ์ง€๋ฅผ ๋…ธ์ด์ฆˆ ์˜ˆ์ธก๊ธฐ์— ์ž…๋ ฅํ•˜๋ฉด ์ด๋ฏธ์ง€์— +ํฌํ•จ๋œ ๋…ธ์ด์ฆˆ์˜ ํŠน์„ฑ์„ ์ถ”์ถœํ•˜์—ฌ ์ž ์žฌ ํ‘œํ˜„์„ ๊ตฌํ•˜๊ณ  ์ด๋ฅผ +๋ฐ”ํƒ•์œผ๋กœ ๋…ธ์ด์ฆˆ๋ฅผ ์˜ˆ์ธกํ•œ๋‹ค. ์ด๋ฏธ์ง€ ์—ฐ์‚ฐ๊ธฐ๋Š” ์ž…๋ ฅ๋œ ํ™•์‚ฐ +์ด๋ฏธ์ง€๋กœ๋ถ€ํ„ฐ ์ด ๋…ธ์ด์ฆˆ๋ฅผ ๋นผ์„œ ํ˜„ ๋‹จ๊ณ„์˜ ๋…ธ์ด์ฆˆ๋ฅผ ์ œ๊ฑฐํ•œ ํ™•์‚ฐ +์ด๋ฏธ์ง€๋ฅผ ์ถœ๋ ฅํ•œ๋‹ค. ํ™•์‚ฐ ์ด๋ฏธ์ง€์— ์ด๋Ÿฐ ๋‹จ๊ณ„๋ฅผ ๋ฐ˜๋ณตํ•˜๋ฉด ๊ฒฐ๊ตญ +๋…ธ์ด์ฆˆ๊ฐ€ ๋Œ€๋ถ€๋ถ„ ์ œ๊ฑฐ๋˜์–ด ์›๋ณธ ์ด๋ฏธ์ง€์— ๊ฐ€๊นŒ์šด ์ด๋ฏธ์ง€๋งŒ ๋‚จ๊ฒŒ +๋œ๋‹ค. +ํ•œํŽธ, ๋งŽ์€ ์ข…๋ฅ˜์˜ ์ด๋ฏธ์ง€๋ฅผ ํ•™์Šต์‹œํ‚จ ํ›„ ํ•™์Šต๋œ ์ด๋ฏธ์ง€์˜ +์ž ์žฌ ํ‘œํ˜„์— ๊ณ ์œ  ๋ฒˆํ˜ธ๋ฅผ ๋ถ™์ด๋ฉด ์—ญํ™•์‚ฐ ๊ณผ์ •์—์„œ ์ด๋ฏธ์ง€๋ฅผ +์„ ํƒํ•˜์—ฌ ์ƒ์„ฑํ•  ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ ์ž ์žฌ ํ‘œํ˜„์˜ ์ˆ˜์น˜๋“ค์„ ์กฐ์ •ํ•˜๋ฉด +๋‹ค๋ฅธ ํŠน์„ฑ์˜ ๋…ธ์ด์ฆˆ๊ฐ€ ์ƒ์„ฑ๋˜์–ด ์—ฌ๋Ÿฌ ์ด๋ฏธ์ง€๋ฅผ ํ˜ผํ•ฉํ•˜๊ฑฐ๋‚˜ +์‹ค์žฌํ•˜์ง€ ์•Š๋Š” ์ด๋ฏธ์ง€๋ฅผ ๋งŒ๋“ค์–ด ๋‚ผ ์ˆ˜๋„ ์žˆ๋‹ค.","{'question': '์ž ์žฌ ํ‘œํ˜„์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์ ์ ˆํ•˜์ง€ ์•Š์€ ๊ฒƒ์€?', 'choices': ['์ž ์žฌ ํ‘œํ˜„์˜ ์ˆ˜์น˜๋“ค์„ ์กฐ์ •ํ•˜๋ฉด ์—ฌ๋Ÿฌ ์ด๋ฏธ์ง€๋ฅผ ํ˜ผํ•ฉํ•  ์ˆ˜ ์žˆ๋‹ค.', '์—ญํ™•์‚ฐ ๊ณผ์ •์—์„œ ์ž ์žฌ ํ‘œํ˜„์ด ๋‹ค๋ฅด๋ฉด ์˜ˆ์ธก๋˜๋Š” ๋…ธ์ด์ฆˆ๊ฐ€ ๋‹ค๋ฅด๋‹ค.', 'ํ™•์‚ฐ ๋ชจ๋ธ์˜ ํ•™์Šต์—๋Š” ์ž ์žฌ ํ‘œํ˜„์„ ๊ตฌํ•˜๋Š” ๋ฐฉ์‹์ด ํฌํ•จ๋˜์–ด\n์žˆ๋‹ค', '์ž ์žฌ ํ‘œํ˜„์€ ์ด๋ฏธ์ง€์— ๋”ํ•ด์ง„ ๋…ธ์ด์ฆˆ์˜ ํฌ๊ธฐ๋‚˜ ๋ถ„ํฌ ์–‘์ƒ์— ๋”ฐ๋ผ\n๋‹ค๋ฅธ ๊ฐ’๋“ค์ด ์–ป์–ด์ง„๋‹ค.', '์ž ์žฌ ํ‘œํ˜„์€ ๋…ธ์ด์ฆˆ ์˜ˆ์ธก๊ธฐ๊ฐ€ ์›๋ณธ ์ด๋ฏธ์ง€๋ฅผ ์ž…๋ ฅ๋ฐ›์•„ ๋…ธ์ด์ฆˆ์˜\nํŠน์„ฑ์„ ์ถ”์ถœํ•œ ๊ฒฐ๊ณผ์ด๋‹ค.'], 'answer': ''}",,5,5,True,[],5 +2025-korean-13,"๋ฌธ์žฅ์ด๋‚˜ ์˜์ƒ, ์Œ์„ฑ์„ ๋งŒ๋“ค์–ด ๋‚ด๋Š” ์ธ๊ณต ์ง€๋Šฅ ์ƒ์„ฑ ๋ชจ๋ธ ์ค‘ +ํ™•์‚ฐ ๋ชจ๋ธ์€ ์˜์ƒ์˜ ๋ณต์›, ์ƒ์„ฑ ๋ฐ ๋ณ€ํ™˜์— ๋›ฐ์–ด๋‚œ ์„ฑ๋Šฅ์„ ๋ณด์ธ๋‹ค. +ํ™•์‚ฐ ๋ชจ๋ธ์˜ ๊ธฐ๋ณธ ๋ฐœ์ƒ์€, ์›๋ณธ ์ด๋ฏธ์ง€์— ๋…ธ์ด์ฆˆ๋ฅผ ์ ์ง„์ ์œผ๋กœ +์ถ”๊ฐ€ํ•˜์˜€๋‹ค๊ฐ€ ๊ทธ ๋…ธ์ด์ฆˆ๋ฅผ ๋‹ค์‹œ ์ œ๊ฑฐํ•ด ๋‚˜๊ฐ€๋ฉด ์›๋ณธ ์ด๋ฏธ์ง€๋ฅผ +๋ณต์›ํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. ๋…ธ์ด์ฆˆ๋Š” ๋ถˆํ•„์š”ํ•˜๊ฑฐ๋‚˜ ์›ํ•˜์ง€ ์•Š๋Š” +๊ฐ’์„ ์˜๋ฏธํ•œ๋‹ค. ์›ํ•˜๋Š” ๊ฐ’๋งŒ ๋“ค์–ด ์žˆ๋Š” ์›๋ณธ ์ด๋ฏธ์ง€์— ๋…ธ์ด์ฆˆ๋ฅผ +๋‹จ๊ณ„๋ณ„๋กœ ๋”ํ•˜๋ฉด ๋…ธ์ด์ฆˆ๊ฐ€ ํฌํ•จ๋œ ํ™•์‚ฐ ์ด๋ฏธ์ง€๊ฐ€ ๋˜๊ณ , ์—ฌ๋Ÿฌ +๋‹จ๊ณ„๋ฅผ ๊ฑฐ์น˜๋ฉด ๊ฒฐ๊ตญ ์›๋ณธ ์ด๋ฏธ์ง€๊ฐ€ ์–ด๋–ค ์ด๋ฏธ์ง€์˜€๋Š”์ง€ ์ „ํ˜€ +์•Œ์•„๋ณผ ์ˆ˜ ์—†๋Š” ๋…ธ์ด์ฆˆ ์ด๋ฏธ์ง€๊ฐ€ ๋œ๋‹ค. ์—ญ์œผ๋กœ, ๋‹จ๊ณ„๋ณ„๋กœ ๋”ํ•ด์ง„ +๋…ธ์ด์ฆˆ๋ฅผ ์•Œ ์ˆ˜ ์žˆ๋‹ค๋ฉด ๋…ธ์ด์ฆˆ ์ด๋ฏธ์ง€์—์„œ ์›๋ณธ ์ด๋ฏธ์ง€๋ฅผ ๋ณต์›ํ•  +์ˆ˜ ์žˆ๋‹ค. ํ™•์‚ฐ ๋ชจ๋ธ์€ ๋…ธ์ด์ฆˆ ์ƒ์„ฑ๊ธฐ, ์ด๋ฏธ์ง€ ์—ฐ์‚ฐ๊ธฐ, ๋…ธ์ด์ฆˆ +์˜ˆ์ธก๊ธฐ๋กœ ๊ตฌ์„ฑ๋˜๋ฉฐ, ์ˆœํ™•์‚ฐ ๊ณผ์ •๊ณผ ์—ญํ™•์‚ฐ ๊ณผ์ • ์ˆœ์œผ๋กœ ์ž‘๋™ํ•œ๋‹ค. +์ˆœํ™•์‚ฐ ๊ณผ์ •์€ ์ด๋ฏธ์ง€์— ๋…ธ์ด์ฆˆ๋ฅผ ์ถ”๊ฐ€ํ•˜๋ฉด์„œ ๋…ธ์ด์ฆˆ ์˜ˆ์ธก๊ธฐ๋ฅผ +ํ•™์Šต์‹œํ‚ค๋Š” ๊ณผ์ •์ด๋‹ค. ์ฒซ ๋‹จ๊ณ„์—์„œ๋Š”, ๋…ธ์ด์ฆˆ ์ƒ์„ฑ๊ธฐ์—์„œ ๋…ธ์ด์ฆˆ๋ฅผ +๋งŒ๋“  ํ›„ ์ด๋ฏธ์ง€ ์—ฐ์‚ฐ๊ธฐ๊ฐ€ ์ด ๋…ธ์ด์ฆˆ๋ฅผ ์›๋ณธ ์ด๋ฏธ์ง€์— ๋”ํ•ด์„œ +๋…ธ์ด์ฆˆ๊ฐ€ ํฌํ•จ๋œ ํ™•์‚ฐ ์ด๋ฏธ์ง€๋ฅผ ์ถœ๋ ฅํ•œ๋‹ค. ๋‹ค์Œ ๋‹จ๊ณ„๋ถ€ํ„ฐ๋Š” +๋…ธ์ด์ฆˆ ์ƒ์„ฑ๊ธฐ์—์„œ ๋งŒ๋“  ๋…ธ์ด์ฆˆ๋ฅผ ์ด์ „ ๋‹จ๊ณ„์—์„œ ์ถœ๋ ฅ๋œ ํ™•์‚ฐ +์ด๋ฏธ์ง€์— ๋”ํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ๋‹จ๊ณ„๋ฅผ ์ถฉ๋ถ„ํžˆ ๋ฐ˜๋ณตํ•˜๋ฉด ์ตœ์ข…์ ์œผ๋กœ +๋…ธ์ด์ฆˆ ์ด๋ฏธ์ง€๊ฐ€ ์ถœ๋ ฅ๋œ๋‹ค. ์ด๋•Œ ๋”ํ•ด์ง€๋Š” ๋…ธ์ด์ฆˆ๋Š” ํฌ๊ธฐ๋‚˜ ๋ถ„ํฌ +์–‘์ƒ ๋“ฑ ๊ทธ ํŠน์„ฑ์ด ๋‹จ๊ณ„๋ณ„๋กœ ๋‹ค๋ฅด๋‹ค. ๋”ฐ๋ผ์„œ ๋…ธ์ด์ฆˆ ์˜ˆ์ธก๊ธฐ๋Š” +๋‹จ๊ณ„๋ณ„๋กœ ํ™•์‚ฐ ์ด๋ฏธ์ง€๋ฅผ ์ž…๋ ฅ๋ฐ›์•„ ์ด๋ฏธ์ง€์— ํฌํ•จ๋œ ๋…ธ์ด์ฆˆ์˜ +ํŠน์„ฑ์„ ์ถ”์ถœํ•˜์—ฌ ์ˆ˜์น˜๋“ค๋กœ ํ‘œํ˜„ํ•˜๊ณ , ์ด ์ˆ˜์น˜๋“ค์„ ๋ฐ”ํƒ•์œผ๋กœ +๋…ธ์ด์ฆˆ๋ฅผ ์˜ˆ์ธกํ•œ๋‹ค. ๋…ธ์ด์ฆˆ ์˜ˆ์ธก๊ธฐ ๋‚ด๋ถ€์˜ ์ด๋Ÿฌํ•œ ์ˆ˜์น˜๋“ค์„ +์ž ์žฌ ํ‘œํ˜„์ด๋ผ๊ณ  ํ•œ๋‹ค. ๋…ธ์ด์ฆˆ ์˜ˆ์ธก๊ธฐ๋Š” ์ž ์žฌ ํ‘œํ˜„์„ ๊ตฌํ•˜๊ณ  +๋…ธ์ด์ฆˆ๋ฅผ ์˜ˆ์ธกํ•˜๋Š” ๋ฐฉ์‹์„ ํ•™์Šตํ•œ๋‹ค. +๋…ธ์ด์ฆˆ ์˜ˆ์ธก๊ธฐ์˜ ํ•™์Šต ๋ฐฉ๋ฒ•์€ ๊ธฐ๊ณ„ ํ•™์Šต ์ค‘์—์„œ ์ง€๋„ ํ•™์Šต์— +ํ•ด๋‹นํ•œ๋‹ค. ์ง€๋„ ํ•™์Šต์€ ํ•™์Šต ๋ฐ์ดํ„ฐ์— ์ •๋‹ต์ด ์ฃผ์–ด์ ธ ์ถœ๋ ฅ๊ณผ +์ •๋‹ต์˜ ์ฐจ์ด๊ฐ€ ์ž‘์•„์ง€๋„๋ก ๋ชจ๋ธ์„ ํ•™์Šต์‹œํ‚ค๋Š” ๋ฐฉ๋ฒ•์ด๋‹ค. ๋…ธ์ด์ฆˆ +์˜ˆ์ธก๊ธฐ๋ฅผ ํ•™์Šต์‹œํ‚ฌ ๋•Œ๋Š” ๋…ธ์ด์ฆˆ ์ƒ์„ฑ๊ธฐ์—์„œ ๋งŒ๋“ค์–ด ๋„ฃ์–ด ์ค€ +๋…ธ์ด์ฆˆ๊ฐ€ ์ •๋‹ต์— ํ•ด๋‹นํ•˜๋ฉฐ ์ด ๋…ธ์ด์ฆˆ์™€ ์˜ˆ์ธก๋œ ๋…ธ์ด์ฆˆ ์‚ฌ์ด์˜ +์ฐจ์ด๊ฐ€ ์ž‘์•„์ง€๋„๋ก ํ•™์Šต์‹œํ‚จ๋‹ค. +์—ญํ™•์‚ฐ ๊ณผ์ •์€ ๋…ธ์ด์ฆˆ ์ด๋ฏธ์ง€์—์„œ ๋…ธ์ด์ฆˆ๋ฅผ ์ œ๊ฑฐํ•˜์—ฌ ์›๋ณธ +์ด๋ฏธ์ง€๋ฅผ ๋ณต์›ํ•˜๋Š” ๊ณผ์ •์ด๋‹ค. ๋…ธ์ด์ฆˆ๋ฅผ ์ œ๊ฑฐํ•˜๋ ค๋ฉด ์ด๋ฏธ์ง€์— +๋‹จ๊ณ„๋ณ„๋กœ ์–ด๋–ค ํŠน์„ฑ์˜ ๋…ธ์ด์ฆˆ๊ฐ€ ๋”ํ•ด์กŒ๋Š”์ง€ ์•Œ์•„์•ผ ํ•˜๋Š”๋ฐ +๋…ธ์ด์ฆˆ ์˜ˆ์ธก๊ธฐ๊ฐ€ ์ด ์—ญํ• ์„ ํ•œ๋‹ค. ๋…ธ์ด์ฆˆ ์ด๋ฏธ์ง€ ๋˜๋Š” ์ค‘๊ฐ„ +๋‹จ๊ณ„์—์„œ์˜ ํ™•์‚ฐ ์ด๋ฏธ์ง€๋ฅผ ๋…ธ์ด์ฆˆ ์˜ˆ์ธก๊ธฐ์— ์ž…๋ ฅํ•˜๋ฉด ์ด๋ฏธ์ง€์— +ํฌํ•จ๋œ ๋…ธ์ด์ฆˆ์˜ ํŠน์„ฑ์„ ์ถ”์ถœํ•˜์—ฌ ์ž ์žฌ ํ‘œํ˜„์„ ๊ตฌํ•˜๊ณ  ์ด๋ฅผ +๋ฐ”ํƒ•์œผ๋กœ ๋…ธ์ด์ฆˆ๋ฅผ ์˜ˆ์ธกํ•œ๋‹ค. ์ด๋ฏธ์ง€ ์—ฐ์‚ฐ๊ธฐ๋Š” ์ž…๋ ฅ๋œ ํ™•์‚ฐ +์ด๋ฏธ์ง€๋กœ๋ถ€ํ„ฐ ์ด ๋…ธ์ด์ฆˆ๋ฅผ ๋นผ์„œ ํ˜„ ๋‹จ๊ณ„์˜ ๋…ธ์ด์ฆˆ๋ฅผ ์ œ๊ฑฐํ•œ ํ™•์‚ฐ +์ด๋ฏธ์ง€๋ฅผ ์ถœ๋ ฅํ•œ๋‹ค. ํ™•์‚ฐ ์ด๋ฏธ์ง€์— ์ด๋Ÿฐ ๋‹จ๊ณ„๋ฅผ ๋ฐ˜๋ณตํ•˜๋ฉด ๊ฒฐ๊ตญ +๋…ธ์ด์ฆˆ๊ฐ€ ๋Œ€๋ถ€๋ถ„ ์ œ๊ฑฐ๋˜์–ด ์›๋ณธ ์ด๋ฏธ์ง€์— ๊ฐ€๊นŒ์šด ์ด๋ฏธ์ง€๋งŒ ๋‚จ๊ฒŒ +๋œ๋‹ค. +ํ•œํŽธ, ๋งŽ์€ ์ข…๋ฅ˜์˜ ์ด๋ฏธ์ง€๋ฅผ ํ•™์Šต์‹œํ‚จ ํ›„ ํ•™์Šต๋œ ์ด๋ฏธ์ง€์˜ +์ž ์žฌ ํ‘œํ˜„์— ๊ณ ์œ  ๋ฒˆํ˜ธ๋ฅผ ๋ถ™์ด๋ฉด ์—ญํ™•์‚ฐ ๊ณผ์ •์—์„œ ์ด๋ฏธ์ง€๋ฅผ +์„ ํƒํ•˜์—ฌ ์ƒ์„ฑํ•  ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ ์ž ์žฌ ํ‘œํ˜„์˜ ์ˆ˜์น˜๋“ค์„ ์กฐ์ •ํ•˜๋ฉด +๋‹ค๋ฅธ ํŠน์„ฑ์˜ ๋…ธ์ด์ฆˆ๊ฐ€ ์ƒ์„ฑ๋˜์–ด ์—ฌ๋Ÿฌ ์ด๋ฏธ์ง€๋ฅผ ํ˜ผํ•ฉํ•˜๊ฑฐ๋‚˜ +์‹ค์žฌํ•˜์ง€ ์•Š๋Š” ์ด๋ฏธ์ง€๋ฅผ ๋งŒ๋“ค์–ด ๋‚ผ ์ˆ˜๋„ ์žˆ๋‹ค.","{'question': '์œ—๊ธ€์„ ๋ฐ”ํƒ•์œผ๋กœ <๋ณด๊ธฐ>๋ฅผ ์ดํ•ดํ•œ ๋‚ด์šฉ์œผ๋กœ ์ ์ ˆํ•˜์ง€ ์•Š์€\n๊ฒƒ์€?', 'choices': ['(๊ฐ€)์— ใ‰ ์ด ์ž…๋ ฅ๋œ๋‹ค๋ฉด, A๋‹จ๊ณ„์˜ ์ด๋ฏธ์ง€ ์—ฐ์‚ฐ๊ธฐ์—์„œ๋Š” ใ‰ ์—\n๋…ธ์ด์ฆˆ๋ฅผ ๋”ํ•˜๊ฒ ๊ตฐ.', '(๋‚˜)์— ใ‰ข์ด ์ถœ๋ ฅ๋œ๋‹ค๋ฉด, A๋‹จ๊ณ„์˜ ๋…ธ์ด์ฆˆ ์ƒ์„ฑ๊ธฐ์—์„œ ์ƒ์„ฑ๋œ\n๋…ธ์ด์ฆˆ๊ฐ€ ์ด๋ฏธ์ง€ ์—ฐ์‚ฐ๊ธฐ์—์„œ ํ™•์‚ฐ ์ด๋ฏธ์ง€์— ๋”ํ•ด์กŒ๊ฒ ๊ตฐ.', '์ˆœํ™•์‚ฐ ๊ณผ์ •์—์„œ (๊ฐ€)์— ใ‰ก์ด ์ž…๋ ฅ๋œ๋‹ค๋ฉด, A๋‹จ๊ณ„์˜ ๋…ธ์ด์ฆˆ\n์˜ˆ์ธก๊ธฐ์—์„œ ์˜ˆ์ธกํ•œ ๋…ธ์ด์ฆˆ๊ฐ€ ์ด๋ฏธ์ง€ ์—ฐ์‚ฐ๊ธฐ์— ์ž…๋ ฅ๋˜๊ฒ ๊ตฐ.', '์—ญํ™•์‚ฐ ๊ณผ์ •์—์„œ (๊ฐ€)์— ใ‰ข์ด ์ž…๋ ฅ๋œ๋‹ค๋ฉด, A๋‹จ๊ณ„์˜ ์ด๋ฏธ์ง€\n์—ฐ์‚ฐ๊ธฐ์—์„œ๋Š” ใ‰ข์—์„œ ๋…ธ์ด์ฆˆ๋ฅผ ๋นผ๊ฒ ๊ตฐ.', '์—ญํ™•์‚ฐ ๊ณผ์ •์—์„œ (๋‚˜)์— ใ‰ก์ด ์ถœ๋ ฅ๋œ๋‹ค๋ฉด, A๋‹จ๊ณ„์˜ ๋…ธ์ด์ฆˆ\n์˜ˆ์ธก๊ธฐ์—์„œ ์˜ˆ์ธกํ•œ ๋…ธ์ด์ฆˆ๊ฐ€ ์ด๋ฏธ์ง€ ์—ฐ์‚ฐ๊ธฐ์— ์ž…๋ ฅ๋˜์—ˆ๊ฒ ๊ตฐ.'], 'answer': '', 'question_plus': 'A๋‹จ๊ณ„๋Š” ํ™•์‚ฐ ๋ชจ๋ธ ๊ณผ์ • ์ค‘ ํ•œ ๋‹จ๊ณ„์ด๋‹ค. ใ‰ ์€ ์›๋ณธ ์ด๋ฏธ์ง€\n์ด๊ณ , ใ‰ก์€ ํ™•์‚ฐ ์ด๋ฏธ์ง€ ์ค‘์˜ ํ•˜๋‚˜์ด๋ฉฐ, ใ‰ข์€ ๋…ธ์ด์ฆˆ ์ด๋ฏธ์ง€\n์ด๋‹ค. (๊ฐ€)๋Š” ์ด๋ฏธ์ง€๊ฐ€ A๋‹จ๊ณ„๋กœ ์ž…๋ ฅ๋˜๋Š” ๋ถ€๋ถ„์ด๊ณ , (๋‚˜)๋Š”\n์ด๋ฏธ์ง€๊ฐ€ A๋‹จ๊ณ„์—์„œ ์ถœ๋ ฅ๋˜๋Š” ๋ถ€๋ถ„์ด๋‹ค.\n(๊ฐ€) โ†’ A๋‹จ๊ณ„ โ†’ (๋‚˜)'}","A๋‹จ๊ณ„๋Š” ํ™•์‚ฐ ๋ชจ๋ธ ๊ณผ์ • ์ค‘ ํ•œ ๋‹จ๊ณ„์ด๋‹ค. ใ‰ ์€ ์›๋ณธ ์ด๋ฏธ์ง€ +์ด๊ณ , ใ‰ก์€ ํ™•์‚ฐ ์ด๋ฏธ์ง€ ์ค‘์˜ ํ•˜๋‚˜์ด๋ฉฐ, ใ‰ข์€ ๋…ธ์ด์ฆˆ ์ด๋ฏธ์ง€ +์ด๋‹ค. (๊ฐ€)๋Š” ์ด๋ฏธ์ง€๊ฐ€ A๋‹จ๊ณ„๋กœ ์ž…๋ ฅ๋˜๋Š” ๋ถ€๋ถ„์ด๊ณ , (๋‚˜)๋Š” +์ด๋ฏธ์ง€๊ฐ€ A๋‹จ๊ณ„์—์„œ ์ถœ๋ ฅ๋˜๋Š” ๋ถ€๋ถ„์ด๋‹ค. +(๊ฐ€) โ†’ A๋‹จ๊ณ„ โ†’ (๋‚˜)",3,3,True,[],3 +2025-korean-14,"๋ฆฌํ”„ํ‚จ์€ ์‚ฌํšŒ์  ์ƒํ˜ธ ์ž‘์šฉ์—์„œ์˜ ์ž๊ธฐํ‘œํ˜„์€ ๋ณธ์งˆ์ ์œผ๋กœ +์—ฐ๊ทน์ ์ด๋ฉฐ, ํ‘œ๋ฉด ์—ฐ๊ธฐ์™€ ์‹ฌ์ธต ์—ฐ๊ธฐ๋กœ โ“์ด๋ฃจ์–ด์ง„๋‹ค๊ณ  ์–ธ๊ธ‰ํ–ˆ๋‹ค. +ํ‘œ๋ฉด ์—ฐ๊ธฐ๋Š” ๋‚ด๋ฉด์˜ ์ž์—ฐ์Šค๋Ÿฌ์šด ๊ฐ์ •๋ณด๋‹ค ์˜๋ก€์ ์ธ ํ‘œํ˜„๊ณผ ๊ฐ™์€ +ํ˜•์‹์— ์ง‘์ค‘ํ•˜์—ฌ ์—ฐ๊ธฐํ•˜๋Š” ๊ฒƒ์ด๊ณ , ์‹ฌ์ธต ์—ฐ๊ธฐ๋Š” ๋‚ด๋ฉด์˜ ์†”์งํ•œ +์ •์„œ๋ฅผ โ“‘๋ถˆ๋Ÿฌ๋‚ด์–ด ์ž์‹ ์˜ ์ง„์ •์„ฑ์„ ๋ณด์—ฌ ์ฃผ๋Š” ๊ฒƒ์ด๋‹ค. ์ธํ„ฐ๋„ท +์—์„œ์˜ ์ปค๋ฎค๋‹ˆ์ผ€์ด์…˜์— ์ฃผ๋ชฉํ•œ ๋ฆฌํ”„ํ‚จ์€ ๊ฐ€์ƒ ๊ณต๊ฐ„์—์„œ ์ž๊ธฐ +ํ‘œํ˜„์ด ๋”์šฑ ํ™œ๋ฐœํžˆ ์ด๋ฃจ์–ด์ง„๋‹ค๊ณ  ๋ณด์•˜๋‹ค. +๊ฐ€์ƒ ๊ณต๊ฐ„์˜ ํŠน์„ฑ์— ์ฃผ๋ชฉํ•œ ์—ฐ๊ตฌ์ž๋“ค์€ ์‚ฌ๋žŒ๋“ค๊ณผ์˜ ๊ด€๊ณ„ ์†์—์„œ +๋“œ๋Ÿฌ๋‚˜๋Š” ๊ณ ์œ ํ•œ ์กด์žฌ๋กœ์„œ์˜ ์œ„์ƒ์„ ๋œปํ•˜๋Š” ์ž๊ธฐ ์ •์ฒด์„ฑ์ด ๊ฐ€์ƒ +๊ณต๊ฐ„์—์„œ ๋‹ค์–‘ํ•˜๊ฒŒ โ“’๋‚˜ํƒ€๋‚œ๋‹ค๊ณ  ๋ณธ๋‹ค. ๊ฐ€์ƒ ๊ณต๊ฐ„์—์„œ๋Š” ์ต๋ช…์„ฑ์ด +์ž‘๋™ํ•˜๋ฏ€๋กœ ํ˜„์‹ค์—์„œ ์œ„์ถ•๋˜๋Š” ์‚ฌ๋žŒ๋„ ์ ๊ทน์ ์œผ๋กœ ์ž๊ธฐํ‘œํ˜„์„ +ํ•  ์ˆ˜ ์žˆ๋‹ค. ์•„์šธ๋Ÿฌ ํ˜„์‹ค์—์„œ์˜ ์ž๊ธฐ ์ •์ฒด์„ฑ์„ โ““๊ฐ์ถ”๊ณ  ๋‹ค๋ฅธ +์ธ๊ฒฉ์ฒด๋กœ ํ™œ๋™ํ•˜๊ฑฐ๋‚˜ ํ˜„์‹ค์—์„œ ์–ต์••๋œ ์ •์„œ๋ฅผ ๊ณต๊ฒฉ์ ์œผ๋กœ ๋“œ๋Ÿฌ +๋‚ด๊ธฐ๋„ ํ•œ๋‹ค. ๊ฒŒ์ž„ ์•„์ด๋””, ๋‹‰๋„ค์ž„, ์•„๋ฐ”ํƒ€ ๋“ฑ ๊ฐ€์ƒ ๊ณต๊ฐ„์—์„œ +๊ฐœ๋ณ„์  ๋Œ€์ƒ์œผ๋กœ ์ธ์‹๋˜๋Š” โ€˜์ธํ„ฐ๋„ท IDโ€™์— ๋Œ€ํ•œ ์‚ฌ์ด๋ฒ„ ํญ๋ ฅ์ด +โ“”๋„˜์ณ ๋‚˜๋Š” ํ˜„์‹ค๋„ ์ด์™€ ๋ฌด๊ด€ํ•˜์ง€ ์•Š๋‹ค. +์‚ฌ์ด๋ฒ„ ํญ๋ ฅ๊ณผ ๊ด€๋ จํ•˜์—ฌ, ์ธํ„ฐ๋„ท ID๋งŒ์„ ์•Œ๊ณ  ์žˆ๋Š” ์ƒํ™ฉ์—์„œ +๊ทธ์— ๋Œ€ํ•ด ๋ช…์˜ˆํ›ผ์†์ด๋‚˜ ๋ชจ์š• ๋“ฑ์˜ ๊ณต๊ฒฉ์ด ์žˆ์„ ๋•Œ ๊ฐ€ํ•ด์ž์—๊ฒŒ +๋ฒ•์ ์ธ ์ฑ…์ž„์„ ๋ฌผ์„ ์ˆ˜ ์žˆ๋Š”์ง€์— ๋Œ€ํ•œ ๋…ผ๋ž€์ด ์žˆ์–ด ์™”๋‹ค. ์ด๋Š” +์ธํ„ฐ๋„ท ID๊ฐ€ ์‚ฌํšŒ์  ํ‰ํŒ์ธ ๋ช…์˜ˆ์˜ ์ฃผ์ฒด๋กœ ์ธ์ •๋  ์ˆ˜ ์žˆ๋Š”๊ฐ€์™€ +๊ด€๋ จ๋œ๋‹ค. ์ธํ„ฐ๋„ท ID์˜ ๋ช…์˜ˆ ์ฃผ์ฒด์„ฑ์„ ใ‰ ์ธ์ •ํ•˜๋Š” ์ž…์žฅ์— +๋”ฐ๋ฅด๋ฉด, ์ž๊ธฐ ์ •์ฒด์„ฑ์€ ์ผ์›์ โ€ค๊ณ ์ •์ ์ธ ๊ฒƒ์ด ์•„๋‹ˆ๋ผ ํ˜„์‹ค +์„ธ๊ณ„์™€ ๊ฐ€์ƒ ๊ณต๊ฐ„์— ๊ฑธ์ณ ์กด์žฌํ•˜๊ณ  ์ƒํ˜ธ ์ž‘์šฉํ•˜๋Š” ๋ณตํ•ฉ์ ์ธ +๊ฒƒ์ด๋‹ค. ์ธํ„ฐ๋„ท์—์„œ์˜ ์ž๊ธฐ ์ •์ฒด์„ฑ์€ ์‚ฌ์šฉ์ž ๊ฐœ์ธ์˜ ์ž๊ธฐ ์ •์ฒด์„ฑ์˜ +์ผ๋ถ€์ด๊ธฐ ๋•Œ๋ฌธ์— ์ž๊ธฐ ์ •์ฒด์„ฑ์„ ๊ฐ€์ง„ ์ธํ„ฐ๋„ท ID์˜ ๋ช…์˜ˆ ์—ญ์‹œ +๋ณดํ˜ธ๋˜์–ด์•ผ ํ•œ๋‹ค. ๋ฐ˜๋ฉด ใ‰ก์ธ์ •ํ•˜์ง€ ์•Š๋Š” ์ž…์žฅ์— ๋”ฐ๋ฅด๋ฉด, ์ƒ์„ฑโ€ค +๋ณ€๊ฒฝโ€ค์†Œ๋ฉธ์ด ์ž์œ ๋กญ๊ณ  ๋ณต์ˆ˜๋กœ ๊ฐœ์„ค์ด ๊ฐ€๋Šฅํ•œ ์ธํ„ฐ๋„ท ID๋Š” ๊ทธ +์‚ฌ์šฉ์ž์ธ ๊ฐœ์ธ์„ ๊ฐ€์ƒ ๊ณต๊ฐ„์—์„œ ๊ตฌ๋ณ„ํ•˜๋Š” ์žฅ์น˜์— ๋ถˆ๊ณผํ•˜๋‹ค. +์ธํ„ฐ๋„ท ID๋Š” ํ˜„์‹ค์—์„œ์˜ ์„ฑ๋ช…๊ณผ ๋‹ฌ๋ฆฌ ๊ทธ ์‚ฌ์šฉ์ž์ธ ๊ฐœ์ธ๊ณผ +๋™์ผ์‹œ๋  ์ˆ˜ ์—†๊ณ , ์ธํ„ฐ๋„ท ID ์ž์ฒด๋Š” ์‚ฌ๋žŒ์ด ์•„๋‹ˆ๋ฏ€๋กœ ๋ช…์˜ˆ +์ฃผ์ฒด์„ฑ์„ ์ธ์ •ํ•  ์ˆ˜ ์—†๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. +ใ‰ฎ๋Œ€๋ฒ•์›์€ ์‹ค๋ช…์„ ๊ฑฐ๋ก ํ•œ ๊ฒฝ์šฐ๋Š” ๋ฌผ๋ก , ์‹ค๋ช…์„ ๊ฑฐ๋ก ํ•˜์ง€ +์•Š์•˜๋”๋ผ๋„ ์ฃผ์œ„ ์‚ฌ์ •์„ ์ข…ํ•ฉํ•  ๋•Œ ์ง€๋ชฉ๋œ ์‚ฌ๋žŒ์ด ๋ˆ„๊ตฌ์ธ์ง€๋ฅผ +์ œ3์ž๊ฐ€ ์•Œ ์ˆ˜ ์žˆ๋Š” ๊ฒฝ์šฐ์—๋Š” ๋ช…์˜ˆํ›ผ์†์ด๋‚˜ ๋ชจ์š•์— ๋Œ€ํ•œ ๊ฐ€ํ•ด์ž์˜ +๋ฒ•์  ์ฑ…์ž„์ด ์„ฑ๋ฆฝํ•œ๋‹ค๊ณ  ํŒ์‹œํ•ด ์™”๋‹ค. ์ด๋ฅผ ์ˆ˜์šฉํ•œ ํ—Œ๋ฒ•์žฌํŒ์†Œ +์—์„œ๋Š” ์ธํ„ฐ๋„ท ID์™€ ๊ด€๋ จ๋œ ๋ช…์˜ˆํ›ผ์†โ€ค๋ชจ์š• ์‚ฌ๊ฑด์˜ ํ—Œ๋ฒ• ์†Œ์›์— +๋Œ€ํ•œ ๊ฒฐ์ •์„ ๋‚ด๋ฆฐ ๋ฐ” ์žˆ๋‹ค. ์ด ๊ฒฐ์ •์—์„œ ใ‰ฏ๋‹ค์ˆ˜ ์˜๊ฒฌ์€ ์ธํ„ฐ๋„ท +ID๋งŒ์„ ์•Œ ์ˆ˜ ์žˆ์„ ๋ฟ ๊ทธ ์‚ฌ์šฉ์ž๊ฐ€ ๋ˆ„๊ตฌ์ธ์ง€ ์ œ3์ž๊ฐ€ ์•Œ ์ˆ˜ +์—†๋‹ค๋ฉด ํ”ผํ•ด์ž๊ฐ€ ํŠน์ •๋˜์ง€ ์•Š์•„ ๋ช…์˜ˆํ›ผ์†์ด๋‚˜ ๋ชจ์š•์— ๋Œ€ํ•œ +๊ฐ€ํ•ด์ž์˜ ๋ฒ•์  ์ฑ…์ž„์ด ์„ฑ๋ฆฝํ•˜์ง€ ์•Š๋Š”๋‹ค๊ณ  ๋ณด์•˜๋‹ค. ๋ฐ˜๋ฉด ์ธํ„ฐ๋„ท +ID๋Š” ๊ฐ€์ƒ ๊ณต๊ฐ„์—์„œ ์„ฑ๋ช…๊ณผ ๊ฐ™์€ ๊ธฐ๋Šฅ์„ ํ•˜๋ฏ€๋กœ ์ œ3์ž์˜ ์ธ์‹ +์—ฌ๋ถ€๊ฐ€ ๋ฒ•์  ์ฑ…์ž„์˜ ๊ทผ๊ฑฐ๊ฐ€ ๋  ์ˆ˜ ์—†๋‹ค๋Š” ใ‰ฐ์†Œ์ˆ˜ ์˜๊ฒฌ๋„ ์ œ์‹œ +๋˜์—ˆ๋‹ค.","{'question': '์œ—๊ธ€์˜ ๋‚ด์šฉ๊ณผ ์ผ์น˜ํ•˜์ง€ ์•Š๋Š” ๊ฒƒ์€?', 'choices': ['์‹ฌ์ธต ์—ฐ๊ธฐ๋Š” ๋‚ด๋ฉด์˜ ์ง„์†”ํ•œ ์ •์„œ๋ฅผ ๋“œ๋Ÿฌ๋‚ด๊ธฐ ์œ„ํ•ด ํ˜•์‹์— ์ง‘์ค‘\nํ•˜๋Š” ์ž๊ธฐํ‘œํ˜„์ด๋‹ค.', '๋ฆฌํ”„ํ‚จ์€ ํ˜„์‹ค ์„ธ๊ณ„๋ณด๋‹ค ๊ฐ€์ƒ ๊ณต๊ฐ„์—์„œ ์ž๊ธฐํ‘œํ˜„์ด ๋”์šฑ ์™•์„ฑ\nํ•˜๊ฒŒ ๋“œ๋Ÿฌ๋‚œ๋‹ค๊ณ  ๋ณด์•˜๋‹ค.', '๊ฐ€์ƒ ๊ณต๊ฐ„์—์„œ ๊ฐœ๋ณ„์ ์ธ ๊ฒƒ์œผ๋กœ ์ธ์‹๋˜๋Š” ์•„๋ฐ”ํƒ€๋Š” ์‚ฌ์ด๋ฒ„\nํญ๋ ฅ์˜ ๋Œ€์ƒ์ด ๋  ์ˆ˜ ์žˆ๋‹ค.', '์ต๋ช…์„ฑ์€ ๊ฐ€์ƒ ๊ณต๊ฐ„์—์„œ ์ž๊ธฐ ์ •์ฒด์„ฑ์ด ๋‹ค์–‘ํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚˜๋Š” ๋ฐ\n์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฐ€์ƒ ๊ณต๊ฐ„์˜ ํŠน์„ฑ์ด๋‹ค.', '๊ฐ€์ƒ ๊ณต๊ฐ„์—์„œ์˜ ์ž๊ธฐ ์ •์ฒด์„ฑ์€ ํ˜„์‹ค์—์„œ์˜ ์ž๊ธฐ ์ •์ฒด์„ฑ๊ณผ\n๋งˆ์ฐฌ๊ฐ€์ง€๋กœ ํƒ€์ธ๊ณผ์˜ ๊ด€๊ณ„ ์†์—์„œ ๋‚˜ํƒ€๋‚œ๋‹ค.'], 'answer': ''}",,1,1,True,[],1 +2025-korean-15,"๋ฆฌํ”„ํ‚จ์€ ์‚ฌํšŒ์  ์ƒํ˜ธ ์ž‘์šฉ์—์„œ์˜ ์ž๊ธฐํ‘œํ˜„์€ ๋ณธ์งˆ์ ์œผ๋กœ +์—ฐ๊ทน์ ์ด๋ฉฐ, ํ‘œ๋ฉด ์—ฐ๊ธฐ์™€ ์‹ฌ์ธต ์—ฐ๊ธฐ๋กœ โ“์ด๋ฃจ์–ด์ง„๋‹ค๊ณ  ์–ธ๊ธ‰ํ–ˆ๋‹ค. +ํ‘œ๋ฉด ์—ฐ๊ธฐ๋Š” ๋‚ด๋ฉด์˜ ์ž์—ฐ์Šค๋Ÿฌ์šด ๊ฐ์ •๋ณด๋‹ค ์˜๋ก€์ ์ธ ํ‘œํ˜„๊ณผ ๊ฐ™์€ +ํ˜•์‹์— ์ง‘์ค‘ํ•˜์—ฌ ์—ฐ๊ธฐํ•˜๋Š” ๊ฒƒ์ด๊ณ , ์‹ฌ์ธต ์—ฐ๊ธฐ๋Š” ๋‚ด๋ฉด์˜ ์†”์งํ•œ +์ •์„œ๋ฅผ โ“‘๋ถˆ๋Ÿฌ๋‚ด์–ด ์ž์‹ ์˜ ์ง„์ •์„ฑ์„ ๋ณด์—ฌ ์ฃผ๋Š” ๊ฒƒ์ด๋‹ค. ์ธํ„ฐ๋„ท +์—์„œ์˜ ์ปค๋ฎค๋‹ˆ์ผ€์ด์…˜์— ์ฃผ๋ชฉํ•œ ๋ฆฌํ”„ํ‚จ์€ ๊ฐ€์ƒ ๊ณต๊ฐ„์—์„œ ์ž๊ธฐ +ํ‘œํ˜„์ด ๋”์šฑ ํ™œ๋ฐœํžˆ ์ด๋ฃจ์–ด์ง„๋‹ค๊ณ  ๋ณด์•˜๋‹ค. +๊ฐ€์ƒ ๊ณต๊ฐ„์˜ ํŠน์„ฑ์— ์ฃผ๋ชฉํ•œ ์—ฐ๊ตฌ์ž๋“ค์€ ์‚ฌ๋žŒ๋“ค๊ณผ์˜ ๊ด€๊ณ„ ์†์—์„œ +๋“œ๋Ÿฌ๋‚˜๋Š” ๊ณ ์œ ํ•œ ์กด์žฌ๋กœ์„œ์˜ ์œ„์ƒ์„ ๋œปํ•˜๋Š” ์ž๊ธฐ ์ •์ฒด์„ฑ์ด ๊ฐ€์ƒ +๊ณต๊ฐ„์—์„œ ๋‹ค์–‘ํ•˜๊ฒŒ โ“’๋‚˜ํƒ€๋‚œ๋‹ค๊ณ  ๋ณธ๋‹ค. ๊ฐ€์ƒ ๊ณต๊ฐ„์—์„œ๋Š” ์ต๋ช…์„ฑ์ด +์ž‘๋™ํ•˜๋ฏ€๋กœ ํ˜„์‹ค์—์„œ ์œ„์ถ•๋˜๋Š” ์‚ฌ๋žŒ๋„ ์ ๊ทน์ ์œผ๋กœ ์ž๊ธฐํ‘œํ˜„์„ +ํ•  ์ˆ˜ ์žˆ๋‹ค. ์•„์šธ๋Ÿฌ ํ˜„์‹ค์—์„œ์˜ ์ž๊ธฐ ์ •์ฒด์„ฑ์„ โ““๊ฐ์ถ”๊ณ  ๋‹ค๋ฅธ +์ธ๊ฒฉ์ฒด๋กœ ํ™œ๋™ํ•˜๊ฑฐ๋‚˜ ํ˜„์‹ค์—์„œ ์–ต์••๋œ ์ •์„œ๋ฅผ ๊ณต๊ฒฉ์ ์œผ๋กœ ๋“œ๋Ÿฌ +๋‚ด๊ธฐ๋„ ํ•œ๋‹ค. ๊ฒŒ์ž„ ์•„์ด๋””, ๋‹‰๋„ค์ž„, ์•„๋ฐ”ํƒ€ ๋“ฑ ๊ฐ€์ƒ ๊ณต๊ฐ„์—์„œ +๊ฐœ๋ณ„์  ๋Œ€์ƒ์œผ๋กœ ์ธ์‹๋˜๋Š” โ€˜์ธํ„ฐ๋„ท IDโ€™์— ๋Œ€ํ•œ ์‚ฌ์ด๋ฒ„ ํญ๋ ฅ์ด +โ“”๋„˜์ณ ๋‚˜๋Š” ํ˜„์‹ค๋„ ์ด์™€ ๋ฌด๊ด€ํ•˜์ง€ ์•Š๋‹ค. +์‚ฌ์ด๋ฒ„ ํญ๋ ฅ๊ณผ ๊ด€๋ จํ•˜์—ฌ, ์ธํ„ฐ๋„ท ID๋งŒ์„ ์•Œ๊ณ  ์žˆ๋Š” ์ƒํ™ฉ์—์„œ +๊ทธ์— ๋Œ€ํ•ด ๋ช…์˜ˆํ›ผ์†์ด๋‚˜ ๋ชจ์š• ๋“ฑ์˜ ๊ณต๊ฒฉ์ด ์žˆ์„ ๋•Œ ๊ฐ€ํ•ด์ž์—๊ฒŒ +๋ฒ•์ ์ธ ์ฑ…์ž„์„ ๋ฌผ์„ ์ˆ˜ ์žˆ๋Š”์ง€์— ๋Œ€ํ•œ ๋…ผ๋ž€์ด ์žˆ์–ด ์™”๋‹ค. ์ด๋Š” +์ธํ„ฐ๋„ท ID๊ฐ€ ์‚ฌํšŒ์  ํ‰ํŒ์ธ ๋ช…์˜ˆ์˜ ์ฃผ์ฒด๋กœ ์ธ์ •๋  ์ˆ˜ ์žˆ๋Š”๊ฐ€์™€ +๊ด€๋ จ๋œ๋‹ค. ์ธํ„ฐ๋„ท ID์˜ ๋ช…์˜ˆ ์ฃผ์ฒด์„ฑ์„ ใ‰ ์ธ์ •ํ•˜๋Š” ์ž…์žฅ์— +๋”ฐ๋ฅด๋ฉด, ์ž๊ธฐ ์ •์ฒด์„ฑ์€ ์ผ์›์ โ€ค๊ณ ์ •์ ์ธ ๊ฒƒ์ด ์•„๋‹ˆ๋ผ ํ˜„์‹ค +์„ธ๊ณ„์™€ ๊ฐ€์ƒ ๊ณต๊ฐ„์— ๊ฑธ์ณ ์กด์žฌํ•˜๊ณ  ์ƒํ˜ธ ์ž‘์šฉํ•˜๋Š” ๋ณตํ•ฉ์ ์ธ +๊ฒƒ์ด๋‹ค. ์ธํ„ฐ๋„ท์—์„œ์˜ ์ž๊ธฐ ์ •์ฒด์„ฑ์€ ์‚ฌ์šฉ์ž ๊ฐœ์ธ์˜ ์ž๊ธฐ ์ •์ฒด์„ฑ์˜ +์ผ๋ถ€์ด๊ธฐ ๋•Œ๋ฌธ์— ์ž๊ธฐ ์ •์ฒด์„ฑ์„ ๊ฐ€์ง„ ์ธํ„ฐ๋„ท ID์˜ ๋ช…์˜ˆ ์—ญ์‹œ +๋ณดํ˜ธ๋˜์–ด์•ผ ํ•œ๋‹ค. ๋ฐ˜๋ฉด ใ‰ก์ธ์ •ํ•˜์ง€ ์•Š๋Š” ์ž…์žฅ์— ๋”ฐ๋ฅด๋ฉด, ์ƒ์„ฑโ€ค +๋ณ€๊ฒฝโ€ค์†Œ๋ฉธ์ด ์ž์œ ๋กญ๊ณ  ๋ณต์ˆ˜๋กœ ๊ฐœ์„ค์ด ๊ฐ€๋Šฅํ•œ ์ธํ„ฐ๋„ท ID๋Š” ๊ทธ +์‚ฌ์šฉ์ž์ธ ๊ฐœ์ธ์„ ๊ฐ€์ƒ ๊ณต๊ฐ„์—์„œ ๊ตฌ๋ณ„ํ•˜๋Š” ์žฅ์น˜์— ๋ถˆ๊ณผํ•˜๋‹ค. +์ธํ„ฐ๋„ท ID๋Š” ํ˜„์‹ค์—์„œ์˜ ์„ฑ๋ช…๊ณผ ๋‹ฌ๋ฆฌ ๊ทธ ์‚ฌ์šฉ์ž์ธ ๊ฐœ์ธ๊ณผ +๋™์ผ์‹œ๋  ์ˆ˜ ์—†๊ณ , ์ธํ„ฐ๋„ท ID ์ž์ฒด๋Š” ์‚ฌ๋žŒ์ด ์•„๋‹ˆ๋ฏ€๋กœ ๋ช…์˜ˆ +์ฃผ์ฒด์„ฑ์„ ์ธ์ •ํ•  ์ˆ˜ ์—†๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. +ใ‰ฎ๋Œ€๋ฒ•์›์€ ์‹ค๋ช…์„ ๊ฑฐ๋ก ํ•œ ๊ฒฝ์šฐ๋Š” ๋ฌผ๋ก , ์‹ค๋ช…์„ ๊ฑฐ๋ก ํ•˜์ง€ +์•Š์•˜๋”๋ผ๋„ ์ฃผ์œ„ ์‚ฌ์ •์„ ์ข…ํ•ฉํ•  ๋•Œ ์ง€๋ชฉ๋œ ์‚ฌ๋žŒ์ด ๋ˆ„๊ตฌ์ธ์ง€๋ฅผ +์ œ3์ž๊ฐ€ ์•Œ ์ˆ˜ ์žˆ๋Š” ๊ฒฝ์šฐ์—๋Š” ๋ช…์˜ˆํ›ผ์†์ด๋‚˜ ๋ชจ์š•์— ๋Œ€ํ•œ ๊ฐ€ํ•ด์ž์˜ +๋ฒ•์  ์ฑ…์ž„์ด ์„ฑ๋ฆฝํ•œ๋‹ค๊ณ  ํŒ์‹œํ•ด ์™”๋‹ค. ์ด๋ฅผ ์ˆ˜์šฉํ•œ ํ—Œ๋ฒ•์žฌํŒ์†Œ +์—์„œ๋Š” ์ธํ„ฐ๋„ท ID์™€ ๊ด€๋ จ๋œ ๋ช…์˜ˆํ›ผ์†โ€ค๋ชจ์š• ์‚ฌ๊ฑด์˜ ํ—Œ๋ฒ• ์†Œ์›์— +๋Œ€ํ•œ ๊ฒฐ์ •์„ ๋‚ด๋ฆฐ ๋ฐ” ์žˆ๋‹ค. ์ด ๊ฒฐ์ •์—์„œ ใ‰ฏ๋‹ค์ˆ˜ ์˜๊ฒฌ์€ ์ธํ„ฐ๋„ท +ID๋งŒ์„ ์•Œ ์ˆ˜ ์žˆ์„ ๋ฟ ๊ทธ ์‚ฌ์šฉ์ž๊ฐ€ ๋ˆ„๊ตฌ์ธ์ง€ ์ œ3์ž๊ฐ€ ์•Œ ์ˆ˜ +์—†๋‹ค๋ฉด ํ”ผํ•ด์ž๊ฐ€ ํŠน์ •๋˜์ง€ ์•Š์•„ ๋ช…์˜ˆํ›ผ์†์ด๋‚˜ ๋ชจ์š•์— ๋Œ€ํ•œ +๊ฐ€ํ•ด์ž์˜ ๋ฒ•์  ์ฑ…์ž„์ด ์„ฑ๋ฆฝํ•˜์ง€ ์•Š๋Š”๋‹ค๊ณ  ๋ณด์•˜๋‹ค. ๋ฐ˜๋ฉด ์ธํ„ฐ๋„ท +ID๋Š” ๊ฐ€์ƒ ๊ณต๊ฐ„์—์„œ ์„ฑ๋ช…๊ณผ ๊ฐ™์€ ๊ธฐ๋Šฅ์„ ํ•˜๋ฏ€๋กœ ์ œ3์ž์˜ ์ธ์‹ +์—ฌ๋ถ€๊ฐ€ ๋ฒ•์  ์ฑ…์ž„์˜ ๊ทผ๊ฑฐ๊ฐ€ ๋  ์ˆ˜ ์—†๋‹ค๋Š” ใ‰ฐ์†Œ์ˆ˜ ์˜๊ฒฌ๋„ ์ œ์‹œ +๋˜์—ˆ๋‹ค.","{'question': 'ใ‰ ๊ณผ ใ‰ก์— ๋Œ€ํ•œ ์ดํ•ด๋กœ ๊ฐ€์žฅ ์ ์ ˆํ•œ ๊ฒƒ์€?', 'choices': ['ใ‰ ์€ ใ‰ก๊ณผ ๋‹ฌ๋ฆฌ ์ž๊ธฐ ์ •์ฒด์„ฑ์„ ๋‹จ์ผํ•˜๊ณ  ๊ณ ์ •์ ์ธ ๊ฒƒ์œผ๋กœ ํŒŒ์•…\nํ•˜๊ฒ ๊ตฐ.', 'ใ‰ ์€ ใ‰ก๊ณผ ๋‹ฌ๋ฆฌ ์ธํ„ฐ๋„ท ID์— ๋Œ€ํ•œ ๊ณต๊ฒฉ์„ ๊ทธ ์‚ฌ์šฉ์ž์ธ ๊ฐœ์ธ์—\n๋Œ€ํ•œ ๊ณต๊ฒฉ์ด๋ผ๊ณ  ๋ณด๊ฒ ๊ตฐ.', 'ใ‰ก์€ ใ‰ ๊ณผ ๋‹ฌ๋ฆฌ ์ธํ„ฐ๋„ท์—์„œ์˜ ์ž๊ธฐ ์ •์ฒด์„ฑ๊ณผ ํ˜„์‹ค ์„ธ๊ณ„์˜ ์ž๊ธฐ\n์ •์ฒด์„ฑ์ด ์ƒํ˜ธ ์ž‘์šฉ์„ ํ•œ๋‹ค๊ณ  ๋ณด๊ฒ ๊ตฐ.', 'ใ‰ก์€ ใ‰ ๊ณผ ๋‹ฌ๋ฆฌ ์ธํ„ฐ๋„ท ID๋Š” ๋ณต์ˆ˜ ๊ฐœ์„ค์ด ๊ฐ€๋Šฅํ•˜๋ฏ€๋กœ ์ž๊ธฐ\n์ •์ฒด์„ฑ์ด ๋ณตํ•ฉ์ ์œผ๋กœ ๊ตฌ์„ฑ๋œ๋‹ค๊ณ  ๋ณด๊ฒ ๊ตฐ.', 'ใ‰ ๊ณผ ใ‰ก์€ ๋ชจ๋‘, ์ธํ„ฐ๋„ท ID๋งˆ๋‹ค ๊ฐœ์ธ์˜ ์ž๊ธฐ ์ •์ฒด์„ฑ์ด ๋‹ค๋ฅด๋‹ค๊ณ \n๋ณด๊ฒ ๊ตฐ.'], 'answer': ''}",,2,2,True,[],2 +2025-korean-16,"๋ฆฌํ”„ํ‚จ์€ ์‚ฌํšŒ์  ์ƒํ˜ธ ์ž‘์šฉ์—์„œ์˜ ์ž๊ธฐํ‘œํ˜„์€ ๋ณธ์งˆ์ ์œผ๋กœ +์—ฐ๊ทน์ ์ด๋ฉฐ, ํ‘œ๋ฉด ์—ฐ๊ธฐ์™€ ์‹ฌ์ธต ์—ฐ๊ธฐ๋กœ โ“์ด๋ฃจ์–ด์ง„๋‹ค๊ณ  ์–ธ๊ธ‰ํ–ˆ๋‹ค. +ํ‘œ๋ฉด ์—ฐ๊ธฐ๋Š” ๋‚ด๋ฉด์˜ ์ž์—ฐ์Šค๋Ÿฌ์šด ๊ฐ์ •๋ณด๋‹ค ์˜๋ก€์ ์ธ ํ‘œํ˜„๊ณผ ๊ฐ™์€ +ํ˜•์‹์— ์ง‘์ค‘ํ•˜์—ฌ ์—ฐ๊ธฐํ•˜๋Š” ๊ฒƒ์ด๊ณ , ์‹ฌ์ธต ์—ฐ๊ธฐ๋Š” ๋‚ด๋ฉด์˜ ์†”์งํ•œ +์ •์„œ๋ฅผ โ“‘๋ถˆ๋Ÿฌ๋‚ด์–ด ์ž์‹ ์˜ ์ง„์ •์„ฑ์„ ๋ณด์—ฌ ์ฃผ๋Š” ๊ฒƒ์ด๋‹ค. ์ธํ„ฐ๋„ท +์—์„œ์˜ ์ปค๋ฎค๋‹ˆ์ผ€์ด์…˜์— ์ฃผ๋ชฉํ•œ ๋ฆฌํ”„ํ‚จ์€ ๊ฐ€์ƒ ๊ณต๊ฐ„์—์„œ ์ž๊ธฐ +ํ‘œํ˜„์ด ๋”์šฑ ํ™œ๋ฐœํžˆ ์ด๋ฃจ์–ด์ง„๋‹ค๊ณ  ๋ณด์•˜๋‹ค. +๊ฐ€์ƒ ๊ณต๊ฐ„์˜ ํŠน์„ฑ์— ์ฃผ๋ชฉํ•œ ์—ฐ๊ตฌ์ž๋“ค์€ ์‚ฌ๋žŒ๋“ค๊ณผ์˜ ๊ด€๊ณ„ ์†์—์„œ +๋“œ๋Ÿฌ๋‚˜๋Š” ๊ณ ์œ ํ•œ ์กด์žฌ๋กœ์„œ์˜ ์œ„์ƒ์„ ๋œปํ•˜๋Š” ์ž๊ธฐ ์ •์ฒด์„ฑ์ด ๊ฐ€์ƒ +๊ณต๊ฐ„์—์„œ ๋‹ค์–‘ํ•˜๊ฒŒ โ“’๋‚˜ํƒ€๋‚œ๋‹ค๊ณ  ๋ณธ๋‹ค. ๊ฐ€์ƒ ๊ณต๊ฐ„์—์„œ๋Š” ์ต๋ช…์„ฑ์ด +์ž‘๋™ํ•˜๋ฏ€๋กœ ํ˜„์‹ค์—์„œ ์œ„์ถ•๋˜๋Š” ์‚ฌ๋žŒ๋„ ์ ๊ทน์ ์œผ๋กœ ์ž๊ธฐํ‘œํ˜„์„ +ํ•  ์ˆ˜ ์žˆ๋‹ค. ์•„์šธ๋Ÿฌ ํ˜„์‹ค์—์„œ์˜ ์ž๊ธฐ ์ •์ฒด์„ฑ์„ โ““๊ฐ์ถ”๊ณ  ๋‹ค๋ฅธ +์ธ๊ฒฉ์ฒด๋กœ ํ™œ๋™ํ•˜๊ฑฐ๋‚˜ ํ˜„์‹ค์—์„œ ์–ต์••๋œ ์ •์„œ๋ฅผ ๊ณต๊ฒฉ์ ์œผ๋กœ ๋“œ๋Ÿฌ +๋‚ด๊ธฐ๋„ ํ•œ๋‹ค. ๊ฒŒ์ž„ ์•„์ด๋””, ๋‹‰๋„ค์ž„, ์•„๋ฐ”ํƒ€ ๋“ฑ ๊ฐ€์ƒ ๊ณต๊ฐ„์—์„œ +๊ฐœ๋ณ„์  ๋Œ€์ƒ์œผ๋กœ ์ธ์‹๋˜๋Š” โ€˜์ธํ„ฐ๋„ท IDโ€™์— ๋Œ€ํ•œ ์‚ฌ์ด๋ฒ„ ํญ๋ ฅ์ด +โ“”๋„˜์ณ ๋‚˜๋Š” ํ˜„์‹ค๋„ ์ด์™€ ๋ฌด๊ด€ํ•˜์ง€ ์•Š๋‹ค. +์‚ฌ์ด๋ฒ„ ํญ๋ ฅ๊ณผ ๊ด€๋ จํ•˜์—ฌ, ์ธํ„ฐ๋„ท ID๋งŒ์„ ์•Œ๊ณ  ์žˆ๋Š” ์ƒํ™ฉ์—์„œ +๊ทธ์— ๋Œ€ํ•ด ๋ช…์˜ˆํ›ผ์†์ด๋‚˜ ๋ชจ์š• ๋“ฑ์˜ ๊ณต๊ฒฉ์ด ์žˆ์„ ๋•Œ ๊ฐ€ํ•ด์ž์—๊ฒŒ +๋ฒ•์ ์ธ ์ฑ…์ž„์„ ๋ฌผ์„ ์ˆ˜ ์žˆ๋Š”์ง€์— ๋Œ€ํ•œ ๋…ผ๋ž€์ด ์žˆ์–ด ์™”๋‹ค. ์ด๋Š” +์ธํ„ฐ๋„ท ID๊ฐ€ ์‚ฌํšŒ์  ํ‰ํŒ์ธ ๋ช…์˜ˆ์˜ ์ฃผ์ฒด๋กœ ์ธ์ •๋  ์ˆ˜ ์žˆ๋Š”๊ฐ€์™€ +๊ด€๋ จ๋œ๋‹ค. ์ธํ„ฐ๋„ท ID์˜ ๋ช…์˜ˆ ์ฃผ์ฒด์„ฑ์„ ใ‰ ์ธ์ •ํ•˜๋Š” ์ž…์žฅ์— +๋”ฐ๋ฅด๋ฉด, ์ž๊ธฐ ์ •์ฒด์„ฑ์€ ์ผ์›์ โ€ค๊ณ ์ •์ ์ธ ๊ฒƒ์ด ์•„๋‹ˆ๋ผ ํ˜„์‹ค +์„ธ๊ณ„์™€ ๊ฐ€์ƒ ๊ณต๊ฐ„์— ๊ฑธ์ณ ์กด์žฌํ•˜๊ณ  ์ƒํ˜ธ ์ž‘์šฉํ•˜๋Š” ๋ณตํ•ฉ์ ์ธ +๊ฒƒ์ด๋‹ค. ์ธํ„ฐ๋„ท์—์„œ์˜ ์ž๊ธฐ ์ •์ฒด์„ฑ์€ ์‚ฌ์šฉ์ž ๊ฐœ์ธ์˜ ์ž๊ธฐ ์ •์ฒด์„ฑ์˜ +์ผ๋ถ€์ด๊ธฐ ๋•Œ๋ฌธ์— ์ž๊ธฐ ์ •์ฒด์„ฑ์„ ๊ฐ€์ง„ ์ธํ„ฐ๋„ท ID์˜ ๋ช…์˜ˆ ์—ญ์‹œ +๋ณดํ˜ธ๋˜์–ด์•ผ ํ•œ๋‹ค. ๋ฐ˜๋ฉด ใ‰ก์ธ์ •ํ•˜์ง€ ์•Š๋Š” ์ž…์žฅ์— ๋”ฐ๋ฅด๋ฉด, ์ƒ์„ฑโ€ค +๋ณ€๊ฒฝโ€ค์†Œ๋ฉธ์ด ์ž์œ ๋กญ๊ณ  ๋ณต์ˆ˜๋กœ ๊ฐœ์„ค์ด ๊ฐ€๋Šฅํ•œ ์ธํ„ฐ๋„ท ID๋Š” ๊ทธ +์‚ฌ์šฉ์ž์ธ ๊ฐœ์ธ์„ ๊ฐ€์ƒ ๊ณต๊ฐ„์—์„œ ๊ตฌ๋ณ„ํ•˜๋Š” ์žฅ์น˜์— ๋ถˆ๊ณผํ•˜๋‹ค. +์ธํ„ฐ๋„ท ID๋Š” ํ˜„์‹ค์—์„œ์˜ ์„ฑ๋ช…๊ณผ ๋‹ฌ๋ฆฌ ๊ทธ ์‚ฌ์šฉ์ž์ธ ๊ฐœ์ธ๊ณผ +๋™์ผ์‹œ๋  ์ˆ˜ ์—†๊ณ , ์ธํ„ฐ๋„ท ID ์ž์ฒด๋Š” ์‚ฌ๋žŒ์ด ์•„๋‹ˆ๋ฏ€๋กœ ๋ช…์˜ˆ +์ฃผ์ฒด์„ฑ์„ ์ธ์ •ํ•  ์ˆ˜ ์—†๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. +ใ‰ฎ๋Œ€๋ฒ•์›์€ ์‹ค๋ช…์„ ๊ฑฐ๋ก ํ•œ ๊ฒฝ์šฐ๋Š” ๋ฌผ๋ก , ์‹ค๋ช…์„ ๊ฑฐ๋ก ํ•˜์ง€ +์•Š์•˜๋”๋ผ๋„ ์ฃผ์œ„ ์‚ฌ์ •์„ ์ข…ํ•ฉํ•  ๋•Œ ์ง€๋ชฉ๋œ ์‚ฌ๋žŒ์ด ๋ˆ„๊ตฌ์ธ์ง€๋ฅผ +์ œ3์ž๊ฐ€ ์•Œ ์ˆ˜ ์žˆ๋Š” ๊ฒฝ์šฐ์—๋Š” ๋ช…์˜ˆํ›ผ์†์ด๋‚˜ ๋ชจ์š•์— ๋Œ€ํ•œ ๊ฐ€ํ•ด์ž์˜ +๋ฒ•์  ์ฑ…์ž„์ด ์„ฑ๋ฆฝํ•œ๋‹ค๊ณ  ํŒ์‹œํ•ด ์™”๋‹ค. ์ด๋ฅผ ์ˆ˜์šฉํ•œ ํ—Œ๋ฒ•์žฌํŒ์†Œ +์—์„œ๋Š” ์ธํ„ฐ๋„ท ID์™€ ๊ด€๋ จ๋œ ๋ช…์˜ˆํ›ผ์†โ€ค๋ชจ์š• ์‚ฌ๊ฑด์˜ ํ—Œ๋ฒ• ์†Œ์›์— +๋Œ€ํ•œ ๊ฒฐ์ •์„ ๋‚ด๋ฆฐ ๋ฐ” ์žˆ๋‹ค. ์ด ๊ฒฐ์ •์—์„œ ใ‰ฏ๋‹ค์ˆ˜ ์˜๊ฒฌ์€ ์ธํ„ฐ๋„ท +ID๋งŒ์„ ์•Œ ์ˆ˜ ์žˆ์„ ๋ฟ ๊ทธ ์‚ฌ์šฉ์ž๊ฐ€ ๋ˆ„๊ตฌ์ธ์ง€ ์ œ3์ž๊ฐ€ ์•Œ ์ˆ˜ +์—†๋‹ค๋ฉด ํ”ผํ•ด์ž๊ฐ€ ํŠน์ •๋˜์ง€ ์•Š์•„ ๋ช…์˜ˆํ›ผ์†์ด๋‚˜ ๋ชจ์š•์— ๋Œ€ํ•œ +๊ฐ€ํ•ด์ž์˜ ๋ฒ•์  ์ฑ…์ž„์ด ์„ฑ๋ฆฝํ•˜์ง€ ์•Š๋Š”๋‹ค๊ณ  ๋ณด์•˜๋‹ค. ๋ฐ˜๋ฉด ์ธํ„ฐ๋„ท +ID๋Š” ๊ฐ€์ƒ ๊ณต๊ฐ„์—์„œ ์„ฑ๋ช…๊ณผ ๊ฐ™์€ ๊ธฐ๋Šฅ์„ ํ•˜๋ฏ€๋กœ ์ œ3์ž์˜ ์ธ์‹ +์—ฌ๋ถ€๊ฐ€ ๋ฒ•์  ์ฑ…์ž„์˜ ๊ทผ๊ฑฐ๊ฐ€ ๋  ์ˆ˜ ์—†๋‹ค๋Š” ใ‰ฐ์†Œ์ˆ˜ ์˜๊ฒฌ๋„ ์ œ์‹œ +๋˜์—ˆ๋‹ค.","{'question': '์œ—๊ธ€์„ ๋ฐ”ํƒ•์œผ๋กœ <๋ณด๊ธฐ>๋ฅผ ์ดํ•ดํ•œ ๋‚ด์šฉ์œผ๋กœ ์ ์ ˆํ•˜์ง€ ์•Š์€\n๊ฒƒ์€?', 'choices': ['ใ‰ฎ๋Š” B๊ฐ€ ๊ฐ€ํ•ด์ž๋กœ์„œ์˜ ๋ฒ•์  ์ฑ…์ž„์„ ์ ธ์•ผ ํ•˜์ง€๋งŒ C๋Š” ๊ฐ€ํ•ด์ž\n๋กœ์„œ์˜ ๋ฒ•์  ์ฑ…์ž„์„ ์ง€์ง€ ์•Š๋Š”๋‹ค๊ณ  ๋ณด๊ฒ ๊ตฐ.', 'ใ‰ฏ๋Š” B๊ฐ€ ๊ฐ€ํ•ด์ž๋กœ์„œ์˜ ๋ฒ•์  ์ฑ…์ž„์„ ์ ธ์•ผ ํ•˜์ง€๋งŒ A๋Š” ๊ฐ€ํ•ด์ž\n๋กœ์„œ์˜ ๋ฒ•์  ์ฑ…์ž„์„ ์ง€์ง€ ์•Š๋Š”๋‹ค๊ณ  ๋ณด๊ฒ ๊ตฐ.', 'ใ‰ฎ์™€ ใ‰ฐ๋Š” A๊ฐ€ ๊ฐ€ํ•ด์ž๋กœ์„œ์˜ ๋ฒ•์  ์ฑ…์ž„์„ ์ ธ์•ผ ํ•˜๋Š”์ง€์˜ ์—ฌ๋ถ€์—\n๋Œ€ํ•ด ๊ฐ™๊ฒŒ ๋ณด๊ฒ ๊ตฐ.', 'ใ‰ฏ์™€ ใ‰ฐ๋Š” B๊ฐ€ ๊ฐ€ํ•ด์ž๋กœ์„œ์˜ ๋ฒ•์  ์ฑ…์ž„์„ ์ ธ์•ผ ํ•˜๋Š”์ง€์˜ ์—ฌ๋ถ€์—\n๋Œ€ํ•ด ๊ฐ™๊ฒŒ ๋ณด๊ฒ ๊ตฐ.', 'ใ‰ฎ, ใ‰ฏ, ใ‰ฐ๊ฐ€, C๊ฐ€ ๊ฐ€ํ•ด์ž๋กœ์„œ์˜ ๋ฒ•์  ์ฑ…์ž„์„ ์ ธ์•ผ ํ•˜๋Š”์ง€์˜\n์—ฌ๋ถ€์— ๋Œ€ํ•ด ํŒ๋‹จํ•œ ๋‚ด์šฉ์ด ๋ชจ๋‘ ๊ฐ™์ง€๋Š” ์•Š๊ฒ ๊ตฐ.'], 'answer': '', 'question_plus': ""โ—‹โ—‹ ์ธํ„ฐ๋„ท ์นดํŽ˜์˜ ์ด์šฉ์ž A๋Š” a, B๋Š” b, C๋Š” c๋ผ๋Š” ID๋ฅผ\n์‚ฌ์šฉํ•œ๋‹ค. ๋ฐ•์‚ฌ ํ•™์œ„ ์†Œ์ง€์ž์ธ A๋Š” โ–กโ–ก ์ „์‹œ๊ด€์˜ ํ•ด์„ค์‚ฌ์ด๊ณ ,\nB๋Š” ๊ฐ™์€ ์ „์‹œ๊ด€์—์„œ ๋ฌผ๊ณ ๊ธฐ ๊ด€๋ฆฌ๋ฅผ ํ˜ผ์ž ์ „๋‹ดํ•œ๋‹ค. ์ด ์ „์‹œ๊ด€์˜\n๋ˆ„๋ฆฌ์ง‘์—๋Š” ์ง๋ฌด๋ณ„๋กœ ๋‹ด๋‹น์ž๊ฐ€ ๊ณต๊ฐœ๋˜์–ด ์žˆ๋‹ค. ์–ด๋–ค ์‚ฌ๋žŒ์ด\nโ–กโ–ก ์ „์‹œ๊ด€์—์„œ A์˜ ํ•ด์„ค์„ ๋“ฃ๊ณ  A์˜ ์‹ค๋ช…์„ ์–ธ๊ธ‰ํ•œ ํ›„๊ธฐ๋ฅผ\n์นดํŽ˜ ๊ฒŒ์‹œํŒ์— ์˜ฌ๋ฆฌ์ž ๋‹ค์Œ๊ณผ ๊ฐ™์€ ๋Œ“๊ธ€์ด ๋‹ฌ๋ ธ๋‹ค.\nA์˜ ํ•ด์„ค์— ๋Œ€ํ•œ ํ›„๊ธฐ : ์•„์ด๋”” b - A๊ฐ€ ๋ฐ•์‚ฌ์ธ์ง€ ์˜์‹ฌ ์Šค๋Ÿฝ๋‹ค. A๋Š” #~#.\n์•„์ด๋”” b์— ๋Œ€ํ•œ ๋‹ต๊ธ€ : ์•„์ด๋”” a - โ–กโ–ก ์ „์‹œ๊ด€์—์„œ ๋ฌผ๊ณ ๊ธฐ๋ฅผ ๊ด€๋ฆฌํ•˜๋Š” b๋Š” #~#.\n์•„์ด๋”” a์— ๋Œ€ํ•œ ๋‹ต๊ธ€ : ์•„์ด๋”” c - ๊ฒŒ์‹œํŒ ๋ถ„์œ„๊ธฐ๋ฅผ ํ๋ฆฌ๋Š” a๋Š” #~#. \n(๋‹จ, '#~#'๋Š” ๋ช…์˜ˆ๋ฅผ ํ›ผ์†ํ•˜๊ฑฐ๋‚˜ ๋ชจ์š•์„ ์ฃผ๋Š” ํ‘œํ˜„์ด๊ณ , A, B, C๋Š” ์‹ค๋ช…์ด๋‹ค. \nID๋กœ๋Š” ๊ทธ ์‚ฌ์šฉ์ž์˜ ๊ฐœ์ธ ์ •๋ณด๋ฅผ ์•Œ ์ˆ˜ ์—†์œผ๋ฉฐ, A, B, C์˜ ๋ฒ•์  ์ฑ…์ž„์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๋‹ค๋ฅธ ์š”์†Œ๋Š” ๊ณ ๋ คํ•˜์ง€ ์•Š๋Š”๋‹ค.)""}","โ—‹โ—‹ ์ธํ„ฐ๋„ท ์นดํŽ˜์˜ ์ด์šฉ์ž A๋Š” a, B๋Š” b, C๋Š” c๋ผ๋Š” ID๋ฅผ +์‚ฌ์šฉํ•œ๋‹ค. ๋ฐ•์‚ฌ ํ•™์œ„ ์†Œ์ง€์ž์ธ A๋Š” โ–กโ–ก ์ „์‹œ๊ด€์˜ ํ•ด์„ค์‚ฌ์ด๊ณ , +B๋Š” ๊ฐ™์€ ์ „์‹œ๊ด€์—์„œ ๋ฌผ๊ณ ๊ธฐ ๊ด€๋ฆฌ๋ฅผ ํ˜ผ์ž ์ „๋‹ดํ•œ๋‹ค. ์ด ์ „์‹œ๊ด€์˜ +๋ˆ„๋ฆฌ์ง‘์—๋Š” ์ง๋ฌด๋ณ„๋กœ ๋‹ด๋‹น์ž๊ฐ€ ๊ณต๊ฐœ๋˜์–ด ์žˆ๋‹ค. ์–ด๋–ค ์‚ฌ๋žŒ์ด +โ–กโ–ก ์ „์‹œ๊ด€์—์„œ A์˜ ํ•ด์„ค์„ ๋“ฃ๊ณ  A์˜ ์‹ค๋ช…์„ ์–ธ๊ธ‰ํ•œ ํ›„๊ธฐ๋ฅผ +์นดํŽ˜ ๊ฒŒ์‹œํŒ์— ์˜ฌ๋ฆฌ์ž ๋‹ค์Œ๊ณผ ๊ฐ™์€ ๋Œ“๊ธ€์ด ๋‹ฌ๋ ธ๋‹ค. +A์˜ ํ•ด์„ค์— ๋Œ€ํ•œ ํ›„๊ธฐ : ์•„์ด๋”” b - A๊ฐ€ ๋ฐ•์‚ฌ์ธ์ง€ ์˜์‹ฌ ์Šค๋Ÿฝ๋‹ค. A๋Š” #~#. +์•„์ด๋”” b์— ๋Œ€ํ•œ ๋‹ต๊ธ€ : ์•„์ด๋”” a - โ–กโ–ก ์ „์‹œ๊ด€์—์„œ ๋ฌผ๊ณ ๊ธฐ๋ฅผ ๊ด€๋ฆฌํ•˜๋Š” b๋Š” #~#. +์•„์ด๋”” a์— ๋Œ€ํ•œ ๋‹ต๊ธ€ : ์•„์ด๋”” c - ๊ฒŒ์‹œํŒ ๋ถ„์œ„๊ธฐ๋ฅผ ํ๋ฆฌ๋Š” a๋Š” #~#. +(๋‹จ, '#~#'๋Š” ๋ช…์˜ˆ๋ฅผ ํ›ผ์†ํ•˜๊ฑฐ๋‚˜ ๋ชจ์š•์„ ์ฃผ๋Š” ํ‘œํ˜„์ด๊ณ , A, B, C๋Š” ์‹ค๋ช…์ด๋‹ค. +ID๋กœ๋Š” ๊ทธ ์‚ฌ์šฉ์ž์˜ ๊ฐœ์ธ ์ •๋ณด๋ฅผ ์•Œ ์ˆ˜ ์—†์œผ๋ฉฐ, A, B, C์˜ ๋ฒ•์  ์ฑ…์ž„์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๋‹ค๋ฅธ ์š”์†Œ๋Š” ๊ณ ๋ คํ•˜์ง€ ์•Š๋Š”๋‹ค.)",2,4,False,[],4 +2025-korean-17,"๋ฆฌํ”„ํ‚จ์€ ์‚ฌํšŒ์  ์ƒํ˜ธ ์ž‘์šฉ์—์„œ์˜ ์ž๊ธฐํ‘œํ˜„์€ ๋ณธ์งˆ์ ์œผ๋กœ +์—ฐ๊ทน์ ์ด๋ฉฐ, ํ‘œ๋ฉด ์—ฐ๊ธฐ์™€ ์‹ฌ์ธต ์—ฐ๊ธฐ๋กœ โ“์ด๋ฃจ์–ด์ง„๋‹ค๊ณ  ์–ธ๊ธ‰ํ–ˆ๋‹ค. +ํ‘œ๋ฉด ์—ฐ๊ธฐ๋Š” ๋‚ด๋ฉด์˜ ์ž์—ฐ์Šค๋Ÿฌ์šด ๊ฐ์ •๋ณด๋‹ค ์˜๋ก€์ ์ธ ํ‘œํ˜„๊ณผ ๊ฐ™์€ +ํ˜•์‹์— ์ง‘์ค‘ํ•˜์—ฌ ์—ฐ๊ธฐํ•˜๋Š” ๊ฒƒ์ด๊ณ , ์‹ฌ์ธต ์—ฐ๊ธฐ๋Š” ๋‚ด๋ฉด์˜ ์†”์งํ•œ +์ •์„œ๋ฅผ โ“‘๋ถˆ๋Ÿฌ๋‚ด์–ด ์ž์‹ ์˜ ์ง„์ •์„ฑ์„ ๋ณด์—ฌ ์ฃผ๋Š” ๊ฒƒ์ด๋‹ค. ์ธํ„ฐ๋„ท +์—์„œ์˜ ์ปค๋ฎค๋‹ˆ์ผ€์ด์…˜์— ์ฃผ๋ชฉํ•œ ๋ฆฌํ”„ํ‚จ์€ ๊ฐ€์ƒ ๊ณต๊ฐ„์—์„œ ์ž๊ธฐ +ํ‘œํ˜„์ด ๋”์šฑ ํ™œ๋ฐœํžˆ ์ด๋ฃจ์–ด์ง„๋‹ค๊ณ  ๋ณด์•˜๋‹ค. +๊ฐ€์ƒ ๊ณต๊ฐ„์˜ ํŠน์„ฑ์— ์ฃผ๋ชฉํ•œ ์—ฐ๊ตฌ์ž๋“ค์€ ์‚ฌ๋žŒ๋“ค๊ณผ์˜ ๊ด€๊ณ„ ์†์—์„œ +๋“œ๋Ÿฌ๋‚˜๋Š” ๊ณ ์œ ํ•œ ์กด์žฌ๋กœ์„œ์˜ ์œ„์ƒ์„ ๋œปํ•˜๋Š” ์ž๊ธฐ ์ •์ฒด์„ฑ์ด ๊ฐ€์ƒ +๊ณต๊ฐ„์—์„œ ๋‹ค์–‘ํ•˜๊ฒŒ โ“’๋‚˜ํƒ€๋‚œ๋‹ค๊ณ  ๋ณธ๋‹ค. ๊ฐ€์ƒ ๊ณต๊ฐ„์—์„œ๋Š” ์ต๋ช…์„ฑ์ด +์ž‘๋™ํ•˜๋ฏ€๋กœ ํ˜„์‹ค์—์„œ ์œ„์ถ•๋˜๋Š” ์‚ฌ๋žŒ๋„ ์ ๊ทน์ ์œผ๋กœ ์ž๊ธฐํ‘œํ˜„์„ +ํ•  ์ˆ˜ ์žˆ๋‹ค. ์•„์šธ๋Ÿฌ ํ˜„์‹ค์—์„œ์˜ ์ž๊ธฐ ์ •์ฒด์„ฑ์„ โ““๊ฐ์ถ”๊ณ  ๋‹ค๋ฅธ +์ธ๊ฒฉ์ฒด๋กœ ํ™œ๋™ํ•˜๊ฑฐ๋‚˜ ํ˜„์‹ค์—์„œ ์–ต์••๋œ ์ •์„œ๋ฅผ ๊ณต๊ฒฉ์ ์œผ๋กœ ๋“œ๋Ÿฌ +๋‚ด๊ธฐ๋„ ํ•œ๋‹ค. ๊ฒŒ์ž„ ์•„์ด๋””, ๋‹‰๋„ค์ž„, ์•„๋ฐ”ํƒ€ ๋“ฑ ๊ฐ€์ƒ ๊ณต๊ฐ„์—์„œ +๊ฐœ๋ณ„์  ๋Œ€์ƒ์œผ๋กœ ์ธ์‹๋˜๋Š” โ€˜์ธํ„ฐ๋„ท IDโ€™์— ๋Œ€ํ•œ ์‚ฌ์ด๋ฒ„ ํญ๋ ฅ์ด +โ“”๋„˜์ณ ๋‚˜๋Š” ํ˜„์‹ค๋„ ์ด์™€ ๋ฌด๊ด€ํ•˜์ง€ ์•Š๋‹ค. +์‚ฌ์ด๋ฒ„ ํญ๋ ฅ๊ณผ ๊ด€๋ จํ•˜์—ฌ, ์ธํ„ฐ๋„ท ID๋งŒ์„ ์•Œ๊ณ  ์žˆ๋Š” ์ƒํ™ฉ์—์„œ +๊ทธ์— ๋Œ€ํ•ด ๋ช…์˜ˆํ›ผ์†์ด๋‚˜ ๋ชจ์š• ๋“ฑ์˜ ๊ณต๊ฒฉ์ด ์žˆ์„ ๋•Œ ๊ฐ€ํ•ด์ž์—๊ฒŒ +๋ฒ•์ ์ธ ์ฑ…์ž„์„ ๋ฌผ์„ ์ˆ˜ ์žˆ๋Š”์ง€์— ๋Œ€ํ•œ ๋…ผ๋ž€์ด ์žˆ์–ด ์™”๋‹ค. ์ด๋Š” +์ธํ„ฐ๋„ท ID๊ฐ€ ์‚ฌํšŒ์  ํ‰ํŒ์ธ ๋ช…์˜ˆ์˜ ์ฃผ์ฒด๋กœ ์ธ์ •๋  ์ˆ˜ ์žˆ๋Š”๊ฐ€์™€ +๊ด€๋ จ๋œ๋‹ค. ์ธํ„ฐ๋„ท ID์˜ ๋ช…์˜ˆ ์ฃผ์ฒด์„ฑ์„ ใ‰ ์ธ์ •ํ•˜๋Š” ์ž…์žฅ์— +๋”ฐ๋ฅด๋ฉด, ์ž๊ธฐ ์ •์ฒด์„ฑ์€ ์ผ์›์ โ€ค๊ณ ์ •์ ์ธ ๊ฒƒ์ด ์•„๋‹ˆ๋ผ ํ˜„์‹ค +์„ธ๊ณ„์™€ ๊ฐ€์ƒ ๊ณต๊ฐ„์— ๊ฑธ์ณ ์กด์žฌํ•˜๊ณ  ์ƒํ˜ธ ์ž‘์šฉํ•˜๋Š” ๋ณตํ•ฉ์ ์ธ +๊ฒƒ์ด๋‹ค. ์ธํ„ฐ๋„ท์—์„œ์˜ ์ž๊ธฐ ์ •์ฒด์„ฑ์€ ์‚ฌ์šฉ์ž ๊ฐœ์ธ์˜ ์ž๊ธฐ ์ •์ฒด์„ฑ์˜ +์ผ๋ถ€์ด๊ธฐ ๋•Œ๋ฌธ์— ์ž๊ธฐ ์ •์ฒด์„ฑ์„ ๊ฐ€์ง„ ์ธํ„ฐ๋„ท ID์˜ ๋ช…์˜ˆ ์—ญ์‹œ +๋ณดํ˜ธ๋˜์–ด์•ผ ํ•œ๋‹ค. ๋ฐ˜๋ฉด ใ‰ก์ธ์ •ํ•˜์ง€ ์•Š๋Š” ์ž…์žฅ์— ๋”ฐ๋ฅด๋ฉด, ์ƒ์„ฑโ€ค +๋ณ€๊ฒฝโ€ค์†Œ๋ฉธ์ด ์ž์œ ๋กญ๊ณ  ๋ณต์ˆ˜๋กœ ๊ฐœ์„ค์ด ๊ฐ€๋Šฅํ•œ ์ธํ„ฐ๋„ท ID๋Š” ๊ทธ +์‚ฌ์šฉ์ž์ธ ๊ฐœ์ธ์„ ๊ฐ€์ƒ ๊ณต๊ฐ„์—์„œ ๊ตฌ๋ณ„ํ•˜๋Š” ์žฅ์น˜์— ๋ถˆ๊ณผํ•˜๋‹ค. +์ธํ„ฐ๋„ท ID๋Š” ํ˜„์‹ค์—์„œ์˜ ์„ฑ๋ช…๊ณผ ๋‹ฌ๋ฆฌ ๊ทธ ์‚ฌ์šฉ์ž์ธ ๊ฐœ์ธ๊ณผ +๋™์ผ์‹œ๋  ์ˆ˜ ์—†๊ณ , ์ธํ„ฐ๋„ท ID ์ž์ฒด๋Š” ์‚ฌ๋žŒ์ด ์•„๋‹ˆ๋ฏ€๋กœ ๋ช…์˜ˆ +์ฃผ์ฒด์„ฑ์„ ์ธ์ •ํ•  ์ˆ˜ ์—†๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. +ใ‰ฎ๋Œ€๋ฒ•์›์€ ์‹ค๋ช…์„ ๊ฑฐ๋ก ํ•œ ๊ฒฝ์šฐ๋Š” ๋ฌผ๋ก , ์‹ค๋ช…์„ ๊ฑฐ๋ก ํ•˜์ง€ +์•Š์•˜๋”๋ผ๋„ ์ฃผ์œ„ ์‚ฌ์ •์„ ์ข…ํ•ฉํ•  ๋•Œ ์ง€๋ชฉ๋œ ์‚ฌ๋žŒ์ด ๋ˆ„๊ตฌ์ธ์ง€๋ฅผ +์ œ3์ž๊ฐ€ ์•Œ ์ˆ˜ ์žˆ๋Š” ๊ฒฝ์šฐ์—๋Š” ๋ช…์˜ˆํ›ผ์†์ด๋‚˜ ๋ชจ์š•์— ๋Œ€ํ•œ ๊ฐ€ํ•ด์ž์˜ +๋ฒ•์  ์ฑ…์ž„์ด ์„ฑ๋ฆฝํ•œ๋‹ค๊ณ  ํŒ์‹œํ•ด ์™”๋‹ค. ์ด๋ฅผ ์ˆ˜์šฉํ•œ ํ—Œ๋ฒ•์žฌํŒ์†Œ +์—์„œ๋Š” ์ธํ„ฐ๋„ท ID์™€ ๊ด€๋ จ๋œ ๋ช…์˜ˆํ›ผ์†โ€ค๋ชจ์š• ์‚ฌ๊ฑด์˜ ํ—Œ๋ฒ• ์†Œ์›์— +๋Œ€ํ•œ ๊ฒฐ์ •์„ ๋‚ด๋ฆฐ ๋ฐ” ์žˆ๋‹ค. ์ด ๊ฒฐ์ •์—์„œ ใ‰ฏ๋‹ค์ˆ˜ ์˜๊ฒฌ์€ ์ธํ„ฐ๋„ท +ID๋งŒ์„ ์•Œ ์ˆ˜ ์žˆ์„ ๋ฟ ๊ทธ ์‚ฌ์šฉ์ž๊ฐ€ ๋ˆ„๊ตฌ์ธ์ง€ ์ œ3์ž๊ฐ€ ์•Œ ์ˆ˜ +์—†๋‹ค๋ฉด ํ”ผํ•ด์ž๊ฐ€ ํŠน์ •๋˜์ง€ ์•Š์•„ ๋ช…์˜ˆํ›ผ์†์ด๋‚˜ ๋ชจ์š•์— ๋Œ€ํ•œ +๊ฐ€ํ•ด์ž์˜ ๋ฒ•์  ์ฑ…์ž„์ด ์„ฑ๋ฆฝํ•˜์ง€ ์•Š๋Š”๋‹ค๊ณ  ๋ณด์•˜๋‹ค. ๋ฐ˜๋ฉด ์ธํ„ฐ๋„ท +ID๋Š” ๊ฐ€์ƒ ๊ณต๊ฐ„์—์„œ ์„ฑ๋ช…๊ณผ ๊ฐ™์€ ๊ธฐ๋Šฅ์„ ํ•˜๋ฏ€๋กœ ์ œ3์ž์˜ ์ธ์‹ +์—ฌ๋ถ€๊ฐ€ ๋ฒ•์  ์ฑ…์ž„์˜ ๊ทผ๊ฑฐ๊ฐ€ ๋  ์ˆ˜ ์—†๋‹ค๋Š” ใ‰ฐ์†Œ์ˆ˜ ์˜๊ฒฌ๋„ ์ œ์‹œ +๋˜์—ˆ๋‹ค.","{'question': '๋ฌธ๋งฅ์ƒ โ“๏ฝžโ“”์™€ ๋ฐ”๊ฟ” ์“ฐ๊ธฐ์— ๊ฐ€์žฅ ์ ์ ˆํ•œ ๊ฒƒ์€?', 'choices': ['โ“:์™„์„ฑ(ๅฎŒๆˆ)๋œ๋‹ค๊ณ ', 'โ“‘:์š”์ฒญ(่ฆ่ซ‹)ํ•˜์—ฌ', 'โ“’:ํ‘œ์ถœ(่กจๅ‡บ)๋œ๋‹ค๊ณ ', 'โ““:๊ธฐ๋งŒ(ๆฌบ็žž)ํ•˜๊ณ ', 'โ“”:ํ™•์ถฉ(ๆ“ดๅ……)๋˜๋Š”'], 'answer': ''}",,3,3,True,[],3 +2025-korean-18,"[์•ž๋ถ€๋ถ„์˜ ์ค„๊ฑฐ๋ฆฌ] ์Šน์ƒ ์ •์„์„ ์ด ์ถœ์ •ํ•œ ์‚ฌ์ด ์ •๋ ฌ๋ถ€์ธ์˜ ๋ชจ๋žต์œผ๋กœ +์ถฉ๋ ฌ๋ถ€์ธ์ด ์˜ฅ์— ๊ฐ‡ํžˆ์ž ์‹œ๋น„ ๊ธˆ์„ฌ์ด ์ถฉ๋ ฌ๋ถ€์ธ์„ ํ”ผ์‹ ์‹œํ‚ค๊ณ  ์ž์ง„ํ•œ๋‹ค. +์˜ฅ์—์„œ ์–ผ๊ตด์ด ์ƒํ•œ ๊ธˆ์„ฌ์˜ ์‹œ์‹ ์ด ๋ฐœ๊ฒฌ๋˜์ž ์™•๋น„๋Š” ์›”๋งค๋ฅผ ๋ฌธ์ดˆํ•œ๋‹ค. +์ „์žฅ์—์„œ ์ •์„์„ ์€ ํ˜ธ์ฒฉ์ด ์ „ํ•œ ํŽธ์ง€๋ฅผ ์ฝ๋Š”๋‹ค. +์›์ˆ˜๊ฐ€ ๋Œ€๊ฒฝํ•˜์—ฌ ํ˜ธ์ฒฉ์„ ๋ถˆ๋Ÿฌ ์—ฐ๊ณ ๋ฅผ ๋ฌผ์œผ์‹œ๊ณ  ์ธํ•˜์—ฌ ์ค‘๊ตฐ์žฅ์—๊ฒŒ +๋ถ„๋ถ€ํ•˜์‹œ๋˜ โ€˜๋‚˜๋Š” ์ง‘์— ๋ณ€์ด ์žˆ์–ด ๋จผ์ € ๊ฐ€๋‹ˆ ์ค‘๊ตฐ์žฅ์€ ์ฐจํ›„์— ์ธ์†” +ํ•˜์—ฌ ์˜ค๋ผ.โ€™ ํ•˜๊ณ  ๋ฐค๋‚ฎ ์‚ผ ์ผ ๋งŒ์— ๋“๋‹ฌํ•˜๋‹ˆ ์ด๋•Œ์— ์™•๋น„์˜ ์‹œ๋น„ +์›”๋งค๊ฐ€ ์ข…์‹œ ํ† ์„ค์น˜ ์•„๋‹ˆํ•˜๋งค ๋งค๋ฅผ ๋งŽ์ด ๋งž๊ณ  ์—ฌ์ญˆ์˜ค๋˜ +โ€œ์–ด์„œ ๋ฐ”์‚ ์ฃฝ์ด์‹œ๋ฉด ๊ธˆ์„ฌ์˜ ๋’ค๋ฅผ ์ซ“์•„๊ฐ€๊ฒ ๋‚˜์ด๋‹ค. +ํ•œ๋ฐ ์™•๋น„ ํฌ๊ฒŒ ๋…ธํ•˜์—ฌ ๋ชฉ์„ ๋ฒ ๋ผ ํ•  ์ฆˆ์Œ์— ์ด๋•Œ ์Šน์ƒ์ด ํ•„๋งˆ๋กœ +๋‹ฌ๋ ค์˜ค๋‹ค๊ฐ€ ์›”๋งค ์ฃฝ์ด๋ ค ํ•˜๋Š” ๊ฑฐ๋™์„ ๋ณด๊ณ  ๊ธ‰ํžˆ ์†Œ๋ฆฌ๋ฅผ ์ง€๋ฅด๋ฉฐ +๋ง์—์„œ ๋‚ด๋ ค ์ด๋ฅผ ๊ตฌํ˜ธํ•˜๋งค ๋ฌธ์™ˆ +โ€œ์ถฉ๋ ฌ๋ถ€์ธ์€ ์–ด๋”” ๊ณ„์‹œ๋ƒ?โ€ +์›”๋งค ์ธ์‚ฌ๋ฅผ ๋ชจ๋ฅด๋‹ค๊ฐ€ ์Šน์ƒ์„ ๋ณด๊ณ  ๋ฐฉ์„ฑํ†ต๊ณก ์™ˆ +โ€œ์Šน์ƒ์€ ๋ฐ”์‚ ์ถฉ๋ ฌ๋ถ€์ธ์„ ์‚ด๋ฆฌ์†Œ์„œ.โ€ +ํ•œ๋ฐ ์Šน์ƒ์ด ๊ธ‰ํžˆ ๋ฌธ์™ˆ +โ€œ์–ด๋”” ๊ณ„์‹œ๋ƒ?โ€ +ํ•œ๋ฐ ์›”๋งค ์šธ๋ฉฐ ์™ˆ +โ€œ์†Œ์ธ์ด ๊ฑท์ง€ ๋ชปํ•˜์˜ค๋‹ˆ ์–ด์ฐŒ ๊ฐ€์˜ค๋ฆฌ๊นŒ?โ€ +ํ•œ๋ฐ ๊ธ‰ํžˆ ์ข…์„ ๋ถˆ๋Ÿฌ ์›”๋งค๋ฅผ ์—…ํžˆ๊ณ  ๊ตฌ๋ฉ์ด๋ฅผ ์ฐพ์•„๊ฐ€ ๋ณด๋‹ˆ ๋ถ€์ธ์ด +์•„๊ธฐ๋ฅผ ์•ˆ๊ณ  ์žˆ๊ฑฐ๋Š˜ ์•„๊ธฐ๋Š” ์ž ์„ ๊นŠ์ด ๋“ค์—ˆ๋Š”์ง€๋ผ. ์Šน์ƒ์ด ํ†ต๊ณก ์™ˆ +โ€œ๋ถ€์ธ์€ ๋ˆˆ์„ ๋–  ๋‚˜๋ฅผ ๋ณด์†Œ์„œ.โ€ +ํ•œ๋ฐ ๋ถ€์ธ์ด ๋ˆˆ์„ ๋–  ๋ณด๋‹ˆ ์Šน์ƒ์ด ์™”๊ฑฐ๋Š˜ ์ •์‹  ์•„๋“ํ•˜์—ฌ ์ธ์‚ฌ๋ฅผ +๋ชจ๋ฅด๋‹ค๊ฐ€ ๊ฒจ์šฐ ์ธ์‚ฌ๋ฅผ ์ฐจ๋ ค ์™ˆ +โ€œ์ด๊ฒƒ์ด ๊ฟˆ์ธ๊ฐ€ ์ƒ์‹œ์ธ๊ฐ€ ๊ตฌ๋…„์ง€์ˆ˜์˜ ํ•ด ๊ฐ™๊ณ  ์น ๋…„๋Œ€ํ•œ์˜ +๋น—๋ฐœ๊ฐ™์ด ๋ฐ”๋ผ๋”๋‹ˆ ์ง€๊ธˆ ๊ตฌ๋ฉ์ด์—์„œ ๋งŒ๋‚  ์ค„ ์•Œ์•˜์œผ๋ฆฌ๊นŒ. +์Šน์ƒ์€ ๋‚˜์˜ ๋ˆ„๋ช…์„ ์”ป๊ฒจ ์ฃผ์†Œ์„œ.โ€ +ํ•˜๋ฉฐ ์ธ์‚ฌ๋ฅผ ๋ชจ๋ฅด๋Š”์ง€๋ผ. ๊ทธ ์ฐธํ˜นํ•œ ํ˜•์ƒ์„ ์–ด๋””์— ๋น„ํ•˜๋ฆฌ์˜ค. ์Šฌํ””์— +๋งค์šฐ ์•ผ์œ„์–ด ๋ผˆ๊ฐ€ ๋“œ๋Ÿฌ๋‚˜๊ฒŒ ๋˜์—ˆ๋Š”์ง€๋ผ. ์Šน์ƒ์ด ์•„๊ธฐ๋ฅผ ์•ˆ์•„ ์›”๋งค๋ฅผ +์ฃผ๊ณ  ๋ถ€์ธ์„ ๊ตฌํ•œ ํ›„์— ์ž๋ฆฌ๋ฅผ ๋งˆ๋ จํ•˜์—ฌ ์˜ฅ์„์„ ๊ตฌ๋ณ„ํ• ์ƒˆ, ์™•๋น„์ „์— +๋ตˆ์˜จ๋Œ€ ์™•๋น„ ๋ชป๋‚ด ๋ฐ˜๊ธฐ์‹œ๋ฉฐ ์‚ฌ์—ฐ์„ ๋‚ฑ๋‚ฑ์ด ์ด๋ฅด์‹œ๋˜ ์Šน์ƒ ์™ˆ +ใ‰ โ€œ์ด ์ผ์€ ์†Œ์ž๊ฐ€ ์ด๋ฏธ ์•„๋Š” ๋ฐ”์ด์˜ค๋‹ˆ ์—ผ๋ ค ๋งˆ์˜ต์†Œ์„œ.โ€ +ํ•˜๋ฉฐ ์™ˆ +ใ‰กโ€œ์ฒ˜์Œ์— ๊ทธ๋†ˆ์ด ์ถฉ๋ ฌ๋ถ€์ธ ๋ฐฉ์— ๊ฐ„ ์ค„ ์–ด์ฐŒ ์•Œ์œผ์…จ๋‚˜์ด๊นŒ?โ€ +์™•๋น„ ์™ˆ +โ€œ์‚ฌ์ดŒ ์˜ค๋ผ๋น„๊ฐ€ ์ด๋ฅด๊ธฐ๋กœ ์•Œ์•˜๋…ธ๋ผ.โ€ +ํ•˜์‹ ๋Œ€ ์Šน์ƒ์ด ๋ณต๋ก์„ ์ฐพ๋Š”๋ฐ ๋ฒŒ์จ ์ œ ์ฃ„๋ฅผ ์•Œ๊ณ  ํ›„์›์— ์˜ฌ๋ผ๊ฐ€ +์ด๋ฏธ ์ฃฝ์—ˆ๋Š”์ง€๋ผ. ํ•˜๋ฆด์—†์–ด ์˜ฅ์กธ์„ ์žก์•„๋“ค์—ฌ ์—„ํžˆ ๋ฌธ์™ˆ +โ€œ๋„ˆํฌ๋Š” ์–ด์ฐŒ ์ถฉ๋ ฌ๋ถ€์ธ ์•„๋‹Œ ์ค„ ์•Œ์•˜๋А๋ƒ? ๋ฐ”๋กœ ์•„๋ขฐ๋ผ.โ€ +ํ•˜์‹ ๋Œ€ ์˜ฅ์กธ์ด ๊ธ‰ํžˆ ์—ฌ์ญˆ์˜ค๋˜ +โ€œ์–ผ๊ตด์ด ์ƒํ•˜์—ฌ ์•„๋ชจ๋ž€ ์ค„ ๋ชจ๋ฅด์˜ค๋‚˜ ์†๊ธธ์ด ๊ณฑ์ง€ ๋ชปํ•˜์˜ค๋งค +์†Œ์ธ ๋“ฑ ์†Œ๊ฒฌ์— ์ถฉ๋ ฌ๋ถ€์ธ์ด ์ฒœํ•˜์ผ์ƒ‰์ด๋ผ ํ•˜๋”๋‹ˆ ์†์ด ๊ณฑ์ง€ +์•„๋‹ˆํ•˜๋”๋ผ ํ•˜์˜ฌ ์ œ ์ •๋ ฌ๋ถ€์ธ์˜ ์‹œ๋น„ ๊ธˆ์—ฐ์ด ์ด๋ฅผ ๋“ฃ๊ณ  ๋ฌป๊ธฐ์— +์ž์„ธํžˆ ์ด๋ฅด๊ณ  ๋ถ€๋”” ๋‹ค๋ฅธ ๋ฐ ๊ฐ€์„œ ์ด ๋ง ๋ง๋ผ ๋‹น๋ถ€ํ•˜์˜ต๋”๋‹ˆ, +ํ•„์—ฐ ๊ธˆ์—ฐ์˜ ์ž…์„ ํ†ตํ•ด ๋ฐœ์„ค์ด ๋œ๊ฐ€ ํ•˜๋‚˜์ด๋‹ค.โ€ +ํ•œ๋ฐ ์Šน์ƒ์ด ๊ธˆ์—ฐ์„ ์žก์•„๋“ค์—ฌ ๋ฌธ์™ˆ +โ€œ์ด ๋ง์„ ๋“ฃ๊ณ  ๋„ค๊ฒŒ ๊ตญ๋ฌธํ•˜๋‹ˆ ๋ฐ”๋ฅธ๋Œ€๋กœ ๊ณ ํ•˜๋ผ.โ€ +ํ•˜๋Š” ์†Œ๋ฆฌ๊ฐ€ ๋ฒผ๋ฝ์ด ๊ผญ๋‘์— ์ž„ํ•œ ๋“ฏํ•˜๊ณ  ๊ถ๊ถ์ด ๋’ค์ง‘ํžˆ๋Š” ๋“ฏ +ํ•˜๋”๋ผ. ์ด๋•Œ์— ์ •๋ ฌ๋ถ€์ธ์ด ์Šน์ƒ์˜ ํ˜ธํ†ต ์†Œ๋ฆฌ๋ฅผ ๋“ฃ๊ณ  ๋˜ฅ์„ ํ•œ +๋ฌด๋”๊ธฐ๋ฅผ ์‹ธ๊ณ  ์ž๋น ์กŒ๋Š”์ง€๋ผ. ๊ธˆ์—ฐ์ด ํ•˜๋ฆด์—†์–ด ๋ฐ”๋กœ ์•„๋ขฐ๋‚˜๋‹ˆ๋ผ +ํ•˜๊ณ  ์ •๋ ฌ๋ถ€์ธ ํ•˜๋˜ ๋ง์ด๋ฉฐ ์ œ๊ฐ€ ๋‚จ๋ณต์„ ํ•˜๊ณ  ์ถฉ๋ ฌ๋ถ€์ธ ์นจ์†Œ๋กœ +๋“ค์–ด๊ฐ„ ๋ง์ด๋ฉฐ ์ด๋ถˆ ์†์— ๋ˆ„์› ๋‹ค๊ฐ€ ๋‹ฌ์•„๋‚œ ๋ง์ด๋ฉฐ ์ •๋ ฌ๋ถ€์ธ์ด +์•“๋Š” ์ฒดํ•˜๊ณ  ๋ˆ„์› ์‚ฌ์˜ค๋งค ์ถฉ๋ ฌ๋ถ€์ธ์ด ์•ฝ์œผ๋กœ ๊ตฌ๋ณ‘ํ•˜๋ฉฐ ๊ณ์— +์žˆ์œผ์‹œ๋งค ์นจ์†Œ๋กœ ๊ฐ€๋ผ ๊ฐ•๊ถŒํ•˜์—ฌ ์นจ์†Œ๋กœ ๋งˆ์ง€๋ชปํ•˜์—ฌ ๊ฐ€์‹œ๋งค +๋ณต๋ก์ด ์™•๋น„๊ป˜ ์ฐธ์†Œํ•˜๋˜ ์—ฐ์œ ๋ฅผ ๋‚ฑ๋‚ฑ์ด ์•„๋ขด๋Œ€ ์™•๋น„ ๊ณ์— ์žˆ๋‹ค๊ฐ€ +์•™์ฒœํ†ต๊ณกํ•˜์‹œ๋ฉฐ ์™ˆ +โ€œ๋‚ด ๋ฐ์ง€ ๋ชปํ•˜์—ฌ ์•…๋…€์˜ ๊พ€์— ๋น ์ ธ ์ถฉ๋ ฌ๋ถ€์ธ์„ ์ฃฝ์ด๋ ค ํ•˜์˜€๋‚˜๋‹ˆ +๋ฌด์Šจ ๋ฉด๋ชฉ์œผ๋กœ ์ถฉ๋ ฌ๋ถ€์ธ์„ ๋ณด๋ฆฌ์˜ค.โ€ +ํ•˜์‹œ๋ฉฐ ์ž๊ฒฐ์ฝ”์ž ํ•˜๊ฑฐ๋Š˜ ์Šน์ƒ์ด ๋ถ™๋“ค๊ณ  ์šธ๋ฉฐ ์™ˆ +โ€œ๋ชจ์นœ์ด ๋„ˆ๋ฌด ๊ณผ๋„ํžˆ ํ•˜์‹œ๋ฉด ์†Œ์ž๊ฐ€ ๋จผ์ € ์ฃฝ์œผ๋ ค ํ•˜๋‚˜์ด๋‹ค.โ€ +์™•๋น„ ๊ธˆ์นจ์— ๋ˆ„์›Œ ์ผ์–ด๋‚˜์ง€ ๋ชปํ•˜๋”๋ผ. ์Šน์ƒ์ด ์ •๋ ฌ๋ถ€์ธ์„ +๊ฒฐ๋ฐ•ํ•˜์—ฌ ๋•…์— ๊ฟ‡๋ฆฌ๊ณ  ํฌ๊ฒŒ ๋…ธํ•˜์—ฌ ์™ˆ +โ€œ๋„ˆ๋Š” ๋ฌด์—‡์ด ๋ถ€์กฑํ•˜์—ฌ ์ถฉ๋ ฌ๋ถ€์ธ์„ ํ•ด์ฝ”์ž ํ•˜๋А๋ƒ. ์–ด์ฐŒ +์ผ์‹œ๋ฅผ ์‚ด๋ฆฌ๋ฆฌ์˜ค. ๋‚ด ์ž„์˜๋กœ๋Š” ์ฃฝ์ด๊ณ  ์‹ถ์œผ๋‚˜ ํ™ฉ์ƒ๊ป˜ ์•„๋ขฐ๊ณ  +์ฃฝ๊ฒŒ ํ•˜๋ฆฌ๋ผ.โ€ +ํ•˜๊ณ  ์ƒ์†Œํ•˜๋‹ˆ ๊ทธ ๊ธ€์— ํ•˜์˜€์œผ๋˜ +โ€œ๋Œ€์‚ฌ๋งˆ ๋Œ€๋„๋… ๋Œ€์›์ˆ˜ ์ •์„์„ ์€ ๋ˆ์ˆ˜๋ฐฑ๋ฐฐํ•˜๊ณ  ์•„๋ขฐ๋‚˜๋‹ˆ ์‹ ์ด +์„œ์œต์„ ์ณ ์‚ฌ๋กœ์žก๊ณ , ๋ฐฑ์„ฑ์„ ์ง„๋ฌดํ•˜๊ณ  ๋Œ์•„์˜ค๋ ค ํ•  ๋•Œ, ์ง‘์—์„œ +๊ธ‰ํ•œ ์†Œ์‹์„ ๋“ฃ๊ณ  ๊ตฐ์‚ฌ๋ฅผ ์ค‘๊ตฐ์žฅ์—๊ฒŒ ๋งก๊ธฐ์˜ต๊ณ  ํ•„๋งˆ๋กœ ์˜ฌ๋ผ์™€ +๋ณธ์ฆ‰, ์ •๋ ฌ๋ถ€์ธ์ด ์ด๋Ÿฌ์ด๋Ÿฌํ•œ ๋ณ€์„ ์ผ์œผ์ผฐ์‚ฌ์˜ค๋‹ˆ ์„ธ์ƒ์— +์ด๋Ÿฌํ•˜์˜จ ์ผ์ด ์žˆ์‚ฌ์˜ค๋‹›๊ฐ€.โ€ +ํ•˜๊ณ  ๊ธˆ์—ฐ์ด ํ‰๊ณ„๋ฅผ ๊พธ๋ฏผ ์ผ๊ณผ ์›”๋งค๊ฐ€ ๋‹นํ•˜๋˜ ๊ณ ์ดˆ๋ฅผ ๋‚ฑ๋‚ฑ์ด +์•„๋ขฐ์—ˆ๋‹ค. +-์ž‘์ž ๋ฏธ์ƒ, ๏ฝข์ •์„์„ ์ „๏ฝฃ-","{'question': 'ใ‰ , ใ‰ก๊ณผ ๊ด€๋ จํ•˜์—ฌ ์œ—๊ธ€์„ ์ดํ•ดํ•œ ๋‚ด์šฉ์œผ๋กœ ์ ์ ˆํ•˜์ง€ ์•Š์€ ๊ฒƒ์€?', 'choices': ['ใ‰ ์„ ๋ณด๋‹ˆ, ํ˜ธ์ฒฉ์—๊ฒŒ ๋ฌผ์€ โ€˜์—ฐ๊ณ โ€™์˜ ๋‚ด์šฉ์€ ์™•๋น„๊ฐ€ ๋งํ•œ โ€˜์‚ฌ์—ฐโ€™์˜\n๋‚ด์šฉ๊ณผ ๊ด€๋ จ์ด ์žˆ๊ฒ ๊ตฐ.', 'ใ‰ ์„ ๋ณด๋‹ˆ, ์Šน์ƒ์ด ํ™ฉ์ƒ์—๊ฒŒ ์˜ฌ๋ฆฐ โ€˜์ƒ์†Œโ€™์— ๋“ค์–ด ์žˆ๋Š” ๋‚ด์šฉ์€\nโ€˜์ด๋ฏธ ์•„๋Š” ๋ฐ”โ€™์™€ ๊ฐ™๊ฒ ๊ตฐ.', 'ใ‰ก์„ ๋ณด๋‹ˆ, ์Šน์ƒ์€ โ€˜์‚ฌ์—ฐโ€™์˜ ์ง„์ƒ์„ ๋ฐํžˆ๋Š” ๋ฐ์— ์™•๋น„๊ฐ€ โ€˜๊ทธ๋†ˆโ€™์˜\nํ–‰์œ„๋ฅผ ์•Œ๊ฒŒ ๋œ ๊ฒฝ์œ„๊ฐ€ ์ค‘์š”ํ•˜๋‹ค๊ณ  ์ƒ๊ฐํ–ˆ๊ฒ ๊ตฐ.', 'ใ‰ก์— ๋Œ€ํ•œ ์™•๋น„์˜ ๋Œ€๋‹ต์„ ๋ณด๋‹ˆ, ์™•๋น„์—๊ฒŒ โ€˜๊ทธ๋†ˆโ€™์˜ ํ–‰์œ„์— ๋Œ€ํ•ด\n์ œ๋ณดํ•œ ์‚ฌ๋žŒ์ด ์žˆ์—ˆ๊ตฐ.', 'ใ‰ก์ด ์ œ์‹œ๋œ ํ›„์— ๋“œ๋Ÿฌ๋‚œ ๋ณต๋ก์˜ ์ƒํ™ฉ์„ ๋ณด๋‹ˆ, ๋ณต๋ก์€ ์ž์‹ ์ด\n์ง€์€ โ€˜์ฃ„โ€™์— ๋Œ€ํ•˜์—ฌ ์‹ฌ๋ฆฌ์  ์ค‘์••๊ฐ์„ ๋А๊ผˆ๊ฒ ๊ตฐ.'], 'answer': ''}",,2,3,False,[],2 +2025-korean-19,"[์•ž๋ถ€๋ถ„์˜ ์ค„๊ฑฐ๋ฆฌ] ์Šน์ƒ ์ •์„์„ ์ด ์ถœ์ •ํ•œ ์‚ฌ์ด ์ •๋ ฌ๋ถ€์ธ์˜ ๋ชจ๋žต์œผ๋กœ +์ถฉ๋ ฌ๋ถ€์ธ์ด ์˜ฅ์— ๊ฐ‡ํžˆ์ž ์‹œ๋น„ ๊ธˆ์„ฌ์ด ์ถฉ๋ ฌ๋ถ€์ธ์„ ํ”ผ์‹ ์‹œํ‚ค๊ณ  ์ž์ง„ํ•œ๋‹ค. +์˜ฅ์—์„œ ์–ผ๊ตด์ด ์ƒํ•œ ๊ธˆ์„ฌ์˜ ์‹œ์‹ ์ด ๋ฐœ๊ฒฌ๋˜์ž ์™•๋น„๋Š” ์›”๋งค๋ฅผ ๋ฌธ์ดˆํ•œ๋‹ค. +์ „์žฅ์—์„œ ์ •์„์„ ์€ ํ˜ธ์ฒฉ์ด ์ „ํ•œ ํŽธ์ง€๋ฅผ ์ฝ๋Š”๋‹ค. +์›์ˆ˜๊ฐ€ ๋Œ€๊ฒฝํ•˜์—ฌ ํ˜ธ์ฒฉ์„ ๋ถˆ๋Ÿฌ ์—ฐ๊ณ ๋ฅผ ๋ฌผ์œผ์‹œ๊ณ  ์ธํ•˜์—ฌ ์ค‘๊ตฐ์žฅ์—๊ฒŒ +๋ถ„๋ถ€ํ•˜์‹œ๋˜ โ€˜๋‚˜๋Š” ์ง‘์— ๋ณ€์ด ์žˆ์–ด ๋จผ์ € ๊ฐ€๋‹ˆ ์ค‘๊ตฐ์žฅ์€ ์ฐจํ›„์— ์ธ์†” +ํ•˜์—ฌ ์˜ค๋ผ.โ€™ ํ•˜๊ณ  ๋ฐค๋‚ฎ ์‚ผ ์ผ ๋งŒ์— ๋“๋‹ฌํ•˜๋‹ˆ ์ด๋•Œ์— ์™•๋น„์˜ ์‹œ๋น„ +์›”๋งค๊ฐ€ ์ข…์‹œ ํ† ์„ค์น˜ ์•„๋‹ˆํ•˜๋งค ๋งค๋ฅผ ๋งŽ์ด ๋งž๊ณ  ์—ฌ์ญˆ์˜ค๋˜ +โ€œ์–ด์„œ ๋ฐ”์‚ ์ฃฝ์ด์‹œ๋ฉด ๊ธˆ์„ฌ์˜ ๋’ค๋ฅผ ์ซ“์•„๊ฐ€๊ฒ ๋‚˜์ด๋‹ค. +ํ•œ๋ฐ ์™•๋น„ ํฌ๊ฒŒ ๋…ธํ•˜์—ฌ ๋ชฉ์„ ๋ฒ ๋ผ ํ•  ์ฆˆ์Œ์— ์ด๋•Œ ์Šน์ƒ์ด ํ•„๋งˆ๋กœ +๋‹ฌ๋ ค์˜ค๋‹ค๊ฐ€ ์›”๋งค ์ฃฝ์ด๋ ค ํ•˜๋Š” ๊ฑฐ๋™์„ ๋ณด๊ณ  ๊ธ‰ํžˆ ์†Œ๋ฆฌ๋ฅผ ์ง€๋ฅด๋ฉฐ +๋ง์—์„œ ๋‚ด๋ ค ์ด๋ฅผ ๊ตฌํ˜ธํ•˜๋งค ๋ฌธ์™ˆ +โ€œ์ถฉ๋ ฌ๋ถ€์ธ์€ ์–ด๋”” ๊ณ„์‹œ๋ƒ?โ€ +์›”๋งค ์ธ์‚ฌ๋ฅผ ๋ชจ๋ฅด๋‹ค๊ฐ€ ์Šน์ƒ์„ ๋ณด๊ณ  ๋ฐฉ์„ฑํ†ต๊ณก ์™ˆ +โ€œ์Šน์ƒ์€ ๋ฐ”์‚ ์ถฉ๋ ฌ๋ถ€์ธ์„ ์‚ด๋ฆฌ์†Œ์„œ.โ€ +ํ•œ๋ฐ ์Šน์ƒ์ด ๊ธ‰ํžˆ ๋ฌธ์™ˆ +โ€œ์–ด๋”” ๊ณ„์‹œ๋ƒ?โ€ +ํ•œ๋ฐ ์›”๋งค ์šธ๋ฉฐ ์™ˆ +โ€œ์†Œ์ธ์ด ๊ฑท์ง€ ๋ชปํ•˜์˜ค๋‹ˆ ์–ด์ฐŒ ๊ฐ€์˜ค๋ฆฌ๊นŒ?โ€ +ํ•œ๋ฐ ๊ธ‰ํžˆ ์ข…์„ ๋ถˆ๋Ÿฌ ์›”๋งค๋ฅผ ์—…ํžˆ๊ณ  ๊ตฌ๋ฉ์ด๋ฅผ ์ฐพ์•„๊ฐ€ ๋ณด๋‹ˆ ๋ถ€์ธ์ด +์•„๊ธฐ๋ฅผ ์•ˆ๊ณ  ์žˆ๊ฑฐ๋Š˜ ์•„๊ธฐ๋Š” ์ž ์„ ๊นŠ์ด ๋“ค์—ˆ๋Š”์ง€๋ผ. ์Šน์ƒ์ด ํ†ต๊ณก ์™ˆ +โ€œ๋ถ€์ธ์€ ๋ˆˆ์„ ๋–  ๋‚˜๋ฅผ ๋ณด์†Œ์„œ.โ€ +ํ•œ๋ฐ ๋ถ€์ธ์ด ๋ˆˆ์„ ๋–  ๋ณด๋‹ˆ ์Šน์ƒ์ด ์™”๊ฑฐ๋Š˜ ์ •์‹  ์•„๋“ํ•˜์—ฌ ์ธ์‚ฌ๋ฅผ +๋ชจ๋ฅด๋‹ค๊ฐ€ ๊ฒจ์šฐ ์ธ์‚ฌ๋ฅผ ์ฐจ๋ ค ์™ˆ +โ€œ์ด๊ฒƒ์ด ๊ฟˆ์ธ๊ฐ€ ์ƒ์‹œ์ธ๊ฐ€ ๊ตฌ๋…„์ง€์ˆ˜์˜ ํ•ด ๊ฐ™๊ณ  ์น ๋…„๋Œ€ํ•œ์˜ +๋น—๋ฐœ๊ฐ™์ด ๋ฐ”๋ผ๋”๋‹ˆ ์ง€๊ธˆ ๊ตฌ๋ฉ์ด์—์„œ ๋งŒ๋‚  ์ค„ ์•Œ์•˜์œผ๋ฆฌ๊นŒ. +์Šน์ƒ์€ ๋‚˜์˜ ๋ˆ„๋ช…์„ ์”ป๊ฒจ ์ฃผ์†Œ์„œ.โ€ +ํ•˜๋ฉฐ ์ธ์‚ฌ๋ฅผ ๋ชจ๋ฅด๋Š”์ง€๋ผ. ๊ทธ ์ฐธํ˜นํ•œ ํ˜•์ƒ์„ ์–ด๋””์— ๋น„ํ•˜๋ฆฌ์˜ค. ์Šฌํ””์— +๋งค์šฐ ์•ผ์œ„์–ด ๋ผˆ๊ฐ€ ๋“œ๋Ÿฌ๋‚˜๊ฒŒ ๋˜์—ˆ๋Š”์ง€๋ผ. ์Šน์ƒ์ด ์•„๊ธฐ๋ฅผ ์•ˆ์•„ ์›”๋งค๋ฅผ +์ฃผ๊ณ  ๋ถ€์ธ์„ ๊ตฌํ•œ ํ›„์— ์ž๋ฆฌ๋ฅผ ๋งˆ๋ จํ•˜์—ฌ ์˜ฅ์„์„ ๊ตฌ๋ณ„ํ• ์ƒˆ, ์™•๋น„์ „์— +๋ตˆ์˜จ๋Œ€ ์™•๋น„ ๋ชป๋‚ด ๋ฐ˜๊ธฐ์‹œ๋ฉฐ ์‚ฌ์—ฐ์„ ๋‚ฑ๋‚ฑ์ด ์ด๋ฅด์‹œ๋˜ ์Šน์ƒ ์™ˆ +ใ‰ โ€œ์ด ์ผ์€ ์†Œ์ž๊ฐ€ ์ด๋ฏธ ์•„๋Š” ๋ฐ”์ด์˜ค๋‹ˆ ์—ผ๋ ค ๋งˆ์˜ต์†Œ์„œ.โ€ +ํ•˜๋ฉฐ ์™ˆ +ใ‰กโ€œ์ฒ˜์Œ์— ๊ทธ๋†ˆ์ด ์ถฉ๋ ฌ๋ถ€์ธ ๋ฐฉ์— ๊ฐ„ ์ค„ ์–ด์ฐŒ ์•Œ์œผ์…จ๋‚˜์ด๊นŒ?โ€ +์™•๋น„ ์™ˆ +โ€œ์‚ฌ์ดŒ ์˜ค๋ผ๋น„๊ฐ€ ์ด๋ฅด๊ธฐ๋กœ ์•Œ์•˜๋…ธ๋ผ.โ€ +ํ•˜์‹ ๋Œ€ ์Šน์ƒ์ด ๋ณต๋ก์„ ์ฐพ๋Š”๋ฐ ๋ฒŒ์จ ์ œ ์ฃ„๋ฅผ ์•Œ๊ณ  ํ›„์›์— ์˜ฌ๋ผ๊ฐ€ +์ด๋ฏธ ์ฃฝ์—ˆ๋Š”์ง€๋ผ. ํ•˜๋ฆด์—†์–ด ์˜ฅ์กธ์„ ์žก์•„๋“ค์—ฌ ์—„ํžˆ ๋ฌธ์™ˆ +โ€œ๋„ˆํฌ๋Š” ์–ด์ฐŒ ์ถฉ๋ ฌ๋ถ€์ธ ์•„๋‹Œ ์ค„ ์•Œ์•˜๋А๋ƒ? ๋ฐ”๋กœ ์•„๋ขฐ๋ผ.โ€ +ํ•˜์‹ ๋Œ€ ์˜ฅ์กธ์ด ๊ธ‰ํžˆ ์—ฌ์ญˆ์˜ค๋˜ +โ€œ์–ผ๊ตด์ด ์ƒํ•˜์—ฌ ์•„๋ชจ๋ž€ ์ค„ ๋ชจ๋ฅด์˜ค๋‚˜ ์†๊ธธ์ด ๊ณฑ์ง€ ๋ชปํ•˜์˜ค๋งค +์†Œ์ธ ๋“ฑ ์†Œ๊ฒฌ์— ์ถฉ๋ ฌ๋ถ€์ธ์ด ์ฒœํ•˜์ผ์ƒ‰์ด๋ผ ํ•˜๋”๋‹ˆ ์†์ด ๊ณฑ์ง€ +์•„๋‹ˆํ•˜๋”๋ผ ํ•˜์˜ฌ ์ œ ์ •๋ ฌ๋ถ€์ธ์˜ ์‹œ๋น„ ๊ธˆ์—ฐ์ด ์ด๋ฅผ ๋“ฃ๊ณ  ๋ฌป๊ธฐ์— +์ž์„ธํžˆ ์ด๋ฅด๊ณ  ๋ถ€๋”” ๋‹ค๋ฅธ ๋ฐ ๊ฐ€์„œ ์ด ๋ง ๋ง๋ผ ๋‹น๋ถ€ํ•˜์˜ต๋”๋‹ˆ, +ํ•„์—ฐ ๊ธˆ์—ฐ์˜ ์ž…์„ ํ†ตํ•ด ๋ฐœ์„ค์ด ๋œ๊ฐ€ ํ•˜๋‚˜์ด๋‹ค.โ€ +ํ•œ๋ฐ ์Šน์ƒ์ด ๊ธˆ์—ฐ์„ ์žก์•„๋“ค์—ฌ ๋ฌธ์™ˆ +โ€œ์ด ๋ง์„ ๋“ฃ๊ณ  ๋„ค๊ฒŒ ๊ตญ๋ฌธํ•˜๋‹ˆ ๋ฐ”๋ฅธ๋Œ€๋กœ ๊ณ ํ•˜๋ผ.โ€ +ํ•˜๋Š” ์†Œ๋ฆฌ๊ฐ€ ๋ฒผ๋ฝ์ด ๊ผญ๋‘์— ์ž„ํ•œ ๋“ฏํ•˜๊ณ  ๊ถ๊ถ์ด ๋’ค์ง‘ํžˆ๋Š” ๋“ฏ +ํ•˜๋”๋ผ. ์ด๋•Œ์— ์ •๋ ฌ๋ถ€์ธ์ด ์Šน์ƒ์˜ ํ˜ธํ†ต ์†Œ๋ฆฌ๋ฅผ ๋“ฃ๊ณ  ๋˜ฅ์„ ํ•œ +๋ฌด๋”๊ธฐ๋ฅผ ์‹ธ๊ณ  ์ž๋น ์กŒ๋Š”์ง€๋ผ. ๊ธˆ์—ฐ์ด ํ•˜๋ฆด์—†์–ด ๋ฐ”๋กœ ์•„๋ขฐ๋‚˜๋‹ˆ๋ผ +ํ•˜๊ณ  ์ •๋ ฌ๋ถ€์ธ ํ•˜๋˜ ๋ง์ด๋ฉฐ ์ œ๊ฐ€ ๋‚จ๋ณต์„ ํ•˜๊ณ  ์ถฉ๋ ฌ๋ถ€์ธ ์นจ์†Œ๋กœ +๋“ค์–ด๊ฐ„ ๋ง์ด๋ฉฐ ์ด๋ถˆ ์†์— ๋ˆ„์› ๋‹ค๊ฐ€ ๋‹ฌ์•„๋‚œ ๋ง์ด๋ฉฐ ์ •๋ ฌ๋ถ€์ธ์ด +์•“๋Š” ์ฒดํ•˜๊ณ  ๋ˆ„์› ์‚ฌ์˜ค๋งค ์ถฉ๋ ฌ๋ถ€์ธ์ด ์•ฝ์œผ๋กœ ๊ตฌ๋ณ‘ํ•˜๋ฉฐ ๊ณ์— +์žˆ์œผ์‹œ๋งค ์นจ์†Œ๋กœ ๊ฐ€๋ผ ๊ฐ•๊ถŒํ•˜์—ฌ ์นจ์†Œ๋กœ ๋งˆ์ง€๋ชปํ•˜์—ฌ ๊ฐ€์‹œ๋งค +๋ณต๋ก์ด ์™•๋น„๊ป˜ ์ฐธ์†Œํ•˜๋˜ ์—ฐ์œ ๋ฅผ ๋‚ฑ๋‚ฑ์ด ์•„๋ขด๋Œ€ ์™•๋น„ ๊ณ์— ์žˆ๋‹ค๊ฐ€ +์•™์ฒœํ†ต๊ณกํ•˜์‹œ๋ฉฐ ์™ˆ +โ€œ๋‚ด ๋ฐ์ง€ ๋ชปํ•˜์—ฌ ์•…๋…€์˜ ๊พ€์— ๋น ์ ธ ์ถฉ๋ ฌ๋ถ€์ธ์„ ์ฃฝ์ด๋ ค ํ•˜์˜€๋‚˜๋‹ˆ +๋ฌด์Šจ ๋ฉด๋ชฉ์œผ๋กœ ์ถฉ๋ ฌ๋ถ€์ธ์„ ๋ณด๋ฆฌ์˜ค.โ€ +ํ•˜์‹œ๋ฉฐ ์ž๊ฒฐ์ฝ”์ž ํ•˜๊ฑฐ๋Š˜ ์Šน์ƒ์ด ๋ถ™๋“ค๊ณ  ์šธ๋ฉฐ ์™ˆ +โ€œ๋ชจ์นœ์ด ๋„ˆ๋ฌด ๊ณผ๋„ํžˆ ํ•˜์‹œ๋ฉด ์†Œ์ž๊ฐ€ ๋จผ์ € ์ฃฝ์œผ๋ ค ํ•˜๋‚˜์ด๋‹ค.โ€ +์™•๋น„ ๊ธˆ์นจ์— ๋ˆ„์›Œ ์ผ์–ด๋‚˜์ง€ ๋ชปํ•˜๋”๋ผ. ์Šน์ƒ์ด ์ •๋ ฌ๋ถ€์ธ์„ +๊ฒฐ๋ฐ•ํ•˜์—ฌ ๋•…์— ๊ฟ‡๋ฆฌ๊ณ  ํฌ๊ฒŒ ๋…ธํ•˜์—ฌ ์™ˆ +โ€œ๋„ˆ๋Š” ๋ฌด์—‡์ด ๋ถ€์กฑํ•˜์—ฌ ์ถฉ๋ ฌ๋ถ€์ธ์„ ํ•ด์ฝ”์ž ํ•˜๋А๋ƒ. ์–ด์ฐŒ +์ผ์‹œ๋ฅผ ์‚ด๋ฆฌ๋ฆฌ์˜ค. ๋‚ด ์ž„์˜๋กœ๋Š” ์ฃฝ์ด๊ณ  ์‹ถ์œผ๋‚˜ ํ™ฉ์ƒ๊ป˜ ์•„๋ขฐ๊ณ  +์ฃฝ๊ฒŒ ํ•˜๋ฆฌ๋ผ.โ€ +ํ•˜๊ณ  ์ƒ์†Œํ•˜๋‹ˆ ๊ทธ ๊ธ€์— ํ•˜์˜€์œผ๋˜ +โ€œ๋Œ€์‚ฌ๋งˆ ๋Œ€๋„๋… ๋Œ€์›์ˆ˜ ์ •์„์„ ์€ ๋ˆ์ˆ˜๋ฐฑ๋ฐฐํ•˜๊ณ  ์•„๋ขฐ๋‚˜๋‹ˆ ์‹ ์ด +์„œ์œต์„ ์ณ ์‚ฌ๋กœ์žก๊ณ , ๋ฐฑ์„ฑ์„ ์ง„๋ฌดํ•˜๊ณ  ๋Œ์•„์˜ค๋ ค ํ•  ๋•Œ, ์ง‘์—์„œ +๊ธ‰ํ•œ ์†Œ์‹์„ ๋“ฃ๊ณ  ๊ตฐ์‚ฌ๋ฅผ ์ค‘๊ตฐ์žฅ์—๊ฒŒ ๋งก๊ธฐ์˜ต๊ณ  ํ•„๋งˆ๋กœ ์˜ฌ๋ผ์™€ +๋ณธ์ฆ‰, ์ •๋ ฌ๋ถ€์ธ์ด ์ด๋Ÿฌ์ด๋Ÿฌํ•œ ๋ณ€์„ ์ผ์œผ์ผฐ์‚ฌ์˜ค๋‹ˆ ์„ธ์ƒ์— +์ด๋Ÿฌํ•˜์˜จ ์ผ์ด ์žˆ์‚ฌ์˜ค๋‹›๊ฐ€.โ€ +ํ•˜๊ณ  ๊ธˆ์—ฐ์ด ํ‰๊ณ„๋ฅผ ๊พธ๋ฏผ ์ผ๊ณผ ์›”๋งค๊ฐ€ ๋‹นํ•˜๋˜ ๊ณ ์ดˆ๋ฅผ ๋‚ฑ๋‚ฑ์ด +์•„๋ขฐ์—ˆ๋‹ค. +-์ž‘์ž ๋ฏธ์ƒ, ๏ฝข์ •์„์„ ์ „๏ฝฃ-","{'question': '๋ˆ„๋ช…๊ณผ ๊ด€๋ จํ•œ ์„ค๋ช…์œผ๋กœ ๊ฐ€์žฅ ์ ์ ˆํ•œ ๊ฒƒ์€?', 'choices': ['๋ˆ„๋ช…์ด ๋ฒ—๊ฒจ์ง€๋ฉด์„œ, ๋ˆ„๋ช…์„ ์ผ๋˜ ์ธ๋ฌผ์€ ์ž์‹ ์˜ ์–ด๋ฆฌ์„์Œ์„\nํƒ“ํ•˜๊ณ  ์žˆ๋‹ค.', '๋ˆ„๋ช…์„ ์“ด ์ธ๋ฌผ์˜ ์š”์ฒญ์œผ๋กœ ๋‚จ์ฃผ์ธ๊ณต์€ ๋ˆ„๋ช…์„ ์”Œ์šด ์ธ๋ฌผ์˜\n์ฒ˜๋ฒŒ์„ ์œ ๋ณดํ•œ๋‹ค.', '๋ˆ„๋ช…์˜ ๋‚ด์šฉ์€ ๋ˆ„๋ช…์„ ์“ด ์ธ๋ฌผ์ด ๋‚จ๋ชฐ๋ž˜ ์ž์‹ ์˜ ์ฒ˜์†Œ์—์„œ ๋ฒ—์–ด๋‚˜\n๊ตฌ๋ฉ์ด์— ์žˆ๋‹ค๋Š” ์‚ฌ์‹ค์ด๋‹ค.', '๋ˆ„๋ช…์„ ์”Œ์šฐ๊ธฐ ์œ„ํ•œ ๊ณ„๋žต์—๋Š” ๋ˆ„๋ช…์„ ์“ฐ๋Š” ์ธ๋ฌผ์„ ํŠน์ • ์žฅ์†Œ๋กœ\n๊ฐ€๊ฒŒ ํ•˜๋Š” ๊ฒƒ์ด ํฌํ•จ๋˜์–ด ์žˆ๋‹ค.', '๋ˆ„๋ช…์ด ๋ฒ—๊ฒจ์ง€๋Š” ๊ณ„๊ธฐ๋Š” ๋‚จ์ฃผ์ธ๊ณต์ด ์ž์‹ ์˜ ์–ด๋จธ๋‹ˆ๊ฐ€ ๊ทน๋‹จ์ \n์„ ํƒ์„ ํ•˜๊ฒ ๋‹ค๋Š” ๊ฒƒ์„ ๋งŒ๋ฅ˜ํ•œ ๊ฒƒ์ด๋‹ค.'], 'answer': ''}",,4,4,True,[],4 +2025-korean-20,"[์•ž๋ถ€๋ถ„์˜ ์ค„๊ฑฐ๋ฆฌ] ์Šน์ƒ ์ •์„์„ ์ด ์ถœ์ •ํ•œ ์‚ฌ์ด ์ •๋ ฌ๋ถ€์ธ์˜ ๋ชจ๋žต์œผ๋กœ +์ถฉ๋ ฌ๋ถ€์ธ์ด ์˜ฅ์— ๊ฐ‡ํžˆ์ž ์‹œ๋น„ ๊ธˆ์„ฌ์ด ์ถฉ๋ ฌ๋ถ€์ธ์„ ํ”ผ์‹ ์‹œํ‚ค๊ณ  ์ž์ง„ํ•œ๋‹ค. +์˜ฅ์—์„œ ์–ผ๊ตด์ด ์ƒํ•œ ๊ธˆ์„ฌ์˜ ์‹œ์‹ ์ด ๋ฐœ๊ฒฌ๋˜์ž ์™•๋น„๋Š” ์›”๋งค๋ฅผ ๋ฌธ์ดˆํ•œ๋‹ค. +์ „์žฅ์—์„œ ์ •์„์„ ์€ ํ˜ธ์ฒฉ์ด ์ „ํ•œ ํŽธ์ง€๋ฅผ ์ฝ๋Š”๋‹ค. +์›์ˆ˜๊ฐ€ ๋Œ€๊ฒฝํ•˜์—ฌ ํ˜ธ์ฒฉ์„ ๋ถˆ๋Ÿฌ ์—ฐ๊ณ ๋ฅผ ๋ฌผ์œผ์‹œ๊ณ  ์ธํ•˜์—ฌ ์ค‘๊ตฐ์žฅ์—๊ฒŒ +๋ถ„๋ถ€ํ•˜์‹œ๋˜ โ€˜๋‚˜๋Š” ์ง‘์— ๋ณ€์ด ์žˆ์–ด ๋จผ์ € ๊ฐ€๋‹ˆ ์ค‘๊ตฐ์žฅ์€ ์ฐจํ›„์— ์ธ์†” +ํ•˜์—ฌ ์˜ค๋ผ.โ€™ ํ•˜๊ณ  ๋ฐค๋‚ฎ ์‚ผ ์ผ ๋งŒ์— ๋“๋‹ฌํ•˜๋‹ˆ ์ด๋•Œ์— ์™•๋น„์˜ ์‹œ๋น„ +์›”๋งค๊ฐ€ ์ข…์‹œ ํ† ์„ค์น˜ ์•„๋‹ˆํ•˜๋งค ๋งค๋ฅผ ๋งŽ์ด ๋งž๊ณ  ์—ฌ์ญˆ์˜ค๋˜ +โ€œ์–ด์„œ ๋ฐ”์‚ ์ฃฝ์ด์‹œ๋ฉด ๊ธˆ์„ฌ์˜ ๋’ค๋ฅผ ์ซ“์•„๊ฐ€๊ฒ ๋‚˜์ด๋‹ค. +ํ•œ๋ฐ ์™•๋น„ ํฌ๊ฒŒ ๋…ธํ•˜์—ฌ ๋ชฉ์„ ๋ฒ ๋ผ ํ•  ์ฆˆ์Œ์— ์ด๋•Œ ์Šน์ƒ์ด ํ•„๋งˆ๋กœ +๋‹ฌ๋ ค์˜ค๋‹ค๊ฐ€ ์›”๋งค ์ฃฝ์ด๋ ค ํ•˜๋Š” ๊ฑฐ๋™์„ ๋ณด๊ณ  ๊ธ‰ํžˆ ์†Œ๋ฆฌ๋ฅผ ์ง€๋ฅด๋ฉฐ +๋ง์—์„œ ๋‚ด๋ ค ์ด๋ฅผ ๊ตฌํ˜ธํ•˜๋งค ๋ฌธ์™ˆ +โ€œ์ถฉ๋ ฌ๋ถ€์ธ์€ ์–ด๋”” ๊ณ„์‹œ๋ƒ?โ€ +์›”๋งค ์ธ์‚ฌ๋ฅผ ๋ชจ๋ฅด๋‹ค๊ฐ€ ์Šน์ƒ์„ ๋ณด๊ณ  ๋ฐฉ์„ฑํ†ต๊ณก ์™ˆ +โ€œ์Šน์ƒ์€ ๋ฐ”์‚ ์ถฉ๋ ฌ๋ถ€์ธ์„ ์‚ด๋ฆฌ์†Œ์„œ.โ€ +ํ•œ๋ฐ ์Šน์ƒ์ด ๊ธ‰ํžˆ ๋ฌธ์™ˆ +โ€œ์–ด๋”” ๊ณ„์‹œ๋ƒ?โ€ +ํ•œ๋ฐ ์›”๋งค ์šธ๋ฉฐ ์™ˆ +โ€œ์†Œ์ธ์ด ๊ฑท์ง€ ๋ชปํ•˜์˜ค๋‹ˆ ์–ด์ฐŒ ๊ฐ€์˜ค๋ฆฌ๊นŒ?โ€ +ํ•œ๋ฐ ๊ธ‰ํžˆ ์ข…์„ ๋ถˆ๋Ÿฌ ์›”๋งค๋ฅผ ์—…ํžˆ๊ณ  ๊ตฌ๋ฉ์ด๋ฅผ ์ฐพ์•„๊ฐ€ ๋ณด๋‹ˆ ๋ถ€์ธ์ด +์•„๊ธฐ๋ฅผ ์•ˆ๊ณ  ์žˆ๊ฑฐ๋Š˜ ์•„๊ธฐ๋Š” ์ž ์„ ๊นŠ์ด ๋“ค์—ˆ๋Š”์ง€๋ผ. ์Šน์ƒ์ด ํ†ต๊ณก ์™ˆ +โ€œ๋ถ€์ธ์€ ๋ˆˆ์„ ๋–  ๋‚˜๋ฅผ ๋ณด์†Œ์„œ.โ€ +ํ•œ๋ฐ ๋ถ€์ธ์ด ๋ˆˆ์„ ๋–  ๋ณด๋‹ˆ ์Šน์ƒ์ด ์™”๊ฑฐ๋Š˜ ์ •์‹  ์•„๋“ํ•˜์—ฌ ์ธ์‚ฌ๋ฅผ +๋ชจ๋ฅด๋‹ค๊ฐ€ ๊ฒจ์šฐ ์ธ์‚ฌ๋ฅผ ์ฐจ๋ ค ์™ˆ +โ€œ์ด๊ฒƒ์ด ๊ฟˆ์ธ๊ฐ€ ์ƒ์‹œ์ธ๊ฐ€ ๊ตฌ๋…„์ง€์ˆ˜์˜ ํ•ด ๊ฐ™๊ณ  ์น ๋…„๋Œ€ํ•œ์˜ +๋น—๋ฐœ๊ฐ™์ด ๋ฐ”๋ผ๋”๋‹ˆ ์ง€๊ธˆ ๊ตฌ๋ฉ์ด์—์„œ ๋งŒ๋‚  ์ค„ ์•Œ์•˜์œผ๋ฆฌ๊นŒ. +์Šน์ƒ์€ ๋‚˜์˜ ๋ˆ„๋ช…์„ ์”ป๊ฒจ ์ฃผ์†Œ์„œ.โ€ +ํ•˜๋ฉฐ ์ธ์‚ฌ๋ฅผ ๋ชจ๋ฅด๋Š”์ง€๋ผ. ๊ทธ ์ฐธํ˜นํ•œ ํ˜•์ƒ์„ ์–ด๋””์— ๋น„ํ•˜๋ฆฌ์˜ค. ์Šฌํ””์— +๋งค์šฐ ์•ผ์œ„์–ด ๋ผˆ๊ฐ€ ๋“œ๋Ÿฌ๋‚˜๊ฒŒ ๋˜์—ˆ๋Š”์ง€๋ผ. ์Šน์ƒ์ด ์•„๊ธฐ๋ฅผ ์•ˆ์•„ ์›”๋งค๋ฅผ +์ฃผ๊ณ  ๋ถ€์ธ์„ ๊ตฌํ•œ ํ›„์— ์ž๋ฆฌ๋ฅผ ๋งˆ๋ จํ•˜์—ฌ ์˜ฅ์„์„ ๊ตฌ๋ณ„ํ• ์ƒˆ, ์™•๋น„์ „์— +๋ตˆ์˜จ๋Œ€ ์™•๋น„ ๋ชป๋‚ด ๋ฐ˜๊ธฐ์‹œ๋ฉฐ ์‚ฌ์—ฐ์„ ๋‚ฑ๋‚ฑ์ด ์ด๋ฅด์‹œ๋˜ ์Šน์ƒ ์™ˆ +ใ‰ โ€œ์ด ์ผ์€ ์†Œ์ž๊ฐ€ ์ด๋ฏธ ์•„๋Š” ๋ฐ”์ด์˜ค๋‹ˆ ์—ผ๋ ค ๋งˆ์˜ต์†Œ์„œ.โ€ +ํ•˜๋ฉฐ ์™ˆ +ใ‰กโ€œ์ฒ˜์Œ์— ๊ทธ๋†ˆ์ด ์ถฉ๋ ฌ๋ถ€์ธ ๋ฐฉ์— ๊ฐ„ ์ค„ ์–ด์ฐŒ ์•Œ์œผ์…จ๋‚˜์ด๊นŒ?โ€ +์™•๋น„ ์™ˆ +โ€œ์‚ฌ์ดŒ ์˜ค๋ผ๋น„๊ฐ€ ์ด๋ฅด๊ธฐ๋กœ ์•Œ์•˜๋…ธ๋ผ.โ€ +ํ•˜์‹ ๋Œ€ ์Šน์ƒ์ด ๋ณต๋ก์„ ์ฐพ๋Š”๋ฐ ๋ฒŒ์จ ์ œ ์ฃ„๋ฅผ ์•Œ๊ณ  ํ›„์›์— ์˜ฌ๋ผ๊ฐ€ +์ด๋ฏธ ์ฃฝ์—ˆ๋Š”์ง€๋ผ. ํ•˜๋ฆด์—†์–ด ์˜ฅ์กธ์„ ์žก์•„๋“ค์—ฌ ์—„ํžˆ ๋ฌธ์™ˆ +โ€œ๋„ˆํฌ๋Š” ์–ด์ฐŒ ์ถฉ๋ ฌ๋ถ€์ธ ์•„๋‹Œ ์ค„ ์•Œ์•˜๋А๋ƒ? ๋ฐ”๋กœ ์•„๋ขฐ๋ผ.โ€ +ํ•˜์‹ ๋Œ€ ์˜ฅ์กธ์ด ๊ธ‰ํžˆ ์—ฌ์ญˆ์˜ค๋˜ +โ€œ์–ผ๊ตด์ด ์ƒํ•˜์—ฌ ์•„๋ชจ๋ž€ ์ค„ ๋ชจ๋ฅด์˜ค๋‚˜ ์†๊ธธ์ด ๊ณฑ์ง€ ๋ชปํ•˜์˜ค๋งค +์†Œ์ธ ๋“ฑ ์†Œ๊ฒฌ์— ์ถฉ๋ ฌ๋ถ€์ธ์ด ์ฒœํ•˜์ผ์ƒ‰์ด๋ผ ํ•˜๋”๋‹ˆ ์†์ด ๊ณฑ์ง€ +์•„๋‹ˆํ•˜๋”๋ผ ํ•˜์˜ฌ ์ œ ์ •๋ ฌ๋ถ€์ธ์˜ ์‹œ๋น„ ๊ธˆ์—ฐ์ด ์ด๋ฅผ ๋“ฃ๊ณ  ๋ฌป๊ธฐ์— +์ž์„ธํžˆ ์ด๋ฅด๊ณ  ๋ถ€๋”” ๋‹ค๋ฅธ ๋ฐ ๊ฐ€์„œ ์ด ๋ง ๋ง๋ผ ๋‹น๋ถ€ํ•˜์˜ต๋”๋‹ˆ, +ํ•„์—ฐ ๊ธˆ์—ฐ์˜ ์ž…์„ ํ†ตํ•ด ๋ฐœ์„ค์ด ๋œ๊ฐ€ ํ•˜๋‚˜์ด๋‹ค.โ€ +ํ•œ๋ฐ ์Šน์ƒ์ด ๊ธˆ์—ฐ์„ ์žก์•„๋“ค์—ฌ ๋ฌธ์™ˆ +โ€œ์ด ๋ง์„ ๋“ฃ๊ณ  ๋„ค๊ฒŒ ๊ตญ๋ฌธํ•˜๋‹ˆ ๋ฐ”๋ฅธ๋Œ€๋กœ ๊ณ ํ•˜๋ผ.โ€ +ํ•˜๋Š” ์†Œ๋ฆฌ๊ฐ€ ๋ฒผ๋ฝ์ด ๊ผญ๋‘์— ์ž„ํ•œ ๋“ฏํ•˜๊ณ  ๊ถ๊ถ์ด ๋’ค์ง‘ํžˆ๋Š” ๋“ฏ +ํ•˜๋”๋ผ. ์ด๋•Œ์— ์ •๋ ฌ๋ถ€์ธ์ด ์Šน์ƒ์˜ ํ˜ธํ†ต ์†Œ๋ฆฌ๋ฅผ ๋“ฃ๊ณ  ๋˜ฅ์„ ํ•œ +๋ฌด๋”๊ธฐ๋ฅผ ์‹ธ๊ณ  ์ž๋น ์กŒ๋Š”์ง€๋ผ. ๊ธˆ์—ฐ์ด ํ•˜๋ฆด์—†์–ด ๋ฐ”๋กœ ์•„๋ขฐ๋‚˜๋‹ˆ๋ผ +ํ•˜๊ณ  ์ •๋ ฌ๋ถ€์ธ ํ•˜๋˜ ๋ง์ด๋ฉฐ ์ œ๊ฐ€ ๋‚จ๋ณต์„ ํ•˜๊ณ  ์ถฉ๋ ฌ๋ถ€์ธ ์นจ์†Œ๋กœ +๋“ค์–ด๊ฐ„ ๋ง์ด๋ฉฐ ์ด๋ถˆ ์†์— ๋ˆ„์› ๋‹ค๊ฐ€ ๋‹ฌ์•„๋‚œ ๋ง์ด๋ฉฐ ์ •๋ ฌ๋ถ€์ธ์ด +์•“๋Š” ์ฒดํ•˜๊ณ  ๋ˆ„์› ์‚ฌ์˜ค๋งค ์ถฉ๋ ฌ๋ถ€์ธ์ด ์•ฝ์œผ๋กœ ๊ตฌ๋ณ‘ํ•˜๋ฉฐ ๊ณ์— +์žˆ์œผ์‹œ๋งค ์นจ์†Œ๋กœ ๊ฐ€๋ผ ๊ฐ•๊ถŒํ•˜์—ฌ ์นจ์†Œ๋กœ ๋งˆ์ง€๋ชปํ•˜์—ฌ ๊ฐ€์‹œ๋งค +๋ณต๋ก์ด ์™•๋น„๊ป˜ ์ฐธ์†Œํ•˜๋˜ ์—ฐ์œ ๋ฅผ ๋‚ฑ๋‚ฑ์ด ์•„๋ขด๋Œ€ ์™•๋น„ ๊ณ์— ์žˆ๋‹ค๊ฐ€ +์•™์ฒœํ†ต๊ณกํ•˜์‹œ๋ฉฐ ์™ˆ +โ€œ๋‚ด ๋ฐ์ง€ ๋ชปํ•˜์—ฌ ์•…๋…€์˜ ๊พ€์— ๋น ์ ธ ์ถฉ๋ ฌ๋ถ€์ธ์„ ์ฃฝ์ด๋ ค ํ•˜์˜€๋‚˜๋‹ˆ +๋ฌด์Šจ ๋ฉด๋ชฉ์œผ๋กœ ์ถฉ๋ ฌ๋ถ€์ธ์„ ๋ณด๋ฆฌ์˜ค.โ€ +ํ•˜์‹œ๋ฉฐ ์ž๊ฒฐ์ฝ”์ž ํ•˜๊ฑฐ๋Š˜ ์Šน์ƒ์ด ๋ถ™๋“ค๊ณ  ์šธ๋ฉฐ ์™ˆ +โ€œ๋ชจ์นœ์ด ๋„ˆ๋ฌด ๊ณผ๋„ํžˆ ํ•˜์‹œ๋ฉด ์†Œ์ž๊ฐ€ ๋จผ์ € ์ฃฝ์œผ๋ ค ํ•˜๋‚˜์ด๋‹ค.โ€ +์™•๋น„ ๊ธˆ์นจ์— ๋ˆ„์›Œ ์ผ์–ด๋‚˜์ง€ ๋ชปํ•˜๋”๋ผ. ์Šน์ƒ์ด ์ •๋ ฌ๋ถ€์ธ์„ +๊ฒฐ๋ฐ•ํ•˜์—ฌ ๋•…์— ๊ฟ‡๋ฆฌ๊ณ  ํฌ๊ฒŒ ๋…ธํ•˜์—ฌ ์™ˆ +โ€œ๋„ˆ๋Š” ๋ฌด์—‡์ด ๋ถ€์กฑํ•˜์—ฌ ์ถฉ๋ ฌ๋ถ€์ธ์„ ํ•ด์ฝ”์ž ํ•˜๋А๋ƒ. ์–ด์ฐŒ +์ผ์‹œ๋ฅผ ์‚ด๋ฆฌ๋ฆฌ์˜ค. ๋‚ด ์ž„์˜๋กœ๋Š” ์ฃฝ์ด๊ณ  ์‹ถ์œผ๋‚˜ ํ™ฉ์ƒ๊ป˜ ์•„๋ขฐ๊ณ  +์ฃฝ๊ฒŒ ํ•˜๋ฆฌ๋ผ.โ€ +ํ•˜๊ณ  ์ƒ์†Œํ•˜๋‹ˆ ๊ทธ ๊ธ€์— ํ•˜์˜€์œผ๋˜ +โ€œ๋Œ€์‚ฌ๋งˆ ๋Œ€๋„๋… ๋Œ€์›์ˆ˜ ์ •์„์„ ์€ ๋ˆ์ˆ˜๋ฐฑ๋ฐฐํ•˜๊ณ  ์•„๋ขฐ๋‚˜๋‹ˆ ์‹ ์ด +์„œ์œต์„ ์ณ ์‚ฌ๋กœ์žก๊ณ , ๋ฐฑ์„ฑ์„ ์ง„๋ฌดํ•˜๊ณ  ๋Œ์•„์˜ค๋ ค ํ•  ๋•Œ, ์ง‘์—์„œ +๊ธ‰ํ•œ ์†Œ์‹์„ ๋“ฃ๊ณ  ๊ตฐ์‚ฌ๋ฅผ ์ค‘๊ตฐ์žฅ์—๊ฒŒ ๋งก๊ธฐ์˜ต๊ณ  ํ•„๋งˆ๋กœ ์˜ฌ๋ผ์™€ +๋ณธ์ฆ‰, ์ •๋ ฌ๋ถ€์ธ์ด ์ด๋Ÿฌ์ด๋Ÿฌํ•œ ๋ณ€์„ ์ผ์œผ์ผฐ์‚ฌ์˜ค๋‹ˆ ์„ธ์ƒ์— +์ด๋Ÿฌํ•˜์˜จ ์ผ์ด ์žˆ์‚ฌ์˜ค๋‹›๊ฐ€.โ€ +ํ•˜๊ณ  ๊ธˆ์—ฐ์ด ํ‰๊ณ„๋ฅผ ๊พธ๋ฏผ ์ผ๊ณผ ์›”๋งค๊ฐ€ ๋‹นํ•˜๋˜ ๊ณ ์ดˆ๋ฅผ ๋‚ฑ๋‚ฑ์ด +์•„๋ขฐ์—ˆ๋‹ค. +-์ž‘์ž ๋ฏธ์ƒ, ๏ฝข์ •์„์„ ์ „๏ฝฃ-","{'question': '<ํ•™์Šต ํ™œ๋™>์„ ์ˆ˜ํ–‰ํ•œ ๊ฒฐ๊ณผ๋กœ ์ ์ ˆํ•˜์ง€ ์•Š์€ ๊ฒƒ์€?', 'choices': ['์ธ๋ฌผ A : ์›์ˆ˜, ์ธ๋ฌผ B : ์ค‘๊ตฐ์žฅ\n์†Œํ†ต์˜ ๋‚ด์šฉ : A๊ฐ€ B์—๊ฒŒ ๊ตฐ์‚ฌ๋ฅผ ์ด๋Œ๊ณ  ๊ฐ€ ์„œ์œต์„\n์‚ฌ๋กœ์žก์œผ๋ผ๊ณ  ๋ช…๋ นํ•จ.', '์ธ๋ฌผ A : ์Šน์ƒ, ์ธ๋ฌผ B : ์›”๋งค\n์†Œํ†ต์˜ ๋‚ด์šฉ : A๊ฐ€ B์—๊ฒŒ ์ถฉ๋ ฌ๋ถ€์ธ์ด ์žˆ๋Š” ๊ณณ์ด ์–ด๋””\n์ธ์ง€ ๋ฌผ์Œ.', '์ธ๋ฌผ A : ์˜ฅ์กธ, ์ธ๋ฌผ B : ๊ธˆ์—ฐ\n์†Œํ†ต์˜ ๋‚ด์šฉ : B๊ฐ€ A๋กœ๋ถ€ํ„ฐ ์˜ฅ์ค‘ ์‹œ์‹ ์˜ ์ •์ฒด์™€ ๊ด€๋ จํ•œ\n์ •๋ณด๋ฅผ ์–ป์Œ.', '์ธ๋ฌผ A : ์˜ฅ์กธ , ์ธ๋ฌผ B : ์Šน์ƒ \n์†Œํ†ต์˜ ๋‚ด์šฉ : A๊ฐ€ B์—๊ฒŒ, ๊ธˆ์—ฐ์ด ์˜ฅ์ค‘ ์‹œ์‹ ์— ๋Œ€ํ•˜์—ฌ\n๋ฐœ์„คํ–ˆ์„ ๊ฒƒ์ด๋ผ๋Š” ์˜ํ˜น์„ ์ œ๊ธฐํ•จ.', '์ธ๋ฌผ A : ๊ธˆ์—ฐ, ์ธ๋ฌผ B : ์Šน์ƒ\n์†Œํ†ต์˜ ๋‚ด์šฉ : B๊ฐ€ A๋กœ๋ถ€ํ„ฐ ์ •๋ ฌ๋ถ€์ธ์ด ๊ฑฐ์ง“์œผ๋กœ ์•“์•„\n๋ˆ„์› ์—ˆ๋‹ค๋Š” ์ •๋ณด๋ฅผ ์–ป์Œ.'], 'answer': '', 'question_plus': '<ํ•™์Šต ํ™œ๋™> : ๏ฝข์ •์„์„ ์ „๏ฝฃ์€ ๋ชจ๋žต์„ ์ค‘์‹ฌ์œผ๋กœ ์‚ฌ๊ฑด์ด ์ „๊ฐœ๋˜๋ฏ€๋กœ ์ธ๋ฌผ\n๊ฐ„ ์†Œํ†ต ์–‘์ƒ์„ ํŒŒ์•…ํ•˜๋Š” ๊ฒƒ์ด ์ค‘์š”ํ•˜๋‹ค. ์œ—๊ธ€์„ ๋ฐ”ํƒ•์œผ๋กœ\n์ธ๋ฌผ ๊ฐ„์— ๋‚˜ํƒ€๋‚œ ์†Œํ†ต์˜ ๋‚ด์šฉ์„ ์ •๋ฆฌํ•ด ๋ณด์ž.'}","<ํ•™์Šต ํ™œ๋™> : ๏ฝข์ •์„์„ ์ „๏ฝฃ์€ ๋ชจ๋žต์„ ์ค‘์‹ฌ์œผ๋กœ ์‚ฌ๊ฑด์ด ์ „๊ฐœ๋˜๋ฏ€๋กœ ์ธ๋ฌผ +๊ฐ„ ์†Œํ†ต ์–‘์ƒ์„ ํŒŒ์•…ํ•˜๋Š” ๊ฒƒ์ด ์ค‘์š”ํ•˜๋‹ค. ์œ—๊ธ€์„ ๋ฐ”ํƒ•์œผ๋กœ +์ธ๋ฌผ ๊ฐ„์— ๋‚˜ํƒ€๋‚œ ์†Œํ†ต์˜ ๋‚ด์šฉ์„ ์ •๋ฆฌํ•ด ๋ณด์ž.",1,1,True,[],1 +2025-korean-21,"[์•ž๋ถ€๋ถ„์˜ ์ค„๊ฑฐ๋ฆฌ] ์Šน์ƒ ์ •์„์„ ์ด ์ถœ์ •ํ•œ ์‚ฌ์ด ์ •๋ ฌ๋ถ€์ธ์˜ ๋ชจ๋žต์œผ๋กœ +์ถฉ๋ ฌ๋ถ€์ธ์ด ์˜ฅ์— ๊ฐ‡ํžˆ์ž ์‹œ๋น„ ๊ธˆ์„ฌ์ด ์ถฉ๋ ฌ๋ถ€์ธ์„ ํ”ผ์‹ ์‹œํ‚ค๊ณ  ์ž์ง„ํ•œ๋‹ค. +์˜ฅ์—์„œ ์–ผ๊ตด์ด ์ƒํ•œ ๊ธˆ์„ฌ์˜ ์‹œ์‹ ์ด ๋ฐœ๊ฒฌ๋˜์ž ์™•๋น„๋Š” ์›”๋งค๋ฅผ ๋ฌธ์ดˆํ•œ๋‹ค. +์ „์žฅ์—์„œ ์ •์„์„ ์€ ํ˜ธ์ฒฉ์ด ์ „ํ•œ ํŽธ์ง€๋ฅผ ์ฝ๋Š”๋‹ค. +์›์ˆ˜๊ฐ€ ๋Œ€๊ฒฝํ•˜์—ฌ ํ˜ธ์ฒฉ์„ ๋ถˆ๋Ÿฌ ์—ฐ๊ณ ๋ฅผ ๋ฌผ์œผ์‹œ๊ณ  ์ธํ•˜์—ฌ ์ค‘๊ตฐ์žฅ์—๊ฒŒ +๋ถ„๋ถ€ํ•˜์‹œ๋˜ โ€˜๋‚˜๋Š” ์ง‘์— ๋ณ€์ด ์žˆ์–ด ๋จผ์ € ๊ฐ€๋‹ˆ ์ค‘๊ตฐ์žฅ์€ ์ฐจํ›„์— ์ธ์†” +ํ•˜์—ฌ ์˜ค๋ผ.โ€™ ํ•˜๊ณ  ๋ฐค๋‚ฎ ์‚ผ ์ผ ๋งŒ์— ๋“๋‹ฌํ•˜๋‹ˆ ์ด๋•Œ์— ์™•๋น„์˜ ์‹œ๋น„ +์›”๋งค๊ฐ€ ์ข…์‹œ ํ† ์„ค์น˜ ์•„๋‹ˆํ•˜๋งค ๋งค๋ฅผ ๋งŽ์ด ๋งž๊ณ  ์—ฌ์ญˆ์˜ค๋˜ +โ€œ์–ด์„œ ๋ฐ”์‚ ์ฃฝ์ด์‹œ๋ฉด ๊ธˆ์„ฌ์˜ ๋’ค๋ฅผ ์ซ“์•„๊ฐ€๊ฒ ๋‚˜์ด๋‹ค. +ํ•œ๋ฐ ์™•๋น„ ํฌ๊ฒŒ ๋…ธํ•˜์—ฌ ๋ชฉ์„ ๋ฒ ๋ผ ํ•  ์ฆˆ์Œ์— ์ด๋•Œ ์Šน์ƒ์ด ํ•„๋งˆ๋กœ +๋‹ฌ๋ ค์˜ค๋‹ค๊ฐ€ ์›”๋งค ์ฃฝ์ด๋ ค ํ•˜๋Š” ๊ฑฐ๋™์„ ๋ณด๊ณ  ๊ธ‰ํžˆ ์†Œ๋ฆฌ๋ฅผ ์ง€๋ฅด๋ฉฐ +๋ง์—์„œ ๋‚ด๋ ค ์ด๋ฅผ ๊ตฌํ˜ธํ•˜๋งค ๋ฌธ์™ˆ +โ€œ์ถฉ๋ ฌ๋ถ€์ธ์€ ์–ด๋”” ๊ณ„์‹œ๋ƒ?โ€ +์›”๋งค ์ธ์‚ฌ๋ฅผ ๋ชจ๋ฅด๋‹ค๊ฐ€ ์Šน์ƒ์„ ๋ณด๊ณ  ๋ฐฉ์„ฑํ†ต๊ณก ์™ˆ +โ€œ์Šน์ƒ์€ ๋ฐ”์‚ ์ถฉ๋ ฌ๋ถ€์ธ์„ ์‚ด๋ฆฌ์†Œ์„œ.โ€ +ํ•œ๋ฐ ์Šน์ƒ์ด ๊ธ‰ํžˆ ๋ฌธ์™ˆ +โ€œ์–ด๋”” ๊ณ„์‹œ๋ƒ?โ€ +ํ•œ๋ฐ ์›”๋งค ์šธ๋ฉฐ ์™ˆ +โ€œ์†Œ์ธ์ด ๊ฑท์ง€ ๋ชปํ•˜์˜ค๋‹ˆ ์–ด์ฐŒ ๊ฐ€์˜ค๋ฆฌ๊นŒ?โ€ +ํ•œ๋ฐ ๊ธ‰ํžˆ ์ข…์„ ๋ถˆ๋Ÿฌ ์›”๋งค๋ฅผ ์—…ํžˆ๊ณ  ๊ตฌ๋ฉ์ด๋ฅผ ์ฐพ์•„๊ฐ€ ๋ณด๋‹ˆ ๋ถ€์ธ์ด +์•„๊ธฐ๋ฅผ ์•ˆ๊ณ  ์žˆ๊ฑฐ๋Š˜ ์•„๊ธฐ๋Š” ์ž ์„ ๊นŠ์ด ๋“ค์—ˆ๋Š”์ง€๋ผ. ์Šน์ƒ์ด ํ†ต๊ณก ์™ˆ +โ€œ๋ถ€์ธ์€ ๋ˆˆ์„ ๋–  ๋‚˜๋ฅผ ๋ณด์†Œ์„œ.โ€ +ํ•œ๋ฐ ๋ถ€์ธ์ด ๋ˆˆ์„ ๋–  ๋ณด๋‹ˆ ์Šน์ƒ์ด ์™”๊ฑฐ๋Š˜ ์ •์‹  ์•„๋“ํ•˜์—ฌ ์ธ์‚ฌ๋ฅผ +๋ชจ๋ฅด๋‹ค๊ฐ€ ๊ฒจ์šฐ ์ธ์‚ฌ๋ฅผ ์ฐจ๋ ค ์™ˆ +โ€œ์ด๊ฒƒ์ด ๊ฟˆ์ธ๊ฐ€ ์ƒ์‹œ์ธ๊ฐ€ ๊ตฌ๋…„์ง€์ˆ˜์˜ ํ•ด ๊ฐ™๊ณ  ์น ๋…„๋Œ€ํ•œ์˜ +๋น—๋ฐœ๊ฐ™์ด ๋ฐ”๋ผ๋”๋‹ˆ ์ง€๊ธˆ ๊ตฌ๋ฉ์ด์—์„œ ๋งŒ๋‚  ์ค„ ์•Œ์•˜์œผ๋ฆฌ๊นŒ. +์Šน์ƒ์€ ๋‚˜์˜ ๋ˆ„๋ช…์„ ์”ป๊ฒจ ์ฃผ์†Œ์„œ.โ€ +ํ•˜๋ฉฐ ์ธ์‚ฌ๋ฅผ ๋ชจ๋ฅด๋Š”์ง€๋ผ. ๊ทธ ์ฐธํ˜นํ•œ ํ˜•์ƒ์„ ์–ด๋””์— ๋น„ํ•˜๋ฆฌ์˜ค. ์Šฌํ””์— +๋งค์šฐ ์•ผ์œ„์–ด ๋ผˆ๊ฐ€ ๋“œ๋Ÿฌ๋‚˜๊ฒŒ ๋˜์—ˆ๋Š”์ง€๋ผ. ์Šน์ƒ์ด ์•„๊ธฐ๋ฅผ ์•ˆ์•„ ์›”๋งค๋ฅผ +์ฃผ๊ณ  ๋ถ€์ธ์„ ๊ตฌํ•œ ํ›„์— ์ž๋ฆฌ๋ฅผ ๋งˆ๋ จํ•˜์—ฌ ์˜ฅ์„์„ ๊ตฌ๋ณ„ํ• ์ƒˆ, ์™•๋น„์ „์— +๋ตˆ์˜จ๋Œ€ ์™•๋น„ ๋ชป๋‚ด ๋ฐ˜๊ธฐ์‹œ๋ฉฐ ์‚ฌ์—ฐ์„ ๋‚ฑ๋‚ฑ์ด ์ด๋ฅด์‹œ๋˜ ์Šน์ƒ ์™ˆ +ใ‰ โ€œ์ด ์ผ์€ ์†Œ์ž๊ฐ€ ์ด๋ฏธ ์•„๋Š” ๋ฐ”์ด์˜ค๋‹ˆ ์—ผ๋ ค ๋งˆ์˜ต์†Œ์„œ.โ€ +ํ•˜๋ฉฐ ์™ˆ +ใ‰กโ€œ์ฒ˜์Œ์— ๊ทธ๋†ˆ์ด ์ถฉ๋ ฌ๋ถ€์ธ ๋ฐฉ์— ๊ฐ„ ์ค„ ์–ด์ฐŒ ์•Œ์œผ์…จ๋‚˜์ด๊นŒ?โ€ +์™•๋น„ ์™ˆ +โ€œ์‚ฌ์ดŒ ์˜ค๋ผ๋น„๊ฐ€ ์ด๋ฅด๊ธฐ๋กœ ์•Œ์•˜๋…ธ๋ผ.โ€ +ํ•˜์‹ ๋Œ€ ์Šน์ƒ์ด ๋ณต๋ก์„ ์ฐพ๋Š”๋ฐ ๋ฒŒ์จ ์ œ ์ฃ„๋ฅผ ์•Œ๊ณ  ํ›„์›์— ์˜ฌ๋ผ๊ฐ€ +์ด๋ฏธ ์ฃฝ์—ˆ๋Š”์ง€๋ผ. ํ•˜๋ฆด์—†์–ด ์˜ฅ์กธ์„ ์žก์•„๋“ค์—ฌ ์—„ํžˆ ๋ฌธ์™ˆ +โ€œ๋„ˆํฌ๋Š” ์–ด์ฐŒ ์ถฉ๋ ฌ๋ถ€์ธ ์•„๋‹Œ ์ค„ ์•Œ์•˜๋А๋ƒ? ๋ฐ”๋กœ ์•„๋ขฐ๋ผ.โ€ +ํ•˜์‹ ๋Œ€ ์˜ฅ์กธ์ด ๊ธ‰ํžˆ ์—ฌ์ญˆ์˜ค๋˜ +โ€œ์–ผ๊ตด์ด ์ƒํ•˜์—ฌ ์•„๋ชจ๋ž€ ์ค„ ๋ชจ๋ฅด์˜ค๋‚˜ ์†๊ธธ์ด ๊ณฑ์ง€ ๋ชปํ•˜์˜ค๋งค +์†Œ์ธ ๋“ฑ ์†Œ๊ฒฌ์— ์ถฉ๋ ฌ๋ถ€์ธ์ด ์ฒœํ•˜์ผ์ƒ‰์ด๋ผ ํ•˜๋”๋‹ˆ ์†์ด ๊ณฑ์ง€ +์•„๋‹ˆํ•˜๋”๋ผ ํ•˜์˜ฌ ์ œ ์ •๋ ฌ๋ถ€์ธ์˜ ์‹œ๋น„ ๊ธˆ์—ฐ์ด ์ด๋ฅผ ๋“ฃ๊ณ  ๋ฌป๊ธฐ์— +์ž์„ธํžˆ ์ด๋ฅด๊ณ  ๋ถ€๋”” ๋‹ค๋ฅธ ๋ฐ ๊ฐ€์„œ ์ด ๋ง ๋ง๋ผ ๋‹น๋ถ€ํ•˜์˜ต๋”๋‹ˆ, +ํ•„์—ฐ ๊ธˆ์—ฐ์˜ ์ž…์„ ํ†ตํ•ด ๋ฐœ์„ค์ด ๋œ๊ฐ€ ํ•˜๋‚˜์ด๋‹ค.โ€ +ํ•œ๋ฐ ์Šน์ƒ์ด ๊ธˆ์—ฐ์„ ์žก์•„๋“ค์—ฌ ๋ฌธ์™ˆ +โ€œ์ด ๋ง์„ ๋“ฃ๊ณ  ๋„ค๊ฒŒ ๊ตญ๋ฌธํ•˜๋‹ˆ ๋ฐ”๋ฅธ๋Œ€๋กœ ๊ณ ํ•˜๋ผ.โ€ +ํ•˜๋Š” ์†Œ๋ฆฌ๊ฐ€ ๋ฒผ๋ฝ์ด ๊ผญ๋‘์— ์ž„ํ•œ ๋“ฏํ•˜๊ณ  ๊ถ๊ถ์ด ๋’ค์ง‘ํžˆ๋Š” ๋“ฏ +ํ•˜๋”๋ผ. ์ด๋•Œ์— ์ •๋ ฌ๋ถ€์ธ์ด ์Šน์ƒ์˜ ํ˜ธํ†ต ์†Œ๋ฆฌ๋ฅผ ๋“ฃ๊ณ  ๋˜ฅ์„ ํ•œ +๋ฌด๋”๊ธฐ๋ฅผ ์‹ธ๊ณ  ์ž๋น ์กŒ๋Š”์ง€๋ผ. ๊ธˆ์—ฐ์ด ํ•˜๋ฆด์—†์–ด ๋ฐ”๋กœ ์•„๋ขฐ๋‚˜๋‹ˆ๋ผ +ํ•˜๊ณ  ์ •๋ ฌ๋ถ€์ธ ํ•˜๋˜ ๋ง์ด๋ฉฐ ์ œ๊ฐ€ ๋‚จ๋ณต์„ ํ•˜๊ณ  ์ถฉ๋ ฌ๋ถ€์ธ ์นจ์†Œ๋กœ +๋“ค์–ด๊ฐ„ ๋ง์ด๋ฉฐ ์ด๋ถˆ ์†์— ๋ˆ„์› ๋‹ค๊ฐ€ ๋‹ฌ์•„๋‚œ ๋ง์ด๋ฉฐ ์ •๋ ฌ๋ถ€์ธ์ด +์•“๋Š” ์ฒดํ•˜๊ณ  ๋ˆ„์› ์‚ฌ์˜ค๋งค ์ถฉ๋ ฌ๋ถ€์ธ์ด ์•ฝ์œผ๋กœ ๊ตฌ๋ณ‘ํ•˜๋ฉฐ ๊ณ์— +์žˆ์œผ์‹œ๋งค ์นจ์†Œ๋กœ ๊ฐ€๋ผ ๊ฐ•๊ถŒํ•˜์—ฌ ์นจ์†Œ๋กœ ๋งˆ์ง€๋ชปํ•˜์—ฌ ๊ฐ€์‹œ๋งค +๋ณต๋ก์ด ์™•๋น„๊ป˜ ์ฐธ์†Œํ•˜๋˜ ์—ฐ์œ ๋ฅผ ๋‚ฑ๋‚ฑ์ด ์•„๋ขด๋Œ€ ์™•๋น„ ๊ณ์— ์žˆ๋‹ค๊ฐ€ +์•™์ฒœํ†ต๊ณกํ•˜์‹œ๋ฉฐ ์™ˆ +โ€œ๋‚ด ๋ฐ์ง€ ๋ชปํ•˜์—ฌ ์•…๋…€์˜ ๊พ€์— ๋น ์ ธ ์ถฉ๋ ฌ๋ถ€์ธ์„ ์ฃฝ์ด๋ ค ํ•˜์˜€๋‚˜๋‹ˆ +๋ฌด์Šจ ๋ฉด๋ชฉ์œผ๋กœ ์ถฉ๋ ฌ๋ถ€์ธ์„ ๋ณด๋ฆฌ์˜ค.โ€ +ํ•˜์‹œ๋ฉฐ ์ž๊ฒฐ์ฝ”์ž ํ•˜๊ฑฐ๋Š˜ ์Šน์ƒ์ด ๋ถ™๋“ค๊ณ  ์šธ๋ฉฐ ์™ˆ +โ€œ๋ชจ์นœ์ด ๋„ˆ๋ฌด ๊ณผ๋„ํžˆ ํ•˜์‹œ๋ฉด ์†Œ์ž๊ฐ€ ๋จผ์ € ์ฃฝ์œผ๋ ค ํ•˜๋‚˜์ด๋‹ค.โ€ +์™•๋น„ ๊ธˆ์นจ์— ๋ˆ„์›Œ ์ผ์–ด๋‚˜์ง€ ๋ชปํ•˜๋”๋ผ. ์Šน์ƒ์ด ์ •๋ ฌ๋ถ€์ธ์„ +๊ฒฐ๋ฐ•ํ•˜์—ฌ ๋•…์— ๊ฟ‡๋ฆฌ๊ณ  ํฌ๊ฒŒ ๋…ธํ•˜์—ฌ ์™ˆ +โ€œ๋„ˆ๋Š” ๋ฌด์—‡์ด ๋ถ€์กฑํ•˜์—ฌ ์ถฉ๋ ฌ๋ถ€์ธ์„ ํ•ด์ฝ”์ž ํ•˜๋А๋ƒ. ์–ด์ฐŒ +์ผ์‹œ๋ฅผ ์‚ด๋ฆฌ๋ฆฌ์˜ค. ๋‚ด ์ž„์˜๋กœ๋Š” ์ฃฝ์ด๊ณ  ์‹ถ์œผ๋‚˜ ํ™ฉ์ƒ๊ป˜ ์•„๋ขฐ๊ณ  +์ฃฝ๊ฒŒ ํ•˜๋ฆฌ๋ผ.โ€ +ํ•˜๊ณ  ์ƒ์†Œํ•˜๋‹ˆ ๊ทธ ๊ธ€์— ํ•˜์˜€์œผ๋˜ +โ€œ๋Œ€์‚ฌ๋งˆ ๋Œ€๋„๋… ๋Œ€์›์ˆ˜ ์ •์„์„ ์€ ๋ˆ์ˆ˜๋ฐฑ๋ฐฐํ•˜๊ณ  ์•„๋ขฐ๋‚˜๋‹ˆ ์‹ ์ด +์„œ์œต์„ ์ณ ์‚ฌ๋กœ์žก๊ณ , ๋ฐฑ์„ฑ์„ ์ง„๋ฌดํ•˜๊ณ  ๋Œ์•„์˜ค๋ ค ํ•  ๋•Œ, ์ง‘์—์„œ +๊ธ‰ํ•œ ์†Œ์‹์„ ๋“ฃ๊ณ  ๊ตฐ์‚ฌ๋ฅผ ์ค‘๊ตฐ์žฅ์—๊ฒŒ ๋งก๊ธฐ์˜ต๊ณ  ํ•„๋งˆ๋กœ ์˜ฌ๋ผ์™€ +๋ณธ์ฆ‰, ์ •๋ ฌ๋ถ€์ธ์ด ์ด๋Ÿฌ์ด๋Ÿฌํ•œ ๋ณ€์„ ์ผ์œผ์ผฐ์‚ฌ์˜ค๋‹ˆ ์„ธ์ƒ์— +์ด๋Ÿฌํ•˜์˜จ ์ผ์ด ์žˆ์‚ฌ์˜ค๋‹›๊ฐ€.โ€ +ํ•˜๊ณ  ๊ธˆ์—ฐ์ด ํ‰๊ณ„๋ฅผ ๊พธ๋ฏผ ์ผ๊ณผ ์›”๋งค๊ฐ€ ๋‹นํ•˜๋˜ ๊ณ ์ดˆ๋ฅผ ๋‚ฑ๋‚ฑ์ด +์•„๋ขฐ์—ˆ๋‹ค. +-์ž‘์ž ๋ฏธ์ƒ, ๏ฝข์ •์„์„ ์ „๏ฝฃ-","{'question': '<๋ณด๊ธฐ>๋ฅผ ์ฐธ๊ณ ํ•˜์—ฌ ์œ—๊ธ€์„ ์ดํ•ดํ•œ ๋‚ด์šฉ์œผ๋กœ ์ ์ ˆํ•˜์ง€ ์•Š์€\n๊ฒƒ์€?', 'choices': ['์ •์„์„ ์ด ํ™ฉ์ƒ์—๊ฒŒ ์˜ฌ๋ฆฐ ์ƒ์†Œ์—์„œ, ๋Œ€์›์ˆ˜์™€ ๊ฐ€์žฅ์œผ๋กœ์„œ์˜\n๋ชจ์Šต์ด ๋“œ๋Ÿฌ๋‚˜๋Š” ๊ฒƒ์œผ๋กœ ๋ณด์•„, ๊ฐ€์ • ์•ˆํŒŽ์˜ ์‚ฌ๊ฑด์— ๋‚จ์ฃผ์ธ๊ณต์ด\n๋‘๋ฃจ ๊ด€์—ฌํ•˜๊ณ  ์žˆ์Œ์„ ์•Œ ์ˆ˜ ์žˆ๊ตฐ.', '์Šน์ƒ์ด ์ถฉ๋ ฌ๋ถ€์ธ์„ ๊ตฌ์ถœํ•˜๋Š” ์žฅ๋ฉด์—์„œ, โ€˜์Šฌํ””์— ๋งค์šฐ ์•ผ์œ„์–ด\n๋ผˆ๊ฐ€ ๋“œ๋Ÿฌโ€™๋‚œ ๋ถ€์ธ์˜ ๋ชจ์Šต๊ณผ โ€˜ํ†ต๊ณกโ€™ํ•˜๋Š” ์Šน์ƒ์˜ ๋ชจ์Šต์€ ์ธ๋ฌผ์˜\n๊ณ ๋‚œ๊ณผ ๊ฐ์ •์ด ๊ทน๋Œ€ํ™”๋œ ํ˜•์ƒ์ž„์„ ์•Œ ์ˆ˜ ์žˆ๊ตฐ.', '์™•๋น„๊ฐ€ โ€˜์•™์ฒœํ†ต๊ณกโ€™ํ•˜๋Š” ์žฅ๋ฉด์—์„œ, ์ถฉ๋ ฌ๋ถ€์ธ์˜ ์ˆ˜๋‚œ์ด โ€˜์•…๋…€โ€™์˜\nํƒ“์ด๋ผ๋Š” ์ธ์‹์ด ๋“œ๋Ÿฌ๋‚˜๋ฉด์„œ ์ผ๋ถ€๋‹ค์ฒ˜์ œ์˜ ๋ฌธ์ œ๊ฐ€ ๊ฐœ์ธ์˜ ์ธ์„ฑ\n๋ฌธ์ œ๋กœ ์ถ•์†Œ๋˜๊ณ  ์žˆ์Œ์„ ์•Œ ์ˆ˜ ์žˆ๊ตฐ.', '์›”๋งค๊ฐ€ โ€˜๋งค๋ฅผโ€™ ๋งž๋Š” ์žฅ๋ฉด์—์„œ, ์›”๋งค๋Š” ์ž์‹ ์ด ๋ชจ์‹œ๋Š” ์ฃผ์ธ์—๊ฒŒ\n์ฃฝ์Œ์„ ๊ฐ์˜คํ•˜๊ณ  ์ง„์‹ค์„ ๋ฐํž˜์œผ๋กœ์จ ๋Šฅ๋™์ ์ธ ํ–‰์œ„์ž๋ฅผ ์ง€ํ–ฅํ•˜๊ณ \n์žˆ์Œ์„ ์•Œ ์ˆ˜ ์žˆ๊ตฐ.', '์ •๋ ฌ๋ถ€์ธ์ด โ€˜์Šน์ƒ์˜ ํ˜ธํ†ต ์†Œ๋ฆฌโ€™์— ๋ฐ˜์‘ํ•˜๋Š” ์žฅ๋ฉด์—์„œ, ๊ฐ€์ •์˜\n์ƒ์ธต ์ธ๋ฌผ์ด ์ž์‹ ์˜ ์œ„์—„์ด ์‹ค์ถ”๋˜๋Š” ํ–‰๋™์„ ๋ณด์ด๋ฉด์„œ ํฌํ™”ํ™”\n๋˜๊ณ  ์žˆ์Œ์„ ์•Œ ์ˆ˜ ์žˆ๊ตฐ'], 'answer': '', 'question_plus': '๏ฝข์ •์„์„ ์ „๏ฝฃ์€ ์˜์›…์†Œ์„ค๊ณผ ๊ฐ€์ •์†Œ์„ค์˜ ์ƒํˆฌ์ ์ธ ๋ฉด๋ชจ๊ฐ€ ํ˜ผ์žฌ\n๋˜์–ด ๋‚˜ํƒ€๋‚œ๋‹ค. ์ด๋ฅผํ…Œ๋ฉด, ๊ฐ€์ • ์•ˆํŒŽ์˜ ์„œ์‚ฌ๋Š” ๋‚จ์ฃผ์ธ๊ณต์„\n๋งค๊ฐœ๋กœ ์—ฐ๊ฒฐ๋˜๊ณ , ์‚ฌ๊ฑด์ด ์„ ์•… ๊ตฌ๋„๋กœ ์ „๊ฐœ๋˜๋ฉฐ, ์ธ๋ฌผ์˜ ๊ณ ๋‚œ๊ณผ\n๊ฐ์ •์€ ๊ทน๋Œ€ํ™”๋œ๋‹ค. ์ด ๊ณผ์ •์—์„œ ์ผ๋ถ€๋‹ค์ฒ˜์ œ์—์„œ ๋น„๋กฏ๋˜๋Š”\n๊ฐ€์ • ๋‚ด ๊ฐˆ๋“ฑ์ด ๊ฐœ์ธ์˜ ์ธ์„ฑ ๋ฌธ์ œ๋กœ ์ถ•์†Œ๋œ๋‹ค. ๊ทธ๋Ÿฌ๋ฉด์„œ๋„\n์ƒ์ „์˜ ์ˆ˜์กฑ์— ๋ถˆ๊ณผํ•œ ํ•˜์ธต์˜ ์‹œ๋น„๊ฐ€ ๋Šฅ๋™์ ์ธ ํ–‰์œ„์ž๋กœ ๋“ฑ์žฅ\nํ•˜๊ฑฐ๋‚˜, ๊ฐ€์ •๊ณผ ์‚ฌํšŒ์—์„œ ์ƒ์ธต์ธ ์ธ๋ฌผ์ด ํฌํ™”ํ™”๋œ๋‹ค.'}","๏ฝข์ •์„์„ ์ „๏ฝฃ์€ ์˜์›…์†Œ์„ค๊ณผ ๊ฐ€์ •์†Œ์„ค์˜ ์ƒํˆฌ์ ์ธ ๋ฉด๋ชจ๊ฐ€ ํ˜ผ์žฌ +๋˜์–ด ๋‚˜ํƒ€๋‚œ๋‹ค. ์ด๋ฅผํ…Œ๋ฉด, ๊ฐ€์ • ์•ˆํŒŽ์˜ ์„œ์‚ฌ๋Š” ๋‚จ์ฃผ์ธ๊ณต์„ +๋งค๊ฐœ๋กœ ์—ฐ๊ฒฐ๋˜๊ณ , ์‚ฌ๊ฑด์ด ์„ ์•… ๊ตฌ๋„๋กœ ์ „๊ฐœ๋˜๋ฉฐ, ์ธ๋ฌผ์˜ ๊ณ ๋‚œ๊ณผ +๊ฐ์ •์€ ๊ทน๋Œ€ํ™”๋œ๋‹ค. ์ด ๊ณผ์ •์—์„œ ์ผ๋ถ€๋‹ค์ฒ˜์ œ์—์„œ ๋น„๋กฏ๋˜๋Š” +๊ฐ€์ • ๋‚ด ๊ฐˆ๋“ฑ์ด ๊ฐœ์ธ์˜ ์ธ์„ฑ ๋ฌธ์ œ๋กœ ์ถ•์†Œ๋œ๋‹ค. ๊ทธ๋Ÿฌ๋ฉด์„œ๋„ +์ƒ์ „์˜ ์ˆ˜์กฑ์— ๋ถˆ๊ณผํ•œ ํ•˜์ธต์˜ ์‹œ๋น„๊ฐ€ ๋Šฅ๋™์ ์ธ ํ–‰์œ„์ž๋กœ ๋“ฑ์žฅ +ํ•˜๊ฑฐ๋‚˜, ๊ฐ€์ •๊ณผ ์‚ฌํšŒ์—์„œ ์ƒ์ธต์ธ ์ธ๋ฌผ์ด ํฌํ™”ํ™”๋œ๋‹ค.",4,4,True,[],4 +2025-korean-22,"(๊ฐ€) +๋ฐฐ๋ฅผ ๋ฏผ๋‹ค +๋ฐฐ๋ฅผ ๋ฐ€์–ด๋ณด๋Š” ๊ฒƒ์€ ์•„์ฃผ ๋“œ๋ฌธ ๊ฒฝํ—˜ +ํฌ๋ฒˆ๋•์ด๋Š” ์ž”์ž”ํ•œ ๊ฐ€์„ ๋ฐ”๋‹ท๋ฌผ ์œ„์— +๋ฐฐ๋ฅผ ๋ฐ€์–ด๋„ฃ๊ณ ๋Š” +์˜จ๋ชธ์ด ์•„์ฃผ ์ถ”๋ฝํ•˜์ง€ ์•Š์„ ์ˆœ๊ฐ„์˜ ํ•œ ํ—ˆ๊ณต์—์„œ +๋ฐ€๋˜ ํž˜์„ ํ•œ๊ป ๋”ํ•ด ๋ฐ€์–ด์ฃผ๊ณ ๋Š” +์•„์Šฌ์•„์Šฌํžˆ ๋ฐฐ์—์„œ ๋–จ์–ด์ง„ ์†, ์ˆœ๊ฐ„ ํ™˜ํ•ด์ง„ ์†์„ +ํ—ˆ๊ณต์œผ๋กœ๋ถ€ํ„ฐ ๊ฑฐ๋‘”๋‹ค +์‚ฌ๋ž‘์€ ์ฐธ ๋ถ€๋“œ๋Ÿฝ๊ฒŒ๋„ ๋– ๋‚˜์ง€ +๋ตˆ์ง€๋„ ์•Š๋Š” ๊ธธ์„ ๋ถ€๋“œ๋Ÿฝ๊ฒŒ๋„ +๋ฐฐ๋ฅผ ํ•œ๊ป ์„ธ๊ฒŒ ๋ฐ€์–ด๋‚ด๋“ฏ์ด ์Šฌํ””๋„ +๊ทธ๋ ‡๊ฒŒ ๋ฐ€์–ด๋‚ด๋Š” ๊ฒƒ์ด์ง€ +๋ฐฐ๊ฐ€ ๋‚˜๊ฐ€๊ณ  ๋‚จ์€ ๋นˆ ๋ฌผ ์œ„์˜ ํ‰ํ„ฐ +์ž ์‹œ ๋จธ๋ฌผ๋‹ค ๊ฐ€๋ผ์•‰๊ณ  +๊ทธ๋Ÿฐ๋ฐ ์˜ค, ๋‚ด ์•ˆ์œผ๋กœ ๋“ค์–ด์˜ค๋Š” ๋ฐฐ์—ฌ +์•„๋ฌด ์†Œ๋ฆฌ ์—†์ด ๋ฐ€๋ ค๋“ค์–ด์˜ค๋Š” ๋ฐฐ์—ฌ +-์žฅ์„๋‚จ, ๏ฝข๋ฐฐ๋ฅผ ๋ฐ€๋ฉฐ๏ฝฃ- +(๋‚˜) +๋‹น์‹ โ€ฆโ€ฆ, ๋‹น์‹ ์ด๋ผ๋Š” ๋ง ์ฐธ ์ข‹์ง€์š”, ๊ทธ๋ž˜์„œ ๋ถˆ๋Ÿฌ๋ด…๋‹ˆ๋‹ค ํ‚ฅํ‚ฅ +๊ฑฐ๋ฆฌ๋ฉฐ ํ•œ๋•Œ ์ ์š”๋กœ์›€์˜ ์šธ์Œ์ด ์žˆ์—ˆ๋˜ ๋•Œ, ํ•œ ์Šฌํ””์ด ๋ฌธ์„ +๋‹ซ์œผ๋ฉด ๋˜ ํ•œ ์Šฌํ””์ด ๋ฌธ์„ ์—ฌ๋Š” ๊ฒƒ์„ ์ด๋งŒํผ ์‚ด์•„์˜ด์˜ ์ƒ์ฒ˜์— +๊ธฐ๋Œ€, ๋‚˜ ํ‚ฅํ‚ฅโ€ฆโ€ฆ +, ๋‹น์‹ ์„ ๋ถ€๋ฆ…๋‹ˆ๋‹ค ๋‹จํ’์˜ ์†๋ฐ”๋‹ฅ, ์€ํ–‰์˜ ๋‘ +๊ฐˆ๋ž˜ ๊ทธ๋ฆฌ๊ณ  ํ•ฉ์นจ ์ € ๊ฐœ๋ง์ดˆ์˜ ์‹œ๋ฆ„, ๋ฐŸํžŒ ํ’€์˜ ํ™์œผ๋กœ ๋Œ์•„๊ฐ +๋‹น์‹ โ€ฆโ€ฆ, ํ‚ฅํ‚ฅ๊ฑฐ๋ฆฌ๋ฉฐ ์„ธ์›”์— ๋Œ€ํ•ด ํ˜น์€ ์‚ฌ๋ž‘๊ณผ ์ƒ์ฒ˜, ์ƒ์ฒ˜์˜ +๋ชธ์ด ๋‚˜์—๊ฒŒ ๊ธฐ๋Œ€์™€ ์ €๋ฅผ ๋ถ€๋นŒ ๋•Œ ๋‹น์‹ โ€ฆโ€ฆ, ๊ทธ๋Œ€๋ผ๋Š” ์ž์—ฐ์˜ +๋‹ฌ๊ณผ ๋ณ„โ€ฆโ€ฆ, ํ‚ฅํ‚ฅ๊ฑฐ๋ฆฌ๋ฉฐ ๋‹น์‹ ์ด๋ผ๊ณ โ€ฆโ€ฆ, ๊ธˆ๋ฐฉ ์šธ ๊ฒƒ ๊ฐ™์€ +์‚ฌ๋‚ด์˜ ์•„๋ฆ„๋‹ค์›€ ๊ทธ ์•„๋ฆ„๋‹ค์›€์— ๊ธฐ๋Œ€ ๋งˆ์Œ์˜ ๋ฌด๋ค์— ๋‚˜ ๋ฒŒ์ดˆํ•˜๋Ÿฌ +์ง„์„ค ์Œ์‹๋„ ์—†์ด ๋งจ ์ˆ  ํ•œ ๋ณ‘ ์ฐจ๊ณ  ๋ณ‘์ž์ฒ˜๋Ÿผ, ๊ทธ๋Ÿฌ๋‚˜ โ“์น˜๋ณ‘*๊ณผ +ํ™˜ํ›„*๋Š” ๊ฐ๊ฐ ๋”ฐ๋กœ์ธ ๊ฒƒ์„ ํ‚ฅํ‚ฅ ๋‹น์‹  ์ด์œ ๋‹น์‹ โ€ฆโ€ฆ +, ๋‹น์‹ ์ด๋ผ๋Š” +๋ง ์ฐธ ์ข‹์ง€์š”, ๋‚ด๊ฐ€ ์•„๋‹ˆ๋ผ์„œ ๋๋‚ด ๋ฒ„๋ฆด ์ˆ˜ ์—†๋Š”, ๋ฌด๋ฅผ ์ˆ˜๋„ ์—†๋Š” +์ฐธํ˜นโ€ฆโ€ฆ, ๊ทธ๋Ÿฌ๋‚˜ ํ‚ฅํ‚ฅ ๋‹น์‹  +-์ด๊ด‘ํ˜ธ, ๏ฝข์ด์   ๋˜๋„๋ก ํŽธ์ง€ ์•ˆ ๋“œ๋ฆฌ๊ฒ ์Šต๋‹ˆ๋‹ค๏ฝฃ - + +*์น˜๋ณ‘:๋ณ‘์„ ๋‹ค์Šค๋ฆผ. +*ํ™˜ํ›„:๋ณ‘์„ ์ •์ค‘ํ•˜๊ฒŒ ์ด๋ฅด๋Š” ๋ง. + +(๋‹ค) +๊ทธ๋…€์—๊ฒŒ ํŽธ์ง€๋ฅผ ์“ฐ๋Š” ๊ฒƒ์ด ์ž์‹ ์˜ ์กด์žฌ๋ฅผ ์ฆ๋ช…ํ•˜๋˜ ์‹œ์ ˆ์ด +์žˆ์—ˆ๋‹ค. ์‚ฌ๋ž‘ํ•˜๋Š” ์‚ฌ๋žŒ์—๊ฒŒ ๋ณด๋‚ด๋Š” ํŽธ์ง€๋งŒํผ ํ‘œํ˜„์˜ ์š•๊ตฌ๋กœ ํ˜๋Ÿฌ +๋„˜์น˜๋Š” ๊ฒƒ๋„ ์—†๋‹ค. ๋ฌด์–ธ๊ฐ€๋ฅผ ํ‘œํ˜„ํ•˜์ง€ ์•Š๊ณ ๋Š” ๊ฒฌ๋”œ ์ˆ˜ ์—†๋Š” +์‹œ๊ฐ„๋“ค์ด ํŽธ์ง€๋ฅผ ์“ฐ๊ฒŒ ํ•œ๋‹ค. ๊ทธ๋Š” ๊ทธ๋…€์—๊ฒŒ ์ž์‹ ์˜ ์‚ฌ๋ž‘์ด ์–ผ๋งˆ๋‚˜ +์–ด๋ ต๊ณ  ์ง„์ •ํ•˜๋ฉฐ ์šด๋ช…์ ์ธ๊ฐ€๋ฅผ ์„ค๋ช…ํ•˜๊ณ  ์‹ถ์—ˆ๋‹ค. ํŽธ์ง€๋Š” ์‚ฌ๋žŒ์„ +์„ค๋“ํ•˜๊ฑฐ๋‚˜ ๋งคํ˜น์‹œํ‚ค๋Š” ๋ฐฉํŽธ์ด ๋ ์ง€๋„ ๋ชจ๋ฅธ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋ชจ๋“  +์‚ฌ๋ž‘์˜ ํŽธ์ง€๋Š” ๋งˆ์ง€๋ง‰ ์ˆœ๊ฐ„, ๋„๊ตฌ์ ์ด์ง€ ๋ชปํ•˜๋‹ค. ์„ธ์ƒ์˜ ๋ชจ๋“  +๊ธ€์“ฐ๊ธฐ๊ฐ€ ์ตœํ›„์˜ ์ˆœ๊ฐ„์—๋Š” ์ฒ˜์Œ์— ํ’ˆ์—ˆ๋˜ ์†Œ์†Œํ•œ ์˜๋„๋ฅผ ๋ฐฐ๋ฐ˜ +ํ•˜๋Š” ๊ฒƒ์ฒ˜๋Ÿผ. ๊ทธ ํ†ต์ œํ•  ์ˆ˜ ์—†๋Š” ์ต๋ช…์˜ ์š•๊ตฌ๊ฐ€ ๊ทธ ํŽธ์ง€์˜ +ํ˜„์‹ค์ ์ธ ๋ชฉํ‘œ๋ฅผ ์žŠ์–ด๋ฒ„๋ฆฌ๊ฒŒ ๋งŒ๋“ค๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ๊ทธ๋Ÿฐ ์ด์œ ๋กœ, ๋ชจ๋“  +์‚ฌ๋ž‘์˜ ํŽธ์ง€์—๋Š” ์•„๋ฌด ์ „์–ธ๋„ ๋“ค์–ด ์žˆ์ง€ ์•Š๋‹ค. +๊ฑฐ๊ธฐ์—๋Š” ๊ฒฐ์ •์ ์ธ ์ •๋ณด๋‚˜ ์ฃผ์žฅ์ด ๋“ค์–ด ์žˆ์ง€ ์•Š๋‹ค. ๋‹ค๋งŒ ๋‚ด +๊ณ ๋ฐฑ์„ ๋ˆ„๊ตฐ๊ฐ€๊ฐ€ ๋“ค์–ด์ค€๋‹ค๋Š” ์ถฉ๋งŒํ•œ ๋А๋‚Œ. ํฌ๋ฏธํ•œ ๋ถˆ๋น› ์•„๋ž˜์„œ +์Šค์Šค๋กœ ์˜ท์„ ๋ฒ—์–ด์•ผ ํ•  ๋•Œ์ฒ˜๋Ÿผ, ์ฃผ์ฒดํ•  ์ˆ˜ ์—†๋Š” ๋ถ€๋„๋Ÿฌ์›€ ๋”ฐ์œ„. +๊ณ ๋ฐฑ์ด๋ž€ ๊ฒฐ๊ตญ 2์ธ์นญ์„ ๊ฒฝ์œ ํ•˜์—ฌ 1์ธ์นญ์œผ๋กœ ๋Œ์•„์˜จ๋‹ค. ๊ทธ์˜ +๋“ค๋“๋Š” ๊ณ ๋ฐฑ์˜ ์–ธ์–ด๋“ค์€ ๊ณ ์Šค๋ž€ํžˆ ์ž์‹ ์—๊ฒŒ ๋Œ์•„์™”๋‹ค. ํ•œ๋™์•ˆ +๊ทธ๋Š”, ์‚ฌ๋ž‘ํ•˜๋Š” โ—‹โ—‹์—๊ฒŒ๋กœ ์‹œ์ž‘๋˜๋Š” ํŽธ์ง€๋ฅผ ์ž์ฃผ ์ผ๋‹ค. ๊ทธ๋…€๋Š” +๊ทธ์˜ ํŽธ์ง€๋ฅผ ์‚ฌ๋ž‘ํ–ˆ๋‹ค. ์ •ํ™•ํ•˜๊ฒŒ ๋งํ•˜๋ฉด โ€˜ํŽธ์ง€ ์†์˜ ๊ทธโ€™๋ฅผ ๊ทธ๋…€๋Š” +์‚ฌ๋ž‘ํ–ˆ๋‹ค. ํŽธ์ง€ ์†์—๋Š” ๊ทธ๊ฐ€ ์ฐพ์•„๋‚ธ ์ž์‹ ์˜ ๋˜ ๋‹ค๋ฅธ ์˜ํ˜ผ์ด +์žˆ์—ˆ๋‹ค. ๋˜ ๋‹ค๋ฅธ ์˜ํ˜ผ์˜ โ€˜๊ทธโ€™๋Š” ์ˆœ์ˆ˜ํ•œ ์—ด์ •๊ณผ ๋ ๋ชจ๋ฅผ ๋™๊ฒฝ๊ณผ +๊นŠ์€ ์ดํ•ด์‹ฌ์„ ๊ฐ€์ง„ ์กด์žฌ์˜€๋‹ค. ๊ทธ๋„ ์—ญ์‹œ ๊ทธ๋…€์ฒ˜๋Ÿผ ์ž์‹ ์˜ ํŽธ์ง€ +์† 1์ธ์นญ ํ™”์ž์—๊ฒŒ ๊นŠ์ด ๋งค๋ฃŒ๋˜์—ˆ๋‹ค. ํ•˜์ง€๋งŒ ๋„ˆ๋ฌด ๋ป”ํ•ด์„œ ๊ฐ€ํ˜น +ํ–ˆ๋˜ ์ง€๋ฆฌ๋ฉธ๋ ฌํ•œ ์‹œ๊ฐ„๋“ค ์†์—์„œ ๊ทธ๋Š” ํŽธ์ง€ ์†์˜ 1์ธ์นญ ์ฃผ์ฒด๋ฅผ +์žŠ์–ด๋ฒ„๋ ธ๋‹ค. +ํŽธ์ง€์กฐ์ฐจ ์“ธ ์ˆ˜ ์—†๋Š” ์‹œ๊ฐ„๋“ค์ด ๋ฌด์‹ฌํ•˜๊ฒŒ ์ง€๋‚˜๊ฐ€๊ณ , ๋‹ค์‹œ ํŽธ์ง€๋ฅผ +์“ฐ๊ณ  ์‹ถ์—ˆ์„ ๋•Œ, ๊ทธ๋Š” ์ด๋ฏธ โ€˜ํŽธ์ง€ ์†์˜ ๊ทธโ€™๊ฐ€ ๋˜์ง€ ๋ชปํ•œ๋‹ค๋Š” ๊ฒƒ์„ +์•Œ์•˜๋‹ค. ๊ทธ๋Š” โ€˜ํŽธ์ง€ ์†์˜ ๊ทธโ€™๋ฅผ ์—ฐ๊ธฐํ•˜๋Š” ๊ฒƒ์ด ๋ถ€๋„๋Ÿฌ์› ๊ณ , ์ž์‹ ์˜ +๋น„๋ฃจํ•จ์„ ๋ผ›์† ๊นŠ์ด ์‹ค๊ฐํ–ˆ๋‹ค. ๊ทธ๋Š” โ€˜์‚ฌ๋ž‘ํ•˜๋Š” โ—‹โ—‹์—๊ฒŒโ€™๋ผ๋Š” +ํŽธ์ง€๋ฅผ ์“ฐ๊ณ  ์‹ถ์–ด ํ•˜๋Š” ์ž์‹  ์†์˜ ์–ด๋–ค ๋Š™์ง€ ์•Š๋Š” ์˜ํ˜ผ์„, ๊ทธ +์ˆœ์ˆ˜ํ•œ ์ธ๊ฒฉ์„ ์™ธ๋ฉดํ•˜๊ณ  ์‹ถ์—ˆ๋‹ค. โ“‘๋ˆ„๊ตฐ๊ฐ€๊ฐ€ ๋“ฃ๊ธฐ๋ฅผ ๋ฐ”๋ผ๋Š” ๋ชจ๋“  +๊ณ ๋ฐฑ์ด๋ž€, ์œ„์„ ์ด ์•„๋‹ˆ๋ฉด ์œ„์•…์ด๋‹ค. +-์ด๊ด‘ํ˜ธ, ๏ฝข์ด์   ๋˜๋„๋ก ํŽธ์ง€ ์•ˆ ๋“œ๋ฆฌ๊ฒ ์Šต๋‹ˆ๋‹ค๏ฝฃ- +","{'question': '(๊ฐ€)๏ฝž(๋‹ค)์˜ ๊ณตํ†ต์ ์œผ๋กœ ๊ฐ€์žฅ ์ ์ ˆํ•œ ๊ฒƒ์€?', 'choices': ['ํ•˜๊ฐ•์  ์ด๋ฏธ์ง€๋ฅผ ํ™œ์šฉํ•˜์—ฌ ์‹œ๊ฐ„์˜ ํ๋ฆ„์„ ๋ณด์—ฌ ์ค€๋‹ค.', '์ž์—ฐ๋ฌผ์— ๋น—๋Œ€์–ด ๋ถ€์ •์  ํ˜„์‹ค์˜ ๊ทน๋ณต ๊ฐ€๋Šฅ์„ฑ์„ ์•”์‹œํ•œ๋‹ค.', '๋™์ผํ•œ ๊ตฌ์ ˆ์˜ ๋ฐ˜๋ณต๊ณผ ๋ณ€์ฃผ๋ฅผ ํ†ตํ•ด ์ƒํ™ฉ์˜ ๋ฐ˜์ „์„ ํ‘œํ˜„ํ•œ๋‹ค.', 'ํŠน์ •ํ•œ ํ–‰์œ„๋ฅผ ์ค‘์‹ฌ์œผ๋กœ ํ–‰์œ„ ์ฃผ์ฒด์™€ ๋Œ€์ƒ์˜ ๊ด€๊ณ„๋ฅผ ๋“œ๋Ÿฌ๋‚ธ๋‹ค.', '๊ณต๊ฐ„์˜ ์ด๋™์— ๋”ฐ๋ผ ๋‚ด์šฉ์„ ์ „๊ฐœํ•˜์—ฌ ์—ญ๋™์  ๋ถ„์œ„๊ธฐ๋ฅผ ๊ฐ•ํ™”\nํ•œ๋‹ค.'], 'answer': ''}",,4,4,True,[],4 +2025-korean-23,"(๊ฐ€) +๋ฐฐ๋ฅผ ๋ฏผ๋‹ค +๋ฐฐ๋ฅผ ๋ฐ€์–ด๋ณด๋Š” ๊ฒƒ์€ ์•„์ฃผ ๋“œ๋ฌธ ๊ฒฝํ—˜ +ํฌ๋ฒˆ๋•์ด๋Š” ์ž”์ž”ํ•œ ๊ฐ€์„ ๋ฐ”๋‹ท๋ฌผ ์œ„์— +๋ฐฐ๋ฅผ ๋ฐ€์–ด๋„ฃ๊ณ ๋Š” +์˜จ๋ชธ์ด ์•„์ฃผ ์ถ”๋ฝํ•˜์ง€ ์•Š์„ ์ˆœ๊ฐ„์˜ ํ•œ ํ—ˆ๊ณต์—์„œ +๋ฐ€๋˜ ํž˜์„ ํ•œ๊ป ๋”ํ•ด ๋ฐ€์–ด์ฃผ๊ณ ๋Š” +์•„์Šฌ์•„์Šฌํžˆ ๋ฐฐ์—์„œ ๋–จ์–ด์ง„ ์†, ์ˆœ๊ฐ„ ํ™˜ํ•ด์ง„ ์†์„ +ํ—ˆ๊ณต์œผ๋กœ๋ถ€ํ„ฐ ๊ฑฐ๋‘”๋‹ค +์‚ฌ๋ž‘์€ ์ฐธ ๋ถ€๋“œ๋Ÿฝ๊ฒŒ๋„ ๋– ๋‚˜์ง€ +๋ตˆ์ง€๋„ ์•Š๋Š” ๊ธธ์„ ๋ถ€๋“œ๋Ÿฝ๊ฒŒ๋„ +๋ฐฐ๋ฅผ ํ•œ๊ป ์„ธ๊ฒŒ ๋ฐ€์–ด๋‚ด๋“ฏ์ด ์Šฌํ””๋„ +๊ทธ๋ ‡๊ฒŒ ๋ฐ€์–ด๋‚ด๋Š” ๊ฒƒ์ด์ง€ +๋ฐฐ๊ฐ€ ๋‚˜๊ฐ€๊ณ  ๋‚จ์€ ๋นˆ ๋ฌผ ์œ„์˜ ํ‰ํ„ฐ +์ž ์‹œ ๋จธ๋ฌผ๋‹ค ๊ฐ€๋ผ์•‰๊ณ  +๊ทธ๋Ÿฐ๋ฐ ์˜ค, ๋‚ด ์•ˆ์œผ๋กœ ๋“ค์–ด์˜ค๋Š” ๋ฐฐ์—ฌ +์•„๋ฌด ์†Œ๋ฆฌ ์—†์ด ๋ฐ€๋ ค๋“ค์–ด์˜ค๋Š” ๋ฐฐ์—ฌ +-์žฅ์„๋‚จ, ๏ฝข๋ฐฐ๋ฅผ ๋ฐ€๋ฉฐ๏ฝฃ- +(๋‚˜) +๋‹น์‹ โ€ฆโ€ฆ, ๋‹น์‹ ์ด๋ผ๋Š” ๋ง ์ฐธ ์ข‹์ง€์š”, ๊ทธ๋ž˜์„œ ๋ถˆ๋Ÿฌ๋ด…๋‹ˆ๋‹ค ํ‚ฅํ‚ฅ +๊ฑฐ๋ฆฌ๋ฉฐ ํ•œ๋•Œ ์ ์š”๋กœ์›€์˜ ์šธ์Œ์ด ์žˆ์—ˆ๋˜ ๋•Œ, ํ•œ ์Šฌํ””์ด ๋ฌธ์„ +๋‹ซ์œผ๋ฉด ๋˜ ํ•œ ์Šฌํ””์ด ๋ฌธ์„ ์—ฌ๋Š” ๊ฒƒ์„ ์ด๋งŒํผ ์‚ด์•„์˜ด์˜ ์ƒ์ฒ˜์— +๊ธฐ๋Œ€, ๋‚˜ ํ‚ฅํ‚ฅโ€ฆโ€ฆ +, ๋‹น์‹ ์„ ๋ถ€๋ฆ…๋‹ˆ๋‹ค ๋‹จํ’์˜ ์†๋ฐ”๋‹ฅ, ์€ํ–‰์˜ ๋‘ +๊ฐˆ๋ž˜ ๊ทธ๋ฆฌ๊ณ  ํ•ฉ์นจ ์ € ๊ฐœ๋ง์ดˆ์˜ ์‹œ๋ฆ„, ๋ฐŸํžŒ ํ’€์˜ ํ™์œผ๋กœ ๋Œ์•„๊ฐ +๋‹น์‹ โ€ฆโ€ฆ, ํ‚ฅํ‚ฅ๊ฑฐ๋ฆฌ๋ฉฐ ์„ธ์›”์— ๋Œ€ํ•ด ํ˜น์€ ์‚ฌ๋ž‘๊ณผ ์ƒ์ฒ˜, ์ƒ์ฒ˜์˜ +๋ชธ์ด ๋‚˜์—๊ฒŒ ๊ธฐ๋Œ€์™€ ์ €๋ฅผ ๋ถ€๋นŒ ๋•Œ ๋‹น์‹ โ€ฆโ€ฆ, ๊ทธ๋Œ€๋ผ๋Š” ์ž์—ฐ์˜ +๋‹ฌ๊ณผ ๋ณ„โ€ฆโ€ฆ, ํ‚ฅํ‚ฅ๊ฑฐ๋ฆฌ๋ฉฐ ๋‹น์‹ ์ด๋ผ๊ณ โ€ฆโ€ฆ, ๊ธˆ๋ฐฉ ์šธ ๊ฒƒ ๊ฐ™์€ +์‚ฌ๋‚ด์˜ ์•„๋ฆ„๋‹ค์›€ ๊ทธ ์•„๋ฆ„๋‹ค์›€์— ๊ธฐ๋Œ€ ๋งˆ์Œ์˜ ๋ฌด๋ค์— ๋‚˜ ๋ฒŒ์ดˆํ•˜๋Ÿฌ +์ง„์„ค ์Œ์‹๋„ ์—†์ด ๋งจ ์ˆ  ํ•œ ๋ณ‘ ์ฐจ๊ณ  ๋ณ‘์ž์ฒ˜๋Ÿผ, ๊ทธ๋Ÿฌ๋‚˜ โ“์น˜๋ณ‘*๊ณผ +ํ™˜ํ›„*๋Š” ๊ฐ๊ฐ ๋”ฐ๋กœ์ธ ๊ฒƒ์„ ํ‚ฅํ‚ฅ ๋‹น์‹  ์ด์œ ๋‹น์‹ โ€ฆโ€ฆ +, ๋‹น์‹ ์ด๋ผ๋Š” +๋ง ์ฐธ ์ข‹์ง€์š”, ๋‚ด๊ฐ€ ์•„๋‹ˆ๋ผ์„œ ๋๋‚ด ๋ฒ„๋ฆด ์ˆ˜ ์—†๋Š”, ๋ฌด๋ฅผ ์ˆ˜๋„ ์—†๋Š” +์ฐธํ˜นโ€ฆโ€ฆ, ๊ทธ๋Ÿฌ๋‚˜ ํ‚ฅํ‚ฅ ๋‹น์‹  +-์ด๊ด‘ํ˜ธ, ๏ฝข์ด์   ๋˜๋„๋ก ํŽธ์ง€ ์•ˆ ๋“œ๋ฆฌ๊ฒ ์Šต๋‹ˆ๋‹ค๏ฝฃ - + +*์น˜๋ณ‘:๋ณ‘์„ ๋‹ค์Šค๋ฆผ. +*ํ™˜ํ›„:๋ณ‘์„ ์ •์ค‘ํ•˜๊ฒŒ ์ด๋ฅด๋Š” ๋ง. + +(๋‹ค) +๊ทธ๋…€์—๊ฒŒ ํŽธ์ง€๋ฅผ ์“ฐ๋Š” ๊ฒƒ์ด ์ž์‹ ์˜ ์กด์žฌ๋ฅผ ์ฆ๋ช…ํ•˜๋˜ ์‹œ์ ˆ์ด +์žˆ์—ˆ๋‹ค. ์‚ฌ๋ž‘ํ•˜๋Š” ์‚ฌ๋žŒ์—๊ฒŒ ๋ณด๋‚ด๋Š” ํŽธ์ง€๋งŒํผ ํ‘œํ˜„์˜ ์š•๊ตฌ๋กœ ํ˜๋Ÿฌ +๋„˜์น˜๋Š” ๊ฒƒ๋„ ์—†๋‹ค. ๋ฌด์–ธ๊ฐ€๋ฅผ ํ‘œํ˜„ํ•˜์ง€ ์•Š๊ณ ๋Š” ๊ฒฌ๋”œ ์ˆ˜ ์—†๋Š” +์‹œ๊ฐ„๋“ค์ด ํŽธ์ง€๋ฅผ ์“ฐ๊ฒŒ ํ•œ๋‹ค. ๊ทธ๋Š” ๊ทธ๋…€์—๊ฒŒ ์ž์‹ ์˜ ์‚ฌ๋ž‘์ด ์–ผ๋งˆ๋‚˜ +์–ด๋ ต๊ณ  ์ง„์ •ํ•˜๋ฉฐ ์šด๋ช…์ ์ธ๊ฐ€๋ฅผ ์„ค๋ช…ํ•˜๊ณ  ์‹ถ์—ˆ๋‹ค. ํŽธ์ง€๋Š” ์‚ฌ๋žŒ์„ +์„ค๋“ํ•˜๊ฑฐ๋‚˜ ๋งคํ˜น์‹œํ‚ค๋Š” ๋ฐฉํŽธ์ด ๋ ์ง€๋„ ๋ชจ๋ฅธ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋ชจ๋“  +์‚ฌ๋ž‘์˜ ํŽธ์ง€๋Š” ๋งˆ์ง€๋ง‰ ์ˆœ๊ฐ„, ๋„๊ตฌ์ ์ด์ง€ ๋ชปํ•˜๋‹ค. ์„ธ์ƒ์˜ ๋ชจ๋“  +๊ธ€์“ฐ๊ธฐ๊ฐ€ ์ตœํ›„์˜ ์ˆœ๊ฐ„์—๋Š” ์ฒ˜์Œ์— ํ’ˆ์—ˆ๋˜ ์†Œ์†Œํ•œ ์˜๋„๋ฅผ ๋ฐฐ๋ฐ˜ +ํ•˜๋Š” ๊ฒƒ์ฒ˜๋Ÿผ. ๊ทธ ํ†ต์ œํ•  ์ˆ˜ ์—†๋Š” ์ต๋ช…์˜ ์š•๊ตฌ๊ฐ€ ๊ทธ ํŽธ์ง€์˜ +ํ˜„์‹ค์ ์ธ ๋ชฉํ‘œ๋ฅผ ์žŠ์–ด๋ฒ„๋ฆฌ๊ฒŒ ๋งŒ๋“ค๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ๊ทธ๋Ÿฐ ์ด์œ ๋กœ, ๋ชจ๋“  +์‚ฌ๋ž‘์˜ ํŽธ์ง€์—๋Š” ์•„๋ฌด ์ „์–ธ๋„ ๋“ค์–ด ์žˆ์ง€ ์•Š๋‹ค. +๊ฑฐ๊ธฐ์—๋Š” ๊ฒฐ์ •์ ์ธ ์ •๋ณด๋‚˜ ์ฃผ์žฅ์ด ๋“ค์–ด ์žˆ์ง€ ์•Š๋‹ค. ๋‹ค๋งŒ ๋‚ด +๊ณ ๋ฐฑ์„ ๋ˆ„๊ตฐ๊ฐ€๊ฐ€ ๋“ค์–ด์ค€๋‹ค๋Š” ์ถฉ๋งŒํ•œ ๋А๋‚Œ. ํฌ๋ฏธํ•œ ๋ถˆ๋น› ์•„๋ž˜์„œ +์Šค์Šค๋กœ ์˜ท์„ ๋ฒ—์–ด์•ผ ํ•  ๋•Œ์ฒ˜๋Ÿผ, ์ฃผ์ฒดํ•  ์ˆ˜ ์—†๋Š” ๋ถ€๋„๋Ÿฌ์›€ ๋”ฐ์œ„. +๊ณ ๋ฐฑ์ด๋ž€ ๊ฒฐ๊ตญ 2์ธ์นญ์„ ๊ฒฝ์œ ํ•˜์—ฌ 1์ธ์นญ์œผ๋กœ ๋Œ์•„์˜จ๋‹ค. ๊ทธ์˜ +๋“ค๋“๋Š” ๊ณ ๋ฐฑ์˜ ์–ธ์–ด๋“ค์€ ๊ณ ์Šค๋ž€ํžˆ ์ž์‹ ์—๊ฒŒ ๋Œ์•„์™”๋‹ค. ํ•œ๋™์•ˆ +๊ทธ๋Š”, ์‚ฌ๋ž‘ํ•˜๋Š” โ—‹โ—‹์—๊ฒŒ๋กœ ์‹œ์ž‘๋˜๋Š” ํŽธ์ง€๋ฅผ ์ž์ฃผ ์ผ๋‹ค. ๊ทธ๋…€๋Š” +๊ทธ์˜ ํŽธ์ง€๋ฅผ ์‚ฌ๋ž‘ํ–ˆ๋‹ค. ์ •ํ™•ํ•˜๊ฒŒ ๋งํ•˜๋ฉด โ€˜ํŽธ์ง€ ์†์˜ ๊ทธโ€™๋ฅผ ๊ทธ๋…€๋Š” +์‚ฌ๋ž‘ํ–ˆ๋‹ค. ํŽธ์ง€ ์†์—๋Š” ๊ทธ๊ฐ€ ์ฐพ์•„๋‚ธ ์ž์‹ ์˜ ๋˜ ๋‹ค๋ฅธ ์˜ํ˜ผ์ด +์žˆ์—ˆ๋‹ค. ๋˜ ๋‹ค๋ฅธ ์˜ํ˜ผ์˜ โ€˜๊ทธโ€™๋Š” ์ˆœ์ˆ˜ํ•œ ์—ด์ •๊ณผ ๋ ๋ชจ๋ฅผ ๋™๊ฒฝ๊ณผ +๊นŠ์€ ์ดํ•ด์‹ฌ์„ ๊ฐ€์ง„ ์กด์žฌ์˜€๋‹ค. ๊ทธ๋„ ์—ญ์‹œ ๊ทธ๋…€์ฒ˜๋Ÿผ ์ž์‹ ์˜ ํŽธ์ง€ +์† 1์ธ์นญ ํ™”์ž์—๊ฒŒ ๊นŠ์ด ๋งค๋ฃŒ๋˜์—ˆ๋‹ค. ํ•˜์ง€๋งŒ ๋„ˆ๋ฌด ๋ป”ํ•ด์„œ ๊ฐ€ํ˜น +ํ–ˆ๋˜ ์ง€๋ฆฌ๋ฉธ๋ ฌํ•œ ์‹œ๊ฐ„๋“ค ์†์—์„œ ๊ทธ๋Š” ํŽธ์ง€ ์†์˜ 1์ธ์นญ ์ฃผ์ฒด๋ฅผ +์žŠ์–ด๋ฒ„๋ ธ๋‹ค. +ํŽธ์ง€์กฐ์ฐจ ์“ธ ์ˆ˜ ์—†๋Š” ์‹œ๊ฐ„๋“ค์ด ๋ฌด์‹ฌํ•˜๊ฒŒ ์ง€๋‚˜๊ฐ€๊ณ , ๋‹ค์‹œ ํŽธ์ง€๋ฅผ +์“ฐ๊ณ  ์‹ถ์—ˆ์„ ๋•Œ, ๊ทธ๋Š” ์ด๋ฏธ โ€˜ํŽธ์ง€ ์†์˜ ๊ทธโ€™๊ฐ€ ๋˜์ง€ ๋ชปํ•œ๋‹ค๋Š” ๊ฒƒ์„ +์•Œ์•˜๋‹ค. ๊ทธ๋Š” โ€˜ํŽธ์ง€ ์†์˜ ๊ทธโ€™๋ฅผ ์—ฐ๊ธฐํ•˜๋Š” ๊ฒƒ์ด ๋ถ€๋„๋Ÿฌ์› ๊ณ , ์ž์‹ ์˜ +๋น„๋ฃจํ•จ์„ ๋ผ›์† ๊นŠ์ด ์‹ค๊ฐํ–ˆ๋‹ค. ๊ทธ๋Š” โ€˜์‚ฌ๋ž‘ํ•˜๋Š” โ—‹โ—‹์—๊ฒŒโ€™๋ผ๋Š” +ํŽธ์ง€๋ฅผ ์“ฐ๊ณ  ์‹ถ์–ด ํ•˜๋Š” ์ž์‹  ์†์˜ ์–ด๋–ค ๋Š™์ง€ ์•Š๋Š” ์˜ํ˜ผ์„, ๊ทธ +์ˆœ์ˆ˜ํ•œ ์ธ๊ฒฉ์„ ์™ธ๋ฉดํ•˜๊ณ  ์‹ถ์—ˆ๋‹ค. โ“‘๋ˆ„๊ตฐ๊ฐ€๊ฐ€ ๋“ฃ๊ธฐ๋ฅผ ๋ฐ”๋ผ๋Š” ๋ชจ๋“  +๊ณ ๋ฐฑ์ด๋ž€, ์œ„์„ ์ด ์•„๋‹ˆ๋ฉด ์œ„์•…์ด๋‹ค. +-์ด๊ด‘ํ˜ธ, ๏ฝข์ด์   ๋˜๋„๋ก ํŽธ์ง€ ์•ˆ ๋“œ๋ฆฌ๊ฒ ์Šต๋‹ˆ๋‹ค๏ฝฃ- +","{'question': '(๊ฐ€)์— ๋Œ€ํ•œ ์ดํ•ด๋กœ ์ ์ ˆํ•˜์ง€ ์•Š์€ ๊ฒƒ์€?', 'choices': ['์•„์ฃผ ์ถ”๋ฝํ•˜์ง€ ์•Š์„ ์ˆœ๊ฐ„โ€™์— โ€˜๋ฐฐโ€™๋ฅผ ๋ฐ€๋˜ โ€˜์†โ€™์ด โ€˜์•„์Šฌ์•„์Šฌํžˆ\n๋ฐฐ์—์„œ ๋–จ์–ด์ง„โ€™๋‹ค๋Š” ๊ฒƒ์€ ์ด๋ณ„์˜ ์ •์„œ์  ๊ธด์žฅ๊ฐ์„ ๋“œ๋Ÿฌ๋‚ธ๋‹ค.', 'โ€˜๋ตˆ์ง€๋„ ์•Š๋Š” ๊ธธโ€™์€ โ€˜์‚ฌ๋ž‘โ€™์ด โ€˜๋– ๋‚˜โ€™๋Š” ๊ธธ์ด๋ผ๋Š” ์ ์—์„œ, ์ด๋ณ„์˜\n๋ง‰๋ง‰ํ•œ ์ƒํ™ฉ์„ ๊ณต๊ฐ„์˜ ํ˜•์ƒ์œผ๋กœ ๋“œ๋Ÿฌ๋‚ธ๋‹ค.', '์Šฌํ””โ€™์„ โ€˜๋ฐ€์–ด๋‚ด๋Š” ๊ฒƒโ€™์„ โ€˜๋ฐฐโ€™๋ฅผ ๋ฐ€๋“ฏ โ€˜ํ•œ๊ป ์„ธ๊ฒŒ ๋ฐ€์–ดโ€™๋‚ธ๋‹ค๊ณ \nํ•œ ๊ฒƒ์€ ์ด๋ณ„์˜ ์•„ํ””์„ ๋–จ์ณ ๋‚ด๋ ค๋Š” ํ™”์ž์˜ ํƒœ๋„๋ฅผ ๋“œ๋Ÿฌ๋‚ธ๋‹ค.', 'โ€˜๋ฐฐ๊ฐ€ ๋‚˜๊ฐ€โ€™๋ฉฐ ์ƒ๊ธด โ€˜ํ‰ํ„ฐโ€™๊ฐ€ โ€˜์ž ์‹œ ๋จธ๋ฌผ๋‹ค ๊ฐ€๋ผ์•‰โ€™๋Š”๋‹ค๋Š” ๊ฒƒ์€\n์ด๋ณ„์˜ ์Šฌํ””์ด ์žฆ์•„๋“  ์ƒํƒœ์— ์žˆ์Œ์„ ๋“œ๋Ÿฌ๋‚ธ๋‹ค.', '๋ฐ€๋ ค๋“ค์–ดโ€™ ์˜จ โ€˜๋ฐฐโ€™๋Š” โ€˜์•„๋ฌด ์†Œ๋ฆฌ ์—†์ดโ€™ ๋‹ค์‹œ ๋Œ์•„์˜จ ๋ฐฐ๋ผ๋Š” ์ ์—์„œ,\n๋Œ€์ƒ๊ณผ์˜ ์žฌํšŒ๊ฐ€ ์˜ˆ์ƒ๋Œ€๋กœ ์ด๋ฃจ์–ด์ง์„ ๋“œ๋Ÿฌ๋‚ธ๋‹ค.'], 'answer': ''}",,5,5,True,[],5 +2025-korean-24,"(๊ฐ€) +๋ฐฐ๋ฅผ ๋ฏผ๋‹ค +๋ฐฐ๋ฅผ ๋ฐ€์–ด๋ณด๋Š” ๊ฒƒ์€ ์•„์ฃผ ๋“œ๋ฌธ ๊ฒฝํ—˜ +ํฌ๋ฒˆ๋•์ด๋Š” ์ž”์ž”ํ•œ ๊ฐ€์„ ๋ฐ”๋‹ท๋ฌผ ์œ„์— +๋ฐฐ๋ฅผ ๋ฐ€์–ด๋„ฃ๊ณ ๋Š” +์˜จ๋ชธ์ด ์•„์ฃผ ์ถ”๋ฝํ•˜์ง€ ์•Š์„ ์ˆœ๊ฐ„์˜ ํ•œ ํ—ˆ๊ณต์—์„œ +๋ฐ€๋˜ ํž˜์„ ํ•œ๊ป ๋”ํ•ด ๋ฐ€์–ด์ฃผ๊ณ ๋Š” +์•„์Šฌ์•„์Šฌํžˆ ๋ฐฐ์—์„œ ๋–จ์–ด์ง„ ์†, ์ˆœ๊ฐ„ ํ™˜ํ•ด์ง„ ์†์„ +ํ—ˆ๊ณต์œผ๋กœ๋ถ€ํ„ฐ ๊ฑฐ๋‘”๋‹ค +์‚ฌ๋ž‘์€ ์ฐธ ๋ถ€๋“œ๋Ÿฝ๊ฒŒ๋„ ๋– ๋‚˜์ง€ +๋ตˆ์ง€๋„ ์•Š๋Š” ๊ธธ์„ ๋ถ€๋“œ๋Ÿฝ๊ฒŒ๋„ +๋ฐฐ๋ฅผ ํ•œ๊ป ์„ธ๊ฒŒ ๋ฐ€์–ด๋‚ด๋“ฏ์ด ์Šฌํ””๋„ +๊ทธ๋ ‡๊ฒŒ ๋ฐ€์–ด๋‚ด๋Š” ๊ฒƒ์ด์ง€ +๋ฐฐ๊ฐ€ ๋‚˜๊ฐ€๊ณ  ๋‚จ์€ ๋นˆ ๋ฌผ ์œ„์˜ ํ‰ํ„ฐ +์ž ์‹œ ๋จธ๋ฌผ๋‹ค ๊ฐ€๋ผ์•‰๊ณ  +๊ทธ๋Ÿฐ๋ฐ ์˜ค, ๋‚ด ์•ˆ์œผ๋กœ ๋“ค์–ด์˜ค๋Š” ๋ฐฐ์—ฌ +์•„๋ฌด ์†Œ๋ฆฌ ์—†์ด ๋ฐ€๋ ค๋“ค์–ด์˜ค๋Š” ๋ฐฐ์—ฌ +-์žฅ์„๋‚จ, ๏ฝข๋ฐฐ๋ฅผ ๋ฐ€๋ฉฐ๏ฝฃ- +(๋‚˜) +๋‹น์‹ โ€ฆโ€ฆ, ๋‹น์‹ ์ด๋ผ๋Š” ๋ง ์ฐธ ์ข‹์ง€์š”, ๊ทธ๋ž˜์„œ ๋ถˆ๋Ÿฌ๋ด…๋‹ˆ๋‹ค ํ‚ฅํ‚ฅ +๊ฑฐ๋ฆฌ๋ฉฐ ํ•œ๋•Œ ์ ์š”๋กœ์›€์˜ ์šธ์Œ์ด ์žˆ์—ˆ๋˜ ๋•Œ, ํ•œ ์Šฌํ””์ด ๋ฌธ์„ +๋‹ซ์œผ๋ฉด ๋˜ ํ•œ ์Šฌํ””์ด ๋ฌธ์„ ์—ฌ๋Š” ๊ฒƒ์„ ์ด๋งŒํผ ์‚ด์•„์˜ด์˜ ์ƒ์ฒ˜์— +๊ธฐ๋Œ€, ๋‚˜ ํ‚ฅํ‚ฅโ€ฆโ€ฆ +, ๋‹น์‹ ์„ ๋ถ€๋ฆ…๋‹ˆ๋‹ค ๋‹จํ’์˜ ์†๋ฐ”๋‹ฅ, ์€ํ–‰์˜ ๋‘ +๊ฐˆ๋ž˜ ๊ทธ๋ฆฌ๊ณ  ํ•ฉ์นจ ์ € ๊ฐœ๋ง์ดˆ์˜ ์‹œ๋ฆ„, ๋ฐŸํžŒ ํ’€์˜ ํ™์œผ๋กœ ๋Œ์•„๊ฐ +๋‹น์‹ โ€ฆโ€ฆ, ํ‚ฅํ‚ฅ๊ฑฐ๋ฆฌ๋ฉฐ ์„ธ์›”์— ๋Œ€ํ•ด ํ˜น์€ ์‚ฌ๋ž‘๊ณผ ์ƒ์ฒ˜, ์ƒ์ฒ˜์˜ +๋ชธ์ด ๋‚˜์—๊ฒŒ ๊ธฐ๋Œ€์™€ ์ €๋ฅผ ๋ถ€๋นŒ ๋•Œ ๋‹น์‹ โ€ฆโ€ฆ, ๊ทธ๋Œ€๋ผ๋Š” ์ž์—ฐ์˜ +๋‹ฌ๊ณผ ๋ณ„โ€ฆโ€ฆ, ํ‚ฅํ‚ฅ๊ฑฐ๋ฆฌ๋ฉฐ ๋‹น์‹ ์ด๋ผ๊ณ โ€ฆโ€ฆ, ๊ธˆ๋ฐฉ ์šธ ๊ฒƒ ๊ฐ™์€ +์‚ฌ๋‚ด์˜ ์•„๋ฆ„๋‹ค์›€ ๊ทธ ์•„๋ฆ„๋‹ค์›€์— ๊ธฐ๋Œ€ ๋งˆ์Œ์˜ ๋ฌด๋ค์— ๋‚˜ ๋ฒŒ์ดˆํ•˜๋Ÿฌ +์ง„์„ค ์Œ์‹๋„ ์—†์ด ๋งจ ์ˆ  ํ•œ ๋ณ‘ ์ฐจ๊ณ  ๋ณ‘์ž์ฒ˜๋Ÿผ, ๊ทธ๋Ÿฌ๋‚˜ โ“์น˜๋ณ‘*๊ณผ +ํ™˜ํ›„*๋Š” ๊ฐ๊ฐ ๋”ฐ๋กœ์ธ ๊ฒƒ์„ ํ‚ฅํ‚ฅ ๋‹น์‹  ์ด์œ ๋‹น์‹ โ€ฆโ€ฆ +, ๋‹น์‹ ์ด๋ผ๋Š” +๋ง ์ฐธ ์ข‹์ง€์š”, ๋‚ด๊ฐ€ ์•„๋‹ˆ๋ผ์„œ ๋๋‚ด ๋ฒ„๋ฆด ์ˆ˜ ์—†๋Š”, ๋ฌด๋ฅผ ์ˆ˜๋„ ์—†๋Š” +์ฐธํ˜นโ€ฆโ€ฆ, ๊ทธ๋Ÿฌ๋‚˜ ํ‚ฅํ‚ฅ ๋‹น์‹  +-์ด๊ด‘ํ˜ธ, ๏ฝข์ด์   ๋˜๋„๋ก ํŽธ์ง€ ์•ˆ ๋“œ๋ฆฌ๊ฒ ์Šต๋‹ˆ๋‹ค๏ฝฃ - + +*์น˜๋ณ‘:๋ณ‘์„ ๋‹ค์Šค๋ฆผ. +*ํ™˜ํ›„:๋ณ‘์„ ์ •์ค‘ํ•˜๊ฒŒ ์ด๋ฅด๋Š” ๋ง. + +(๋‹ค) +๊ทธ๋…€์—๊ฒŒ ํŽธ์ง€๋ฅผ ์“ฐ๋Š” ๊ฒƒ์ด ์ž์‹ ์˜ ์กด์žฌ๋ฅผ ์ฆ๋ช…ํ•˜๋˜ ์‹œ์ ˆ์ด +์žˆ์—ˆ๋‹ค. ์‚ฌ๋ž‘ํ•˜๋Š” ์‚ฌ๋žŒ์—๊ฒŒ ๋ณด๋‚ด๋Š” ํŽธ์ง€๋งŒํผ ํ‘œํ˜„์˜ ์š•๊ตฌ๋กœ ํ˜๋Ÿฌ +๋„˜์น˜๋Š” ๊ฒƒ๋„ ์—†๋‹ค. ๋ฌด์–ธ๊ฐ€๋ฅผ ํ‘œํ˜„ํ•˜์ง€ ์•Š๊ณ ๋Š” ๊ฒฌ๋”œ ์ˆ˜ ์—†๋Š” +์‹œ๊ฐ„๋“ค์ด ํŽธ์ง€๋ฅผ ์“ฐ๊ฒŒ ํ•œ๋‹ค. ๊ทธ๋Š” ๊ทธ๋…€์—๊ฒŒ ์ž์‹ ์˜ ์‚ฌ๋ž‘์ด ์–ผ๋งˆ๋‚˜ +์–ด๋ ต๊ณ  ์ง„์ •ํ•˜๋ฉฐ ์šด๋ช…์ ์ธ๊ฐ€๋ฅผ ์„ค๋ช…ํ•˜๊ณ  ์‹ถ์—ˆ๋‹ค. ํŽธ์ง€๋Š” ์‚ฌ๋žŒ์„ +์„ค๋“ํ•˜๊ฑฐ๋‚˜ ๋งคํ˜น์‹œํ‚ค๋Š” ๋ฐฉํŽธ์ด ๋ ์ง€๋„ ๋ชจ๋ฅธ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋ชจ๋“  +์‚ฌ๋ž‘์˜ ํŽธ์ง€๋Š” ๋งˆ์ง€๋ง‰ ์ˆœ๊ฐ„, ๋„๊ตฌ์ ์ด์ง€ ๋ชปํ•˜๋‹ค. ์„ธ์ƒ์˜ ๋ชจ๋“  +๊ธ€์“ฐ๊ธฐ๊ฐ€ ์ตœํ›„์˜ ์ˆœ๊ฐ„์—๋Š” ์ฒ˜์Œ์— ํ’ˆ์—ˆ๋˜ ์†Œ์†Œํ•œ ์˜๋„๋ฅผ ๋ฐฐ๋ฐ˜ +ํ•˜๋Š” ๊ฒƒ์ฒ˜๋Ÿผ. ๊ทธ ํ†ต์ œํ•  ์ˆ˜ ์—†๋Š” ์ต๋ช…์˜ ์š•๊ตฌ๊ฐ€ ๊ทธ ํŽธ์ง€์˜ +ํ˜„์‹ค์ ์ธ ๋ชฉํ‘œ๋ฅผ ์žŠ์–ด๋ฒ„๋ฆฌ๊ฒŒ ๋งŒ๋“ค๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ๊ทธ๋Ÿฐ ์ด์œ ๋กœ, ๋ชจ๋“  +์‚ฌ๋ž‘์˜ ํŽธ์ง€์—๋Š” ์•„๋ฌด ์ „์–ธ๋„ ๋“ค์–ด ์žˆ์ง€ ์•Š๋‹ค. +๊ฑฐ๊ธฐ์—๋Š” ๊ฒฐ์ •์ ์ธ ์ •๋ณด๋‚˜ ์ฃผ์žฅ์ด ๋“ค์–ด ์žˆ์ง€ ์•Š๋‹ค. ๋‹ค๋งŒ ๋‚ด +๊ณ ๋ฐฑ์„ ๋ˆ„๊ตฐ๊ฐ€๊ฐ€ ๋“ค์–ด์ค€๋‹ค๋Š” ์ถฉ๋งŒํ•œ ๋А๋‚Œ. ํฌ๋ฏธํ•œ ๋ถˆ๋น› ์•„๋ž˜์„œ +์Šค์Šค๋กœ ์˜ท์„ ๋ฒ—์–ด์•ผ ํ•  ๋•Œ์ฒ˜๋Ÿผ, ์ฃผ์ฒดํ•  ์ˆ˜ ์—†๋Š” ๋ถ€๋„๋Ÿฌ์›€ ๋”ฐ์œ„. +๊ณ ๋ฐฑ์ด๋ž€ ๊ฒฐ๊ตญ 2์ธ์นญ์„ ๊ฒฝ์œ ํ•˜์—ฌ 1์ธ์นญ์œผ๋กœ ๋Œ์•„์˜จ๋‹ค. ๊ทธ์˜ +๋“ค๋“๋Š” ๊ณ ๋ฐฑ์˜ ์–ธ์–ด๋“ค์€ ๊ณ ์Šค๋ž€ํžˆ ์ž์‹ ์—๊ฒŒ ๋Œ์•„์™”๋‹ค. ํ•œ๋™์•ˆ +๊ทธ๋Š”, ์‚ฌ๋ž‘ํ•˜๋Š” โ—‹โ—‹์—๊ฒŒ๋กœ ์‹œ์ž‘๋˜๋Š” ํŽธ์ง€๋ฅผ ์ž์ฃผ ์ผ๋‹ค. ๊ทธ๋…€๋Š” +๊ทธ์˜ ํŽธ์ง€๋ฅผ ์‚ฌ๋ž‘ํ–ˆ๋‹ค. ์ •ํ™•ํ•˜๊ฒŒ ๋งํ•˜๋ฉด โ€˜ํŽธ์ง€ ์†์˜ ๊ทธโ€™๋ฅผ ๊ทธ๋…€๋Š” +์‚ฌ๋ž‘ํ–ˆ๋‹ค. ํŽธ์ง€ ์†์—๋Š” ๊ทธ๊ฐ€ ์ฐพ์•„๋‚ธ ์ž์‹ ์˜ ๋˜ ๋‹ค๋ฅธ ์˜ํ˜ผ์ด +์žˆ์—ˆ๋‹ค. ๋˜ ๋‹ค๋ฅธ ์˜ํ˜ผ์˜ โ€˜๊ทธโ€™๋Š” ์ˆœ์ˆ˜ํ•œ ์—ด์ •๊ณผ ๋ ๋ชจ๋ฅผ ๋™๊ฒฝ๊ณผ +๊นŠ์€ ์ดํ•ด์‹ฌ์„ ๊ฐ€์ง„ ์กด์žฌ์˜€๋‹ค. ๊ทธ๋„ ์—ญ์‹œ ๊ทธ๋…€์ฒ˜๋Ÿผ ์ž์‹ ์˜ ํŽธ์ง€ +์† 1์ธ์นญ ํ™”์ž์—๊ฒŒ ๊นŠ์ด ๋งค๋ฃŒ๋˜์—ˆ๋‹ค. ํ•˜์ง€๋งŒ ๋„ˆ๋ฌด ๋ป”ํ•ด์„œ ๊ฐ€ํ˜น +ํ–ˆ๋˜ ์ง€๋ฆฌ๋ฉธ๋ ฌํ•œ ์‹œ๊ฐ„๋“ค ์†์—์„œ ๊ทธ๋Š” ํŽธ์ง€ ์†์˜ 1์ธ์นญ ์ฃผ์ฒด๋ฅผ +์žŠ์–ด๋ฒ„๋ ธ๋‹ค. +ํŽธ์ง€์กฐ์ฐจ ์“ธ ์ˆ˜ ์—†๋Š” ์‹œ๊ฐ„๋“ค์ด ๋ฌด์‹ฌํ•˜๊ฒŒ ์ง€๋‚˜๊ฐ€๊ณ , ๋‹ค์‹œ ํŽธ์ง€๋ฅผ +์“ฐ๊ณ  ์‹ถ์—ˆ์„ ๋•Œ, ๊ทธ๋Š” ์ด๋ฏธ โ€˜ํŽธ์ง€ ์†์˜ ๊ทธโ€™๊ฐ€ ๋˜์ง€ ๋ชปํ•œ๋‹ค๋Š” ๊ฒƒ์„ +์•Œ์•˜๋‹ค. ๊ทธ๋Š” โ€˜ํŽธ์ง€ ์†์˜ ๊ทธโ€™๋ฅผ ์—ฐ๊ธฐํ•˜๋Š” ๊ฒƒ์ด ๋ถ€๋„๋Ÿฌ์› ๊ณ , ์ž์‹ ์˜ +๋น„๋ฃจํ•จ์„ ๋ผ›์† ๊นŠ์ด ์‹ค๊ฐํ–ˆ๋‹ค. ๊ทธ๋Š” โ€˜์‚ฌ๋ž‘ํ•˜๋Š” โ—‹โ—‹์—๊ฒŒโ€™๋ผ๋Š” +ํŽธ์ง€๋ฅผ ์“ฐ๊ณ  ์‹ถ์–ด ํ•˜๋Š” ์ž์‹  ์†์˜ ์–ด๋–ค ๋Š™์ง€ ์•Š๋Š” ์˜ํ˜ผ์„, ๊ทธ +์ˆœ์ˆ˜ํ•œ ์ธ๊ฒฉ์„ ์™ธ๋ฉดํ•˜๊ณ  ์‹ถ์—ˆ๋‹ค. โ“‘๋ˆ„๊ตฐ๊ฐ€๊ฐ€ ๋“ฃ๊ธฐ๋ฅผ ๋ฐ”๋ผ๋Š” ๋ชจ๋“  +๊ณ ๋ฐฑ์ด๋ž€, ์œ„์„ ์ด ์•„๋‹ˆ๋ฉด ์œ„์•…์ด๋‹ค. +-์ด๊ด‘ํ˜ธ, ๏ฝข์ด์   ๋˜๋„๋ก ํŽธ์ง€ ์•ˆ ๋“œ๋ฆฌ๊ฒ ์Šต๋‹ˆ๋‹ค๏ฝฃ- +","{'question': '(๋‚˜)์˜ โ€˜๋‹น์‹ โ€™์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์ ์ ˆํ•˜์ง€ ์•Š์€ ๊ฒƒ์€?', 'choices': ['ํ™”์ž์™€ โ€˜ํ•œ๋•Œโ€™์˜ ๊ธฐ์–ต์„ ์ž‡๋Š” ๋งค๊ฐœ์  ์กด์žฌ์ด๋‹ค.', 'ํ™”์ž์˜ ๋‚ด๋ฉด์— ์‚ด๊ณ  ์žˆ๋Š” โ€˜๋ณ‘์žโ€™๋กœ์„œ ์—ฐ๋ฏผ์˜ ๋Œ€์ƒ์ด๋‹ค.', 'ํ™”์ž์˜ ๋ˆˆ์•ž์— ์—†์ง€๋งŒ โ€˜๋ถ€โ€™๋ฆ„์œผ๋กœ์จ ํ™˜๊ธฐ๋˜๋Š” ๋Œ€์ƒ์ด๋‹ค.', 'ํ™”์ž๊ฐ€ โ€˜๋ฒ„๋ฆด ์ˆ˜ ์—†โ€™๊ณ  โ€˜๋ฌด๋ฅผ ์ˆ˜๋„ ์—†๋Š”โ€™ ์ˆ™๋ช…์  ์กด์žฌ์ด๋‹ค.', 'ํ™”์ž์—๊ฒŒ โ€˜์‚ฌ๋ž‘โ€™๊ณผ โ€˜์Šฌํ””โ€™์„ ๊ฒฝํ—˜ํ•˜๊ฒŒ ํ•˜๋Š” ์ด์ค‘์  ์กด์žฌ์ด๋‹ค.'], 'answer': ''}",,2,2,True,[],2 +2025-korean-25,"(๊ฐ€) +๋ฐฐ๋ฅผ ๋ฏผ๋‹ค +๋ฐฐ๋ฅผ ๋ฐ€์–ด๋ณด๋Š” ๊ฒƒ์€ ์•„์ฃผ ๋“œ๋ฌธ ๊ฒฝํ—˜ +ํฌ๋ฒˆ๋•์ด๋Š” ์ž”์ž”ํ•œ ๊ฐ€์„ ๋ฐ”๋‹ท๋ฌผ ์œ„์— +๋ฐฐ๋ฅผ ๋ฐ€์–ด๋„ฃ๊ณ ๋Š” +์˜จ๋ชธ์ด ์•„์ฃผ ์ถ”๋ฝํ•˜์ง€ ์•Š์„ ์ˆœ๊ฐ„์˜ ํ•œ ํ—ˆ๊ณต์—์„œ +๋ฐ€๋˜ ํž˜์„ ํ•œ๊ป ๋”ํ•ด ๋ฐ€์–ด์ฃผ๊ณ ๋Š” +์•„์Šฌ์•„์Šฌํžˆ ๋ฐฐ์—์„œ ๋–จ์–ด์ง„ ์†, ์ˆœ๊ฐ„ ํ™˜ํ•ด์ง„ ์†์„ +ํ—ˆ๊ณต์œผ๋กœ๋ถ€ํ„ฐ ๊ฑฐ๋‘”๋‹ค +์‚ฌ๋ž‘์€ ์ฐธ ๋ถ€๋“œ๋Ÿฝ๊ฒŒ๋„ ๋– ๋‚˜์ง€ +๋ตˆ์ง€๋„ ์•Š๋Š” ๊ธธ์„ ๋ถ€๋“œ๋Ÿฝ๊ฒŒ๋„ +๋ฐฐ๋ฅผ ํ•œ๊ป ์„ธ๊ฒŒ ๋ฐ€์–ด๋‚ด๋“ฏ์ด ์Šฌํ””๋„ +๊ทธ๋ ‡๊ฒŒ ๋ฐ€์–ด๋‚ด๋Š” ๊ฒƒ์ด์ง€ +๋ฐฐ๊ฐ€ ๋‚˜๊ฐ€๊ณ  ๋‚จ์€ ๋นˆ ๋ฌผ ์œ„์˜ ํ‰ํ„ฐ +์ž ์‹œ ๋จธ๋ฌผ๋‹ค ๊ฐ€๋ผ์•‰๊ณ  +๊ทธ๋Ÿฐ๋ฐ ์˜ค, ๋‚ด ์•ˆ์œผ๋กœ ๋“ค์–ด์˜ค๋Š” ๋ฐฐ์—ฌ +์•„๋ฌด ์†Œ๋ฆฌ ์—†์ด ๋ฐ€๋ ค๋“ค์–ด์˜ค๋Š” ๋ฐฐ์—ฌ +-์žฅ์„๋‚จ, ๏ฝข๋ฐฐ๋ฅผ ๋ฐ€๋ฉฐ๏ฝฃ- +(๋‚˜) +๋‹น์‹ โ€ฆโ€ฆ, ๋‹น์‹ ์ด๋ผ๋Š” ๋ง ์ฐธ ์ข‹์ง€์š”, ๊ทธ๋ž˜์„œ ๋ถˆ๋Ÿฌ๋ด…๋‹ˆ๋‹ค ํ‚ฅํ‚ฅ +๊ฑฐ๋ฆฌ๋ฉฐ ํ•œ๋•Œ ์ ์š”๋กœ์›€์˜ ์šธ์Œ์ด ์žˆ์—ˆ๋˜ ๋•Œ, ํ•œ ์Šฌํ””์ด ๋ฌธ์„ +๋‹ซ์œผ๋ฉด ๋˜ ํ•œ ์Šฌํ””์ด ๋ฌธ์„ ์—ฌ๋Š” ๊ฒƒ์„ ์ด๋งŒํผ ์‚ด์•„์˜ด์˜ ์ƒ์ฒ˜์— +๊ธฐ๋Œ€, ๋‚˜ ํ‚ฅํ‚ฅโ€ฆโ€ฆ +, ๋‹น์‹ ์„ ๋ถ€๋ฆ…๋‹ˆ๋‹ค ๋‹จํ’์˜ ์†๋ฐ”๋‹ฅ, ์€ํ–‰์˜ ๋‘ +๊ฐˆ๋ž˜ ๊ทธ๋ฆฌ๊ณ  ํ•ฉ์นจ ์ € ๊ฐœ๋ง์ดˆ์˜ ์‹œ๋ฆ„, ๋ฐŸํžŒ ํ’€์˜ ํ™์œผ๋กœ ๋Œ์•„๊ฐ +๋‹น์‹ โ€ฆโ€ฆ, ํ‚ฅํ‚ฅ๊ฑฐ๋ฆฌ๋ฉฐ ์„ธ์›”์— ๋Œ€ํ•ด ํ˜น์€ ์‚ฌ๋ž‘๊ณผ ์ƒ์ฒ˜, ์ƒ์ฒ˜์˜ +๋ชธ์ด ๋‚˜์—๊ฒŒ ๊ธฐ๋Œ€์™€ ์ €๋ฅผ ๋ถ€๋นŒ ๋•Œ ๋‹น์‹ โ€ฆโ€ฆ, ๊ทธ๋Œ€๋ผ๋Š” ์ž์—ฐ์˜ +๋‹ฌ๊ณผ ๋ณ„โ€ฆโ€ฆ, ํ‚ฅํ‚ฅ๊ฑฐ๋ฆฌ๋ฉฐ ๋‹น์‹ ์ด๋ผ๊ณ โ€ฆโ€ฆ, ๊ธˆ๋ฐฉ ์šธ ๊ฒƒ ๊ฐ™์€ +์‚ฌ๋‚ด์˜ ์•„๋ฆ„๋‹ค์›€ ๊ทธ ์•„๋ฆ„๋‹ค์›€์— ๊ธฐ๋Œ€ ๋งˆ์Œ์˜ ๋ฌด๋ค์— ๋‚˜ ๋ฒŒ์ดˆํ•˜๋Ÿฌ +์ง„์„ค ์Œ์‹๋„ ์—†์ด ๋งจ ์ˆ  ํ•œ ๋ณ‘ ์ฐจ๊ณ  ๋ณ‘์ž์ฒ˜๋Ÿผ, ๊ทธ๋Ÿฌ๋‚˜ โ“์น˜๋ณ‘*๊ณผ +ํ™˜ํ›„*๋Š” ๊ฐ๊ฐ ๋”ฐ๋กœ์ธ ๊ฒƒ์„ ํ‚ฅํ‚ฅ ๋‹น์‹  ์ด์œ ๋‹น์‹ โ€ฆโ€ฆ +, ๋‹น์‹ ์ด๋ผ๋Š” +๋ง ์ฐธ ์ข‹์ง€์š”, ๋‚ด๊ฐ€ ์•„๋‹ˆ๋ผ์„œ ๋๋‚ด ๋ฒ„๋ฆด ์ˆ˜ ์—†๋Š”, ๋ฌด๋ฅผ ์ˆ˜๋„ ์—†๋Š” +์ฐธํ˜นโ€ฆโ€ฆ, ๊ทธ๋Ÿฌ๋‚˜ ํ‚ฅํ‚ฅ ๋‹น์‹  +-์ด๊ด‘ํ˜ธ, ๏ฝข์ด์   ๋˜๋„๋ก ํŽธ์ง€ ์•ˆ ๋“œ๋ฆฌ๊ฒ ์Šต๋‹ˆ๋‹ค๏ฝฃ - + +*์น˜๋ณ‘:๋ณ‘์„ ๋‹ค์Šค๋ฆผ. +*ํ™˜ํ›„:๋ณ‘์„ ์ •์ค‘ํ•˜๊ฒŒ ์ด๋ฅด๋Š” ๋ง. + +(๋‹ค) +๊ทธ๋…€์—๊ฒŒ ํŽธ์ง€๋ฅผ ์“ฐ๋Š” ๊ฒƒ์ด ์ž์‹ ์˜ ์กด์žฌ๋ฅผ ์ฆ๋ช…ํ•˜๋˜ ์‹œ์ ˆ์ด +์žˆ์—ˆ๋‹ค. ์‚ฌ๋ž‘ํ•˜๋Š” ์‚ฌ๋žŒ์—๊ฒŒ ๋ณด๋‚ด๋Š” ํŽธ์ง€๋งŒํผ ํ‘œํ˜„์˜ ์š•๊ตฌ๋กœ ํ˜๋Ÿฌ +๋„˜์น˜๋Š” ๊ฒƒ๋„ ์—†๋‹ค. ๋ฌด์–ธ๊ฐ€๋ฅผ ํ‘œํ˜„ํ•˜์ง€ ์•Š๊ณ ๋Š” ๊ฒฌ๋”œ ์ˆ˜ ์—†๋Š” +์‹œ๊ฐ„๋“ค์ด ํŽธ์ง€๋ฅผ ์“ฐ๊ฒŒ ํ•œ๋‹ค. ๊ทธ๋Š” ๊ทธ๋…€์—๊ฒŒ ์ž์‹ ์˜ ์‚ฌ๋ž‘์ด ์–ผ๋งˆ๋‚˜ +์–ด๋ ต๊ณ  ์ง„์ •ํ•˜๋ฉฐ ์šด๋ช…์ ์ธ๊ฐ€๋ฅผ ์„ค๋ช…ํ•˜๊ณ  ์‹ถ์—ˆ๋‹ค. ํŽธ์ง€๋Š” ์‚ฌ๋žŒ์„ +์„ค๋“ํ•˜๊ฑฐ๋‚˜ ๋งคํ˜น์‹œํ‚ค๋Š” ๋ฐฉํŽธ์ด ๋ ์ง€๋„ ๋ชจ๋ฅธ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋ชจ๋“  +์‚ฌ๋ž‘์˜ ํŽธ์ง€๋Š” ๋งˆ์ง€๋ง‰ ์ˆœ๊ฐ„, ๋„๊ตฌ์ ์ด์ง€ ๋ชปํ•˜๋‹ค. ์„ธ์ƒ์˜ ๋ชจ๋“  +๊ธ€์“ฐ๊ธฐ๊ฐ€ ์ตœํ›„์˜ ์ˆœ๊ฐ„์—๋Š” ์ฒ˜์Œ์— ํ’ˆ์—ˆ๋˜ ์†Œ์†Œํ•œ ์˜๋„๋ฅผ ๋ฐฐ๋ฐ˜ +ํ•˜๋Š” ๊ฒƒ์ฒ˜๋Ÿผ. ๊ทธ ํ†ต์ œํ•  ์ˆ˜ ์—†๋Š” ์ต๋ช…์˜ ์š•๊ตฌ๊ฐ€ ๊ทธ ํŽธ์ง€์˜ +ํ˜„์‹ค์ ์ธ ๋ชฉํ‘œ๋ฅผ ์žŠ์–ด๋ฒ„๋ฆฌ๊ฒŒ ๋งŒ๋“ค๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ๊ทธ๋Ÿฐ ์ด์œ ๋กœ, ๋ชจ๋“  +์‚ฌ๋ž‘์˜ ํŽธ์ง€์—๋Š” ์•„๋ฌด ์ „์–ธ๋„ ๋“ค์–ด ์žˆ์ง€ ์•Š๋‹ค. +๊ฑฐ๊ธฐ์—๋Š” ๊ฒฐ์ •์ ์ธ ์ •๋ณด๋‚˜ ์ฃผ์žฅ์ด ๋“ค์–ด ์žˆ์ง€ ์•Š๋‹ค. ๋‹ค๋งŒ ๋‚ด +๊ณ ๋ฐฑ์„ ๋ˆ„๊ตฐ๊ฐ€๊ฐ€ ๋“ค์–ด์ค€๋‹ค๋Š” ์ถฉ๋งŒํ•œ ๋А๋‚Œ. ํฌ๋ฏธํ•œ ๋ถˆ๋น› ์•„๋ž˜์„œ +์Šค์Šค๋กœ ์˜ท์„ ๋ฒ—์–ด์•ผ ํ•  ๋•Œ์ฒ˜๋Ÿผ, ์ฃผ์ฒดํ•  ์ˆ˜ ์—†๋Š” ๋ถ€๋„๋Ÿฌ์›€ ๋”ฐ์œ„. +๊ณ ๋ฐฑ์ด๋ž€ ๊ฒฐ๊ตญ 2์ธ์นญ์„ ๊ฒฝ์œ ํ•˜์—ฌ 1์ธ์นญ์œผ๋กœ ๋Œ์•„์˜จ๋‹ค. ๊ทธ์˜ +๋“ค๋“๋Š” ๊ณ ๋ฐฑ์˜ ์–ธ์–ด๋“ค์€ ๊ณ ์Šค๋ž€ํžˆ ์ž์‹ ์—๊ฒŒ ๋Œ์•„์™”๋‹ค. ํ•œ๋™์•ˆ +๊ทธ๋Š”, ์‚ฌ๋ž‘ํ•˜๋Š” โ—‹โ—‹์—๊ฒŒ๋กœ ์‹œ์ž‘๋˜๋Š” ํŽธ์ง€๋ฅผ ์ž์ฃผ ์ผ๋‹ค. ๊ทธ๋…€๋Š” +๊ทธ์˜ ํŽธ์ง€๋ฅผ ์‚ฌ๋ž‘ํ–ˆ๋‹ค. ์ •ํ™•ํ•˜๊ฒŒ ๋งํ•˜๋ฉด โ€˜ํŽธ์ง€ ์†์˜ ๊ทธโ€™๋ฅผ ๊ทธ๋…€๋Š” +์‚ฌ๋ž‘ํ–ˆ๋‹ค. ํŽธ์ง€ ์†์—๋Š” ๊ทธ๊ฐ€ ์ฐพ์•„๋‚ธ ์ž์‹ ์˜ ๋˜ ๋‹ค๋ฅธ ์˜ํ˜ผ์ด +์žˆ์—ˆ๋‹ค. ๋˜ ๋‹ค๋ฅธ ์˜ํ˜ผ์˜ โ€˜๊ทธโ€™๋Š” ์ˆœ์ˆ˜ํ•œ ์—ด์ •๊ณผ ๋ ๋ชจ๋ฅผ ๋™๊ฒฝ๊ณผ +๊นŠ์€ ์ดํ•ด์‹ฌ์„ ๊ฐ€์ง„ ์กด์žฌ์˜€๋‹ค. ๊ทธ๋„ ์—ญ์‹œ ๊ทธ๋…€์ฒ˜๋Ÿผ ์ž์‹ ์˜ ํŽธ์ง€ +์† 1์ธ์นญ ํ™”์ž์—๊ฒŒ ๊นŠ์ด ๋งค๋ฃŒ๋˜์—ˆ๋‹ค. ํ•˜์ง€๋งŒ ๋„ˆ๋ฌด ๋ป”ํ•ด์„œ ๊ฐ€ํ˜น +ํ–ˆ๋˜ ์ง€๋ฆฌ๋ฉธ๋ ฌํ•œ ์‹œ๊ฐ„๋“ค ์†์—์„œ ๊ทธ๋Š” ํŽธ์ง€ ์†์˜ 1์ธ์นญ ์ฃผ์ฒด๋ฅผ +์žŠ์–ด๋ฒ„๋ ธ๋‹ค. +ํŽธ์ง€์กฐ์ฐจ ์“ธ ์ˆ˜ ์—†๋Š” ์‹œ๊ฐ„๋“ค์ด ๋ฌด์‹ฌํ•˜๊ฒŒ ์ง€๋‚˜๊ฐ€๊ณ , ๋‹ค์‹œ ํŽธ์ง€๋ฅผ +์“ฐ๊ณ  ์‹ถ์—ˆ์„ ๋•Œ, ๊ทธ๋Š” ์ด๋ฏธ โ€˜ํŽธ์ง€ ์†์˜ ๊ทธโ€™๊ฐ€ ๋˜์ง€ ๋ชปํ•œ๋‹ค๋Š” ๊ฒƒ์„ +์•Œ์•˜๋‹ค. ๊ทธ๋Š” โ€˜ํŽธ์ง€ ์†์˜ ๊ทธโ€™๋ฅผ ์—ฐ๊ธฐํ•˜๋Š” ๊ฒƒ์ด ๋ถ€๋„๋Ÿฌ์› ๊ณ , ์ž์‹ ์˜ +๋น„๋ฃจํ•จ์„ ๋ผ›์† ๊นŠ์ด ์‹ค๊ฐํ–ˆ๋‹ค. ๊ทธ๋Š” โ€˜์‚ฌ๋ž‘ํ•˜๋Š” โ—‹โ—‹์—๊ฒŒโ€™๋ผ๋Š” +ํŽธ์ง€๋ฅผ ์“ฐ๊ณ  ์‹ถ์–ด ํ•˜๋Š” ์ž์‹  ์†์˜ ์–ด๋–ค ๋Š™์ง€ ์•Š๋Š” ์˜ํ˜ผ์„, ๊ทธ +์ˆœ์ˆ˜ํ•œ ์ธ๊ฒฉ์„ ์™ธ๋ฉดํ•˜๊ณ  ์‹ถ์—ˆ๋‹ค. โ“‘๋ˆ„๊ตฐ๊ฐ€๊ฐ€ ๋“ฃ๊ธฐ๋ฅผ ๋ฐ”๋ผ๋Š” ๋ชจ๋“  +๊ณ ๋ฐฑ์ด๋ž€, ์œ„์„ ์ด ์•„๋‹ˆ๋ฉด ์œ„์•…์ด๋‹ค. +-์ด๊ด‘ํ˜ธ, ๏ฝข์ด์   ๋˜๋„๋ก ํŽธ์ง€ ์•ˆ ๋“œ๋ฆฌ๊ฒ ์Šต๋‹ˆ๋‹ค๏ฝฃ- +","{'question': '<๋ณด๊ธฐ>๋ฅผ ์ฐธ๊ณ ํ•˜์—ฌ (๋‚˜)๋ฅผ ๊ฐ์ƒํ•œ ๋‚ด์šฉ์œผ๋กœ ์ ์ ˆํ•˜์ง€ ์•Š์€\n๊ฒƒ์€? ', 'choices': ['ํ‚ฅํ‚ฅโ€™์€ ๋ฐ˜๋ณต์ ์œผ๋กœ ์ถœํ˜„ํ•˜๋Š” ์›ƒ์Œ์˜ ์˜์„ฑ์–ด๋กœ์„œ, ์‚ฌ๋ž‘๊ณผ ์Šฌํ””์ด\n๋‚ด์žฌ๋œ ํ™”์ž์˜ ๋ณตํ•ฉ์ ์ธ ์ •์„œ๋ฅผ ์ƒ์ƒํ•˜๊ฒŒ ๋“œ๋Ÿฌ๋‚ด๋Š” ํ‘œํ˜„์ด๊ฒ ๊ตฐ', 'โ€˜์ƒ์ฒ˜์— ๊ธฐ๋Œ€, ๋‚˜ ํ‚ฅํ‚ฅโ€ฆโ€ฆ, ๋‹น์‹ ์„ ๋ถ€๋ฆ…๋‹ˆ๋‹คโ€™๋Š” ๋ง์ค„์ž„ํ‘œ์™€\n์‰ผํ‘œ๋ฅผ ์‚ฌ์šฉํ•œ ์„œ์ˆ ๋กœ์„œ, ์ƒ์‹ค์˜ ๊ณ ํ†ต์œผ๋กœ ์ธํ•˜์—ฌ ์‚ฌ๋ž‘์˜ ๊ธฐ์–ต์ด\nํฌ๋ฏธํ•ด์ง€๋Š” ํ™”์ž์˜ ์‹ฌ์  ์ƒํƒœ๋ฅผ ๋ณด์—ฌ ์ฃผ๋Š” ํ‘œํ˜„์ด๊ฒ ๊ตฐ', 'ํ‚ฅํ‚ฅ๊ฑฐ๋ฆฌ๋ฉฐ ์„ธ์›”์— ๋Œ€ํ•ด ํ˜น์€ ์‚ฌ๋ž‘๊ณผ ์ƒ์ฒ˜,โ€™๋Š” ๋งฅ๋ฝ์ด ์—†์–ด\n๋ณด์ด๋Š” ํ‘œํ˜„๋“ค์ด ํ•œ๋ฐ ์ด์–ด์ง„ ์„œ์ˆ ๋กœ์„œ, ๊ฐ์ •๋“ค์ด ๋’ค์„ž์ธ\nํ™”์ž์˜ ๋‚ด๋ฉด์„ ๋ณด์—ฌ ์ฃผ๋Š” ํ‘œํ˜„์ด๊ฒ ๊ตฐ.', 'โ€˜๋งˆ์Œ์˜ ๋ฌด๋คโ€™์€ ํ™”์ž์˜ ์‹ฌ์  ์ƒํƒœ๋ฅผ ํ˜•์ƒํ™”ํ•œ ์„œ์ˆ ๋กœ์„œ, ์ƒ์‹ค์˜\n๊ณ ํ†ต์„ ์•ˆ๊ณ  ์ƒ์„ ์‚ด์•„ ๋‚ด์•ผ ํ•˜๋Š” ํ™”์ž์˜ ๋‚ด๋ฉด์„ ๋น„์œ ํ•œ ํ‘œํ˜„์ด๊ฒ ๊ตฐ.', 'โ€˜์ด์œ ๋‹น์‹ โ€ฆโ€ฆ, ๋‹น์‹ ์ด๋ผ๋Š” ๋ง ์ฐธ ์ข‹์ง€์š”,โ€™๋Š” ๋Š์–ด์งˆ ๋“ฏ ์ด์–ด์ง€๋Š”\n์„œ์ˆ ๋กœ์„œ, ๋Œ€์ƒ์— ๋Œ€ํ•˜์—ฌ ์‚ฌ๋ž‘์˜ ๊ฐ์ •์„ ํ’ˆ๊ณ  ์žˆ๋Š” ํ™”์ž์˜ ๋‚ด๋ฉด์„\n๋ณด์—ฌ ์ฃผ๋Š” ํ‘œํ˜„์ด๊ฒ ๊ตฐ.'], 'answer': '', 'question_plus': '์‹œ๋Š” ํ‘œํ˜„ํ•˜๊ณ ์ž ํ•˜๋Š” ๋ฐ”๋ฅผ ์–ด๋–ค ์‹ฌ์  ์ƒํƒœ์— ๋†“์ธ ํ™”์ž์˜\n๋ฐœํ™”๋กœ์จ ํ˜•์ƒํ™”ํ•œ๋‹ค. (๋‚˜)์— ๋‚˜ํƒ€๋‚˜ ์žˆ๋Š” ๋…ํŠนํ•œ ๋ฐœํ™” ๋ฐฉ์‹,\n์ฆ‰ ๋Š์–ด์งˆ ๋“ฏ ์ด์–ด์ง€๋Š” ์„œ์ˆ , ์–ดํœ˜์˜ ๋ฐ˜๋ณต์  ์ถœํ˜„, ๋งฅ๋ฝ์ด\n์—†์–ด ๋ณด์ด๋Š” ๊ตฌ์ ˆ๋“ค์˜ ๋ฐฐ์—ด, ์ˆ˜์‹œ๋กœ ๋“ฑ์žฅํ•˜๋Š” ๋ง์ค„์ž„ํ‘œ์™€ ์‰ผํ‘œ\n๋“ฑ์€ ์‚ฌ๋ž‘์˜ ๊ธฐ์–ต์„ ๋– ์˜ฌ๋ฆฌ๊ฑฐ๋‚˜ ์ƒ์ฒ˜๋ฅผ ์น˜์œ ํ•˜์ง€ ๋ชปํ•œ ํ™”์ž์˜\n๋‚ด๋ฉด์„ ๋“œ๋Ÿฌ๋‚ด๋Š” ์‹œ์  ์žฅ์น˜๋“ค์ด๋‹ค. ์ด๋Ÿฌํ•œ ์žฅ์น˜๋“ค์€ ์‚ฌ๋ž‘์˜\n๊ธฐ์–ต๊ณผ ํ•จ๊ป˜ ์ƒ์‹ค์˜ ๊ณ ํ†ต์„ ์•ˆ๊ณ  ๋‚จ์€ ์ƒ์„ ์‚ด์•„ ๋‚ด์•ผ ํ•˜๋Š”\nํ™”์ž์˜ ๋ณตํ•ฉ์ ์ธ ๋‚ด๋ฉด์„ ์ƒ์ƒํ•˜๊ฒŒ ๊ทธ๋ ค ๋‚ด๋Š” ์—ญํ• ์„ ํ•œ๋‹ค.'}","์‹œ๋Š” ํ‘œํ˜„ํ•˜๊ณ ์ž ํ•˜๋Š” ๋ฐ”๋ฅผ ์–ด๋–ค ์‹ฌ์  ์ƒํƒœ์— ๋†“์ธ ํ™”์ž์˜ +๋ฐœํ™”๋กœ์จ ํ˜•์ƒํ™”ํ•œ๋‹ค. (๋‚˜)์— ๋‚˜ํƒ€๋‚˜ ์žˆ๋Š” ๋…ํŠนํ•œ ๋ฐœํ™” ๋ฐฉ์‹, +์ฆ‰ ๋Š์–ด์งˆ ๋“ฏ ์ด์–ด์ง€๋Š” ์„œ์ˆ , ์–ดํœ˜์˜ ๋ฐ˜๋ณต์  ์ถœํ˜„, ๋งฅ๋ฝ์ด +์—†์–ด ๋ณด์ด๋Š” ๊ตฌ์ ˆ๋“ค์˜ ๋ฐฐ์—ด, ์ˆ˜์‹œ๋กœ ๋“ฑ์žฅํ•˜๋Š” ๋ง์ค„์ž„ํ‘œ์™€ ์‰ผํ‘œ +๋“ฑ์€ ์‚ฌ๋ž‘์˜ ๊ธฐ์–ต์„ ๋– ์˜ฌ๋ฆฌ๊ฑฐ๋‚˜ ์ƒ์ฒ˜๋ฅผ ์น˜์œ ํ•˜์ง€ ๋ชปํ•œ ํ™”์ž์˜ +๋‚ด๋ฉด์„ ๋“œ๋Ÿฌ๋‚ด๋Š” ์‹œ์  ์žฅ์น˜๋“ค์ด๋‹ค. ์ด๋Ÿฌํ•œ ์žฅ์น˜๋“ค์€ ์‚ฌ๋ž‘์˜ +๊ธฐ์–ต๊ณผ ํ•จ๊ป˜ ์ƒ์‹ค์˜ ๊ณ ํ†ต์„ ์•ˆ๊ณ  ๋‚จ์€ ์ƒ์„ ์‚ด์•„ ๋‚ด์•ผ ํ•˜๋Š” +ํ™”์ž์˜ ๋ณตํ•ฉ์ ์ธ ๋‚ด๋ฉด์„ ์ƒ์ƒํ•˜๊ฒŒ ๊ทธ๋ ค ๋‚ด๋Š” ์—ญํ• ์„ ํ•œ๋‹ค.",2,2,True,[],2 +2025-korean-26,"(๊ฐ€) +๋ฐฐ๋ฅผ ๋ฏผ๋‹ค +๋ฐฐ๋ฅผ ๋ฐ€์–ด๋ณด๋Š” ๊ฒƒ์€ ์•„์ฃผ ๋“œ๋ฌธ ๊ฒฝํ—˜ +ํฌ๋ฒˆ๋•์ด๋Š” ์ž”์ž”ํ•œ ๊ฐ€์„ ๋ฐ”๋‹ท๋ฌผ ์œ„์— +๋ฐฐ๋ฅผ ๋ฐ€์–ด๋„ฃ๊ณ ๋Š” +์˜จ๋ชธ์ด ์•„์ฃผ ์ถ”๋ฝํ•˜์ง€ ์•Š์„ ์ˆœ๊ฐ„์˜ ํ•œ ํ—ˆ๊ณต์—์„œ +๋ฐ€๋˜ ํž˜์„ ํ•œ๊ป ๋”ํ•ด ๋ฐ€์–ด์ฃผ๊ณ ๋Š” +์•„์Šฌ์•„์Šฌํžˆ ๋ฐฐ์—์„œ ๋–จ์–ด์ง„ ์†, ์ˆœ๊ฐ„ ํ™˜ํ•ด์ง„ ์†์„ +ํ—ˆ๊ณต์œผ๋กœ๋ถ€ํ„ฐ ๊ฑฐ๋‘”๋‹ค +์‚ฌ๋ž‘์€ ์ฐธ ๋ถ€๋“œ๋Ÿฝ๊ฒŒ๋„ ๋– ๋‚˜์ง€ +๋ตˆ์ง€๋„ ์•Š๋Š” ๊ธธ์„ ๋ถ€๋“œ๋Ÿฝ๊ฒŒ๋„ +๋ฐฐ๋ฅผ ํ•œ๊ป ์„ธ๊ฒŒ ๋ฐ€์–ด๋‚ด๋“ฏ์ด ์Šฌํ””๋„ +๊ทธ๋ ‡๊ฒŒ ๋ฐ€์–ด๋‚ด๋Š” ๊ฒƒ์ด์ง€ +๋ฐฐ๊ฐ€ ๋‚˜๊ฐ€๊ณ  ๋‚จ์€ ๋นˆ ๋ฌผ ์œ„์˜ ํ‰ํ„ฐ +์ž ์‹œ ๋จธ๋ฌผ๋‹ค ๊ฐ€๋ผ์•‰๊ณ  +๊ทธ๋Ÿฐ๋ฐ ์˜ค, ๋‚ด ์•ˆ์œผ๋กœ ๋“ค์–ด์˜ค๋Š” ๋ฐฐ์—ฌ +์•„๋ฌด ์†Œ๋ฆฌ ์—†์ด ๋ฐ€๋ ค๋“ค์–ด์˜ค๋Š” ๋ฐฐ์—ฌ +-์žฅ์„๋‚จ, ๏ฝข๋ฐฐ๋ฅผ ๋ฐ€๋ฉฐ๏ฝฃ- +(๋‚˜) +๋‹น์‹ โ€ฆโ€ฆ, ๋‹น์‹ ์ด๋ผ๋Š” ๋ง ์ฐธ ์ข‹์ง€์š”, ๊ทธ๋ž˜์„œ ๋ถˆ๋Ÿฌ๋ด…๋‹ˆ๋‹ค ํ‚ฅํ‚ฅ +๊ฑฐ๋ฆฌ๋ฉฐ ํ•œ๋•Œ ์ ์š”๋กœ์›€์˜ ์šธ์Œ์ด ์žˆ์—ˆ๋˜ ๋•Œ, ํ•œ ์Šฌํ””์ด ๋ฌธ์„ +๋‹ซ์œผ๋ฉด ๋˜ ํ•œ ์Šฌํ””์ด ๋ฌธ์„ ์—ฌ๋Š” ๊ฒƒ์„ ์ด๋งŒํผ ์‚ด์•„์˜ด์˜ ์ƒ์ฒ˜์— +๊ธฐ๋Œ€, ๋‚˜ ํ‚ฅํ‚ฅโ€ฆโ€ฆ +, ๋‹น์‹ ์„ ๋ถ€๋ฆ…๋‹ˆ๋‹ค ๋‹จํ’์˜ ์†๋ฐ”๋‹ฅ, ์€ํ–‰์˜ ๋‘ +๊ฐˆ๋ž˜ ๊ทธ๋ฆฌ๊ณ  ํ•ฉ์นจ ์ € ๊ฐœ๋ง์ดˆ์˜ ์‹œ๋ฆ„, ๋ฐŸํžŒ ํ’€์˜ ํ™์œผ๋กœ ๋Œ์•„๊ฐ +๋‹น์‹ โ€ฆโ€ฆ, ํ‚ฅํ‚ฅ๊ฑฐ๋ฆฌ๋ฉฐ ์„ธ์›”์— ๋Œ€ํ•ด ํ˜น์€ ์‚ฌ๋ž‘๊ณผ ์ƒ์ฒ˜, ์ƒ์ฒ˜์˜ +๋ชธ์ด ๋‚˜์—๊ฒŒ ๊ธฐ๋Œ€์™€ ์ €๋ฅผ ๋ถ€๋นŒ ๋•Œ ๋‹น์‹ โ€ฆโ€ฆ, ๊ทธ๋Œ€๋ผ๋Š” ์ž์—ฐ์˜ +๋‹ฌ๊ณผ ๋ณ„โ€ฆโ€ฆ, ํ‚ฅํ‚ฅ๊ฑฐ๋ฆฌ๋ฉฐ ๋‹น์‹ ์ด๋ผ๊ณ โ€ฆโ€ฆ, ๊ธˆ๋ฐฉ ์šธ ๊ฒƒ ๊ฐ™์€ +์‚ฌ๋‚ด์˜ ์•„๋ฆ„๋‹ค์›€ ๊ทธ ์•„๋ฆ„๋‹ค์›€์— ๊ธฐ๋Œ€ ๋งˆ์Œ์˜ ๋ฌด๋ค์— ๋‚˜ ๋ฒŒ์ดˆํ•˜๋Ÿฌ +์ง„์„ค ์Œ์‹๋„ ์—†์ด ๋งจ ์ˆ  ํ•œ ๋ณ‘ ์ฐจ๊ณ  ๋ณ‘์ž์ฒ˜๋Ÿผ, ๊ทธ๋Ÿฌ๋‚˜ โ“์น˜๋ณ‘*๊ณผ +ํ™˜ํ›„*๋Š” ๊ฐ๊ฐ ๋”ฐ๋กœ์ธ ๊ฒƒ์„ ํ‚ฅํ‚ฅ ๋‹น์‹  ์ด์œ ๋‹น์‹ โ€ฆโ€ฆ +, ๋‹น์‹ ์ด๋ผ๋Š” +๋ง ์ฐธ ์ข‹์ง€์š”, ๋‚ด๊ฐ€ ์•„๋‹ˆ๋ผ์„œ ๋๋‚ด ๋ฒ„๋ฆด ์ˆ˜ ์—†๋Š”, ๋ฌด๋ฅผ ์ˆ˜๋„ ์—†๋Š” +์ฐธํ˜นโ€ฆโ€ฆ, ๊ทธ๋Ÿฌ๋‚˜ ํ‚ฅํ‚ฅ ๋‹น์‹  +-์ด๊ด‘ํ˜ธ, ๏ฝข์ด์   ๋˜๋„๋ก ํŽธ์ง€ ์•ˆ ๋“œ๋ฆฌ๊ฒ ์Šต๋‹ˆ๋‹ค๏ฝฃ - + +*์น˜๋ณ‘:๋ณ‘์„ ๋‹ค์Šค๋ฆผ. +*ํ™˜ํ›„:๋ณ‘์„ ์ •์ค‘ํ•˜๊ฒŒ ์ด๋ฅด๋Š” ๋ง. + +(๋‹ค) +๊ทธ๋…€์—๊ฒŒ ํŽธ์ง€๋ฅผ ์“ฐ๋Š” ๊ฒƒ์ด ์ž์‹ ์˜ ์กด์žฌ๋ฅผ ์ฆ๋ช…ํ•˜๋˜ ์‹œ์ ˆ์ด +์žˆ์—ˆ๋‹ค. ์‚ฌ๋ž‘ํ•˜๋Š” ์‚ฌ๋žŒ์—๊ฒŒ ๋ณด๋‚ด๋Š” ํŽธ์ง€๋งŒํผ ํ‘œํ˜„์˜ ์š•๊ตฌ๋กœ ํ˜๋Ÿฌ +๋„˜์น˜๋Š” ๊ฒƒ๋„ ์—†๋‹ค. ๋ฌด์–ธ๊ฐ€๋ฅผ ํ‘œํ˜„ํ•˜์ง€ ์•Š๊ณ ๋Š” ๊ฒฌ๋”œ ์ˆ˜ ์—†๋Š” +์‹œ๊ฐ„๋“ค์ด ํŽธ์ง€๋ฅผ ์“ฐ๊ฒŒ ํ•œ๋‹ค. ๊ทธ๋Š” ๊ทธ๋…€์—๊ฒŒ ์ž์‹ ์˜ ์‚ฌ๋ž‘์ด ์–ผ๋งˆ๋‚˜ +์–ด๋ ต๊ณ  ์ง„์ •ํ•˜๋ฉฐ ์šด๋ช…์ ์ธ๊ฐ€๋ฅผ ์„ค๋ช…ํ•˜๊ณ  ์‹ถ์—ˆ๋‹ค. ํŽธ์ง€๋Š” ์‚ฌ๋žŒ์„ +์„ค๋“ํ•˜๊ฑฐ๋‚˜ ๋งคํ˜น์‹œํ‚ค๋Š” ๋ฐฉํŽธ์ด ๋ ์ง€๋„ ๋ชจ๋ฅธ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋ชจ๋“  +์‚ฌ๋ž‘์˜ ํŽธ์ง€๋Š” ๋งˆ์ง€๋ง‰ ์ˆœ๊ฐ„, ๋„๊ตฌ์ ์ด์ง€ ๋ชปํ•˜๋‹ค. ์„ธ์ƒ์˜ ๋ชจ๋“  +๊ธ€์“ฐ๊ธฐ๊ฐ€ ์ตœํ›„์˜ ์ˆœ๊ฐ„์—๋Š” ์ฒ˜์Œ์— ํ’ˆ์—ˆ๋˜ ์†Œ์†Œํ•œ ์˜๋„๋ฅผ ๋ฐฐ๋ฐ˜ +ํ•˜๋Š” ๊ฒƒ์ฒ˜๋Ÿผ. ๊ทธ ํ†ต์ œํ•  ์ˆ˜ ์—†๋Š” ์ต๋ช…์˜ ์š•๊ตฌ๊ฐ€ ๊ทธ ํŽธ์ง€์˜ +ํ˜„์‹ค์ ์ธ ๋ชฉํ‘œ๋ฅผ ์žŠ์–ด๋ฒ„๋ฆฌ๊ฒŒ ๋งŒ๋“ค๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ๊ทธ๋Ÿฐ ์ด์œ ๋กœ, ๋ชจ๋“  +์‚ฌ๋ž‘์˜ ํŽธ์ง€์—๋Š” ์•„๋ฌด ์ „์–ธ๋„ ๋“ค์–ด ์žˆ์ง€ ์•Š๋‹ค. +๊ฑฐ๊ธฐ์—๋Š” ๊ฒฐ์ •์ ์ธ ์ •๋ณด๋‚˜ ์ฃผ์žฅ์ด ๋“ค์–ด ์žˆ์ง€ ์•Š๋‹ค. ๋‹ค๋งŒ ๋‚ด +๊ณ ๋ฐฑ์„ ๋ˆ„๊ตฐ๊ฐ€๊ฐ€ ๋“ค์–ด์ค€๋‹ค๋Š” ์ถฉ๋งŒํ•œ ๋А๋‚Œ. ํฌ๋ฏธํ•œ ๋ถˆ๋น› ์•„๋ž˜์„œ +์Šค์Šค๋กœ ์˜ท์„ ๋ฒ—์–ด์•ผ ํ•  ๋•Œ์ฒ˜๋Ÿผ, ์ฃผ์ฒดํ•  ์ˆ˜ ์—†๋Š” ๋ถ€๋„๋Ÿฌ์›€ ๋”ฐ์œ„. +๊ณ ๋ฐฑ์ด๋ž€ ๊ฒฐ๊ตญ 2์ธ์นญ์„ ๊ฒฝ์œ ํ•˜์—ฌ 1์ธ์นญ์œผ๋กœ ๋Œ์•„์˜จ๋‹ค. ๊ทธ์˜ +๋“ค๋“๋Š” ๊ณ ๋ฐฑ์˜ ์–ธ์–ด๋“ค์€ ๊ณ ์Šค๋ž€ํžˆ ์ž์‹ ์—๊ฒŒ ๋Œ์•„์™”๋‹ค. ํ•œ๋™์•ˆ +๊ทธ๋Š”, ์‚ฌ๋ž‘ํ•˜๋Š” โ—‹โ—‹์—๊ฒŒ๋กœ ์‹œ์ž‘๋˜๋Š” ํŽธ์ง€๋ฅผ ์ž์ฃผ ์ผ๋‹ค. ๊ทธ๋…€๋Š” +๊ทธ์˜ ํŽธ์ง€๋ฅผ ์‚ฌ๋ž‘ํ–ˆ๋‹ค. ์ •ํ™•ํ•˜๊ฒŒ ๋งํ•˜๋ฉด โ€˜ํŽธ์ง€ ์†์˜ ๊ทธโ€™๋ฅผ ๊ทธ๋…€๋Š” +์‚ฌ๋ž‘ํ–ˆ๋‹ค. ํŽธ์ง€ ์†์—๋Š” ๊ทธ๊ฐ€ ์ฐพ์•„๋‚ธ ์ž์‹ ์˜ ๋˜ ๋‹ค๋ฅธ ์˜ํ˜ผ์ด +์žˆ์—ˆ๋‹ค. ๋˜ ๋‹ค๋ฅธ ์˜ํ˜ผ์˜ โ€˜๊ทธโ€™๋Š” ์ˆœ์ˆ˜ํ•œ ์—ด์ •๊ณผ ๋ ๋ชจ๋ฅผ ๋™๊ฒฝ๊ณผ +๊นŠ์€ ์ดํ•ด์‹ฌ์„ ๊ฐ€์ง„ ์กด์žฌ์˜€๋‹ค. ๊ทธ๋„ ์—ญ์‹œ ๊ทธ๋…€์ฒ˜๋Ÿผ ์ž์‹ ์˜ ํŽธ์ง€ +์† 1์ธ์นญ ํ™”์ž์—๊ฒŒ ๊นŠ์ด ๋งค๋ฃŒ๋˜์—ˆ๋‹ค. ํ•˜์ง€๋งŒ ๋„ˆ๋ฌด ๋ป”ํ•ด์„œ ๊ฐ€ํ˜น +ํ–ˆ๋˜ ์ง€๋ฆฌ๋ฉธ๋ ฌํ•œ ์‹œ๊ฐ„๋“ค ์†์—์„œ ๊ทธ๋Š” ํŽธ์ง€ ์†์˜ 1์ธ์นญ ์ฃผ์ฒด๋ฅผ +์žŠ์–ด๋ฒ„๋ ธ๋‹ค. +ํŽธ์ง€์กฐ์ฐจ ์“ธ ์ˆ˜ ์—†๋Š” ์‹œ๊ฐ„๋“ค์ด ๋ฌด์‹ฌํ•˜๊ฒŒ ์ง€๋‚˜๊ฐ€๊ณ , ๋‹ค์‹œ ํŽธ์ง€๋ฅผ +์“ฐ๊ณ  ์‹ถ์—ˆ์„ ๋•Œ, ๊ทธ๋Š” ์ด๋ฏธ โ€˜ํŽธ์ง€ ์†์˜ ๊ทธโ€™๊ฐ€ ๋˜์ง€ ๋ชปํ•œ๋‹ค๋Š” ๊ฒƒ์„ +์•Œ์•˜๋‹ค. ๊ทธ๋Š” โ€˜ํŽธ์ง€ ์†์˜ ๊ทธโ€™๋ฅผ ์—ฐ๊ธฐํ•˜๋Š” ๊ฒƒ์ด ๋ถ€๋„๋Ÿฌ์› ๊ณ , ์ž์‹ ์˜ +๋น„๋ฃจํ•จ์„ ๋ผ›์† ๊นŠ์ด ์‹ค๊ฐํ–ˆ๋‹ค. ๊ทธ๋Š” โ€˜์‚ฌ๋ž‘ํ•˜๋Š” โ—‹โ—‹์—๊ฒŒโ€™๋ผ๋Š” +ํŽธ์ง€๋ฅผ ์“ฐ๊ณ  ์‹ถ์–ด ํ•˜๋Š” ์ž์‹  ์†์˜ ์–ด๋–ค ๋Š™์ง€ ์•Š๋Š” ์˜ํ˜ผ์„, ๊ทธ +์ˆœ์ˆ˜ํ•œ ์ธ๊ฒฉ์„ ์™ธ๋ฉดํ•˜๊ณ  ์‹ถ์—ˆ๋‹ค. โ“‘๋ˆ„๊ตฐ๊ฐ€๊ฐ€ ๋“ฃ๊ธฐ๋ฅผ ๋ฐ”๋ผ๋Š” ๋ชจ๋“  +๊ณ ๋ฐฑ์ด๋ž€, ์œ„์„ ์ด ์•„๋‹ˆ๋ฉด ์œ„์•…์ด๋‹ค. +-์ด๊ด‘ํ˜ธ, ๏ฝข์ด์   ๋˜๋„๋ก ํŽธ์ง€ ์•ˆ ๋“œ๋ฆฌ๊ฒ ์Šต๋‹ˆ๋‹ค๏ฝฃ- +","{'question': 'โ“, โ“‘์— ๋Œ€ํ•œ ์ดํ•ด๋กœ ๊ฐ€์žฅ ์ ์ ˆํ•œ ๊ฒƒ์€?', 'choices': ['โ“๋Š” ์น˜๋ณ‘์˜ ๋…ธ๋ ฅ์œผ๋กœ๋„ ํ™˜ํ›„๊ฐ€ ์‚ฌ๋ผ์ง€๋Š” ๊ฒƒ์€ ์•„๋‹ˆ๋ผ๋Š”\nํ™”์ž์˜ ์ธ์‹์„ ๋งํ•œ๋‹ค.', 'โ“๋Š” ํ™”์ž๊ฐ€ ๋Œ€์ƒ์˜ ์•„๋ฆ„๋‹ค์›€์„ ๋ฐœ๊ฒฌํ•จ์œผ๋กœ์จ ์ž์‹ ์˜ ํ™˜ํ›„๋ฅผ\n์˜์‹ํ•˜์ง€ ์•Š๊ฒŒ ๋˜์—ˆ์Œ์„ ๋งํ•œ๋‹ค.', 'โ“‘๋Š” ์‚ฌ๋ž‘์˜ ํŽธ์ง€๊ฐ€ ์ƒ๋Œ€๋ฅผ ํ–ฅํ•œ ํ‘œํ˜„์ผ ๋•Œ, ์œ„์„ ๊ณผ ์œ„์•…์—์„œ\n๋ฒ—์–ด๋‚  ์ˆ˜ ์žˆ์Œ์„ ๋งํ•œ๋‹ค.', 'โ“‘๋Š” ๋” ๋‚˜์€ ์ž์‹ ์„ ๋“œ๋Ÿฌ๋‚ด๋ ค๋Š” ์š•๋ง์ด์•ผ๋ง๋กœ ์ƒ๋Œ€๋ฅผ ๋งคํ˜น\nํ•˜๋Š” ์ง„์ •ํ•œ ์š”์ธ์ž„์„ ๋งํ•œ๋‹ค.', 'โ“์™€ โ“‘๋Š” ๋ชจ๋‘, ์•„ํ””์„ ๊ฒช๋Š” ์ด๋‚˜ ๊ณ ๋ฐฑ์„ ํ•˜๋Š” ์ด๊ฐ€ ๊ทธ ์•„ํ””์ด๋‚˜\n๊ณ ๋ฐฑ์˜ ์‹ค์ฒด๋ฅผ ์ง€๊ฐํ•˜์ง€ ๋ชปํ•จ์„ ๋งํ•œ๋‹ค'], 'answer': ''}",,1,1,True,[],5 +2025-korean-27,"(๊ฐ€) +๋ฐฐ๋ฅผ ๋ฏผ๋‹ค +๋ฐฐ๋ฅผ ๋ฐ€์–ด๋ณด๋Š” ๊ฒƒ์€ ์•„์ฃผ ๋“œ๋ฌธ ๊ฒฝํ—˜ +ํฌ๋ฒˆ๋•์ด๋Š” ์ž”์ž”ํ•œ ๊ฐ€์„ ๋ฐ”๋‹ท๋ฌผ ์œ„์— +๋ฐฐ๋ฅผ ๋ฐ€์–ด๋„ฃ๊ณ ๋Š” +์˜จ๋ชธ์ด ์•„์ฃผ ์ถ”๋ฝํ•˜์ง€ ์•Š์„ ์ˆœ๊ฐ„์˜ ํ•œ ํ—ˆ๊ณต์—์„œ +๋ฐ€๋˜ ํž˜์„ ํ•œ๊ป ๋”ํ•ด ๋ฐ€์–ด์ฃผ๊ณ ๋Š” +์•„์Šฌ์•„์Šฌํžˆ ๋ฐฐ์—์„œ ๋–จ์–ด์ง„ ์†, ์ˆœ๊ฐ„ ํ™˜ํ•ด์ง„ ์†์„ +ํ—ˆ๊ณต์œผ๋กœ๋ถ€ํ„ฐ ๊ฑฐ๋‘”๋‹ค +์‚ฌ๋ž‘์€ ์ฐธ ๋ถ€๋“œ๋Ÿฝ๊ฒŒ๋„ ๋– ๋‚˜์ง€ +๋ตˆ์ง€๋„ ์•Š๋Š” ๊ธธ์„ ๋ถ€๋“œ๋Ÿฝ๊ฒŒ๋„ +๋ฐฐ๋ฅผ ํ•œ๊ป ์„ธ๊ฒŒ ๋ฐ€์–ด๋‚ด๋“ฏ์ด ์Šฌํ””๋„ +๊ทธ๋ ‡๊ฒŒ ๋ฐ€์–ด๋‚ด๋Š” ๊ฒƒ์ด์ง€ +๋ฐฐ๊ฐ€ ๋‚˜๊ฐ€๊ณ  ๋‚จ์€ ๋นˆ ๋ฌผ ์œ„์˜ ํ‰ํ„ฐ +์ž ์‹œ ๋จธ๋ฌผ๋‹ค ๊ฐ€๋ผ์•‰๊ณ  +๊ทธ๋Ÿฐ๋ฐ ์˜ค, ๋‚ด ์•ˆ์œผ๋กœ ๋“ค์–ด์˜ค๋Š” ๋ฐฐ์—ฌ +์•„๋ฌด ์†Œ๋ฆฌ ์—†์ด ๋ฐ€๋ ค๋“ค์–ด์˜ค๋Š” ๋ฐฐ์—ฌ +-์žฅ์„๋‚จ, ๏ฝข๋ฐฐ๋ฅผ ๋ฐ€๋ฉฐ๏ฝฃ- +(๋‚˜) +๋‹น์‹ โ€ฆโ€ฆ, ๋‹น์‹ ์ด๋ผ๋Š” ๋ง ์ฐธ ์ข‹์ง€์š”, ๊ทธ๋ž˜์„œ ๋ถˆ๋Ÿฌ๋ด…๋‹ˆ๋‹ค ํ‚ฅํ‚ฅ +๊ฑฐ๋ฆฌ๋ฉฐ ํ•œ๋•Œ ์ ์š”๋กœ์›€์˜ ์šธ์Œ์ด ์žˆ์—ˆ๋˜ ๋•Œ, ํ•œ ์Šฌํ””์ด ๋ฌธ์„ +๋‹ซ์œผ๋ฉด ๋˜ ํ•œ ์Šฌํ””์ด ๋ฌธ์„ ์—ฌ๋Š” ๊ฒƒ์„ ์ด๋งŒํผ ์‚ด์•„์˜ด์˜ ์ƒ์ฒ˜์— +๊ธฐ๋Œ€, ๋‚˜ ํ‚ฅํ‚ฅโ€ฆโ€ฆ +, ๋‹น์‹ ์„ ๋ถ€๋ฆ…๋‹ˆ๋‹ค ๋‹จํ’์˜ ์†๋ฐ”๋‹ฅ, ์€ํ–‰์˜ ๋‘ +๊ฐˆ๋ž˜ ๊ทธ๋ฆฌ๊ณ  ํ•ฉ์นจ ์ € ๊ฐœ๋ง์ดˆ์˜ ์‹œ๋ฆ„, ๋ฐŸํžŒ ํ’€์˜ ํ™์œผ๋กœ ๋Œ์•„๊ฐ +๋‹น์‹ โ€ฆโ€ฆ, ํ‚ฅํ‚ฅ๊ฑฐ๋ฆฌ๋ฉฐ ์„ธ์›”์— ๋Œ€ํ•ด ํ˜น์€ ์‚ฌ๋ž‘๊ณผ ์ƒ์ฒ˜, ์ƒ์ฒ˜์˜ +๋ชธ์ด ๋‚˜์—๊ฒŒ ๊ธฐ๋Œ€์™€ ์ €๋ฅผ ๋ถ€๋นŒ ๋•Œ ๋‹น์‹ โ€ฆโ€ฆ, ๊ทธ๋Œ€๋ผ๋Š” ์ž์—ฐ์˜ +๋‹ฌ๊ณผ ๋ณ„โ€ฆโ€ฆ, ํ‚ฅํ‚ฅ๊ฑฐ๋ฆฌ๋ฉฐ ๋‹น์‹ ์ด๋ผ๊ณ โ€ฆโ€ฆ, ๊ธˆ๋ฐฉ ์šธ ๊ฒƒ ๊ฐ™์€ +์‚ฌ๋‚ด์˜ ์•„๋ฆ„๋‹ค์›€ ๊ทธ ์•„๋ฆ„๋‹ค์›€์— ๊ธฐ๋Œ€ ๋งˆ์Œ์˜ ๋ฌด๋ค์— ๋‚˜ ๋ฒŒ์ดˆํ•˜๋Ÿฌ +์ง„์„ค ์Œ์‹๋„ ์—†์ด ๋งจ ์ˆ  ํ•œ ๋ณ‘ ์ฐจ๊ณ  ๋ณ‘์ž์ฒ˜๋Ÿผ, ๊ทธ๋Ÿฌ๋‚˜ โ“์น˜๋ณ‘*๊ณผ +ํ™˜ํ›„*๋Š” ๊ฐ๊ฐ ๋”ฐ๋กœ์ธ ๊ฒƒ์„ ํ‚ฅํ‚ฅ ๋‹น์‹  ์ด์œ ๋‹น์‹ โ€ฆโ€ฆ +, ๋‹น์‹ ์ด๋ผ๋Š” +๋ง ์ฐธ ์ข‹์ง€์š”, ๋‚ด๊ฐ€ ์•„๋‹ˆ๋ผ์„œ ๋๋‚ด ๋ฒ„๋ฆด ์ˆ˜ ์—†๋Š”, ๋ฌด๋ฅผ ์ˆ˜๋„ ์—†๋Š” +์ฐธํ˜นโ€ฆโ€ฆ, ๊ทธ๋Ÿฌ๋‚˜ ํ‚ฅํ‚ฅ ๋‹น์‹  +-์ด๊ด‘ํ˜ธ, ๏ฝข์ด์   ๋˜๋„๋ก ํŽธ์ง€ ์•ˆ ๋“œ๋ฆฌ๊ฒ ์Šต๋‹ˆ๋‹ค๏ฝฃ - + +*์น˜๋ณ‘:๋ณ‘์„ ๋‹ค์Šค๋ฆผ. +*ํ™˜ํ›„:๋ณ‘์„ ์ •์ค‘ํ•˜๊ฒŒ ์ด๋ฅด๋Š” ๋ง. + +(๋‹ค) +๊ทธ๋…€์—๊ฒŒ ํŽธ์ง€๋ฅผ ์“ฐ๋Š” ๊ฒƒ์ด ์ž์‹ ์˜ ์กด์žฌ๋ฅผ ์ฆ๋ช…ํ•˜๋˜ ์‹œ์ ˆ์ด +์žˆ์—ˆ๋‹ค. ์‚ฌ๋ž‘ํ•˜๋Š” ์‚ฌ๋žŒ์—๊ฒŒ ๋ณด๋‚ด๋Š” ํŽธ์ง€๋งŒํผ ํ‘œํ˜„์˜ ์š•๊ตฌ๋กœ ํ˜๋Ÿฌ +๋„˜์น˜๋Š” ๊ฒƒ๋„ ์—†๋‹ค. ๋ฌด์–ธ๊ฐ€๋ฅผ ํ‘œํ˜„ํ•˜์ง€ ์•Š๊ณ ๋Š” ๊ฒฌ๋”œ ์ˆ˜ ์—†๋Š” +์‹œ๊ฐ„๋“ค์ด ํŽธ์ง€๋ฅผ ์“ฐ๊ฒŒ ํ•œ๋‹ค. ๊ทธ๋Š” ๊ทธ๋…€์—๊ฒŒ ์ž์‹ ์˜ ์‚ฌ๋ž‘์ด ์–ผ๋งˆ๋‚˜ +์–ด๋ ต๊ณ  ์ง„์ •ํ•˜๋ฉฐ ์šด๋ช…์ ์ธ๊ฐ€๋ฅผ ์„ค๋ช…ํ•˜๊ณ  ์‹ถ์—ˆ๋‹ค. ํŽธ์ง€๋Š” ์‚ฌ๋žŒ์„ +์„ค๋“ํ•˜๊ฑฐ๋‚˜ ๋งคํ˜น์‹œํ‚ค๋Š” ๋ฐฉํŽธ์ด ๋ ์ง€๋„ ๋ชจ๋ฅธ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋ชจ๋“  +์‚ฌ๋ž‘์˜ ํŽธ์ง€๋Š” ๋งˆ์ง€๋ง‰ ์ˆœ๊ฐ„, ๋„๊ตฌ์ ์ด์ง€ ๋ชปํ•˜๋‹ค. ์„ธ์ƒ์˜ ๋ชจ๋“  +๊ธ€์“ฐ๊ธฐ๊ฐ€ ์ตœํ›„์˜ ์ˆœ๊ฐ„์—๋Š” ์ฒ˜์Œ์— ํ’ˆ์—ˆ๋˜ ์†Œ์†Œํ•œ ์˜๋„๋ฅผ ๋ฐฐ๋ฐ˜ +ํ•˜๋Š” ๊ฒƒ์ฒ˜๋Ÿผ. ๊ทธ ํ†ต์ œํ•  ์ˆ˜ ์—†๋Š” ์ต๋ช…์˜ ์š•๊ตฌ๊ฐ€ ๊ทธ ํŽธ์ง€์˜ +ํ˜„์‹ค์ ์ธ ๋ชฉํ‘œ๋ฅผ ์žŠ์–ด๋ฒ„๋ฆฌ๊ฒŒ ๋งŒ๋“ค๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ๊ทธ๋Ÿฐ ์ด์œ ๋กœ, ๋ชจ๋“  +์‚ฌ๋ž‘์˜ ํŽธ์ง€์—๋Š” ์•„๋ฌด ์ „์–ธ๋„ ๋“ค์–ด ์žˆ์ง€ ์•Š๋‹ค. +๊ฑฐ๊ธฐ์—๋Š” ๊ฒฐ์ •์ ์ธ ์ •๋ณด๋‚˜ ์ฃผ์žฅ์ด ๋“ค์–ด ์žˆ์ง€ ์•Š๋‹ค. ๋‹ค๋งŒ ๋‚ด +๊ณ ๋ฐฑ์„ ๋ˆ„๊ตฐ๊ฐ€๊ฐ€ ๋“ค์–ด์ค€๋‹ค๋Š” ์ถฉ๋งŒํ•œ ๋А๋‚Œ. ํฌ๋ฏธํ•œ ๋ถˆ๋น› ์•„๋ž˜์„œ +์Šค์Šค๋กœ ์˜ท์„ ๋ฒ—์–ด์•ผ ํ•  ๋•Œ์ฒ˜๋Ÿผ, ์ฃผ์ฒดํ•  ์ˆ˜ ์—†๋Š” ๋ถ€๋„๋Ÿฌ์›€ ๋”ฐ์œ„. +๊ณ ๋ฐฑ์ด๋ž€ ๊ฒฐ๊ตญ 2์ธ์นญ์„ ๊ฒฝ์œ ํ•˜์—ฌ 1์ธ์นญ์œผ๋กœ ๋Œ์•„์˜จ๋‹ค. ๊ทธ์˜ +๋“ค๋“๋Š” ๊ณ ๋ฐฑ์˜ ์–ธ์–ด๋“ค์€ ๊ณ ์Šค๋ž€ํžˆ ์ž์‹ ์—๊ฒŒ ๋Œ์•„์™”๋‹ค. ํ•œ๋™์•ˆ +๊ทธ๋Š”, ์‚ฌ๋ž‘ํ•˜๋Š” โ—‹โ—‹์—๊ฒŒ๋กœ ์‹œ์ž‘๋˜๋Š” ํŽธ์ง€๋ฅผ ์ž์ฃผ ์ผ๋‹ค. ๊ทธ๋…€๋Š” +๊ทธ์˜ ํŽธ์ง€๋ฅผ ์‚ฌ๋ž‘ํ–ˆ๋‹ค. ์ •ํ™•ํ•˜๊ฒŒ ๋งํ•˜๋ฉด โ€˜ํŽธ์ง€ ์†์˜ ๊ทธโ€™๋ฅผ ๊ทธ๋…€๋Š” +์‚ฌ๋ž‘ํ–ˆ๋‹ค. ํŽธ์ง€ ์†์—๋Š” ๊ทธ๊ฐ€ ์ฐพ์•„๋‚ธ ์ž์‹ ์˜ ๋˜ ๋‹ค๋ฅธ ์˜ํ˜ผ์ด +์žˆ์—ˆ๋‹ค. ๋˜ ๋‹ค๋ฅธ ์˜ํ˜ผ์˜ โ€˜๊ทธโ€™๋Š” ์ˆœ์ˆ˜ํ•œ ์—ด์ •๊ณผ ๋ ๋ชจ๋ฅผ ๋™๊ฒฝ๊ณผ +๊นŠ์€ ์ดํ•ด์‹ฌ์„ ๊ฐ€์ง„ ์กด์žฌ์˜€๋‹ค. ๊ทธ๋„ ์—ญ์‹œ ๊ทธ๋…€์ฒ˜๋Ÿผ ์ž์‹ ์˜ ํŽธ์ง€ +์† 1์ธ์นญ ํ™”์ž์—๊ฒŒ ๊นŠ์ด ๋งค๋ฃŒ๋˜์—ˆ๋‹ค. ํ•˜์ง€๋งŒ ๋„ˆ๋ฌด ๋ป”ํ•ด์„œ ๊ฐ€ํ˜น +ํ–ˆ๋˜ ์ง€๋ฆฌ๋ฉธ๋ ฌํ•œ ์‹œ๊ฐ„๋“ค ์†์—์„œ ๊ทธ๋Š” ํŽธ์ง€ ์†์˜ 1์ธ์นญ ์ฃผ์ฒด๋ฅผ +์žŠ์–ด๋ฒ„๋ ธ๋‹ค. +ํŽธ์ง€์กฐ์ฐจ ์“ธ ์ˆ˜ ์—†๋Š” ์‹œ๊ฐ„๋“ค์ด ๋ฌด์‹ฌํ•˜๊ฒŒ ์ง€๋‚˜๊ฐ€๊ณ , ๋‹ค์‹œ ํŽธ์ง€๋ฅผ +์“ฐ๊ณ  ์‹ถ์—ˆ์„ ๋•Œ, ๊ทธ๋Š” ์ด๋ฏธ โ€˜ํŽธ์ง€ ์†์˜ ๊ทธโ€™๊ฐ€ ๋˜์ง€ ๋ชปํ•œ๋‹ค๋Š” ๊ฒƒ์„ +์•Œ์•˜๋‹ค. ๊ทธ๋Š” โ€˜ํŽธ์ง€ ์†์˜ ๊ทธโ€™๋ฅผ ์—ฐ๊ธฐํ•˜๋Š” ๊ฒƒ์ด ๋ถ€๋„๋Ÿฌ์› ๊ณ , ์ž์‹ ์˜ +๋น„๋ฃจํ•จ์„ ๋ผ›์† ๊นŠ์ด ์‹ค๊ฐํ–ˆ๋‹ค. ๊ทธ๋Š” โ€˜์‚ฌ๋ž‘ํ•˜๋Š” โ—‹โ—‹์—๊ฒŒโ€™๋ผ๋Š” +ํŽธ์ง€๋ฅผ ์“ฐ๊ณ  ์‹ถ์–ด ํ•˜๋Š” ์ž์‹  ์†์˜ ์–ด๋–ค ๋Š™์ง€ ์•Š๋Š” ์˜ํ˜ผ์„, ๊ทธ +์ˆœ์ˆ˜ํ•œ ์ธ๊ฒฉ์„ ์™ธ๋ฉดํ•˜๊ณ  ์‹ถ์—ˆ๋‹ค. โ“‘๋ˆ„๊ตฐ๊ฐ€๊ฐ€ ๋“ฃ๊ธฐ๋ฅผ ๋ฐ”๋ผ๋Š” ๋ชจ๋“  +๊ณ ๋ฐฑ์ด๋ž€, ์œ„์„ ์ด ์•„๋‹ˆ๋ฉด ์œ„์•…์ด๋‹ค. +-์ด๊ด‘ํ˜ธ, ๏ฝข์ด์   ๋˜๋„๋ก ํŽธ์ง€ ์•ˆ ๋“œ๋ฆฌ๊ฒ ์Šต๋‹ˆ๋‹ค๏ฝฃ- +","{'question': '<๋ณด๊ธฐ>๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ (๋‹ค)๋ฅผ ์ดํ•ดํ•œ ๋‚ด์šฉ์œผ๋กœ ์ ์ ˆํ•˜์ง€ ์•Š์€\n๊ฒƒ์€?', 'choices': ['โ€˜์ต๋ช…์˜ ์š•๊ตฌโ€™๋ฅผ โ€˜ํ†ต์ œํ•  ์ˆ˜ ์—†โ€™๋‹ค๋Š” ๊ฒƒ์€ ์ƒ๋Œ€๋ฅผ ํ–ฅํ•œ โ€˜๊ทธโ€™์˜\n์‚ฌ๋ž‘์ด ์šด๋ช…์ ์ธ ๊ฒƒ์ด์–ด์„œ ์‚ฌ๋ž‘์„ ๋ฉˆ์ถœ ์ˆ˜ ์—†์Œ์„ ๋งํ•˜๋Š”๊ตฐ.', 'โ€˜์•„๋ฌด ์ „์–ธ๋„ ๋“ค์–ด ์žˆ์ง€ ์•Š๋‹คโ€™๋Š” ๊ฒƒ์€ โ€˜์ฒ˜์Œ์— ํ’ˆ์—ˆ๋˜ ์†Œ์†Œํ•œ\n์˜๋„โ€™๋ฅผ ์žŠ์Œ์œผ๋กœ์จ, ์ƒ๋Œ€๋ฅผ ํ–ฅํ•œ ๊ธ€์“ฐ๊ธฐ์˜ โ€˜ํ˜„์‹ค์ ์ธ ๋ชฉํ‘œโ€™๊ฐ€\n์‹คํŒจ๋กœ ๋Œ์•„๊ฐ”์Œ์„ ๋งํ•˜๋Š”๊ตฐ.', 'โ€˜2์ธ์นญ์„ ๊ฒฝ์œ ํ•˜์—ฌ 1์ธ์นญ์œผ๋กœ ๋Œ์•„์˜จ๋‹คโ€™๋Š” ๊ฒƒ์€ ํŽธ์ง€๊ฐ€ ์ƒ๋Œ€๋ฅผ\nํ–ฅํ•œ โ€˜๋„๊ตฌ์ โ€™ ๊ธฐ๋Šฅ์„ ํ•˜์ง€ ๋ชปํ•˜๊ณ  ์ž๊ธฐ ๊ณ ๋ฐฑ์— ๊ทธ์น˜๊ฒŒ ๋จ์„\n๋งํ•˜๋Š”๊ตฐ.', 'ํŽธ์ง€ ์†์˜ ๊ทธโ€™๋ฅผ ๊ทธ๋…€๋Š” ์‚ฌ๋ž‘ํ–ˆ๋‹คโ€™๋Š” ๊ฒƒ์€ ํŽธ์ง€๋ฅผ ๋ฐ›์€ ๊ทธ๋…€๊ฐ€\n์‚ฌ๋ž‘ํ•œ ์ƒ๋Œ€๋Š” ํŽธ์ง€ ์†์˜ โ€˜๋˜ ๋‹ค๋ฅธ ์˜ํ˜ผโ€™์ž„์„ ๋งํ•˜๋Š”๊ตฐ.', 'โ€˜์ž์‹ ์˜ ๋น„๋ฃจํ•จ์„ ๋ผ›์† ๊นŠ์ด ์‹ค๊ฐํ–ˆ๋‹คโ€™๋Š” ๊ฒƒ์€ ์‹ค์ œ ์ž์‹ ๊ณผ\n์ด์ƒํ™”๋œ ์ž์‹  ์‚ฌ์ด์˜ ๊ฐ„๊ทน์„ ์ž๊ฐํ•œ โ€˜๊ทธโ€™๊ฐ€ ๋ถ€๋„๋Ÿฌ์›€์— ๋น ์ ธ\n์žˆ์Œ์„ ๋งํ•˜๋Š”๊ตฐ.'], 'answer': '', 'question_plus': '(๋‹ค)์—์„œ ํŽธ์ง€๋Š” ๋ฐ›๋Š” ์‚ฌ๋žŒ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์“ฐ๋Š” ์‚ฌ๋žŒ ์ž์‹ ์„\nํ–ฅํ•œ ๊ฒƒ์ด๊ธฐ๋„ ํ•˜๋‹ค. ์ƒ๋Œ€์— ๋Œ€ํ•œ ์—ด๋ง์œผ๋กœ ์‚ฌ๋ž‘์˜ ํŽธ์ง€๋ฅผ\n์“ฐ์ง€๋งŒ ๊ฒฐ๊ตญ ๊ทธ๊ฒƒ์€ ์ž์‹ ์„ ํ‘œํ˜„ํ•˜๋Š” ๊ธ€์ด๋‹ค. ์ž์‹ ์„ ์ด์ƒํ™”\nํ•˜๋ ค๋Š” ์š•๊ตฌ์— ๋น ์ ธ ์žˆ๊ธฐ์— ํŽธ์ง€๋Š” โ€˜๊ทธ๋…€โ€™๊ฐ€ ์‚ฌ๋ž‘ํ•  ๋งŒํ•œ โ€˜๊ทธโ€™๋กœ\n์ฑ„์›Œ์ง„๋‹ค. ์‚ฌ๋ž‘์˜ ํŽธ์ง€๋ฅผ ๋ฐ›์€ โ€˜๊ทธ๋…€โ€™๋Š” โ€˜ํŽธ์ง€ ์†์˜ ๊ทธโ€™๋ฅผ\n์‚ฌ๋ž‘ํ•˜๊ณ , ํŽธ์ง€๋ฅผ ์“ฐ๋Š” โ€˜๊ทธโ€™๋„ โ€˜ํŽธ์ง€ ์†์˜ ๊ทธโ€™์—๊ฒŒ ๋งค๋ฃŒ๋˜์–ด\n์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด๋Ÿฐ ์‹์˜ ์ž๊ธฐ ๊ณ ๋ฐฑ์ด ์ง€์†๋  ์ˆ˜ ์—†๋Š” ๊นŒ๋‹ญ์€\n์ด ์ด์ƒํ™”๋œ โ€˜๊ทธโ€™์™€ ์‹ค์ œ์˜ โ€˜๊ทธโ€™ ์‚ฌ์ด์˜ ๊ฐ„๊ทน์ด ์ฃผ๋Š” ๋ถ€๋„๋Ÿฌ์›€\n๋•Œ๋ฌธ์ด๋‹ค.'}","(๋‹ค)์—์„œ ํŽธ์ง€๋Š” ๋ฐ›๋Š” ์‚ฌ๋žŒ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์“ฐ๋Š” ์‚ฌ๋žŒ ์ž์‹ ์„ +ํ–ฅํ•œ ๊ฒƒ์ด๊ธฐ๋„ ํ•˜๋‹ค. ์ƒ๋Œ€์— ๋Œ€ํ•œ ์—ด๋ง์œผ๋กœ ์‚ฌ๋ž‘์˜ ํŽธ์ง€๋ฅผ +์“ฐ์ง€๋งŒ ๊ฒฐ๊ตญ ๊ทธ๊ฒƒ์€ ์ž์‹ ์„ ํ‘œํ˜„ํ•˜๋Š” ๊ธ€์ด๋‹ค. ์ž์‹ ์„ ์ด์ƒํ™” +ํ•˜๋ ค๋Š” ์š•๊ตฌ์— ๋น ์ ธ ์žˆ๊ธฐ์— ํŽธ์ง€๋Š” โ€˜๊ทธ๋…€โ€™๊ฐ€ ์‚ฌ๋ž‘ํ•  ๋งŒํ•œ โ€˜๊ทธโ€™๋กœ +์ฑ„์›Œ์ง„๋‹ค. ์‚ฌ๋ž‘์˜ ํŽธ์ง€๋ฅผ ๋ฐ›์€ โ€˜๊ทธ๋…€โ€™๋Š” โ€˜ํŽธ์ง€ ์†์˜ ๊ทธโ€™๋ฅผ +์‚ฌ๋ž‘ํ•˜๊ณ , ํŽธ์ง€๋ฅผ ์“ฐ๋Š” โ€˜๊ทธโ€™๋„ โ€˜ํŽธ์ง€ ์†์˜ ๊ทธโ€™์—๊ฒŒ ๋งค๋ฃŒ๋˜์–ด +์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด๋Ÿฐ ์‹์˜ ์ž๊ธฐ ๊ณ ๋ฐฑ์ด ์ง€์†๋  ์ˆ˜ ์—†๋Š” ๊นŒ๋‹ญ์€ +์ด ์ด์ƒํ™”๋œ โ€˜๊ทธโ€™์™€ ์‹ค์ œ์˜ โ€˜๊ทธโ€™ ์‚ฌ์ด์˜ ๊ฐ„๊ทน์ด ์ฃผ๋Š” ๋ถ€๋„๋Ÿฌ์›€ +๋•Œ๋ฌธ์ด๋‹ค.",1,2,False,[],2 +2025-korean-28,"ใ‰ ๋ถˆํŽธ์Šค๋Ÿฐ ์ผ์ด ํ•œ๋‘ ๊ฐ€์ง€๊ฐ€ ์•„๋‹ˆ์—ˆ๋‹ค. ํ•˜์ง€๋งŒ ํ—ˆ์›์€ ๊ทธ๋ ‡๊ฒŒ +๊ทธ๋Š” ์–ด๋А๋ง ๋ฐฐ๊ผฝ์— ๋Œ€ํ•ด ๋‹น๋‹นํ•œ ์ผ๊ฐ€๊ฒฌ์„ ์ด๋ฃฌ ๋ฐฐ๊ผฝ ์ „๋ฌธ๊ฐ€๊ฐ€ +๋˜์–ด ๊ฐ€๊ณ  ์žˆ์—ˆ๋‹ค. +ใ‰ฃ์–ด๋А ํ•ด ์—ฌ๋ฆ„์ด์—ˆ๋‹ค. ํ•˜๋‹ˆ๊นŒ ๊ทธ๊ฒƒ์€ ํ—ˆ์›์ด ์ž์‹ ์˜ ๋ฐฐ๊ผฝ์„ +์Šค์Šค๋กœ ์ฃผ์˜ํ•˜๊ณ  ๊ณ ํ†ต์„ ๊ฐ๋‚ดํ•ด ๋ƒˆ๊ธฐ ๋•Œ๋ฌธ์— ์ž์‹ ์˜ ๋น„๋ฐ€์„ +๋‚จ ์•ž์— ๊ฐ์ชฝ๊ฐ™์ด ์ˆจ๊ฒจ ๋‚˜๊ฐˆ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์•„๋ฌด๋„ ๊ทธ์˜ ๋น„๋ฐ€์„ +๋ˆˆ์น˜์ฑˆ ์‚ฌ๋žŒ์ด ์—†์—ˆ๋‹ค. ๋น„๋ฐ€์ด ํƒ„๋กœ ๋‚˜์ง€ ์•Š๋Š” ํ•œ ๊ทธ์˜ ์ผ์ƒ +์ƒํ™œ์€ ๋” ์ด์ƒ ๋ถˆํŽธ์„ ๊ฒช์„ ํ•„์š”๋„ ์—†์—ˆ๋‹ค. ์ธ์ฒด ์ƒ๋ฆฌ๋‚˜ ํ•ด๋ถ€ํ•™ +์„œ์  ๊ฐ™์€ ๊ฑธ ๋’ค์ ธ ๋ด๋„ ์„ฑ์ธ์˜ ๋ฐฐ๊ผฝ์€ ๊ฑฐ์˜ ์•„๋ฌด๋Ÿฐ ๊ธฐ๋Šฅ๋„ +์ˆ˜ํ–‰ํ•˜์ง€ ์•Š์Œ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ ์–ด๋„ ๊ทธ์˜ ์™ธ๋ชจ๋‚˜ ๋ฐ”๊นฅ ์ƒํ™œ์€ +์ •์ƒ์„ ์œ ์ง€ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๊ทธ ์ ๋งŒ์ด๋ผ๋„ ๋ฌด์ฒ™ ๋‹คํ–‰์ด์—ˆ๋‹ค. ๊ทธ๋Š” +์ผ๋‹จ ์•ˆ๋„์˜ ํ•œ์ˆจ์„ ๋‚ด์‰ฌ์—ˆ๋‹ค. +ใ‰กโ€•๊ทธ๊นŸ ๋†ˆ์˜ ๋ฐฐ๊ผฝ, ์•ˆ ๊ฐ€์ง€๊ณ  ์žˆ์Œ ์–ด๋•Œ. +์žƒ์–ด๋ฒ„๋ฆฌ๊ณ  ๋‚˜์„œ ๋ถˆํŽธํ•˜๊ธฐ ๊ทธ์ง€์—†๋Š” ์„ธ ๋ฒˆ์งธ์˜ ์—ฌ๋ฆ„์„ ๋งž๊ณ  ์žˆ์„ +๋•Œ์˜€๋‹ค. ๊ทธ๋Š” ๋ฌผ๋ก  ๋ฐฐ๊ผฝ์„ ์žƒ์–ด๋ฒ„๋ฆฐ ์ž์‹ ์— ๋Œ€ํ•ด ์•„์ง๋„ ์™„์ „ํžŒ +๊ทธ์ฏค ์ฒด๋…์„ ํ•˜๊ณ  ๋  ์ˆ˜ ์žˆ์œผ๋ฉด ๋ฐฐ๊ผฝ์— ๊ด€ํ•œ ์ผ๋“ค์„ ์žŠ์–ด๋ฒ„๋ฆฌ๋ ค +ํ–ˆ๋‹ค. ใ‰ข์ž์‹ ์œผ๋กœ๋ถ€ํ„ฐ ๋ฐฐ๊ผฝ์ด ์‚ฌ๋ผ์ ธ ๋ฒ„๋ฆฐ ์‚ฌ์‹ค์„, ๊ทธ๋ฆฌ๊ณ  ๊ทธ +๋•Œ๋ฌธ์— ์ƒ๊ธด ๋ชจ๋“  ๋ถˆํŽธ์„ ์žŠ๊ณ , ๊ทธ ๋ฐฐ๊ผฝ ์—†๋Š” ์ƒํ™œ์— ์Šค์Šค๋กœ +์ต์ˆ™ํ•ด์ ธ ๋ฒ„๋ฆฌ๊ธฐ๋ฅผ ๋ฐ”๋ผ ๋งˆ์ง€์•Š์•˜๋‹ค. ํ•˜์ง€๋งŒ ๋ฌธ์ œ๋Š” ๊ทธ๋ ‡๊ฒŒ +๊ฐ„๋‹จํ•˜์ง€ ์•Š์•˜๋‹ค. ์•„๋ฌด๋ฆฌ ์ผ์ƒ์ƒํ™œ์—์„  ๋“œ๋Ÿฌ๋‚˜๊ฒŒ ๋ถˆํŽธํ•œ ์ ์ด +์—†๋‹ค ํ•ด๋„ ๊ทธ๋Š” ์—ญ์‹œ ๋ฐฐ๊ผฝ์ด ์—†๋Š” ์ž์‹ ์— ๋Œ€ํ•ด ์ข€์ฒ˜๋Ÿผ ์ต์ˆ™ํ•ด์งˆ +์ˆ˜๊ฐ€ ์—†์—ˆ๋‹ค. ๊ทธ๋Š” ์ž๊พธ๋งŒ ํ—ˆ์ „ํ•ด์„œ ๊ฒฌ๋”œ ์ˆ˜๊ฐ€ ์—†์–ด์ง€๊ณค ํ–ˆ๋‹ค. +์žˆ๋А๋‹ˆ๋ผ ์—ฌ๊ธฐ๊ณ  ์ง€๋‚ผ ๋•Œ๋Š” ๊ทธ์ฒ˜๋Ÿผ ๋ฌด์‹ฌ์Šค๋Ÿฝ๋˜ ์ผ์ด ๊ทธ๋Ÿฐ ์‹์œผ๋กœ +ํ•œ๋ฒˆ ์˜์‹์˜ ๋ˆ์„ ๊ฑด๋“œ๋ ค ์˜ค์ž ํ—ˆ์›์˜ ์ƒ๋…์€ ์ž ์‹œ๋„ ๊ทธ ์žƒ์–ด +๋ฒ„๋ฆฐ ๋ฐฐ๊ผฝ์—์„œ ๋– ๋‚˜ ์žˆ์„ ์ˆ˜๊ฐ€ ์—†์—ˆ๋‹ค. +๊ทธ๋Š” ๋งˆ์นจ๋‚ด ํšŒ์‚ฌ ์ถœ๊ทผ๋งˆ์ € ๋‹จ๋…ํ•˜๊ธฐ์— ์ด๋ฅด๋ €๋‹ค. ๊ทธ๋Ÿฌ์ž ์‹ ํ†ต +ํ•˜๊ฒŒ๋„ ๋Šฆ์ž  ๋ฒ„๋ฆ‡์ด ๊นจ๋—์ด ์ž์ทจ๋ฅผ ๊ฐ์ถฐ ๋ฒ„๋ ธ๋‹ค. ๊ทธ๋Š” ๋ˆˆ๋งŒ ๋œจ๋ฉด +์‚ฌ๋ผ์ ธ ์—†์–ด์ง„ ๋ฐฐ๊ผฝ ๋•Œ๋ฌธ์— ๊ธฐ๋ถ„์ด ํ—ˆ์ „ํ–ˆ๊ณ , ๊ทธ๋Ÿฌ๋ฉด ๊ทธ ํ—ˆ๋ง๊ฐ์„ +์ซ“๊ธฐ ์œ„ํ•ด ๋ฐฐ๊ผฝ์— ๊ด€ํ•œ ๋์—†๋Š” ์ƒ๋…๋“ค์„ ์Œ“๊ธฐ ์‹œ์ž‘ํ–ˆ๋‹ค. +(์ค‘๋žต) +๊ทธ๋ฆฌํ•˜์—ฌ ๋ฐฐ๊ผฝ์— ๊ด€ํ•œ ํ—ˆ์›์˜ ์ง€์‹๊ณผ ์‚ฌ๋…์€ ์ž๊พธ ๋” ์‹ฌ์˜คํ•˜ +๊ณ  ์ถ”์ƒ์ ์ธ ๊ฒƒ์ด ๋˜์–ด ๊ฐ”๋‹ค. ๊ทธ์—๊ฒŒ๋Š” ์–ด๋А๋ง ๊ทธ ๋‚˜๋ฆ„์˜ ๋…ํŠนํ•œ +๋ฐฐ๊ผฝ๋ก  ๊ฐ™์€ ๊ฒƒ์ด ์œค๊ณฝ์„ ์ง€์–ด ๊ฐ€๊ณ  ์žˆ์—ˆ๋‹ค. ํ•˜์ง€๋งŒ ๊ทธ๋Ÿฌ๋ฉด ๊ทธ๋Ÿด +์ˆ˜๋ก ํ—ˆ์›์€ ๋”์šฑ๋” ํ—ˆ์ „ํ•ด์ง€๊ณ , ์•„๋ฌด ๊ณณ์—๋„ ๋ฐœ์ด ๋‹ฟ์•„ ์žˆ๋Š” ๊ฒƒ +๊ฐ™์ง€ ์•Š๊ณ , ํ˜ผ์ž์„œ ์™ธ๋กญ๊ฒŒ ํ—ˆ๊ณต์„ ๋‘ฅ๋‘ฅ ๋– ๋‹ค๋‹ˆ๊ณ  ์žˆ๋Š” ๊ฒƒ์ฒ˜๋Ÿผ +๋А๊ปด์กŒ๋‹ค. ๊ทธ๋Ÿฌ๋ฉด ๊ทธ๋Š” ๋˜ ๊ฑฐ๋“ญ ๊ทธ ํ—ˆ๋ง๊ฐ์„ ์ซ“๊ธฐ ์œ„ํ•ด ์ž์‹ ์˜ +๋ฐฐ๊ผฝ๋ก ์„ ์™„๋ฒฝํ•˜๊ฒŒ ๋ฐœ์ „์‹œ์ผœ ๋‚˜๊ฐ”๋‹ค. ๋งˆ์น˜ ๊ทธ๋ ‡๊ฒŒ ํ•˜์—ฌ ๊ทธ๋Š” +์ž์‹ ์˜ ์‚ฌ๋… ์†์—์„œ ์žƒ์–ด๋ฒ„๋ฆฐ ๋ฐฐ๊ผฝ์„ ๋˜์ฐพ์•„๋‚ด๊ณ , ๊ทธ๊ฒƒ์œผ๋กœ ๊ทธ +์‹ค๋ฌผ์„ ๋Œ€์‹ ํ•ด ์–ด๋–ค ์‹์œผ๋กœ๋“  ์ž์‹ ๊ณผ ์„ธ์ƒ ๊ฐ„์— ํฐ ๋ถˆํŽธ์ด ์—†๋„๋ก +ํ™”ํ•ด์‹œํ‚ค๊ณ  ๊ทธ๊ฒƒ์œผ๋กœ ๊ทธ ๋‚œ๊ฐ์Šค๋Ÿฐ ํ—ˆ๋ง๊ฐ์„ ์ฑ„์šฐ๋ ค๋Š” ๋“ฏ์ด. ๊ทธ์˜ +๋ฐฐ๊ผฝ๋ก ์€ ๊ฐ€๋ น ์ด๋Ÿฐ ์‹์œผ๋กœ๊นŒ์ง€ ๋ฐœ์ „๋˜์–ด ์žˆ์—ˆ๋‹ค. + +โ€•์šฐ๋ฆฌ๋Š” ๋ˆ„๊ตฌ๋‚˜ ๋ฐฐ๊ผฝ์„ ๊ฐ€์ง€๊ณ  ์žˆ๋‹คโ€ฆโ€ฆ ์šฐ๋ฆฌ๋Š” ์šฐ๋ฆฌ๋“ค์˜ +์–ด๋จธ๋‹ˆ๋กœ๋ถ€ํ„ฐ ํƒฏ์ค„์ด ๋Š์–ด์ง€๋Š” ์ˆœ๊ฐ„ ์ด ์šฐ์ฃผ์˜ ํ•œ ๋‹จ์ž(ๅ–ฎๅญ)๋กœ์„œ +๊ณ ๋…ํ•˜๊ฒŒ ์กด์žฌํ•˜๊ฒŒ ๋˜์—ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์šฐ๋ฆฌ๋Š” ์˜์›ํžˆ ๊ทธ ํƒฏ์ค„์˜ +๊ธฐ์–ต์„ ์žŠ์ง€ ์•Š๋Š”๋‹ค. ์šฐ๋ฆฌ ์˜ํ˜ผ์€ ์–ธ์ œ๊นŒ์ง€๋‚˜ ๊ทธ ์–ด๋จธ๋‹ˆ์˜ ํƒฏ์ค„๊ณผ +์ด์–ด์ง€๋ ค ํ•˜๊ณ , ๋˜๋‹ค์‹œ ๊ทธ ์–ด๋จธ๋‹ˆ์˜ ์–ด๋จธ๋‹ˆ์˜ ํƒฏ์ค„๊ณผ ์ด์–ด์ ธ +๋‚˜๊ฐ€๋ฉด์„œ ์šฐ๋ฆฌ ์กด์žฌ๋ฅผ ์„ค๋ช…ํ•˜๊ณ  ๊ทผ์›์„ ๋ฐํ˜€ ๋‚˜๊ฐ€๋ฉฐ, ๋งˆ์นจ๋‚ด๋Š” +๋งˆ์ง€๋ง‰ ์–ด๋จธ๋‹ˆ์˜ ํƒฏ์ค„์ด ์ด์–ด์ง€๋Š” ์šฐ๋ฆฌ๋“ค์˜ ์šฐ์ฃผ์™€ ๋งŒ๋‚˜๊ฒŒ +๋œ๋‹คโ€ฆโ€ฆ ์šฐ๋ฆฌ์˜ ๋ฐฐ๊ผฝ์€ ์šฐ๋ฆฌ๊ฐ€ ๊ทธ ๋งˆ์ง€๋ง‰ ์šฐ์ฃผ์™€ ๋งŒ๋‚˜๊ณ ์ž ํ•˜๋Š” +ํ–ฅ์ˆ˜์˜ ํ‘œ์ƒ์ด๋ฉฐ ๊ฐ€๋Šฅ์„ฑ์˜ ์ƒ์ง•์ด๋ฉฐ ์กด์žฌ์˜ ๋น„๋ฐ€๋กœ ๋‚˜์•„๊ฐ€๋Š” +ํ˜•์ด์ƒํ•™์ด๋‹ค. ๊ทธ ๋น„๋ฐ€์˜ ๋ฌธ์ด๋‹คโ€ฆโ€ฆ +๊ทธ๋Š” ์–ด๋А๋ง ๋ฐฐ๊ผฝ์— ๋Œ€ํ•ด ๋‹น๋‹นํ•œ ์ผ๊ฐ€๊ฒฌ์„ ์ด๋ฃฌ ๋ฐฐ๊ผฝ ์ „๋ฌธ๊ฐ€๊ฐ€ +๋˜์–ด ๊ฐ€๊ณ  ์žˆ์—ˆ๋‹ค. +ใ‰ฃ์–ด๋А ํ•ด ์—ฌ๋ฆ„์ด์—ˆ๋‹ค. ํ•˜๋‹ˆ๊นŒ ๊ทธ๊ฒƒ์€ ํ—ˆ์›์ด ์ž์‹ ์˜ ๋ฐฐ๊ผฝ์„ +์žƒ์–ด๋ฒ„๋ฆฌ๊ณ  ๋‚˜์„œ ๋ถˆํŽธํ•˜๊ธฐ ๊ทธ์ง€์—†๋Š” ์„ธ ๋ฒˆ์งธ์˜ ์—ฌ๋ฆ„์„ ๋งž๊ณ  ์žˆ์„ +๋•Œ์˜€๋‹ค. ๊ทธ๋Š” ๋ฌผ๋ก  ๋ฐฐ๊ผฝ์„ ์žƒ์–ด๋ฒ„๋ฆฐ ์ž์‹ ์— ๋Œ€ํ•ด ์•„์ง๋„ ์™„์ „ํžŒ +์ต์ˆ™ํ•ด์ง€์งˆ ๋ชปํ•˜๊ณ  ์žˆ์—ˆ๋‹ค. ๊ทธ์˜ ์‚ฌ๋… ์—ญ์‹œ ์–ธ์ œ๋‚˜ ๊ทธ ๋ˆˆ์— +๋ณด์ด์ง€ ์•Š๋Š” ๋ฐฐ๊ผฝ์— ๋งค๋‹ฌ๋ ค ๊ฑฐ๊ธฐ์—์„œ๋ฐ–์—๋Š” ์˜์˜ ๋” ์ด์ƒ ์ž์œ  +๋กœ์›Œ์งˆ ์ˆ˜๊ฐ€ ์—†์—ˆ๋‹ค. ๊ทธ ๋Œ€์‹  ํ—ˆ์›์€ ์ด์ œ ๊ทธ ์ž์‹ ์˜ ๋ฐฐ๊ผฝ๋ก ์— +๋Œ€ํ•ด์„  ๋งค์šฐ ํ™•๊ณ ํ•œ ๊ฒฝ์ง€์— ๋„๋‹ฌํ•ด ์žˆ์—ˆ๋‹ค. +๊ทธ๋Ÿด ์ฆˆ์Œ์ด์—ˆ๋‹ค. ํ—ˆ์›์€ ๋ฌธ๋“ ์„ธ์ƒ ์‚ฌ๋žŒ๋“ค์ด ์ˆ˜์ƒ์ฉ์–ด์ง€๊ธฐ +์‹œ์ž‘ํ–ˆ๋‹ค. ์–ด๋А ๋•Œ๋ถ€ํ„ด์ง€๋Š” ํ™•์‹คํžˆ ์•Œ ์ˆ˜ ์—†์—ˆ์ง€๋งŒ, ์„ธ์ƒ ์‚ฌ๋žŒ๋“ค +์—ญ์‹œ ๋ฌด์Šจ ์ด์œ ์—์„ ์ง€ ์ด ์ธ๊ฐ„ ์žฅ๊ธฐ์˜ ํ•œ ์กฐ๊ทธ๋งŒ ํ”์ ์— ๋Œ€ํ•ด +์‹ฌ์ƒ์ฐฎ์€ ๊ด€์‹ฌ์„ ๋‚˜ํƒ€๋‚ด๊ธฐ ์‹œ์ž‘ํ•œ ๊ฒƒ์ด๋‹ค. ๋ฐฐ๊ผฝ์— ๋Œ€ํ•œ ์‚ฌ๋žŒ๋“ค์˜ +๊ด€์‹ฌ ์—ญ์‹œ ๊ธฐ์™•๋ถ€ํ„ฐ ์žˆ์–ด ์˜จ ๊ฒƒ์„ ์—ฌํƒœ๊นŒ์ง€ ์„œ๋กœ ๋ชจ๋ฅด๊ณ  ์ง€๋‚ด +์˜ค๋‹ค๊ฐ€ ๋น„๋กœ์†Œ ์–ด๋–ค ๊ธฐ๋ฏธ๋ฅผ ์•Œ์•„์ฐจ๋ฆฌ๊ฒŒ ๋œ ๊ฒƒ์ธ์ง€, ํ˜น์€ ์‚ฌ๋žŒ๋“ค๋กœ +ํ•˜์—ฌ๊ธˆ ๊ทธ๋Ÿฐ ๊ด€์‹ฌ์„ ๋‚ด๋ณด์ด๊ฒŒ ํ•  ๋งŒํ•œ ๋ฌด์Šจ ์šฐ์—ฐ์ฐฎ์€ ๊ณ„๊ธฐ๊ฐ€ +๋งˆ๋ จ๋˜์—ˆ๋Š”์ง€๋Š” ํ™•์‹ค์น˜๊ฐ€ ์•Š์•˜๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๋ฌด์—‡ ๋•Œ๋ฌธ์— ์‚ฌ๋žŒ๋“ค +์—๊ฒŒ์„œ ๊ทธ๋Ÿฐ ๊ด€์‹ฌ์ด ์‹œ์ž‘๋˜์—ˆ๋Š”์ง€ ๊ทธ ์ด์œ ๋ฅผ ์•Œ ์ˆ˜๋„ ์—†์—ˆ๋‹ค. +ํ•˜์ง€๋งŒ ๊ทธ๊ฒƒ์€ ์–ด์จŒ๋“  ์‚ฌ์‹ค์ด์—ˆ๋‹ค. ์ฃผ์˜๋ฅผ ๊ธฐ์šธ์—ฌ ๋ณด๋‹ˆ ๊ด€์‹ฌ์˜ +์ •๋„๋„ ์—ฌ๊ฐ„์ด ์•„๋‹ˆ์—ˆ๋‹ค. ํ•œ๋‘ ์‚ฌ๋žŒ, ํ•œ๋‘ ๊ณณ์—์„œ๋งŒ ๋‚˜ํƒ€๋‚œ +ํ˜„์ƒ์ด ์•„๋‹ˆ์—ˆ๋‹ค. ๊ทธ๊ฒƒ์€ ์ด๋ฏธ ์ผ๋ฐ˜์ ์ธ ํ˜„์ƒ์ด ๋˜์–ด ๊ฐ€๊ณ  +์žˆ์—ˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๊ทธ๋ ‡๋“ฏ ๋ฐฐ๊ผฝ ์ด์•ผ๊ธฐ๊ฐ€ ์ผ๋ฐ˜ํ™”์˜ ๊ธฐ๋ฏธ๋ฅผ ์—ฟ๋ณด์ด๊ธฐ +์‹œ์ž‘ํ•˜์ž ์‚ฌ๋žŒ๋“ค์€ ์ด์ œ ๊ทธ๊ฑธ ์‹ ํ˜ธ๋กœ ์•„๋ฌด ํ‰ํ—ˆ๋ฌผ ์—†์ด ํ„ฐ๋†“๊ณ  +์ง€๊ป„์ด๊ฑฐ๋‚˜ ์‹ ๋ฌธ, ์žก์ง€ ๊ฐ™์€ ๋ฐ์„œ ์ง„์ง€ํ•˜๊ฒŒ ๋…ผ์˜์˜ ๋Œ€์ƒ์„ +์‚ผ๊ธฐ๋„ ํ•˜์˜€๋‹ค. ใ‰ค๋ฐฐ๊ผฝ์— ๊ด€ํ•œ ๋…ผ์˜๊ฐ€ ๊ทธ๋ ‡๋“ฏ ๊ฐ‘์ž๊ธฐ +์‹œ์ค‘ ์ผ๋ฐ˜์—๊นŒ์ง€ ์„ฑํ–‰ํ•˜๊ธฐ ์‹œ์ž‘ํ•œ ๊ฒƒ์ด๋‹ค. +๊ธฐ๋ฌ˜ํ•œ ํ˜„์ƒ์ด์—ˆ๋‹ค. +-์ด์ฒญ์ค€, ๏ฝข๋ฐฐ๊ผฝ์„ ์ฃผ์ œ๋กœ ํ•œ ๋ณ€์ฃผ๊ณก๏ฝฃ +","{'question': 'ใ‰ ๏ฝžใ‰ค์˜ ์„œ์ˆ  ๋ฐฉ์‹์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ๊ฐ€์žฅ ์ ์ ˆํ•œ ๊ฒƒ์€?', 'choices': ['ใ‰ :๋ˆ„๊ตฌ์˜ ์ƒ๊ฐ์„ ๋ˆ„๊ฐ€ ๋งํ•˜๋Š”์ง€ ๋ช…์‹œํ•œ ํ‘œํ˜„์„ ๋‚˜ํƒ€๋‚ด์–ด\n์„œ์ˆ ํ•˜๊ณ  ์žˆ๋‹ค.', 'ใ‰ก:์ธ๋ฌผ์˜ ์ƒ๊ฐ์„ ์„œ์ˆ ์ž๊ฐ€ ํ‰๊ฐ€ํ•˜๋ฉฐ ๊ทธ ์‹ฌํ™”๋œ ์˜๋ฏธ๋ฅผ ํ•จ์ถ•ํ•˜์—ฌ\n์„œ์ˆ ํ•˜๊ณ  ์žˆ๋‹ค.', 'ใ‰ข:์ธ๋ฌผ์˜ ์˜์‹์„ ์ธ๋ฌผ ์ž์‹ ์˜ ์ƒ์ƒํ•œ ๋ชฉ์†Œ๋ฆฌ๋ฅผ ํ†ตํ•ด ์„œ์ˆ \nํ•˜๊ณ  ์žˆ๋‹ค.', 'ใ‰ฃ:์ธ๋ฌผ์˜ ์ƒํ™ฉ์— ๊ด€๋ จ๋œ ์ •๋ณด๋ฅผ ๋ถ€๊ฐ€ํ•˜์—ฌ ์„œ์ˆ ํ•˜๊ณ  ์žˆ๋‹ค.', 'ใ‰ค:์ธ๋ฌผ ํ–‰๋™์˜ ์ง„ํ–‰ ๊ณผ์ •์„ ์ˆœ์ฐจ์ ์œผ๋กœ ์„œ์ˆ ํ•˜๊ณ  ์žˆ๋‹ค.'], 'answer': ''}",,4,4,True,[],4 +2025-korean-29,"ใ‰ ๋ถˆํŽธ์Šค๋Ÿฐ ์ผ์ด ํ•œ๋‘ ๊ฐ€์ง€๊ฐ€ ์•„๋‹ˆ์—ˆ๋‹ค. ํ•˜์ง€๋งŒ ํ—ˆ์›์€ ๊ทธ๋ ‡๊ฒŒ +๊ทธ๋Š” ์–ด๋А๋ง ๋ฐฐ๊ผฝ์— ๋Œ€ํ•ด ๋‹น๋‹นํ•œ ์ผ๊ฐ€๊ฒฌ์„ ์ด๋ฃฌ ๋ฐฐ๊ผฝ ์ „๋ฌธ๊ฐ€๊ฐ€ +๋˜์–ด ๊ฐ€๊ณ  ์žˆ์—ˆ๋‹ค. +ใ‰ฃ์–ด๋А ํ•ด ์—ฌ๋ฆ„์ด์—ˆ๋‹ค. ํ•˜๋‹ˆ๊นŒ ๊ทธ๊ฒƒ์€ ํ—ˆ์›์ด ์ž์‹ ์˜ ๋ฐฐ๊ผฝ์„ +์Šค์Šค๋กœ ์ฃผ์˜ํ•˜๊ณ  ๊ณ ํ†ต์„ ๊ฐ๋‚ดํ•ด ๋ƒˆ๊ธฐ ๋•Œ๋ฌธ์— ์ž์‹ ์˜ ๋น„๋ฐ€์„ +๋‚จ ์•ž์— ๊ฐ์ชฝ๊ฐ™์ด ์ˆจ๊ฒจ ๋‚˜๊ฐˆ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์•„๋ฌด๋„ ๊ทธ์˜ ๋น„๋ฐ€์„ +๋ˆˆ์น˜์ฑˆ ์‚ฌ๋žŒ์ด ์—†์—ˆ๋‹ค. ๋น„๋ฐ€์ด ํƒ„๋กœ ๋‚˜์ง€ ์•Š๋Š” ํ•œ ๊ทธ์˜ ์ผ์ƒ +์ƒํ™œ์€ ๋” ์ด์ƒ ๋ถˆํŽธ์„ ๊ฒช์„ ํ•„์š”๋„ ์—†์—ˆ๋‹ค. ์ธ์ฒด ์ƒ๋ฆฌ๋‚˜ ํ•ด๋ถ€ํ•™ +์„œ์  ๊ฐ™์€ ๊ฑธ ๋’ค์ ธ ๋ด๋„ ์„ฑ์ธ์˜ ๋ฐฐ๊ผฝ์€ ๊ฑฐ์˜ ์•„๋ฌด๋Ÿฐ ๊ธฐ๋Šฅ๋„ +์ˆ˜ํ–‰ํ•˜์ง€ ์•Š์Œ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ ์–ด๋„ ๊ทธ์˜ ์™ธ๋ชจ๋‚˜ ๋ฐ”๊นฅ ์ƒํ™œ์€ +์ •์ƒ์„ ์œ ์ง€ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๊ทธ ์ ๋งŒ์ด๋ผ๋„ ๋ฌด์ฒ™ ๋‹คํ–‰์ด์—ˆ๋‹ค. ๊ทธ๋Š” +์ผ๋‹จ ์•ˆ๋„์˜ ํ•œ์ˆจ์„ ๋‚ด์‰ฌ์—ˆ๋‹ค. +ใ‰กโ€•๊ทธ๊นŸ ๋†ˆ์˜ ๋ฐฐ๊ผฝ, ์•ˆ ๊ฐ€์ง€๊ณ  ์žˆ์Œ ์–ด๋•Œ. +์žƒ์–ด๋ฒ„๋ฆฌ๊ณ  ๋‚˜์„œ ๋ถˆํŽธํ•˜๊ธฐ ๊ทธ์ง€์—†๋Š” ์„ธ ๋ฒˆ์งธ์˜ ์—ฌ๋ฆ„์„ ๋งž๊ณ  ์žˆ์„ +๋•Œ์˜€๋‹ค. ๊ทธ๋Š” ๋ฌผ๋ก  ๋ฐฐ๊ผฝ์„ ์žƒ์–ด๋ฒ„๋ฆฐ ์ž์‹ ์— ๋Œ€ํ•ด ์•„์ง๋„ ์™„์ „ํžŒ +๊ทธ์ฏค ์ฒด๋…์„ ํ•˜๊ณ  ๋  ์ˆ˜ ์žˆ์œผ๋ฉด ๋ฐฐ๊ผฝ์— ๊ด€ํ•œ ์ผ๋“ค์„ ์žŠ์–ด๋ฒ„๋ฆฌ๋ ค +ํ–ˆ๋‹ค. ใ‰ข์ž์‹ ์œผ๋กœ๋ถ€ํ„ฐ ๋ฐฐ๊ผฝ์ด ์‚ฌ๋ผ์ ธ ๋ฒ„๋ฆฐ ์‚ฌ์‹ค์„, ๊ทธ๋ฆฌ๊ณ  ๊ทธ +๋•Œ๋ฌธ์— ์ƒ๊ธด ๋ชจ๋“  ๋ถˆํŽธ์„ ์žŠ๊ณ , ๊ทธ ๋ฐฐ๊ผฝ ์—†๋Š” ์ƒํ™œ์— ์Šค์Šค๋กœ +์ต์ˆ™ํ•ด์ ธ ๋ฒ„๋ฆฌ๊ธฐ๋ฅผ ๋ฐ”๋ผ ๋งˆ์ง€์•Š์•˜๋‹ค. ํ•˜์ง€๋งŒ ๋ฌธ์ œ๋Š” ๊ทธ๋ ‡๊ฒŒ +๊ฐ„๋‹จํ•˜์ง€ ์•Š์•˜๋‹ค. ์•„๋ฌด๋ฆฌ ์ผ์ƒ์ƒํ™œ์—์„  ๋“œ๋Ÿฌ๋‚˜๊ฒŒ ๋ถˆํŽธํ•œ ์ ์ด +์—†๋‹ค ํ•ด๋„ ๊ทธ๋Š” ์—ญ์‹œ ๋ฐฐ๊ผฝ์ด ์—†๋Š” ์ž์‹ ์— ๋Œ€ํ•ด ์ข€์ฒ˜๋Ÿผ ์ต์ˆ™ํ•ด์งˆ +์ˆ˜๊ฐ€ ์—†์—ˆ๋‹ค. ๊ทธ๋Š” ์ž๊พธ๋งŒ ํ—ˆ์ „ํ•ด์„œ ๊ฒฌ๋”œ ์ˆ˜๊ฐ€ ์—†์–ด์ง€๊ณค ํ–ˆ๋‹ค. +์žˆ๋А๋‹ˆ๋ผ ์—ฌ๊ธฐ๊ณ  ์ง€๋‚ผ ๋•Œ๋Š” ๊ทธ์ฒ˜๋Ÿผ ๋ฌด์‹ฌ์Šค๋Ÿฝ๋˜ ์ผ์ด ๊ทธ๋Ÿฐ ์‹์œผ๋กœ +ํ•œ๋ฒˆ ์˜์‹์˜ ๋ˆ์„ ๊ฑด๋“œ๋ ค ์˜ค์ž ํ—ˆ์›์˜ ์ƒ๋…์€ ์ž ์‹œ๋„ ๊ทธ ์žƒ์–ด +๋ฒ„๋ฆฐ ๋ฐฐ๊ผฝ์—์„œ ๋– ๋‚˜ ์žˆ์„ ์ˆ˜๊ฐ€ ์—†์—ˆ๋‹ค. +๊ทธ๋Š” ๋งˆ์นจ๋‚ด ํšŒ์‚ฌ ์ถœ๊ทผ๋งˆ์ € ๋‹จ๋…ํ•˜๊ธฐ์— ์ด๋ฅด๋ €๋‹ค. ๊ทธ๋Ÿฌ์ž ์‹ ํ†ต +ํ•˜๊ฒŒ๋„ ๋Šฆ์ž  ๋ฒ„๋ฆ‡์ด ๊นจ๋—์ด ์ž์ทจ๋ฅผ ๊ฐ์ถฐ ๋ฒ„๋ ธ๋‹ค. ๊ทธ๋Š” ๋ˆˆ๋งŒ ๋œจ๋ฉด +์‚ฌ๋ผ์ ธ ์—†์–ด์ง„ ๋ฐฐ๊ผฝ ๋•Œ๋ฌธ์— ๊ธฐ๋ถ„์ด ํ—ˆ์ „ํ–ˆ๊ณ , ๊ทธ๋Ÿฌ๋ฉด ๊ทธ ํ—ˆ๋ง๊ฐ์„ +์ซ“๊ธฐ ์œ„ํ•ด ๋ฐฐ๊ผฝ์— ๊ด€ํ•œ ๋์—†๋Š” ์ƒ๋…๋“ค์„ ์Œ“๊ธฐ ์‹œ์ž‘ํ–ˆ๋‹ค. +(์ค‘๋žต) +๊ทธ๋ฆฌํ•˜์—ฌ ๋ฐฐ๊ผฝ์— ๊ด€ํ•œ ํ—ˆ์›์˜ ์ง€์‹๊ณผ ์‚ฌ๋…์€ ์ž๊พธ ๋” ์‹ฌ์˜คํ•˜ +๊ณ  ์ถ”์ƒ์ ์ธ ๊ฒƒ์ด ๋˜์–ด ๊ฐ”๋‹ค. ๊ทธ์—๊ฒŒ๋Š” ์–ด๋А๋ง ๊ทธ ๋‚˜๋ฆ„์˜ ๋…ํŠนํ•œ +๋ฐฐ๊ผฝ๋ก  ๊ฐ™์€ ๊ฒƒ์ด ์œค๊ณฝ์„ ์ง€์–ด ๊ฐ€๊ณ  ์žˆ์—ˆ๋‹ค. ํ•˜์ง€๋งŒ ๊ทธ๋Ÿฌ๋ฉด ๊ทธ๋Ÿด +์ˆ˜๋ก ํ—ˆ์›์€ ๋”์šฑ๋” ํ—ˆ์ „ํ•ด์ง€๊ณ , ์•„๋ฌด ๊ณณ์—๋„ ๋ฐœ์ด ๋‹ฟ์•„ ์žˆ๋Š” ๊ฒƒ +๊ฐ™์ง€ ์•Š๊ณ , ํ˜ผ์ž์„œ ์™ธ๋กญ๊ฒŒ ํ—ˆ๊ณต์„ ๋‘ฅ๋‘ฅ ๋– ๋‹ค๋‹ˆ๊ณ  ์žˆ๋Š” ๊ฒƒ์ฒ˜๋Ÿผ +๋А๊ปด์กŒ๋‹ค. ๊ทธ๋Ÿฌ๋ฉด ๊ทธ๋Š” ๋˜ ๊ฑฐ๋“ญ ๊ทธ ํ—ˆ๋ง๊ฐ์„ ์ซ“๊ธฐ ์œ„ํ•ด ์ž์‹ ์˜ +๋ฐฐ๊ผฝ๋ก ์„ ์™„๋ฒฝํ•˜๊ฒŒ ๋ฐœ์ „์‹œ์ผœ ๋‚˜๊ฐ”๋‹ค. ๋งˆ์น˜ ๊ทธ๋ ‡๊ฒŒ ํ•˜์—ฌ ๊ทธ๋Š” +์ž์‹ ์˜ ์‚ฌ๋… ์†์—์„œ ์žƒ์–ด๋ฒ„๋ฆฐ ๋ฐฐ๊ผฝ์„ ๋˜์ฐพ์•„๋‚ด๊ณ , ๊ทธ๊ฒƒ์œผ๋กœ ๊ทธ +์‹ค๋ฌผ์„ ๋Œ€์‹ ํ•ด ์–ด๋–ค ์‹์œผ๋กœ๋“  ์ž์‹ ๊ณผ ์„ธ์ƒ ๊ฐ„์— ํฐ ๋ถˆํŽธ์ด ์—†๋„๋ก +ํ™”ํ•ด์‹œํ‚ค๊ณ  ๊ทธ๊ฒƒ์œผ๋กœ ๊ทธ ๋‚œ๊ฐ์Šค๋Ÿฐ ํ—ˆ๋ง๊ฐ์„ ์ฑ„์šฐ๋ ค๋Š” ๋“ฏ์ด. ๊ทธ์˜ +๋ฐฐ๊ผฝ๋ก ์€ ๊ฐ€๋ น ์ด๋Ÿฐ ์‹์œผ๋กœ๊นŒ์ง€ ๋ฐœ์ „๋˜์–ด ์žˆ์—ˆ๋‹ค. + +โ€•์šฐ๋ฆฌ๋Š” ๋ˆ„๊ตฌ๋‚˜ ๋ฐฐ๊ผฝ์„ ๊ฐ€์ง€๊ณ  ์žˆ๋‹คโ€ฆโ€ฆ ์šฐ๋ฆฌ๋Š” ์šฐ๋ฆฌ๋“ค์˜ +์–ด๋จธ๋‹ˆ๋กœ๋ถ€ํ„ฐ ํƒฏ์ค„์ด ๋Š์–ด์ง€๋Š” ์ˆœ๊ฐ„ ์ด ์šฐ์ฃผ์˜ ํ•œ ๋‹จ์ž(ๅ–ฎๅญ)๋กœ์„œ +๊ณ ๋…ํ•˜๊ฒŒ ์กด์žฌํ•˜๊ฒŒ ๋˜์—ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์šฐ๋ฆฌ๋Š” ์˜์›ํžˆ ๊ทธ ํƒฏ์ค„์˜ +๊ธฐ์–ต์„ ์žŠ์ง€ ์•Š๋Š”๋‹ค. ์šฐ๋ฆฌ ์˜ํ˜ผ์€ ์–ธ์ œ๊นŒ์ง€๋‚˜ ๊ทธ ์–ด๋จธ๋‹ˆ์˜ ํƒฏ์ค„๊ณผ +์ด์–ด์ง€๋ ค ํ•˜๊ณ , ๋˜๋‹ค์‹œ ๊ทธ ์–ด๋จธ๋‹ˆ์˜ ์–ด๋จธ๋‹ˆ์˜ ํƒฏ์ค„๊ณผ ์ด์–ด์ ธ +๋‚˜๊ฐ€๋ฉด์„œ ์šฐ๋ฆฌ ์กด์žฌ๋ฅผ ์„ค๋ช…ํ•˜๊ณ  ๊ทผ์›์„ ๋ฐํ˜€ ๋‚˜๊ฐ€๋ฉฐ, ๋งˆ์นจ๋‚ด๋Š” +๋งˆ์ง€๋ง‰ ์–ด๋จธ๋‹ˆ์˜ ํƒฏ์ค„์ด ์ด์–ด์ง€๋Š” ์šฐ๋ฆฌ๋“ค์˜ ์šฐ์ฃผ์™€ ๋งŒ๋‚˜๊ฒŒ +๋œ๋‹คโ€ฆโ€ฆ ์šฐ๋ฆฌ์˜ ๋ฐฐ๊ผฝ์€ ์šฐ๋ฆฌ๊ฐ€ ๊ทธ ๋งˆ์ง€๋ง‰ ์šฐ์ฃผ์™€ ๋งŒ๋‚˜๊ณ ์ž ํ•˜๋Š” +ํ–ฅ์ˆ˜์˜ ํ‘œ์ƒ์ด๋ฉฐ ๊ฐ€๋Šฅ์„ฑ์˜ ์ƒ์ง•์ด๋ฉฐ ์กด์žฌ์˜ ๋น„๋ฐ€๋กœ ๋‚˜์•„๊ฐ€๋Š” +ํ˜•์ด์ƒํ•™์ด๋‹ค. ๊ทธ ๋น„๋ฐ€์˜ ๋ฌธ์ด๋‹คโ€ฆโ€ฆ +๊ทธ๋Š” ์–ด๋А๋ง ๋ฐฐ๊ผฝ์— ๋Œ€ํ•ด ๋‹น๋‹นํ•œ ์ผ๊ฐ€๊ฒฌ์„ ์ด๋ฃฌ ๋ฐฐ๊ผฝ ์ „๋ฌธ๊ฐ€๊ฐ€ +๋˜์–ด ๊ฐ€๊ณ  ์žˆ์—ˆ๋‹ค. +ใ‰ฃ์–ด๋А ํ•ด ์—ฌ๋ฆ„์ด์—ˆ๋‹ค. ํ•˜๋‹ˆ๊นŒ ๊ทธ๊ฒƒ์€ ํ—ˆ์›์ด ์ž์‹ ์˜ ๋ฐฐ๊ผฝ์„ +์žƒ์–ด๋ฒ„๋ฆฌ๊ณ  ๋‚˜์„œ ๋ถˆํŽธํ•˜๊ธฐ ๊ทธ์ง€์—†๋Š” ์„ธ ๋ฒˆ์งธ์˜ ์—ฌ๋ฆ„์„ ๋งž๊ณ  ์žˆ์„ +๋•Œ์˜€๋‹ค. ๊ทธ๋Š” ๋ฌผ๋ก  ๋ฐฐ๊ผฝ์„ ์žƒ์–ด๋ฒ„๋ฆฐ ์ž์‹ ์— ๋Œ€ํ•ด ์•„์ง๋„ ์™„์ „ํžŒ +์ต์ˆ™ํ•ด์ง€์งˆ ๋ชปํ•˜๊ณ  ์žˆ์—ˆ๋‹ค. ๊ทธ์˜ ์‚ฌ๋… ์—ญ์‹œ ์–ธ์ œ๋‚˜ ๊ทธ ๋ˆˆ์— +๋ณด์ด์ง€ ์•Š๋Š” ๋ฐฐ๊ผฝ์— ๋งค๋‹ฌ๋ ค ๊ฑฐ๊ธฐ์—์„œ๋ฐ–์—๋Š” ์˜์˜ ๋” ์ด์ƒ ์ž์œ  +๋กœ์›Œ์งˆ ์ˆ˜๊ฐ€ ์—†์—ˆ๋‹ค. ๊ทธ ๋Œ€์‹  ํ—ˆ์›์€ ์ด์ œ ๊ทธ ์ž์‹ ์˜ ๋ฐฐ๊ผฝ๋ก ์— +๋Œ€ํ•ด์„  ๋งค์šฐ ํ™•๊ณ ํ•œ ๊ฒฝ์ง€์— ๋„๋‹ฌํ•ด ์žˆ์—ˆ๋‹ค. +๊ทธ๋Ÿด ์ฆˆ์Œ์ด์—ˆ๋‹ค. ํ—ˆ์›์€ ๋ฌธ๋“ ์„ธ์ƒ ์‚ฌ๋žŒ๋“ค์ด ์ˆ˜์ƒ์ฉ์–ด์ง€๊ธฐ +์‹œ์ž‘ํ–ˆ๋‹ค. ์–ด๋А ๋•Œ๋ถ€ํ„ด์ง€๋Š” ํ™•์‹คํžˆ ์•Œ ์ˆ˜ ์—†์—ˆ์ง€๋งŒ, ์„ธ์ƒ ์‚ฌ๋žŒ๋“ค +์—ญ์‹œ ๋ฌด์Šจ ์ด์œ ์—์„ ์ง€ ์ด ์ธ๊ฐ„ ์žฅ๊ธฐ์˜ ํ•œ ์กฐ๊ทธ๋งŒ ํ”์ ์— ๋Œ€ํ•ด +์‹ฌ์ƒ์ฐฎ์€ ๊ด€์‹ฌ์„ ๋‚˜ํƒ€๋‚ด๊ธฐ ์‹œ์ž‘ํ•œ ๊ฒƒ์ด๋‹ค. ๋ฐฐ๊ผฝ์— ๋Œ€ํ•œ ์‚ฌ๋žŒ๋“ค์˜ +๊ด€์‹ฌ ์—ญ์‹œ ๊ธฐ์™•๋ถ€ํ„ฐ ์žˆ์–ด ์˜จ ๊ฒƒ์„ ์—ฌํƒœ๊นŒ์ง€ ์„œ๋กœ ๋ชจ๋ฅด๊ณ  ์ง€๋‚ด +์˜ค๋‹ค๊ฐ€ ๋น„๋กœ์†Œ ์–ด๋–ค ๊ธฐ๋ฏธ๋ฅผ ์•Œ์•„์ฐจ๋ฆฌ๊ฒŒ ๋œ ๊ฒƒ์ธ์ง€, ํ˜น์€ ์‚ฌ๋žŒ๋“ค๋กœ +ํ•˜์—ฌ๊ธˆ ๊ทธ๋Ÿฐ ๊ด€์‹ฌ์„ ๋‚ด๋ณด์ด๊ฒŒ ํ•  ๋งŒํ•œ ๋ฌด์Šจ ์šฐ์—ฐ์ฐฎ์€ ๊ณ„๊ธฐ๊ฐ€ +๋งˆ๋ จ๋˜์—ˆ๋Š”์ง€๋Š” ํ™•์‹ค์น˜๊ฐ€ ์•Š์•˜๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๋ฌด์—‡ ๋•Œ๋ฌธ์— ์‚ฌ๋žŒ๋“ค +์—๊ฒŒ์„œ ๊ทธ๋Ÿฐ ๊ด€์‹ฌ์ด ์‹œ์ž‘๋˜์—ˆ๋Š”์ง€ ๊ทธ ์ด์œ ๋ฅผ ์•Œ ์ˆ˜๋„ ์—†์—ˆ๋‹ค. +ํ•˜์ง€๋งŒ ๊ทธ๊ฒƒ์€ ์–ด์จŒ๋“  ์‚ฌ์‹ค์ด์—ˆ๋‹ค. ์ฃผ์˜๋ฅผ ๊ธฐ์šธ์—ฌ ๋ณด๋‹ˆ ๊ด€์‹ฌ์˜ +์ •๋„๋„ ์—ฌ๊ฐ„์ด ์•„๋‹ˆ์—ˆ๋‹ค. ํ•œ๋‘ ์‚ฌ๋žŒ, ํ•œ๋‘ ๊ณณ์—์„œ๋งŒ ๋‚˜ํƒ€๋‚œ +ํ˜„์ƒ์ด ์•„๋‹ˆ์—ˆ๋‹ค. ๊ทธ๊ฒƒ์€ ์ด๋ฏธ ์ผ๋ฐ˜์ ์ธ ํ˜„์ƒ์ด ๋˜์–ด ๊ฐ€๊ณ  +์žˆ์—ˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๊ทธ๋ ‡๋“ฏ ๋ฐฐ๊ผฝ ์ด์•ผ๊ธฐ๊ฐ€ ์ผ๋ฐ˜ํ™”์˜ ๊ธฐ๋ฏธ๋ฅผ ์—ฟ๋ณด์ด๊ธฐ +์‹œ์ž‘ํ•˜์ž ์‚ฌ๋žŒ๋“ค์€ ์ด์ œ ๊ทธ๊ฑธ ์‹ ํ˜ธ๋กœ ์•„๋ฌด ํ‰ํ—ˆ๋ฌผ ์—†์ด ํ„ฐ๋†“๊ณ  +์ง€๊ป„์ด๊ฑฐ๋‚˜ ์‹ ๋ฌธ, ์žก์ง€ ๊ฐ™์€ ๋ฐ์„œ ์ง„์ง€ํ•˜๊ฒŒ ๋…ผ์˜์˜ ๋Œ€์ƒ์„ +์‚ผ๊ธฐ๋„ ํ•˜์˜€๋‹ค. ใ‰ค๋ฐฐ๊ผฝ์— ๊ด€ํ•œ ๋…ผ์˜๊ฐ€ ๊ทธ๋ ‡๋“ฏ ๊ฐ‘์ž๊ธฐ +์‹œ์ค‘ ์ผ๋ฐ˜์—๊นŒ์ง€ ์„ฑํ–‰ํ•˜๊ธฐ ์‹œ์ž‘ํ•œ ๊ฒƒ์ด๋‹ค. +๊ธฐ๋ฌ˜ํ•œ ํ˜„์ƒ์ด์—ˆ๋‹ค. +-์ด์ฒญ์ค€, ๏ฝข๋ฐฐ๊ผฝ์„ ์ฃผ์ œ๋กœ ํ•œ ๋ณ€์ฃผ๊ณก๏ฝฃ +","{'question': '๋น„๋ฐ€์˜ ์„œ์‚ฌ์  ๊ธฐ๋Šฅ์œผ๋กœ ๊ฐ€์žฅ ์ ์ ˆํ•œ ๊ฒƒ์€?', 'choices': ['์ž์‹ ์˜ ์‹ ๋…์„ ์ธ๋ฌผ์ด ๋Œ์ด์ผœ ๋ณธ ๊ฒฐ๊ณผ๋กœ, ์ƒˆ๋กœ์šด ์„ธ๊ณ„๊ด€์„\n๋ฐ”ํƒ•์œผ๋กœ ํ•˜๋Š” ์ฃผ์ œ๋ฅผ ํ˜•์„ฑํ•œ๋‹ค.', '์–ฝํžŒ ์ธ๊ฐ„๊ด€๊ณ„๋ฅผ ์ธ๋ฌผ์ด ์„ฑ์ฐฐํ•˜๋Š” ์ „ํ™˜์ ์œผ๋กœ, ๊ฐˆ๋“ฑ์œผ๋กœ\n์ธํ•œ ์œ„๊ธฐ๊ฐ์„ ์™„ํ™”ํ•œ๋‹ค.', '์ผ์ƒ์ ์ด์ง€ ์•Š์€ ๊ฒฝํ—˜์„ ์ธ๋ฌผ์ด ์˜์‹ํ•œ๋‹ค๋Š” ํ‘œ์ง€๋กœ, ์ธ๋ฌผ์˜\n์‹ฌ๋ฆฌ์  ๋™์š”๋ฅผ ๋ถ€๋ฅธ๋‹ค.', '์ƒ์ถฉ๋œ ์ดํ•ด๊ด€๊ณ„๋ฅผ ์ธ๋ฌผ์ด ์กฐ์ •ํ•˜๋Š” ๋‹จ์„œ๋กœ, ์‹ฌํ™”๋œ ์‚ฌํšŒ์ \n๊ฐˆ๋“ฑ์„ ํ•ด์†Œํ•œ๋‹ค.', '๊ธฐ์„ฑ์˜ ์งˆ์„œ์— ์ธ๋ฌผ์ด ์ €ํ•ญํ•œ๋‹ค๋Š” ์‹ ํ˜ธ๋กœ, ๋Œ๋ฐœ์  ์‚ฌ๊ฑด์˜\n๋ฐœ์ƒ์„ ์•Œ๋ฆฐ๋‹ค'], 'answer': ''}",,3,3,True,[],3 +2025-korean-30,"ใ‰ ๋ถˆํŽธ์Šค๋Ÿฐ ์ผ์ด ํ•œ๋‘ ๊ฐ€์ง€๊ฐ€ ์•„๋‹ˆ์—ˆ๋‹ค. ํ•˜์ง€๋งŒ ํ—ˆ์›์€ ๊ทธ๋ ‡๊ฒŒ +๊ทธ๋Š” ์–ด๋А๋ง ๋ฐฐ๊ผฝ์— ๋Œ€ํ•ด ๋‹น๋‹นํ•œ ์ผ๊ฐ€๊ฒฌ์„ ์ด๋ฃฌ ๋ฐฐ๊ผฝ ์ „๋ฌธ๊ฐ€๊ฐ€ +๋˜์–ด ๊ฐ€๊ณ  ์žˆ์—ˆ๋‹ค. +ใ‰ฃ์–ด๋А ํ•ด ์—ฌ๋ฆ„์ด์—ˆ๋‹ค. ํ•˜๋‹ˆ๊นŒ ๊ทธ๊ฒƒ์€ ํ—ˆ์›์ด ์ž์‹ ์˜ ๋ฐฐ๊ผฝ์„ +์Šค์Šค๋กœ ์ฃผ์˜ํ•˜๊ณ  ๊ณ ํ†ต์„ ๊ฐ๋‚ดํ•ด ๋ƒˆ๊ธฐ ๋•Œ๋ฌธ์— ์ž์‹ ์˜ ๋น„๋ฐ€์„ +๋‚จ ์•ž์— ๊ฐ์ชฝ๊ฐ™์ด ์ˆจ๊ฒจ ๋‚˜๊ฐˆ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์•„๋ฌด๋„ ๊ทธ์˜ ๋น„๋ฐ€์„ +๋ˆˆ์น˜์ฑˆ ์‚ฌ๋žŒ์ด ์—†์—ˆ๋‹ค. ๋น„๋ฐ€์ด ํƒ„๋กœ ๋‚˜์ง€ ์•Š๋Š” ํ•œ ๊ทธ์˜ ์ผ์ƒ +์ƒํ™œ์€ ๋” ์ด์ƒ ๋ถˆํŽธ์„ ๊ฒช์„ ํ•„์š”๋„ ์—†์—ˆ๋‹ค. ์ธ์ฒด ์ƒ๋ฆฌ๋‚˜ ํ•ด๋ถ€ํ•™ +์„œ์  ๊ฐ™์€ ๊ฑธ ๋’ค์ ธ ๋ด๋„ ์„ฑ์ธ์˜ ๋ฐฐ๊ผฝ์€ ๊ฑฐ์˜ ์•„๋ฌด๋Ÿฐ ๊ธฐ๋Šฅ๋„ +์ˆ˜ํ–‰ํ•˜์ง€ ์•Š์Œ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ ์–ด๋„ ๊ทธ์˜ ์™ธ๋ชจ๋‚˜ ๋ฐ”๊นฅ ์ƒํ™œ์€ +์ •์ƒ์„ ์œ ์ง€ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๊ทธ ์ ๋งŒ์ด๋ผ๋„ ๋ฌด์ฒ™ ๋‹คํ–‰์ด์—ˆ๋‹ค. ๊ทธ๋Š” +์ผ๋‹จ ์•ˆ๋„์˜ ํ•œ์ˆจ์„ ๋‚ด์‰ฌ์—ˆ๋‹ค. +ใ‰กโ€•๊ทธ๊นŸ ๋†ˆ์˜ ๋ฐฐ๊ผฝ, ์•ˆ ๊ฐ€์ง€๊ณ  ์žˆ์Œ ์–ด๋•Œ. +์žƒ์–ด๋ฒ„๋ฆฌ๊ณ  ๋‚˜์„œ ๋ถˆํŽธํ•˜๊ธฐ ๊ทธ์ง€์—†๋Š” ์„ธ ๋ฒˆ์งธ์˜ ์—ฌ๋ฆ„์„ ๋งž๊ณ  ์žˆ์„ +๋•Œ์˜€๋‹ค. ๊ทธ๋Š” ๋ฌผ๋ก  ๋ฐฐ๊ผฝ์„ ์žƒ์–ด๋ฒ„๋ฆฐ ์ž์‹ ์— ๋Œ€ํ•ด ์•„์ง๋„ ์™„์ „ํžŒ +๊ทธ์ฏค ์ฒด๋…์„ ํ•˜๊ณ  ๋  ์ˆ˜ ์žˆ์œผ๋ฉด ๋ฐฐ๊ผฝ์— ๊ด€ํ•œ ์ผ๋“ค์„ ์žŠ์–ด๋ฒ„๋ฆฌ๋ ค +ํ–ˆ๋‹ค. ใ‰ข์ž์‹ ์œผ๋กœ๋ถ€ํ„ฐ ๋ฐฐ๊ผฝ์ด ์‚ฌ๋ผ์ ธ ๋ฒ„๋ฆฐ ์‚ฌ์‹ค์„, ๊ทธ๋ฆฌ๊ณ  ๊ทธ +๋•Œ๋ฌธ์— ์ƒ๊ธด ๋ชจ๋“  ๋ถˆํŽธ์„ ์žŠ๊ณ , ๊ทธ ๋ฐฐ๊ผฝ ์—†๋Š” ์ƒํ™œ์— ์Šค์Šค๋กœ +์ต์ˆ™ํ•ด์ ธ ๋ฒ„๋ฆฌ๊ธฐ๋ฅผ ๋ฐ”๋ผ ๋งˆ์ง€์•Š์•˜๋‹ค. ํ•˜์ง€๋งŒ ๋ฌธ์ œ๋Š” ๊ทธ๋ ‡๊ฒŒ +๊ฐ„๋‹จํ•˜์ง€ ์•Š์•˜๋‹ค. ์•„๋ฌด๋ฆฌ ์ผ์ƒ์ƒํ™œ์—์„  ๋“œ๋Ÿฌ๋‚˜๊ฒŒ ๋ถˆํŽธํ•œ ์ ์ด +์—†๋‹ค ํ•ด๋„ ๊ทธ๋Š” ์—ญ์‹œ ๋ฐฐ๊ผฝ์ด ์—†๋Š” ์ž์‹ ์— ๋Œ€ํ•ด ์ข€์ฒ˜๋Ÿผ ์ต์ˆ™ํ•ด์งˆ +์ˆ˜๊ฐ€ ์—†์—ˆ๋‹ค. ๊ทธ๋Š” ์ž๊พธ๋งŒ ํ—ˆ์ „ํ•ด์„œ ๊ฒฌ๋”œ ์ˆ˜๊ฐ€ ์—†์–ด์ง€๊ณค ํ–ˆ๋‹ค. +์žˆ๋А๋‹ˆ๋ผ ์—ฌ๊ธฐ๊ณ  ์ง€๋‚ผ ๋•Œ๋Š” ๊ทธ์ฒ˜๋Ÿผ ๋ฌด์‹ฌ์Šค๋Ÿฝ๋˜ ์ผ์ด ๊ทธ๋Ÿฐ ์‹์œผ๋กœ +ํ•œ๋ฒˆ ์˜์‹์˜ ๋ˆ์„ ๊ฑด๋“œ๋ ค ์˜ค์ž ํ—ˆ์›์˜ ์ƒ๋…์€ ์ž ์‹œ๋„ ๊ทธ ์žƒ์–ด +๋ฒ„๋ฆฐ ๋ฐฐ๊ผฝ์—์„œ ๋– ๋‚˜ ์žˆ์„ ์ˆ˜๊ฐ€ ์—†์—ˆ๋‹ค. +๊ทธ๋Š” ๋งˆ์นจ๋‚ด ํšŒ์‚ฌ ์ถœ๊ทผ๋งˆ์ € ๋‹จ๋…ํ•˜๊ธฐ์— ์ด๋ฅด๋ €๋‹ค. ๊ทธ๋Ÿฌ์ž ์‹ ํ†ต +ํ•˜๊ฒŒ๋„ ๋Šฆ์ž  ๋ฒ„๋ฆ‡์ด ๊นจ๋—์ด ์ž์ทจ๋ฅผ ๊ฐ์ถฐ ๋ฒ„๋ ธ๋‹ค. ๊ทธ๋Š” ๋ˆˆ๋งŒ ๋œจ๋ฉด +์‚ฌ๋ผ์ ธ ์—†์–ด์ง„ ๋ฐฐ๊ผฝ ๋•Œ๋ฌธ์— ๊ธฐ๋ถ„์ด ํ—ˆ์ „ํ–ˆ๊ณ , ๊ทธ๋Ÿฌ๋ฉด ๊ทธ ํ—ˆ๋ง๊ฐ์„ +์ซ“๊ธฐ ์œ„ํ•ด ๋ฐฐ๊ผฝ์— ๊ด€ํ•œ ๋์—†๋Š” ์ƒ๋…๋“ค์„ ์Œ“๊ธฐ ์‹œ์ž‘ํ–ˆ๋‹ค. +(์ค‘๋žต) +๊ทธ๋ฆฌํ•˜์—ฌ ๋ฐฐ๊ผฝ์— ๊ด€ํ•œ ํ—ˆ์›์˜ ์ง€์‹๊ณผ ์‚ฌ๋…์€ ์ž๊พธ ๋” ์‹ฌ์˜คํ•˜ +๊ณ  ์ถ”์ƒ์ ์ธ ๊ฒƒ์ด ๋˜์–ด ๊ฐ”๋‹ค. ๊ทธ์—๊ฒŒ๋Š” ์–ด๋А๋ง ๊ทธ ๋‚˜๋ฆ„์˜ ๋…ํŠนํ•œ +๋ฐฐ๊ผฝ๋ก  ๊ฐ™์€ ๊ฒƒ์ด ์œค๊ณฝ์„ ์ง€์–ด ๊ฐ€๊ณ  ์žˆ์—ˆ๋‹ค. ํ•˜์ง€๋งŒ ๊ทธ๋Ÿฌ๋ฉด ๊ทธ๋Ÿด +์ˆ˜๋ก ํ—ˆ์›์€ ๋”์šฑ๋” ํ—ˆ์ „ํ•ด์ง€๊ณ , ์•„๋ฌด ๊ณณ์—๋„ ๋ฐœ์ด ๋‹ฟ์•„ ์žˆ๋Š” ๊ฒƒ +๊ฐ™์ง€ ์•Š๊ณ , ํ˜ผ์ž์„œ ์™ธ๋กญ๊ฒŒ ํ—ˆ๊ณต์„ ๋‘ฅ๋‘ฅ ๋– ๋‹ค๋‹ˆ๊ณ  ์žˆ๋Š” ๊ฒƒ์ฒ˜๋Ÿผ +๋А๊ปด์กŒ๋‹ค. ๊ทธ๋Ÿฌ๋ฉด ๊ทธ๋Š” ๋˜ ๊ฑฐ๋“ญ ๊ทธ ํ—ˆ๋ง๊ฐ์„ ์ซ“๊ธฐ ์œ„ํ•ด ์ž์‹ ์˜ +๋ฐฐ๊ผฝ๋ก ์„ ์™„๋ฒฝํ•˜๊ฒŒ ๋ฐœ์ „์‹œ์ผœ ๋‚˜๊ฐ”๋‹ค. ๋งˆ์น˜ ๊ทธ๋ ‡๊ฒŒ ํ•˜์—ฌ ๊ทธ๋Š” +์ž์‹ ์˜ ์‚ฌ๋… ์†์—์„œ ์žƒ์–ด๋ฒ„๋ฆฐ ๋ฐฐ๊ผฝ์„ ๋˜์ฐพ์•„๋‚ด๊ณ , ๊ทธ๊ฒƒ์œผ๋กœ ๊ทธ +์‹ค๋ฌผ์„ ๋Œ€์‹ ํ•ด ์–ด๋–ค ์‹์œผ๋กœ๋“  ์ž์‹ ๊ณผ ์„ธ์ƒ ๊ฐ„์— ํฐ ๋ถˆํŽธ์ด ์—†๋„๋ก +ํ™”ํ•ด์‹œํ‚ค๊ณ  ๊ทธ๊ฒƒ์œผ๋กœ ๊ทธ ๋‚œ๊ฐ์Šค๋Ÿฐ ํ—ˆ๋ง๊ฐ์„ ์ฑ„์šฐ๋ ค๋Š” ๋“ฏ์ด. ๊ทธ์˜ +๋ฐฐ๊ผฝ๋ก ์€ ๊ฐ€๋ น ์ด๋Ÿฐ ์‹์œผ๋กœ๊นŒ์ง€ ๋ฐœ์ „๋˜์–ด ์žˆ์—ˆ๋‹ค. + +โ€•์šฐ๋ฆฌ๋Š” ๋ˆ„๊ตฌ๋‚˜ ๋ฐฐ๊ผฝ์„ ๊ฐ€์ง€๊ณ  ์žˆ๋‹คโ€ฆโ€ฆ ์šฐ๋ฆฌ๋Š” ์šฐ๋ฆฌ๋“ค์˜ +์–ด๋จธ๋‹ˆ๋กœ๋ถ€ํ„ฐ ํƒฏ์ค„์ด ๋Š์–ด์ง€๋Š” ์ˆœ๊ฐ„ ์ด ์šฐ์ฃผ์˜ ํ•œ ๋‹จ์ž(ๅ–ฎๅญ)๋กœ์„œ +๊ณ ๋…ํ•˜๊ฒŒ ์กด์žฌํ•˜๊ฒŒ ๋˜์—ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์šฐ๋ฆฌ๋Š” ์˜์›ํžˆ ๊ทธ ํƒฏ์ค„์˜ +๊ธฐ์–ต์„ ์žŠ์ง€ ์•Š๋Š”๋‹ค. ์šฐ๋ฆฌ ์˜ํ˜ผ์€ ์–ธ์ œ๊นŒ์ง€๋‚˜ ๊ทธ ์–ด๋จธ๋‹ˆ์˜ ํƒฏ์ค„๊ณผ +์ด์–ด์ง€๋ ค ํ•˜๊ณ , ๋˜๋‹ค์‹œ ๊ทธ ์–ด๋จธ๋‹ˆ์˜ ์–ด๋จธ๋‹ˆ์˜ ํƒฏ์ค„๊ณผ ์ด์–ด์ ธ +๋‚˜๊ฐ€๋ฉด์„œ ์šฐ๋ฆฌ ์กด์žฌ๋ฅผ ์„ค๋ช…ํ•˜๊ณ  ๊ทผ์›์„ ๋ฐํ˜€ ๋‚˜๊ฐ€๋ฉฐ, ๋งˆ์นจ๋‚ด๋Š” +๋งˆ์ง€๋ง‰ ์–ด๋จธ๋‹ˆ์˜ ํƒฏ์ค„์ด ์ด์–ด์ง€๋Š” ์šฐ๋ฆฌ๋“ค์˜ ์šฐ์ฃผ์™€ ๋งŒ๋‚˜๊ฒŒ +๋œ๋‹คโ€ฆโ€ฆ ์šฐ๋ฆฌ์˜ ๋ฐฐ๊ผฝ์€ ์šฐ๋ฆฌ๊ฐ€ ๊ทธ ๋งˆ์ง€๋ง‰ ์šฐ์ฃผ์™€ ๋งŒ๋‚˜๊ณ ์ž ํ•˜๋Š” +ํ–ฅ์ˆ˜์˜ ํ‘œ์ƒ์ด๋ฉฐ ๊ฐ€๋Šฅ์„ฑ์˜ ์ƒ์ง•์ด๋ฉฐ ์กด์žฌ์˜ ๋น„๋ฐ€๋กœ ๋‚˜์•„๊ฐ€๋Š” +ํ˜•์ด์ƒํ•™์ด๋‹ค. ๊ทธ ๋น„๋ฐ€์˜ ๋ฌธ์ด๋‹คโ€ฆโ€ฆ +๊ทธ๋Š” ์–ด๋А๋ง ๋ฐฐ๊ผฝ์— ๋Œ€ํ•ด ๋‹น๋‹นํ•œ ์ผ๊ฐ€๊ฒฌ์„ ์ด๋ฃฌ ๋ฐฐ๊ผฝ ์ „๋ฌธ๊ฐ€๊ฐ€ +๋˜์–ด ๊ฐ€๊ณ  ์žˆ์—ˆ๋‹ค. +ใ‰ฃ์–ด๋А ํ•ด ์—ฌ๋ฆ„์ด์—ˆ๋‹ค. ํ•˜๋‹ˆ๊นŒ ๊ทธ๊ฒƒ์€ ํ—ˆ์›์ด ์ž์‹ ์˜ ๋ฐฐ๊ผฝ์„ +์žƒ์–ด๋ฒ„๋ฆฌ๊ณ  ๋‚˜์„œ ๋ถˆํŽธํ•˜๊ธฐ ๊ทธ์ง€์—†๋Š” ์„ธ ๋ฒˆ์งธ์˜ ์—ฌ๋ฆ„์„ ๋งž๊ณ  ์žˆ์„ +๋•Œ์˜€๋‹ค. ๊ทธ๋Š” ๋ฌผ๋ก  ๋ฐฐ๊ผฝ์„ ์žƒ์–ด๋ฒ„๋ฆฐ ์ž์‹ ์— ๋Œ€ํ•ด ์•„์ง๋„ ์™„์ „ํžŒ +์ต์ˆ™ํ•ด์ง€์งˆ ๋ชปํ•˜๊ณ  ์žˆ์—ˆ๋‹ค. ๊ทธ์˜ ์‚ฌ๋… ์—ญ์‹œ ์–ธ์ œ๋‚˜ ๊ทธ ๋ˆˆ์— +๋ณด์ด์ง€ ์•Š๋Š” ๋ฐฐ๊ผฝ์— ๋งค๋‹ฌ๋ ค ๊ฑฐ๊ธฐ์—์„œ๋ฐ–์—๋Š” ์˜์˜ ๋” ์ด์ƒ ์ž์œ  +๋กœ์›Œ์งˆ ์ˆ˜๊ฐ€ ์—†์—ˆ๋‹ค. ๊ทธ ๋Œ€์‹  ํ—ˆ์›์€ ์ด์ œ ๊ทธ ์ž์‹ ์˜ ๋ฐฐ๊ผฝ๋ก ์— +๋Œ€ํ•ด์„  ๋งค์šฐ ํ™•๊ณ ํ•œ ๊ฒฝ์ง€์— ๋„๋‹ฌํ•ด ์žˆ์—ˆ๋‹ค. +๊ทธ๋Ÿด ์ฆˆ์Œ์ด์—ˆ๋‹ค. ํ—ˆ์›์€ ๋ฌธ๋“ ์„ธ์ƒ ์‚ฌ๋žŒ๋“ค์ด ์ˆ˜์ƒ์ฉ์–ด์ง€๊ธฐ +์‹œ์ž‘ํ–ˆ๋‹ค. ์–ด๋А ๋•Œ๋ถ€ํ„ด์ง€๋Š” ํ™•์‹คํžˆ ์•Œ ์ˆ˜ ์—†์—ˆ์ง€๋งŒ, ์„ธ์ƒ ์‚ฌ๋žŒ๋“ค +์—ญ์‹œ ๋ฌด์Šจ ์ด์œ ์—์„ ์ง€ ์ด ์ธ๊ฐ„ ์žฅ๊ธฐ์˜ ํ•œ ์กฐ๊ทธ๋งŒ ํ”์ ์— ๋Œ€ํ•ด +์‹ฌ์ƒ์ฐฎ์€ ๊ด€์‹ฌ์„ ๋‚˜ํƒ€๋‚ด๊ธฐ ์‹œ์ž‘ํ•œ ๊ฒƒ์ด๋‹ค. ๋ฐฐ๊ผฝ์— ๋Œ€ํ•œ ์‚ฌ๋žŒ๋“ค์˜ +๊ด€์‹ฌ ์—ญ์‹œ ๊ธฐ์™•๋ถ€ํ„ฐ ์žˆ์–ด ์˜จ ๊ฒƒ์„ ์—ฌํƒœ๊นŒ์ง€ ์„œ๋กœ ๋ชจ๋ฅด๊ณ  ์ง€๋‚ด +์˜ค๋‹ค๊ฐ€ ๋น„๋กœ์†Œ ์–ด๋–ค ๊ธฐ๋ฏธ๋ฅผ ์•Œ์•„์ฐจ๋ฆฌ๊ฒŒ ๋œ ๊ฒƒ์ธ์ง€, ํ˜น์€ ์‚ฌ๋žŒ๋“ค๋กœ +ํ•˜์—ฌ๊ธˆ ๊ทธ๋Ÿฐ ๊ด€์‹ฌ์„ ๋‚ด๋ณด์ด๊ฒŒ ํ•  ๋งŒํ•œ ๋ฌด์Šจ ์šฐ์—ฐ์ฐฎ์€ ๊ณ„๊ธฐ๊ฐ€ +๋งˆ๋ จ๋˜์—ˆ๋Š”์ง€๋Š” ํ™•์‹ค์น˜๊ฐ€ ์•Š์•˜๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๋ฌด์—‡ ๋•Œ๋ฌธ์— ์‚ฌ๋žŒ๋“ค +์—๊ฒŒ์„œ ๊ทธ๋Ÿฐ ๊ด€์‹ฌ์ด ์‹œ์ž‘๋˜์—ˆ๋Š”์ง€ ๊ทธ ์ด์œ ๋ฅผ ์•Œ ์ˆ˜๋„ ์—†์—ˆ๋‹ค. +ํ•˜์ง€๋งŒ ๊ทธ๊ฒƒ์€ ์–ด์จŒ๋“  ์‚ฌ์‹ค์ด์—ˆ๋‹ค. ์ฃผ์˜๋ฅผ ๊ธฐ์šธ์—ฌ ๋ณด๋‹ˆ ๊ด€์‹ฌ์˜ +์ •๋„๋„ ์—ฌ๊ฐ„์ด ์•„๋‹ˆ์—ˆ๋‹ค. ํ•œ๋‘ ์‚ฌ๋žŒ, ํ•œ๋‘ ๊ณณ์—์„œ๋งŒ ๋‚˜ํƒ€๋‚œ +ํ˜„์ƒ์ด ์•„๋‹ˆ์—ˆ๋‹ค. ๊ทธ๊ฒƒ์€ ์ด๋ฏธ ์ผ๋ฐ˜์ ์ธ ํ˜„์ƒ์ด ๋˜์–ด ๊ฐ€๊ณ  +์žˆ์—ˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๊ทธ๋ ‡๋“ฏ ๋ฐฐ๊ผฝ ์ด์•ผ๊ธฐ๊ฐ€ ์ผ๋ฐ˜ํ™”์˜ ๊ธฐ๋ฏธ๋ฅผ ์—ฟ๋ณด์ด๊ธฐ +์‹œ์ž‘ํ•˜์ž ์‚ฌ๋žŒ๋“ค์€ ์ด์ œ ๊ทธ๊ฑธ ์‹ ํ˜ธ๋กœ ์•„๋ฌด ํ‰ํ—ˆ๋ฌผ ์—†์ด ํ„ฐ๋†“๊ณ  +์ง€๊ป„์ด๊ฑฐ๋‚˜ ์‹ ๋ฌธ, ์žก์ง€ ๊ฐ™์€ ๋ฐ์„œ ์ง„์ง€ํ•˜๊ฒŒ ๋…ผ์˜์˜ ๋Œ€์ƒ์„ +์‚ผ๊ธฐ๋„ ํ•˜์˜€๋‹ค. ใ‰ค๋ฐฐ๊ผฝ์— ๊ด€ํ•œ ๋…ผ์˜๊ฐ€ ๊ทธ๋ ‡๋“ฏ ๊ฐ‘์ž๊ธฐ +์‹œ์ค‘ ์ผ๋ฐ˜์—๊นŒ์ง€ ์„ฑํ–‰ํ•˜๊ธฐ ์‹œ์ž‘ํ•œ ๊ฒƒ์ด๋‹ค. +๊ธฐ๋ฌ˜ํ•œ ํ˜„์ƒ์ด์—ˆ๋‹ค. +-์ด์ฒญ์ค€, ๏ฝข๋ฐฐ๊ผฝ์„ ์ฃผ์ œ๋กœ ํ•œ ๋ณ€์ฃผ๊ณก๏ฝฃ +","{'question': 'โ€˜ํ—ˆ์›โ€™์„ ์ค‘์‹ฌ์œผ๋กœ ์œ—๊ธ€์„ ์ดํ•ดํ•œ ๋‚ด์šฉ์œผ๋กœ ์ ์ ˆํ•˜์ง€ ์•Š์€ ๊ฒƒ์€?', 'choices': ['โ€˜ํ—ˆ์›โ€™์€ โ€˜์‹ค๋ฌผโ€™๊ณผ ๊ด€๋ จํ•˜์—ฌ ์‹œ์ž‘๋œ โ€˜์‚ฌ๋…โ€™์„ ํ†ตํ•ด โ€˜์กด์žฌโ€™์˜ ์˜๋ฏธ๋ฅผ\n๋ฐœ๊ฒฌํ•ด ๊ฐ„๋‹ค', 'ํ—ˆ์›โ€™์€ โ€˜์‹ค๋ฌผโ€™์ด ๋ชธ์—์„œ ํฐ ๊ธฐ๋Šฅ์„ ํ•˜์ง€ ์•Š๋Š”๋‹ค๋Š” ๊ฒƒ์„ ์•Œ๊ณ \n์ผ๋‹จ ์•ˆ๋„๊ฐ์„ ๋А๋ผ๊ฒŒ ๋œ๋‹ค.', 'ํ—ˆ์›โ€™์€ โ€˜์‚ฌ๋…โ€™์„ ๋ฐฉํŽธ์œผ๋กœ ์‚ผ์•„ ์ž์‹ ์˜ ํ˜„์žฌ ์ƒํƒœ์— ๋Œ€ํ•ด\n๋‹ค๋ฅธ ๋ฐฉํ–ฅ์—์„œ ์ ‘๊ทผํ•˜๊ณ ์ž ํ•œ๋‹ค', 'โ€˜ํ—ˆ์›โ€™์€ โ€˜์‹ฌ์ƒ์ฐฎ์€ ๊ด€์‹ฌโ€™์˜ ์›์ธ์— ๋Œ€ํ•ด ๊ถ๊ธˆํ•ดํ•˜๋ฉด์„œ โ€˜์„ธ์ƒ\n์‚ฌ๋žŒ๋“คโ€™์—๊ฒŒ ์ฃผ์˜๋ฅผ ๊ธฐ์šธ์ด๊ฒŒ ๋œ๋‹ค.', 'ํ—ˆ์›โ€™์€ โ€˜์‹ค๋ฌผโ€™์— ๋Œ€ํ•œ ์ธ์‹์„ โ€˜์„ธ์ƒ ์‚ฌ๋žŒ๋“คโ€™๊ณผ ๊ณต์œ ํ•˜๊ฒŒ ๋˜๋ฉด์„œ,\n๊ทธ๊ฐ„ ์ด์–ด ์˜จ โ€˜์‚ฌ๋…โ€™์„ ๋” ์ด์ƒ ์ง€์†ํ•˜์ง€ ์•Š๊ฒŒ ๋œ๋‹ค.'], 'answer': ''}",,5,5,True,[],5 +2025-korean-31,"ใ‰ ๋ถˆํŽธ์Šค๋Ÿฐ ์ผ์ด ํ•œ๋‘ ๊ฐ€์ง€๊ฐ€ ์•„๋‹ˆ์—ˆ๋‹ค. ํ•˜์ง€๋งŒ ํ—ˆ์›์€ ๊ทธ๋ ‡๊ฒŒ +๊ทธ๋Š” ์–ด๋А๋ง ๋ฐฐ๊ผฝ์— ๋Œ€ํ•ด ๋‹น๋‹นํ•œ ์ผ๊ฐ€๊ฒฌ์„ ์ด๋ฃฌ ๋ฐฐ๊ผฝ ์ „๋ฌธ๊ฐ€๊ฐ€ +๋˜์–ด ๊ฐ€๊ณ  ์žˆ์—ˆ๋‹ค. +ใ‰ฃ์–ด๋А ํ•ด ์—ฌ๋ฆ„์ด์—ˆ๋‹ค. ํ•˜๋‹ˆ๊นŒ ๊ทธ๊ฒƒ์€ ํ—ˆ์›์ด ์ž์‹ ์˜ ๋ฐฐ๊ผฝ์„ +์Šค์Šค๋กœ ์ฃผ์˜ํ•˜๊ณ  ๊ณ ํ†ต์„ ๊ฐ๋‚ดํ•ด ๋ƒˆ๊ธฐ ๋•Œ๋ฌธ์— ์ž์‹ ์˜ ๋น„๋ฐ€์„ +๋‚จ ์•ž์— ๊ฐ์ชฝ๊ฐ™์ด ์ˆจ๊ฒจ ๋‚˜๊ฐˆ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์•„๋ฌด๋„ ๊ทธ์˜ ๋น„๋ฐ€์„ +๋ˆˆ์น˜์ฑˆ ์‚ฌ๋žŒ์ด ์—†์—ˆ๋‹ค. ๋น„๋ฐ€์ด ํƒ„๋กœ ๋‚˜์ง€ ์•Š๋Š” ํ•œ ๊ทธ์˜ ์ผ์ƒ +์ƒํ™œ์€ ๋” ์ด์ƒ ๋ถˆํŽธ์„ ๊ฒช์„ ํ•„์š”๋„ ์—†์—ˆ๋‹ค. ์ธ์ฒด ์ƒ๋ฆฌ๋‚˜ ํ•ด๋ถ€ํ•™ +์„œ์  ๊ฐ™์€ ๊ฑธ ๋’ค์ ธ ๋ด๋„ ์„ฑ์ธ์˜ ๋ฐฐ๊ผฝ์€ ๊ฑฐ์˜ ์•„๋ฌด๋Ÿฐ ๊ธฐ๋Šฅ๋„ +์ˆ˜ํ–‰ํ•˜์ง€ ์•Š์Œ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ ์–ด๋„ ๊ทธ์˜ ์™ธ๋ชจ๋‚˜ ๋ฐ”๊นฅ ์ƒํ™œ์€ +์ •์ƒ์„ ์œ ์ง€ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๊ทธ ์ ๋งŒ์ด๋ผ๋„ ๋ฌด์ฒ™ ๋‹คํ–‰์ด์—ˆ๋‹ค. ๊ทธ๋Š” +์ผ๋‹จ ์•ˆ๋„์˜ ํ•œ์ˆจ์„ ๋‚ด์‰ฌ์—ˆ๋‹ค. +ใ‰กโ€•๊ทธ๊นŸ ๋†ˆ์˜ ๋ฐฐ๊ผฝ, ์•ˆ ๊ฐ€์ง€๊ณ  ์žˆ์Œ ์–ด๋•Œ. +์žƒ์–ด๋ฒ„๋ฆฌ๊ณ  ๋‚˜์„œ ๋ถˆํŽธํ•˜๊ธฐ ๊ทธ์ง€์—†๋Š” ์„ธ ๋ฒˆ์งธ์˜ ์—ฌ๋ฆ„์„ ๋งž๊ณ  ์žˆ์„ +๋•Œ์˜€๋‹ค. ๊ทธ๋Š” ๋ฌผ๋ก  ๋ฐฐ๊ผฝ์„ ์žƒ์–ด๋ฒ„๋ฆฐ ์ž์‹ ์— ๋Œ€ํ•ด ์•„์ง๋„ ์™„์ „ํžŒ +๊ทธ์ฏค ์ฒด๋…์„ ํ•˜๊ณ  ๋  ์ˆ˜ ์žˆ์œผ๋ฉด ๋ฐฐ๊ผฝ์— ๊ด€ํ•œ ์ผ๋“ค์„ ์žŠ์–ด๋ฒ„๋ฆฌ๋ ค +ํ–ˆ๋‹ค. ใ‰ข์ž์‹ ์œผ๋กœ๋ถ€ํ„ฐ ๋ฐฐ๊ผฝ์ด ์‚ฌ๋ผ์ ธ ๋ฒ„๋ฆฐ ์‚ฌ์‹ค์„, ๊ทธ๋ฆฌ๊ณ  ๊ทธ +๋•Œ๋ฌธ์— ์ƒ๊ธด ๋ชจ๋“  ๋ถˆํŽธ์„ ์žŠ๊ณ , ๊ทธ ๋ฐฐ๊ผฝ ์—†๋Š” ์ƒํ™œ์— ์Šค์Šค๋กœ +์ต์ˆ™ํ•ด์ ธ ๋ฒ„๋ฆฌ๊ธฐ๋ฅผ ๋ฐ”๋ผ ๋งˆ์ง€์•Š์•˜๋‹ค. ํ•˜์ง€๋งŒ ๋ฌธ์ œ๋Š” ๊ทธ๋ ‡๊ฒŒ +๊ฐ„๋‹จํ•˜์ง€ ์•Š์•˜๋‹ค. ์•„๋ฌด๋ฆฌ ์ผ์ƒ์ƒํ™œ์—์„  ๋“œ๋Ÿฌ๋‚˜๊ฒŒ ๋ถˆํŽธํ•œ ์ ์ด +์—†๋‹ค ํ•ด๋„ ๊ทธ๋Š” ์—ญ์‹œ ๋ฐฐ๊ผฝ์ด ์—†๋Š” ์ž์‹ ์— ๋Œ€ํ•ด ์ข€์ฒ˜๋Ÿผ ์ต์ˆ™ํ•ด์งˆ +์ˆ˜๊ฐ€ ์—†์—ˆ๋‹ค. ๊ทธ๋Š” ์ž๊พธ๋งŒ ํ—ˆ์ „ํ•ด์„œ ๊ฒฌ๋”œ ์ˆ˜๊ฐ€ ์—†์–ด์ง€๊ณค ํ–ˆ๋‹ค. +์žˆ๋А๋‹ˆ๋ผ ์—ฌ๊ธฐ๊ณ  ์ง€๋‚ผ ๋•Œ๋Š” ๊ทธ์ฒ˜๋Ÿผ ๋ฌด์‹ฌ์Šค๋Ÿฝ๋˜ ์ผ์ด ๊ทธ๋Ÿฐ ์‹์œผ๋กœ +ํ•œ๋ฒˆ ์˜์‹์˜ ๋ˆ์„ ๊ฑด๋“œ๋ ค ์˜ค์ž ํ—ˆ์›์˜ ์ƒ๋…์€ ์ž ์‹œ๋„ ๊ทธ ์žƒ์–ด +๋ฒ„๋ฆฐ ๋ฐฐ๊ผฝ์—์„œ ๋– ๋‚˜ ์žˆ์„ ์ˆ˜๊ฐ€ ์—†์—ˆ๋‹ค. +๊ทธ๋Š” ๋งˆ์นจ๋‚ด ํšŒ์‚ฌ ์ถœ๊ทผ๋งˆ์ € ๋‹จ๋…ํ•˜๊ธฐ์— ์ด๋ฅด๋ €๋‹ค. ๊ทธ๋Ÿฌ์ž ์‹ ํ†ต +ํ•˜๊ฒŒ๋„ ๋Šฆ์ž  ๋ฒ„๋ฆ‡์ด ๊นจ๋—์ด ์ž์ทจ๋ฅผ ๊ฐ์ถฐ ๋ฒ„๋ ธ๋‹ค. ๊ทธ๋Š” ๋ˆˆ๋งŒ ๋œจ๋ฉด +์‚ฌ๋ผ์ ธ ์—†์–ด์ง„ ๋ฐฐ๊ผฝ ๋•Œ๋ฌธ์— ๊ธฐ๋ถ„์ด ํ—ˆ์ „ํ–ˆ๊ณ , ๊ทธ๋Ÿฌ๋ฉด ๊ทธ ํ—ˆ๋ง๊ฐ์„ +์ซ“๊ธฐ ์œ„ํ•ด ๋ฐฐ๊ผฝ์— ๊ด€ํ•œ ๋์—†๋Š” ์ƒ๋…๋“ค์„ ์Œ“๊ธฐ ์‹œ์ž‘ํ–ˆ๋‹ค. +(์ค‘๋žต) +๊ทธ๋ฆฌํ•˜์—ฌ ๋ฐฐ๊ผฝ์— ๊ด€ํ•œ ํ—ˆ์›์˜ ์ง€์‹๊ณผ ์‚ฌ๋…์€ ์ž๊พธ ๋” ์‹ฌ์˜คํ•˜ +๊ณ  ์ถ”์ƒ์ ์ธ ๊ฒƒ์ด ๋˜์–ด ๊ฐ”๋‹ค. ๊ทธ์—๊ฒŒ๋Š” ์–ด๋А๋ง ๊ทธ ๋‚˜๋ฆ„์˜ ๋…ํŠนํ•œ +๋ฐฐ๊ผฝ๋ก  ๊ฐ™์€ ๊ฒƒ์ด ์œค๊ณฝ์„ ์ง€์–ด ๊ฐ€๊ณ  ์žˆ์—ˆ๋‹ค. ํ•˜์ง€๋งŒ ๊ทธ๋Ÿฌ๋ฉด ๊ทธ๋Ÿด +์ˆ˜๋ก ํ—ˆ์›์€ ๋”์šฑ๋” ํ—ˆ์ „ํ•ด์ง€๊ณ , ์•„๋ฌด ๊ณณ์—๋„ ๋ฐœ์ด ๋‹ฟ์•„ ์žˆ๋Š” ๊ฒƒ +๊ฐ™์ง€ ์•Š๊ณ , ํ˜ผ์ž์„œ ์™ธ๋กญ๊ฒŒ ํ—ˆ๊ณต์„ ๋‘ฅ๋‘ฅ ๋– ๋‹ค๋‹ˆ๊ณ  ์žˆ๋Š” ๊ฒƒ์ฒ˜๋Ÿผ +๋А๊ปด์กŒ๋‹ค. ๊ทธ๋Ÿฌ๋ฉด ๊ทธ๋Š” ๋˜ ๊ฑฐ๋“ญ ๊ทธ ํ—ˆ๋ง๊ฐ์„ ์ซ“๊ธฐ ์œ„ํ•ด ์ž์‹ ์˜ +๋ฐฐ๊ผฝ๋ก ์„ ์™„๋ฒฝํ•˜๊ฒŒ ๋ฐœ์ „์‹œ์ผœ ๋‚˜๊ฐ”๋‹ค. ๋งˆ์น˜ ๊ทธ๋ ‡๊ฒŒ ํ•˜์—ฌ ๊ทธ๋Š” +์ž์‹ ์˜ ์‚ฌ๋… ์†์—์„œ ์žƒ์–ด๋ฒ„๋ฆฐ ๋ฐฐ๊ผฝ์„ ๋˜์ฐพ์•„๋‚ด๊ณ , ๊ทธ๊ฒƒ์œผ๋กœ ๊ทธ +์‹ค๋ฌผ์„ ๋Œ€์‹ ํ•ด ์–ด๋–ค ์‹์œผ๋กœ๋“  ์ž์‹ ๊ณผ ์„ธ์ƒ ๊ฐ„์— ํฐ ๋ถˆํŽธ์ด ์—†๋„๋ก +ํ™”ํ•ด์‹œํ‚ค๊ณ  ๊ทธ๊ฒƒ์œผ๋กœ ๊ทธ ๋‚œ๊ฐ์Šค๋Ÿฐ ํ—ˆ๋ง๊ฐ์„ ์ฑ„์šฐ๋ ค๋Š” ๋“ฏ์ด. ๊ทธ์˜ +๋ฐฐ๊ผฝ๋ก ์€ ๊ฐ€๋ น ์ด๋Ÿฐ ์‹์œผ๋กœ๊นŒ์ง€ ๋ฐœ์ „๋˜์–ด ์žˆ์—ˆ๋‹ค. + +โ€•์šฐ๋ฆฌ๋Š” ๋ˆ„๊ตฌ๋‚˜ ๋ฐฐ๊ผฝ์„ ๊ฐ€์ง€๊ณ  ์žˆ๋‹คโ€ฆโ€ฆ ์šฐ๋ฆฌ๋Š” ์šฐ๋ฆฌ๋“ค์˜ +์–ด๋จธ๋‹ˆ๋กœ๋ถ€ํ„ฐ ํƒฏ์ค„์ด ๋Š์–ด์ง€๋Š” ์ˆœ๊ฐ„ ์ด ์šฐ์ฃผ์˜ ํ•œ ๋‹จ์ž(ๅ–ฎๅญ)๋กœ์„œ +๊ณ ๋…ํ•˜๊ฒŒ ์กด์žฌํ•˜๊ฒŒ ๋˜์—ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์šฐ๋ฆฌ๋Š” ์˜์›ํžˆ ๊ทธ ํƒฏ์ค„์˜ +๊ธฐ์–ต์„ ์žŠ์ง€ ์•Š๋Š”๋‹ค. ์šฐ๋ฆฌ ์˜ํ˜ผ์€ ์–ธ์ œ๊นŒ์ง€๋‚˜ ๊ทธ ์–ด๋จธ๋‹ˆ์˜ ํƒฏ์ค„๊ณผ +์ด์–ด์ง€๋ ค ํ•˜๊ณ , ๋˜๋‹ค์‹œ ๊ทธ ์–ด๋จธ๋‹ˆ์˜ ์–ด๋จธ๋‹ˆ์˜ ํƒฏ์ค„๊ณผ ์ด์–ด์ ธ +๋‚˜๊ฐ€๋ฉด์„œ ์šฐ๋ฆฌ ์กด์žฌ๋ฅผ ์„ค๋ช…ํ•˜๊ณ  ๊ทผ์›์„ ๋ฐํ˜€ ๋‚˜๊ฐ€๋ฉฐ, ๋งˆ์นจ๋‚ด๋Š” +๋งˆ์ง€๋ง‰ ์–ด๋จธ๋‹ˆ์˜ ํƒฏ์ค„์ด ์ด์–ด์ง€๋Š” ์šฐ๋ฆฌ๋“ค์˜ ์šฐ์ฃผ์™€ ๋งŒ๋‚˜๊ฒŒ +๋œ๋‹คโ€ฆโ€ฆ ์šฐ๋ฆฌ์˜ ๋ฐฐ๊ผฝ์€ ์šฐ๋ฆฌ๊ฐ€ ๊ทธ ๋งˆ์ง€๋ง‰ ์šฐ์ฃผ์™€ ๋งŒ๋‚˜๊ณ ์ž ํ•˜๋Š” +ํ–ฅ์ˆ˜์˜ ํ‘œ์ƒ์ด๋ฉฐ ๊ฐ€๋Šฅ์„ฑ์˜ ์ƒ์ง•์ด๋ฉฐ ์กด์žฌ์˜ ๋น„๋ฐ€๋กœ ๋‚˜์•„๊ฐ€๋Š” +ํ˜•์ด์ƒํ•™์ด๋‹ค. ๊ทธ ๋น„๋ฐ€์˜ ๋ฌธ์ด๋‹คโ€ฆโ€ฆ +๊ทธ๋Š” ์–ด๋А๋ง ๋ฐฐ๊ผฝ์— ๋Œ€ํ•ด ๋‹น๋‹นํ•œ ์ผ๊ฐ€๊ฒฌ์„ ์ด๋ฃฌ ๋ฐฐ๊ผฝ ์ „๋ฌธ๊ฐ€๊ฐ€ +๋˜์–ด ๊ฐ€๊ณ  ์žˆ์—ˆ๋‹ค. +ใ‰ฃ์–ด๋А ํ•ด ์—ฌ๋ฆ„์ด์—ˆ๋‹ค. ํ•˜๋‹ˆ๊นŒ ๊ทธ๊ฒƒ์€ ํ—ˆ์›์ด ์ž์‹ ์˜ ๋ฐฐ๊ผฝ์„ +์žƒ์–ด๋ฒ„๋ฆฌ๊ณ  ๋‚˜์„œ ๋ถˆํŽธํ•˜๊ธฐ ๊ทธ์ง€์—†๋Š” ์„ธ ๋ฒˆ์งธ์˜ ์—ฌ๋ฆ„์„ ๋งž๊ณ  ์žˆ์„ +๋•Œ์˜€๋‹ค. ๊ทธ๋Š” ๋ฌผ๋ก  ๋ฐฐ๊ผฝ์„ ์žƒ์–ด๋ฒ„๋ฆฐ ์ž์‹ ์— ๋Œ€ํ•ด ์•„์ง๋„ ์™„์ „ํžŒ +์ต์ˆ™ํ•ด์ง€์งˆ ๋ชปํ•˜๊ณ  ์žˆ์—ˆ๋‹ค. ๊ทธ์˜ ์‚ฌ๋… ์—ญ์‹œ ์–ธ์ œ๋‚˜ ๊ทธ ๋ˆˆ์— +๋ณด์ด์ง€ ์•Š๋Š” ๋ฐฐ๊ผฝ์— ๋งค๋‹ฌ๋ ค ๊ฑฐ๊ธฐ์—์„œ๋ฐ–์—๋Š” ์˜์˜ ๋” ์ด์ƒ ์ž์œ  +๋กœ์›Œ์งˆ ์ˆ˜๊ฐ€ ์—†์—ˆ๋‹ค. ๊ทธ ๋Œ€์‹  ํ—ˆ์›์€ ์ด์ œ ๊ทธ ์ž์‹ ์˜ ๋ฐฐ๊ผฝ๋ก ์— +๋Œ€ํ•ด์„  ๋งค์šฐ ํ™•๊ณ ํ•œ ๊ฒฝ์ง€์— ๋„๋‹ฌํ•ด ์žˆ์—ˆ๋‹ค. +๊ทธ๋Ÿด ์ฆˆ์Œ์ด์—ˆ๋‹ค. ํ—ˆ์›์€ ๋ฌธ๋“ ์„ธ์ƒ ์‚ฌ๋žŒ๋“ค์ด ์ˆ˜์ƒ์ฉ์–ด์ง€๊ธฐ +์‹œ์ž‘ํ–ˆ๋‹ค. ์–ด๋А ๋•Œ๋ถ€ํ„ด์ง€๋Š” ํ™•์‹คํžˆ ์•Œ ์ˆ˜ ์—†์—ˆ์ง€๋งŒ, ์„ธ์ƒ ์‚ฌ๋žŒ๋“ค +์—ญ์‹œ ๋ฌด์Šจ ์ด์œ ์—์„ ์ง€ ์ด ์ธ๊ฐ„ ์žฅ๊ธฐ์˜ ํ•œ ์กฐ๊ทธ๋งŒ ํ”์ ์— ๋Œ€ํ•ด +์‹ฌ์ƒ์ฐฎ์€ ๊ด€์‹ฌ์„ ๋‚˜ํƒ€๋‚ด๊ธฐ ์‹œ์ž‘ํ•œ ๊ฒƒ์ด๋‹ค. ๋ฐฐ๊ผฝ์— ๋Œ€ํ•œ ์‚ฌ๋žŒ๋“ค์˜ +๊ด€์‹ฌ ์—ญ์‹œ ๊ธฐ์™•๋ถ€ํ„ฐ ์žˆ์–ด ์˜จ ๊ฒƒ์„ ์—ฌํƒœ๊นŒ์ง€ ์„œ๋กœ ๋ชจ๋ฅด๊ณ  ์ง€๋‚ด +์˜ค๋‹ค๊ฐ€ ๋น„๋กœ์†Œ ์–ด๋–ค ๊ธฐ๋ฏธ๋ฅผ ์•Œ์•„์ฐจ๋ฆฌ๊ฒŒ ๋œ ๊ฒƒ์ธ์ง€, ํ˜น์€ ์‚ฌ๋žŒ๋“ค๋กœ +ํ•˜์—ฌ๊ธˆ ๊ทธ๋Ÿฐ ๊ด€์‹ฌ์„ ๋‚ด๋ณด์ด๊ฒŒ ํ•  ๋งŒํ•œ ๋ฌด์Šจ ์šฐ์—ฐ์ฐฎ์€ ๊ณ„๊ธฐ๊ฐ€ +๋งˆ๋ จ๋˜์—ˆ๋Š”์ง€๋Š” ํ™•์‹ค์น˜๊ฐ€ ์•Š์•˜๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๋ฌด์—‡ ๋•Œ๋ฌธ์— ์‚ฌ๋žŒ๋“ค +์—๊ฒŒ์„œ ๊ทธ๋Ÿฐ ๊ด€์‹ฌ์ด ์‹œ์ž‘๋˜์—ˆ๋Š”์ง€ ๊ทธ ์ด์œ ๋ฅผ ์•Œ ์ˆ˜๋„ ์—†์—ˆ๋‹ค. +ํ•˜์ง€๋งŒ ๊ทธ๊ฒƒ์€ ์–ด์จŒ๋“  ์‚ฌ์‹ค์ด์—ˆ๋‹ค. ์ฃผ์˜๋ฅผ ๊ธฐ์šธ์—ฌ ๋ณด๋‹ˆ ๊ด€์‹ฌ์˜ +์ •๋„๋„ ์—ฌ๊ฐ„์ด ์•„๋‹ˆ์—ˆ๋‹ค. ํ•œ๋‘ ์‚ฌ๋žŒ, ํ•œ๋‘ ๊ณณ์—์„œ๋งŒ ๋‚˜ํƒ€๋‚œ +ํ˜„์ƒ์ด ์•„๋‹ˆ์—ˆ๋‹ค. ๊ทธ๊ฒƒ์€ ์ด๋ฏธ ์ผ๋ฐ˜์ ์ธ ํ˜„์ƒ์ด ๋˜์–ด ๊ฐ€๊ณ  +์žˆ์—ˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๊ทธ๋ ‡๋“ฏ ๋ฐฐ๊ผฝ ์ด์•ผ๊ธฐ๊ฐ€ ์ผ๋ฐ˜ํ™”์˜ ๊ธฐ๋ฏธ๋ฅผ ์—ฟ๋ณด์ด๊ธฐ +์‹œ์ž‘ํ•˜์ž ์‚ฌ๋žŒ๋“ค์€ ์ด์ œ ๊ทธ๊ฑธ ์‹ ํ˜ธ๋กœ ์•„๋ฌด ํ‰ํ—ˆ๋ฌผ ์—†์ด ํ„ฐ๋†“๊ณ  +์ง€๊ป„์ด๊ฑฐ๋‚˜ ์‹ ๋ฌธ, ์žก์ง€ ๊ฐ™์€ ๋ฐ์„œ ์ง„์ง€ํ•˜๊ฒŒ ๋…ผ์˜์˜ ๋Œ€์ƒ์„ +์‚ผ๊ธฐ๋„ ํ•˜์˜€๋‹ค. ใ‰ค๋ฐฐ๊ผฝ์— ๊ด€ํ•œ ๋…ผ์˜๊ฐ€ ๊ทธ๋ ‡๋“ฏ ๊ฐ‘์ž๊ธฐ +์‹œ์ค‘ ์ผ๋ฐ˜์—๊นŒ์ง€ ์„ฑํ–‰ํ•˜๊ธฐ ์‹œ์ž‘ํ•œ ๊ฒƒ์ด๋‹ค. +๊ธฐ๋ฌ˜ํ•œ ํ˜„์ƒ์ด์—ˆ๋‹ค. +-์ด์ฒญ์ค€, ๏ฝข๋ฐฐ๊ผฝ์„ ์ฃผ์ œ๋กœ ํ•œ ๋ณ€์ฃผ๊ณก๏ฝฃ +","{'question': '<๋ณด๊ธฐ>๋ฅผ ์ฐธ๊ณ ํ•˜์—ฌ ์œ—๊ธ€์„ ๊ฐ์ƒํ•œ ๋‚ด์šฉ์œผ๋กœ ์ ์ ˆํ•˜์ง€ ์•Š์€\n๊ฒƒ์€? ', 'choices': ['์˜์‹์˜ ๋ˆโ€™์ด โ€˜๊ฑด๋“œ๋ คโ€™์ง์œผ๋กœ์จ ์ฃผ์ธ๊ณต์ด ๋น„์ •์ƒ์  ๋ฌธ์ œ ์ƒํ™ฉ์—\n์ง€์†์ ์œผ๋กœ ์ฃผ๋ชฉํ•˜๊ฒŒ ๋œ ๊ฒƒ์ด๊ฒ ๊ตฐ.', 'ํšŒ์‚ฌ ์ถœ๊ทผโ€™์„ ํฌ๊ธฐํ•˜๊ฒŒ ๋˜๊ณ  โ€˜๋Šฆ์ž  ๋ฒ„๋ฆ‡โ€™์ด ์‚ฌ๋ผ์ง„ ์ƒํ™ฉ์€,\n์ฃผ์ธ๊ณต์˜ ์ผ์ƒ์ด ๋ณ€ํ™”๋œ ๋ชจ์Šต์„ ๋ณด์—ฌ ์ค€๋‹ค๊ณ  ํ•  ์ˆ˜ ์žˆ๊ฒ ๊ตฐ.', '๋ฐฐ๊ผฝโ€™์„ โ€˜ํƒฏ์ค„โ€™์— ์—ฐ๊ด€ํ•˜์—ฌ ์ดํ•ดํ•˜๋Š” ๊ฒƒ์€, ๊ฐœ์ธ์— ๊ด€๋ จ๋œ ์ƒ๊ฐ์„\nโ€˜์šฐ์ฃผ์™€ ๋งŒ๋‚˜โ€™๋Š” โ€˜์‹ฌ์˜คํ•˜๊ณ  ์ถ”์ƒ์ ์ธโ€™ ์ƒ๊ฐ์œผ๋กœ ํ™•์žฅํ•˜๋Š” ์‹ค๋งˆ๋ฆฌ๊ฐ€\n๋œ๋‹ค๊ณ  ํ•  ์ˆ˜ ์žˆ๊ฒ ๊ตฐ', 'โ€˜๊ทธ์˜ ์‚ฌ๋…โ€™์ด ๋„๋‹ฌํ•œ โ€˜๋ฐฐ๊ผฝ๋ก โ€™์˜ โ€˜ํ™•๊ณ ํ•œ ๊ฒฝ์ง€โ€™๋Š” ์‚ฌ์†Œํ•œ ๊ฒƒ์˜\n์‹ฌ์ธต์  ์˜๋ฏธ๋ฅผ ํƒ์ƒ‰ํ•  ๋•Œ ์ด๋ฅผ ์ˆ˜ ์žˆ์œผ๋ฏ€๋กœ, ๊ทธ ์‚ฌ์†Œํ•œ ๊ฒƒ์—\n์–ฝ๋งค์ด์ง€ ์•Š๋Š” ์ž์œ ๋กœ์šด ์ƒํƒœ์—์„œ ์‹คํ˜„์ด ๊ฐ€๋Šฅํ•ด์ง€๊ฒ ๊ตฐ.', 'โ€˜๊ธฐ๋ฌ˜ํ•œ ํ˜„์ƒโ€™์€, โ€˜๋ฐฐ๊ผฝ ์ด์•ผ๊ธฐโ€™๊ฐ€ โ€˜์ผ๋ฐ˜ํ™”โ€™๋˜๋Š” ์ƒํ™ฉ์ด ๋œป๋ฐ–์ด์ง€๋งŒ\nโ€˜์‚ฌ์‹คโ€™๋กœ ๋‚˜ํƒ€๋‚˜๋Š” ํ˜„์ƒ์„ ๋‘๊ณ  ์ผ์ปฌ์€ ๋ง์ด๋ผ๊ณ  ํ•  ์ˆ˜ ์žˆ๊ฒ ๊ตฐ.'], 'answer': '', 'question_plus': '๏ฝข๋ฐฐ๊ผฝ์„ ์ฃผ์ œ๋กœ ํ•œ ๋ณ€์ฃผ๊ณก๏ฝฃ์€ ์ฃผ์ธ๊ณต์ด ๋ฐฐ๊ผฝ์„ ์žƒ์–ด๋ฒ„๋ ธ๋‹ค๋Š”\nํ—ˆ๊ตฌ์  ์„ค์ •์œผ๋กœ ์‹œ์ž‘ํ•˜์—ฌ, ์ดํ›„ ๋ฐฐ๊ผฝ์„ ๋‘˜๋Ÿฌ์‹ผ ํฌํ™”์  ์—ํ”ผ์†Œ๋“œ\n๋“ค์ด ์ด์–ด์ง„๋‹ค. ์ฃผ์ธ๊ณต์€ ์œผ๋ ˆ ์žˆ์–ด์•ผ ํ•  ๊ฒƒ์ด ์—†์–ด์ ธ ๋ถˆํŽธํ•œ\n์ƒํ™œ์„ ์ด์–ด ๊ฐ€๋˜ ์ค‘ ๋ฐฐ๊ผฝ์— ๊ด€์‹ฌ์„ ๊ฐ–๋Š” ์ด๋“ค์ด ๋Š˜์–ด๋‚˜๊ณ \n์žˆ์Œ์„ ์•Œ๊ฒŒ ๋œ๋‹ค. ์ด ๊ณผ์ •์—์„œ ๋ฐฐ๊ผฝ์— ๊ด€๋ จ๋œ ๊ฐœ์ธ์  ์ƒํ™ฉ์€\n๋ฌผ๋ก  ์ธ๊ฐ„ ์กด์žฌ์™€ ์‚ฌํšŒ ์ƒํ™ฉ์— ๋Œ€ํ•œ ์‹ฌ์ธต์  ์˜๋ฏธ์˜ ํƒ์ƒ‰์ด\n์ด๋ฃจ์–ด์ง„๋‹ค'}","๏ฝข๋ฐฐ๊ผฝ์„ ์ฃผ์ œ๋กœ ํ•œ ๋ณ€์ฃผ๊ณก๏ฝฃ์€ ์ฃผ์ธ๊ณต์ด ๋ฐฐ๊ผฝ์„ ์žƒ์–ด๋ฒ„๋ ธ๋‹ค๋Š” +ํ—ˆ๊ตฌ์  ์„ค์ •์œผ๋กœ ์‹œ์ž‘ํ•˜์—ฌ, ์ดํ›„ ๋ฐฐ๊ผฝ์„ ๋‘˜๋Ÿฌ์‹ผ ํฌํ™”์  ์—ํ”ผ์†Œ๋“œ +๋“ค์ด ์ด์–ด์ง„๋‹ค. ์ฃผ์ธ๊ณต์€ ์œผ๋ ˆ ์žˆ์–ด์•ผ ํ•  ๊ฒƒ์ด ์—†์–ด์ ธ ๋ถˆํŽธํ•œ +์ƒํ™œ์„ ์ด์–ด ๊ฐ€๋˜ ์ค‘ ๋ฐฐ๊ผฝ์— ๊ด€์‹ฌ์„ ๊ฐ–๋Š” ์ด๋“ค์ด ๋Š˜์–ด๋‚˜๊ณ  +์žˆ์Œ์„ ์•Œ๊ฒŒ ๋œ๋‹ค. ์ด ๊ณผ์ •์—์„œ ๋ฐฐ๊ผฝ์— ๊ด€๋ จ๋œ ๊ฐœ์ธ์  ์ƒํ™ฉ์€ +๋ฌผ๋ก  ์ธ๊ฐ„ ์กด์žฌ์™€ ์‚ฌํšŒ ์ƒํ™ฉ์— ๋Œ€ํ•œ ์‹ฌ์ธต์  ์˜๋ฏธ์˜ ํƒ์ƒ‰์ด +์ด๋ฃจ์–ด์ง„๋‹ค",4,4,True,[],4 +2025-korean-32,"(๊ฐ€) +์–ด์ ธ ์–ด์ ธ ์ €๊ธฐ ๊ฐ€๋Š” ์ € ์‚ฌ๋žŒ์•„ +๋„ค ํ–‰์ƒ‰์„ ๋ณด์•„ ํ•˜๋‹ˆ ๊ตฐ์‚ฌ ๋„๋ง ๋„ค๋กœ๊ตฌ๋‚˜ +ํ—ˆ๋ฆฌ ์œ„๋กœ ๋ณผ์ž‘์‹œ๋ฉด ๋ฒ ์ ์‚ผ์ด ๊นƒ๋งŒ ๋‚จ๊ณ  +ํ—ˆ๋ฆฌ ์•„๋ž˜ ๊ตฝ์–ด๋ณด๋‹ˆ ํ—Œ ์ž ๋ฐฉ์ด ๋…ธ๋‹ฅ๋…ธ๋‹ฅ +๊ณฑ์žฅ ํ• ๋ฏธ ์•ž์— ๊ฐ€๊ณ  ์ „ํƒœ๋ฐœ์ด ๋’ค์— ๊ฐ„๋‹ค +์‹ญ ๋ฆฌ ๊ธธ์„ ํ•˜๋ฃจ ๊ฐ€๋‹ˆ ๋ช‡ ๋ฆฌ ๊ฐ€์„œ ์—Ž์–ด์ง€๋ฆฌ +๋‚ด ๊ณ ์„์˜ ์–‘๋ฐ˜ ์‚ฌ๋žŒ ํƒ€๋„ ํƒ€๊ด€ ์˜ฎ๊ฒจ ์‚ด๋ฉด +์ฒœํžˆ ๋˜๊ธฐ ์ƒ์‚ฌ์—ฌ๋“  ๋ณธํ†  ๊ตฐ์ •(่ปไธ) ์‹ซ๋‹ค ํ•˜๊ณ  +์ž๋„ค ๋˜ํ•œ ๋„๋งํ•˜๋ฉด ์ผ๊ตญ ์ผํ† (ไธ€ๅœŸ) ํ•œ ์ธ์‹ฌ์— +๊ทผ๋ณธ ์ˆจ๊ฒจ ์‚ด๋ ค ํ•œ๋“ค ์–ด๋ฐ ๊ฐ„๋“ค ๋ฉดํ• ์œ๊ฐ€ +์ฐจ๋ผ๋ฆฌ ๋„ค ์‚ด๋˜ ๊ณณ์— ์•„๋ฌด๋ ‡๊ฒŒ๋‚˜ ๋ฟŒ๋ฆฌ๋ฐ•ํ˜€ +์น ํŒ”์›”์— ใ‰ ์ธ์‚ผ ์บ๊ณ  ๊ตฌ์‹œ์›”์— ๋ˆํ”ผ* ์žก์•„ +๊ณต์ฑ„ ์‹ ์—ญ ๊ฐš์€ ํ›„์— ๊ทธ ๋‚˜๋จธ์ง€ ๋‘์—ˆ๋‹ค๊ฐ€ +ํ•จํฅ ๋ถ์ฒญ ํ™์› ์žฅ์‚ฌ ๋Œ์•„๋“ค์–ด ์ž ๋งคํ•  ๋•Œ +ํ›„ํ•œ ๊ฐ’์— ํŒ”์•„ ๋‚ด์–ด ์‚ด๊ธฐ ์ข‹์€ ๋„“์€ ๊ณณ์— +๊ฐ€์‚ฌ ์ „ํ† (ๅฎถ่ˆ็”ฐๅœŸ) ๋‹ค์‹œ ์‚ฌ๊ณ  ์‚ด๋ฆผ์‚ด์ด ์žฅ๋งŒํ•˜์—ฌ +๋ถ€๋ชจ์ฒ˜์ž ๋ณด์ „ํ•˜๊ณ  ์ƒˆ ์ฆ๊ฑฐ์›€ ๋ˆ„๋ฆฌ๋ ค๋ฌด๋‚˜ +์–ด์™€ ์ƒ์›์ธ์ง€ ์ดˆ๊ด€์ธ์ง€ +๊ทธ๋Œ€ ๋ง์”€ ๊ทธ๋งŒ๋‘๊ณ  ์ด๋‚ด ๋ง์”€ ๋“ค์–ด ๋ณด์†Œ +์ด ๋‚ด ๋˜ํ•œ ๊ฐ‘๋ฏผ(็”ฒๆฐ‘)*์ด๋ผ ์ด ๋•…์—์„œ ์ƒ์žฅํ•˜๋‹ˆ ์ด๋•Œ ์ผ์„ +๋ชจ๋ฅผ์˜๋ƒ +์šฐ๋ฆฌ ์กฐ์ƒ ๋‚จ์ชฝ ์–‘๋ฐ˜ ์ง„์‚ฌ ๊ธ‰์ œ ๊ณ„์†ํ•˜์—ฌ +๊ธˆ์žฅ ์˜ฅํŒจ ๋น—๊ธฐ ์ฐจ๊ณ  ์‹œ์ข…์‹ ์„ ๋‹ค๋‹ˆ๋‹ค๊ฐ€ +์‹œ๊ธฐ์ธ์˜ ์ฐธ์†Œ ์ž…์–ด ๋ณ€๋ฐฉ์œผ๋กœ ์ซ“๊ฒจ ์™€์„œ +๊ตญ๋‚ด ๋ณ€๋ฐฉ ์ด ๋•…์—์„œ ์น ํŒ” ๋Œ€๋ฅผ ์‚ด์•„์˜ค๋‹ˆ +์กฐ์ƒ ๋•์— ํ•˜๋Š” ์ผ์ด ์์ค‘ ๊ตฌ์‹ค ์ฒซ์งธ๋กœ๋‹ค +๋“ค์–ด๊ฐ€๋ฉด ์ขŒ์ˆ˜ ๋ณ„๊ฐ ๋‚˜๊ฐ€์„œ๋Š” ํ’ํ—Œ ๊ฐ๊ด€ +์œ ์‚ฌ ์žฅ์˜ ์ฑ„์ง€ ๋‚˜๋ฉด ์ฒด๋ฉด ๋ณด์•„ ์‚ฌ์–‘ํ„ฐ๋‹ˆ +์• ์Šฌํ”„๋‹ค ๋‚ด ์‹œ์ ˆ์— ์›์ˆ˜์ธ์˜ ๋ชจํ•ด๋กœ์จ +๊ตฐ์‚ฌ ๊ฐ•์ • ๋˜๋‹จ ๋ง๊ฐ€ ๋‚ด ํ•œ ๋ชธ์ด ํ—์–ด ๋‚˜๋‹ˆ +์ขŒ์šฐ์ „ํ›„ ์ˆ˜๋‹ค ์ผ๊ฐ€ ์ฐจ์ฐจ ์ถฉ๊ตฐ(ๅ……่ป) ๋˜๊ฒƒ๊ณ ์•ผ +์กฐ์ƒ ์ œ์‚ฌ ์ด๋‚ด ๋ชธ์€ ํ•˜๋ฆด์—†์ด ๋งค์—ฌ ์žˆ๊ณ  +์‹œ๋ฆ„์—†๋Š” ์นœ์กฑ๋“ค์€ ์ž์ทจ ์—†์ด ๋„๋งํ•˜๊ณ  +์—ฌ๋Ÿฌ ์‚ฌ๋žŒ ๋ชจ๋“  ์‹ ์—ญ ๋‚ด ํ•œ ๋ชธ์— ๋ชจ๋‘ ๋ฌด๋‹ˆ +ํ•œ ๋ชธ ์‹ ์—ญ ์‚ผ ๋ƒฅ ์˜ค ์ „ ๋ˆํ”ผ ๋‘ ์žฅ ์˜๋ฒ•์ด๋ผ +์—ด๋‘ ์‚ฌ๋žŒ ์—†๋Š” ๊ตฌ์‹ค ํ•ฉ์ณ ๋ณด๋ฉด ์‚ฌ์‹ญ์œก ๋ƒฅ +ํ•ด๋งˆ๋‹ค ๋งก์•„ ๋ฌด๋‹ˆ ์„์ˆญ*์ธ๋“ค ๋‹นํ• ์˜๋ƒ +-์ž‘์ž ๋ฏธ์ƒ, ๏ฝข๊ฐ‘๋ฏผ๊ฐ€๏ฝฃ- +*๋ˆํ”ผ:๋‹ด๋น„ ๊ฐ€์ฃฝ. +*๊ฐ‘๋ฏผ:๊ฐ‘์‚ฐ์˜ ๋ฐฑ์„ฑ. +*์„์ˆญ:์ค‘๊ตญ ์ง„๋‚˜๋ผ ๋•Œ์˜ ๋ถ€์ž. + +(๋‚˜) +๋…น์–‘๋ฐฉ์ดˆ ์–ธ๋•์— ์†Œ ๋จน์ด๋Š” ์•„ํฌ๋“ค์•„ +์•ž๋‚ด ใ‰ก๊ณ ๊ธฐ ๋’ท๋‚ด ๊ณ ๊ธฐ๋ฅผ ๋‹ค ๋ชฝ๋•… ์žก์•„๋‚ด ๋‹ค๋ž˜๋ผ*์— ๋„ฃ์–ด +์ฃผ๊ฑฐ๋“  ๋„ค ์†Œ ๊ถ๋‘ฅ์ด์— ์–น์–ด๋‹ค๊ฐ€ ์ฃผ๋ ด +์šฐ๋ฆฌ๋„ ์„œ์ฃผ(่ฅฟ็–‡)*์— ์ผ์ด ๋งŽ์•„ ๋ฐ”์‚ ๊ฐ€๋Š” ๊ธธ์ด๋งค ๊ฐ€ ์ „ํ• ๋™ +๋ง๋™ ํ•˜์—ฌ๋ผ +-์ž‘์ž ๋ฏธ์ƒ, ์‚ฌ์„ค์‹œ์กฐ- +*๋‹ค๋ž˜๋ผ:๋ฌผ๊ณ ๊ธฐ๋‚˜ ์ž‘์€ ๋ฌผ๊ฑด ๋“ฑ์„ ๋„ฃ๋Š” ๋ฐ”๊ตฌ๋‹ˆ. +*์„œ์ฃผ:์„œ์ชฝ ๋ฐญ.","{'question': '(๊ฐ€)์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์ ์ ˆํ•˜์ง€ ์•Š์€ ๊ฒƒ์€?', 'choices': ['๋Œ€๊ตฌ ํ‘œํ˜„์œผ๋กœ ์™ธ์–‘์„ ๋ฌ˜์‚ฌํ•˜์—ฌ ๋Œ€์ƒ์˜ ์ฒ˜์ง€๋ฅผ ๋“œ๋Ÿฌ๋‚ธ๋‹ค.', 'ํ–‰์œ„์˜ ์‹คํ–‰์„ ๊ฐ€์ •ํ•˜์—ฌ ๋ถ€์ •์  ์ „๋ง์„ ์ œ์‹œํ•œ๋‹ค.', '์˜๋ฌธ์˜ ํ‘œํ˜„์„ ์‚ฌ์šฉํ•˜์—ฌ ์ƒ๋Œ€์˜ ํ–‰์ ์— ๋Œ€ํ•ด ์˜์‹ฌํ•œ๋‹ค.', '๊ณผ๊ฑฐ์™€ ํ˜„์žฌ๋ฅผ ๋Œ€๋น„ํ•˜์—ฌ ์•…ํ™”๋œ ์ฒ˜์ง€๋ฅผ ๋ณด์—ฌ ์ค€๋‹ค.', '๊ตฌ์ฒด์  ์ˆ˜์น˜๋ฅผ ์ œ์‹œํ•˜์—ฌ ๊ฐ๋‹นํ•˜๊ธฐ ํž˜๋“  ํ˜„์‹ค์„ ๋“œ๋Ÿฌ๋‚ธ๋‹ค'], 'answer': ''}",,3,3,True,[],3 +2025-korean-33,"(๊ฐ€) +์–ด์ ธ ์–ด์ ธ ์ €๊ธฐ ๊ฐ€๋Š” ์ € ์‚ฌ๋žŒ์•„ +๋„ค ํ–‰์ƒ‰์„ ๋ณด์•„ ํ•˜๋‹ˆ ๊ตฐ์‚ฌ ๋„๋ง ๋„ค๋กœ๊ตฌ๋‚˜ +ํ—ˆ๋ฆฌ ์œ„๋กœ ๋ณผ์ž‘์‹œ๋ฉด ๋ฒ ์ ์‚ผ์ด ๊นƒ๋งŒ ๋‚จ๊ณ  +ํ—ˆ๋ฆฌ ์•„๋ž˜ ๊ตฝ์–ด๋ณด๋‹ˆ ํ—Œ ์ž ๋ฐฉ์ด ๋…ธ๋‹ฅ๋…ธ๋‹ฅ +๊ณฑ์žฅ ํ• ๋ฏธ ์•ž์— ๊ฐ€๊ณ  ์ „ํƒœ๋ฐœ์ด ๋’ค์— ๊ฐ„๋‹ค +์‹ญ ๋ฆฌ ๊ธธ์„ ํ•˜๋ฃจ ๊ฐ€๋‹ˆ ๋ช‡ ๋ฆฌ ๊ฐ€์„œ ์—Ž์–ด์ง€๋ฆฌ +๋‚ด ๊ณ ์„์˜ ์–‘๋ฐ˜ ์‚ฌ๋žŒ ํƒ€๋„ ํƒ€๊ด€ ์˜ฎ๊ฒจ ์‚ด๋ฉด +์ฒœํžˆ ๋˜๊ธฐ ์ƒ์‚ฌ์—ฌ๋“  ๋ณธํ†  ๊ตฐ์ •(่ปไธ) ์‹ซ๋‹ค ํ•˜๊ณ  +์ž๋„ค ๋˜ํ•œ ๋„๋งํ•˜๋ฉด ์ผ๊ตญ ์ผํ† (ไธ€ๅœŸ) ํ•œ ์ธ์‹ฌ์— +๊ทผ๋ณธ ์ˆจ๊ฒจ ์‚ด๋ ค ํ•œ๋“ค ์–ด๋ฐ ๊ฐ„๋“ค ๋ฉดํ• ์œ๊ฐ€ +์ฐจ๋ผ๋ฆฌ ๋„ค ์‚ด๋˜ ๊ณณ์— ์•„๋ฌด๋ ‡๊ฒŒ๋‚˜ ๋ฟŒ๋ฆฌ๋ฐ•ํ˜€ +์น ํŒ”์›”์— ใ‰ ์ธ์‚ผ ์บ๊ณ  ๊ตฌ์‹œ์›”์— ๋ˆํ”ผ* ์žก์•„ +๊ณต์ฑ„ ์‹ ์—ญ ๊ฐš์€ ํ›„์— ๊ทธ ๋‚˜๋จธ์ง€ ๋‘์—ˆ๋‹ค๊ฐ€ +ํ•จํฅ ๋ถ์ฒญ ํ™์› ์žฅ์‚ฌ ๋Œ์•„๋“ค์–ด ์ž ๋งคํ•  ๋•Œ +ํ›„ํ•œ ๊ฐ’์— ํŒ”์•„ ๋‚ด์–ด ์‚ด๊ธฐ ์ข‹์€ ๋„“์€ ๊ณณ์— +๊ฐ€์‚ฌ ์ „ํ† (ๅฎถ่ˆ็”ฐๅœŸ) ๋‹ค์‹œ ์‚ฌ๊ณ  ์‚ด๋ฆผ์‚ด์ด ์žฅ๋งŒํ•˜์—ฌ +๋ถ€๋ชจ์ฒ˜์ž ๋ณด์ „ํ•˜๊ณ  ์ƒˆ ์ฆ๊ฑฐ์›€ ๋ˆ„๋ฆฌ๋ ค๋ฌด๋‚˜ +์–ด์™€ ์ƒ์›์ธ์ง€ ์ดˆ๊ด€์ธ์ง€ +๊ทธ๋Œ€ ๋ง์”€ ๊ทธ๋งŒ๋‘๊ณ  ์ด๋‚ด ๋ง์”€ ๋“ค์–ด ๋ณด์†Œ +์ด ๋‚ด ๋˜ํ•œ ๊ฐ‘๋ฏผ(็”ฒๆฐ‘)*์ด๋ผ ์ด ๋•…์—์„œ ์ƒ์žฅํ•˜๋‹ˆ ์ด๋•Œ ์ผ์„ +๋ชจ๋ฅผ์˜๋ƒ +์šฐ๋ฆฌ ์กฐ์ƒ ๋‚จ์ชฝ ์–‘๋ฐ˜ ์ง„์‚ฌ ๊ธ‰์ œ ๊ณ„์†ํ•˜์—ฌ +๊ธˆ์žฅ ์˜ฅํŒจ ๋น—๊ธฐ ์ฐจ๊ณ  ์‹œ์ข…์‹ ์„ ๋‹ค๋‹ˆ๋‹ค๊ฐ€ +์‹œ๊ธฐ์ธ์˜ ์ฐธ์†Œ ์ž…์–ด ๋ณ€๋ฐฉ์œผ๋กœ ์ซ“๊ฒจ ์™€์„œ +๊ตญ๋‚ด ๋ณ€๋ฐฉ ์ด ๋•…์—์„œ ์น ํŒ” ๋Œ€๋ฅผ ์‚ด์•„์˜ค๋‹ˆ +์กฐ์ƒ ๋•์— ํ•˜๋Š” ์ผ์ด ์์ค‘ ๊ตฌ์‹ค ์ฒซ์งธ๋กœ๋‹ค +๋“ค์–ด๊ฐ€๋ฉด ์ขŒ์ˆ˜ ๋ณ„๊ฐ ๋‚˜๊ฐ€์„œ๋Š” ํ’ํ—Œ ๊ฐ๊ด€ +์œ ์‚ฌ ์žฅ์˜ ์ฑ„์ง€ ๋‚˜๋ฉด ์ฒด๋ฉด ๋ณด์•„ ์‚ฌ์–‘ํ„ฐ๋‹ˆ +์• ์Šฌํ”„๋‹ค ๋‚ด ์‹œ์ ˆ์— ์›์ˆ˜์ธ์˜ ๋ชจํ•ด๋กœ์จ +๊ตฐ์‚ฌ ๊ฐ•์ • ๋˜๋‹จ ๋ง๊ฐ€ ๋‚ด ํ•œ ๋ชธ์ด ํ—์–ด ๋‚˜๋‹ˆ +์ขŒ์šฐ์ „ํ›„ ์ˆ˜๋‹ค ์ผ๊ฐ€ ์ฐจ์ฐจ ์ถฉ๊ตฐ(ๅ……่ป) ๋˜๊ฒƒ๊ณ ์•ผ +์กฐ์ƒ ์ œ์‚ฌ ์ด๋‚ด ๋ชธ์€ ํ•˜๋ฆด์—†์ด ๋งค์—ฌ ์žˆ๊ณ  +์‹œ๋ฆ„์—†๋Š” ์นœ์กฑ๋“ค์€ ์ž์ทจ ์—†์ด ๋„๋งํ•˜๊ณ  +์—ฌ๋Ÿฌ ์‚ฌ๋žŒ ๋ชจ๋“  ์‹ ์—ญ ๋‚ด ํ•œ ๋ชธ์— ๋ชจ๋‘ ๋ฌด๋‹ˆ +ํ•œ ๋ชธ ์‹ ์—ญ ์‚ผ ๋ƒฅ ์˜ค ์ „ ๋ˆํ”ผ ๋‘ ์žฅ ์˜๋ฒ•์ด๋ผ +์—ด๋‘ ์‚ฌ๋žŒ ์—†๋Š” ๊ตฌ์‹ค ํ•ฉ์ณ ๋ณด๋ฉด ์‚ฌ์‹ญ์œก ๋ƒฅ +ํ•ด๋งˆ๋‹ค ๋งก์•„ ๋ฌด๋‹ˆ ์„์ˆญ*์ธ๋“ค ๋‹นํ• ์˜๋ƒ +-์ž‘์ž ๋ฏธ์ƒ, ๏ฝข๊ฐ‘๋ฏผ๊ฐ€๏ฝฃ- +*๋ˆํ”ผ:๋‹ด๋น„ ๊ฐ€์ฃฝ. +*๊ฐ‘๋ฏผ:๊ฐ‘์‚ฐ์˜ ๋ฐฑ์„ฑ. +*์„์ˆญ:์ค‘๊ตญ ์ง„๋‚˜๋ผ ๋•Œ์˜ ๋ถ€์ž. + +(๋‚˜) +๋…น์–‘๋ฐฉ์ดˆ ์–ธ๋•์— ์†Œ ๋จน์ด๋Š” ์•„ํฌ๋“ค์•„ +์•ž๋‚ด ใ‰ก๊ณ ๊ธฐ ๋’ท๋‚ด ๊ณ ๊ธฐ๋ฅผ ๋‹ค ๋ชฝ๋•… ์žก์•„๋‚ด ๋‹ค๋ž˜๋ผ*์— ๋„ฃ์–ด +์ฃผ๊ฑฐ๋“  ๋„ค ์†Œ ๊ถ๋‘ฅ์ด์— ์–น์–ด๋‹ค๊ฐ€ ์ฃผ๋ ด +์šฐ๋ฆฌ๋„ ์„œ์ฃผ(่ฅฟ็–‡)*์— ์ผ์ด ๋งŽ์•„ ๋ฐ”์‚ ๊ฐ€๋Š” ๊ธธ์ด๋งค ๊ฐ€ ์ „ํ• ๋™ +๋ง๋™ ํ•˜์—ฌ๋ผ +-์ž‘์ž ๋ฏธ์ƒ, ์‚ฌ์„ค์‹œ์กฐ- +*๋‹ค๋ž˜๋ผ:๋ฌผ๊ณ ๊ธฐ๋‚˜ ์ž‘์€ ๋ฌผ๊ฑด ๋“ฑ์„ ๋„ฃ๋Š” ๋ฐ”๊ตฌ๋‹ˆ. +*์„œ์ฃผ:์„œ์ชฝ ๋ฐญ.","{'question': 'ใ‰ , ใ‰ก์— ๋Œ€ํ•œ ์ดํ•ด๋กœ ๊ฐ€์žฅ ์ ์ ˆํ•œ ๊ฒƒ์€?', 'choices': ['ใ‰ ์€ ใ‰ ์„ ์–ธ๊ธ‰ํ•˜๋Š” ํ™”์ž๊ฐ€ ์ด์ฃผํ•ด ๊ฐ€๋ ค๋Š” ๋•…์—์„œ ์žฌ๋ฐฐํ•  ์•ฝ์žฌ\n์ด๋‹ค.', 'ใ‰ก์€ ใ‰ก์„ ์–ธ๊ธ‰ํ•˜๋Š” ํ™”์ž๊ฐ€ ๋ง์„ ๊ฑด๋„ค๋Š” ์ƒ๋Œ€์—๊ฒŒ ๋…ธ๋™์˜ ๋Œ€๊ฐ€๋กœ\n์ฃผ๋Š” ๋ณด์ƒ์ด๋‹ค.', 'ใ‰ ๊ณผ ใ‰ก์€ ๋ชจ๋‘, ๊ฐ๊ฐ์„ ์–ธ๊ธ‰ํ•˜๋Š” ํ™”์ž๊ฐ€ ์œ ํฅ์„ ๋ชฉ์ ์œผ๋กœ\n๊ตฌํ•˜๋ ค๋Š” ๋ฌผํ’ˆ์ด๋‹ค.', 'ใ‰ ๊ณผ ใ‰ก์€ ๋ชจ๋‘, ๊ฐ๊ฐ์„ ์–ธ๊ธ‰ํ•˜๋Š” ํ™”์ž๊ฐ€ ํš๋“ํ•˜๋ ค๋ฉด ์ƒ๋Œ€์˜\n๋„์›€์ด ํ•„์š”ํ•œ ๋Œ€์ƒ์ด๋‹ค.', 'ใ‰ ๊ณผ ใ‰ก์€ ๋ชจ๋‘, ๊ฐ๊ฐ์„ ์–ธ๊ธ‰ํ•˜๋Š” ํ™”์ž๊ฐ€ ๋ณด๊ธฐ์— ์ƒ๋Œ€๊ฐ€ ํ–ˆ์œผ๋ฉด\nํ•˜๋Š” ํ–‰์œ„์˜ ๋Œ€์ƒ์ด๋‹ค.'], 'answer': ''}",,5,5,True,[],5 +2025-korean-34,"(๊ฐ€) +์–ด์ ธ ์–ด์ ธ ์ €๊ธฐ ๊ฐ€๋Š” ์ € ์‚ฌ๋žŒ์•„ +๋„ค ํ–‰์ƒ‰์„ ๋ณด์•„ ํ•˜๋‹ˆ ๊ตฐ์‚ฌ ๋„๋ง ๋„ค๋กœ๊ตฌ๋‚˜ +ํ—ˆ๋ฆฌ ์œ„๋กœ ๋ณผ์ž‘์‹œ๋ฉด ๋ฒ ์ ์‚ผ์ด ๊นƒ๋งŒ ๋‚จ๊ณ  +ํ—ˆ๋ฆฌ ์•„๋ž˜ ๊ตฝ์–ด๋ณด๋‹ˆ ํ—Œ ์ž ๋ฐฉ์ด ๋…ธ๋‹ฅ๋…ธ๋‹ฅ +๊ณฑ์žฅ ํ• ๋ฏธ ์•ž์— ๊ฐ€๊ณ  ์ „ํƒœ๋ฐœ์ด ๋’ค์— ๊ฐ„๋‹ค +์‹ญ ๋ฆฌ ๊ธธ์„ ํ•˜๋ฃจ ๊ฐ€๋‹ˆ ๋ช‡ ๋ฆฌ ๊ฐ€์„œ ์—Ž์–ด์ง€๋ฆฌ +๋‚ด ๊ณ ์„์˜ ์–‘๋ฐ˜ ์‚ฌ๋žŒ ํƒ€๋„ ํƒ€๊ด€ ์˜ฎ๊ฒจ ์‚ด๋ฉด +์ฒœํžˆ ๋˜๊ธฐ ์ƒ์‚ฌ์—ฌ๋“  ๋ณธํ†  ๊ตฐ์ •(่ปไธ) ์‹ซ๋‹ค ํ•˜๊ณ  +์ž๋„ค ๋˜ํ•œ ๋„๋งํ•˜๋ฉด ์ผ๊ตญ ์ผํ† (ไธ€ๅœŸ) ํ•œ ์ธ์‹ฌ์— +๊ทผ๋ณธ ์ˆจ๊ฒจ ์‚ด๋ ค ํ•œ๋“ค ์–ด๋ฐ ๊ฐ„๋“ค ๋ฉดํ• ์œ๊ฐ€ +์ฐจ๋ผ๋ฆฌ ๋„ค ์‚ด๋˜ ๊ณณ์— ์•„๋ฌด๋ ‡๊ฒŒ๋‚˜ ๋ฟŒ๋ฆฌ๋ฐ•ํ˜€ +์น ํŒ”์›”์— ใ‰ ์ธ์‚ผ ์บ๊ณ  ๊ตฌ์‹œ์›”์— ๋ˆํ”ผ* ์žก์•„ +๊ณต์ฑ„ ์‹ ์—ญ ๊ฐš์€ ํ›„์— ๊ทธ ๋‚˜๋จธ์ง€ ๋‘์—ˆ๋‹ค๊ฐ€ +ํ•จํฅ ๋ถ์ฒญ ํ™์› ์žฅ์‚ฌ ๋Œ์•„๋“ค์–ด ์ž ๋งคํ•  ๋•Œ +ํ›„ํ•œ ๊ฐ’์— ํŒ”์•„ ๋‚ด์–ด ์‚ด๊ธฐ ์ข‹์€ ๋„“์€ ๊ณณ์— +๊ฐ€์‚ฌ ์ „ํ† (ๅฎถ่ˆ็”ฐๅœŸ) ๋‹ค์‹œ ์‚ฌ๊ณ  ์‚ด๋ฆผ์‚ด์ด ์žฅ๋งŒํ•˜์—ฌ +๋ถ€๋ชจ์ฒ˜์ž ๋ณด์ „ํ•˜๊ณ  ์ƒˆ ์ฆ๊ฑฐ์›€ ๋ˆ„๋ฆฌ๋ ค๋ฌด๋‚˜ +์–ด์™€ ์ƒ์›์ธ์ง€ ์ดˆ๊ด€์ธ์ง€ +๊ทธ๋Œ€ ๋ง์”€ ๊ทธ๋งŒ๋‘๊ณ  ์ด๋‚ด ๋ง์”€ ๋“ค์–ด ๋ณด์†Œ +์ด ๋‚ด ๋˜ํ•œ ๊ฐ‘๋ฏผ(็”ฒๆฐ‘)*์ด๋ผ ์ด ๋•…์—์„œ ์ƒ์žฅํ•˜๋‹ˆ ์ด๋•Œ ์ผ์„ +๋ชจ๋ฅผ์˜๋ƒ +์šฐ๋ฆฌ ์กฐ์ƒ ๋‚จ์ชฝ ์–‘๋ฐ˜ ์ง„์‚ฌ ๊ธ‰์ œ ๊ณ„์†ํ•˜์—ฌ +๊ธˆ์žฅ ์˜ฅํŒจ ๋น—๊ธฐ ์ฐจ๊ณ  ์‹œ์ข…์‹ ์„ ๋‹ค๋‹ˆ๋‹ค๊ฐ€ +์‹œ๊ธฐ์ธ์˜ ์ฐธ์†Œ ์ž…์–ด ๋ณ€๋ฐฉ์œผ๋กœ ์ซ“๊ฒจ ์™€์„œ +๊ตญ๋‚ด ๋ณ€๋ฐฉ ์ด ๋•…์—์„œ ์น ํŒ” ๋Œ€๋ฅผ ์‚ด์•„์˜ค๋‹ˆ +์กฐ์ƒ ๋•์— ํ•˜๋Š” ์ผ์ด ์์ค‘ ๊ตฌ์‹ค ์ฒซ์งธ๋กœ๋‹ค +๋“ค์–ด๊ฐ€๋ฉด ์ขŒ์ˆ˜ ๋ณ„๊ฐ ๋‚˜๊ฐ€์„œ๋Š” ํ’ํ—Œ ๊ฐ๊ด€ +์œ ์‚ฌ ์žฅ์˜ ์ฑ„์ง€ ๋‚˜๋ฉด ์ฒด๋ฉด ๋ณด์•„ ์‚ฌ์–‘ํ„ฐ๋‹ˆ +์• ์Šฌํ”„๋‹ค ๋‚ด ์‹œ์ ˆ์— ์›์ˆ˜์ธ์˜ ๋ชจํ•ด๋กœ์จ +๊ตฐ์‚ฌ ๊ฐ•์ • ๋˜๋‹จ ๋ง๊ฐ€ ๋‚ด ํ•œ ๋ชธ์ด ํ—์–ด ๋‚˜๋‹ˆ +์ขŒ์šฐ์ „ํ›„ ์ˆ˜๋‹ค ์ผ๊ฐ€ ์ฐจ์ฐจ ์ถฉ๊ตฐ(ๅ……่ป) ๋˜๊ฒƒ๊ณ ์•ผ +์กฐ์ƒ ์ œ์‚ฌ ์ด๋‚ด ๋ชธ์€ ํ•˜๋ฆด์—†์ด ๋งค์—ฌ ์žˆ๊ณ  +์‹œ๋ฆ„์—†๋Š” ์นœ์กฑ๋“ค์€ ์ž์ทจ ์—†์ด ๋„๋งํ•˜๊ณ  +์—ฌ๋Ÿฌ ์‚ฌ๋žŒ ๋ชจ๋“  ์‹ ์—ญ ๋‚ด ํ•œ ๋ชธ์— ๋ชจ๋‘ ๋ฌด๋‹ˆ +ํ•œ ๋ชธ ์‹ ์—ญ ์‚ผ ๋ƒฅ ์˜ค ์ „ ๋ˆํ”ผ ๋‘ ์žฅ ์˜๋ฒ•์ด๋ผ +์—ด๋‘ ์‚ฌ๋žŒ ์—†๋Š” ๊ตฌ์‹ค ํ•ฉ์ณ ๋ณด๋ฉด ์‚ฌ์‹ญ์œก ๋ƒฅ +ํ•ด๋งˆ๋‹ค ๋งก์•„ ๋ฌด๋‹ˆ ์„์ˆญ*์ธ๋“ค ๋‹นํ• ์˜๋ƒ +-์ž‘์ž ๋ฏธ์ƒ, ๏ฝข๊ฐ‘๋ฏผ๊ฐ€๏ฝฃ- +*๋ˆํ”ผ:๋‹ด๋น„ ๊ฐ€์ฃฝ. +*๊ฐ‘๋ฏผ:๊ฐ‘์‚ฐ์˜ ๋ฐฑ์„ฑ. +*์„์ˆญ:์ค‘๊ตญ ์ง„๋‚˜๋ผ ๋•Œ์˜ ๋ถ€์ž. + +(๋‚˜) +๋…น์–‘๋ฐฉ์ดˆ ์–ธ๋•์— ์†Œ ๋จน์ด๋Š” ์•„ํฌ๋“ค์•„ +์•ž๋‚ด ใ‰ก๊ณ ๊ธฐ ๋’ท๋‚ด ๊ณ ๊ธฐ๋ฅผ ๋‹ค ๋ชฝ๋•… ์žก์•„๋‚ด ๋‹ค๋ž˜๋ผ*์— ๋„ฃ์–ด +์ฃผ๊ฑฐ๋“  ๋„ค ์†Œ ๊ถ๋‘ฅ์ด์— ์–น์–ด๋‹ค๊ฐ€ ์ฃผ๋ ด +์šฐ๋ฆฌ๋„ ์„œ์ฃผ(่ฅฟ็–‡)*์— ์ผ์ด ๋งŽ์•„ ๋ฐ”์‚ ๊ฐ€๋Š” ๊ธธ์ด๋งค ๊ฐ€ ์ „ํ• ๋™ +๋ง๋™ ํ•˜์—ฌ๋ผ +-์ž‘์ž ๋ฏธ์ƒ, ์‚ฌ์„ค์‹œ์กฐ- +*๋‹ค๋ž˜๋ผ:๋ฌผ๊ณ ๊ธฐ๋‚˜ ์ž‘์€ ๋ฌผ๊ฑด ๋“ฑ์„ ๋„ฃ๋Š” ๋ฐ”๊ตฌ๋‹ˆ. +*์„œ์ฃผ:์„œ์ชฝ ๋ฐญ.","{'question': '<๋ณด๊ธฐ>๋ฅผ ์ฐธ๊ณ ํ•˜์—ฌ (๊ฐ€), (๋‚˜)๋ฅผ ๊ฐ์ƒํ•œ ๋‚ด์šฉ์œผ๋กœ ์ ์ ˆํ•˜์ง€\n์•Š์€ ๊ฒƒ์€?', 'choices': ['(๊ฐ€)์˜ โ€˜๊ทธ๋Œ€โ€™๊ฐ€ โ€˜์ž๋„คโ€™์˜ ์„ ํƒ๊ณผ ๋‹ค๋ฅธ ๊ถŒ์œ ๋ฅผ ํ•จ์œผ๋กœ์จ โ€˜์ž๋„คโ€™๊ฐ€\nํ’€์–ด๋‚ธ ์‚ฌ์—ฐ์€, ๋‹น์‹œ ๊ฐ‘์‚ฐ ๋ฐฑ์„ฑ์ด ๊ฒช์—ˆ์Œ ์งํ•œ ๊ณ ํ†ต์„ ์‚ฌ์‹ค์ ์œผ๋กœ\n๋ณด์—ฌ ์ฃผ๋Š”๊ตฐ.', '๊ฐ€)์˜ โ€˜์ด๋‚ดโ€™ ๋ง์”€์€ ์ง‘์•ˆ์˜ ๋‚ด๋ ฅ๊ณผ ์‚ฌํšŒ์  ์ง€์œ„๋ฅผ ๊ตฌ์ฒด์ ์œผ๋กœ\n์–ธ๊ธ‰ํ•˜๋ฉฐ ์‚ฌํšŒ์˜ ๋ถ€์กฐ๋ฆฌ๋ฅผ ํ•ด๊ฒฐํ•˜์ž๋Š” ์ž…์žฅ์œผ๋กœ, โ€˜๊ทธ๋Œ€โ€™ ๋ง์”€๊ณผ\n์˜๊ฒฌ์ด ์ผ์น˜ํ•˜์ง€ ์•Š๋Š”๊ตฐ.', '(๋‚˜)๋Š” ์„ ํ–‰ํ•˜๋Š” ํ™”์ž์˜ ์š”์ฒญ์— ๋Œ€ํ•ด โ€˜์šฐ๋ฆฌโ€™๊ฐ€ ์„ ํ–‰ํ•˜๋Š” ํ™”์ž์˜\n๊ธฐ๋Œ€์— ์–ด๊ธ‹๋‚œ ๋Œ€๋‹ต์„ ํ•˜๋ฉด์„œ ๋Œ€ํ™”๊ฐ€ ์˜์™ธ์˜ ์ƒํ™ฉ์œผ๋กœ ํŽผ์ณ\n์ง€๋Š”๊ตฐ.', '(๋‚˜)์˜ ์„ ํ–‰ํ•˜๋Š” ํ™”์ž๊ฐ€ โ€˜๊ณ ๊ธฐโ€™๋ฅผ ๋ˆ„๊ตฌ์—๊ฒŒ ์ฃผ๋ผ๊ณ  ํ•˜๋Š”์ง€ ๋ช…์‹œํ•˜์ง€\n์•Š์•„ ๋ถˆ์™„์ „ํ•œ ํ‘œํ˜„์ด ๋œ ๊ฒƒ์€ ์ด ์ž‘ํ’ˆ์ด ๋‚ด์šฉ๋ณด๋‹ค ๋Œ€ํ™”์˜ ์ „๊ฐœ\n์–‘์ƒ์— ์ฃผ๋ชฉํ•œ๋‹ค๋Š” ๊ฒƒ์„ ๋“œ๋Ÿฌ๋‚ด๋Š”๊ตฐ.', '(๊ฐ€)์˜ โ€˜๊ทธ๋Œ€โ€™๋Š” ๊ธธ ๊ฐ€๋Š” โ€˜์ž๋„คโ€™๋ฅผ, (๋‚˜)์˜ ์„ ํ–‰ํ•˜๋Š” ํ™”์ž๋Š”\n์†Œ ๋จน์ด๋Š” โ€˜์•„ํฌ๋“คโ€™์„ ๋ถˆ๋Ÿฌ ๋ง์„ ๊ฑด๋„ค๊ณ  ์žˆ์–ด ์ž‘ํ’ˆ์˜ ์ƒํ™ฉ์ด\n๊ทน ์ค‘ ์žฅ๋ฉด์ฒ˜๋Ÿผ ๋ณด์ด๋Š”๊ตฐ.'], 'answer': '', 'question_plus': '์กฐ์„  ํ›„๊ธฐ์˜ ๊ฐ€์‚ฌ๋‚˜ ์‚ฌ์„ค์‹œ์กฐ์—์„œ๋Š” ์ž…์žฅ์ด ๋‹ค๋ฅธ ๋ฐœํ™”์ž๊ฐ€\n๋“ฑ์žฅํ•˜๋Š” ๋Œ€ํ™”์ฒด๋ฅผ ์‚ฌ์šฉํ•ด ์ž‘์ค‘ ์ƒํ™ฉ์„ ๊ทน์˜ ํ•œ ์žฅ๋ฉด์ฒ˜๋Ÿผ\n๋งŒ๋“ค๊ธฐ๋„ ํ•œ๋‹ค. ๋Œ€ํ™”๋ฅผ ํ†ตํ•ด ์‚ฌ์‹ค์„ฑ์„ ์ถ”๊ตฌํ•˜๋Š” ์ž‘ํ’ˆ์˜ ๊ฒฝ์šฐ,\n๊ตฌ์ฒด์  ์†Œ์žฌ์™€ ๋‹ค๊ฐ์ ์ธ ๋‚ด์šฉ์œผ๋กœ ๊ทธ ์‹œ๋Œ€ ์‚ถ์˜ ๋ชจ์Šต์„ ๋ณด์—ฌ\n์ค€๋‹ค. ๋Œ€ํ™”๋ฅผ ํ†ตํ•ด ์œ ํฌ์„ฑ์„ ๋ณด์ด๋Š” ์ž‘ํ’ˆ์˜ ๊ฒฝ์šฐ, ๋Œ€ํ™”๊ฐ€\n๋…ผ์Ÿ, ์˜๊ฒฌ ๋ถˆ์ผ์น˜ ๋“ฑ ์˜์™ธ์˜ ์ƒํ™ฉ์œผ๋กœ ์ „๊ฐœ๋˜๋ฉด์„œ ์žฌ๋ฏธ๊ฐ€\n์ƒ๊ฒจ๋‚˜๋ฉฐ, ๋•Œ๋กœ ๋“ฑ์žฅํ•˜๋Š” ๋ถˆ์™„์ „ํ•œ ํ‘œํ˜„์€ ์ด๋Ÿฌํ•œ ์ž‘ํ’ˆ์ด ๋‚ด์šฉ\n์ž์ฒด๋ณด๋‹ค ๋Œ€ํ™”์˜ ์ „๊ฐœ ์–‘์ƒ์— ์ฃผ๋ชฉํ•จ์„ ๋ณด์—ฌ ์ค€๋‹ค.'}","์กฐ์„  ํ›„๊ธฐ์˜ ๊ฐ€์‚ฌ๋‚˜ ์‚ฌ์„ค์‹œ์กฐ์—์„œ๋Š” ์ž…์žฅ์ด ๋‹ค๋ฅธ ๋ฐœํ™”์ž๊ฐ€ +๋“ฑ์žฅํ•˜๋Š” ๋Œ€ํ™”์ฒด๋ฅผ ์‚ฌ์šฉํ•ด ์ž‘์ค‘ ์ƒํ™ฉ์„ ๊ทน์˜ ํ•œ ์žฅ๋ฉด์ฒ˜๋Ÿผ +๋งŒ๋“ค๊ธฐ๋„ ํ•œ๋‹ค. ๋Œ€ํ™”๋ฅผ ํ†ตํ•ด ์‚ฌ์‹ค์„ฑ์„ ์ถ”๊ตฌํ•˜๋Š” ์ž‘ํ’ˆ์˜ ๊ฒฝ์šฐ, +๊ตฌ์ฒด์  ์†Œ์žฌ์™€ ๋‹ค๊ฐ์ ์ธ ๋‚ด์šฉ์œผ๋กœ ๊ทธ ์‹œ๋Œ€ ์‚ถ์˜ ๋ชจ์Šต์„ ๋ณด์—ฌ +์ค€๋‹ค. ๋Œ€ํ™”๋ฅผ ํ†ตํ•ด ์œ ํฌ์„ฑ์„ ๋ณด์ด๋Š” ์ž‘ํ’ˆ์˜ ๊ฒฝ์šฐ, ๋Œ€ํ™”๊ฐ€ +๋…ผ์Ÿ, ์˜๊ฒฌ ๋ถˆ์ผ์น˜ ๋“ฑ ์˜์™ธ์˜ ์ƒํ™ฉ์œผ๋กœ ์ „๊ฐœ๋˜๋ฉด์„œ ์žฌ๋ฏธ๊ฐ€ +์ƒ๊ฒจ๋‚˜๋ฉฐ, ๋•Œ๋กœ ๋“ฑ์žฅํ•˜๋Š” ๋ถˆ์™„์ „ํ•œ ํ‘œํ˜„์€ ์ด๋Ÿฌํ•œ ์ž‘ํ’ˆ์ด ๋‚ด์šฉ +์ž์ฒด๋ณด๋‹ค ๋Œ€ํ™”์˜ ์ „๊ฐœ ์–‘์ƒ์— ์ฃผ๋ชฉํ•จ์„ ๋ณด์—ฌ ์ค€๋‹ค.",2,2,True,[],2 +2025-korean-35,"์•ˆ๋…•ํ•˜์„ธ์š”? ์˜ค๋Š˜ ๋ฐœํ‘œ๋ฅผ ๋งก์€ โ—‹โ—‹โ—‹์ž…๋‹ˆ๋‹ค. ์˜ค๋Š˜์€ ์‹๋ฌผ์ด +์‚ด์•„๊ฐ€๋Š” ๋ช‡ ๊ฐ€์ง€ ๋…ํŠนํ•œ ๋ฐฉ์‹์— ๋Œ€ํ•ด ์†Œ๊ฐœํ•˜๋ ค๊ณ  ํ•ฉ๋‹ˆ๋‹ค. +ํฅ๋ฏธ๋กœ์šด ๋‚ด์šฉ์ด ์žˆ์œผ๋‹ˆ ์ง‘์ค‘ํ•ด์„œ ๋“ค์–ด ์ฃผ์„ธ์š”. +์ƒ์กด์„ ์œ„ํ•ด ๋‹ค๋ฅธ ์‹๋ฌผ์— ๋ถ™์–ด์„œ ์‚ฌ๋Š” ์‹๋ฌผ๋“ค์ด ์žˆ์Šต๋‹ˆ๋‹ค. +๋จผ์ €, ๋ผํ”Œ๋ ˆ์‹œ์•„๋ผ๋Š” ์‹๋ฌผ์ด์—์š”. (์ž๋ฃŒ ์ œ์‹œ) ์ด ์‹๋ฌผ์€ ํŠน์ด +ํ•˜๊ฒŒ๋„ ์žŽ๋„ ์ค„๊ธฐ๋„ ๋ฟŒ๋ฆฌ๋„ ์—†์ด ๊ฝƒ๋งŒ ์žˆ์Šต๋‹ˆ๋‹ค. ๊ฝƒ๋งŒ์œผ๋กœ๋Š” +๊ด‘ํ•ฉ์„ฑ์„ ํ•  ์ˆ˜ ์—†๊ธฐ ๋•Œ๋ฌธ์— ์ˆ™์ฃผ์ธ ๋ฉ๊ตด ์‹๋ฌผ์— ๊ธฐ์ƒํ•˜์—ฌ ์–‘๋ถ„์„ +ํก์ˆ˜ํ•œ๋‹ต๋‹ˆ๋‹ค. ๋ฉ๊ตด์— ๋ถ™์–ด ์žˆ๋Š” ๊ฒƒ ์ „์ฒด๊ฐ€ ๊ฝƒ์ธ๋ฐ์š”, ๊ฝƒ์˜ ๋ฌด๊ฒŒ๊ฐ€ +10kg๊ฐ€๋Ÿ‰์ด๊ณ  ์ง€๋ฆ„์ด ๊ฑฐ์˜ 1m๊ฐ€ ๋œ๋‹ค๋‹ˆ, ์ •๋ง ๋†€๋ž์ง€ ์•Š๋‚˜์š”? +๋‹ค๋ฅธ ์‹๋ฌผ์— ๋ถ™์–ด์„œ ์‚ด์•„๊ฐ€๋Š” ์‹๋ฌผ์ด ๋˜ ์žˆ์Šต๋‹ˆ๋‹ค. (๊ณ ๊ฐœ๋ฅผ ์ €์œผ๋ฉฐ) +์•„, ๋‹ค๋ฅธ ์‹๋ฌผ์—์„œ ์–‘๋ถ„์„ ํก์ˆ˜ํ•˜๋Š” ๊ฑด ์•„๋‹ˆ์—์š”. (์ž๋ฃŒ ์ œ์‹œ) +์ด ์‹๋ฌผ์€ ํŒŒ์ธ์• ํ”Œ๊ณผ์— ์†ํ•˜๋Š” ์ˆ˜์—ผํ‹ธ๋ž€๋“œ์‹œ์•„์ธ๋ฐ์š”, ์—ฌ๊ธฐ ์ด +๋ถ€๋ถ„์€ ๊ณต๊ธฐ ์ค‘์— ๋…ธ์ถœ๋˜์–ด ์žˆ๋Š” ๊ณต๊ธฐ๋ฟŒ๋ฆฌ๋ž๋‹ˆ๋‹ค. ๋•…์†๋ฟŒ๋ฆฌ๊ฐ€ ์—†์–ด +๊ณต๊ธฐ๋ฟŒ๋ฆฌ๋ฅผ ์ด์šฉํ•˜์—ฌ ๋‹ค๋ฅธ ์‹๋ฌผ์— ๋ถ™์–ด์„œ ์‚ด์•„๊ฐ€๋Š” ๊ฑฐ์ง€์š”. ๋ฟŒ๋ฆฌ๊ฐ€ +๋•…์†์— ์žˆ๋Š” ๊ฒŒ ์•„๋‹Œ๋ฐ ์–‘๋ถ„๊ณผ ์ˆ˜๋ถ„์€ ์–ด๋–ป๊ฒŒ ์–ป์„๊นŒ์š”? (์ž๋ฃŒ +์ œ์‹œ) ๋ณด์‹œ๋Š” ๊ฒƒ์ฒ˜๋Ÿผ ์ˆ˜์—ผํ‹ธ๋ž€๋“œ์‹œ์•„๋Š” ์žŽ์— ์žˆ๋Š” ๋น„๋Š˜์ฒ˜๋Ÿผ ์ƒ๊ธด +ํ„ธ์„ ํ†ตํ•ด ๊ณต๊ธฐ์— ์žˆ๋Š” ์–‘๋ถ„๊ณผ ์ˆ˜๋ถ„์„ ์–ป๋Š”๋‹ต๋‹ˆ๋‹ค. +๋ฒˆ์‹์„ ์œ„ํ•ด ๊ณค์ถฉ์„ ์†์ด๋Š” ์‹๋ฌผ๋„ ์žˆ๋‹ค๋Š” ๊ฑธ ์•„์‹œ๋‚˜์š”? (์ฒญ์ค‘ +์„ ๋‘˜๋Ÿฌ๋ณด๋ฉฐ) ๊ฑฐ์˜ ๋ชจ๋ฅด์‹œ๋Š”๊ตฐ์š”. ๊ฐœ๋‹ค๋ž˜๋Š” ๊ณค์ถฉ์„ ์œ ์ธํ•˜๊ธฐ +์œ„ํ•ด ์žŽ์˜ ์ƒ‰๊น”์„ ๋ฐ”๊พธ๋Š” ๋‚˜๋ฌด์ž…๋‹ˆ๋‹ค. (์ž๋ฃŒ ์ œ์‹œ) ์˜์ƒ์—์„œ +๊ฐœ๋‹ค๋ž˜์˜ ์žŽ ์ƒ‰๊น”์ด ๋‹ฌ๋ผ์ง€๋Š” ๊ฑฐ ๋ณด์…จ๋‚˜์š”? ๊ฐœ๋‹ค๋ž˜์˜ ์žŽ์€ +๊ฝƒ๊ฐ€๋ฃจ๋ฐ›์ด ๊ธฐ๊ฐ„์— ํฐ์ƒ‰์œผ๋กœ ๋ณ€ํ–ˆ๋‹ค๊ฐ€ ๊ฝƒ์ด ์ˆ˜์ •๋˜๊ณ  ๋‚˜๋ฉด +์›๋ž˜์˜ ๋…น์ƒ‰์œผ๋กœ ๋Œ์•„์˜ต๋‹ˆ๋‹ค. ๊ฐœ๋‹ค๋ž˜์˜ ๊ฝƒ์€ ์ž‘๊ณ  ์žŽ์— ๊ฐ€๋ ค์ ธ +์žˆ์–ด ๊ณค์ถฉ๋“ค์ด ์ž˜ ๋ณผ ์ˆ˜ ์—†๋Š”๋ฐ์š”, ์žŽ์„ ๊ฝƒ์ฒ˜๋Ÿผ ๋ณด์ด๊ฒŒ ํ•ด์„œ +๊ณค์ถฉ์„ ์œ ์ธํ•˜๊ณ  ๋ฒˆ์‹์— ์ด์šฉํ•˜๋Š” ๊ฒƒ์ด์ฃ . ๋‹ค์Œ ์‹๋ฌผ์€ ์šฐ๋ฆฌ์—๊ฒŒ +์ต์ˆ™ํ•œ ํ•ด๋ฐ”๋ผ๊ธฐ์ž…๋‹ˆ๋‹ค. (์ž๋ฃŒ ์ œ์‹œ) ์—ฌ๊ธฐ ๋ณด์ด๋Š” ๊ฝƒ์†ก์ด๊ฐ€ +ํ•˜๋‚˜์˜ ๊ฝƒ์ฒ˜๋Ÿผ ๋ณด์ด์‹œ์ฃ ? ์‚ฌ์‹ค ํ•ด๋ฐ”๋ผ๊ธฐ ๊ฝƒ์˜ ๊ฐ€์šด๋ฐ ๊ฐˆ์ƒ‰ +๋ถ€๋ถ„์€ ์•„์ฃผ ๋งŽ์€ ๊ฝƒ๋“ค์ด ๋ชจ์—ฌ ์žˆ๋Š” ๊ฑฐ์˜ˆ์š”. ์—ฌ๊ธฐ ๊ฐ€์žฅ์ž๋ฆฌ์— +๋…ธ๋ž€ ๊ฝƒ์žŽ์ฒ˜๋Ÿผ ๋ณด์ด๋Š” ๊ฒƒ๋“ค๋„ ํ•˜๋‚˜ํ•˜๋‚˜๊ฐ€ ๊ฝƒ์ด๋ž๋‹ˆ๋‹ค. ์ž‘์€ ๊ฝƒ +๋“ค์ด ๋ชจ์—ฌ ์ปค๋‹ค๋ž€ ๊ฝƒ์ฒ˜๋Ÿผ ๋ณด์ด๊ฒŒ ํ•ด์„œ ๊ณค์ถฉ์„ ๋Œ์–ด๋“ค์ด๋Š” ๊ฒƒ์ด์ฃ . +์‹๋ฌผ์ด ์‚ด์•„๊ฐ€๋Š” ๋ชจ์Šต, ์‹ ๊ธฐํ•˜์ง€ ์•Š๋‚˜์š”? ์ œ ๋ฐœํ‘œ๊ฐ€ ์—ฌ๋Ÿฌ๋ถ„์˜ +์ƒ์‹์„ ๋„“ํžˆ๋Š” ๋ฐ ๋„์›€์ด ๋˜์—ˆ๊ธฐ๋ฅผ ๋ฐ”๋ž๋‹ˆ๋‹ค. ๋ฐœํ‘œ ๋งˆ์น˜๊ฒ ์Šต๋‹ˆ๋‹ค","{'question': '์œ„ ๋ฐœํ‘œ์ž์˜ ๋งํ•˜๊ธฐ ๋ฐฉ์‹์œผ๋กœ ๊ฐ€์žฅ ์ ์ ˆํ•œ ๊ฒƒ์€?', 'choices': ['๋น„์–ธ์–ด์  ํ‘œํ˜„์„ ํ™œ์šฉํ•˜์—ฌ ์ฒญ์ค‘์˜ ํ–‰๋™ ๋ณ€ํ™”๋ฅผ ์š”๊ตฌํ•˜๊ณ  ์žˆ๋‹ค.', '๋ฐœํ‘œ ๋‚ด์šฉ๊ณผ ๊ด€๋ จํ•œ ์งˆ๋ฌธ์„ ํ•˜์—ฌ ์ฒญ์ค‘์˜ ๋ฐฐ๊ฒฝ์ง€์‹์„ ํ™•์ธํ•˜๊ณ \n์žˆ๋‹ค.', '๋‚ฏ์„  ์šฉ์–ด์˜ ๊ฐœ๋…์„ ์ •์˜ํ•˜์—ฌ ๋ฐœํ‘œ ๋‚ด์šฉ์— ๋Œ€ํ•œ ์ฒญ์ค‘์˜ ์ดํ•ด๋ฅผ\n๋•๊ณ  ์žˆ๋‹ค.', '๋ฐœํ‘œ ์ค‘๊ฐ„์ค‘๊ฐ„์— ์•ž์„œ ์–ธ๊ธ‰ํ•œ ์ฃผ์š” ๋‚ด์šฉ์„ ์š”์•ฝํ•˜์—ฌ ์ฃผ์ œ๋ฅผ\n๊ฐ•์กฐํ•˜๊ณ  ์žˆ๋‹ค.', '์ฒญ์ค‘์ด ๋ฐœํ‘œ ๋‚ด์šฉ์„ ํ†ตํ•ด ์–ป์„ ์ˆ˜ ์žˆ๋Š” ํšจ์šฉ์„ ์ œ์‹œํ•˜๋ฉฐ ํ™”์ œ๋ฅผ\n์ „ํ™˜ํ•˜๊ณ  ์žˆ๋‹ค.'], 'answer': ''}",,2,5,False,[],5 +2025-korean-36,"์•ˆ๋…•ํ•˜์„ธ์š”? ์˜ค๋Š˜ ๋ฐœํ‘œ๋ฅผ ๋งก์€ โ—‹โ—‹โ—‹์ž…๋‹ˆ๋‹ค. ์˜ค๋Š˜์€ ์‹๋ฌผ์ด +์‚ด์•„๊ฐ€๋Š” ๋ช‡ ๊ฐ€์ง€ ๋…ํŠนํ•œ ๋ฐฉ์‹์— ๋Œ€ํ•ด ์†Œ๊ฐœํ•˜๋ ค๊ณ  ํ•ฉ๋‹ˆ๋‹ค. +ํฅ๋ฏธ๋กœ์šด ๋‚ด์šฉ์ด ์žˆ์œผ๋‹ˆ ์ง‘์ค‘ํ•ด์„œ ๋“ค์–ด ์ฃผ์„ธ์š”. +์ƒ์กด์„ ์œ„ํ•ด ๋‹ค๋ฅธ ์‹๋ฌผ์— ๋ถ™์–ด์„œ ์‚ฌ๋Š” ์‹๋ฌผ๋“ค์ด ์žˆ์Šต๋‹ˆ๋‹ค. +๋จผ์ €, ๋ผํ”Œ๋ ˆ์‹œ์•„๋ผ๋Š” ์‹๋ฌผ์ด์—์š”. (์ž๋ฃŒ ์ œ์‹œ) ์ด ์‹๋ฌผ์€ ํŠน์ด +ํ•˜๊ฒŒ๋„ ์žŽ๋„ ์ค„๊ธฐ๋„ ๋ฟŒ๋ฆฌ๋„ ์—†์ด ๊ฝƒ๋งŒ ์žˆ์Šต๋‹ˆ๋‹ค. ๊ฝƒ๋งŒ์œผ๋กœ๋Š” +๊ด‘ํ•ฉ์„ฑ์„ ํ•  ์ˆ˜ ์—†๊ธฐ ๋•Œ๋ฌธ์— ์ˆ™์ฃผ์ธ ๋ฉ๊ตด ์‹๋ฌผ์— ๊ธฐ์ƒํ•˜์—ฌ ์–‘๋ถ„์„ +ํก์ˆ˜ํ•œ๋‹ต๋‹ˆ๋‹ค. ๋ฉ๊ตด์— ๋ถ™์–ด ์žˆ๋Š” ๊ฒƒ ์ „์ฒด๊ฐ€ ๊ฝƒ์ธ๋ฐ์š”, ๊ฝƒ์˜ ๋ฌด๊ฒŒ๊ฐ€ +10kg๊ฐ€๋Ÿ‰์ด๊ณ  ์ง€๋ฆ„์ด ๊ฑฐ์˜ 1m๊ฐ€ ๋œ๋‹ค๋‹ˆ, ์ •๋ง ๋†€๋ž์ง€ ์•Š๋‚˜์š”? +๋‹ค๋ฅธ ์‹๋ฌผ์— ๋ถ™์–ด์„œ ์‚ด์•„๊ฐ€๋Š” ์‹๋ฌผ์ด ๋˜ ์žˆ์Šต๋‹ˆ๋‹ค. (๊ณ ๊ฐœ๋ฅผ ์ €์œผ๋ฉฐ) +์•„, ๋‹ค๋ฅธ ์‹๋ฌผ์—์„œ ์–‘๋ถ„์„ ํก์ˆ˜ํ•˜๋Š” ๊ฑด ์•„๋‹ˆ์—์š”. (์ž๋ฃŒ ์ œ์‹œ) +์ด ์‹๋ฌผ์€ ํŒŒ์ธ์• ํ”Œ๊ณผ์— ์†ํ•˜๋Š” ์ˆ˜์—ผํ‹ธ๋ž€๋“œ์‹œ์•„์ธ๋ฐ์š”, ์—ฌ๊ธฐ ์ด +๋ถ€๋ถ„์€ ๊ณต๊ธฐ ์ค‘์— ๋…ธ์ถœ๋˜์–ด ์žˆ๋Š” ๊ณต๊ธฐ๋ฟŒ๋ฆฌ๋ž๋‹ˆ๋‹ค. ๋•…์†๋ฟŒ๋ฆฌ๊ฐ€ ์—†์–ด +๊ณต๊ธฐ๋ฟŒ๋ฆฌ๋ฅผ ์ด์šฉํ•˜์—ฌ ๋‹ค๋ฅธ ์‹๋ฌผ์— ๋ถ™์–ด์„œ ์‚ด์•„๊ฐ€๋Š” ๊ฑฐ์ง€์š”. ๋ฟŒ๋ฆฌ๊ฐ€ +๋•…์†์— ์žˆ๋Š” ๊ฒŒ ์•„๋‹Œ๋ฐ ์–‘๋ถ„๊ณผ ์ˆ˜๋ถ„์€ ์–ด๋–ป๊ฒŒ ์–ป์„๊นŒ์š”? (์ž๋ฃŒ +์ œ์‹œ) ๋ณด์‹œ๋Š” ๊ฒƒ์ฒ˜๋Ÿผ ์ˆ˜์—ผํ‹ธ๋ž€๋“œ์‹œ์•„๋Š” ์žŽ์— ์žˆ๋Š” ๋น„๋Š˜์ฒ˜๋Ÿผ ์ƒ๊ธด +ํ„ธ์„ ํ†ตํ•ด ๊ณต๊ธฐ์— ์žˆ๋Š” ์–‘๋ถ„๊ณผ ์ˆ˜๋ถ„์„ ์–ป๋Š”๋‹ต๋‹ˆ๋‹ค. +๋ฒˆ์‹์„ ์œ„ํ•ด ๊ณค์ถฉ์„ ์†์ด๋Š” ์‹๋ฌผ๋„ ์žˆ๋‹ค๋Š” ๊ฑธ ์•„์‹œ๋‚˜์š”? (์ฒญ์ค‘ +์„ ๋‘˜๋Ÿฌ๋ณด๋ฉฐ) ๊ฑฐ์˜ ๋ชจ๋ฅด์‹œ๋Š”๊ตฐ์š”. ๊ฐœ๋‹ค๋ž˜๋Š” ๊ณค์ถฉ์„ ์œ ์ธํ•˜๊ธฐ +์œ„ํ•ด ์žŽ์˜ ์ƒ‰๊น”์„ ๋ฐ”๊พธ๋Š” ๋‚˜๋ฌด์ž…๋‹ˆ๋‹ค. (์ž๋ฃŒ ์ œ์‹œ) ์˜์ƒ์—์„œ +๊ฐœ๋‹ค๋ž˜์˜ ์žŽ ์ƒ‰๊น”์ด ๋‹ฌ๋ผ์ง€๋Š” ๊ฑฐ ๋ณด์…จ๋‚˜์š”? ๊ฐœ๋‹ค๋ž˜์˜ ์žŽ์€ +๊ฝƒ๊ฐ€๋ฃจ๋ฐ›์ด ๊ธฐ๊ฐ„์— ํฐ์ƒ‰์œผ๋กœ ๋ณ€ํ–ˆ๋‹ค๊ฐ€ ๊ฝƒ์ด ์ˆ˜์ •๋˜๊ณ  ๋‚˜๋ฉด +์›๋ž˜์˜ ๋…น์ƒ‰์œผ๋กœ ๋Œ์•„์˜ต๋‹ˆ๋‹ค. ๊ฐœ๋‹ค๋ž˜์˜ ๊ฝƒ์€ ์ž‘๊ณ  ์žŽ์— ๊ฐ€๋ ค์ ธ +์žˆ์–ด ๊ณค์ถฉ๋“ค์ด ์ž˜ ๋ณผ ์ˆ˜ ์—†๋Š”๋ฐ์š”, ์žŽ์„ ๊ฝƒ์ฒ˜๋Ÿผ ๋ณด์ด๊ฒŒ ํ•ด์„œ +๊ณค์ถฉ์„ ์œ ์ธํ•˜๊ณ  ๋ฒˆ์‹์— ์ด์šฉํ•˜๋Š” ๊ฒƒ์ด์ฃ . ๋‹ค์Œ ์‹๋ฌผ์€ ์šฐ๋ฆฌ์—๊ฒŒ +์ต์ˆ™ํ•œ ํ•ด๋ฐ”๋ผ๊ธฐ์ž…๋‹ˆ๋‹ค. (์ž๋ฃŒ ์ œ์‹œ) ์—ฌ๊ธฐ ๋ณด์ด๋Š” ๊ฝƒ์†ก์ด๊ฐ€ +ํ•˜๋‚˜์˜ ๊ฝƒ์ฒ˜๋Ÿผ ๋ณด์ด์‹œ์ฃ ? ์‚ฌ์‹ค ํ•ด๋ฐ”๋ผ๊ธฐ ๊ฝƒ์˜ ๊ฐ€์šด๋ฐ ๊ฐˆ์ƒ‰ +๋ถ€๋ถ„์€ ์•„์ฃผ ๋งŽ์€ ๊ฝƒ๋“ค์ด ๋ชจ์—ฌ ์žˆ๋Š” ๊ฑฐ์˜ˆ์š”. ์—ฌ๊ธฐ ๊ฐ€์žฅ์ž๋ฆฌ์— +๋…ธ๋ž€ ๊ฝƒ์žŽ์ฒ˜๋Ÿผ ๋ณด์ด๋Š” ๊ฒƒ๋“ค๋„ ํ•˜๋‚˜ํ•˜๋‚˜๊ฐ€ ๊ฝƒ์ด๋ž๋‹ˆ๋‹ค. ์ž‘์€ ๊ฝƒ +๋“ค์ด ๋ชจ์—ฌ ์ปค๋‹ค๋ž€ ๊ฝƒ์ฒ˜๋Ÿผ ๋ณด์ด๊ฒŒ ํ•ด์„œ ๊ณค์ถฉ์„ ๋Œ์–ด๋“ค์ด๋Š” ๊ฒƒ์ด์ฃ . +์‹๋ฌผ์ด ์‚ด์•„๊ฐ€๋Š” ๋ชจ์Šต, ์‹ ๊ธฐํ•˜์ง€ ์•Š๋‚˜์š”? ์ œ ๋ฐœํ‘œ๊ฐ€ ์—ฌ๋Ÿฌ๋ถ„์˜ +์ƒ์‹์„ ๋„“ํžˆ๋Š” ๋ฐ ๋„์›€์ด ๋˜์—ˆ๊ธฐ๋ฅผ ๋ฐ”๋ž๋‹ˆ๋‹ค. ๋ฐœํ‘œ ๋งˆ์น˜๊ฒ ์Šต๋‹ˆ๋‹ค","{'question': '๋‹ค์Œ์€ ๋ฐœํ‘œ์ž๊ฐ€ ๋ฐœํ‘œ์— ํ™œ์šฉํ•œ ์ž๋ฃŒ์˜ ๋ชฉ๋ก์ด๋‹ค. ๋ฐœํ‘œ ๋‚ด์šฉ์„\n๊ณ ๋ คํ•  ๋•Œ, ์ž๋ฃŒ ํ™œ์šฉ์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์ ์ ˆํ•˜์ง€ ์•Š์€ ๊ฒƒ์€?', 'choices': ['ใ‰ ์€ ์‚ฌ์ง„ ์† ์‹๋ฌผ์ด ์ˆ™์ฃผ์— ๊ธฐ์ƒํ•˜์—ฌ ์–‘๋ถ„์„ ์–ป๋Š”๋‹ค๋Š” ๊ฒƒ์„\n์„ค๋ช…ํ•˜๋Š” ๋ฐ์— ํ™œ์šฉ๋˜์—ˆ๋‹ค.', 'ใ‰ก์€ ์‚ฌ์ง„ ์† ์‹๋ฌผ์˜ ๊ณต๊ธฐ๋ฟŒ๋ฆฌ๊ฐ€ ํ•˜๋Š” ์—ญํ• ์„ ์„ค๋ช…ํ•˜๋Š” ๋ฐ์—\nํ™œ์šฉ๋˜์—ˆ๋‹ค.', 'ใ‰ข์€ ์‚ฌ์ง„ ์† ์‹๋ฌผ์˜ ์žŽ์— ์žˆ๋Š” ํ„ธ์˜ ๊ธฐ๋Šฅ์„ ์„ค๋ช…ํ•˜๋Š” ๋ฐ์—\nํ™œ์šฉ๋˜์—ˆ๋‹ค.', 'ใ‰ฃ์€ ๋™์˜์ƒ ์† ์‹๋ฌผ์˜ ๊ฝƒ์ด ์ž‘๊ณ  ์žŽ์— ๊ฐ€๋ ค์ ธ ์žˆ๋Š” ์ด์œ ๋ฅผ\n์„ค๋ช…ํ•˜๋Š” ๋ฐ์— ํ™œ์šฉ๋˜์—ˆ๋‹ค.', 'ใ‰ค์€ ์‚ฌ์ง„ ์† ์‹๋ฌผ์˜ ๊ฝƒ์†ก์ด๊ฐ€ ๋‚ฑ๋‚ฑ์˜ ๊ฝƒ๋“ค์ด ํ•œ๋ฐ ๋ชจ์—ฌ\n์ด๋ฃจ์–ด์ ธ ์žˆ๋‹ค๋Š” ๋‚ด์šฉ์„ ์„ค๋ช…ํ•˜๋Š” ๋ฐ์— ํ™œ์šฉ๋˜์—ˆ๋‹ค.'], 'answer': '', 'question_plus': 'ใ‰  : ๋ผํ”Œ๋ ˆ์‹œ์•„๊ฐ€ ๋ฉ๊ตด ์‹๋ฌผ์— ๋ถ™์–ด ์žˆ๋Š” ์‚ฌ์ง„ ์ž๋ฃŒ\nใ‰ก : ์ˆ˜์—ผํ‹ธ๋ž€๋“œ์‹œ์•„๊ฐ€ ๋‚˜๋ญ‡๊ฐ€์ง€์— ๋ถ™์–ด ์žˆ๋Š” ์‚ฌ์ง„ ์ž๋ฃŒ\nใ‰ข : ์ˆ˜์—ผํ‹ธ๋ž€๋“œ์‹œ์•„ ์žŽ์„ ํ™•๋Œ€ํ•œ ์‚ฌ์ง„ ์ž๋ฃŒ\nใ‰ฃ : ๊ฝƒ๊ฐ€๋ฃจ๋ฐ›์ด ๊ธฐ๊ฐ„์ธ ๋•Œ์™€ ์•„๋‹Œ ๋•Œ์˜ ๊ฐœ๋‹ค๋ž˜๋ฅผ ์ดฌ์˜ํ•œ ๋™์˜์ƒ ์ž๋ฃŒ\nใ‰ค : ํ•ด๋ฐ”๋ผ๊ธฐ์˜ ๊ฝƒ์†ก์ด ์ „์ฒด๊ฐ€ ์ž˜ ๋“œ๋Ÿฌ๋‚˜๋Š” ์‚ฌ์ง„์ž๋ฃŒ'}","ใ‰  : ๋ผํ”Œ๋ ˆ์‹œ์•„๊ฐ€ ๋ฉ๊ตด ์‹๋ฌผ์— ๋ถ™์–ด ์žˆ๋Š” ์‚ฌ์ง„ ์ž๋ฃŒ +ใ‰ก : ์ˆ˜์—ผํ‹ธ๋ž€๋“œ์‹œ์•„๊ฐ€ ๋‚˜๋ญ‡๊ฐ€์ง€์— ๋ถ™์–ด ์žˆ๋Š” ์‚ฌ์ง„ ์ž๋ฃŒ +ใ‰ข : ์ˆ˜์—ผํ‹ธ๋ž€๋“œ์‹œ์•„ ์žŽ์„ ํ™•๋Œ€ํ•œ ์‚ฌ์ง„ ์ž๋ฃŒ +ใ‰ฃ : ๊ฝƒ๊ฐ€๋ฃจ๋ฐ›์ด ๊ธฐ๊ฐ„์ธ ๋•Œ์™€ ์•„๋‹Œ ๋•Œ์˜ ๊ฐœ๋‹ค๋ž˜๋ฅผ ์ดฌ์˜ํ•œ ๋™์˜์ƒ ์ž๋ฃŒ +ใ‰ค : ํ•ด๋ฐ”๋ผ๊ธฐ์˜ ๊ฝƒ์†ก์ด ์ „์ฒด๊ฐ€ ์ž˜ ๋“œ๋Ÿฌ๋‚˜๋Š” ์‚ฌ์ง„์ž๋ฃŒ",4,4,True,[],4 +2025-korean-37,"์•ˆ๋…•ํ•˜์„ธ์š”? ์˜ค๋Š˜ ๋ฐœํ‘œ๋ฅผ ๋งก์€ โ—‹โ—‹โ—‹์ž…๋‹ˆ๋‹ค. ์˜ค๋Š˜์€ ์‹๋ฌผ์ด +์‚ด์•„๊ฐ€๋Š” ๋ช‡ ๊ฐ€์ง€ ๋…ํŠนํ•œ ๋ฐฉ์‹์— ๋Œ€ํ•ด ์†Œ๊ฐœํ•˜๋ ค๊ณ  ํ•ฉ๋‹ˆ๋‹ค. +ํฅ๋ฏธ๋กœ์šด ๋‚ด์šฉ์ด ์žˆ์œผ๋‹ˆ ์ง‘์ค‘ํ•ด์„œ ๋“ค์–ด ์ฃผ์„ธ์š”. +์ƒ์กด์„ ์œ„ํ•ด ๋‹ค๋ฅธ ์‹๋ฌผ์— ๋ถ™์–ด์„œ ์‚ฌ๋Š” ์‹๋ฌผ๋“ค์ด ์žˆ์Šต๋‹ˆ๋‹ค. +๋จผ์ €, ๋ผํ”Œ๋ ˆ์‹œ์•„๋ผ๋Š” ์‹๋ฌผ์ด์—์š”. (์ž๋ฃŒ ์ œ์‹œ) ์ด ์‹๋ฌผ์€ ํŠน์ด +ํ•˜๊ฒŒ๋„ ์žŽ๋„ ์ค„๊ธฐ๋„ ๋ฟŒ๋ฆฌ๋„ ์—†์ด ๊ฝƒ๋งŒ ์žˆ์Šต๋‹ˆ๋‹ค. ๊ฝƒ๋งŒ์œผ๋กœ๋Š” +๊ด‘ํ•ฉ์„ฑ์„ ํ•  ์ˆ˜ ์—†๊ธฐ ๋•Œ๋ฌธ์— ์ˆ™์ฃผ์ธ ๋ฉ๊ตด ์‹๋ฌผ์— ๊ธฐ์ƒํ•˜์—ฌ ์–‘๋ถ„์„ +ํก์ˆ˜ํ•œ๋‹ต๋‹ˆ๋‹ค. ๋ฉ๊ตด์— ๋ถ™์–ด ์žˆ๋Š” ๊ฒƒ ์ „์ฒด๊ฐ€ ๊ฝƒ์ธ๋ฐ์š”, ๊ฝƒ์˜ ๋ฌด๊ฒŒ๊ฐ€ +10kg๊ฐ€๋Ÿ‰์ด๊ณ  ์ง€๋ฆ„์ด ๊ฑฐ์˜ 1m๊ฐ€ ๋œ๋‹ค๋‹ˆ, ์ •๋ง ๋†€๋ž์ง€ ์•Š๋‚˜์š”? +๋‹ค๋ฅธ ์‹๋ฌผ์— ๋ถ™์–ด์„œ ์‚ด์•„๊ฐ€๋Š” ์‹๋ฌผ์ด ๋˜ ์žˆ์Šต๋‹ˆ๋‹ค. (๊ณ ๊ฐœ๋ฅผ ์ €์œผ๋ฉฐ) +์•„, ๋‹ค๋ฅธ ์‹๋ฌผ์—์„œ ์–‘๋ถ„์„ ํก์ˆ˜ํ•˜๋Š” ๊ฑด ์•„๋‹ˆ์—์š”. (์ž๋ฃŒ ์ œ์‹œ) +์ด ์‹๋ฌผ์€ ํŒŒ์ธ์• ํ”Œ๊ณผ์— ์†ํ•˜๋Š” ์ˆ˜์—ผํ‹ธ๋ž€๋“œ์‹œ์•„์ธ๋ฐ์š”, ์—ฌ๊ธฐ ์ด +๋ถ€๋ถ„์€ ๊ณต๊ธฐ ์ค‘์— ๋…ธ์ถœ๋˜์–ด ์žˆ๋Š” ๊ณต๊ธฐ๋ฟŒ๋ฆฌ๋ž๋‹ˆ๋‹ค. ๋•…์†๋ฟŒ๋ฆฌ๊ฐ€ ์—†์–ด +๊ณต๊ธฐ๋ฟŒ๋ฆฌ๋ฅผ ์ด์šฉํ•˜์—ฌ ๋‹ค๋ฅธ ์‹๋ฌผ์— ๋ถ™์–ด์„œ ์‚ด์•„๊ฐ€๋Š” ๊ฑฐ์ง€์š”. ๋ฟŒ๋ฆฌ๊ฐ€ +๋•…์†์— ์žˆ๋Š” ๊ฒŒ ์•„๋‹Œ๋ฐ ์–‘๋ถ„๊ณผ ์ˆ˜๋ถ„์€ ์–ด๋–ป๊ฒŒ ์–ป์„๊นŒ์š”? (์ž๋ฃŒ +์ œ์‹œ) ๋ณด์‹œ๋Š” ๊ฒƒ์ฒ˜๋Ÿผ ์ˆ˜์—ผํ‹ธ๋ž€๋“œ์‹œ์•„๋Š” ์žŽ์— ์žˆ๋Š” ๋น„๋Š˜์ฒ˜๋Ÿผ ์ƒ๊ธด +ํ„ธ์„ ํ†ตํ•ด ๊ณต๊ธฐ์— ์žˆ๋Š” ์–‘๋ถ„๊ณผ ์ˆ˜๋ถ„์„ ์–ป๋Š”๋‹ต๋‹ˆ๋‹ค. +๋ฒˆ์‹์„ ์œ„ํ•ด ๊ณค์ถฉ์„ ์†์ด๋Š” ์‹๋ฌผ๋„ ์žˆ๋‹ค๋Š” ๊ฑธ ์•„์‹œ๋‚˜์š”? (์ฒญ์ค‘ +์„ ๋‘˜๋Ÿฌ๋ณด๋ฉฐ) ๊ฑฐ์˜ ๋ชจ๋ฅด์‹œ๋Š”๊ตฐ์š”. ๊ฐœ๋‹ค๋ž˜๋Š” ๊ณค์ถฉ์„ ์œ ์ธํ•˜๊ธฐ +์œ„ํ•ด ์žŽ์˜ ์ƒ‰๊น”์„ ๋ฐ”๊พธ๋Š” ๋‚˜๋ฌด์ž…๋‹ˆ๋‹ค. (์ž๋ฃŒ ์ œ์‹œ) ์˜์ƒ์—์„œ +๊ฐœ๋‹ค๋ž˜์˜ ์žŽ ์ƒ‰๊น”์ด ๋‹ฌ๋ผ์ง€๋Š” ๊ฑฐ ๋ณด์…จ๋‚˜์š”? ๊ฐœ๋‹ค๋ž˜์˜ ์žŽ์€ +๊ฝƒ๊ฐ€๋ฃจ๋ฐ›์ด ๊ธฐ๊ฐ„์— ํฐ์ƒ‰์œผ๋กœ ๋ณ€ํ–ˆ๋‹ค๊ฐ€ ๊ฝƒ์ด ์ˆ˜์ •๋˜๊ณ  ๋‚˜๋ฉด +์›๋ž˜์˜ ๋…น์ƒ‰์œผ๋กœ ๋Œ์•„์˜ต๋‹ˆ๋‹ค. ๊ฐœ๋‹ค๋ž˜์˜ ๊ฝƒ์€ ์ž‘๊ณ  ์žŽ์— ๊ฐ€๋ ค์ ธ +์žˆ์–ด ๊ณค์ถฉ๋“ค์ด ์ž˜ ๋ณผ ์ˆ˜ ์—†๋Š”๋ฐ์š”, ์žŽ์„ ๊ฝƒ์ฒ˜๋Ÿผ ๋ณด์ด๊ฒŒ ํ•ด์„œ +๊ณค์ถฉ์„ ์œ ์ธํ•˜๊ณ  ๋ฒˆ์‹์— ์ด์šฉํ•˜๋Š” ๊ฒƒ์ด์ฃ . ๋‹ค์Œ ์‹๋ฌผ์€ ์šฐ๋ฆฌ์—๊ฒŒ +์ต์ˆ™ํ•œ ํ•ด๋ฐ”๋ผ๊ธฐ์ž…๋‹ˆ๋‹ค. (์ž๋ฃŒ ์ œ์‹œ) ์—ฌ๊ธฐ ๋ณด์ด๋Š” ๊ฝƒ์†ก์ด๊ฐ€ +ํ•˜๋‚˜์˜ ๊ฝƒ์ฒ˜๋Ÿผ ๋ณด์ด์‹œ์ฃ ? ์‚ฌ์‹ค ํ•ด๋ฐ”๋ผ๊ธฐ ๊ฝƒ์˜ ๊ฐ€์šด๋ฐ ๊ฐˆ์ƒ‰ +๋ถ€๋ถ„์€ ์•„์ฃผ ๋งŽ์€ ๊ฝƒ๋“ค์ด ๋ชจ์—ฌ ์žˆ๋Š” ๊ฑฐ์˜ˆ์š”. ์—ฌ๊ธฐ ๊ฐ€์žฅ์ž๋ฆฌ์— +๋…ธ๋ž€ ๊ฝƒ์žŽ์ฒ˜๋Ÿผ ๋ณด์ด๋Š” ๊ฒƒ๋“ค๋„ ํ•˜๋‚˜ํ•˜๋‚˜๊ฐ€ ๊ฝƒ์ด๋ž๋‹ˆ๋‹ค. ์ž‘์€ ๊ฝƒ +๋“ค์ด ๋ชจ์—ฌ ์ปค๋‹ค๋ž€ ๊ฝƒ์ฒ˜๋Ÿผ ๋ณด์ด๊ฒŒ ํ•ด์„œ ๊ณค์ถฉ์„ ๋Œ์–ด๋“ค์ด๋Š” ๊ฒƒ์ด์ฃ . +์‹๋ฌผ์ด ์‚ด์•„๊ฐ€๋Š” ๋ชจ์Šต, ์‹ ๊ธฐํ•˜์ง€ ์•Š๋‚˜์š”? ์ œ ๋ฐœํ‘œ๊ฐ€ ์—ฌ๋Ÿฌ๋ถ„์˜ +์ƒ์‹์„ ๋„“ํžˆ๋Š” ๋ฐ ๋„์›€์ด ๋˜์—ˆ๊ธฐ๋ฅผ ๋ฐ”๋ž๋‹ˆ๋‹ค. ๋ฐœํ‘œ ๋งˆ์น˜๊ฒ ์Šต๋‹ˆ๋‹ค","{'question': '๋ฐœํ‘œ ๋‚ด์šฉ์„ ๋ฐ”ํƒ•์œผ๋กœ ํ•  ๋•Œ, <๋ณด๊ธฐ>์— ๋‚˜ํƒ€๋‚œ ํ•™์ƒ๋“ค์˜ ๋ฐ˜์‘์—\n๋Œ€ํ•œ ์ดํ•ด๋กœ ์ ์ ˆํ•˜์ง€ ์•Š์€ ๊ฒƒ์€?', 'choices': ['ํ•™์ƒ1โ€™์€ ๋ฐœํ‘œ ๋‚ด์šฉ๊ณผ ๊ด€๋ จํ•˜์—ฌ ์ž์‹ ์˜ ๊ธฐ์–ต์„ ๋– ์˜ฌ๋ฆฌ๊ณ  ์žˆ๋‹ค.', 'โ€˜ํ•™์ƒ2โ€™๋Š” ์ž์‹ ์ด ์ดํ•ดํ•œ ๋‚ด์šฉ์ด ๋งž๋Š”์ง€ ์ƒ๋Œ€์—๊ฒŒ ํ™•์ธํ•˜๊ณ \n์žˆ๋‹ค.', 'โ€˜ํ•™์ƒ1โ€™์˜ ์˜๋ฌธ์— ๋Œ€ํ•ด, โ€˜ํ•™์ƒ2โ€™๋Š” ๋ฐœํ‘œ์—์„œ ์ œ๊ณตํ•˜์ง€ ์•Š์€\n๋‚ด์šฉ์„ ์ถ”๋ก ํ•˜๊ณ  ์žˆ๋‹ค.', 'โ€˜ํ•™์ƒ1โ€™๊ณผ โ€˜ํ•™์ƒ2โ€™๋Š” ๋ชจ๋‘, ๋ฐœํ‘œ์—์„œ ๊ถ๊ธˆํ•œ ๋‚ด์šฉ์ด ๋‹ค๋ค„์ง€์ง€\n์•Š์•˜์Œ์„ ์•„์‰ฌ์›Œํ•˜๊ณ  ์žˆ๋‹ค.', 'ํ•™์ƒ1โ€™๊ณผ ๋‹ฌ๋ฆฌ, โ€˜ํ•™์ƒ2โ€™๋Š” ๋ฐœํ‘œ ๋‚ด์šฉ ์™ธ์˜ ์ถ”๊ฐ€์ ์ธ ์ •๋ณด๋ฅผ\nํƒ์ƒ‰ํ•˜๋ ค ํ•˜๊ณ  ์žˆ๋‹ค.'], 'answer': '', 'question_plus': '<๋ณด๊ธฐ>\nํ•™์ƒ1:์˜ค๋Š˜ ๋ฐœํ‘œ์— ๋‚˜์˜จ ๋ผํ”Œ๋ ˆ์‹œ์•„์— ๋Œ€ํ•œ ๋‚ด์šฉ์„ ์ธํ„ฐ๋„ท\n์—์„œ ๋ณธ ์ ์ด ์žˆ์–ด. ๊ทธ ๊ฝƒ์€ ์‹ฌํ•œ ์•…์ทจ๋ฅผ ํ’๊ฒจ์„œ ํŒŒ๋ฆฌ๋ฅผ\n์œ ์ธํ•˜๋Š”๋ฐ, ๋ฒˆ์‹์„ ์œ„ํ•ด์„œ ๊ทธ๋Ÿฐ ๊ฑฐ๋ž˜.\nํ•™์ƒ2:๊ทธ๋ž˜? 1m๋‚˜ ๋˜๋Š” ํฐ ๊ฝƒ์ด ์•…์ทจ๋ฅผ ํ’๊ธฐ๋ฉด ์—„์ฒญ๋‚˜๊ฒ ๋Š”๊ฑธ?\n๊ทผ๋ฐ ์ˆ˜์—ผํ‹ธ๋ž€๋“œ์‹œ์•„๋Š” ๋‹ค๋ฅธ ์‹๋ฌผ์— ๊ธฐ์ƒํ•˜๋Š” ๊ฑด ์•„๋‹ˆ๋ผ๋Š”\n๊ฑฐ์ง€?\nํ•™์ƒ1:์‘, ๋งž์•„. ๋‚˜๋Š” ์ˆ˜์—ผํ‹ธ๋ž€๋“œ์‹œ์•„๊ฐ€ ์–ด๋–ป๊ฒŒ ๋ฒˆ์‹ํ•˜๋Š”์ง€\n์•Œ๊ณ  ์‹ถ์—ˆ๋Š”๋ฐ ๊ทธ ๋‚ด์šฉ์ด ์—†์–ด์„œ ์•„์‰ฌ์› ์–ด.\nํ•™์ƒ2:๋‚˜๋„ ๊ทธ๋žฌ์–ด. ๊ทธ ๋ถ€๋ถ„์— ๋Œ€ํ•œ ์„ค๋ช…์ด ์žˆ์—ˆ์œผ๋ฉด ๋”\n์ข‹์•˜์„ ํ…๋ฐ. ์ˆ˜์—ผํ‹ธ๋ž€๋“œ์‹œ์•„๊ฐ€ ๋ฒˆ์‹์„ ์–ด๋–ป๊ฒŒ ํ•˜๋Š”์ง€\n์ฐพ์•„๋ด์•ผ๊ฒ ์–ด.'}","<๋ณด๊ธฐ> +ํ•™์ƒ1:์˜ค๋Š˜ ๋ฐœํ‘œ์— ๋‚˜์˜จ ๋ผํ”Œ๋ ˆ์‹œ์•„์— ๋Œ€ํ•œ ๋‚ด์šฉ์„ ์ธํ„ฐ๋„ท +์—์„œ ๋ณธ ์ ์ด ์žˆ์–ด. ๊ทธ ๊ฝƒ์€ ์‹ฌํ•œ ์•…์ทจ๋ฅผ ํ’๊ฒจ์„œ ํŒŒ๋ฆฌ๋ฅผ +์œ ์ธํ•˜๋Š”๋ฐ, ๋ฒˆ์‹์„ ์œ„ํ•ด์„œ ๊ทธ๋Ÿฐ ๊ฑฐ๋ž˜. +ํ•™์ƒ2:๊ทธ๋ž˜? 1m๋‚˜ ๋˜๋Š” ํฐ ๊ฝƒ์ด ์•…์ทจ๋ฅผ ํ’๊ธฐ๋ฉด ์—„์ฒญ๋‚˜๊ฒ ๋Š”๊ฑธ? +๊ทผ๋ฐ ์ˆ˜์—ผํ‹ธ๋ž€๋“œ์‹œ์•„๋Š” ๋‹ค๋ฅธ ์‹๋ฌผ์— ๊ธฐ์ƒํ•˜๋Š” ๊ฑด ์•„๋‹ˆ๋ผ๋Š” +๊ฑฐ์ง€? +ํ•™์ƒ1:์‘, ๋งž์•„. ๋‚˜๋Š” ์ˆ˜์—ผํ‹ธ๋ž€๋“œ์‹œ์•„๊ฐ€ ์–ด๋–ป๊ฒŒ ๋ฒˆ์‹ํ•˜๋Š”์ง€ +์•Œ๊ณ  ์‹ถ์—ˆ๋Š”๋ฐ ๊ทธ ๋‚ด์šฉ์ด ์—†์–ด์„œ ์•„์‰ฌ์› ์–ด. +ํ•™์ƒ2:๋‚˜๋„ ๊ทธ๋žฌ์–ด. ๊ทธ ๋ถ€๋ถ„์— ๋Œ€ํ•œ ์„ค๋ช…์ด ์žˆ์—ˆ์œผ๋ฉด ๋” +์ข‹์•˜์„ ํ…๋ฐ. ์ˆ˜์—ผํ‹ธ๋ž€๋“œ์‹œ์•„๊ฐ€ ๋ฒˆ์‹์„ ์–ด๋–ป๊ฒŒ ํ•˜๋Š”์ง€ +์ฐพ์•„๋ด์•ผ๊ฒ ์–ด.",3,3,True,[],3 +2025-korean-38,"(๊ฐ€)๋Š” ํ•™์ƒํšŒ ํ•™์ƒ๋“ค์˜ ๋Œ€ํ™”์ด๊ณ , (๋‚˜)๋Š” ํ•™์ƒํšŒ ๋Œ€ํ‘œ๊ฐ€ +์“ด ๊ฑด์˜๋ฌธ์ด๋‹ค. ๋ฌผ์Œ์— ๋‹ตํ•˜์‹œ์˜ค. +(๊ฐ€) +ํ•™์ƒ1:์–˜๋“ค์•„, ์–ด์ œ ๋‰ด์Šค ๋ดค์–ด? ์ธ๊ทผ์— ์žˆ๋Š” โ—‹โ—‹๊ณ ๊ฐ€ ๊ฐœ๊ต +60์ฃผ๋…„์„ ์•ž๋‘๊ณ  ๊ต๊ฐ€ ๊ฐ€์‚ฌ๋ฅผ ๋ฐ”๊ฟจ๋‹ค๊ณ  ํ•˜๋”๋ผ. +ํ•™์ƒ2:ใ‰ ์ฒ˜์Œ ๋“ค์–ด๋ณด๋Š”๋ฐ, ๊ต๊ฐ€ ๊ฐ€์‚ฌ๋ฅผ ์™œ ๋ฐ”๊พผ ๊ฑฐ์•ผ? +ํ•™์ƒ1:๊ต๊ฐ€ ๊ฐ€์‚ฌ์— โ€˜์”ฉ์”ฉํ•œ ๊ฑด์•„์—ฌ, ์ฒญ๋…„ ์ผ๊พผ์ด์—ฌโ€™๋ผ๋Š” ๊ตฌ์ ˆ์ด +๋ฐ˜๋ณต๋˜์—ˆ๋‹ค๊ณ  ํ•ด. ์ด๋Ÿฐ ๊ฐ€์‚ฌ๋Š” ๊ฐœ๊ต ๋‹น์‹œ ์‚ฌํšŒ์—์„œ ์š”๊ตฌ๋˜๋˜ +ํŠน์ • ์—ญํ• ๋งŒ์„ ๊ฐ•์กฐํ•œ ๊ฑฐ๋ผ๊ณ  ๋‰ด์Šค์—์„œ ๊ทธ๋Ÿฌ๋”๋ผ. +ํ•™์ƒ3:์•„, ๊ทธ๋ž˜? ๊ทธ๋Ÿฐ ๊ฐ€์‚ฌ ๋‚ด์šฉ์ด ๊ฐœ๊ต ๋‹น์‹œ์—๋Š” ์ค‘์š”ํ•œ +๊ฐ€์น˜๋กœ ์—ฌ๊ฒจ์กŒ๊ฒ ์ง€๋งŒ ์ง€๊ธˆ์€ ๊ทธ๋ ‡์ง€ ์•Š์œผ๋‹ˆ ๋ฐ”๊พผ ๊ฑฐ๊ตฌ๋‚˜. +๊ทผ๋ฐ ๋น„์Šทํ•œ ์‹œ๊ธฐ์— ๊ฐœ๊ตํ•œ ์šฐ๋ฆฌ ํ•™๊ต ๊ตํ›ˆ๋„ ๋ฌธ์ œ๊ฐ€ +์žˆ์ง€ ์•Š๋‹ˆ? - [A] +ํ•™์ƒ2:ใ‰ก๋งž์•„. ๋“ฑ๊ตํ•  ๋•Œ ๊ตํ›ˆ์„ ๋ณด๋ฉด ๋งˆ์Œ์ด ์ข€ ๋ถˆํŽธํ•˜๋”๋ผ. +๊ทธ๋ž˜์„œ ๊ตํ›ˆ์„ ๋ฐ”๊ฟจ์œผ๋ฉด ์ข‹๊ฒ ๋‹ค๊ณ  ์ƒ๊ฐํ•œ ์ ์ด ์žˆ์–ด. +ํ•™์ƒ3:๋‚˜๋„ ๊ทธ๋žฌ๋Š”๋ฐ. ์šฐ๋ฆฌ ํ•™๊ต ๊ตํ›ˆ๋„ โ—‹โ—‹๊ณ  ๊ต๊ฐ€์ฒ˜๋Ÿผ ํŠน์ • +์—ญํ• ๋งŒ์ด ๋‘๋“œ๋Ÿฌ์ง€๋Š” ๊ฒƒ ๊ฐ™์•„. +ํ•™์ƒ2:์‘. ๊ทธ๋ž˜์„œ ๋งŽ์€ ํ•™์ƒ๋“ค์ด ๊ณต๊ฐํ•˜๊ธฐ ์–ด๋ ค์šด ๊ฒƒ๋„ ์‚ฌ์‹ค์ด์•ผ. +ํ•™์ƒ3:๊ตํ›ˆ์€ ์ง€๊ธˆ ์‹œ๋Œ€์—๋„ ๋งž๋Š” ๋ณดํŽธ์  ๊ฐ€์น˜๋ฅผ ๋‹ด๊ณ  +์žˆ์–ด์•ผ ํ•˜๋Š” ๊ฑฐ ์•„๋ƒ? ๋ˆ„๊ตฌ๋‚˜ ๊ณต๊ฐํ•  ์ˆ˜ ์žˆ์–ด์•ผ ํ•˜๊ณ . +๊ทธ๋Ÿฐ๋ฐ ์šฐ๋ฆฌ ๊ตํ›ˆ์€ ๊ทธ๋ ‡์ง€ ์•Š์€ ๊ฒƒ ๊ฐ™์•„. - [B] +ํ•™์ƒ1:ใ‰ข๋‚˜๋Š” ์šฐ๋ฆฌ ํ•™๊ต ๊ตํ›ˆ์ด ๊ดœ์ฐฎ๋‹ค๊ณ  ์ƒ๊ฐํ–ˆ๋Š”๋ฐ, ๋“ฃ๊ณ  +๋ณด๋‹ˆ ๋ฐ”๊ฟ”์•ผ๊ฒ ๋‹ค๋Š” ์ƒ๊ฐ์ด ๋“ค์–ด. ๊ทธ๋Ÿฐ๋ฐ ๋‰ด์Šค์— ๋”ฐ๋ฅด๋ฉด โ—‹โ—‹๊ณ  +์—์„œ๋Š” ๋™๋ฌธํšŒ๋ฅผ ์„ค๋“ํ•˜๋Š” ๊ฒƒ์ด ์‰ฝ์ง€ ์•Š์•˜๋‹ค๊ณ  ํ•˜๋”๋ผ๊ณ . +ํ•™์ƒ2:์•„๋ฌด๋ž˜๋„ ํ•™๊ต ์„ค๋ฆฝ ์ดํ›„ ์˜ค๋žœ ๊ธฐ๊ฐ„ ๊ต๊ฐ€๋ฅผ ๋ถˆ๋Ÿฌ ์™”์œผ๋‹ˆ๊นŒ, +๋™๋ฌธ ์„ ๋ฐฐ๋“ค์€ ๊ต๊ฐ€๊ฐ€ ๋ชจ๊ต๋ฅผ ์ƒ์ง•ํ•œ๋‹ค๊ณ  ์ƒ๊ฐํ•  ๊ฒƒ ๊ฐ™์•„. ๊ทธ๋งŒํผ +๊ต๊ฐ€์— ์• ์ •์ด ์žˆ๋Š” ์‚ฌ๋žŒ๋„ ๋งŽ์„ ๊ฑฐ์•ผ. +ํ•™์ƒ1:๋งž์•„. ๊ตํ›ˆ์„ ๋ฐ”๊พธ๋Š” ์ผ๋„ ๊ต๊ฐ€๋ฅผ ๋ฐ”๊พธ๋Š” ๊ฒƒ๋งŒํผ +์–ด๋ ค์šธ ๊ฒƒ ๊ฐ™์•„. - [C] +ํ•™์ƒ3:๊ทธ๋Ÿฌ๋‹ˆ๊นŒ ๋™๋ฌธ ์„ ๋ฐฐ๋“ค๊ณผ ํ•™๊ต ๊ตฌ์„ฑ์›์˜ ์˜๊ฒฌ๋„ ์ถฉ๋ถ„ํžˆ +๋“ค์–ด์•ผ ํ•  ๊ฒƒ ๊ฐ™์•„. ใ‰ฃ๊ตํ›ˆ ๋ณ€๊ฒฝ์ด ์™œ ํ•„์š”ํ•˜๊ณ  ์–ด๋–ค ํšจ๊ณผ๊ฐ€ +์žˆ๋Š”์ง€ ์•Œ๋ฆฌ๋Š” ๊ฒƒ๋„ ์ค‘์š”ํ•˜๊ฒ ์ง€? +ํ•™์ƒ2:์‘. ๊ทผ๋ฐ ๊ตํ›ˆ์„ ๋ฐ”๊พธ๋ฉด ์–ด๋–ค ํšจ๊ณผ๊ฐ€ ์žˆ์„๊นŒ? +ํ•™์ƒ3:๊ตํ›ˆ์„ ๋ณด๋ฉด์„œ ๋А๊ผˆ๋˜ ๋ถˆํŽธํ•œ ๋งˆ์Œ์ด ์‚ฌ๋ผ์ง€์ง€ ์•Š์„๊นŒ? +์ƒˆ๋กœ์šด ๊ตํ›ˆ์œผ๋กœ๋ถ€ํ„ฐ ์•Œ๊ฒŒ ๋ชจ๋ฅด๊ฒŒ ๊ธ์ •์ ์ธ ์˜ํ–ฅ๋„ ๋ฐ›์„ ์ˆ˜ +์žˆ์„ ๊ฑฐ๊ณ . +ํ•™์ƒ1:๊ทธ๋ž˜. ๊ตํ›ˆ์„ ๋ฐ”๊พธ๋Š” ๊ณผ์ •์—์„œ ํ•™๊ต์— ๋Œ€ํ•œ ๊ตฌ์„ฑ์›์˜ +๊ด€์‹ฌ์ด ๋†’์•„์ง€๊ณ  ๊ฒฐ์†๋ ฅ๋„ ์ปค์งˆ ๊ฑฐ์•ผ. ๊ทธ๋ฆฌ๊ณ  ๊ตํ›ˆ์„ ๋ฐ”๊พธ๊ณ  +์‹ถ์€ ๋‹ค๋ฅธ ํ•™๊ต์—๋„ ์ข‹์€ ๋ณธ๋ณด๊ธฐ๊ฐ€ ๋  ๊ฒƒ ๊ฐ™์•„. +ํ•™์ƒ3:๊ทธ๋Ÿผ ๊ตํ›ˆ ๋ณ€๊ฒฝ์„ ์ถ”์ง„ํ• ์ง€ ๋ง์ง€ ํ•™์ƒํšŒ ํšŒ์˜ ์•ˆ๊ฑด์œผ๋กœ +์˜ฌ๋ ค ๋ณด์ž. ์˜ค๋Š˜ ๋‚˜๋ˆˆ ์ด์•ผ๊ธฐ๋Š” ๋‚ด๊ฐ€ ์ •๋ฆฌํ• ๊ฒŒ. +ํ•™์ƒ1:๋‚˜๋Š” ๋‹ค๋ฅธ ํ•™๊ต์˜ ์‚ฌ๋ก€๋ฅผ ๋” ์ฐพ์•„์„œ ํšŒ์˜ ๋•Œ ๊ณต์œ ํ• ๊ฒŒ. +ํ•™์ƒ2:์ข‹์•„. ํšŒ์˜์—์„œ ํ†ต๊ณผ๋˜๋ฉด ๊ตํ›ˆ ๋ณ€๊ฒฝ์— ๋Œ€ํ•œ ํ•™์ƒ๋“ค์˜ +์˜๊ฒฌ์„ ์กฐ์‚ฌํ•ด ๋ณด์ž. ์„ค๋ฌธ ์กฐ์‚ฌ๋ฅผ ํ•˜๋Š” ๋ฐฐ๊ฒฝ๋„ ๊ฐ™์ด ์•ˆ๋‚ดํ•˜๋ฉด +์ข‹๊ฒ ์–ด. +ํ•™์ƒ1:์ฐฌ๋ฐ˜ ์˜๊ฒฌ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์ƒˆ๋กœ์šด ๊ตํ›ˆ๋„ ์ œ์•ˆ๋ฐ›์•„ ๋ณด์ž. +ํ•™์ƒ3:ใ‰ค๊ตํ›ˆ์„ ๋ฏธ๋ฆฌ ์ œ์•ˆ๋ฐ›์œผ๋ฉด ๊ตํ›ˆ ๋ณ€๊ฒฝ์ด ํ™•์ •๋œ ๊ฒƒ์ฒ˜๋Ÿผ +์˜คํ•ดํ•  ์ˆ˜ ์žˆ์œผ๋‹ˆ ๊ทธ ๋‚ด์šฉ์€ ๋นผ๋Š” ๊ฒŒ ์–ด๋•Œ? +ํ•™์ƒ1:๊ทธ๊ฒŒ ๋‚ซ๊ฒ ๋‹ค. ๊ตํ›ˆ ๋ณ€๊ฒฝ์— ๋Œ€ํ•œ ์ฐฌ๋ฐ˜ ์˜๊ฒฌ์„ ์กฐ์‚ฌํ•˜๊ณ , +๊ตํ›ˆ์„ ๋ฐ”๊พธ์ž๋Š” ์˜๊ฒฌ์ด ๋งŽ์œผ๋ฉด ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ํ•™๊ต์— +๊ฑด์˜ํ•˜๋ฉด ๋  ๊ฒƒ ๊ฐ™์•„. +ํ•™์ƒ2:๊ทธ๋ž˜! ๊ทธ๊ฒƒ๊ณผ ํ•จ๊ป˜ ๋™๋ฌธ ์„ ๋ฐฐ๋“ค์˜ ์˜๊ฒฌ์„ ๋ชจ์•„ ๋‹ฌ๋ผ๊ณ ๋„ +๋ถ€ํƒํ•ด ๋ณด์ž. ์šฐ๋ฆฌ ์ž˜ํ•ด ๋ณด์ž. + +(๋‚˜) +๊ต์žฅ ์„ ์ƒ๋‹˜, ์•ˆ๋…•ํ•˜์„ธ์š”? โ“์ €๋Š” ํ•™์ƒํšŒ ๋Œ€ํ‘œ ์•ˆโ–ณโ–ณ์ž…๋‹ˆ๋‹ค. +๋Š˜ ํ•™๊ต ๋ฐœ์ „๊ณผ ํ•™์ƒ๋“ค์˜ ์„ฑ์žฅ์„ ์œ„ํ•ด ์• ์“ฐ์‹œ๋Š” ๊ต์žฅ ์„ ์ƒ๋‹˜๊ป˜ +๊ฐ์‚ฌ์˜ ๋ง์”€์„ ๋“œ๋ฆฝ๋‹ˆ๋‹ค. +โ“‘์ตœ๊ทผ โ—‡โ—‡๋ฐฉ์†ก ๋‰ด์Šค์— ๋”ฐ๋ฅด๋ฉด ์ธ๊ทผ ํ•™๊ต์ธ โ—‹โ—‹๊ณ ๊ฐ€ ํ•™๊ต +๊ตฌ์„ฑ์›์˜ ๋…ธ๋ ฅ ๋์— ๊ต๊ฐ€ ๊ฐ€์‚ฌ๋ฅผ ๋ณ€๊ฒฝํ•˜์˜€๋‹ค๊ณ  ํ•ฉ๋‹ˆ๋‹ค. โ—‹โ—‹๊ณ ์˜ +๋ณ€๊ฒฝ๋œ ๊ต๊ฐ€ ๊ฐ€์‚ฌ๋Š” ์ด์ „๊ณผ ๋‹ฌ๋ฆฌ, ํ•™์ƒ๋“ค์˜ ๋ฏธ๋ž˜์™€ ํ–‰๋ณตํ•œ ์‚ถ์„ +๊ฐ•์กฐํ•œ๋‹ค๊ณ  ํ•ฉ๋‹ˆ๋‹ค. โ—‹โ—‹๊ณ ์™€ ๊ฐ™์ด ์šฐ๋ฆฌ ํ•™๊ต์—์„œ๋„ ๊ตํ›ˆ์„ +๋ฐ”๊พธ์ž๋Š” ํ•™์ƒ๋“ค์˜ ๋ชฉ์†Œ๋ฆฌ๊ฐ€ ์ปค์ง€๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. +โ“’๊ตํ›ˆ์€ ํ•™๊ต์˜ ์ด๋…์„ ํ‘œํ˜„ํ•˜์ง€๋งŒ, ๋‹จ์ˆœํžˆ ํ‘œํ˜„์—๋งŒ ๊ทธ์น˜๋Š” +๊ฒƒ์€ ์•„๋‹™๋‹ˆ๋‹ค. ๊ตํ›ˆ์€ ํ•™๊ต์˜ ์ด๋…๊ณผ ๋ชฉํ‘œ๋ฅผ ๋“œ๋Ÿฌ๋‚ผ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ +โ““๊ตฌ์„ฑ์› ๋ชจ๋‘๊ฐ€ ์ง€ํ–ฅํ•˜๋Š” ์ •์‹ ์  ๊ฐ€์น˜๋ฅผ ๋‹ด๋Š” ๊ทธ๋ฆ‡์ž…๋‹ˆ๋‹ค. ํ•˜์ง€๋งŒ +์ง€๊ธˆ ์šฐ๋ฆฌ ํ•™๊ต์˜ ๊ตํ›ˆ์€ ๊ฐœ๊ต ๋‹น์‹œ ์š”๊ตฌ๋˜๋˜ ํŠน์ • ์—ญํ• ๋งŒ์„ +๋ถ€๊ฐํ•˜๊ณ  ์žˆ์–ด, ํ˜„์žฌ์™€ ๋ฏธ๋ž˜์˜ ๊ตฌ์„ฑ์›์ด ์ง€ํ–ฅํ•ด์•ผ ํ•˜๋Š” ๊ฐ€์น˜๋ฅผ +๋ฐ˜์˜ํ•˜์ง€ ๋ชปํ•˜๋Š” ๋ฌธ์ œ๊ฐ€ ์žˆ์Šต๋‹ˆ๋‹ค. ๊ทธ๋ž˜์„œ ๋‹น์‹œ์™€๋Š” ๋‹ฌ๋ฆฌ ์ง€๊ธˆ์€ +ํ•™์ƒ๋“ค์˜ ๊ณต๊ฐ์„ ์–ป์ง€ ๋ชปํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. +์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ๊ณต์‹์ ์ธ ์ ˆ์ฐจ๋ฅผ ์ถ”์ง„ํ•˜์—ฌ +๊ตํ›ˆ์„ ๋ณ€๊ฒฝํ•ด ์ฃผ์‹œ๊ธฐ๋ฅผ ๊ฑด์˜๋“œ๋ฆฝ๋‹ˆ๋‹ค. โ“”ํ•™๊ต์—์„œ๋Š” ๊ต์ง์›, +๋™๋ฌธ ์„ ๋ฐฐ, ํ•™๋ถ€๋ชจ์—๊ฒŒ ๊ตํ›ˆ ๋ณ€๊ฒฝ์˜ ์ทจ์ง€๋ฅผ ์„ค๋ช…ํ•˜๊ณ , ๊ทธ๋ถ„๋“ค์˜ +์˜๊ฒฌ์„ ์ˆ˜๋ ดํ•œ ํ›„, ํ•™๊ต์šด์˜์œ„์›ํšŒ์—์„œ ์‹ฌ์˜ํ•˜๋„๋ก ํ•ด ์ฃผ์‹œ๋ฉด +์ข‹๊ฒ ์Šต๋‹ˆ๋‹ค. ๊ต์žฅ ์„ ์ƒ๋‹˜๊ป˜์„œ๋„ ์•Œ๊ณ  ๊ณ„์‹œ๋“ฏ์ด, ํ•™์ƒํšŒ์—์„œ ์„ค๋ฌธ +์กฐ์‚ฌ๋กœ ํ•™์ƒ๋“ค์˜ ์˜๊ฒฌ์„ ์ˆ˜๋ ดํ•œ ๊ฒฐ๊ณผ ์ „๊ต์ƒ์˜ 91.8%๊ฐ€ ๊ตํ›ˆ +๋ณ€๊ฒฝ์— ์ฐฌ์„ฑํ–ˆ์Šต๋‹ˆ๋‹ค. ํ•™์ƒ๋“ค ์‚ฌ์ด์—๋Š” ์ด๋ฏธ ๊ตํ›ˆ ๋ณ€๊ฒฝ์— ๋Œ€ํ•œ +๊ณต๊ฐ๋Œ€๊ฐ€ ํ˜•์„ฑ๋˜์—ˆ๋‹ค๊ณ  ๋ณผ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. +๋ˆ„๊ตฌ๋‚˜ ๊ณต๊ฐํ•  ์ˆ˜ ์žˆ๋Š” ๊ตํ›ˆ์œผ๋กœ ๋ฐ”๊พธ๋ฉด ๊ตํ›ˆ์„ ๋ณด๋ฉด์„œ ๋А๊ผˆ๋˜ +๋ถˆํŽธํ•œ ๋งˆ์Œ์ด ์‚ฌ๋ผ์ง€๊ณ  ํ•™์ƒ๋“ค์˜ ๋…ธ๋ ฅ์œผ๋กœ ๊ตํ›ˆ์„ ๋ฐ”๊ฟจ๋‹ค๋Š” +์ž๋ถ€์‹ฌ์„ ๋А๋ผ๊ฒŒ ๋  ๊ฒƒ์ž…๋‹ˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๊ทธ ๊ณผ์ •์—์„œ ํ•™์ƒ๋“ค์€ +๋ฌผ๋ก  ๋ถ€๋ชจ๋‹˜๋“ค๊ณผ ์„ ์ƒ๋‹˜๋“ค๋„ ํ•™๊ต์— ๊ด€์‹ฌ์„ ๋” ๊ฐ–๊ฒŒ ๋˜๋ฉด์„œ +์ž์—ฐ์Šค๋Ÿฝ๊ฒŒ ์• ๊ต์‹ฌ๊ณผ ํ•™๊ต์— ๋Œ€ํ•œ ๊ธ์ง€๊ฐ€ ๋†’์•„์งˆ ๊ฒƒ์ž…๋‹ˆ๋‹ค. ์šฐ๋ฆฌ +ํ•™๊ต์˜ ๊ตํ›ˆ ๋ณ€๊ฒฝ์€ ๊ตํ›ˆ์„ ๋ฐ”๊พธ๊ณ  ์‹ถ์€ ๋‹ค๋ฅธ ํ•™๊ต์—๋„ ์ข‹์€ +์˜ํ–ฅ์„ ๋ผ์น  ๊ฒƒ์ž…๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋ณ€ํ™”๋Š” ํ•™์ƒ๋“ค์—๊ฒŒ ๊ต์œก์ ์œผ๋กœ๋„ +๊ธ์ •์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์น  ๊ฒƒ์ด๋ผ๊ณ  ํ™•์‹ ํ•ฉ๋‹ˆ๋‹ค. +ํ•™์ƒ๋“ค์ด ์ƒˆ๋กœ์šด ๊ตํ›ˆ ์•„๋ž˜์—์„œ ์„ฑ์žฅํ•˜๊ณ  ์‹œ๋Œ€์— ๋ฐœ๋งž์ถฐ ๊ฐˆ +์ˆ˜ ์žˆ๋„๋ก ๊ตํ›ˆ์„ ๋ณ€๊ฒฝํ•ด ์ฃผ์‹œ๊ธธ ๋ถ€ํƒ๋“œ๋ฆฝ๋‹ˆ๋‹ค. ๊ณ ๋ง™์Šต๋‹ˆ๋‹ค. + +","{'question': '(๊ฐ€)์˜ ใ‰ ๏ฝžใ‰ค์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์ ์ ˆํ•˜์ง€ ์•Š์€ ๊ฒƒ์€?', 'choices': ['ใ‰ :์ƒ๋Œ€๊ฐ€ ์–ธ๊ธ‰ํ•œ ๋‚ด์šฉ์— ๋Œ€ํ•ด ๊ด€๋ จ ์ •๋ณด๋ฅผ ์š”์ฒญํ•˜๊ณ  ์žˆ๋‹ค.', 'ใ‰ก:์ƒ๋Œ€์˜ ์ƒ๊ฐ์— ์ˆ˜๊ธํ•œ ํ›„ ์ž์‹ ์˜ ๊ฒฝํ—˜์„ ์ œ์‹œํ•˜๊ณ  ์žˆ๋‹ค.', 'ใ‰ข:์ƒ๋Œ€์˜ ๊ฒฌํ•ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๊ธฐ์กด์˜ ์ธ์‹์„ ์ „ํ™˜ํ•˜๊ณ  ์žˆ๋‹ค.', 'ใ‰ฃ:์ƒ๋Œ€๊ฐ€ ์ œ์‹œํ•œ ๋Œ€์•ˆ์— ๋Œ€ํ•ด ๋ฌธ์ œ๋ฅผ ์ œ๊ธฐํ•˜๊ณ  ์žˆ๋‹ค.', 'ใ‰ค:์ƒ๋Œ€์˜ ์ƒ๊ฐ๊ณผ ๋‹ค๋ฅธ ์ž์‹ ์˜ ์˜๊ฒฌ์„ ์ œ์•ˆํ•˜๊ณ  ์žˆ๋‹ค.'], 'answer': ''}",,4,4,True,[],4 +2025-korean-39,"(๊ฐ€)๋Š” ํ•™์ƒํšŒ ํ•™์ƒ๋“ค์˜ ๋Œ€ํ™”์ด๊ณ , (๋‚˜)๋Š” ํ•™์ƒํšŒ ๋Œ€ํ‘œ๊ฐ€ +์“ด ๊ฑด์˜๋ฌธ์ด๋‹ค. ๋ฌผ์Œ์— ๋‹ตํ•˜์‹œ์˜ค. +(๊ฐ€) +ํ•™์ƒ1:์–˜๋“ค์•„, ์–ด์ œ ๋‰ด์Šค ๋ดค์–ด? ์ธ๊ทผ์— ์žˆ๋Š” โ—‹โ—‹๊ณ ๊ฐ€ ๊ฐœ๊ต +60์ฃผ๋…„์„ ์•ž๋‘๊ณ  ๊ต๊ฐ€ ๊ฐ€์‚ฌ๋ฅผ ๋ฐ”๊ฟจ๋‹ค๊ณ  ํ•˜๋”๋ผ. +ํ•™์ƒ2:ใ‰ ์ฒ˜์Œ ๋“ค์–ด๋ณด๋Š”๋ฐ, ๊ต๊ฐ€ ๊ฐ€์‚ฌ๋ฅผ ์™œ ๋ฐ”๊พผ ๊ฑฐ์•ผ? +ํ•™์ƒ1:๊ต๊ฐ€ ๊ฐ€์‚ฌ์— โ€˜์”ฉ์”ฉํ•œ ๊ฑด์•„์—ฌ, ์ฒญ๋…„ ์ผ๊พผ์ด์—ฌโ€™๋ผ๋Š” ๊ตฌ์ ˆ์ด +๋ฐ˜๋ณต๋˜์—ˆ๋‹ค๊ณ  ํ•ด. ์ด๋Ÿฐ ๊ฐ€์‚ฌ๋Š” ๊ฐœ๊ต ๋‹น์‹œ ์‚ฌํšŒ์—์„œ ์š”๊ตฌ๋˜๋˜ +ํŠน์ • ์—ญํ• ๋งŒ์„ ๊ฐ•์กฐํ•œ ๊ฑฐ๋ผ๊ณ  ๋‰ด์Šค์—์„œ ๊ทธ๋Ÿฌ๋”๋ผ. +ํ•™์ƒ3:์•„, ๊ทธ๋ž˜? ๊ทธ๋Ÿฐ ๊ฐ€์‚ฌ ๋‚ด์šฉ์ด ๊ฐœ๊ต ๋‹น์‹œ์—๋Š” ์ค‘์š”ํ•œ +๊ฐ€์น˜๋กœ ์—ฌ๊ฒจ์กŒ๊ฒ ์ง€๋งŒ ์ง€๊ธˆ์€ ๊ทธ๋ ‡์ง€ ์•Š์œผ๋‹ˆ ๋ฐ”๊พผ ๊ฑฐ๊ตฌ๋‚˜. +๊ทผ๋ฐ ๋น„์Šทํ•œ ์‹œ๊ธฐ์— ๊ฐœ๊ตํ•œ ์šฐ๋ฆฌ ํ•™๊ต ๊ตํ›ˆ๋„ ๋ฌธ์ œ๊ฐ€ +์žˆ์ง€ ์•Š๋‹ˆ? - [A] +ํ•™์ƒ2:ใ‰ก๋งž์•„. ๋“ฑ๊ตํ•  ๋•Œ ๊ตํ›ˆ์„ ๋ณด๋ฉด ๋งˆ์Œ์ด ์ข€ ๋ถˆํŽธํ•˜๋”๋ผ. +๊ทธ๋ž˜์„œ ๊ตํ›ˆ์„ ๋ฐ”๊ฟจ์œผ๋ฉด ์ข‹๊ฒ ๋‹ค๊ณ  ์ƒ๊ฐํ•œ ์ ์ด ์žˆ์–ด. +ํ•™์ƒ3:๋‚˜๋„ ๊ทธ๋žฌ๋Š”๋ฐ. ์šฐ๋ฆฌ ํ•™๊ต ๊ตํ›ˆ๋„ โ—‹โ—‹๊ณ  ๊ต๊ฐ€์ฒ˜๋Ÿผ ํŠน์ • +์—ญํ• ๋งŒ์ด ๋‘๋“œ๋Ÿฌ์ง€๋Š” ๊ฒƒ ๊ฐ™์•„. +ํ•™์ƒ2:์‘. ๊ทธ๋ž˜์„œ ๋งŽ์€ ํ•™์ƒ๋“ค์ด ๊ณต๊ฐํ•˜๊ธฐ ์–ด๋ ค์šด ๊ฒƒ๋„ ์‚ฌ์‹ค์ด์•ผ. +ํ•™์ƒ3:๊ตํ›ˆ์€ ์ง€๊ธˆ ์‹œ๋Œ€์—๋„ ๋งž๋Š” ๋ณดํŽธ์  ๊ฐ€์น˜๋ฅผ ๋‹ด๊ณ  +์žˆ์–ด์•ผ ํ•˜๋Š” ๊ฑฐ ์•„๋ƒ? ๋ˆ„๊ตฌ๋‚˜ ๊ณต๊ฐํ•  ์ˆ˜ ์žˆ์–ด์•ผ ํ•˜๊ณ . +๊ทธ๋Ÿฐ๋ฐ ์šฐ๋ฆฌ ๊ตํ›ˆ์€ ๊ทธ๋ ‡์ง€ ์•Š์€ ๊ฒƒ ๊ฐ™์•„. - [B] +ํ•™์ƒ1:ใ‰ข๋‚˜๋Š” ์šฐ๋ฆฌ ํ•™๊ต ๊ตํ›ˆ์ด ๊ดœ์ฐฎ๋‹ค๊ณ  ์ƒ๊ฐํ–ˆ๋Š”๋ฐ, ๋“ฃ๊ณ  +๋ณด๋‹ˆ ๋ฐ”๊ฟ”์•ผ๊ฒ ๋‹ค๋Š” ์ƒ๊ฐ์ด ๋“ค์–ด. ๊ทธ๋Ÿฐ๋ฐ ๋‰ด์Šค์— ๋”ฐ๋ฅด๋ฉด โ—‹โ—‹๊ณ  +์—์„œ๋Š” ๋™๋ฌธํšŒ๋ฅผ ์„ค๋“ํ•˜๋Š” ๊ฒƒ์ด ์‰ฝ์ง€ ์•Š์•˜๋‹ค๊ณ  ํ•˜๋”๋ผ๊ณ . +ํ•™์ƒ2:์•„๋ฌด๋ž˜๋„ ํ•™๊ต ์„ค๋ฆฝ ์ดํ›„ ์˜ค๋žœ ๊ธฐ๊ฐ„ ๊ต๊ฐ€๋ฅผ ๋ถˆ๋Ÿฌ ์™”์œผ๋‹ˆ๊นŒ, +๋™๋ฌธ ์„ ๋ฐฐ๋“ค์€ ๊ต๊ฐ€๊ฐ€ ๋ชจ๊ต๋ฅผ ์ƒ์ง•ํ•œ๋‹ค๊ณ  ์ƒ๊ฐํ•  ๊ฒƒ ๊ฐ™์•„. ๊ทธ๋งŒํผ +๊ต๊ฐ€์— ์• ์ •์ด ์žˆ๋Š” ์‚ฌ๋žŒ๋„ ๋งŽ์„ ๊ฑฐ์•ผ. +ํ•™์ƒ1:๋งž์•„. ๊ตํ›ˆ์„ ๋ฐ”๊พธ๋Š” ์ผ๋„ ๊ต๊ฐ€๋ฅผ ๋ฐ”๊พธ๋Š” ๊ฒƒ๋งŒํผ +์–ด๋ ค์šธ ๊ฒƒ ๊ฐ™์•„. - [C] +ํ•™์ƒ3:๊ทธ๋Ÿฌ๋‹ˆ๊นŒ ๋™๋ฌธ ์„ ๋ฐฐ๋“ค๊ณผ ํ•™๊ต ๊ตฌ์„ฑ์›์˜ ์˜๊ฒฌ๋„ ์ถฉ๋ถ„ํžˆ +๋“ค์–ด์•ผ ํ•  ๊ฒƒ ๊ฐ™์•„. ใ‰ฃ๊ตํ›ˆ ๋ณ€๊ฒฝ์ด ์™œ ํ•„์š”ํ•˜๊ณ  ์–ด๋–ค ํšจ๊ณผ๊ฐ€ +์žˆ๋Š”์ง€ ์•Œ๋ฆฌ๋Š” ๊ฒƒ๋„ ์ค‘์š”ํ•˜๊ฒ ์ง€? +ํ•™์ƒ2:์‘. ๊ทผ๋ฐ ๊ตํ›ˆ์„ ๋ฐ”๊พธ๋ฉด ์–ด๋–ค ํšจ๊ณผ๊ฐ€ ์žˆ์„๊นŒ? +ํ•™์ƒ3:๊ตํ›ˆ์„ ๋ณด๋ฉด์„œ ๋А๊ผˆ๋˜ ๋ถˆํŽธํ•œ ๋งˆ์Œ์ด ์‚ฌ๋ผ์ง€์ง€ ์•Š์„๊นŒ? +์ƒˆ๋กœ์šด ๊ตํ›ˆ์œผ๋กœ๋ถ€ํ„ฐ ์•Œ๊ฒŒ ๋ชจ๋ฅด๊ฒŒ ๊ธ์ •์ ์ธ ์˜ํ–ฅ๋„ ๋ฐ›์„ ์ˆ˜ +์žˆ์„ ๊ฑฐ๊ณ . +ํ•™์ƒ1:๊ทธ๋ž˜. ๊ตํ›ˆ์„ ๋ฐ”๊พธ๋Š” ๊ณผ์ •์—์„œ ํ•™๊ต์— ๋Œ€ํ•œ ๊ตฌ์„ฑ์›์˜ +๊ด€์‹ฌ์ด ๋†’์•„์ง€๊ณ  ๊ฒฐ์†๋ ฅ๋„ ์ปค์งˆ ๊ฑฐ์•ผ. ๊ทธ๋ฆฌ๊ณ  ๊ตํ›ˆ์„ ๋ฐ”๊พธ๊ณ  +์‹ถ์€ ๋‹ค๋ฅธ ํ•™๊ต์—๋„ ์ข‹์€ ๋ณธ๋ณด๊ธฐ๊ฐ€ ๋  ๊ฒƒ ๊ฐ™์•„. +ํ•™์ƒ3:๊ทธ๋Ÿผ ๊ตํ›ˆ ๋ณ€๊ฒฝ์„ ์ถ”์ง„ํ• ์ง€ ๋ง์ง€ ํ•™์ƒํšŒ ํšŒ์˜ ์•ˆ๊ฑด์œผ๋กœ +์˜ฌ๋ ค ๋ณด์ž. ์˜ค๋Š˜ ๋‚˜๋ˆˆ ์ด์•ผ๊ธฐ๋Š” ๋‚ด๊ฐ€ ์ •๋ฆฌํ• ๊ฒŒ. +ํ•™์ƒ1:๋‚˜๋Š” ๋‹ค๋ฅธ ํ•™๊ต์˜ ์‚ฌ๋ก€๋ฅผ ๋” ์ฐพ์•„์„œ ํšŒ์˜ ๋•Œ ๊ณต์œ ํ• ๊ฒŒ. +ํ•™์ƒ2:์ข‹์•„. ํšŒ์˜์—์„œ ํ†ต๊ณผ๋˜๋ฉด ๊ตํ›ˆ ๋ณ€๊ฒฝ์— ๋Œ€ํ•œ ํ•™์ƒ๋“ค์˜ +์˜๊ฒฌ์„ ์กฐ์‚ฌํ•ด ๋ณด์ž. ์„ค๋ฌธ ์กฐ์‚ฌ๋ฅผ ํ•˜๋Š” ๋ฐฐ๊ฒฝ๋„ ๊ฐ™์ด ์•ˆ๋‚ดํ•˜๋ฉด +์ข‹๊ฒ ์–ด. +ํ•™์ƒ1:์ฐฌ๋ฐ˜ ์˜๊ฒฌ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์ƒˆ๋กœ์šด ๊ตํ›ˆ๋„ ์ œ์•ˆ๋ฐ›์•„ ๋ณด์ž. +ํ•™์ƒ3:ใ‰ค๊ตํ›ˆ์„ ๋ฏธ๋ฆฌ ์ œ์•ˆ๋ฐ›์œผ๋ฉด ๊ตํ›ˆ ๋ณ€๊ฒฝ์ด ํ™•์ •๋œ ๊ฒƒ์ฒ˜๋Ÿผ +์˜คํ•ดํ•  ์ˆ˜ ์žˆ์œผ๋‹ˆ ๊ทธ ๋‚ด์šฉ์€ ๋นผ๋Š” ๊ฒŒ ์–ด๋•Œ? +ํ•™์ƒ1:๊ทธ๊ฒŒ ๋‚ซ๊ฒ ๋‹ค. ๊ตํ›ˆ ๋ณ€๊ฒฝ์— ๋Œ€ํ•œ ์ฐฌ๋ฐ˜ ์˜๊ฒฌ์„ ์กฐ์‚ฌํ•˜๊ณ , +๊ตํ›ˆ์„ ๋ฐ”๊พธ์ž๋Š” ์˜๊ฒฌ์ด ๋งŽ์œผ๋ฉด ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ํ•™๊ต์— +๊ฑด์˜ํ•˜๋ฉด ๋  ๊ฒƒ ๊ฐ™์•„. +ํ•™์ƒ2:๊ทธ๋ž˜! ๊ทธ๊ฒƒ๊ณผ ํ•จ๊ป˜ ๋™๋ฌธ ์„ ๋ฐฐ๋“ค์˜ ์˜๊ฒฌ์„ ๋ชจ์•„ ๋‹ฌ๋ผ๊ณ ๋„ +๋ถ€ํƒํ•ด ๋ณด์ž. ์šฐ๋ฆฌ ์ž˜ํ•ด ๋ณด์ž. + +(๋‚˜) +๊ต์žฅ ์„ ์ƒ๋‹˜, ์•ˆ๋…•ํ•˜์„ธ์š”? โ“์ €๋Š” ํ•™์ƒํšŒ ๋Œ€ํ‘œ ์•ˆโ–ณโ–ณ์ž…๋‹ˆ๋‹ค. +๋Š˜ ํ•™๊ต ๋ฐœ์ „๊ณผ ํ•™์ƒ๋“ค์˜ ์„ฑ์žฅ์„ ์œ„ํ•ด ์• ์“ฐ์‹œ๋Š” ๊ต์žฅ ์„ ์ƒ๋‹˜๊ป˜ +๊ฐ์‚ฌ์˜ ๋ง์”€์„ ๋“œ๋ฆฝ๋‹ˆ๋‹ค. +โ“‘์ตœ๊ทผ โ—‡โ—‡๋ฐฉ์†ก ๋‰ด์Šค์— ๋”ฐ๋ฅด๋ฉด ์ธ๊ทผ ํ•™๊ต์ธ โ—‹โ—‹๊ณ ๊ฐ€ ํ•™๊ต +๊ตฌ์„ฑ์›์˜ ๋…ธ๋ ฅ ๋์— ๊ต๊ฐ€ ๊ฐ€์‚ฌ๋ฅผ ๋ณ€๊ฒฝํ•˜์˜€๋‹ค๊ณ  ํ•ฉ๋‹ˆ๋‹ค. โ—‹โ—‹๊ณ ์˜ +๋ณ€๊ฒฝ๋œ ๊ต๊ฐ€ ๊ฐ€์‚ฌ๋Š” ์ด์ „๊ณผ ๋‹ฌ๋ฆฌ, ํ•™์ƒ๋“ค์˜ ๋ฏธ๋ž˜์™€ ํ–‰๋ณตํ•œ ์‚ถ์„ +๊ฐ•์กฐํ•œ๋‹ค๊ณ  ํ•ฉ๋‹ˆ๋‹ค. โ—‹โ—‹๊ณ ์™€ ๊ฐ™์ด ์šฐ๋ฆฌ ํ•™๊ต์—์„œ๋„ ๊ตํ›ˆ์„ +๋ฐ”๊พธ์ž๋Š” ํ•™์ƒ๋“ค์˜ ๋ชฉ์†Œ๋ฆฌ๊ฐ€ ์ปค์ง€๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. +โ“’๊ตํ›ˆ์€ ํ•™๊ต์˜ ์ด๋…์„ ํ‘œํ˜„ํ•˜์ง€๋งŒ, ๋‹จ์ˆœํžˆ ํ‘œํ˜„์—๋งŒ ๊ทธ์น˜๋Š” +๊ฒƒ์€ ์•„๋‹™๋‹ˆ๋‹ค. ๊ตํ›ˆ์€ ํ•™๊ต์˜ ์ด๋…๊ณผ ๋ชฉํ‘œ๋ฅผ ๋“œ๋Ÿฌ๋‚ผ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ +โ““๊ตฌ์„ฑ์› ๋ชจ๋‘๊ฐ€ ์ง€ํ–ฅํ•˜๋Š” ์ •์‹ ์  ๊ฐ€์น˜๋ฅผ ๋‹ด๋Š” ๊ทธ๋ฆ‡์ž…๋‹ˆ๋‹ค. ํ•˜์ง€๋งŒ +์ง€๊ธˆ ์šฐ๋ฆฌ ํ•™๊ต์˜ ๊ตํ›ˆ์€ ๊ฐœ๊ต ๋‹น์‹œ ์š”๊ตฌ๋˜๋˜ ํŠน์ • ์—ญํ• ๋งŒ์„ +๋ถ€๊ฐํ•˜๊ณ  ์žˆ์–ด, ํ˜„์žฌ์™€ ๋ฏธ๋ž˜์˜ ๊ตฌ์„ฑ์›์ด ์ง€ํ–ฅํ•ด์•ผ ํ•˜๋Š” ๊ฐ€์น˜๋ฅผ +๋ฐ˜์˜ํ•˜์ง€ ๋ชปํ•˜๋Š” ๋ฌธ์ œ๊ฐ€ ์žˆ์Šต๋‹ˆ๋‹ค. ๊ทธ๋ž˜์„œ ๋‹น์‹œ์™€๋Š” ๋‹ฌ๋ฆฌ ์ง€๊ธˆ์€ +ํ•™์ƒ๋“ค์˜ ๊ณต๊ฐ์„ ์–ป์ง€ ๋ชปํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. +์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ๊ณต์‹์ ์ธ ์ ˆ์ฐจ๋ฅผ ์ถ”์ง„ํ•˜์—ฌ +๊ตํ›ˆ์„ ๋ณ€๊ฒฝํ•ด ์ฃผ์‹œ๊ธฐ๋ฅผ ๊ฑด์˜๋“œ๋ฆฝ๋‹ˆ๋‹ค. โ“”ํ•™๊ต์—์„œ๋Š” ๊ต์ง์›, +๋™๋ฌธ ์„ ๋ฐฐ, ํ•™๋ถ€๋ชจ์—๊ฒŒ ๊ตํ›ˆ ๋ณ€๊ฒฝ์˜ ์ทจ์ง€๋ฅผ ์„ค๋ช…ํ•˜๊ณ , ๊ทธ๋ถ„๋“ค์˜ +์˜๊ฒฌ์„ ์ˆ˜๋ ดํ•œ ํ›„, ํ•™๊ต์šด์˜์œ„์›ํšŒ์—์„œ ์‹ฌ์˜ํ•˜๋„๋ก ํ•ด ์ฃผ์‹œ๋ฉด +์ข‹๊ฒ ์Šต๋‹ˆ๋‹ค. ๊ต์žฅ ์„ ์ƒ๋‹˜๊ป˜์„œ๋„ ์•Œ๊ณ  ๊ณ„์‹œ๋“ฏ์ด, ํ•™์ƒํšŒ์—์„œ ์„ค๋ฌธ +์กฐ์‚ฌ๋กœ ํ•™์ƒ๋“ค์˜ ์˜๊ฒฌ์„ ์ˆ˜๋ ดํ•œ ๊ฒฐ๊ณผ ์ „๊ต์ƒ์˜ 91.8%๊ฐ€ ๊ตํ›ˆ +๋ณ€๊ฒฝ์— ์ฐฌ์„ฑํ–ˆ์Šต๋‹ˆ๋‹ค. ํ•™์ƒ๋“ค ์‚ฌ์ด์—๋Š” ์ด๋ฏธ ๊ตํ›ˆ ๋ณ€๊ฒฝ์— ๋Œ€ํ•œ +๊ณต๊ฐ๋Œ€๊ฐ€ ํ˜•์„ฑ๋˜์—ˆ๋‹ค๊ณ  ๋ณผ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. +๋ˆ„๊ตฌ๋‚˜ ๊ณต๊ฐํ•  ์ˆ˜ ์žˆ๋Š” ๊ตํ›ˆ์œผ๋กœ ๋ฐ”๊พธ๋ฉด ๊ตํ›ˆ์„ ๋ณด๋ฉด์„œ ๋А๊ผˆ๋˜ +๋ถˆํŽธํ•œ ๋งˆ์Œ์ด ์‚ฌ๋ผ์ง€๊ณ  ํ•™์ƒ๋“ค์˜ ๋…ธ๋ ฅ์œผ๋กœ ๊ตํ›ˆ์„ ๋ฐ”๊ฟจ๋‹ค๋Š” +์ž๋ถ€์‹ฌ์„ ๋А๋ผ๊ฒŒ ๋  ๊ฒƒ์ž…๋‹ˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๊ทธ ๊ณผ์ •์—์„œ ํ•™์ƒ๋“ค์€ +๋ฌผ๋ก  ๋ถ€๋ชจ๋‹˜๋“ค๊ณผ ์„ ์ƒ๋‹˜๋“ค๋„ ํ•™๊ต์— ๊ด€์‹ฌ์„ ๋” ๊ฐ–๊ฒŒ ๋˜๋ฉด์„œ +์ž์—ฐ์Šค๋Ÿฝ๊ฒŒ ์• ๊ต์‹ฌ๊ณผ ํ•™๊ต์— ๋Œ€ํ•œ ๊ธ์ง€๊ฐ€ ๋†’์•„์งˆ ๊ฒƒ์ž…๋‹ˆ๋‹ค. ์šฐ๋ฆฌ +ํ•™๊ต์˜ ๊ตํ›ˆ ๋ณ€๊ฒฝ์€ ๊ตํ›ˆ์„ ๋ฐ”๊พธ๊ณ  ์‹ถ์€ ๋‹ค๋ฅธ ํ•™๊ต์—๋„ ์ข‹์€ +์˜ํ–ฅ์„ ๋ผ์น  ๊ฒƒ์ž…๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋ณ€ํ™”๋Š” ํ•™์ƒ๋“ค์—๊ฒŒ ๊ต์œก์ ์œผ๋กœ๋„ +๊ธ์ •์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์น  ๊ฒƒ์ด๋ผ๊ณ  ํ™•์‹ ํ•ฉ๋‹ˆ๋‹ค. +ํ•™์ƒ๋“ค์ด ์ƒˆ๋กœ์šด ๊ตํ›ˆ ์•„๋ž˜์—์„œ ์„ฑ์žฅํ•˜๊ณ  ์‹œ๋Œ€์— ๋ฐœ๋งž์ถฐ ๊ฐˆ +์ˆ˜ ์žˆ๋„๋ก ๊ตํ›ˆ์„ ๋ณ€๊ฒฝํ•ด ์ฃผ์‹œ๊ธธ ๋ถ€ํƒ๋“œ๋ฆฝ๋‹ˆ๋‹ค. ๊ณ ๋ง™์Šต๋‹ˆ๋‹ค. + +","{'question': '๋‹ค์Œ์€ โ€˜ํ•™์ƒ3โ€™์ด ํ•™์ƒํšŒ ํšŒ์˜๋ฅผ ์ค€๋น„ํ•˜๋ฉด์„œ (๊ฐ€)์˜ ๋Œ€ํ™” ๋‚ด์šฉ์„\n์ •๋ฆฌํ•œ ๋ฉ”๋ชจ์˜ ์ผ๋ถ€์ด๋‹ค. ๋ฉ”๋ชจ์˜ ๋‚ด์šฉ์œผ๋กœ ์ ์ ˆํ•˜์ง€ ์•Š์€ ๊ฒƒ์€?', 'choices': ['๋ฐฐ๊ฒฝ \n- โ—‹โ—‹๊ณ ๋Š” ๊ฐœ๊ต 60์ฃผ๋…„์„ ์•ž๋‘๊ณ  ์‹œ๋Œ€์— ๋งž์ง€ ์•Š๋Š” ๊ต๊ฐ€ ๊ฐ€์‚ฌ๋ฅผ ๋ฐ”๊ฟˆ\n- ์šฐ๋ฆฌ๋„ ๊ตํ›ˆ ๋ณ€๊ฒฝ์„ ๋…ผ์˜ํ•˜๋ฉด ์ข‹์„ ๋“ฏํ•จ', '๋ฐฐ๊ฒฝ\n์šฐ๋ฆฌ ํ•™๊ต ๊ตํ›ˆ๋„ ํŠน์ • ์—ญํ• ๋งŒ์ด ๋ถ€๊ฐ๋˜๊ณ  ์žˆ์Œ.\n- ๋งŽ์€ ํ•™์ƒ๋“ค์ด ๊ตํ›ˆ์— ๊ณต๊ฐํ•˜๊ธฐ ์–ด๋ ค์›€', '๊ณ ๋ คํ•  ์ \n- โ—‹โ—‹๊ณ ๋Š” ๋™๋ฌธํšŒ๋ฅผ ์„ค๋“ํ•˜๋Š” ๋ฐ ์–ด๋ ค์›€์„ ๊ฒช์Œ.\n- ์šฐ๋ฆฌ๋Š” ๋™๋ฌธ ์„ ๋ฐฐ๋“ค์˜ ์˜๊ฒฌ์„ ๋น„๋กฏํ•œ ์—ฌ๋Ÿฌ ์˜๊ฒฌ์„ ๊ฒฝ์ฒญํ•ด์•ผ ํ•จ.', '์šฐ๋ฆฌ๊ฐ€ ํ•  ์ผ\n- ๊ตํ›ˆ ๋ณ€๊ฒฝ ์ถ”์ง„ ์—ฌ๋ถ€๋ฅผ ํ•™์ƒํšŒ ํšŒ์˜ ์•ˆ๊ฑด์œผ๋กœ ์ƒ์ •ํ•˜๊ธฐ.\n- ๋‹ค๋ฅธ ํ•™๊ต ์‚ฌ๋ก€๋ฅผ ์ฐพ์•„์„œ ๊ณต์œ ํ•ด์•ผ ํ•จ.', '์šฐ๋ฆฌ๊ฐ€ ํ•  ์ผ\n- ํ•™์ƒํšŒ ํšŒ์˜ ์ „์— ๋™๋ฌธ ์„ ๋ฐฐ๋“ค์˜ ์˜๊ฒฌ ์ˆ˜๋ ดํ•˜๊ธฐ.\n- ๊ตํ›ˆ ๋ณ€๊ฒฝ ์ถ”์ง„์— ๋Œ€ํ•œ ์ฐฌ๋ฐ˜ ์˜๊ฒฌ์„ ์กฐ์‚ฌํ•ด์•ผ ํ•จ.'], 'answer': ''}",,5,5,True,[],5 +2025-korean-40,"(๊ฐ€)๋Š” ํ•™์ƒํšŒ ํ•™์ƒ๋“ค์˜ ๋Œ€ํ™”์ด๊ณ , (๋‚˜)๋Š” ํ•™์ƒํšŒ ๋Œ€ํ‘œ๊ฐ€ +์“ด ๊ฑด์˜๋ฌธ์ด๋‹ค. ๋ฌผ์Œ์— ๋‹ตํ•˜์‹œ์˜ค. +(๊ฐ€) +ํ•™์ƒ1:์–˜๋“ค์•„, ์–ด์ œ ๋‰ด์Šค ๋ดค์–ด? ์ธ๊ทผ์— ์žˆ๋Š” โ—‹โ—‹๊ณ ๊ฐ€ ๊ฐœ๊ต +60์ฃผ๋…„์„ ์•ž๋‘๊ณ  ๊ต๊ฐ€ ๊ฐ€์‚ฌ๋ฅผ ๋ฐ”๊ฟจ๋‹ค๊ณ  ํ•˜๋”๋ผ. +ํ•™์ƒ2:ใ‰ ์ฒ˜์Œ ๋“ค์–ด๋ณด๋Š”๋ฐ, ๊ต๊ฐ€ ๊ฐ€์‚ฌ๋ฅผ ์™œ ๋ฐ”๊พผ ๊ฑฐ์•ผ? +ํ•™์ƒ1:๊ต๊ฐ€ ๊ฐ€์‚ฌ์— โ€˜์”ฉ์”ฉํ•œ ๊ฑด์•„์—ฌ, ์ฒญ๋…„ ์ผ๊พผ์ด์—ฌโ€™๋ผ๋Š” ๊ตฌ์ ˆ์ด +๋ฐ˜๋ณต๋˜์—ˆ๋‹ค๊ณ  ํ•ด. ์ด๋Ÿฐ ๊ฐ€์‚ฌ๋Š” ๊ฐœ๊ต ๋‹น์‹œ ์‚ฌํšŒ์—์„œ ์š”๊ตฌ๋˜๋˜ +ํŠน์ • ์—ญํ• ๋งŒ์„ ๊ฐ•์กฐํ•œ ๊ฑฐ๋ผ๊ณ  ๋‰ด์Šค์—์„œ ๊ทธ๋Ÿฌ๋”๋ผ. +ํ•™์ƒ3:์•„, ๊ทธ๋ž˜? ๊ทธ๋Ÿฐ ๊ฐ€์‚ฌ ๋‚ด์šฉ์ด ๊ฐœ๊ต ๋‹น์‹œ์—๋Š” ์ค‘์š”ํ•œ +๊ฐ€์น˜๋กœ ์—ฌ๊ฒจ์กŒ๊ฒ ์ง€๋งŒ ์ง€๊ธˆ์€ ๊ทธ๋ ‡์ง€ ์•Š์œผ๋‹ˆ ๋ฐ”๊พผ ๊ฑฐ๊ตฌ๋‚˜. +๊ทผ๋ฐ ๋น„์Šทํ•œ ์‹œ๊ธฐ์— ๊ฐœ๊ตํ•œ ์šฐ๋ฆฌ ํ•™๊ต ๊ตํ›ˆ๋„ ๋ฌธ์ œ๊ฐ€ +์žˆ์ง€ ์•Š๋‹ˆ? - [A] +ํ•™์ƒ2:ใ‰ก๋งž์•„. ๋“ฑ๊ตํ•  ๋•Œ ๊ตํ›ˆ์„ ๋ณด๋ฉด ๋งˆ์Œ์ด ์ข€ ๋ถˆํŽธํ•˜๋”๋ผ. +๊ทธ๋ž˜์„œ ๊ตํ›ˆ์„ ๋ฐ”๊ฟจ์œผ๋ฉด ์ข‹๊ฒ ๋‹ค๊ณ  ์ƒ๊ฐํ•œ ์ ์ด ์žˆ์–ด. +ํ•™์ƒ3:๋‚˜๋„ ๊ทธ๋žฌ๋Š”๋ฐ. ์šฐ๋ฆฌ ํ•™๊ต ๊ตํ›ˆ๋„ โ—‹โ—‹๊ณ  ๊ต๊ฐ€์ฒ˜๋Ÿผ ํŠน์ • +์—ญํ• ๋งŒ์ด ๋‘๋“œ๋Ÿฌ์ง€๋Š” ๊ฒƒ ๊ฐ™์•„. +ํ•™์ƒ2:์‘. ๊ทธ๋ž˜์„œ ๋งŽ์€ ํ•™์ƒ๋“ค์ด ๊ณต๊ฐํ•˜๊ธฐ ์–ด๋ ค์šด ๊ฒƒ๋„ ์‚ฌ์‹ค์ด์•ผ. +ํ•™์ƒ3:๊ตํ›ˆ์€ ์ง€๊ธˆ ์‹œ๋Œ€์—๋„ ๋งž๋Š” ๋ณดํŽธ์  ๊ฐ€์น˜๋ฅผ ๋‹ด๊ณ  +์žˆ์–ด์•ผ ํ•˜๋Š” ๊ฑฐ ์•„๋ƒ? ๋ˆ„๊ตฌ๋‚˜ ๊ณต๊ฐํ•  ์ˆ˜ ์žˆ์–ด์•ผ ํ•˜๊ณ . +๊ทธ๋Ÿฐ๋ฐ ์šฐ๋ฆฌ ๊ตํ›ˆ์€ ๊ทธ๋ ‡์ง€ ์•Š์€ ๊ฒƒ ๊ฐ™์•„. - [B] +ํ•™์ƒ1:ใ‰ข๋‚˜๋Š” ์šฐ๋ฆฌ ํ•™๊ต ๊ตํ›ˆ์ด ๊ดœ์ฐฎ๋‹ค๊ณ  ์ƒ๊ฐํ–ˆ๋Š”๋ฐ, ๋“ฃ๊ณ  +๋ณด๋‹ˆ ๋ฐ”๊ฟ”์•ผ๊ฒ ๋‹ค๋Š” ์ƒ๊ฐ์ด ๋“ค์–ด. ๊ทธ๋Ÿฐ๋ฐ ๋‰ด์Šค์— ๋”ฐ๋ฅด๋ฉด โ—‹โ—‹๊ณ  +์—์„œ๋Š” ๋™๋ฌธํšŒ๋ฅผ ์„ค๋“ํ•˜๋Š” ๊ฒƒ์ด ์‰ฝ์ง€ ์•Š์•˜๋‹ค๊ณ  ํ•˜๋”๋ผ๊ณ . +ํ•™์ƒ2:์•„๋ฌด๋ž˜๋„ ํ•™๊ต ์„ค๋ฆฝ ์ดํ›„ ์˜ค๋žœ ๊ธฐ๊ฐ„ ๊ต๊ฐ€๋ฅผ ๋ถˆ๋Ÿฌ ์™”์œผ๋‹ˆ๊นŒ, +๋™๋ฌธ ์„ ๋ฐฐ๋“ค์€ ๊ต๊ฐ€๊ฐ€ ๋ชจ๊ต๋ฅผ ์ƒ์ง•ํ•œ๋‹ค๊ณ  ์ƒ๊ฐํ•  ๊ฒƒ ๊ฐ™์•„. ๊ทธ๋งŒํผ +๊ต๊ฐ€์— ์• ์ •์ด ์žˆ๋Š” ์‚ฌ๋žŒ๋„ ๋งŽ์„ ๊ฑฐ์•ผ. +ํ•™์ƒ1:๋งž์•„. ๊ตํ›ˆ์„ ๋ฐ”๊พธ๋Š” ์ผ๋„ ๊ต๊ฐ€๋ฅผ ๋ฐ”๊พธ๋Š” ๊ฒƒ๋งŒํผ +์–ด๋ ค์šธ ๊ฒƒ ๊ฐ™์•„. - [C] +ํ•™์ƒ3:๊ทธ๋Ÿฌ๋‹ˆ๊นŒ ๋™๋ฌธ ์„ ๋ฐฐ๋“ค๊ณผ ํ•™๊ต ๊ตฌ์„ฑ์›์˜ ์˜๊ฒฌ๋„ ์ถฉ๋ถ„ํžˆ +๋“ค์–ด์•ผ ํ•  ๊ฒƒ ๊ฐ™์•„. ใ‰ฃ๊ตํ›ˆ ๋ณ€๊ฒฝ์ด ์™œ ํ•„์š”ํ•˜๊ณ  ์–ด๋–ค ํšจ๊ณผ๊ฐ€ +์žˆ๋Š”์ง€ ์•Œ๋ฆฌ๋Š” ๊ฒƒ๋„ ์ค‘์š”ํ•˜๊ฒ ์ง€? +ํ•™์ƒ2:์‘. ๊ทผ๋ฐ ๊ตํ›ˆ์„ ๋ฐ”๊พธ๋ฉด ์–ด๋–ค ํšจ๊ณผ๊ฐ€ ์žˆ์„๊นŒ? +ํ•™์ƒ3:๊ตํ›ˆ์„ ๋ณด๋ฉด์„œ ๋А๊ผˆ๋˜ ๋ถˆํŽธํ•œ ๋งˆ์Œ์ด ์‚ฌ๋ผ์ง€์ง€ ์•Š์„๊นŒ? +์ƒˆ๋กœ์šด ๊ตํ›ˆ์œผ๋กœ๋ถ€ํ„ฐ ์•Œ๊ฒŒ ๋ชจ๋ฅด๊ฒŒ ๊ธ์ •์ ์ธ ์˜ํ–ฅ๋„ ๋ฐ›์„ ์ˆ˜ +์žˆ์„ ๊ฑฐ๊ณ . +ํ•™์ƒ1:๊ทธ๋ž˜. ๊ตํ›ˆ์„ ๋ฐ”๊พธ๋Š” ๊ณผ์ •์—์„œ ํ•™๊ต์— ๋Œ€ํ•œ ๊ตฌ์„ฑ์›์˜ +๊ด€์‹ฌ์ด ๋†’์•„์ง€๊ณ  ๊ฒฐ์†๋ ฅ๋„ ์ปค์งˆ ๊ฑฐ์•ผ. ๊ทธ๋ฆฌ๊ณ  ๊ตํ›ˆ์„ ๋ฐ”๊พธ๊ณ  +์‹ถ์€ ๋‹ค๋ฅธ ํ•™๊ต์—๋„ ์ข‹์€ ๋ณธ๋ณด๊ธฐ๊ฐ€ ๋  ๊ฒƒ ๊ฐ™์•„. +ํ•™์ƒ3:๊ทธ๋Ÿผ ๊ตํ›ˆ ๋ณ€๊ฒฝ์„ ์ถ”์ง„ํ• ์ง€ ๋ง์ง€ ํ•™์ƒํšŒ ํšŒ์˜ ์•ˆ๊ฑด์œผ๋กœ +์˜ฌ๋ ค ๋ณด์ž. ์˜ค๋Š˜ ๋‚˜๋ˆˆ ์ด์•ผ๊ธฐ๋Š” ๋‚ด๊ฐ€ ์ •๋ฆฌํ• ๊ฒŒ. +ํ•™์ƒ1:๋‚˜๋Š” ๋‹ค๋ฅธ ํ•™๊ต์˜ ์‚ฌ๋ก€๋ฅผ ๋” ์ฐพ์•„์„œ ํšŒ์˜ ๋•Œ ๊ณต์œ ํ• ๊ฒŒ. +ํ•™์ƒ2:์ข‹์•„. ํšŒ์˜์—์„œ ํ†ต๊ณผ๋˜๋ฉด ๊ตํ›ˆ ๋ณ€๊ฒฝ์— ๋Œ€ํ•œ ํ•™์ƒ๋“ค์˜ +์˜๊ฒฌ์„ ์กฐ์‚ฌํ•ด ๋ณด์ž. ์„ค๋ฌธ ์กฐ์‚ฌ๋ฅผ ํ•˜๋Š” ๋ฐฐ๊ฒฝ๋„ ๊ฐ™์ด ์•ˆ๋‚ดํ•˜๋ฉด +์ข‹๊ฒ ์–ด. +ํ•™์ƒ1:์ฐฌ๋ฐ˜ ์˜๊ฒฌ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์ƒˆ๋กœ์šด ๊ตํ›ˆ๋„ ์ œ์•ˆ๋ฐ›์•„ ๋ณด์ž. +ํ•™์ƒ3:ใ‰ค๊ตํ›ˆ์„ ๋ฏธ๋ฆฌ ์ œ์•ˆ๋ฐ›์œผ๋ฉด ๊ตํ›ˆ ๋ณ€๊ฒฝ์ด ํ™•์ •๋œ ๊ฒƒ์ฒ˜๋Ÿผ +์˜คํ•ดํ•  ์ˆ˜ ์žˆ์œผ๋‹ˆ ๊ทธ ๋‚ด์šฉ์€ ๋นผ๋Š” ๊ฒŒ ์–ด๋•Œ? +ํ•™์ƒ1:๊ทธ๊ฒŒ ๋‚ซ๊ฒ ๋‹ค. ๊ตํ›ˆ ๋ณ€๊ฒฝ์— ๋Œ€ํ•œ ์ฐฌ๋ฐ˜ ์˜๊ฒฌ์„ ์กฐ์‚ฌํ•˜๊ณ , +๊ตํ›ˆ์„ ๋ฐ”๊พธ์ž๋Š” ์˜๊ฒฌ์ด ๋งŽ์œผ๋ฉด ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ํ•™๊ต์— +๊ฑด์˜ํ•˜๋ฉด ๋  ๊ฒƒ ๊ฐ™์•„. +ํ•™์ƒ2:๊ทธ๋ž˜! ๊ทธ๊ฒƒ๊ณผ ํ•จ๊ป˜ ๋™๋ฌธ ์„ ๋ฐฐ๋“ค์˜ ์˜๊ฒฌ์„ ๋ชจ์•„ ๋‹ฌ๋ผ๊ณ ๋„ +๋ถ€ํƒํ•ด ๋ณด์ž. ์šฐ๋ฆฌ ์ž˜ํ•ด ๋ณด์ž. + +(๋‚˜) +๊ต์žฅ ์„ ์ƒ๋‹˜, ์•ˆ๋…•ํ•˜์„ธ์š”? โ“์ €๋Š” ํ•™์ƒํšŒ ๋Œ€ํ‘œ ์•ˆโ–ณโ–ณ์ž…๋‹ˆ๋‹ค. +๋Š˜ ํ•™๊ต ๋ฐœ์ „๊ณผ ํ•™์ƒ๋“ค์˜ ์„ฑ์žฅ์„ ์œ„ํ•ด ์• ์“ฐ์‹œ๋Š” ๊ต์žฅ ์„ ์ƒ๋‹˜๊ป˜ +๊ฐ์‚ฌ์˜ ๋ง์”€์„ ๋“œ๋ฆฝ๋‹ˆ๋‹ค. +โ“‘์ตœ๊ทผ โ—‡โ—‡๋ฐฉ์†ก ๋‰ด์Šค์— ๋”ฐ๋ฅด๋ฉด ์ธ๊ทผ ํ•™๊ต์ธ โ—‹โ—‹๊ณ ๊ฐ€ ํ•™๊ต +๊ตฌ์„ฑ์›์˜ ๋…ธ๋ ฅ ๋์— ๊ต๊ฐ€ ๊ฐ€์‚ฌ๋ฅผ ๋ณ€๊ฒฝํ•˜์˜€๋‹ค๊ณ  ํ•ฉ๋‹ˆ๋‹ค. โ—‹โ—‹๊ณ ์˜ +๋ณ€๊ฒฝ๋œ ๊ต๊ฐ€ ๊ฐ€์‚ฌ๋Š” ์ด์ „๊ณผ ๋‹ฌ๋ฆฌ, ํ•™์ƒ๋“ค์˜ ๋ฏธ๋ž˜์™€ ํ–‰๋ณตํ•œ ์‚ถ์„ +๊ฐ•์กฐํ•œ๋‹ค๊ณ  ํ•ฉ๋‹ˆ๋‹ค. โ—‹โ—‹๊ณ ์™€ ๊ฐ™์ด ์šฐ๋ฆฌ ํ•™๊ต์—์„œ๋„ ๊ตํ›ˆ์„ +๋ฐ”๊พธ์ž๋Š” ํ•™์ƒ๋“ค์˜ ๋ชฉ์†Œ๋ฆฌ๊ฐ€ ์ปค์ง€๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. +โ“’๊ตํ›ˆ์€ ํ•™๊ต์˜ ์ด๋…์„ ํ‘œํ˜„ํ•˜์ง€๋งŒ, ๋‹จ์ˆœํžˆ ํ‘œํ˜„์—๋งŒ ๊ทธ์น˜๋Š” +๊ฒƒ์€ ์•„๋‹™๋‹ˆ๋‹ค. ๊ตํ›ˆ์€ ํ•™๊ต์˜ ์ด๋…๊ณผ ๋ชฉํ‘œ๋ฅผ ๋“œ๋Ÿฌ๋‚ผ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ +โ““๊ตฌ์„ฑ์› ๋ชจ๋‘๊ฐ€ ์ง€ํ–ฅํ•˜๋Š” ์ •์‹ ์  ๊ฐ€์น˜๋ฅผ ๋‹ด๋Š” ๊ทธ๋ฆ‡์ž…๋‹ˆ๋‹ค. ํ•˜์ง€๋งŒ +์ง€๊ธˆ ์šฐ๋ฆฌ ํ•™๊ต์˜ ๊ตํ›ˆ์€ ๊ฐœ๊ต ๋‹น์‹œ ์š”๊ตฌ๋˜๋˜ ํŠน์ • ์—ญํ• ๋งŒ์„ +๋ถ€๊ฐํ•˜๊ณ  ์žˆ์–ด, ํ˜„์žฌ์™€ ๋ฏธ๋ž˜์˜ ๊ตฌ์„ฑ์›์ด ์ง€ํ–ฅํ•ด์•ผ ํ•˜๋Š” ๊ฐ€์น˜๋ฅผ +๋ฐ˜์˜ํ•˜์ง€ ๋ชปํ•˜๋Š” ๋ฌธ์ œ๊ฐ€ ์žˆ์Šต๋‹ˆ๋‹ค. ๊ทธ๋ž˜์„œ ๋‹น์‹œ์™€๋Š” ๋‹ฌ๋ฆฌ ์ง€๊ธˆ์€ +ํ•™์ƒ๋“ค์˜ ๊ณต๊ฐ์„ ์–ป์ง€ ๋ชปํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. +์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ๊ณต์‹์ ์ธ ์ ˆ์ฐจ๋ฅผ ์ถ”์ง„ํ•˜์—ฌ +๊ตํ›ˆ์„ ๋ณ€๊ฒฝํ•ด ์ฃผ์‹œ๊ธฐ๋ฅผ ๊ฑด์˜๋“œ๋ฆฝ๋‹ˆ๋‹ค. โ“”ํ•™๊ต์—์„œ๋Š” ๊ต์ง์›, +๋™๋ฌธ ์„ ๋ฐฐ, ํ•™๋ถ€๋ชจ์—๊ฒŒ ๊ตํ›ˆ ๋ณ€๊ฒฝ์˜ ์ทจ์ง€๋ฅผ ์„ค๋ช…ํ•˜๊ณ , ๊ทธ๋ถ„๋“ค์˜ +์˜๊ฒฌ์„ ์ˆ˜๋ ดํ•œ ํ›„, ํ•™๊ต์šด์˜์œ„์›ํšŒ์—์„œ ์‹ฌ์˜ํ•˜๋„๋ก ํ•ด ์ฃผ์‹œ๋ฉด +์ข‹๊ฒ ์Šต๋‹ˆ๋‹ค. ๊ต์žฅ ์„ ์ƒ๋‹˜๊ป˜์„œ๋„ ์•Œ๊ณ  ๊ณ„์‹œ๋“ฏ์ด, ํ•™์ƒํšŒ์—์„œ ์„ค๋ฌธ +์กฐ์‚ฌ๋กœ ํ•™์ƒ๋“ค์˜ ์˜๊ฒฌ์„ ์ˆ˜๋ ดํ•œ ๊ฒฐ๊ณผ ์ „๊ต์ƒ์˜ 91.8%๊ฐ€ ๊ตํ›ˆ +๋ณ€๊ฒฝ์— ์ฐฌ์„ฑํ–ˆ์Šต๋‹ˆ๋‹ค. ํ•™์ƒ๋“ค ์‚ฌ์ด์—๋Š” ์ด๋ฏธ ๊ตํ›ˆ ๋ณ€๊ฒฝ์— ๋Œ€ํ•œ +๊ณต๊ฐ๋Œ€๊ฐ€ ํ˜•์„ฑ๋˜์—ˆ๋‹ค๊ณ  ๋ณผ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. +๋ˆ„๊ตฌ๋‚˜ ๊ณต๊ฐํ•  ์ˆ˜ ์žˆ๋Š” ๊ตํ›ˆ์œผ๋กœ ๋ฐ”๊พธ๋ฉด ๊ตํ›ˆ์„ ๋ณด๋ฉด์„œ ๋А๊ผˆ๋˜ +๋ถˆํŽธํ•œ ๋งˆ์Œ์ด ์‚ฌ๋ผ์ง€๊ณ  ํ•™์ƒ๋“ค์˜ ๋…ธ๋ ฅ์œผ๋กœ ๊ตํ›ˆ์„ ๋ฐ”๊ฟจ๋‹ค๋Š” +์ž๋ถ€์‹ฌ์„ ๋А๋ผ๊ฒŒ ๋  ๊ฒƒ์ž…๋‹ˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๊ทธ ๊ณผ์ •์—์„œ ํ•™์ƒ๋“ค์€ +๋ฌผ๋ก  ๋ถ€๋ชจ๋‹˜๋“ค๊ณผ ์„ ์ƒ๋‹˜๋“ค๋„ ํ•™๊ต์— ๊ด€์‹ฌ์„ ๋” ๊ฐ–๊ฒŒ ๋˜๋ฉด์„œ +์ž์—ฐ์Šค๋Ÿฝ๊ฒŒ ์• ๊ต์‹ฌ๊ณผ ํ•™๊ต์— ๋Œ€ํ•œ ๊ธ์ง€๊ฐ€ ๋†’์•„์งˆ ๊ฒƒ์ž…๋‹ˆ๋‹ค. ์šฐ๋ฆฌ +ํ•™๊ต์˜ ๊ตํ›ˆ ๋ณ€๊ฒฝ์€ ๊ตํ›ˆ์„ ๋ฐ”๊พธ๊ณ  ์‹ถ์€ ๋‹ค๋ฅธ ํ•™๊ต์—๋„ ์ข‹์€ +์˜ํ–ฅ์„ ๋ผ์น  ๊ฒƒ์ž…๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋ณ€ํ™”๋Š” ํ•™์ƒ๋“ค์—๊ฒŒ ๊ต์œก์ ์œผ๋กœ๋„ +๊ธ์ •์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์น  ๊ฒƒ์ด๋ผ๊ณ  ํ™•์‹ ํ•ฉ๋‹ˆ๋‹ค. +ํ•™์ƒ๋“ค์ด ์ƒˆ๋กœ์šด ๊ตํ›ˆ ์•„๋ž˜์—์„œ ์„ฑ์žฅํ•˜๊ณ  ์‹œ๋Œ€์— ๋ฐœ๋งž์ถฐ ๊ฐˆ +์ˆ˜ ์žˆ๋„๋ก ๊ตํ›ˆ์„ ๋ณ€๊ฒฝํ•ด ์ฃผ์‹œ๊ธธ ๋ถ€ํƒ๋“œ๋ฆฝ๋‹ˆ๋‹ค. ๊ณ ๋ง™์Šต๋‹ˆ๋‹ค. + +","{'question': '๋‹ค์Œ์€ (๋‚˜)๋ฅผ ์“ธ ๋•Œ ๊ณ„ํšํ•œ ๋‚ด์šฉ ์ „๊ฐœ ๊ณผ์ •์ด๋‹ค. (๊ฐ€)์˜\n[A]๏ฝž[E]๊ฐ€ ใ‰ฎ๏ฝžใ‰ฒ๋ฅผ ๊ณ ๋ คํ•˜์—ฌ (๋‚˜)์— ๋ฐ˜์˜๋˜์—ˆ๋‹ค๊ณ  ํ•  ๋•Œ, ์ด์—\n๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ๊ฐ€์žฅ ์ ์ ˆํ•œ ๊ฒƒ์€?', 'choices': ['โ—‹โ—‹๊ณ ์˜ ์˜ˆ์ „ ๊ต๊ฐ€ ๊ฐ€์‚ฌ์— ๋‹ด๊ธด ๊ฐ€์น˜์˜ ์ค‘์š”๋„๊ฐ€ ์ง€๊ธˆ์€ ๋‹ฌ๋ผ\n์กŒ๋‹ค๋Š” [A]์˜ ๋‚ด์šฉ์€, ใ‰ฏ๋ฅผ ๊ณ ๋ คํ•˜์—ฌ (๋‚˜)์—์„œ ํ•™์ƒ๋“ค์˜ ์‚ถ์ด\n์˜ˆ์ „๋ณด๋‹ค ํ–‰๋ณตํ•ด์กŒ์Œ์„ ๊ฐ•์กฐํ•˜๊ธฐ ์œ„ํ•œ ์‚ฌ๋ก€๋กœ ๋ฐ˜์˜๋˜์—ˆ๋‹ค.', '๊ตํ›ˆ ๋‚ด์šฉ์ด ๋ฌธ์ œ๊ฐ€ ์žˆ๋‹ค๋Š” [B]์˜ ๋‚ด์šฉ์€, ใ‰ฐ๋ฅผ ๊ณ ๋ คํ•˜์—ฌ\n(๋‚˜)์—์„œ ๊ตํ›ˆ ๋‚ด์šฉ์ด ๊ตฌ์„ฑ์›์˜ ๊ณผ๊ฑฐ์™€ ํ˜„์žฌ์˜ ๊ฐ€์น˜๋ฅผ ๋‹ด๊ณ \n์žˆ์ง€ ์•Š๋‹ค๋Š” ๋ฌธ์ œ ์ƒํ™ฉ์œผ๋กœ ๋ฐ˜์˜๋˜์—ˆ๋‹ค.', '๊ต๊ฐ€๋ณด๋‹ค ๊ตํ›ˆ์„ ๋ฐ”๊พธ๊ธฐ ์–ด๋ ต๋‹ค๋Š” [C]์˜ ๋‚ด์šฉ์€, ใ‰ฒ๋ฅผ ๊ณ ๋ ค\nํ•˜์—ฌ (๋‚˜)์—์„œ ์ƒˆ๋กœ์šด ๊ตํ›ˆ์„ ์ œ์•ˆ๋ฐ›์•„ ๋‹ฌ๋ผ๋Š” ๊ฑด์˜ ๋‚ด์šฉ์„\n์žฌํ™•์ธํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋ฐ˜์˜๋˜์—ˆ๋‹ค.', 'ํ•™์ƒ๋“ค์˜ ์˜๊ฒฌ ์กฐ์‚ฌ๋ฅผ ์ œ์•ˆํ•œ [D]์˜ ๋‚ด์šฉ์€, ใ‰ฑ๋ฅผ ๊ณ ๋ คํ•˜์—ฌ\n(๋‚˜)์—์„œ ๊ตํ›ˆ์„ ๋ณ€๊ฒฝํ•ด ๋‹ฌ๋ผ๋Š” ๊ฑด์˜ ๋‚ด์šฉ์— ๋Œ€ํ•œ ๊ทผ๊ฑฐ๋ฅผ ์„ค๋ฌธ\n์กฐ์‚ฌ ๋ฐฉ๋ฒ•์„ ํ†ตํ•ด ๋งˆ๋ จํ•œ ๊ฒƒ์œผ๋กœ ๋ฐ˜์˜๋˜์—ˆ๋‹ค.', '๊ตํ›ˆ ๋ณ€๊ฒฝ์„ ํ•™๊ต์— ๊ฑด์˜ํ•˜์ž๋Š” [E]์˜ ๋‚ด์šฉ์€, ใ‰ฎ๋ฅผ ๊ณ ๋ คํ•˜์—ฌ\n(๋‚˜)์—์„œ ๊ต์žฅ ์„ ์ƒ๋‹˜์„ ์˜ˆ์ƒ ๋…์ž๋กœ ํ•˜์—ฌ ์ง€์—ญ ํ•™๊ต๋“ค๊ณผ์˜\n๊ณต๊ฐ๋Œ€๋ฅผ ํ˜•์„ฑํ•ด์•ผ ํ•˜๋Š” ์ด์œ ๋กœ ๋ฐ˜์˜๋˜์—ˆ๋‹ค.'], 'answer': '', 'question_plus': 'ใ‰ฎ: ์˜ˆ์ƒ ๋…์ž ๋ฐ ์ธ์‚ฌ๋ง ์ œ์‹œ\nโ†’ ใ‰ฏ: ๊ด€๋ จ ์‚ฌ๋ก€ ์ œ์‹œ\nโ†’ ใ‰ฐ: ๋ฌธ์ œ ์ƒํ™ฉ ์ œ์‹œ\nโ†’ ใ‰ฑ: ๊ฑด์˜ ๋‚ด์šฉ ๋ฐ ๊ทผ๊ฑฐ ์ œ์‹œ\nโ†’ ๊ธฐ๋Œ€ ํšจ๊ณผ ์ œ์‹œ\nโ†’ ใ‰ฒ: ๊ฑด์˜ ๋‚ด์šฉ ์žฌํ™•์ธ ๋ฐ ๋งˆ๋ฌด๋ฆฌ ์ธ์‚ฌ'}","ใ‰ฎ: ์˜ˆ์ƒ ๋…์ž ๋ฐ ์ธ์‚ฌ๋ง ์ œ์‹œ +โ†’ ใ‰ฏ: ๊ด€๋ จ ์‚ฌ๋ก€ ์ œ์‹œ +โ†’ ใ‰ฐ: ๋ฌธ์ œ ์ƒํ™ฉ ์ œ์‹œ +โ†’ ใ‰ฑ: ๊ฑด์˜ ๋‚ด์šฉ ๋ฐ ๊ทผ๊ฑฐ ์ œ์‹œ +โ†’ ๊ธฐ๋Œ€ ํšจ๊ณผ ์ œ์‹œ +โ†’ ใ‰ฒ: ๊ฑด์˜ ๋‚ด์šฉ ์žฌํ™•์ธ ๋ฐ ๋งˆ๋ฌด๋ฆฌ ์ธ์‚ฌ",4,4,True,[],2 +2025-korean-41,"(๊ฐ€)๋Š” ํ•™์ƒํšŒ ํ•™์ƒ๋“ค์˜ ๋Œ€ํ™”์ด๊ณ , (๋‚˜)๋Š” ํ•™์ƒํšŒ ๋Œ€ํ‘œ๊ฐ€ +์“ด ๊ฑด์˜๋ฌธ์ด๋‹ค. ๋ฌผ์Œ์— ๋‹ตํ•˜์‹œ์˜ค. +(๊ฐ€) +ํ•™์ƒ1:์–˜๋“ค์•„, ์–ด์ œ ๋‰ด์Šค ๋ดค์–ด? ์ธ๊ทผ์— ์žˆ๋Š” โ—‹โ—‹๊ณ ๊ฐ€ ๊ฐœ๊ต +60์ฃผ๋…„์„ ์•ž๋‘๊ณ  ๊ต๊ฐ€ ๊ฐ€์‚ฌ๋ฅผ ๋ฐ”๊ฟจ๋‹ค๊ณ  ํ•˜๋”๋ผ. +ํ•™์ƒ2:ใ‰ ์ฒ˜์Œ ๋“ค์–ด๋ณด๋Š”๋ฐ, ๊ต๊ฐ€ ๊ฐ€์‚ฌ๋ฅผ ์™œ ๋ฐ”๊พผ ๊ฑฐ์•ผ? +ํ•™์ƒ1:๊ต๊ฐ€ ๊ฐ€์‚ฌ์— โ€˜์”ฉ์”ฉํ•œ ๊ฑด์•„์—ฌ, ์ฒญ๋…„ ์ผ๊พผ์ด์—ฌโ€™๋ผ๋Š” ๊ตฌ์ ˆ์ด +๋ฐ˜๋ณต๋˜์—ˆ๋‹ค๊ณ  ํ•ด. ์ด๋Ÿฐ ๊ฐ€์‚ฌ๋Š” ๊ฐœ๊ต ๋‹น์‹œ ์‚ฌํšŒ์—์„œ ์š”๊ตฌ๋˜๋˜ +ํŠน์ • ์—ญํ• ๋งŒ์„ ๊ฐ•์กฐํ•œ ๊ฑฐ๋ผ๊ณ  ๋‰ด์Šค์—์„œ ๊ทธ๋Ÿฌ๋”๋ผ. +ํ•™์ƒ3:์•„, ๊ทธ๋ž˜? ๊ทธ๋Ÿฐ ๊ฐ€์‚ฌ ๋‚ด์šฉ์ด ๊ฐœ๊ต ๋‹น์‹œ์—๋Š” ์ค‘์š”ํ•œ +๊ฐ€์น˜๋กœ ์—ฌ๊ฒจ์กŒ๊ฒ ์ง€๋งŒ ์ง€๊ธˆ์€ ๊ทธ๋ ‡์ง€ ์•Š์œผ๋‹ˆ ๋ฐ”๊พผ ๊ฑฐ๊ตฌ๋‚˜. +๊ทผ๋ฐ ๋น„์Šทํ•œ ์‹œ๊ธฐ์— ๊ฐœ๊ตํ•œ ์šฐ๋ฆฌ ํ•™๊ต ๊ตํ›ˆ๋„ ๋ฌธ์ œ๊ฐ€ +์žˆ์ง€ ์•Š๋‹ˆ? - [A] +ํ•™์ƒ2:ใ‰ก๋งž์•„. ๋“ฑ๊ตํ•  ๋•Œ ๊ตํ›ˆ์„ ๋ณด๋ฉด ๋งˆ์Œ์ด ์ข€ ๋ถˆํŽธํ•˜๋”๋ผ. +๊ทธ๋ž˜์„œ ๊ตํ›ˆ์„ ๋ฐ”๊ฟจ์œผ๋ฉด ์ข‹๊ฒ ๋‹ค๊ณ  ์ƒ๊ฐํ•œ ์ ์ด ์žˆ์–ด. +ํ•™์ƒ3:๋‚˜๋„ ๊ทธ๋žฌ๋Š”๋ฐ. ์šฐ๋ฆฌ ํ•™๊ต ๊ตํ›ˆ๋„ โ—‹โ—‹๊ณ  ๊ต๊ฐ€์ฒ˜๋Ÿผ ํŠน์ • +์—ญํ• ๋งŒ์ด ๋‘๋“œ๋Ÿฌ์ง€๋Š” ๊ฒƒ ๊ฐ™์•„. +ํ•™์ƒ2:์‘. ๊ทธ๋ž˜์„œ ๋งŽ์€ ํ•™์ƒ๋“ค์ด ๊ณต๊ฐํ•˜๊ธฐ ์–ด๋ ค์šด ๊ฒƒ๋„ ์‚ฌ์‹ค์ด์•ผ. +ํ•™์ƒ3:๊ตํ›ˆ์€ ์ง€๊ธˆ ์‹œ๋Œ€์—๋„ ๋งž๋Š” ๋ณดํŽธ์  ๊ฐ€์น˜๋ฅผ ๋‹ด๊ณ  +์žˆ์–ด์•ผ ํ•˜๋Š” ๊ฑฐ ์•„๋ƒ? ๋ˆ„๊ตฌ๋‚˜ ๊ณต๊ฐํ•  ์ˆ˜ ์žˆ์–ด์•ผ ํ•˜๊ณ . +๊ทธ๋Ÿฐ๋ฐ ์šฐ๋ฆฌ ๊ตํ›ˆ์€ ๊ทธ๋ ‡์ง€ ์•Š์€ ๊ฒƒ ๊ฐ™์•„. - [B] +ํ•™์ƒ1:ใ‰ข๋‚˜๋Š” ์šฐ๋ฆฌ ํ•™๊ต ๊ตํ›ˆ์ด ๊ดœ์ฐฎ๋‹ค๊ณ  ์ƒ๊ฐํ–ˆ๋Š”๋ฐ, ๋“ฃ๊ณ  +๋ณด๋‹ˆ ๋ฐ”๊ฟ”์•ผ๊ฒ ๋‹ค๋Š” ์ƒ๊ฐ์ด ๋“ค์–ด. ๊ทธ๋Ÿฐ๋ฐ ๋‰ด์Šค์— ๋”ฐ๋ฅด๋ฉด โ—‹โ—‹๊ณ  +์—์„œ๋Š” ๋™๋ฌธํšŒ๋ฅผ ์„ค๋“ํ•˜๋Š” ๊ฒƒ์ด ์‰ฝ์ง€ ์•Š์•˜๋‹ค๊ณ  ํ•˜๋”๋ผ๊ณ . +ํ•™์ƒ2:์•„๋ฌด๋ž˜๋„ ํ•™๊ต ์„ค๋ฆฝ ์ดํ›„ ์˜ค๋žœ ๊ธฐ๊ฐ„ ๊ต๊ฐ€๋ฅผ ๋ถˆ๋Ÿฌ ์™”์œผ๋‹ˆ๊นŒ, +๋™๋ฌธ ์„ ๋ฐฐ๋“ค์€ ๊ต๊ฐ€๊ฐ€ ๋ชจ๊ต๋ฅผ ์ƒ์ง•ํ•œ๋‹ค๊ณ  ์ƒ๊ฐํ•  ๊ฒƒ ๊ฐ™์•„. ๊ทธ๋งŒํผ +๊ต๊ฐ€์— ์• ์ •์ด ์žˆ๋Š” ์‚ฌ๋žŒ๋„ ๋งŽ์„ ๊ฑฐ์•ผ. +ํ•™์ƒ1:๋งž์•„. ๊ตํ›ˆ์„ ๋ฐ”๊พธ๋Š” ์ผ๋„ ๊ต๊ฐ€๋ฅผ ๋ฐ”๊พธ๋Š” ๊ฒƒ๋งŒํผ +์–ด๋ ค์šธ ๊ฒƒ ๊ฐ™์•„. - [C] +ํ•™์ƒ3:๊ทธ๋Ÿฌ๋‹ˆ๊นŒ ๋™๋ฌธ ์„ ๋ฐฐ๋“ค๊ณผ ํ•™๊ต ๊ตฌ์„ฑ์›์˜ ์˜๊ฒฌ๋„ ์ถฉ๋ถ„ํžˆ +๋“ค์–ด์•ผ ํ•  ๊ฒƒ ๊ฐ™์•„. ใ‰ฃ๊ตํ›ˆ ๋ณ€๊ฒฝ์ด ์™œ ํ•„์š”ํ•˜๊ณ  ์–ด๋–ค ํšจ๊ณผ๊ฐ€ +์žˆ๋Š”์ง€ ์•Œ๋ฆฌ๋Š” ๊ฒƒ๋„ ์ค‘์š”ํ•˜๊ฒ ์ง€? +ํ•™์ƒ2:์‘. ๊ทผ๋ฐ ๊ตํ›ˆ์„ ๋ฐ”๊พธ๋ฉด ์–ด๋–ค ํšจ๊ณผ๊ฐ€ ์žˆ์„๊นŒ? +ํ•™์ƒ3:๊ตํ›ˆ์„ ๋ณด๋ฉด์„œ ๋А๊ผˆ๋˜ ๋ถˆํŽธํ•œ ๋งˆ์Œ์ด ์‚ฌ๋ผ์ง€์ง€ ์•Š์„๊นŒ? +์ƒˆ๋กœ์šด ๊ตํ›ˆ์œผ๋กœ๋ถ€ํ„ฐ ์•Œ๊ฒŒ ๋ชจ๋ฅด๊ฒŒ ๊ธ์ •์ ์ธ ์˜ํ–ฅ๋„ ๋ฐ›์„ ์ˆ˜ +์žˆ์„ ๊ฑฐ๊ณ . +ํ•™์ƒ1:๊ทธ๋ž˜. ๊ตํ›ˆ์„ ๋ฐ”๊พธ๋Š” ๊ณผ์ •์—์„œ ํ•™๊ต์— ๋Œ€ํ•œ ๊ตฌ์„ฑ์›์˜ +๊ด€์‹ฌ์ด ๋†’์•„์ง€๊ณ  ๊ฒฐ์†๋ ฅ๋„ ์ปค์งˆ ๊ฑฐ์•ผ. ๊ทธ๋ฆฌ๊ณ  ๊ตํ›ˆ์„ ๋ฐ”๊พธ๊ณ  +์‹ถ์€ ๋‹ค๋ฅธ ํ•™๊ต์—๋„ ์ข‹์€ ๋ณธ๋ณด๊ธฐ๊ฐ€ ๋  ๊ฒƒ ๊ฐ™์•„. +ํ•™์ƒ3:๊ทธ๋Ÿผ ๊ตํ›ˆ ๋ณ€๊ฒฝ์„ ์ถ”์ง„ํ• ์ง€ ๋ง์ง€ ํ•™์ƒํšŒ ํšŒ์˜ ์•ˆ๊ฑด์œผ๋กœ +์˜ฌ๋ ค ๋ณด์ž. ์˜ค๋Š˜ ๋‚˜๋ˆˆ ์ด์•ผ๊ธฐ๋Š” ๋‚ด๊ฐ€ ์ •๋ฆฌํ• ๊ฒŒ. +ํ•™์ƒ1:๋‚˜๋Š” ๋‹ค๋ฅธ ํ•™๊ต์˜ ์‚ฌ๋ก€๋ฅผ ๋” ์ฐพ์•„์„œ ํšŒ์˜ ๋•Œ ๊ณต์œ ํ• ๊ฒŒ. +ํ•™์ƒ2:์ข‹์•„. ํšŒ์˜์—์„œ ํ†ต๊ณผ๋˜๋ฉด ๊ตํ›ˆ ๋ณ€๊ฒฝ์— ๋Œ€ํ•œ ํ•™์ƒ๋“ค์˜ +์˜๊ฒฌ์„ ์กฐ์‚ฌํ•ด ๋ณด์ž. ์„ค๋ฌธ ์กฐ์‚ฌ๋ฅผ ํ•˜๋Š” ๋ฐฐ๊ฒฝ๋„ ๊ฐ™์ด ์•ˆ๋‚ดํ•˜๋ฉด +์ข‹๊ฒ ์–ด. +ํ•™์ƒ1:์ฐฌ๋ฐ˜ ์˜๊ฒฌ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์ƒˆ๋กœ์šด ๊ตํ›ˆ๋„ ์ œ์•ˆ๋ฐ›์•„ ๋ณด์ž. +ํ•™์ƒ3:ใ‰ค๊ตํ›ˆ์„ ๋ฏธ๋ฆฌ ์ œ์•ˆ๋ฐ›์œผ๋ฉด ๊ตํ›ˆ ๋ณ€๊ฒฝ์ด ํ™•์ •๋œ ๊ฒƒ์ฒ˜๋Ÿผ +์˜คํ•ดํ•  ์ˆ˜ ์žˆ์œผ๋‹ˆ ๊ทธ ๋‚ด์šฉ์€ ๋นผ๋Š” ๊ฒŒ ์–ด๋•Œ? +ํ•™์ƒ1:๊ทธ๊ฒŒ ๋‚ซ๊ฒ ๋‹ค. ๊ตํ›ˆ ๋ณ€๊ฒฝ์— ๋Œ€ํ•œ ์ฐฌ๋ฐ˜ ์˜๊ฒฌ์„ ์กฐ์‚ฌํ•˜๊ณ , +๊ตํ›ˆ์„ ๋ฐ”๊พธ์ž๋Š” ์˜๊ฒฌ์ด ๋งŽ์œผ๋ฉด ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ํ•™๊ต์— +๊ฑด์˜ํ•˜๋ฉด ๋  ๊ฒƒ ๊ฐ™์•„. +ํ•™์ƒ2:๊ทธ๋ž˜! ๊ทธ๊ฒƒ๊ณผ ํ•จ๊ป˜ ๋™๋ฌธ ์„ ๋ฐฐ๋“ค์˜ ์˜๊ฒฌ์„ ๋ชจ์•„ ๋‹ฌ๋ผ๊ณ ๋„ +๋ถ€ํƒํ•ด ๋ณด์ž. ์šฐ๋ฆฌ ์ž˜ํ•ด ๋ณด์ž. + +(๋‚˜) +๊ต์žฅ ์„ ์ƒ๋‹˜, ์•ˆ๋…•ํ•˜์„ธ์š”? โ“์ €๋Š” ํ•™์ƒํšŒ ๋Œ€ํ‘œ ์•ˆโ–ณโ–ณ์ž…๋‹ˆ๋‹ค. +๋Š˜ ํ•™๊ต ๋ฐœ์ „๊ณผ ํ•™์ƒ๋“ค์˜ ์„ฑ์žฅ์„ ์œ„ํ•ด ์• ์“ฐ์‹œ๋Š” ๊ต์žฅ ์„ ์ƒ๋‹˜๊ป˜ +๊ฐ์‚ฌ์˜ ๋ง์”€์„ ๋“œ๋ฆฝ๋‹ˆ๋‹ค. +โ“‘์ตœ๊ทผ โ—‡โ—‡๋ฐฉ์†ก ๋‰ด์Šค์— ๋”ฐ๋ฅด๋ฉด ์ธ๊ทผ ํ•™๊ต์ธ โ—‹โ—‹๊ณ ๊ฐ€ ํ•™๊ต +๊ตฌ์„ฑ์›์˜ ๋…ธ๋ ฅ ๋์— ๊ต๊ฐ€ ๊ฐ€์‚ฌ๋ฅผ ๋ณ€๊ฒฝํ•˜์˜€๋‹ค๊ณ  ํ•ฉ๋‹ˆ๋‹ค. โ—‹โ—‹๊ณ ์˜ +๋ณ€๊ฒฝ๋œ ๊ต๊ฐ€ ๊ฐ€์‚ฌ๋Š” ์ด์ „๊ณผ ๋‹ฌ๋ฆฌ, ํ•™์ƒ๋“ค์˜ ๋ฏธ๋ž˜์™€ ํ–‰๋ณตํ•œ ์‚ถ์„ +๊ฐ•์กฐํ•œ๋‹ค๊ณ  ํ•ฉ๋‹ˆ๋‹ค. โ—‹โ—‹๊ณ ์™€ ๊ฐ™์ด ์šฐ๋ฆฌ ํ•™๊ต์—์„œ๋„ ๊ตํ›ˆ์„ +๋ฐ”๊พธ์ž๋Š” ํ•™์ƒ๋“ค์˜ ๋ชฉ์†Œ๋ฆฌ๊ฐ€ ์ปค์ง€๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. +โ“’๊ตํ›ˆ์€ ํ•™๊ต์˜ ์ด๋…์„ ํ‘œํ˜„ํ•˜์ง€๋งŒ, ๋‹จ์ˆœํžˆ ํ‘œํ˜„์—๋งŒ ๊ทธ์น˜๋Š” +๊ฒƒ์€ ์•„๋‹™๋‹ˆ๋‹ค. ๊ตํ›ˆ์€ ํ•™๊ต์˜ ์ด๋…๊ณผ ๋ชฉํ‘œ๋ฅผ ๋“œ๋Ÿฌ๋‚ผ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ +โ““๊ตฌ์„ฑ์› ๋ชจ๋‘๊ฐ€ ์ง€ํ–ฅํ•˜๋Š” ์ •์‹ ์  ๊ฐ€์น˜๋ฅผ ๋‹ด๋Š” ๊ทธ๋ฆ‡์ž…๋‹ˆ๋‹ค. ํ•˜์ง€๋งŒ +์ง€๊ธˆ ์šฐ๋ฆฌ ํ•™๊ต์˜ ๊ตํ›ˆ์€ ๊ฐœ๊ต ๋‹น์‹œ ์š”๊ตฌ๋˜๋˜ ํŠน์ • ์—ญํ• ๋งŒ์„ +๋ถ€๊ฐํ•˜๊ณ  ์žˆ์–ด, ํ˜„์žฌ์™€ ๋ฏธ๋ž˜์˜ ๊ตฌ์„ฑ์›์ด ์ง€ํ–ฅํ•ด์•ผ ํ•˜๋Š” ๊ฐ€์น˜๋ฅผ +๋ฐ˜์˜ํ•˜์ง€ ๋ชปํ•˜๋Š” ๋ฌธ์ œ๊ฐ€ ์žˆ์Šต๋‹ˆ๋‹ค. ๊ทธ๋ž˜์„œ ๋‹น์‹œ์™€๋Š” ๋‹ฌ๋ฆฌ ์ง€๊ธˆ์€ +ํ•™์ƒ๋“ค์˜ ๊ณต๊ฐ์„ ์–ป์ง€ ๋ชปํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. +์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ๊ณต์‹์ ์ธ ์ ˆ์ฐจ๋ฅผ ์ถ”์ง„ํ•˜์—ฌ +๊ตํ›ˆ์„ ๋ณ€๊ฒฝํ•ด ์ฃผ์‹œ๊ธฐ๋ฅผ ๊ฑด์˜๋“œ๋ฆฝ๋‹ˆ๋‹ค. โ“”ํ•™๊ต์—์„œ๋Š” ๊ต์ง์›, +๋™๋ฌธ ์„ ๋ฐฐ, ํ•™๋ถ€๋ชจ์—๊ฒŒ ๊ตํ›ˆ ๋ณ€๊ฒฝ์˜ ์ทจ์ง€๋ฅผ ์„ค๋ช…ํ•˜๊ณ , ๊ทธ๋ถ„๋“ค์˜ +์˜๊ฒฌ์„ ์ˆ˜๋ ดํ•œ ํ›„, ํ•™๊ต์šด์˜์œ„์›ํšŒ์—์„œ ์‹ฌ์˜ํ•˜๋„๋ก ํ•ด ์ฃผ์‹œ๋ฉด +์ข‹๊ฒ ์Šต๋‹ˆ๋‹ค. ๊ต์žฅ ์„ ์ƒ๋‹˜๊ป˜์„œ๋„ ์•Œ๊ณ  ๊ณ„์‹œ๋“ฏ์ด, ํ•™์ƒํšŒ์—์„œ ์„ค๋ฌธ +์กฐ์‚ฌ๋กœ ํ•™์ƒ๋“ค์˜ ์˜๊ฒฌ์„ ์ˆ˜๋ ดํ•œ ๊ฒฐ๊ณผ ์ „๊ต์ƒ์˜ 91.8%๊ฐ€ ๊ตํ›ˆ +๋ณ€๊ฒฝ์— ์ฐฌ์„ฑํ–ˆ์Šต๋‹ˆ๋‹ค. ํ•™์ƒ๋“ค ์‚ฌ์ด์—๋Š” ์ด๋ฏธ ๊ตํ›ˆ ๋ณ€๊ฒฝ์— ๋Œ€ํ•œ +๊ณต๊ฐ๋Œ€๊ฐ€ ํ˜•์„ฑ๋˜์—ˆ๋‹ค๊ณ  ๋ณผ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. +๋ˆ„๊ตฌ๋‚˜ ๊ณต๊ฐํ•  ์ˆ˜ ์žˆ๋Š” ๊ตํ›ˆ์œผ๋กœ ๋ฐ”๊พธ๋ฉด ๊ตํ›ˆ์„ ๋ณด๋ฉด์„œ ๋А๊ผˆ๋˜ +๋ถˆํŽธํ•œ ๋งˆ์Œ์ด ์‚ฌ๋ผ์ง€๊ณ  ํ•™์ƒ๋“ค์˜ ๋…ธ๋ ฅ์œผ๋กœ ๊ตํ›ˆ์„ ๋ฐ”๊ฟจ๋‹ค๋Š” +์ž๋ถ€์‹ฌ์„ ๋А๋ผ๊ฒŒ ๋  ๊ฒƒ์ž…๋‹ˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๊ทธ ๊ณผ์ •์—์„œ ํ•™์ƒ๋“ค์€ +๋ฌผ๋ก  ๋ถ€๋ชจ๋‹˜๋“ค๊ณผ ์„ ์ƒ๋‹˜๋“ค๋„ ํ•™๊ต์— ๊ด€์‹ฌ์„ ๋” ๊ฐ–๊ฒŒ ๋˜๋ฉด์„œ +์ž์—ฐ์Šค๋Ÿฝ๊ฒŒ ์• ๊ต์‹ฌ๊ณผ ํ•™๊ต์— ๋Œ€ํ•œ ๊ธ์ง€๊ฐ€ ๋†’์•„์งˆ ๊ฒƒ์ž…๋‹ˆ๋‹ค. ์šฐ๋ฆฌ +ํ•™๊ต์˜ ๊ตํ›ˆ ๋ณ€๊ฒฝ์€ ๊ตํ›ˆ์„ ๋ฐ”๊พธ๊ณ  ์‹ถ์€ ๋‹ค๋ฅธ ํ•™๊ต์—๋„ ์ข‹์€ +์˜ํ–ฅ์„ ๋ผ์น  ๊ฒƒ์ž…๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋ณ€ํ™”๋Š” ํ•™์ƒ๋“ค์—๊ฒŒ ๊ต์œก์ ์œผ๋กœ๋„ +๊ธ์ •์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์น  ๊ฒƒ์ด๋ผ๊ณ  ํ™•์‹ ํ•ฉ๋‹ˆ๋‹ค. +ํ•™์ƒ๋“ค์ด ์ƒˆ๋กœ์šด ๊ตํ›ˆ ์•„๋ž˜์—์„œ ์„ฑ์žฅํ•˜๊ณ  ์‹œ๋Œ€์— ๋ฐœ๋งž์ถฐ ๊ฐˆ +์ˆ˜ ์žˆ๋„๋ก ๊ตํ›ˆ์„ ๋ณ€๊ฒฝํ•ด ์ฃผ์‹œ๊ธธ ๋ถ€ํƒ๋“œ๋ฆฝ๋‹ˆ๋‹ค. ๊ณ ๋ง™์Šต๋‹ˆ๋‹ค. + +","{'question': '(๋‚˜)์˜ โ“๏ฝžโ“”์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์ ์ ˆํ•˜์ง€ ์•Š์€ ๊ฒƒ์€?', 'choices': ['โ“:๊ธ€์˜ ํŠน์„ฑ์„ ๊ณ ๋ คํ•˜์—ฌ ๊ฑด์˜์˜ ์ฃผ์ฒด๋ฅผ ์ œ์‹œํ–ˆ๋‹ค.', 'โ“‘:์ •๋ณด์˜ ์‹ ๋ขฐ์„ฑ์„ ๋†’์ด๊ธฐ ์œ„ํ•ด ์ถœ์ฒ˜๋ฅผ ์ œ์‹œํ–ˆ๋‹ค', 'โ“’:์„ค๋“๋ ฅ์„ ๋†’์ด๊ธฐ ์œ„ํ•ด ์˜ˆ์ƒ๋˜๋Š” ๋ฐ˜๋ก ์„ ์ œ์‹œํ–ˆ๋‹ค', 'โ““:ํ™”์ œ์˜ ์ค‘์š”์„ฑ์„ ํ™˜๊ธฐํ•˜๊ธฐ ์œ„ํ•ด ๋น„์œ ์  ํ‘œํ˜„์„ ์ œ์‹œํ–ˆ๋‹ค.', 'โ“”:๊ฑด์˜ ๋‚ด์šฉ์„ ์‹คํ˜„ํ•˜๊ธฐ ์œ„ํ•ด ๊ฑฐ์ณ์•ผ ํ•˜๋Š” ๊ณผ์ •์„ ์ œ์‹œํ–ˆ๋‹ค.'], 'answer': ''}",,3,3,True,[],3 +2025-korean-42,"(๊ฐ€)๋Š” ํ•™์ƒํšŒ ํ•™์ƒ๋“ค์˜ ๋Œ€ํ™”์ด๊ณ , (๋‚˜)๋Š” ํ•™์ƒํšŒ ๋Œ€ํ‘œ๊ฐ€ +์“ด ๊ฑด์˜๋ฌธ์ด๋‹ค. ๋ฌผ์Œ์— ๋‹ตํ•˜์‹œ์˜ค. +(๊ฐ€) +ํ•™์ƒ1:์–˜๋“ค์•„, ์–ด์ œ ๋‰ด์Šค ๋ดค์–ด? ์ธ๊ทผ์— ์žˆ๋Š” โ—‹โ—‹๊ณ ๊ฐ€ ๊ฐœ๊ต +60์ฃผ๋…„์„ ์•ž๋‘๊ณ  ๊ต๊ฐ€ ๊ฐ€์‚ฌ๋ฅผ ๋ฐ”๊ฟจ๋‹ค๊ณ  ํ•˜๋”๋ผ. +ํ•™์ƒ2:ใ‰ ์ฒ˜์Œ ๋“ค์–ด๋ณด๋Š”๋ฐ, ๊ต๊ฐ€ ๊ฐ€์‚ฌ๋ฅผ ์™œ ๋ฐ”๊พผ ๊ฑฐ์•ผ? +ํ•™์ƒ1:๊ต๊ฐ€ ๊ฐ€์‚ฌ์— โ€˜์”ฉ์”ฉํ•œ ๊ฑด์•„์—ฌ, ์ฒญ๋…„ ์ผ๊พผ์ด์—ฌโ€™๋ผ๋Š” ๊ตฌ์ ˆ์ด +๋ฐ˜๋ณต๋˜์—ˆ๋‹ค๊ณ  ํ•ด. ์ด๋Ÿฐ ๊ฐ€์‚ฌ๋Š” ๊ฐœ๊ต ๋‹น์‹œ ์‚ฌํšŒ์—์„œ ์š”๊ตฌ๋˜๋˜ +ํŠน์ • ์—ญํ• ๋งŒ์„ ๊ฐ•์กฐํ•œ ๊ฑฐ๋ผ๊ณ  ๋‰ด์Šค์—์„œ ๊ทธ๋Ÿฌ๋”๋ผ. +ํ•™์ƒ3:์•„, ๊ทธ๋ž˜? ๊ทธ๋Ÿฐ ๊ฐ€์‚ฌ ๋‚ด์šฉ์ด ๊ฐœ๊ต ๋‹น์‹œ์—๋Š” ์ค‘์š”ํ•œ +๊ฐ€์น˜๋กœ ์—ฌ๊ฒจ์กŒ๊ฒ ์ง€๋งŒ ์ง€๊ธˆ์€ ๊ทธ๋ ‡์ง€ ์•Š์œผ๋‹ˆ ๋ฐ”๊พผ ๊ฑฐ๊ตฌ๋‚˜. +๊ทผ๋ฐ ๋น„์Šทํ•œ ์‹œ๊ธฐ์— ๊ฐœ๊ตํ•œ ์šฐ๋ฆฌ ํ•™๊ต ๊ตํ›ˆ๋„ ๋ฌธ์ œ๊ฐ€ +์žˆ์ง€ ์•Š๋‹ˆ? - [A] +ํ•™์ƒ2:ใ‰ก๋งž์•„. ๋“ฑ๊ตํ•  ๋•Œ ๊ตํ›ˆ์„ ๋ณด๋ฉด ๋งˆ์Œ์ด ์ข€ ๋ถˆํŽธํ•˜๋”๋ผ. +๊ทธ๋ž˜์„œ ๊ตํ›ˆ์„ ๋ฐ”๊ฟจ์œผ๋ฉด ์ข‹๊ฒ ๋‹ค๊ณ  ์ƒ๊ฐํ•œ ์ ์ด ์žˆ์–ด. +ํ•™์ƒ3:๋‚˜๋„ ๊ทธ๋žฌ๋Š”๋ฐ. ์šฐ๋ฆฌ ํ•™๊ต ๊ตํ›ˆ๋„ โ—‹โ—‹๊ณ  ๊ต๊ฐ€์ฒ˜๋Ÿผ ํŠน์ • +์—ญํ• ๋งŒ์ด ๋‘๋“œ๋Ÿฌ์ง€๋Š” ๊ฒƒ ๊ฐ™์•„. +ํ•™์ƒ2:์‘. ๊ทธ๋ž˜์„œ ๋งŽ์€ ํ•™์ƒ๋“ค์ด ๊ณต๊ฐํ•˜๊ธฐ ์–ด๋ ค์šด ๊ฒƒ๋„ ์‚ฌ์‹ค์ด์•ผ. +ํ•™์ƒ3:๊ตํ›ˆ์€ ์ง€๊ธˆ ์‹œ๋Œ€์—๋„ ๋งž๋Š” ๋ณดํŽธ์  ๊ฐ€์น˜๋ฅผ ๋‹ด๊ณ  +์žˆ์–ด์•ผ ํ•˜๋Š” ๊ฑฐ ์•„๋ƒ? ๋ˆ„๊ตฌ๋‚˜ ๊ณต๊ฐํ•  ์ˆ˜ ์žˆ์–ด์•ผ ํ•˜๊ณ . +๊ทธ๋Ÿฐ๋ฐ ์šฐ๋ฆฌ ๊ตํ›ˆ์€ ๊ทธ๋ ‡์ง€ ์•Š์€ ๊ฒƒ ๊ฐ™์•„. - [B] +ํ•™์ƒ1:ใ‰ข๋‚˜๋Š” ์šฐ๋ฆฌ ํ•™๊ต ๊ตํ›ˆ์ด ๊ดœ์ฐฎ๋‹ค๊ณ  ์ƒ๊ฐํ–ˆ๋Š”๋ฐ, ๋“ฃ๊ณ  +๋ณด๋‹ˆ ๋ฐ”๊ฟ”์•ผ๊ฒ ๋‹ค๋Š” ์ƒ๊ฐ์ด ๋“ค์–ด. ๊ทธ๋Ÿฐ๋ฐ ๋‰ด์Šค์— ๋”ฐ๋ฅด๋ฉด โ—‹โ—‹๊ณ  +์—์„œ๋Š” ๋™๋ฌธํšŒ๋ฅผ ์„ค๋“ํ•˜๋Š” ๊ฒƒ์ด ์‰ฝ์ง€ ์•Š์•˜๋‹ค๊ณ  ํ•˜๋”๋ผ๊ณ . +ํ•™์ƒ2:์•„๋ฌด๋ž˜๋„ ํ•™๊ต ์„ค๋ฆฝ ์ดํ›„ ์˜ค๋žœ ๊ธฐ๊ฐ„ ๊ต๊ฐ€๋ฅผ ๋ถˆ๋Ÿฌ ์™”์œผ๋‹ˆ๊นŒ, +๋™๋ฌธ ์„ ๋ฐฐ๋“ค์€ ๊ต๊ฐ€๊ฐ€ ๋ชจ๊ต๋ฅผ ์ƒ์ง•ํ•œ๋‹ค๊ณ  ์ƒ๊ฐํ•  ๊ฒƒ ๊ฐ™์•„. ๊ทธ๋งŒํผ +๊ต๊ฐ€์— ์• ์ •์ด ์žˆ๋Š” ์‚ฌ๋žŒ๋„ ๋งŽ์„ ๊ฑฐ์•ผ. +ํ•™์ƒ1:๋งž์•„. ๊ตํ›ˆ์„ ๋ฐ”๊พธ๋Š” ์ผ๋„ ๊ต๊ฐ€๋ฅผ ๋ฐ”๊พธ๋Š” ๊ฒƒ๋งŒํผ +์–ด๋ ค์šธ ๊ฒƒ ๊ฐ™์•„. - [C] +ํ•™์ƒ3:๊ทธ๋Ÿฌ๋‹ˆ๊นŒ ๋™๋ฌธ ์„ ๋ฐฐ๋“ค๊ณผ ํ•™๊ต ๊ตฌ์„ฑ์›์˜ ์˜๊ฒฌ๋„ ์ถฉ๋ถ„ํžˆ +๋“ค์–ด์•ผ ํ•  ๊ฒƒ ๊ฐ™์•„. ใ‰ฃ๊ตํ›ˆ ๋ณ€๊ฒฝ์ด ์™œ ํ•„์š”ํ•˜๊ณ  ์–ด๋–ค ํšจ๊ณผ๊ฐ€ +์žˆ๋Š”์ง€ ์•Œ๋ฆฌ๋Š” ๊ฒƒ๋„ ์ค‘์š”ํ•˜๊ฒ ์ง€? +ํ•™์ƒ2:์‘. ๊ทผ๋ฐ ๊ตํ›ˆ์„ ๋ฐ”๊พธ๋ฉด ์–ด๋–ค ํšจ๊ณผ๊ฐ€ ์žˆ์„๊นŒ? +ํ•™์ƒ3:๊ตํ›ˆ์„ ๋ณด๋ฉด์„œ ๋А๊ผˆ๋˜ ๋ถˆํŽธํ•œ ๋งˆ์Œ์ด ์‚ฌ๋ผ์ง€์ง€ ์•Š์„๊นŒ? +์ƒˆ๋กœ์šด ๊ตํ›ˆ์œผ๋กœ๋ถ€ํ„ฐ ์•Œ๊ฒŒ ๋ชจ๋ฅด๊ฒŒ ๊ธ์ •์ ์ธ ์˜ํ–ฅ๋„ ๋ฐ›์„ ์ˆ˜ +์žˆ์„ ๊ฑฐ๊ณ . +ํ•™์ƒ1:๊ทธ๋ž˜. ๊ตํ›ˆ์„ ๋ฐ”๊พธ๋Š” ๊ณผ์ •์—์„œ ํ•™๊ต์— ๋Œ€ํ•œ ๊ตฌ์„ฑ์›์˜ +๊ด€์‹ฌ์ด ๋†’์•„์ง€๊ณ  ๊ฒฐ์†๋ ฅ๋„ ์ปค์งˆ ๊ฑฐ์•ผ. ๊ทธ๋ฆฌ๊ณ  ๊ตํ›ˆ์„ ๋ฐ”๊พธ๊ณ  +์‹ถ์€ ๋‹ค๋ฅธ ํ•™๊ต์—๋„ ์ข‹์€ ๋ณธ๋ณด๊ธฐ๊ฐ€ ๋  ๊ฒƒ ๊ฐ™์•„. +ํ•™์ƒ3:๊ทธ๋Ÿผ ๊ตํ›ˆ ๋ณ€๊ฒฝ์„ ์ถ”์ง„ํ• ์ง€ ๋ง์ง€ ํ•™์ƒํšŒ ํšŒ์˜ ์•ˆ๊ฑด์œผ๋กœ +์˜ฌ๋ ค ๋ณด์ž. ์˜ค๋Š˜ ๋‚˜๋ˆˆ ์ด์•ผ๊ธฐ๋Š” ๋‚ด๊ฐ€ ์ •๋ฆฌํ• ๊ฒŒ. +ํ•™์ƒ1:๋‚˜๋Š” ๋‹ค๋ฅธ ํ•™๊ต์˜ ์‚ฌ๋ก€๋ฅผ ๋” ์ฐพ์•„์„œ ํšŒ์˜ ๋•Œ ๊ณต์œ ํ• ๊ฒŒ. +ํ•™์ƒ2:์ข‹์•„. ํšŒ์˜์—์„œ ํ†ต๊ณผ๋˜๋ฉด ๊ตํ›ˆ ๋ณ€๊ฒฝ์— ๋Œ€ํ•œ ํ•™์ƒ๋“ค์˜ +์˜๊ฒฌ์„ ์กฐ์‚ฌํ•ด ๋ณด์ž. ์„ค๋ฌธ ์กฐ์‚ฌ๋ฅผ ํ•˜๋Š” ๋ฐฐ๊ฒฝ๋„ ๊ฐ™์ด ์•ˆ๋‚ดํ•˜๋ฉด +์ข‹๊ฒ ์–ด. +ํ•™์ƒ1:์ฐฌ๋ฐ˜ ์˜๊ฒฌ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์ƒˆ๋กœ์šด ๊ตํ›ˆ๋„ ์ œ์•ˆ๋ฐ›์•„ ๋ณด์ž. +ํ•™์ƒ3:ใ‰ค๊ตํ›ˆ์„ ๋ฏธ๋ฆฌ ์ œ์•ˆ๋ฐ›์œผ๋ฉด ๊ตํ›ˆ ๋ณ€๊ฒฝ์ด ํ™•์ •๋œ ๊ฒƒ์ฒ˜๋Ÿผ +์˜คํ•ดํ•  ์ˆ˜ ์žˆ์œผ๋‹ˆ ๊ทธ ๋‚ด์šฉ์€ ๋นผ๋Š” ๊ฒŒ ์–ด๋•Œ? +ํ•™์ƒ1:๊ทธ๊ฒŒ ๋‚ซ๊ฒ ๋‹ค. ๊ตํ›ˆ ๋ณ€๊ฒฝ์— ๋Œ€ํ•œ ์ฐฌ๋ฐ˜ ์˜๊ฒฌ์„ ์กฐ์‚ฌํ•˜๊ณ , +๊ตํ›ˆ์„ ๋ฐ”๊พธ์ž๋Š” ์˜๊ฒฌ์ด ๋งŽ์œผ๋ฉด ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ํ•™๊ต์— +๊ฑด์˜ํ•˜๋ฉด ๋  ๊ฒƒ ๊ฐ™์•„. +ํ•™์ƒ2:๊ทธ๋ž˜! ๊ทธ๊ฒƒ๊ณผ ํ•จ๊ป˜ ๋™๋ฌธ ์„ ๋ฐฐ๋“ค์˜ ์˜๊ฒฌ์„ ๋ชจ์•„ ๋‹ฌ๋ผ๊ณ ๋„ +๋ถ€ํƒํ•ด ๋ณด์ž. ์šฐ๋ฆฌ ์ž˜ํ•ด ๋ณด์ž. + +(๋‚˜) +๊ต์žฅ ์„ ์ƒ๋‹˜, ์•ˆ๋…•ํ•˜์„ธ์š”? โ“์ €๋Š” ํ•™์ƒํšŒ ๋Œ€ํ‘œ ์•ˆโ–ณโ–ณ์ž…๋‹ˆ๋‹ค. +๋Š˜ ํ•™๊ต ๋ฐœ์ „๊ณผ ํ•™์ƒ๋“ค์˜ ์„ฑ์žฅ์„ ์œ„ํ•ด ์• ์“ฐ์‹œ๋Š” ๊ต์žฅ ์„ ์ƒ๋‹˜๊ป˜ +๊ฐ์‚ฌ์˜ ๋ง์”€์„ ๋“œ๋ฆฝ๋‹ˆ๋‹ค. +โ“‘์ตœ๊ทผ โ—‡โ—‡๋ฐฉ์†ก ๋‰ด์Šค์— ๋”ฐ๋ฅด๋ฉด ์ธ๊ทผ ํ•™๊ต์ธ โ—‹โ—‹๊ณ ๊ฐ€ ํ•™๊ต +๊ตฌ์„ฑ์›์˜ ๋…ธ๋ ฅ ๋์— ๊ต๊ฐ€ ๊ฐ€์‚ฌ๋ฅผ ๋ณ€๊ฒฝํ•˜์˜€๋‹ค๊ณ  ํ•ฉ๋‹ˆ๋‹ค. โ—‹โ—‹๊ณ ์˜ +๋ณ€๊ฒฝ๋œ ๊ต๊ฐ€ ๊ฐ€์‚ฌ๋Š” ์ด์ „๊ณผ ๋‹ฌ๋ฆฌ, ํ•™์ƒ๋“ค์˜ ๋ฏธ๋ž˜์™€ ํ–‰๋ณตํ•œ ์‚ถ์„ +๊ฐ•์กฐํ•œ๋‹ค๊ณ  ํ•ฉ๋‹ˆ๋‹ค. โ—‹โ—‹๊ณ ์™€ ๊ฐ™์ด ์šฐ๋ฆฌ ํ•™๊ต์—์„œ๋„ ๊ตํ›ˆ์„ +๋ฐ”๊พธ์ž๋Š” ํ•™์ƒ๋“ค์˜ ๋ชฉ์†Œ๋ฆฌ๊ฐ€ ์ปค์ง€๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. +โ“’๊ตํ›ˆ์€ ํ•™๊ต์˜ ์ด๋…์„ ํ‘œํ˜„ํ•˜์ง€๋งŒ, ๋‹จ์ˆœํžˆ ํ‘œํ˜„์—๋งŒ ๊ทธ์น˜๋Š” +๊ฒƒ์€ ์•„๋‹™๋‹ˆ๋‹ค. ๊ตํ›ˆ์€ ํ•™๊ต์˜ ์ด๋…๊ณผ ๋ชฉํ‘œ๋ฅผ ๋“œ๋Ÿฌ๋‚ผ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ +โ““๊ตฌ์„ฑ์› ๋ชจ๋‘๊ฐ€ ์ง€ํ–ฅํ•˜๋Š” ์ •์‹ ์  ๊ฐ€์น˜๋ฅผ ๋‹ด๋Š” ๊ทธ๋ฆ‡์ž…๋‹ˆ๋‹ค. ํ•˜์ง€๋งŒ +์ง€๊ธˆ ์šฐ๋ฆฌ ํ•™๊ต์˜ ๊ตํ›ˆ์€ ๊ฐœ๊ต ๋‹น์‹œ ์š”๊ตฌ๋˜๋˜ ํŠน์ • ์—ญํ• ๋งŒ์„ +๋ถ€๊ฐํ•˜๊ณ  ์žˆ์–ด, ํ˜„์žฌ์™€ ๋ฏธ๋ž˜์˜ ๊ตฌ์„ฑ์›์ด ์ง€ํ–ฅํ•ด์•ผ ํ•˜๋Š” ๊ฐ€์น˜๋ฅผ +๋ฐ˜์˜ํ•˜์ง€ ๋ชปํ•˜๋Š” ๋ฌธ์ œ๊ฐ€ ์žˆ์Šต๋‹ˆ๋‹ค. ๊ทธ๋ž˜์„œ ๋‹น์‹œ์™€๋Š” ๋‹ฌ๋ฆฌ ์ง€๊ธˆ์€ +ํ•™์ƒ๋“ค์˜ ๊ณต๊ฐ์„ ์–ป์ง€ ๋ชปํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. +์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ๊ณต์‹์ ์ธ ์ ˆ์ฐจ๋ฅผ ์ถ”์ง„ํ•˜์—ฌ +๊ตํ›ˆ์„ ๋ณ€๊ฒฝํ•ด ์ฃผ์‹œ๊ธฐ๋ฅผ ๊ฑด์˜๋“œ๋ฆฝ๋‹ˆ๋‹ค. โ“”ํ•™๊ต์—์„œ๋Š” ๊ต์ง์›, +๋™๋ฌธ ์„ ๋ฐฐ, ํ•™๋ถ€๋ชจ์—๊ฒŒ ๊ตํ›ˆ ๋ณ€๊ฒฝ์˜ ์ทจ์ง€๋ฅผ ์„ค๋ช…ํ•˜๊ณ , ๊ทธ๋ถ„๋“ค์˜ +์˜๊ฒฌ์„ ์ˆ˜๋ ดํ•œ ํ›„, ํ•™๊ต์šด์˜์œ„์›ํšŒ์—์„œ ์‹ฌ์˜ํ•˜๋„๋ก ํ•ด ์ฃผ์‹œ๋ฉด +์ข‹๊ฒ ์Šต๋‹ˆ๋‹ค. ๊ต์žฅ ์„ ์ƒ๋‹˜๊ป˜์„œ๋„ ์•Œ๊ณ  ๊ณ„์‹œ๋“ฏ์ด, ํ•™์ƒํšŒ์—์„œ ์„ค๋ฌธ +์กฐ์‚ฌ๋กœ ํ•™์ƒ๋“ค์˜ ์˜๊ฒฌ์„ ์ˆ˜๋ ดํ•œ ๊ฒฐ๊ณผ ์ „๊ต์ƒ์˜ 91.8%๊ฐ€ ๊ตํ›ˆ +๋ณ€๊ฒฝ์— ์ฐฌ์„ฑํ–ˆ์Šต๋‹ˆ๋‹ค. ํ•™์ƒ๋“ค ์‚ฌ์ด์—๋Š” ์ด๋ฏธ ๊ตํ›ˆ ๋ณ€๊ฒฝ์— ๋Œ€ํ•œ +๊ณต๊ฐ๋Œ€๊ฐ€ ํ˜•์„ฑ๋˜์—ˆ๋‹ค๊ณ  ๋ณผ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. +๋ˆ„๊ตฌ๋‚˜ ๊ณต๊ฐํ•  ์ˆ˜ ์žˆ๋Š” ๊ตํ›ˆ์œผ๋กœ ๋ฐ”๊พธ๋ฉด ๊ตํ›ˆ์„ ๋ณด๋ฉด์„œ ๋А๊ผˆ๋˜ +๋ถˆํŽธํ•œ ๋งˆ์Œ์ด ์‚ฌ๋ผ์ง€๊ณ  ํ•™์ƒ๋“ค์˜ ๋…ธ๋ ฅ์œผ๋กœ ๊ตํ›ˆ์„ ๋ฐ”๊ฟจ๋‹ค๋Š” +์ž๋ถ€์‹ฌ์„ ๋А๋ผ๊ฒŒ ๋  ๊ฒƒ์ž…๋‹ˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๊ทธ ๊ณผ์ •์—์„œ ํ•™์ƒ๋“ค์€ +๋ฌผ๋ก  ๋ถ€๋ชจ๋‹˜๋“ค๊ณผ ์„ ์ƒ๋‹˜๋“ค๋„ ํ•™๊ต์— ๊ด€์‹ฌ์„ ๋” ๊ฐ–๊ฒŒ ๋˜๋ฉด์„œ +์ž์—ฐ์Šค๋Ÿฝ๊ฒŒ ์• ๊ต์‹ฌ๊ณผ ํ•™๊ต์— ๋Œ€ํ•œ ๊ธ์ง€๊ฐ€ ๋†’์•„์งˆ ๊ฒƒ์ž…๋‹ˆ๋‹ค. ์šฐ๋ฆฌ +ํ•™๊ต์˜ ๊ตํ›ˆ ๋ณ€๊ฒฝ์€ ๊ตํ›ˆ์„ ๋ฐ”๊พธ๊ณ  ์‹ถ์€ ๋‹ค๋ฅธ ํ•™๊ต์—๋„ ์ข‹์€ +์˜ํ–ฅ์„ ๋ผ์น  ๊ฒƒ์ž…๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋ณ€ํ™”๋Š” ํ•™์ƒ๋“ค์—๊ฒŒ ๊ต์œก์ ์œผ๋กœ๋„ +๊ธ์ •์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์น  ๊ฒƒ์ด๋ผ๊ณ  ํ™•์‹ ํ•ฉ๋‹ˆ๋‹ค. +ํ•™์ƒ๋“ค์ด ์ƒˆ๋กœ์šด ๊ตํ›ˆ ์•„๋ž˜์—์„œ ์„ฑ์žฅํ•˜๊ณ  ์‹œ๋Œ€์— ๋ฐœ๋งž์ถฐ ๊ฐˆ +์ˆ˜ ์žˆ๋„๋ก ๊ตํ›ˆ์„ ๋ณ€๊ฒฝํ•ด ์ฃผ์‹œ๊ธธ ๋ถ€ํƒ๋“œ๋ฆฝ๋‹ˆ๋‹ค. ๊ณ ๋ง™์Šต๋‹ˆ๋‹ค. + +","{'question': '๋‹ค์Œ์€ (๋‚˜)์˜ 5๋ฌธ๋‹จ ์ดˆ๊ณ ์™€ ๊ทธ์— ๋Œ€ํ•œ ์นœ๊ตฌ์˜ ์กฐ์–ธ์ด๋‹ค. ์นœ๊ตฌ์˜\n์กฐ์–ธ์ด (๋‚˜)์— ๋ฐ˜์˜๋˜์—ˆ๋‹ค๊ณ  ํ•  ๋•Œ โ’ถ ์— ๋“ค์–ด๊ฐˆ ๋‚ด์šฉ์œผ๋กœ\n๊ฐ€์žฅ ์ ์ ˆํ•œ ๊ฒƒ์€?', 'choices': ['ํ•™๊ต ๊ตฌ์„ฑ์› ์ž…์žฅ์—์„œ์˜ ๊ธ์ •์ ์ธ ์ธก๋ฉด์„', '๋‹ค๋ฅธ ํ•™๊ต๊ฐ€ ์ฐธ๊ณ ํ•  ์ˆ˜ ์žˆ๋Š” ์œ ์šฉํ•œ ์ •๋ณด๋ฅผ', '๊ตํ›ˆ ๋‚ด์šฉ์ด ํ•™๊ต์ƒํ™œ์˜ ์ง€์นจ์ด ๋œ๋‹ค๋Š” ์ ์„', '์ง€์—ญ ์‚ฌํšŒ์—์„œ ํ•™๊ต์˜ ์œ„์ƒ์ด ๊ฐ•ํ™”๋œ๋‹ค๋Š” ์ธก๋ฉด์„', '๊ฑด์˜๋ฅผ ๋ฐ›๋Š” ๋Œ€์ƒ์ด ํ•™์ƒ์˜ ์„ฑ์žฅ์„ ์ด๋Œ ์ˆ˜ ์žˆ๋‹ค๋Š” ์ ์„'], 'answer': '', 'question_plus': '[5๋ฌธ๋‹จ ์ดˆ๊ณ ]\n ์šฐ๋ฆฌ ํ•™๊ต์˜ ๊ตํ›ˆ ๋ณ€๊ฒฝ์€ ๊ตํ›ˆ์„ ๋ฐ”๊พธ๊ณ  ์‹ถ์€ ๋‹ค๋ฅธ ํ•™๊ต์—๋„ \n์ข‹์€ ์˜ํ–ฅ์„ ๋ผ์น  ๊ฒƒ์ž…๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋ณ€ํ™”๋Š” ํ•™์ƒ๋“ค์—๊ฒŒ ๊ต์œก์ ์œผ๋กœ๋„\n๊ธ์ •์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์น  ๊ฒƒ์ด๋ผ๊ณ  ํ™•์‹ ํ•ฉ๋‹ˆ๋‹ค.\n[์นœ๊ตฌ์˜ ์กฐ์–ธ]\n 5๋ฌธ๋‹จ ์ดˆ๊ณ ๋Š” ๊ธฐ๋Œ€ํšจ๊ณผ๊ฐ€ ์ข€ ๋ถ€์กฑํ•œ ๊ฒƒ ๊ฐ™์•„.\n๊ธ€์˜ ํ๋ฆ„์„ ๊ณ ๋ คํ•˜์—ฌ โ’ถ ์ถ”๊ฐ€ํ•˜๋ฉด ์–ด๋•Œ?\\'}","[5๋ฌธ๋‹จ ์ดˆ๊ณ ] + ์šฐ๋ฆฌ ํ•™๊ต์˜ ๊ตํ›ˆ ๋ณ€๊ฒฝ์€ ๊ตํ›ˆ์„ ๋ฐ”๊พธ๊ณ  ์‹ถ์€ ๋‹ค๋ฅธ ํ•™๊ต์—๋„ +์ข‹์€ ์˜ํ–ฅ์„ ๋ผ์น  ๊ฒƒ์ž…๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋ณ€ํ™”๋Š” ํ•™์ƒ๋“ค์—๊ฒŒ ๊ต์œก์ ์œผ๋กœ๋„ +๊ธ์ •์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์น  ๊ฒƒ์ด๋ผ๊ณ  ํ™•์‹ ํ•ฉ๋‹ˆ๋‹ค. +[์นœ๊ตฌ์˜ ์กฐ์–ธ] + 5๋ฌธ๋‹จ ์ดˆ๊ณ ๋Š” ๊ธฐ๋Œ€ํšจ๊ณผ๊ฐ€ ์ข€ ๋ถ€์กฑํ•œ ๊ฒƒ ๊ฐ™์•„. +๊ธ€์˜ ํ๋ฆ„์„ ๊ณ ๋ คํ•˜์—ฌ โ’ถ ์ถ”๊ฐ€ํ•˜๋ฉด ์–ด๋•Œ?\",1,1,True,[],5 +2025-korean-43,"๋‹ค์Œ์€ ์ž‘๋ฌธ ์ƒํ™ฉ๊ณผ ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ํ•™์ƒ์ด ์ž‘์„ฑํ•œ ์ดˆ๊ณ ์˜ +์ผ๋ถ€์ด๋‹ค. ๋ฌผ์Œ์— ๋‹ตํ•˜์‹œ์˜ค. +[์ž‘๋ฌธ ์ƒํ™ฉ] +๊ธ€์„ ์“ฐ๋ ค ํ•จ. +ํ™˜๊ฒฝ์˜ ๋‚ ์„ ๋งž์•„ ๊ต์ง€์— ์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ ์— ๋Œ€ํ•ด ์•Œ๋ฆฌ๋Š” +[ํ•™์ƒ์˜ ์ดˆ๊ณ ] +2017๋…„ ์ฆˆ์Œ์— ์Šค์›จ๋ด์—์„œ โ€˜ํ”Œ๋คผ๊ทธ์Šค์บ„(Flygskam)โ€™ ์šด๋™์ด +์ผ์–ด๋‚ฌ๋‹ค. ์ด๋Š” โ€˜๋น„ํ–‰๊ธฐ ํƒ€๋Š” ๊ฒƒ์„ ๋ถ€๋„๋Ÿฝ๊ฒŒ ์—ฌ๊ธด๋‹ค.โ€™๋ผ๋Š” ์˜๋ฏธ๋กœ, +์‹œ๊ฐ„์ด ๋” ๊ฑธ๋ฆฌ๋”๋ผ๋„ ํ™˜๊ฒฝ์„ ์ƒ๊ฐํ•˜์—ฌ ๋น„ํ–‰๊ธฐ ๋Œ€์‹  ๊ธฐ์ฐจ๋ฅผ +ํƒ€์ž๋Š” ๊ฒƒ์ด๋‹ค. ๊ทธ๋Ÿฐ๋ฐ ๋น„ํ–‰๊ธฐ๋ฅผ ํƒ€์•ผ ํ•  ๋•Œ ํ™˜๊ฒฝ์„ ์œ„ํ•œ ์„ ํƒ์€ +์—†์„๊นŒ? ์ตœ๊ทผ ๊ธฐ์กด ํ•ญ๊ณต์œ ์˜ ๋Œ€์•ˆ์œผ๋กœ ์ฃผ๋ชฉ๋ฐ›๋Š” ์นœํ™˜๊ฒฝ ์—ฐ๋ฃŒ์ธ +์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ ์— ๋Œ€ํ•ด ์•Œ์•„๋ณด์ž. +์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ ๋ž€ ๊ธฐ์กด ํ•ญ๊ณต์œ ์™€ ํ™”ํ•™์  ๊ตฌ์กฐ๊ฐ€ ์œ ์‚ฌํ•˜๋ฉด์„œ๋„ +ํ™”์„ ์—ฐ๋ฃŒ๊ฐ€ ์•„๋‹Œ ํ๊ธฐ๋ฌผ์ด๋‚˜ ์ž‘๋ฌผ ๋“ฑ์„ ์›๋ฃŒ๋กœ ์ƒ์‚ฐ๋œ ์—ฐ๋ฃŒ์ด๋‹ค. +ํ๊ธฐ๋ฌผ๋กœ๋Š” ํ์‹์šฉ์œ , ํ๋ชฉ์žฌ ๋“ฑ์ด ์‚ฌ์šฉ๋˜๊ณ , ์ž‘๋ฌผ๋กœ๋Š” ๊ธฐ๋ฆ„์•ผ์ž๋‚˜ +์˜ฅ์ˆ˜์ˆ˜ ๋“ฑ์ด ์“ฐ์ด๋Š”๋ฐ, ์œ ๋Ÿฝ์—ฐํ•ฉ์€ ์ž‘๋ฌผ ๊ธฐ๋ฐ˜ ๋ฐ”์ด์˜ค ์—ฐ๋ฃŒ์˜ ์‚ฌ์šฉ์€ +๊ทœ์ œํ•˜๊ณ  ์žˆ๋‹ค. +์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ ๋Š” ๋ณ„๋„๋กœ ๋น„ํ–‰๊ธฐ๋ฅผ ๊ฐœ์กฐํ•  ํ•„์š” ์—†์ด ๊ธฐ์กด +ํ•ญ๊ณต์œ ์™€ ํ˜ผํ•ฉํ•˜์—ฌ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ์–ด ํšจ์œจ์ ์ด๋‹ค. ๊ทธ๋ฆฌ๊ณ  ํƒ„์†Œ +๋ฐฐ์ถœ๋Ÿ‰์„ ์ค„์ผ ์ˆ˜ ์žˆ์–ด ์นœํ™˜๊ฒฝ์ ์ด๋‹ค. ์›๋ฃŒ์˜ ์ƒ์‚ฐ๋ถ€ํ„ฐ ์—ฐ๋ฃŒ +์†Œ๋น„๊นŒ์ง€์˜ ์ „ ๊ณผ์ •์—์„œ ๋ฐœ์ƒํ•˜๋Š” ํƒ„์†Œ ๋ฐฐ์ถœ๋Ÿ‰์€, ์šด์†ก ์ˆ˜๋‹จ +์ค‘ ๋น„ํ–‰๊ธฐ๊ฐ€ ๊ฐ€์žฅ ๋งŽ์€๋ฐ, ์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ ๋Š” ํ™”์„ ์—ฐ๋ฃŒ๋กœ ๋งŒ๋“  +๊ธฐ์กด ํ•ญ๊ณต์œ ์— ๋น„ํ•ด ํƒ„์†Œ ๋ฐฐ์ถœ๋Ÿ‰์„ ์ตœ๋Œ€ 80%๊ฐ€๋Ÿ‰ ์ค„์ผ ์ˆ˜ ์žˆ๋‹ค. +์ด๋Ÿฌํ•œ ์žฅ์  ๋•Œ๋ฌธ์— ์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ  ์‚ฌ์šฉ์„ ์˜๋ฌดํ™”ํ•˜๋Š” +๊ตญ๊ฐ€๊ฐ€ ์ ์  ๋Š˜๊ณ  ์žˆ๋‹ค. ์œ ๋Ÿฝ์—ฐํ•ฉ์€ 2025๋…„๋ถ€ํ„ฐ ์ง€์† ๊ฐ€๋Šฅ +ํ•ญ๊ณต์œ ๋ฅผ ์ตœ์†Œ 2% ์ด์ƒ ์„ž๋„๋ก ์˜๋ฌดํ™”ํ•˜๊ณ , ํ˜ผํ•ฉ ๋น„์œจ์„ ์ ์ฐจ +๋†’์—ฌ 2050๋…„์—๋Š” 70%๊นŒ์ง€ ๋†’์ผ ์˜ˆ์ •์ด๋‹ค. ์šฐ๋ฆฌ๋‚˜๋ผ๋„ 2027๋…„์— +1% ๋‚ด์™ธ๋กœ ํ˜ผํ•ฉํ•˜๋„๋ก ์˜๋ฌดํ™”ํ•˜๋Š” ์ œ๋„๋ฅผ ์‹œํ–‰ํ•  ์˜ˆ์ •์ด๋‹ค. +์šฐ๋ฆฌ๋‚˜๋ผ๋Š” ์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ  ์‚ฌ์šฉ์˜ ์˜๋ฌดํ™”๋ฅผ ์•ž๋‘๊ณ , ์ง€์† +๊ฐ€๋Šฅ ํ•ญ๊ณต์œ ์˜ ๋ถ€์กฑ ์ƒํ™ฉ์— ๋Œ€๋น„ํ•˜๊ณ  ์žˆ๋‹ค. ์ •๋ถ€๋Š” ๊ธฐ์—…์„ +์ง€์›ํ•˜์—ฌ ๋‹ค์–‘ํ•œ ์›๋ฃŒ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ ์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ ์˜ ์ƒ์‚ฐ +๊ธฐ์ˆ ์„ ๊ณ ๋„ํ™”ํ•˜๊ณ  ์„์œ  ์‚ฌ์—…๋ฒ• ๊ฐœ์ •์— ๋”ฐ๋ฅธ ๊ด€๋ จ ์ œ๋„๋ฅผ +์ •๋น„ํ•จ์œผ๋กœ์จ, ์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ ์˜ ๊ณต๊ธ‰ ์—ญ๋Ÿ‰์„ ๊ฐ•ํ™”ํ•˜๊ณ  ์žˆ๋‹ค. +ํ•ญ๊ณต ๋ถ€๋ฌธ์—์„œ ํ™˜๊ฒฝ์„ ์œ„ํ•œ ์ •๋ถ€์˜ ๋ฐœ๊ฑธ์Œ์ด ์ ์ฐจ ๋นจ๋ผ์ง€๊ณ  +์žˆ๋‹ค. [A]","{'question': 'ํ•™์ƒ์˜ ์ดˆ๊ณ โ€™์— ํ™œ์šฉ๋œ ๊ธ€์“ฐ๊ธฐ ๋ฐฉ์‹์œผ๋กœ ๊ฐ€์žฅ ์ ์ ˆํ•œ ๊ฒƒ์€?', 'choices': ['์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ ๋ฅผ ๊ธฐ์กด ํ•ญ๊ณต์œ ์™€ ๋Œ€๋น„ํ•˜์—ฌ ์„œ์ˆ ํ•˜์˜€๋‹ค.', '์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ ์˜ ์ƒ์‚ฐ ๊ณผ์ •์„ ๋‹จ๊ณ„์ ์œผ๋กœ ์„œ์ˆ ํ•˜์˜€๋‹ค.', '์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ ์˜ ์žฅ๋‹จ์ ์„ ๋ฌป๊ณ  ๋‹ตํ•˜๋Š” ๋ฐฉ์‹์œผ๋กœ ์„œ์ˆ \nํ•˜์˜€๋‹ค.', '์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ ์˜ ๋„์ž… ๊ณผ์ •์—์„œ ์˜ˆ์ƒ๋˜๋Š” ๋ฌธ์ œ์ ์„ ์‹œ๊ธฐ๋ณ„๋กœ\n์„œ์ˆ ํ•˜์˜€๋‹ค.', '์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ ๋ฅผ ์‚ฌ์šฉํ•  ๋•Œ์˜ ๊ฒฝ์ œ์  ํšจ๊ณผ๋ฅผ ๊ตญ๊ฐ€๋ณ„๋กœ\n๋ถ„์„ํ•˜์—ฌ ์„œ์ˆ ํ•˜์˜€๋‹ค.'], 'answer': ''}",,1,1,True,[],1 +2025-korean-44,"๋‹ค์Œ์€ ์ž‘๋ฌธ ์ƒํ™ฉ๊ณผ ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ํ•™์ƒ์ด ์ž‘์„ฑํ•œ ์ดˆ๊ณ ์˜ +์ผ๋ถ€์ด๋‹ค. ๋ฌผ์Œ์— ๋‹ตํ•˜์‹œ์˜ค. +[์ž‘๋ฌธ ์ƒํ™ฉ] +๊ธ€์„ ์“ฐ๋ ค ํ•จ. +ํ™˜๊ฒฝ์˜ ๋‚ ์„ ๋งž์•„ ๊ต์ง€์— ์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ ์— ๋Œ€ํ•ด ์•Œ๋ฆฌ๋Š” +[ํ•™์ƒ์˜ ์ดˆ๊ณ ] +2017๋…„ ์ฆˆ์Œ์— ์Šค์›จ๋ด์—์„œ โ€˜ํ”Œ๋คผ๊ทธ์Šค์บ„(Flygskam)โ€™ ์šด๋™์ด +์ผ์–ด๋‚ฌ๋‹ค. ์ด๋Š” โ€˜๋น„ํ–‰๊ธฐ ํƒ€๋Š” ๊ฒƒ์„ ๋ถ€๋„๋Ÿฝ๊ฒŒ ์—ฌ๊ธด๋‹ค.โ€™๋ผ๋Š” ์˜๋ฏธ๋กœ, +์‹œ๊ฐ„์ด ๋” ๊ฑธ๋ฆฌ๋”๋ผ๋„ ํ™˜๊ฒฝ์„ ์ƒ๊ฐํ•˜์—ฌ ๋น„ํ–‰๊ธฐ ๋Œ€์‹  ๊ธฐ์ฐจ๋ฅผ +ํƒ€์ž๋Š” ๊ฒƒ์ด๋‹ค. ๊ทธ๋Ÿฐ๋ฐ ๋น„ํ–‰๊ธฐ๋ฅผ ํƒ€์•ผ ํ•  ๋•Œ ํ™˜๊ฒฝ์„ ์œ„ํ•œ ์„ ํƒ์€ +์—†์„๊นŒ? ์ตœ๊ทผ ๊ธฐ์กด ํ•ญ๊ณต์œ ์˜ ๋Œ€์•ˆ์œผ๋กœ ์ฃผ๋ชฉ๋ฐ›๋Š” ์นœํ™˜๊ฒฝ ์—ฐ๋ฃŒ์ธ +์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ ์— ๋Œ€ํ•ด ์•Œ์•„๋ณด์ž. +์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ ๋ž€ ๊ธฐ์กด ํ•ญ๊ณต์œ ์™€ ํ™”ํ•™์  ๊ตฌ์กฐ๊ฐ€ ์œ ์‚ฌํ•˜๋ฉด์„œ๋„ +ํ™”์„ ์—ฐ๋ฃŒ๊ฐ€ ์•„๋‹Œ ํ๊ธฐ๋ฌผ์ด๋‚˜ ์ž‘๋ฌผ ๋“ฑ์„ ์›๋ฃŒ๋กœ ์ƒ์‚ฐ๋œ ์—ฐ๋ฃŒ์ด๋‹ค. +ํ๊ธฐ๋ฌผ๋กœ๋Š” ํ์‹์šฉ์œ , ํ๋ชฉ์žฌ ๋“ฑ์ด ์‚ฌ์šฉ๋˜๊ณ , ์ž‘๋ฌผ๋กœ๋Š” ๊ธฐ๋ฆ„์•ผ์ž๋‚˜ +์˜ฅ์ˆ˜์ˆ˜ ๋“ฑ์ด ์“ฐ์ด๋Š”๋ฐ, ์œ ๋Ÿฝ์—ฐํ•ฉ์€ ์ž‘๋ฌผ ๊ธฐ๋ฐ˜ ๋ฐ”์ด์˜ค ์—ฐ๋ฃŒ์˜ ์‚ฌ์šฉ์€ +๊ทœ์ œํ•˜๊ณ  ์žˆ๋‹ค. +์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ ๋Š” ๋ณ„๋„๋กœ ๋น„ํ–‰๊ธฐ๋ฅผ ๊ฐœ์กฐํ•  ํ•„์š” ์—†์ด ๊ธฐ์กด +ํ•ญ๊ณต์œ ์™€ ํ˜ผํ•ฉํ•˜์—ฌ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ์–ด ํšจ์œจ์ ์ด๋‹ค. ๊ทธ๋ฆฌ๊ณ  ํƒ„์†Œ +๋ฐฐ์ถœ๋Ÿ‰์„ ์ค„์ผ ์ˆ˜ ์žˆ์–ด ์นœํ™˜๊ฒฝ์ ์ด๋‹ค. ์›๋ฃŒ์˜ ์ƒ์‚ฐ๋ถ€ํ„ฐ ์—ฐ๋ฃŒ +์†Œ๋น„๊นŒ์ง€์˜ ์ „ ๊ณผ์ •์—์„œ ๋ฐœ์ƒํ•˜๋Š” ํƒ„์†Œ ๋ฐฐ์ถœ๋Ÿ‰์€, ์šด์†ก ์ˆ˜๋‹จ +์ค‘ ๋น„ํ–‰๊ธฐ๊ฐ€ ๊ฐ€์žฅ ๋งŽ์€๋ฐ, ์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ ๋Š” ํ™”์„ ์—ฐ๋ฃŒ๋กœ ๋งŒ๋“  +๊ธฐ์กด ํ•ญ๊ณต์œ ์— ๋น„ํ•ด ํƒ„์†Œ ๋ฐฐ์ถœ๋Ÿ‰์„ ์ตœ๋Œ€ 80%๊ฐ€๋Ÿ‰ ์ค„์ผ ์ˆ˜ ์žˆ๋‹ค. +์ด๋Ÿฌํ•œ ์žฅ์  ๋•Œ๋ฌธ์— ์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ  ์‚ฌ์šฉ์„ ์˜๋ฌดํ™”ํ•˜๋Š” +๊ตญ๊ฐ€๊ฐ€ ์ ์  ๋Š˜๊ณ  ์žˆ๋‹ค. ์œ ๋Ÿฝ์—ฐํ•ฉ์€ 2025๋…„๋ถ€ํ„ฐ ์ง€์† ๊ฐ€๋Šฅ +ํ•ญ๊ณต์œ ๋ฅผ ์ตœ์†Œ 2% ์ด์ƒ ์„ž๋„๋ก ์˜๋ฌดํ™”ํ•˜๊ณ , ํ˜ผํ•ฉ ๋น„์œจ์„ ์ ์ฐจ +๋†’์—ฌ 2050๋…„์—๋Š” 70%๊นŒ์ง€ ๋†’์ผ ์˜ˆ์ •์ด๋‹ค. ์šฐ๋ฆฌ๋‚˜๋ผ๋„ 2027๋…„์— +1% ๋‚ด์™ธ๋กœ ํ˜ผํ•ฉํ•˜๋„๋ก ์˜๋ฌดํ™”ํ•˜๋Š” ์ œ๋„๋ฅผ ์‹œํ–‰ํ•  ์˜ˆ์ •์ด๋‹ค. +์šฐ๋ฆฌ๋‚˜๋ผ๋Š” ์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ  ์‚ฌ์šฉ์˜ ์˜๋ฌดํ™”๋ฅผ ์•ž๋‘๊ณ , ์ง€์† +๊ฐ€๋Šฅ ํ•ญ๊ณต์œ ์˜ ๋ถ€์กฑ ์ƒํ™ฉ์— ๋Œ€๋น„ํ•˜๊ณ  ์žˆ๋‹ค. ์ •๋ถ€๋Š” ๊ธฐ์—…์„ +์ง€์›ํ•˜์—ฌ ๋‹ค์–‘ํ•œ ์›๋ฃŒ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ ์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ ์˜ ์ƒ์‚ฐ +๊ธฐ์ˆ ์„ ๊ณ ๋„ํ™”ํ•˜๊ณ  ์„์œ  ์‚ฌ์—…๋ฒ• ๊ฐœ์ •์— ๋”ฐ๋ฅธ ๊ด€๋ จ ์ œ๋„๋ฅผ +์ •๋น„ํ•จ์œผ๋กœ์จ, ์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ ์˜ ๊ณต๊ธ‰ ์—ญ๋Ÿ‰์„ ๊ฐ•ํ™”ํ•˜๊ณ  ์žˆ๋‹ค. +ํ•ญ๊ณต ๋ถ€๋ฌธ์—์„œ ํ™˜๊ฒฝ์„ ์œ„ํ•œ ์ •๋ถ€์˜ ๋ฐœ๊ฑธ์Œ์ด ์ ์ฐจ ๋นจ๋ผ์ง€๊ณ  +์žˆ๋‹ค. [A]","{'question': '๋‹ค์Œ์€ ํ•™์ƒ์ด ์ดˆ๊ณ ๋ฅผ ์ž‘์„ฑํ•˜๋ฉฐ ๋– ์˜ฌ๋ฆฐ ์ƒ๊ฐ์ด๋‹ค. ์ด๋ฅผ ๊ณ ๋ คํ• \n๋•Œ [A]์— ๋“ค์–ด๊ฐˆ ๋‚ด์šฉ์œผ๋กœ ๊ฐ€์žฅ ์ ์ ˆํ•œ ๊ฒƒ์€?', 'choices': ['์•ž์œผ๋กœ ํ•ญ๊ณตํŽธ์„ ์„ ํƒํ•  ๋•Œ๋Š” ๋น„์šฉ๋ณด๋‹ค๋Š” ํ™˜๊ฒฝ์„ ๊ณ ๋ คํ•ด ๋ณด๋ฉด\n์–ด๋–จ๊นŒ? ์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ ๋ฅผ ์‚ฌ์šฉํ•œ ๋น„ํ–‰๊ธฐ๋ฅผ ์„ ํƒํ•œ๋‹ค๋ฉด ์ง€๊ตฌ\n์˜จ๋‚œํ™”๋ฅผ ๋Šฆ์ถœ ์ˆ˜ ์žˆ๋‹ค.', '์ด์ œ๋Š” ์ผ์ƒ์ƒํ™œ์—์„œ๋„ ํƒ„์†Œ ๋ฐฐ์ถœ๋Ÿ‰์„ ์ค„์ด๊ธฐ ์œ„ํ•œ ๋…ธ๋ ฅ์ด\nํ•„์š”ํ•œ ์‹œ์ ์ด๋‹ค. ๋น„ํ–‰๊ธฐ๋กœ ์—ฌํ–‰ํ•  ๋•Œ ์ˆ˜ํ•˜๋ฌผ์˜ ๋ฌด๊ฒŒ๋ฅผ ์ค„์—ฌ\nํ™˜๊ฒฝ์„ ์œ„ํ•œ ๋ฐœ๊ฑธ์Œ์— ๋™์ฐธํ•˜๋ฉด ์–ด๋–จ๊นŒ?', '์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ ์˜ ํ˜ผํ•ฉ ๋น„์œจ์„ ๋” ๋†’์ผ์ˆ˜๋ก ํƒ„์†Œ ๋ฐฐ์ถœ๋Ÿ‰์„\n๋” ๋งŽ์ด ๊ฐ์ถ•ํ•  ์ˆ˜ ์žˆ๋‹ค. ํ™˜๊ฒฝ์„ ์œ„ํ•ด ์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ ์˜\nํ˜ผํ•ฉ ๋น„์œจ์„ ์ ์ฐจ ๋†’์—ฌ ๊ฐ€๋Š” ๊ฒƒ์€ ์–ด๋–จ๊นŒ?', '๋งŽ์€ ๊ตญ๊ฐ€๋“ค์ด ์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ ์˜ ์‚ฌ์šฉ์— ๋™์ฐธํ•œ๋‹ค๋ฉด\nํ™”์„ ์—ฐ๋ฃŒ ์‚ฌ์šฉ๋Ÿ‰์„ ์ค„์ผ ์ˆ˜ ์žˆ๋‹ค. ํ™”์„ ์—ฐ๋ฃŒ ์‚ฌ์šฉ๋Ÿ‰์ด ์ค„์–ด\n๋“ค ๋•Œ ์ง€๊ตฌ๋Š” ๋” ๊ฑด๊ฐ•ํ•ด์งˆ ์ˆ˜ ์žˆ์ง€ ์•Š์„๊นŒ?', '์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ ์˜ ์‚ฌ์šฉ์„ ํ™•๋Œ€ํ•˜๋ฉด ํƒ„์†Œ ๋ฐฐ์ถœ๋Ÿ‰์„ ์ค„์—ฌ ๊ธฐํ›„\n์œ„๊ธฐ์— ๋Œ€์‘ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋น„ํ–‰๊ธฐ๋ฅผ ํƒ€์•ผ ํ•œ๋‹ค๋ฉด, ๋˜๋„๋ก ํƒ„์†Œ\n๋ฐฐ์ถœ๋Ÿ‰์ด ๋” ์ ์€ ํ•ญ๊ณตํŽธ์„ ์ด์šฉํ•˜๋ฉด ์–ด๋–จ๊นŒ?'], 'answer': '', 'question_plus': '๊ธ€์„ ๋งˆ๋ฌด๋ฆฌํ•  ๋•Œ๋Š” ์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ  ์‚ฌ์šฉ์˜ ์˜์˜๋ฅผ ์ œ์‹œํ•œ\nํ›„, ํ™˜๊ฒฝ๊ณผ ๊ด€๋ จํ•˜์—ฌ ํ•™์ƒ๋“ค์˜ ์‹ค์ฒœ์„ ์ œ์•ˆํ•˜๋Š” ๋‚ด์šฉ์„ ์จ์•ผ๊ฒ ์–ด.'}","๊ธ€์„ ๋งˆ๋ฌด๋ฆฌํ•  ๋•Œ๋Š” ์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ  ์‚ฌ์šฉ์˜ ์˜์˜๋ฅผ ์ œ์‹œํ•œ +ํ›„, ํ™˜๊ฒฝ๊ณผ ๊ด€๋ จํ•˜์—ฌ ํ•™์ƒ๋“ค์˜ ์‹ค์ฒœ์„ ์ œ์•ˆํ•˜๋Š” ๋‚ด์šฉ์„ ์จ์•ผ๊ฒ ์–ด.",5,5,True,[],5 +2025-korean-45,"๋‹ค์Œ์€ ์ž‘๋ฌธ ์ƒํ™ฉ๊ณผ ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ํ•™์ƒ์ด ์ž‘์„ฑํ•œ ์ดˆ๊ณ ์˜ +์ผ๋ถ€์ด๋‹ค. ๋ฌผ์Œ์— ๋‹ตํ•˜์‹œ์˜ค. +[์ž‘๋ฌธ ์ƒํ™ฉ] +๊ธ€์„ ์“ฐ๋ ค ํ•จ. +ํ™˜๊ฒฝ์˜ ๋‚ ์„ ๋งž์•„ ๊ต์ง€์— ์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ ์— ๋Œ€ํ•ด ์•Œ๋ฆฌ๋Š” +[ํ•™์ƒ์˜ ์ดˆ๊ณ ] +2017๋…„ ์ฆˆ์Œ์— ์Šค์›จ๋ด์—์„œ โ€˜ํ”Œ๋คผ๊ทธ์Šค์บ„(Flygskam)โ€™ ์šด๋™์ด +์ผ์–ด๋‚ฌ๋‹ค. ์ด๋Š” โ€˜๋น„ํ–‰๊ธฐ ํƒ€๋Š” ๊ฒƒ์„ ๋ถ€๋„๋Ÿฝ๊ฒŒ ์—ฌ๊ธด๋‹ค.โ€™๋ผ๋Š” ์˜๋ฏธ๋กœ, +์‹œ๊ฐ„์ด ๋” ๊ฑธ๋ฆฌ๋”๋ผ๋„ ํ™˜๊ฒฝ์„ ์ƒ๊ฐํ•˜์—ฌ ๋น„ํ–‰๊ธฐ ๋Œ€์‹  ๊ธฐ์ฐจ๋ฅผ +ํƒ€์ž๋Š” ๊ฒƒ์ด๋‹ค. ๊ทธ๋Ÿฐ๋ฐ ๋น„ํ–‰๊ธฐ๋ฅผ ํƒ€์•ผ ํ•  ๋•Œ ํ™˜๊ฒฝ์„ ์œ„ํ•œ ์„ ํƒ์€ +์—†์„๊นŒ? ์ตœ๊ทผ ๊ธฐ์กด ํ•ญ๊ณต์œ ์˜ ๋Œ€์•ˆ์œผ๋กœ ์ฃผ๋ชฉ๋ฐ›๋Š” ์นœํ™˜๊ฒฝ ์—ฐ๋ฃŒ์ธ +์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ ์— ๋Œ€ํ•ด ์•Œ์•„๋ณด์ž. +์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ ๋ž€ ๊ธฐ์กด ํ•ญ๊ณต์œ ์™€ ํ™”ํ•™์  ๊ตฌ์กฐ๊ฐ€ ์œ ์‚ฌํ•˜๋ฉด์„œ๋„ +ํ™”์„ ์—ฐ๋ฃŒ๊ฐ€ ์•„๋‹Œ ํ๊ธฐ๋ฌผ์ด๋‚˜ ์ž‘๋ฌผ ๋“ฑ์„ ์›๋ฃŒ๋กœ ์ƒ์‚ฐ๋œ ์—ฐ๋ฃŒ์ด๋‹ค. +ํ๊ธฐ๋ฌผ๋กœ๋Š” ํ์‹์šฉ์œ , ํ๋ชฉ์žฌ ๋“ฑ์ด ์‚ฌ์šฉ๋˜๊ณ , ์ž‘๋ฌผ๋กœ๋Š” ๊ธฐ๋ฆ„์•ผ์ž๋‚˜ +์˜ฅ์ˆ˜์ˆ˜ ๋“ฑ์ด ์“ฐ์ด๋Š”๋ฐ, ์œ ๋Ÿฝ์—ฐํ•ฉ์€ ์ž‘๋ฌผ ๊ธฐ๋ฐ˜ ๋ฐ”์ด์˜ค ์—ฐ๋ฃŒ์˜ ์‚ฌ์šฉ์€ +๊ทœ์ œํ•˜๊ณ  ์žˆ๋‹ค. +์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ ๋Š” ๋ณ„๋„๋กœ ๋น„ํ–‰๊ธฐ๋ฅผ ๊ฐœ์กฐํ•  ํ•„์š” ์—†์ด ๊ธฐ์กด +ํ•ญ๊ณต์œ ์™€ ํ˜ผํ•ฉํ•˜์—ฌ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ์–ด ํšจ์œจ์ ์ด๋‹ค. ๊ทธ๋ฆฌ๊ณ  ํƒ„์†Œ +๋ฐฐ์ถœ๋Ÿ‰์„ ์ค„์ผ ์ˆ˜ ์žˆ์–ด ์นœํ™˜๊ฒฝ์ ์ด๋‹ค. ์›๋ฃŒ์˜ ์ƒ์‚ฐ๋ถ€ํ„ฐ ์—ฐ๋ฃŒ +์†Œ๋น„๊นŒ์ง€์˜ ์ „ ๊ณผ์ •์—์„œ ๋ฐœ์ƒํ•˜๋Š” ํƒ„์†Œ ๋ฐฐ์ถœ๋Ÿ‰์€, ์šด์†ก ์ˆ˜๋‹จ +์ค‘ ๋น„ํ–‰๊ธฐ๊ฐ€ ๊ฐ€์žฅ ๋งŽ์€๋ฐ, ์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ ๋Š” ํ™”์„ ์—ฐ๋ฃŒ๋กœ ๋งŒ๋“  +๊ธฐ์กด ํ•ญ๊ณต์œ ์— ๋น„ํ•ด ํƒ„์†Œ ๋ฐฐ์ถœ๋Ÿ‰์„ ์ตœ๋Œ€ 80%๊ฐ€๋Ÿ‰ ์ค„์ผ ์ˆ˜ ์žˆ๋‹ค. +์ด๋Ÿฌํ•œ ์žฅ์  ๋•Œ๋ฌธ์— ์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ  ์‚ฌ์šฉ์„ ์˜๋ฌดํ™”ํ•˜๋Š” +๊ตญ๊ฐ€๊ฐ€ ์ ์  ๋Š˜๊ณ  ์žˆ๋‹ค. ์œ ๋Ÿฝ์—ฐํ•ฉ์€ 2025๋…„๋ถ€ํ„ฐ ์ง€์† ๊ฐ€๋Šฅ +ํ•ญ๊ณต์œ ๋ฅผ ์ตœ์†Œ 2% ์ด์ƒ ์„ž๋„๋ก ์˜๋ฌดํ™”ํ•˜๊ณ , ํ˜ผํ•ฉ ๋น„์œจ์„ ์ ์ฐจ +๋†’์—ฌ 2050๋…„์—๋Š” 70%๊นŒ์ง€ ๋†’์ผ ์˜ˆ์ •์ด๋‹ค. ์šฐ๋ฆฌ๋‚˜๋ผ๋„ 2027๋…„์— +1% ๋‚ด์™ธ๋กœ ํ˜ผํ•ฉํ•˜๋„๋ก ์˜๋ฌดํ™”ํ•˜๋Š” ์ œ๋„๋ฅผ ์‹œํ–‰ํ•  ์˜ˆ์ •์ด๋‹ค. +์šฐ๋ฆฌ๋‚˜๋ผ๋Š” ์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ  ์‚ฌ์šฉ์˜ ์˜๋ฌดํ™”๋ฅผ ์•ž๋‘๊ณ , ์ง€์† +๊ฐ€๋Šฅ ํ•ญ๊ณต์œ ์˜ ๋ถ€์กฑ ์ƒํ™ฉ์— ๋Œ€๋น„ํ•˜๊ณ  ์žˆ๋‹ค. ์ •๋ถ€๋Š” ๊ธฐ์—…์„ +์ง€์›ํ•˜์—ฌ ๋‹ค์–‘ํ•œ ์›๋ฃŒ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ ์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ ์˜ ์ƒ์‚ฐ +๊ธฐ์ˆ ์„ ๊ณ ๋„ํ™”ํ•˜๊ณ  ์„์œ  ์‚ฌ์—…๋ฒ• ๊ฐœ์ •์— ๋”ฐ๋ฅธ ๊ด€๋ จ ์ œ๋„๋ฅผ +์ •๋น„ํ•จ์œผ๋กœ์จ, ์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ ์˜ ๊ณต๊ธ‰ ์—ญ๋Ÿ‰์„ ๊ฐ•ํ™”ํ•˜๊ณ  ์žˆ๋‹ค. +ํ•ญ๊ณต ๋ถ€๋ฌธ์—์„œ ํ™˜๊ฒฝ์„ ์œ„ํ•œ ์ •๋ถ€์˜ ๋ฐœ๊ฑธ์Œ์ด ์ ์ฐจ ๋นจ๋ผ์ง€๊ณ  +์žˆ๋‹ค. [A]","{'question': '<๋ณด๊ธฐ>๋Š” ํ•™์ƒ์ด ์ดˆ๊ณ ๋ฅผ ๋ณด์™„ํ•˜๊ธฐ ์œ„ํ•ด ์ถ”๊ฐ€๋กœ ์ˆ˜์ง‘ํ•œ ์ž๋ฃŒ์ด๋‹ค.\n์ž๋ฃŒ ํ™œ์šฉ ๋ฐฉ์•ˆ์œผ๋กœ ์ ์ ˆํ•˜์ง€ ์•Š์€ ๊ฒƒ์€? ', 'choices': ['ใ„ฑ-1์„ ํ™œ์šฉํ•˜์—ฌ, ๋น„ํ–‰๊ธฐ๊ฐ€ ํƒ„์†Œ ๋ฐฐ์ถœ๋Ÿ‰์ด ๊ฐ€์žฅ ๋งŽ๊ณ  ๊ธฐ์ฐจ๊ฐ€\n๊ฐ€์žฅ ์ ๋‹ค๋Š” ๋‚ด์šฉ์œผ๋กœ, ์‹œ๊ฐ„ ์†์‹ค์„ ๊ฐ์ˆ˜ํ•˜๊ณ  ๋น„ํ–‰๊ธฐ ๋Œ€์‹  ๊ธฐ์ฐจ๋ฅผ\nํƒ€์ž๋Š” ์šด๋™์ด ์ผ์–ด๋‚˜๊ฒŒ ๋œ ๋ฐฐ๊ฒฝ์„ 1๋ฌธ๋‹จ์— ๋ณด๊ฐ•ํ•œ๋‹ค.', 'ใ„ฑ-2๋ฅผ ํ™œ์šฉํ•˜์—ฌ, ์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ ๊ฐ€ 2050๋…„์—๋Š” 2025๋…„๋ณด๋‹ค\n50๋ฐฐ ์ด์ƒ ํ•„์š”ํ•˜๋‹ค๋Š” ๋‚ด์šฉ์„, ์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ ์˜ ํ˜ผํ•ฉ ๋น„์œจ์„\n2050๋…„์— 70%๊นŒ์ง€ ๋†’์ด๋Š” ๊ทผ๊ฑฐ๋กœ 4๋ฌธ๋‹จ์— ์ถ”๊ฐ€ํ•œ๋‹ค.', 'ใ„ท์„ ํ™œ์šฉํ•˜์—ฌ, ์ž‘๋ฌผ ์›๋ฃŒ์˜ ์‚ฌ์šฉ์ด ์‚ผ๋ฆผ๊ณผ ์‹๋Ÿ‰ ๊ณต๊ธ‰์—\n๋ถ€์ •์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์นœ๋‹ค๋Š” ๋‚ด์šฉ์„, ์œ ๋Ÿฝ์—ฐํ•ฉ์—์„œ ์ž‘๋ฌผ ๊ธฐ๋ฐ˜\n๋ฐ”์ด์˜ค ์—ฐ๋ฃŒ์˜ ์‚ฌ์šฉ์„ ์ œํ•œํ•˜๊ฒŒ ๋œ ์ด์œ ๋กœ 2๋ฌธ๋‹จ์— ์ถ”๊ฐ€ํ•œ๋‹ค.', 'ใ„ฑ-1๊ณผ ใ„ด์„ ํ™œ์šฉํ•˜์—ฌ, ๋‹ค๋ฅธ ์šด์†ก ์ˆ˜๋‹จ ๋Œ€๋น„ ํƒ„์†Œ ๋ฐฐ์ถœ๋Ÿ‰์ด\n๋งŽ์€ ๋น„ํ–‰๊ธฐ์— ์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ ๋ฅผ ์‚ฌ์šฉํ•˜๋ฉด ํƒ„์†Œ ๋ฐฐ์ถœ๋Ÿ‰ ๊ฐ์ถ•์—\nํšจ๊ณผ์ ์ด๋ผ๋Š” ๋‚ด์šฉ์œผ๋กœ, ์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ ์˜ ์นœํ™˜๊ฒฝ์  ํŠน์ง•์„\n๋ณด์—ฌ ์ฃผ๋Š” ๊ทผ๊ฑฐ๋ฅผ 3๋ฌธ๋‹จ์— ๋ณด๊ฐ•ํ•œ๋‹ค.', 'ใ„ฑ-2์™€ ใ„ท์„ ํ™œ์šฉํ•˜์—ฌ, ์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ ์˜ ์˜ˆ์ƒ ์ˆ˜์š”๊ฐ€ ์ง€์†์ \n์œผ๋กœ ์ฆ๊ฐ€ํ•˜์ง€๋งŒ ๊ณต๊ธ‰์— ์ œ์•ฝ์ด ์žˆ๋‹ค๋Š” ๋‚ด์šฉ์œผ๋กœ, ์ •๋ถ€๊ฐ€ ๊ธฐ์—…์„\n์ง€์›ํ•˜์—ฌ ์ƒ์‚ฐ ๊ธฐ์ˆ ์˜ ๊ณ ๋„ํ™”๋ฅผ ํ†ตํ•ด ์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ ์˜ ๊ณต๊ธ‰\n์—ญ๋Ÿ‰์„ ๊ฐ•ํ™”ํ•˜๋ ค๋Š” ์ด์œ ๋ฅผ 5๋ฌธ๋‹จ์— ๋ณด๊ฐ•ํ•œ๋‹ค.'], 'answer': '', 'question_plus': 'ใ„ฑ.ํ†ต๊ณ„ ์ž๋ฃŒ\nใ„ฑ-1. ์šด์†ก์ˆ˜๋‹จ๋ณ„ ํƒ„์†Œ๋ฐฐ์ถœ๋Ÿ‰\n์šด์†ก์ˆ˜๋‹จ - ํƒ„์†Œ ๋ฐฐ์ถœ๋Ÿ‰ : (๊ธฐ์ฐจ - 14, ๋ฒ„์Šค - 68, ๋น„ํ–‰๊ธฐ - 285, ์†Œํ˜• ์Šน์šฉ์ฐจ - 104) (๋‹จ์œ„ g์ด๊ณ  1km๋‹น ์Šน๊ฐ 1๋ช… ์ด๋™ ๊ธฐ์ค€, ์ž๋ฃŒ์ถœ์ฒ˜ ์œ ๋Ÿฝํ™˜๊ฒฝ์ฒญ 2014๋…„)\nใ„ฑ-2. ์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ  ํ•„์š”๋Ÿ‰ ์ „๋ง\n์—ฐ๋„ - L(๋‹จ์œ„ : ์–ต L) : {2025 - 80, 2030 - 230, 2035 - 900, 2040 - 2290, 2045 - 3460, 2050 - 4490}\n2050๋…„ - 2025๋…„ ๊ธฐ์ค€ 56.125๋ฐฐ\nใ„ด. ์‹ ๋ฌธ ๊ธฐ์‚ฌ\n2023๋…„ ์šฐ๋ฆฌ๋‚˜๋ผ ๊ตญ์ œ์„  ํ•ญ๊ณต๊ธฐ์˜ ํƒ„์†Œ ๋ฐฐ์ถœ๋Ÿ‰์ธ ์•ฝ 2์ฒœ๋งŒ ํ†ค์„\n๊ธฐ์ค€์œผ๋กœ ์‚ฐ์ •ํ•˜๋ฉด, ์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ ๋ฅผ 1% ํ˜ผํ•ฉํ•  ๊ฒฝ์šฐ ์•ฝ 16๋งŒ ํ†ค\n์ •๋„์˜ ํƒ„์†Œ ๋ฐฐ์ถœ ๊ฐ์ถ• ํšจ๊ณผ๊ฐ€ ์žˆ๋‹ค. 16๋งŒ ํ†ค์€ ์Šน์šฉ์ฐจ ์•ฝ 5๋งŒ\n3์ฒœ ๋Œ€์˜ 1๋…„๊ฐ„ ํƒ„์†Œ ๋ฐฐ์ถœ๋Ÿ‰์— ํ•ด๋‹นํ•œ๋‹ค.\nใ„ท.์ „๋ฌธ๊ฐ€ ์ธํ„ฐ๋ทฐ\nโ€œ์ž‘๋ฌผ์„ ์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ ์˜ ์›๋ฃŒ๋กœ ์‚ฌ์šฉํ•˜๋ฉด ์ž‘๋ฌผ ์žฌ๋ฐฐ๋กœ ์ธํ•œ\n์‚ผ๋ฆผ ํ›ผ์†๊ณผ ์‹๋Ÿ‰ ๋ถ€์กฑ ๋“ฑ์ด ์šฐ๋ ค๋ฉ๋‹ˆ๋‹ค. ํ๊ธฐ๋ฌผ์€ ์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋Š”\n์—†์ง€๋งŒ ์–‘์ด ํ•œ์ •๋˜์–ด ์žˆ์Šต๋‹ˆ๋‹ค. ๋ฏธ์„ธ ์กฐ๋ฅ˜, ์ด์‚ฐํ™” ํƒ„์†Œ ๋“ฑ์ด\n์›๋ฃŒ์˜ ๋Œ€์•ˆ์œผ๋กœ ๋– ์˜ค๋ฅด๊ณ  ์žˆ์œผ๋‚˜ ๊ตญ๋‚ด ์ƒ์šฉํ™”๋ฅผ ์œ„ํ•œ ๊ธฐ์ˆ  ๊ฐœ๋ฐœ์ด\n๋” ํ•„์š”ํ•œ ์‹ค์ •์ž…๋‹ˆ๋‹ค.โ€'}","ใ„ฑ.ํ†ต๊ณ„ ์ž๋ฃŒ +ใ„ฑ-1. ์šด์†ก์ˆ˜๋‹จ๋ณ„ ํƒ„์†Œ๋ฐฐ์ถœ๋Ÿ‰ +์šด์†ก์ˆ˜๋‹จ - ํƒ„์†Œ ๋ฐฐ์ถœ๋Ÿ‰ : (๊ธฐ์ฐจ - 14, ๋ฒ„์Šค - 68, ๋น„ํ–‰๊ธฐ - 285, ์†Œํ˜• ์Šน์šฉ์ฐจ - 104) (๋‹จ์œ„ g์ด๊ณ  1km๋‹น ์Šน๊ฐ 1๋ช… ์ด๋™ ๊ธฐ์ค€, ์ž๋ฃŒ์ถœ์ฒ˜ ์œ ๋Ÿฝํ™˜๊ฒฝ์ฒญ 2014๋…„) +ใ„ฑ-2. ์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ  ํ•„์š”๋Ÿ‰ ์ „๋ง +์—ฐ๋„ - L(๋‹จ์œ„ : ์–ต L) : (2025 - 80, 2030 - 230, 2035 - 900, 2040 - 2290, 2045 - 3460, 2050 - 4490) +2050๋…„ - 2025๋…„ ๊ธฐ์ค€ 56.125๋ฐฐ +ใ„ด. ์‹ ๋ฌธ ๊ธฐ์‚ฌ +2023๋…„ ์šฐ๋ฆฌ๋‚˜๋ผ ๊ตญ์ œ์„  ํ•ญ๊ณต๊ธฐ์˜ ํƒ„์†Œ ๋ฐฐ์ถœ๋Ÿ‰์ธ ์•ฝ 2์ฒœ๋งŒ ํ†ค์„ +๊ธฐ์ค€์œผ๋กœ ์‚ฐ์ •ํ•˜๋ฉด, ์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ ๋ฅผ 1% ํ˜ผํ•ฉํ•  ๊ฒฝ์šฐ ์•ฝ 16๋งŒ ํ†ค +์ •๋„์˜ ํƒ„์†Œ ๋ฐฐ์ถœ ๊ฐ์ถ• ํšจ๊ณผ๊ฐ€ ์žˆ๋‹ค. 16๋งŒ ํ†ค์€ ์Šน์šฉ์ฐจ ์•ฝ 5๋งŒ +3์ฒœ ๋Œ€์˜ 1๋…„๊ฐ„ ํƒ„์†Œ ๋ฐฐ์ถœ๋Ÿ‰์— ํ•ด๋‹นํ•œ๋‹ค. +ใ„ท.์ „๋ฌธ๊ฐ€ ์ธํ„ฐ๋ทฐ +โ€œ์ž‘๋ฌผ์„ ์ง€์† ๊ฐ€๋Šฅ ํ•ญ๊ณต์œ ์˜ ์›๋ฃŒ๋กœ ์‚ฌ์šฉํ•˜๋ฉด ์ž‘๋ฌผ ์žฌ๋ฐฐ๋กœ ์ธํ•œ +์‚ผ๋ฆผ ํ›ผ์†๊ณผ ์‹๋Ÿ‰ ๋ถ€์กฑ ๋“ฑ์ด ์šฐ๋ ค๋ฉ๋‹ˆ๋‹ค. ํ๊ธฐ๋ฌผ์€ ์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋Š” +์—†์ง€๋งŒ ์–‘์ด ํ•œ์ •๋˜์–ด ์žˆ์Šต๋‹ˆ๋‹ค. ๋ฏธ์„ธ ์กฐ๋ฅ˜, ์ด์‚ฐํ™” ํƒ„์†Œ ๋“ฑ์ด +์›๋ฃŒ์˜ ๋Œ€์•ˆ์œผ๋กœ ๋– ์˜ค๋ฅด๊ณ  ์žˆ์œผ๋‚˜ ๊ตญ๋‚ด ์ƒ์šฉํ™”๋ฅผ ์œ„ํ•œ ๊ธฐ์ˆ  ๊ฐœ๋ฐœ์ด +๋” ํ•„์š”ํ•œ ์‹ค์ •์ž…๋‹ˆ๋‹ค.โ€",2,2,True,[],2 +2025-DS-01,"ํ•ญ์šฐ๋ฅผ ๋ฌผ๋ฆฌ์น˜๊ณ  ์ฒœํ•˜๋ฅผ ์ฐจ์ง€ํ•œ (๊ฐ€) ์˜ ํ™ฉ์ œ๋Š” ์ข…์‹ค์˜ +์ž์ œ๋“ค์„ ์™•์œผ๋กœ ๋Œ€๊ฑฐ ๋ด‰ํ•˜์˜€๋‹ค. ๋™์„ฑ(ๅŒๅง“)์œผ๋กœ ์™•์ด ๋œ ์ž๋Š” +9๋ช…์ด๊ณ , ์ด๋“ค์€ ์—ฐโ€ค๋Œ€โ€ค์ œโ€ค์กฐโ€ค์–‘โ€ค์ดˆโ€คํ˜•โ€คํšŒ๋‚จโ€คํšŒ์–‘์˜ +์ œํ›„๊ตญ์„ ๊ฐ๊ฐ ๋‹ค์Šค๋ ธ๋‹ค. ๋˜ํ•œ ๊ณต์‹ ์œผ๋กœ์„œ ์ œํ›„๊ฐ€ ๋œ ์ž๋Š” ๋ฐฑ์—ฌ +๋ช…์ด๋‹ค. ๊ฐ•๋ฆ‰์—์„œ ์„œ์ชฝ์œผ๋กœ ์ด‰๊นŒ์ง€, ๋ถ์ชฝ์œผ๋กœ ์šด์ค‘์—์„œ ๋†์„œ์— +์ด๋ฅด๊ธฐ๊นŒ์ง€ 15๊ฐœ ๊ตฐ์€ ํ™ฉ์ œ๊ฐ€ ์ง์ ‘ ๋‹ค์Šค๋ ธ๋‹ค.","{'question': '(๊ฐ€) ๊ตญ๊ฐ€์— ๋Œ€ํ•œ ํƒ๊ตฌ ํ™œ๋™์œผ๋กœ ๊ฐ€์žฅ ์ ์ ˆํ•œ ๊ฒƒ์€?', 'choices': ['8์กฐ๋ฒ•์˜ ๋‚ด์šฉ๊ณผ ์„ฑ๊ฒฉ์„ ๋ถ„์„ํ•œ๋‹ค.', '์ƒ์•™์ด ์‹ค์‹œํ•œ ๊ฐœํ˜ ์ •์ฑ…์„ ์กฐ์‚ฌํ•œ๋‹ค.', '๋‹ค์ด์นด ๊ฐœ์‹ ์„ ์ฃผ๋„ํ•œ ์„ธ๋ ฅ์„ ์•Œ์•„๋ณธ๋‹ค.', '3์„ฑ 6๋ถ€์ œ๊ฐ€ ์ฃผ๋ณ€ ๊ตญ๊ฐ€์— ๋ผ์นœ ์˜ํ–ฅ์„ ํŒŒ์•…ํ•œ๋‹ค.', '์œ ๊ต๋ฅผ ํ†ต์น˜ ์ด๋…์œผ๋กœ ์ฑ„ํƒํ•˜๋Š” ๊ณผ์ •์„ ์ฐพ์•„๋ณธ๋‹ค.'], 'answer': ''}",,5,5,True,"['๋˜,์–‘์ „ ์‚ฌ์—…๊ณผ ํ˜ธ๊ตฌ ํŒŒ์•…์— ๋…ธ๋ ฅ์„ ๊ธฐ์šธ์˜€์œผ๋ฉฐ, ํ˜ธํŒจ๋ฒ•์„ ์‹ค์‹œํ•˜์˜€๊ณ , ์‚ฌ์›์˜ ํ† ์ง€๋ฅผ ๋ชฐ์ˆ˜ํ•˜๊ณ , ์–ต์šธํ•œ๋…ธ๋น„๋ฅผ ์กฐ์‚ฌํ•˜์—ฌ ํ•ด๋ฐฉ์‹œ์ผฐ๋‹ค. ์•„์šธ๋Ÿฌ์‚ฌ๋ณ‘์„ ์—†์•  ์™•์ด ๊ตฐ์‚ฌ ์ง€ํœ˜๊ถŒ์„ ์žฅ์•…ํ•˜๋ฉด์„œ ์นœ์œ„ ๊ตฐ์‚ฌ๋ฅผ ๋Š˜๋ ธ๋‹ค. ์‹ ์ง„ ์‚ฌ๋Œ€๋ถ€ ์˜ ๋ถ„ํ™” ์˜จ๊ฑด ๊ฐœํ˜ํŒŒ๋Š” ๋น„๋ฆฌ์˜ ํ•ต์‹ฌ ์„ธ๋ ฅ์„ ์ œ๊ฑฐํ•˜๊ณ  ๋Œ€ํ† ์ง€ ์‚ฌ์œ ๋Š” ์ •๋ฆฌํ•˜๋˜, ์™•์กฐ ์งˆ์„œ๋ฅผ ํŒŒ๊ดดํ•˜๊ฑฐ๋‚˜ ์ „๋ฉด์ ์ธ ํ† ์ง€ ๊ฐœํ˜์—๋Š” ๋ฐ˜๋Œ€ํ•˜์˜€๋‹ค. ๋ฐ˜๋ฉด, ๊ธ‰์ง„ ๊ฐœํ˜ํŒŒ๋Š” ์—ญ์„ฑ ํ˜๋ช… ์„ ์ฐฌ์„ฑํ•˜๊ณ , ๊ถŒ์„ธ๊ฐ€์— ์˜ํ•œ ํ† ์ง€ ์‚ฌ์œ ๋ฅผ ์ถ•์†Œ์‹œํ‚ค๋ ค ํ•˜์˜€๋‹ค. ์ •๋„์ „ ์˜ ์ •์น˜ ์‚ฌ์ƒ ์ •๋„์ „ ์€ ํ›Œ๋ฅญํ•œ ์žฌ์ƒ์„ ์„ ํƒํ•˜์—ฌ, ์žฌ์ƒ์—๊ฒŒ ์ •์น˜์˜ ์‹ค๊ถŒ์„ ๋ถ€์—ฌํ•˜์—ฌ ์œ„๋กœ๋Š” ์ž„๊ธˆ์„ ๋ฐ›๋“ค์–ด ์˜ฌ๋ฐ”๋ฅด๊ฒŒ ์ธ๋„ํ•˜๊ณ , ์•„๋ž˜๋กœ๋Š” ๋ฐฑ๊ด€์„ ํ†ต๊ด„ํ•˜๊ณ  ๋งŒ๋ฏผ์„ ๋‹ค์Šค๋ฆฌ๋Š” ์ค‘์ฑ…์„ ๋ถ€์—ฌํ•˜์ž๊ณ  ์ฃผ์žฅํ•˜์˜€๋‹ค. 6์กฐ ์ง๊ณ„์ œ 6์กฐ ์—์„œ ์˜์ •๋ถ€ ๋ฅผ ๊ฑฐ์น˜์ง€ ์•Š๊ณ  ๊ณง๋ฐ”๋กœ ์‚ฌ์•ˆ์„ ๊ตญ์™•์—๊ฒŒ ์˜ฌ๋ ค ์žฌ๊ฐ€๋ฅผ ๋ฐ›์•„ ์‹œํ–‰ํ•˜๋Š” ์ œ๋„']",5 +2025-DS-03,"์‹ (่‡ฃ)์€ ์น™๋ช…์„ ๋ฐ›์€ ํ›„ ๋ณ€๊ฒฝ(ๆฑดไบฌ)์„ ๋– ๋‚˜ ๋ฐฑ๊ตฌ๊ต๋ฅผ ๊ฑด๋„ˆ +๋ถ์กฐ์˜ ๋•…์— ์ด๋ฅด๋ €์Šต๋‹ˆ๋‹ค. ๊ทธ๋Ÿฌ์ž ์ƒ๊ฒฝ ์ž„ํ™ฉ๋ถ€๋กœ๋ถ€ํ„ฐ ํŒŒ๊ฒฌ๋œ +์ ‘๋ฐ˜์‚ฌ์™€ ์ ‘๋ฐ˜๋ถ€์‚ฌ๊ฐ€ ๋ง์„ ์„ธ์šฐ๊ณ  ๊ธฐ๋‹ค๋ฆฌ๊ณ  ์žˆ์—ˆ์Šต๋‹ˆ๋‹ค. +์ ‘๋ฐ˜๋ถ€์‚ฌ๋Š” ๋‚จ์กฐ ํ™ฉ์ œ์˜ ์„ฑ์ฒด(่–้ซ”)๊ฐ€ ๊ฑด๊ฐ•ํ•˜์‹ ๊ฐ€ ๋ฌผ์—ˆ๊ณ , ์‹ ๋„ +๋ถ์กฐ์˜ ๊ตฐ์ฃผ ๋ฐ ํƒœํ›„์˜ ์•ˆ๋ถ€๋ฅผ ๋ฌผ์—ˆ์Šต๋‹ˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์„œ๋กœ ์(ๆ–)ํ•˜๊ณ  +๋ถ์ •(ๅŒ—ไบญ)์— ์ด๋ฅด๋ €์Šต๋‹ˆ๋‹ค. ์šฐ๋ฆฌ ์กฐ์ •์ด ์˜ค๋Œ€์˜ ๋’ค๋ฅผ ์ด์–ด ์ฒœํ•˜๋ฅผ +ํ‰์ •ํ–ˆ์„ ๋•Œ๋Š” ์ด๋Ÿฌํ•œ ์ธ์‚ฌ๋ฒ•์„ ํ–‰ํ•˜์ง€ ์•Š์•˜์Šต๋‹ˆ๋‹ค. ํ•˜์ง€๋งŒ +์ €๋“ค๊ณผ ๊ฐ•ํ™”ํ•œ ์ด๋ž˜ ์‚ฌ์ž๋ฅผ ์ฃผ๊ณ ๋ฐ›์„ ๋•Œ๋งˆ๋‹ค ๊ด€ํ–‰์ ์œผ๋กœ ์ด๋ฅผ +๋ฐ˜๋ณตํ•ด ์˜ค๊ณ  ์žˆ๋‹ค ๋“ค์—ˆ์Šต๋‹ˆ๋‹ค.","{'question': '๋ฐ‘์ค„ ์นœ โ€˜๋ถ์กฐโ€™์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['๋‚จ์†ก๊ณผ ๊ตฐ์‹  ๊ด€๊ณ„๋ฅผ ๋งบ์—ˆ๋‹ค.', '๊ธˆ์˜ ๊ณต๊ฒฉ์„ ๋ฐ›์•„ ๋ฉธ๋งํ•˜์˜€๋‹ค.', 'ํ‰์„ฑ ๋ฐฑ๋“ฑ์‚ฐ ์ „ํˆฌ์—์„œ ์Šน๋ฆฌํ•˜์˜€๋‹ค.', '๊ณ ๊ตฌ๋ ค์™€ ์กฐ๊ณตโ€ค์ฑ…๋ด‰ ๊ด€๊ณ„๋ฅผ ์ˆ˜๋ฆฝํ•˜์˜€๋‹ค', '์นด๋ผ์ฝ”๋ฃธ์— ์ˆ˜๋„๋ฅผ ๋‘๊ณ  ์„œ๋ฐฉ ์›์ •๊ตฐ์„ ํŒŒ๊ฒฌํ•˜์˜€๋‹ค.'], 'answer': ''}",,2,1,False,[],1 +2025-DS-04,"๊ณต์˜ ์„ ์กฐ๋Š” ํ•œ๊ฐ• ์ด๋‚จ์œผ๋กœ ๋‚ด๋ ค์™€ ํ‘์น˜์— ๋ด‰ํ•ด์กŒ๊ธฐ ๋•Œ๋ฌธ์— +์ž์†๋“ค์ด ํ‘์น˜๋ฅผ ์„ฑ์”จ๋กœ ์‚ผ์•˜๋‹ค. ๊ทธ ๊ฐ€๋ฌธ์€ ๋Œ€๋Œ€๋กœ ๋‹ฌ์†”์˜ ์ง์œ„์— +์˜ฌ๋ž๋Š”๋ฐ ๋‹ฌ์†”์€ (๊ฐ€) ์˜ ๋ณ‘๋ถ€ ์ƒ์„œ์— ํ•ด๋‹นํ•œ๋‹ค. ์ฆ์กฐ๋ถ€์˜ +์ด๋ฆ„์€ ๋ฌธ๋Œ€์ด๊ณ , ํ• ์•„๋ฒ„์ง€์˜ ์ด๋ฆ„์€ ๋•ํ˜„์ด๋ฉฐ, ์•„๋ฒ„์ง€์˜ ์ด๋ฆ„์€ +์‚ฌ์ฐจ์ด๋‹ค. ์šฐ๋ฆฌ ํ™ฉ์ œ๊ป˜์„œ ํ˜•๊ตญ๊ณต์„ ๋ณด๋‚ด์–ด ์‹ ๋ผ์™€ ์—ฐํ•ฉํ•˜์—ฌ +์„/๋ฅผ ํ‰์ •ํ•˜์ž, ๊ทธ ์ž„๊ธˆ ๋ถ€์—ฌ์œต๊ณผ ํ•จ๊ป˜ ์ž…์กฐํ•˜์˜€์œผ๋ฏ€๋กœ +๊ฑฐ๋‘ฌ๋“ค์—ฌ ๋งŒ๋…„ํ˜„ ์‚ฌ๋žŒ์œผ๋กœ ์‚ผ์•˜๋‹ค.","{'question': '(๊ฐ€), (๋‚˜) ๊ตญ๊ฐ€์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['(๊ฐ€)-๋…์„œ์‚ผํ’ˆ๊ณผ๋ฅผ ์‹ค์‹œํ•˜์˜€๋‹ค.', '(๊ฐ€)-๋ถ์ถ”๋ฐ€์›๊ณผ ๋‚จ์ถ”๋ฐ€์›์„ ์„ค์น˜ํ•˜์˜€๋‹ค.', '(๋‚˜)-์ˆ˜์˜ ๊ณต๊ฒฉ์„ ๋ฐ›์•˜๋‹ค.', '(๋‚˜)-ํ•œ์‚ฌ๊ตฐ ์ค‘ ๋‚™๋ž‘์˜ ์œ ๋ฏผ์„ ๋ฐ›์•„๋“ค์˜€๋‹ค.', '(๋‚˜)-์ค‘์•™ ๊ธฐ๊ตฌ๋กœ 2๊ด€ 8์„ฑ์ œ๋ฅผ ์šด์˜ํ•˜์˜€๋‹ค.'], 'answer': ''}",,4,3,False,"['๋ฐ•์‚ฌ(ๅšๅฃซ\u2009:\u2009ๅ“ๅ›ž)๊ฐ€ ์ •์žฌ(ๆŒบๆ‰)๋ฅผ ๋‚ณ์•˜๊ณ , ์ •์žฌ๊ฐ€ ๋‚จ์ˆ˜(ๅ—็ง€)๋ฅผ ๋‚ณ์•˜๊ณ , ๋‚จ์ˆ˜๊ฐ€ ์†Œ(้‚ต)๋ฅผ ๋‚ณ์•˜๊ณ , ์†Œ(้‚ต)๊ฐ€ ํœ˜ ๋ถ€์‹ฌ(ๅฏŒๆทฑ)์„ ๋‚ณ์•˜๋Š”๋ฐ, ๊ณต(ๅ…ฌ)์—๊ฒŒ ์ฆ์กฐ๋ถ€๊ฐ€ ๋œ๋‹ค. ๋ฐ•์‚ฌ๋กœ๋ถ€ํ„ฐ ์•„๋ž˜๋กœ 4๋Œ€์— ๊ฑธ์ณ ๊ณ„์†ํ•ด์„œ ๊ณผ๊ฑฐ์— ์˜ฌ๋ž๋‹ค. ์ฆ์กฐ๋ถ€์™€ ๊ทธ ํ˜•๋‹˜ ํ˜ธ(ๆตฉ) ๋ฐ ๋ถ€(ๆ‰ถ)๋„ ๋ชจ๋‘ ๊ณผ๊ฑฐ์— ์˜ฌ๋ž๋‹ค. ๋‚˜๋ผ์˜ ์ œ๋„์— ๋”ฐ๋ผ ๊ทธ ์–ด๋จธ๋‹ˆ์—๊ฒŒ ๋…น(็ฅฟ)์„ ์ง€๊ธ‰ํ–ˆ์œผ๋ฏ€๋กœ ๋‹น์‹œ ์‚ฌ๋žŒ๋“ค์ด ๋ถ€๋Ÿฌ์›Œํ•˜์˜€๋‹ค. ์กฐ๋ถ€ ํœ˜ ์‹(ๆนœ)์€ ์ฆ์ˆœ์ถฉ๋ณด์กฐ๊ณต์‹ (่ดˆ็ด”ๅฟ ่ฃœ็ฅšๅŠŸ่‡ฃ) ๋ณด๊ตญ์ˆญ๋ก๋Œ€๋ถ€(่ผ”ๅœ‹ๅด‡็ฅฟๅคงๅคซ) ํŒ์‚ฌํ‰๋ถ€์‚ฌ(ๅˆคๅธๅนณๅบœไบ‹) ์ง„๊ฐ•๊ตฐ(ๆ™‹ๅบทๅ›)์ด๋‹ค. ๋ถ€์นœ ํœ˜ ์‹œ์›(ๆƒๆบ)์€ ์ฆ์ˆœ์ถฉ์ ๋•๋ณ‘์˜๋ณด์กฐ๊ณต์‹ (่ดˆ็ด”ๅฟ ็ฉๅพท็ง‰็พฉ่ฃœ็ฅšๅŠŸ่‡ฃ) ๋Œ€๊ด‘๋ณด๊ตญ์ˆญ๋ก๋Œ€๋ถ€(ๅคงๅŒก่ผ”ๅœ‹ๅด‡็ฅฟๅคงๅคซ) ์˜์ •๋ถ€(่ญฐๆ”ฟๅบœ) ์šฐ์ •์Šน(ๅณๆ”ฟไธž) ํŒ๋ณ‘์กฐ์‚ฌ(ๅˆคๅ…ตๆ›นไบ‹) ์ง„๊ฐ•๋ถ€์›๊ตฐ(ๆ™‹ๅบทๅบœ้™ขๅ›)์ด๋‹ค. โ€ฆโ€ฆํ›„๋žตโ€ฆโ€ฆโ€ ํ•œํŽธ, ์ง„์ฃผ(ๆ™‰ๅทž)๋ฅผ ๊ด€ํ–ฅ(่ฒซ้„•)์œผ๋กœ ํ•˜๋Š” ํ•˜์”จ(ๆฒณๆฐ)์—๋Š” ๊ณ ๋ ค ์ •์ข…ยท๋ฌธ์ข… ์—ฐ๊ฐ„์— ์‚ฌ์ง(ๅธ็›ด)์„ ์ง€๋‚ธ ํ•˜์ง„(ๆฒณ็) ๊ณต์„ ์‹œ์กฐ๋กœ ์‚ผ์€ ์‚ฌ์ง๊ณตํŒŒ(ๅธ็›ดๅ…ฌๆดพ)์™€ ๊ณ ๋ ค ์ค‘์—ฝ์— ์ฃผ๋ถ€(ๆณจ็ฐฟ)๋ฅผ ์ง€๋‚ธ ํ•˜์„ฑ(ๆฒณๆˆ) ๊ณต์„ ์‹œ์กฐ๋กœ ์‚ผ์€ ๋‹จ๊ณ„๊ณตํŒŒ(ไธนๆบชๅ…ฌๆดพ)๊ฐ€ ์žˆ๋‹ค', '้ซ˜็ฅ–์ธ ๆตฉ๊ป˜์„œ๋Š” ์ƒ์›(็”Ÿๅ“ก)์ด์‹œ๊ณ  ์ฆ์กฐ(ๆ›พ็ฅ–) ์ฐจ์ •(ๆฌกๆฅจ)๊ป˜์„œ๋Š” ํšจ๋ ด(ๅญๅป‰)์œผ๋กœ ์ฒœ๊ฑฐ(่–ฆๆ“ง)๋˜์–ด ์ฐธ๋ด‰(ๅƒๅฅ‰)์— ์ œ์ˆ˜(้™คๆŽˆ)๋˜์…จ๊ณ  ํ• ์•„๋ฒ„์ง€์˜ ํœ˜๋Š” ์šฐ(ๅฎ‡)์ด์‹ ๋ฐ ์ฒจ์ง€์ค‘์ถ”๋ถ€์‚ฌ(ๅƒ‰็Ÿฅไธญๆจžๅบœไบ‹)๋ฅผ ์ง€๋‚ด์…จ๊ณ , ์•„๋ฒ„์ง€์˜ ํœ˜๋Š” ๏ฅฆไธญ์ด์‹œ๋‹ค. ์–ด๋จธ๋‹ˆ ์ฒญ์†ก์‹ฌ์”จ(้‘ๆพ๏ฅฒๆฐ)๋Š” ์‘์‹ (ๆ‡‰ไฟก)์˜ ๋”ฐ๋‹˜์œผ๋กœ ๋งŒ๋ ฅ(่ฌๆ›†) ๊ณ„์‚ฌ๋…„ 8์›”์— ๊ณต(ๅ…ฌ)์„ ์žฅ๋‚ด(ๅขปๅ…ง) ๋ณธ๊ฐ€(ๆœฌๅฎถ)์—์„œ ๋‚ณ์œผ์…จ๋‹ค. ๊ณต(ๅ…ฌ)์€ ์–ด๋ ค์„œ๋ถ€ํ„ฐ ํ’ˆ์„ฑ(็จŸๆ€ง)์ด ์˜๋งค(่‹ฑ้‚)ํ•˜์‹œ๊ณ  ํšจ์„ฑ(ๅญ่ช )๊ณผ ๊ณต๊ฒฝ(ๆญๆ•ฌ)ํ•จ์ด ๋‹จ์ •ํ•˜๊ณ  ๊ณง์•˜์œผ๋ฉฐ ๋ช…๋ฆฌ(ๅๅˆฉ)๋ฅผ ๋Œ€๋‹จ์น˜ ์•Š๊ฒŒ ์ƒ๊ฐํ•˜์…จ๋‹ค. ์ž๋ผ์„œ๋Š” ์ด์›ƒ๋งˆ์„์— ๋ถ„๊ฐ€(ๅˆ†ๅฎถ)ํ•˜์—ฌ ์‚ด์•˜์œผ๋‚˜, ์ •์„ฑ(ๅฎš็œ)(์•„์นจ์ €๋…์œผ๋กœ ๋ถ€๋ชจ์—๊ฒŒ ๋ฌธ์•ˆ๋“œ๋ฆฌ๋Š” ๊ฒƒ)์„ ํํ•˜์ง€ ์•Š์œผ์…จ๊ณ , ์ƒ์„ ๋‹นํ•ด์„œ๋Š” ์‹œ๋ฌ˜์‚ด์ด๋ฅผ ํ•˜๋ฉด์„œ 3๋…„์„ ํ”ผ๋ˆˆ๋ฌผ๋กœ ์šธ์—ˆ๋‹ค. ์ผ์ฐ์ด ์‚ฌ๊ณ„ ๊น€์„ ์ƒ(ๆฒ™ๆบช ๏คŠๅ…ˆ็”Ÿ)์—๊ฒŒ ๋ฌธ์žฅ(ๆ–‡็ซ )๊ณผ ๋•ํ–‰(ๅพท่กŒ)์„ ๋ฐฐ์šฐ๊ณ  ๋‹น์„ธ(็•ถไธ–)์— ์ถ”์ค‘(ๆŽจ้‡)์„ ๋ฐ›์œผ์…จ๋‹ค. ์ธ์กฐ(ไป็ฅ–) ๋ณ‘์ž๋…„(ไธ™ๅญๅนด)์— ๊ตฐ์ •(่ปไธ)์„ ๋ชจ์ง‘ํ•˜๊ณ  ๊ตฐ๋Ÿ‰(่ป็ณง)์„ ๋ชจ์•„์„œ ๊ทผ์™•(ๅ‹ค็Ž‹)ํ•˜๋ ค๊ณ  ์„œ์šธ๋กœ ๋‹ฌ๋ ค๊ฐ”์œผ๋‚˜, ์ค‘๋„(ไธญ้€”)์—์„œ ๋‚จํ•œ์‚ฐ์„ฑ(ๅ—ๆผขๅฑฑๅŸŽ)์˜ ๊ฐ•ํ™”๊ฐ€ ์ด๋ฃจ์–ด์กŒ๋‹ค๋Š” ๋ง์„ ๋“ฃ๊ณ ๋Š” ๋ฏธ์น  ๋“ฏ์ด ํ†ต๋ถ„ํ•ด ํ•˜์˜€๋‹ค. ๋“œ๋””์–ด ์ƒ์†Œ๋ฅผ ์˜ฌ๋ฆฌ๊ณ  ๋ฐฑ์•” ๋ช…์ •์‚ฐ์ค‘์œผ๋กœ ๋ฐœ์ž์ทจ๋ฅผ ๊ฐ์ถ”์–ด ํ˜ธ๋ฅผ ๋ช…์‚ฐ(ๆ˜Žๅฑฑ)์ด๋ผ๊ณ  ํ•˜๊ณ  ๋ฌธ์„ค์ฃผ์— ๋…๋ช…ํ—Œ(็จๆ˜Ž่ป’)์ด๋ผ๊ณ  ํ˜„ํŒ(ๆ‡ธๆฟ)์„ ๊ฑธ์—ˆ์œผ๋ฉฐ ์ง€์ฒœ ์ตœ๋ช…๊ธธ(้ฒๅท ๅด”้ณดๅ‰)์ด ์—ฌ๋Ÿฌ๋ฒˆ ์ฒœ์šฉ(่–ฆ็”จ)ํ•˜๋ ค๊ณ ํ•˜์˜€์œผ๋‚˜ ์‘(ๆ‡‰)ํ•˜์ง€ ์•Š์•˜๋‹ค']",3 +2025-DS-06,"โ—ฆ (๊ฐ€) ์€/๋Š” ์ค‘์•™์•„์‹œ์•„ ์ผ๋Œ€๋ฅผ ์–ป๊ธฐ ์œ„ํ•ด ์ถœ์ •ํ•˜์—ฌ ๋‹ญ์˜ +ํ•ด์— ์ž์‹ ์˜ ์žฅ๋ง‰์œผ๋กœ ๋Œ์•„์™”๋‹ค. ์ด๋กœ์จ ๋ถ€ํ•˜๋ผ, ๋ฐ”๋‹ฅ์ƒจ, +์นด์Šˆ๊ฐ€๋ฅด ์ผ๋Œ€๋ฅผ ์ง€๋ฐฐํ•˜๊ฒŒ ๋˜์—ˆ๋‹ค. ๊ทธ ํ›„ ํƒ•๊ตฌํŠธ์˜ ๊ตฐ์ฃผ๊ฐ€ +๋ฐ˜๋ž€์„ ์ผ์œผ์ผฐ๋‹ค๋Š” ์†Œ์‹์„ ๋“ค์—ˆ๋‹ค. ๊ทธ๋Š” ์ด๋ฅผ ์ง•๋ฒŒํ•˜๊ณ ์ž +ํƒ•๊ตฌํŠธ๋ฅผ ๊ณต๊ฒฉํ•˜๊ฒŒ ํ•˜์˜€๋‹ค. +โ—ฆ๋Œ€๋ฆฌ๋ฅผ ์ •๋ณตํ•œ ์ง€ ์ด์‹ญ์—ฌ ๋…„์ด ์ง€๋‚ฌ๋‹ค. (๋‚˜) ์€/๋Š” ๋ฐ”์–€์—๊ฒŒ +๋ช…ํ•˜์—ฌ ๋Œ€๊ตฐ์„ ์ด๋Œ๊ณ  ๋‚จํ•˜ํ•˜์—ฌ ๋งˆ์ง€๋ง‰ ๋‚จ์€ ๊ฐ•๋‚จ์„ ์ •๋ณตํ•˜๊ฒŒ +ํ•˜์˜€๋‹ค. ๋ฐ”์–€์˜ ๊ตฐ๋Œ€๋Š” ์ฐฝ์žฅ๊ฐ•์„ ๊ฑด๋„Œ ๋’ค ๊ฑด๊ฐ•์—์„œ ์„ธ ๊ธธ๋กœ +๋‚˜๋ˆ„์–ด ๊ณ ์ •์‚ฐ์— ๋„๋‹ฌํ•˜์˜€๋‹ค. ๊ทธ๋Ÿฌ์ž ๊ทธ๊ณณ์˜ ๊ตฐ์ฃผ๊ฐ€ ์‹ ํ•˜๋ฅผ +๋ณด๋‚ด ๊ตญ์ƒˆ๋ฅผ ์ง€๋‹ˆ๊ณ  ์™€์„œ ํ•ญ๋ณต์˜ ํ‘œ๋ฌธ์„ ๋ฐ”์ณค๋‹ค.","{'question': '(๊ฐ€), (๋‚˜) ๊ตฐ์ฃผ์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['(๊ฐ€)-๊ตญํ˜ธ๋ฅผ ์›์œผ๋กœ ๋ฐ”๊ฟจ๋‹ค.', '(๊ฐ€)-๊ณ ๋ ค ์ •๋ฒŒ์„ ์‹œ์ž‘ํ•˜์˜€๋‹ค.', '(๊ฐ€)-๋ฒ ํŠธ๋‚จ์— ์›์ •๊ตฐ์„ ํŒŒ๊ฒฌํ•˜์˜€๋‹ค.', '(๋‚˜)-๋‚œ์ง•์—์„œ ๋ฒ ์ด์ง•์œผ๋กœ ์ฒœ๋„ํ•˜์˜€๋‹ค.', '(๋‚˜)-๋‘ ์ฐจ๋ก€์— ๊ฑธ์ณ ์ผ๋ณธ ์›์ •์„ ๋‹จํ–‰ํ•˜์˜€๋‹ค.'], 'answer': ''}",,5,3,False,[],5 +2025-DS-07,"์šฐ๋ฆฌ๋Š” ๋‚ด์ฃผ์— ์žˆ๋Š” ๋…ธ์‚ฐ์— ๋„์ฐฉํ•˜์˜€๋Š”๋ฐ, ๊ทธ๊ณณ ์‚ฌ๋žŒ๋“ค์ด +๋งํ•˜๊ธฐ๋ฅผ ์ผ๋ณธ์˜ ์Šน๋ ค์™€ ์†์ธ๋“ค์ด ํ˜„์žฌ ๋“ฑ์ฃผ ๋ฒ•ํ™”์›์— ์žˆ๋‹ค๊ณ  +ํ•˜๊ธฐ์—, ๋Œ€์‚ฌ๋ฅผ ๋งŒ๋‚˜๊ธฐ ์œ„ํ•ด ๊ทธ๊ณณ์œผ๋กœ ๊ฐ€๋ ค๊ณ  ํ–ˆ์Šต๋‹ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ +์šฐ๋ฆฌ๊ฐ€ ์ถœ๋ฐœํ•˜๊ธฐ๋กœ ํ•œ ๋‚ , ๋Œ€์‚ฌ๊ป˜์„œ๋Š” ์ผ๋ณธ์œผ๋กœ ๊ฐ€๋Š” ์ผ๋ณธ +๋ฐฐ๋ฅผ ํƒ€๊ธฐ ์œ„ํ•ด ์ด๋ฏธ ๋‚จ์ชฝ์œผ๋กœ ๋– ๋‚ฌ๋‹ค๋Š” ์†Œ์‹์„ ๋“ค์—ˆ์Šต๋‹ˆ๋‹ค. +ํ˜„์žฌ ์šฐ๋ฆฌ๋Š” ๋…ธ์‚ฐ์—์„œ ๊ธฐ๋‹ค๋ฆฌ๊ณ  ์žˆ์‚ฌ์˜ค๋‹ˆ ๋ชจ์ชผ๋ก ์ด๊ณณ์œผ๋กœ +๋Œ์•„์˜ค์‹œ๊ธฐ ๋ฐ”๋ž๋‹ˆ๋‹ค. +โ—ฆ์ „๋“ฑ๋Œ€๋ฒ•์‚ฌ ์šด์‹ ์ด ์‚ผ๊ฐ€ ์•„๋ฃ๋‹ˆ๋‹ค. ์˜›๋‚  ๋Œ€์‚ฌ๊ป˜์„œ๋Š” ๋ฒ•์„ +๊ตฌํ•˜๊ธฐ ์œ„ํ•ด ๋‹น์˜ ์˜ค๋Œ€์‚ฐ์— ์˜ฌ๋ผ ๋ฌธ์ˆ˜๋ณด์‚ด์˜ ํ™”์‹ ์„ ๋งŒ๋‚ฌ +์Šต๋‹ˆ๋‹ค. ๋ฌธ์ˆ˜๋ณด์‚ด์˜ ๊ฐํ™”์— ์˜์ง€ํ•˜์—ฌ ๋งˆ์นจ๋‚ด ๊ตฌํ•˜๋Š” ๋ฐ”๋ฅผ +์–ป์—ˆ์Šต๋‹ˆ๋‹ค. ๊ทธ ๊ฐ€๋ฅด์นจ์„ ๋ณธ์กฐ์— ๋“ค์—ฌ์™€ ๊ตญ๊ฐ€๋ฅผ ์ง„ํ˜ธ(้Žญ่ญท) +ํ•˜๊ณ  ๋ฐฑ์„ฑ์„ ์ด๋กญ๊ฒŒ ํ•˜๊ณ ์ž ํ•˜์˜€์Šต๋‹ˆ๋‹ค.","{'question': '๋ฐ‘์ค„ ์นœ โ€˜๋Œ€์‚ฌโ€™์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['์™•์˜ค์ฒœ์ถ•๊ตญ์ „์„ ์ง‘ํ•„ํ•˜์˜€๋‹ค.', '์‡ผํ† ์ฟ  ํƒœ์ž์˜ ์Šค์Šน์ด ๋˜์—ˆ๋‹ค.', '์ž…๋‹น๊ตฌ๋ฒ•์ˆœ๋ก€ํ–‰๊ธฐ๋ฅผ ์ €์ˆ ํ•˜์˜€๋‹ค.', '์ฐธ์„ ์„ ์ค‘์‹œํ•˜๋Š” ์„ ์ข…์„ ๊ฐœ์ฐฝํ•˜์˜€๋‹ค.', '์ผ๋ณธ์— ๊ณ„์œจ๊ณผ ์ˆ˜๊ณ„ ๋ฐฉ์‹์„ ์ „ํ•˜์˜€๋‹ค.'], 'answer': ''}",,3,3,True,[],5 +2025-DS-10,"โ—ฆ์˜์ •๋ถ€์—์„œ ์•„๋ขฐ๊ธฐ๋ฅผ, โ€œ์ค‘๊ตญ ๊ตฐํ•จ์ด ๊ณง ์™€์„œ ์ •๋ฐ•ํ•œ๋‹ค๊ณ  ํ•˜๋‹ˆ, +์˜์ ‘ํ•˜๋Š” ์ ˆ์ฐจ๋ฅผ ์กฐ๊ธˆ๋„ ๋Šฆ์ถœ ์ˆ˜ ์—†์Šต๋‹ˆ๋‹ค. ๊ณต์กฐ ์ฐธํŒ ์ด์ค‘ํ•˜๋ฅผ +์˜์ ‘๊ด€์œผ๋กœ ๋ณด๋‚ด ์ผ์„ ์ฒ˜๋ฆฌํ•˜๊ฒŒ ํ•˜๋Š” ๊ฒƒ์ด ์–ด๋–ป๊ฒ ์Šต๋‹ˆ๊นŒ?โ€๋ผ๊ณ  +ํ•˜์—ฌ, ์œคํ—ˆํ•˜์˜€๋‹ค. ใ‰ ์ ๋„๊ฐ€ ์ด๋ฏธ ์ „์ฃผ๋ฅผ ํ•จ๋ฝํ•˜๊ณ  ์„ธ๋ ฅ์ด ์„ฑํ•ด์ ธ +์ •๋ถ€์—์„œ ๋น„๋ฐ€๋ฆฌ์— ์œ„์•ˆ์Šค์นด์ด์™€ ์˜๋…ผํ•˜๊ณ  ๊ตฌ์›์„ ์ฒญํ•œ ๋ฐ” +์žˆ์—ˆ๋‹ค. +โ—ฆ์‚ฐ๋‘ฅ์„ฑ์—์„œ ๋ด‰๊ธฐํ•œ ์ฒญ๊ตญ ใ‰กํญ๋„์—๊ฒŒ ๋ฒ ์ด์ง• ๋ฐ ํ†ˆ์ง„ ๋ถ€๊ทผ์—์„œ +์˜๊ตญ์ธ ์„ ๊ต์‚ฌ ํ•œ ๋ช…๊ณผ ์•ฝ 60๋ช…์˜ ์ค‘๊ตญ์ธ ํฌ๋ฆฌ์ŠคํŠธ๊ต ์‹ ์ž๊ฐ€ +์‚ดํ•ด๋˜์—ˆ์Šต๋‹ˆ๋‹ค. ์ฒ ๋กœ์™€ ์ „์‹ ์„ ๋„ ์—ฌ๋Ÿฌ ๋ฒˆ ํŒŒ๊ดด๋˜์—ˆ์Šต๋‹ˆ๋‹ค. +๊ทธ๋“ค์—๊ฒŒ ๋ฒ ์ด์ง•์˜ ๊ณต์‚ฌ๊ด€์ด ์œ„ํ˜‘๋ฐ›์ž ์™ธ๊ตญ ๋Œ€ํ‘œ๋“ค์€ ์—ฌ๋Ÿฌ +์ฐจ๋ก€์˜ ํšŒํ•ฉ์„ ๊ฐ€์ง€๊ณ  ์‹ ์†ํ•˜๊ฒŒ ์ด ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•ด ์ค„ ๊ฒƒ์„ +์ฒญ๊ตญ ์ •๋ถ€์— ๊ฑด์˜ํ•˜์˜€์Šต๋‹ˆ๋‹ค.","{'question': '๋ฐ‘์ค„ ์นœ ใ‰ , ใ‰ก์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['ใ‰ -๊ตฐ๋ฒŒ ์ œ๊ฑฐ๋ฅผ ์œ„ํ•œ ๋ถ๋ฒŒ์— ์ฐธ์—ฌํ•˜์˜€๋‹ค.', 'ใ‰ -๋ฉ”์ด์ง€ ์œ ์‹ ์„ ๋ณธ๋ฐ›์•„ ์ •๋ณ€์„ ์ฃผ๋„ํ•˜์˜€๋‹ค.', 'ใ‰ก-ํ™์ˆ˜์ „์˜ ์ง€ํœ˜๋ฅผ ๋ฐ›์•˜๋‹ค.', 'ใ‰ก-์™ธ์„ธ์˜ ๊ฐœ์ž…์— ์˜ํ•ด ์ง„์••๋˜์—ˆ๋‹ค.', 'ใ‰ ๊ณผ ใ‰ก-์˜ํšŒ ๊ฐœ์„ค์„ ์š”๊ตฌํ•˜์˜€๋‹ค.'], 'answer': ''}",,4,4,True,[],4 +2025-DS-11,"์ผ๋ณธ์— ๊ฐ”๋˜ ๋‚˜ํฅ์œ ๊ฐ€ (๊ฐ€) ์˜ ๋‹ต์‹ ์„ ๊ฐ€์ง€๊ณ  ๋Œ์•„์™”๋‹ค. +๋‹ต์‹ ์— ์ด๋ฅด๊ธฐ๋ฅผ, โ€œ์ง€๊ธˆ ๊ท€๊ตญ์— ์ถœ๋ชฐํ•˜๋Š” ํ•ด์ ์€ ๊ทœ์Šˆ ์ง€์—ญ์˜ +๋‚œ์‹ ๋“ค๋กœ ๋งˆ์“ฐ์šฐ๋ผ ๋“ฑ ์„œ์ชฝ ์„ฌ์— ํ• ๊ฑฐํ•ด ๋…ธ๋žต์งˆํ•˜๋Š” ์ž๋“ค์ž…๋‹ˆ๋‹ค. +๊ทœ์Šˆ๋Š” ์„œํ•ด๋„(่ฅฟๆตท้“)์— ์†ํ•ด ์žˆ์œผ๋‚˜, ๊ณ ๋‹ค์ด๊ณ  ์ฒœํ™ฉ์ด ๋‚จ์ชฝ์œผ๋กœ +๋‚ด๋ ค๊ฐ„ ์ดํ›„ ์—ญ์‹ ๋“ค์ด ๋‚œ๋ฆฝํ•˜์—ฌ ์šฐ๋ฆฌ์˜ ์ง€๋ฐฐ๊ฐ€ ๋ฏธ์น˜์ง€ ์•Š๋Š” +์ƒํƒœ์ž…๋‹ˆ๋‹ค. ์‚ฌ์ •์ด ์ด๋Ÿฌํ•˜๋ฏ€๋กœ ๊ฐํžˆ ๊ณง๋ฐ”๋กœ ๊ธˆ์ง€ํ•˜๊ฒ ๋‹ค๋Š” ์•ฝ์†์„ +๋“œ๋ฆด ์ˆ˜ ์—†์Šต๋‹ˆ๋‹ค.โ€๋ผ๊ณ  ํ•˜์˜€๋‹ค.","{'question': '(๊ฐ€) ๋ง‰๋ถ€์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['์‹ ํŒจ๋ฅผ ๋ฐœ๊ธ‰ํ•˜์˜€๋‹ค.', '๋‹น์— ์‚ฌ์‹ ์„ ํŒŒ๊ฒฌํ•˜์˜€๋‹ค', '๊ฐํ•ฉ ๋ฌด์—ญ์„ ์‹ค์‹œํ•˜์˜€๋‹ค.', '์ดˆ๋Ÿ‰ ์™œ๊ด€์„ ์„ค์น˜ํ•ด ๊ต์—ญํ•˜์˜€๋‹ค.', '๋ฏธ๋‚˜๋ชจํ† ๋…ธ ์š”๋ฆฌํ† ๋ชจ์— ์˜ํ•ด ์ˆ˜๋ฆฝ๋˜์—ˆ๋‹ค.'], 'answer': ''}",,3,1,False,[],2 +2025-DS-13,"์ €๋“ค์€ ์‹œ๋ชจ๋…ธ์„ธํ‚ค์—์„œ ์กฐ์ธํ•œ ๋ฐ”์— ์˜๊ฑฐํ•˜์—ฌ (๊ฐ€) ์™€/๊ณผ +(๋‚˜) ์„ฌ ์ „์ฒด ๋ฐ ๋ถ€์† ๋„์„œ, ํŽ‘ํ›„ ์—ด๋„์— ๋Œ€ํ•œ ํ• ์–‘๊ณผ ๋ฐฐ์ƒ๊ธˆ +2์–ต ๋ƒฅ์˜ ์ง€๋ถˆ์„ ์š”๊ตฌํ•˜๊ณ  ์žˆ๋‹ค. ์ €๋“ค์€ ์ „์Ÿ์—์„œ ๊ฑฐ๋“ญ ์Šน๋ฆฌํ•œ +๊ฒƒ์„ ๋ฏฟ๊ณ , ์ด ๋‘ ๊ฐ€์ง€ ์กฐ๊ฑด์ด ์ดํ–‰๋˜์ง€ ์•Š์œผ๋ฉด ์ „์Ÿ์„ ์žฌ๊ฐœํ•  +๊ฒƒ์ด๋ผ๊ณ  ๊ณต์–ธํ•˜๊ณ  ์žˆ๋‹ค.","{'question': '(๊ฐ€), (๋‚˜) ์ง€์—ญ์— ๋Œ€ํ•œ ์˜ณ์€ ์„ค๋ช…๋งŒ์„ <๋ณด๊ธฐ>์—์„œ ๊ณ ๋ฅธ ๊ฒƒ์€?', 'choices': ['ใ„ฑ, ใ„ด', 'ใ„ฑ, ใ„ท', 'ใ„ด, ใ„ท', 'ใ„ด, ใ„น', 'ใ„ท, ใ„น'], 'answer': '', 'question_plus': 'ใ„ฑ.(๊ฐ€)-๋Ÿฌ์‹œ์•„์˜ ๋ฐœํŠธ ํ•จ๋Œ€๊ฐ€ ๊ฒฉํŒŒ๋œ ๊ณณ์ด๋‹ค.\nใ„ด.(๊ฐ€)-21๊ฐœ์กฐ ์š”๊ตฌ์—์„œ ์ด๊ถŒ์ด ๊ฑฐ๋ก ๋œ ๋„์‹œ๋ฅผ ํฌํ•จํ•˜๊ณ  ์žˆ๋‹ค.\nใ„ท.(๋‚˜)-์žฅ์ œ์Šค๊ฐ€ ์ด๋„๋Š” ๊ตญ๋ฏผ๋‹น ์ •๋ถ€๊ฐ€ ์˜ฎ๊ฒจ ๊ฐ„ ๊ณณ์ด๋‹ค.\nใ„น.(๋‚˜)-ํŒŒ๋ฆฌ ๊ฐ•ํ™” ํšŒ์˜์—์„œ ์ผ๋ณธ์˜ ๊ถŒ์ต์œผ๋กœ ์ธ์ •๋˜์—ˆ๋‹ค.'}","ใ„ฑ.(๊ฐ€)-๋Ÿฌ์‹œ์•„์˜ ๋ฐœํŠธ ํ•จ๋Œ€๊ฐ€ ๊ฒฉํŒŒ๋œ ๊ณณ์ด๋‹ค. +ใ„ด.(๊ฐ€)-21๊ฐœ์กฐ ์š”๊ตฌ์—์„œ ์ด๊ถŒ์ด ๊ฑฐ๋ก ๋œ ๋„์‹œ๋ฅผ ํฌํ•จํ•˜๊ณ  ์žˆ๋‹ค. +ใ„ท.(๋‚˜)-์žฅ์ œ์Šค๊ฐ€ ์ด๋„๋Š” ๊ตญ๋ฏผ๋‹น ์ •๋ถ€๊ฐ€ ์˜ฎ๊ฒจ ๊ฐ„ ๊ณณ์ด๋‹ค. +ใ„น.(๋‚˜)-ํŒŒ๋ฆฌ ๊ฐ•ํ™” ํšŒ์˜์—์„œ ์ผ๋ณธ์˜ ๊ถŒ์ต์œผ๋กœ ์ธ์ •๋˜์—ˆ๋‹ค.",3,3,True,[],3 +2025-DS-14,"๊ทธ๋ฆผ์€ ํฌ๋ณ‘์ด ์˜› ์—๋„์„ฑ์—์„œ ์˜คํฌ(ๅˆ็ ฒ)๋ฅผ ์˜๋Š” ์žฅ๋ฉด์ด๋‹ค. +์ผ๋ณธ์ธ๋“ค์€ ๊ฐœํ•ญ ํ›„ ์„œ์–‘ ๋ฌธ๋ฌผ์ด ๋“ค์–ด์˜ค๋ฉด์„œ ๊ทผ๋Œ€์  ์‹œ๊ฐ„์„ +์ ‘ํ•˜๊ฒŒ ๋˜์—ˆ๋‹ค. ๋‹น์‹œ ๋ชจ๋“  ์‚ฌ๋žŒ์ด ์‹œ๊ณ„๋ฅผ ๊ฐ€์งˆ ์ˆ˜๋Š” ์—†์—ˆ๊ธฐ ๋•Œ๋ฌธ์— +12์‹œ ์ •๊ฐ์„ ์•Œ๋ฆฌ๋Š” ์˜คํฌ๋ฅผ ์˜๋„๋ก ํƒœ์ •๊ด€ ํฌ๊ณ ๋ฅผ ๋‚ด๋ ธ๋‹ค. +๊ทธ ์ด๋“ฌํ•ด ์š”์ฝ”ํ•˜๋งˆ์™€ ๋„์ฟ„ ์‚ฌ์ด๋ฅผ ์ž‡๋Š” ์ฒ ๋„๊ฐ€ ์ตœ์ดˆ๋กœ ๊ฐœํ†ต +๋˜๋ฉด์„œ ๊ทผ๋Œ€์  ์‹œ๊ฐ„ ๊ฐœ๋…์€ ๋”์šฑ ํ™•์‹คํ•ด์ ธ ๊ฐ”๋‹ค.","{'question': '๋ฐ‘์ค„ ์นœ โ€˜ํฌ๊ณ โ€™๊ฐ€ ๋ฐœํ‘œ๋œ ์‹œ๊ธฐ์— ๋™์•„์‹œ์•„์—์„œ ๋ณผ ์ˆ˜ ์žˆ๋Š” ๋ชจ์Šต์œผ๋กœ\n๊ฐ€์žฅ ์ ์ ˆํ•œ ๊ฒƒ์€?', 'choices': ['ํ•œ๊ตญ-์—ฌ๊ถŒํ†ต๋ฌธ์˜ ๋ฐœํ‘œ๋ฅผ ์ทจ์žฌํ•˜๋Š” ๊ธฐ์ž', '์ค‘๊ตญ-ํƒœํ‰์ฒœ๊ตญ์˜ ์ˆ˜๋„๋ฅผ ํ•จ๋ฝํ•˜๋Š” ํ•œ์ธ ๊ด€๋ฃŒ', '์ค‘๊ตญ-์ƒค๋จผ์—์„œ ์˜๊ตญ ์˜์‚ฌ์—๊ฒŒ ์žฌํŒ์„ ๋ฐ›๋Š” ์˜๊ตญ ์ƒ์ธ', '์ผ๋ณธ-๊ต์œก ์น™์–ด๋ฅผ ๋‚ญ๋…ํ•˜๋Š” ๊ต์‚ฌ', '์ผ๋ณธ-๋„์ฟ„ ๋Œ€ํ•™ ๊ฐœ๊ต์‹์— ์ฐธ์„ํ•˜๋Š” ๊ด€๋ฆฌ'], 'answer': ''}",,3,4,False,[],5 +2025-DS-16,"์‚ฌ์ง„์˜ ์ธ๋ฌผ์€ ์ค‘ํ™” ์ธ๋ฏผ ๊ณตํ™”๊ตญ์˜ ์ œ2๋Œ€ +๊ตญ๊ฐ€ ์ฃผ์„์„ ์—ญ์ž„ํ•œ ๋ฅ˜์‚ฌ์˜ค์น˜์ด๋‹ค. ๊ทธ๋Š” +๋งˆ์˜ค์ฉŒ๋‘ฅ ์ฃผ๋„ํ•˜์— ์ธ๋ฏผ๊ณต์‚ฌ ๋“ฑ์„ ์„ค๋ฆฝํ•˜๋ฉฐ +์ถ”์ง„๋˜์—ˆ๋˜ (๊ฐ€) ์˜ ๋ฌธ์ œ์ ์„ ์ง€์ ํ•˜๋ฉด์„œ +์‚ฌํšŒ์ฃผ์˜ ๊ฒฝ์ œ ์ •์ฑ…์˜ ์ˆ˜์ •์„ ์ฃผ์žฅํ•˜์˜€๋‹ค. +๊ทธ๋Ÿฌ๋‚˜ ๋งˆ์˜ค์ฉŒ๋‘ฅ์ด ์ž๋ณธ์ฃผ์˜์  ์š”์†Œ์˜ +๋„์ž…์„ ์ฃผ์žฅํ•˜๋Š” ์„ธ๋ ฅ์„ ์ˆ™์ฒญํ•˜๊ณ ์ž ์ฃผ๋„ํ•œ (๋‚˜) +๋‹น์‹œ ํ™์œ„๋ณ‘๋“ค์˜ ํ‘œ์ ์ด ๋˜์–ด ๋น„ํŒ๊ณผ ๋ฐ•ํ•ด๋ฅผ ๋ฐ›์•„ +๊ตญ๊ฐ€ ์ฃผ์„์—์„œ ๋ฌผ๋Ÿฌ๋‚œ ํ›„ ์‚ฌ๋งํ•˜์˜€๋‹ค. ๊ทธ๋Š” ๋ฉ์ƒค์˜คํ•‘์ด ์ง‘๊ถŒํ•œ +์ดํ›„์—์•ผ ์ •์น˜์ ์œผ๋กœ ๋ณต๊ถŒ๋˜์—ˆ๋‹ค.","{'question': '(๊ฐ€), (๋‚˜)๊ฐ€ ์ „๊ฐœ๋œ ์‹œ๊ธฐ์— ์žˆ์—ˆ๋˜ ์‚ฌ์‹ค๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['(๊ฐ€)-๋„์ด๋จธ์ด ์ •์ฑ…์ด ์ถ”์ง„๋˜์—ˆ๋‹ค.', '(๊ฐ€)-์ผ๋ณธ์—์„œ ๊ฑฐํ’ˆ ๊ฒฝ์ œ๊ฐ€ ๋ถ•๊ดด๋˜์—ˆ๋‹ค.', '(๋‚˜)-4โ€ค19 ํ˜๋ช…์ด ์ „๊ฐœ๋˜์—ˆ๋‹ค.', '(๋‚˜)-์ค‘โ€ค์ผ ๊ณต๋™ ์„ฑ๋ช…์ด ๋ฐœํ‘œ๋˜์—ˆ๋‹ค.', '(๋‚˜)-๋ฏธ๊ตญ๊ณผ ์ค‘๊ตญ์ด ๊ตญ๊ต๋ฅผ ์ˆ˜๋ฆฝํ•˜์˜€๋‹ค.'], 'answer': ''}",,4,3,False,[],5 +2025-DS-17,"ํ™ฉ์ œ์˜ ์ˆ™๋ถ€์ธ ์„ญ์ •์™•์ด ์˜์„ ๋‚ด๋ ธ๋‹ค. +โ€œ๋‚จ๋ฐฉ์˜ ๋ฌธ๋ฌด ๊ด€์›์—๊ฒŒ ์œ ์‹œ๋ฅผ ๋‚ด๋ฆฌ๋‹ˆ ์ž˜ ์œ ๋…ํ† ๋ก ํ•˜๋ผ. +๋„ˆํฌ๋Š” ์ˆญ์ •์ œ๊ฐ€ ์ˆœ์ฒœ๋ถ€์˜ ๊ฒฝ์‚ฌ(ไบฌๅธซ)์—์„œ ๋‚œ๋ฆฌ๋กœ ์ธํ•ด ์ƒ์„ +๋งˆ๊ฐํ•˜์—ฌ (๊ฐ€) ์ด/๊ฐ€ ๋ฉธ๋งํ•  ๋•Œ, ๋งˆ์น˜ ๊ตด์†์˜ ํ˜ธ๋ž‘์ด์ฒ˜๋Ÿผ +์ˆจ์–ด ์žˆ์—ˆ๋‹ค. ์ง€๋‚œ๋‚  ํƒœ์กฐ๊ป˜์„œ ํฅ๊ฒฝ ํ—ˆํˆฌ์•Œ๋ผ์—์„œ ๊ฑด๊ตญํ•œ ํ›„ +ํƒœ์ข…๊ป˜์„œ ๊ตญํ˜ธ๋ฅผ (๋‚˜) (์œผ)๋กœ ๋ฐ”๊พธ์—ˆ๊ณ , ์ง€๊ธˆ ํ™ฉ์ œ๊ป˜์„œ๋Š” +์‚ฐํ•˜์ด๊ด€์„ ๋„˜์–ด ์ค‘์›์œผ๋กœ ๋“ค์–ด์™”๋‹ค. ๋„ˆํฌ๋“ค์ด ์ด์ „์˜ ๊ณผ์˜ค๋ฅผ +๋‰˜์šฐ์น˜๊ณ  ํž˜์จ ์šฐ๋ฆฌ ์กฐ์ •์„ ๋•์ง€ ์•Š๋Š”๋‹ค๋ฉด ๊ทธ ๋‚˜ํƒœํ•จ์„ ๋ฐ˜๋“œ์‹œ +์ง•๋ฒŒํ•  ๊ฒƒ์ด๋‹ค.โ€","{'question': '(๊ฐ€), (๋‚˜) ์™•์กฐ์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['(๊ฐ€)-์ฒœ๊ณ„๋ น์„ ํ•ด์ œํ•˜์˜€๋‹ค.', '(๊ฐ€)-๋งน์•ˆโ€ค๋ชจ๊ทน์ œ๋ฅผ ์‹ค์‹œํ•˜์˜€๋‹ค.', '(๋‚˜)-์ผ๋ณธ๊ณผ ์กฐ๊ณตโ€ค์ฑ…๋ด‰ ๊ด€๊ณ„๋ฅผ ๋งบ์—ˆ๋‹ค.', '(๋‚˜)-์žฅ๊ฑฐ์ •์„ ๋“ฑ์šฉํ•˜์—ฌ ๊ฐœํ˜์„ ์ถ”์ง„ํ•˜์˜€๋‹ค.', '(๊ฐ€)์™€ (๋‚˜)-ํ•œ๋ฐ˜๋„์— ๊ตฐ๋Œ€๋ฅผ ๋ณด๋ƒˆ๋‹ค.'], 'answer': ''}",,5,5,True,[],5 +2025-SM-02,"์‚ฌํšŒ ์ฒด๊ณ„ ์•ˆ์—์„œ ์ธ๊ฐ„์˜ ์ƒํ˜ธ ์ž‘์šฉ์ด ์ž‘๋™ํ•˜๋Š” ์ด์œ ๋Š” ํ–‰์œ„์ž๋“ค์—๊ฒŒ +ํ• ๋‹น๋˜๋Š”, ๋ถ„ํ™”๋œ ์—ญํ•  ๊ตฌ์กฐ๊ฐ€ ์žˆ๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ๊ฐœ์ธ์€ ์—ญํ•  ๊ตฌ์กฐ ์†์—์„œ +์‚ฌํšŒ๊ฐ€ ๊ธฐ๋Œ€ํ•˜๋Š” ํ–‰๋™์„ ์ˆ˜ํ–‰ํ•˜๊ฒŒ ๋œ๋‹ค. ์ด๋ ‡๊ฒŒ ๊ฐœ์ธ์ด ์‚ฌํšŒ์˜ ํ•œ ๋ถ€๋ถ„์œผ๋กœ์„œ +๊ณต์œ ๋œ ๊ธฐ๋Œ€์— ๋ถ€์‘ํ•˜์—ฌ ๋‹ค๋ฅธ ๋ถ€๋ถ„๊ณผ ์œ ๊ธฐ์ ์œผ๋กœ ์ƒํ˜ธ ์ž‘์šฉ์„ ํ•จ์— ๋”ฐ๋ผ +์‚ฌํšŒ๋ผ๋Š” ์™„์ „์ฒด๊ฐ€ ํ˜•์„ฑ๋œ๋‹ค.","{'question': '๋‹ค์Œ ๊ธ€์—์„œ ์‚ฌํšŒโ€ค๋ฌธํ™” ํ˜„์ƒ์„ ๋ฐ”๋ผ๋ณด๋Š” ํ•„์ž์˜ ๊ด€์ ์— ๋Œ€ํ•œ ์˜ณ์€ ์„ค๋ช…๋งŒ์„ <๋ณด๊ธฐ>์—์„œ ๊ณ ๋ฅธ ๊ฒƒ์€?', 'choices': ['ใ„ฑ, ใ„ด', 'ใ„ฑ, ใ„ท', 'ใ„ด,ใ„ท', 'ใ„ด, ใ„น', 'ใ„ท, ใ„น'], 'answer': '', 'question_plus': 'ใ„ฑ. ์ƒํ™ฉ ์ •์˜์— ๊ธฐ์ดˆํ•œ ๊ฐœ์ธ ๊ฐ„ ์ƒํ˜ธ ์ž‘์šฉ์„ ์ค‘์‹œํ•œ๋‹ค.\r\nใ„ด. ๊ฐœ์ธ ํ–‰์œ„์ž์˜ ๋Šฅ๋™์ ์ด๊ณ  ์ž์œจ์ ์ธ ์ธก๋ฉด์„ ์ค‘์‹œํ•œ๋‹ค.\r\nใ„ท. ์‚ฌํšŒ์˜ ๊ฐ ๋ถ€๋ถ„์ด ์ƒํ˜ธ ์˜์กด์  ๊ด€๊ณ„๋ฅผ ๋งบ๋Š”๋‹ค๊ณ  ๋ณธ๋‹ค.\r\nใ„น. ์‚ฌํšŒ๋Š” ์Šค์Šค๋กœ ๊ท ํ˜•์„ ์œ ์ง€ํ•˜๋ ค๋Š” ์†์„ฑ์„ ์ง€๋‹Œ๋‹ค๊ณ  ๋ณธ๋‹ค.'}","ใ„ฑ. ์ƒํ™ฉ ์ •์˜์— ๊ธฐ์ดˆํ•œ ๊ฐœ์ธ ๊ฐ„ ์ƒํ˜ธ ์ž‘์šฉ์„ ์ค‘์‹œํ•œ๋‹ค. +ใ„ด. ๊ฐœ์ธ ํ–‰์œ„์ž์˜ ๋Šฅ๋™์ ์ด๊ณ  ์ž์œจ์ ์ธ ์ธก๋ฉด์„ ์ค‘์‹œํ•œ๋‹ค. +ใ„ท. ์‚ฌํšŒ์˜ ๊ฐ ๋ถ€๋ถ„์ด ์ƒํ˜ธ ์˜์กด์  ๊ด€๊ณ„๋ฅผ ๋งบ๋Š”๋‹ค๊ณ  ๋ณธ๋‹ค. +ใ„น. ์‚ฌํšŒ๋Š” ์Šค์Šค๋กœ ๊ท ํ˜•์„ ์œ ์ง€ํ•˜๋ ค๋Š” ์†์„ฑ์„ ์ง€๋‹Œ๋‹ค๊ณ  ๋ณธ๋‹ค.",5,5,True,[],5 +2025-SM-03,"๋„์„œ ์ถœํŒ ๊ณผ์ •์—๋Š” ํŽธ์ง‘, ๋””์ž์ธ, ์ธ์‡„ ๋“ฑ ์—ฌ๋Ÿฌ ๊ณต์ •์ด ์žˆ๋‹ค. โ—‹โ—‹์ถœํŒ +ํšŒ์‚ฌ๋Š” ์ˆ˜ํ‰์ ์œผ๋กœ ๋ถ„๊ถŒํ™”๋œ ์กฐ์ง์„ ํ†ตํ•ด ๊ตฌ์„ฑ์›๋“ค์ด ํ•จ๊ป˜ ๊ฒฐ์ •์„ ๋‚ด๋ ค +์ถœํŒ ๊ณต์ •์„ ๊ด€๋ฆฌํ•˜๊ณ  ๋„์„œ๋ฅผ ์ถœ๊ฐ„ํ•œ๋‹ค. โ–กโ–ก์ถœํŒ ํšŒ์‚ฌ๋Š” ์„ธ๋ถ€์ ์œผ๋กœ +๋ถ„์—…ํ™”๋œ ์กฐ์ง์„ ํ†ตํ•ด ํ•ด๋‹น ๋ถ„์•ผ์˜ ๋‹ด๋‹น์ž๋“ค์ด ์ •ํ•ด์ง„ ์„œ์—ด๊ณผ ์ ˆ์ฐจ์— +๋”ฐ๋ผ ๊ฐ ๊ณต์ •์„ ์ง„ํ–‰ํ•˜์—ฌ ๋„์„œ๋ฅผ ์ถœ๊ฐ„ํ•œ๋‹ค. โ—‹โ—‹์ถœํŒ ํšŒ์‚ฌ๋Š” A์˜ ์šด์˜ +์›๋ฆฌ๊ฐ€, โ–กโ–ก์ถœํŒ ํšŒ์‚ฌ๋Š” B์˜ ์šด์˜ ์›๋ฆฌ๊ฐ€ ๊ฐ•์กฐ๋œ๋‹ค.","{'question': 'A, B์˜ ์ผ๋ฐ˜์ ์ธ ํŠน์ง•์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€? (๋‹จ, A, B๋Š” ๊ฐ๊ฐ ๊ด€๋ฃŒ์ œ์™€ ํƒˆ๊ด€๋ฃŒ์ œ ์ค‘ ํ•˜๋‚˜์ž„.)', 'choices': ['A์— ๋น„ํ•ด B๋Š” ์กฐ์ง ๊ตฌ์„ฑ์›์˜ ์—…๋ฌด ์žฌ๋Ÿ‰๊ถŒ ๋ฐ ์ž์œจ์„ฑ์ด ๋‚ฎ๋‹ค.', 'A์— ๋น„ํ•ด B๋Š” ์™ธ๋ถ€ ํ™˜๊ฒฝ ๋ณ€ํ™”์— ๋Œ€ํ•œ ์œ ์—ฐํ•œ ๋Œ€์ฒ˜๊ฐ€ ์šฉ์ดํ•˜๋‹ค.', 'B์— ๋น„ํ•ด A๋Š” ์—…๋ฌด์˜ ํ‘œ์ค€ํ™”์™€ ์„ธ๋ถ„ํ™”๊ฐ€ ๊ฐ•์กฐ๋œ๋‹ค.', 'B์— ๋น„ํ•ด A๋Š” ๋ชฉ์  ์ „์น˜ ํ˜„์ƒ์ด ๋‚˜ํƒ€๋‚  ๊ฐ€๋Šฅ์„ฑ์ด ๋†’๋‹ค.', 'A๋Š” ๊ฒฝ๋ ฅ์— ๋”ฐ๋ฅธ ๋ณด์ƒ์„, B๋Š” ์„ฑ๊ณผ์— ๋”ฐ๋ฅธ ๋ณด์ƒ์„ ์ค‘์‹œํ•œ๋‹ค.'], 'answer': ''}",,1,2,False,[],2 +2025-SM-05,"๊ฐ‘์€ ๊ณ ๋“ฑํ•™์ƒ์˜ ํ•™์—… ์„ฑ์ทจ๋„์™€ ๋ฌธํ•ด๋ ฅ ๊ฐ„์˜ ๊ด€๊ณ„๋ฅผ ํŒŒ์•…ํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. +์ด๋ฅผ ์œ„ํ•ด โ–กโ–ก์ง€์—ญ ๊ณ ๋“ฑํ•™์ƒ 200๋ช…์„ ๋Œ€์ƒ์œผ๋กœ ์งˆ๋ฌธ์ง€๋ฅผ ํ†ตํ•ด ํ•™์—… +์„ฑ์ทจ๋„์™€ ใ‰ ๋ฌธํ•ด๋ ฅ ์ˆ˜์ค€์„ ์ธก์ • +ํ•˜์˜€๋‹ค. ์ด ์ž๋ฃŒ์—์„œ ๋ฌธํ•ด๋ ฅ์„ ๊ธฐ์ค€์œผ๋กœ, +์ƒ์œ„ 100๋ช…(A์ง‘๋‹จ)๊ณผ ํ•˜์œ„ 100๋ช…(B์ง‘๋‹จ)์œผ๋กœ ๊ตฌ๋ถ„ํ•˜์—ฌ ํ•™์—… ์„ฑ์ทจ๋„๋ฅผ +๋ถ„์„ํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ ใ‰กB +์ง‘๋‹จ์˜ ํ•™์—… ์„ฑ์ทจ๋„๊ฐ€ A +์ง‘๋‹จ์˜ ํ•™์—… ์„ฑ์ทจ๋„๋ณด๋‹ค +์œ ์˜๋ฏธํ•˜๊ฒŒ ๋‚ฎ์•˜๋‹ค. +์„์€ ใ‰ขโ—‹โ—‹ +๋…์„œ ํ”„๋กœ๊ทธ๋žจ์ด ๊ณ ๋“ฑํ•™์ƒ์˜ ๋ฌธํ•ด๋ ฅ ์ฆ์ง„์— ํšจ๊ณผ๊ฐ€ ์žˆ์„ ๊ฒƒ +์ด๋ผ ์ƒ๊ฐํ•˜๊ณ  ์ด๋ฅผ ์•Œ์•„๋ณด๊ธฐ ์œ„ํ•ด ๋‹ค์Œ๊ณผ ๊ฐ™์ด ์—ฐ๊ตฌ๋ฅผ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ๊ทธ๋Š” +๊ฐ‘๊ณผ ์—ฐ๊ตฌ ๋Œ€์ƒ์ž์˜ ๋™์˜๋ฅผ ๋ฐ›์•„, ๊ฐ‘์˜ ์—ฐ๊ตฌ์—์„œ ๋ฌธํ•ด๋ ฅ์ด ๋‚ฎ์€ ๊ฒƒ์œผ๋กœ +ํŒ๋ช…๋œ B์ง‘๋‹จ์„ ๋ฌด์ž‘์œ„๋กœ 50๋ช…์”ฉ C์ง‘๋‹จ๊ณผ D์ง‘๋‹จ์œผ๋กœ ๋‚˜๋ˆˆ ํ›„ C์ง‘๋‹จ์—๊ฒŒ๋งŒ +4์ฃผ๊ฐ„ โ—‹โ—‹๋…์„œ ํ”„๋กœ๊ทธ๋žจ์„ ์ ์šฉํ•˜์˜€๋‹ค. ๋…์„œ ํ”„๋กœ๊ทธ๋žจ ์ข…๋ฃŒ ์‹œ์ ์— +๊ฐ‘์ด ํ™œ์šฉํ•œ ์ธก์ • ๋„๊ตฌ๋กœ ใ‰ฃ๋ฌธํ•ด๋ ฅ ์ˆ˜์ค€์„ ์ธก์ •ํ•œ ๊ฒฐ๊ณผ, C์ง‘๋‹จ์˜ ๋ฌธํ•ด๋ ฅ +์ˆ˜์ค€์€ ์œ ์˜๋ฏธํ•˜๊ฒŒ ๋†’์•„์กŒ์œผ๋‚˜ D์ง‘๋‹จ์˜ ๋ฌธํ•ด๋ ฅ ์ˆ˜์ค€์€ ์ด์ „๊ณผ ์ฐจ์ด๊ฐ€ +์—†์—ˆ๋‹ค. ์ดํ›„ ์„์€ D์ง‘๋‹จ์—๊ฒŒ๋งŒ โ—‹โ—‹๋…์„œ ํ”„๋กœ๊ทธ๋žจ์„ 4์ฃผ๊ฐ„ ์ ์šฉํ•˜์˜€๋‹ค. +๊ทธ ๊ฒฐ๊ณผ D์ง‘๋‹จ์˜ ๋ฌธํ•ด๋ ฅ ์ˆ˜์ค€์ด ๋†’์•„์ ธ ์ตœ์ข…์ ์œผ๋กœ ใ‰คC +์ง‘๋‹จ๊ณผ D +์ง‘๋‹จ +๊ฐ„์—๋Š” ๋ฌธํ•ด๋ ฅ ์ˆ˜์ค€์ด ์œ ์˜๋ฏธํ•œ ์ฐจ์ด๋ฅผ ๋ณด์ด์ง€ ์•Š์•˜๋‹ค.","{'question': '๋‹ค์Œ ์ž๋ฃŒ์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['๊ฐ‘์˜ ์—ฐ๊ตฌ์—์„œ ๋ชจ์ง‘๋‹จ์€ โ–กโ–ก์ง€์—ญ ๊ณ ๋“ฑํ•™์ƒ์ด๋‹ค.', 'ใ‰ ์€ ์„์˜ ์—ฐ๊ตฌ์—์„œ ์‚ฌ์ „ ๊ฒ€์‚ฌ๋กœ ํ™œ์šฉ๋˜์—ˆ๋‹ค.', 'ใ‰ก์€ ๋ฌธํ•ด๋ ฅ๊ณผ ํ•™์—… ์„ฑ์ทจ๋„ ๊ฐ„์˜ ๋ถ€(-)์˜ ๊ด€๊ณ„๋ฅผ ๋ณด์—ฌ ์ค€๋‹ค.', 'ใ‰ฃ์€ ์„์˜ ์—ฐ๊ตฌ์—์„œ ์‹คํ—˜ ์ฒ˜์น˜์— ํ•ด๋‹นํ•œ๋‹ค.', 'ใ‰ค์€ ใ‰ข์„ ์ง€์ง€ํ•˜๋Š” ๊ทผ๊ฑฐ๋กœ ์‚ฌ์šฉํ•  ์ˆ˜ ์—†๋‹ค.'], 'answer': ''}",,2,5,False,[],5 +2025-SM-06,"๊ฐ‘๊ตญ์—์„œ๋Š” ์†์„ ์”ป์œผ๋ฉด ์˜ํ˜ผ์ด ์˜ค์—ผ๋˜์–ด ๋ชฉ์ˆจ์ด ์œ„ํ—˜ํ•ด์ง„๋‹ค๋Š” +ใ‰ ์ „ํ†ต์  ๋ฏฟ์Œ ๋•Œ๋ฌธ์— ์†์„ ์ž˜ ์”ป์ง€ ์•Š๋Š” ๊ด€์Šต +์ด ์žˆ์—ˆ๋‹ค. ์ด๋กœ ์ธํ•ด ๋งŽ์€ +์‚ฌ๋žŒ์ด ๊ฐ์—ผ๋ณ‘์œผ๋กœ ๋ชฉ์ˆจ์„ ์žƒ์—ˆ๋‹ค. ํ•œ ์˜์‚ฌ๊ฐ€ ์† ์”ป๊ธฐ๋กœ ๊ฑด๊ฐ•์„ ์œ ์ง€ํ•˜๊ณ  +์ƒ๋ช…์„ ์ง€ํ‚ฌ ์ˆ˜ ์žˆ๋‹ค๋Š” ์‚ฌ์‹ค์„ ์•Œ๋ฆฌ๋ฉด์„œ ๊ฐ‘๊ตญ์˜ A์ง€์—ญ์—์„œ๋Š” ใ‰ก์†์„ ์ž˜ +์”ป๋Š” ๋ฌธํ™”๊ฐ€ ํ˜•์„ฑ๋˜์—ˆ๋‹ค. ใ‰ข์ด๋Ÿฌํ•œ ๋ฌธํ™”๊ฐ€ ์กฐ๊ธˆ์”ฉ ํผ์ ธ ๋‚˜๊ฐ€์ž ๋Œ€๋‹ค์ˆ˜ +๊ฐ‘๊ตญ ์‚ฌ๋žŒ๋“ค์€ ์ž์‹ ๋“ค์˜ ๋ฏฟ์Œ์„ ํ•ด์นœ๋‹ค๋Š” ์ด์œ ๋กœ A +์ง€์—ญ ์‚ฌ๋žŒ์„ ๋น„๋‚œ +ํ•˜๋ฉฐ +ใ‰ฃ์ž์‹ ๋“ค์˜ ๋ฌธํ™”๋ฅผ ์ง€ํ‚ค๊ธฐ ์œ„ํ•ด ์ €ํ•ญํ•˜์˜€๋‹ค. ๊ฐ‘๊ตญ์—์„œ ๊ฐ์—ผ๋ณ‘์ด ์œ ํ–‰ +ํ–ˆ์„ ๋•Œ, A์ง€์—ญ ์‚ฌ๋ง๋ฅ ์€ ๋‹ค๋ฅธ ์ง€์—ญ์— ๋น„ํ•ด ํ˜„์ €ํžˆ ๋‚ฎ์•˜๋‹ค. ์† ์”ป๋Š” ๊ฐ„๋‹จํ•œ +ํ–‰์œ„๋กœ ์งˆ๋ณ‘์„ ์˜ˆ๋ฐฉํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ์‚ฌ์‹ค์„ ๊นจ๋‹ซ์ž ๊ฐ‘๊ตญ์—์„œ๋Š” ใ‰ค์†์„ ์ž˜ ์”ป์–ด +์œ„์ƒ ๊ด€๋ฆฌ๋ฅผ ์ฒ ์ €ํžˆ ํ•˜๋Š” ์ƒํ™œ ์Šต๊ด€์ด ๋ณดํŽธํ™”๋˜์—ˆ๋‹ค.","{'question': '๋ฐ‘์ค„ ์นœ ใ‰ ๏ฝžใ‰ค์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['ใ‰ ์€ ์ง€๋ฐฐ ์„ธ๋ ฅ์— ๋ฐ˜๋ฐœํ•˜์—ฌ ์‚ฌํšŒ ํ†ตํ•ฉ์ด ์ด๋ฃจ์–ด์ง„ ์‚ฌ๋ก€์ด๋‹ค.', 'ใ‰ก์€ ์ฃผ๋ฅ˜ ๋ฌธํ™”๊ฐ€ ํ•˜์œ„๋ฌธํ™”๋กœ ๋ณ€ํ•œ ์‚ฌ๋ก€์ด๋‹ค.', 'ใ‰ข์€ ๋ฌธํ™” ๋ณ€๋™์ด ๋น ๋ฅด๊ฒŒ ์ง„ํ–‰๋˜์–ด ๋‚˜ํƒ€๋‚œ ๋ฌธํ™” ์ง€์ฒด ์‚ฌ๋ก€์ด๋‹ค.', 'ใ‰ฃ์€ ์ง€์—ญ ๋ฌธํ™”๊ฐ€ ์ฃผ๋ฅ˜ ๋ฌธํ™”์— ๋Œ€ํ•ญํ•œ ๋ฐ˜๋ฌธํ™” ์‚ฌ๋ก€์ด๋‹ค.', 'ใ‰ค์€ ํ•˜์œ„๋ฌธํ™”๊ฐ€ ์ฃผ๋ฅ˜ ๋ฌธํ™”๋กœ ๋ณ€ํ•œ ์‚ฌ๋ก€์ด๋‹ค'], 'answer': ''}",,5,5,True,[],5 +2025-SM-07,"๊ธ‰๋ณ€ํ•˜๋Š” ์„ธ์ƒ์—์„œ ์‚ฌ๋žŒ๋“ค์€ ๋ฌดํ•œํžˆ ์ œ๊ณต๋˜๋Š” ์ •๋ณด๋ฅผ ๋ชจ๋‘ ์‚ดํŽด๋ณผ +์—ฌ์œ ๊ฐ€ ์—†๋‹ค. ๊ทธ๋กœ ์ธํ•ด ์‚ฌํšŒ ์ด์Šˆ๋ฅผ ์ง๊ด€์ ์œผ๋กœ ์ดํ•ดํ•  ์ˆ˜ ์žˆ๊ฒŒ ๊ฐ€๊ณตํ•œ +์ฝ˜ํ…์ธ ๋“ค์ด ์ธ๊ธฐ๋ฅผ ์–ป๋Š”๋‹ค. ์‚ฌ๋žŒ๋“ค์€ ๊ฐ€๊ณต๋œ ์ฝ˜ํ…์ธ ๋ฅผ ์†Œ๋น„ํ•  ๋•Œ ์ž์‹ ์ด +์ •๋ณด๋ฅผ ์ฐพ๊ณ  ์Šค์Šค๋กœ ์ƒ๊ฐํ•ด ํŒ๋‹จํ•œ๋‹ค๊ณ  ๋А๋‚€๋‹ค. ํ•˜์ง€๋งŒ ํ•ด๋‹น ์ฝ˜ํ…์ธ ์—๋Š” +์ œ์ž‘์ž์˜ ํŽธํ–ฅ๋œ ์‹œ๊ฐ์ด ๋ฐ˜์˜๋˜์–ด ์žˆ์–ด ์ •๋ณด๋ฅผ ๋ฐ›์•„๋“ค์ด๋Š” ๋Œ€์ค‘์€ +์ œ์ž‘์ž์˜ ์‹œ๊ฐ์— ๋™ํ™”๋œ๋‹ค. ์ด์ฒ˜๋Ÿผ ์‚ฌ์œ ๋ฅผ ์™ธ์ฃผํ™”ํ•˜๋Š” ์‚ฌ๋žŒ๋“ค์ด ๋งŽ์•„์ง€๋ฉด +๋น„์Šทํ•œ ์ƒ๊ฐ์„ ๊ฐ€์ง„ ์‚ฌ๋žŒ๋“ค์ด ํ์‡„์  ์ง‘๋‹จ์— ๋จธ๋ฌผ๋ฉฐ ๋‹ค๋ฅธ ์ƒ๊ฐ์„ ๊ฐ€์ง„ +์‚ฌ๋žŒ๋“ค์„ ๋ฐฐ์ฒ™ํ•˜๋Š” ์ƒํ™ฉ์ด ๋ฐœ์ƒํ•œ๋‹ค. ์ด๋Š” ๋‹ค์›ํ™”๋œ ๋ฏผ์ฃผ ์‚ฌํšŒ์˜ ํ˜•์„ฑ์„ +์–ด๋ ต๊ฒŒ ๋งŒ๋“ ๋‹ค. ๋””์ง€ํ„ธ ๊ธฐ์ˆ ์ด ์ •๋ณด์˜ ์†Œ๋น„ ์„ ํƒ์„ฑ๊ณผ ์ƒ์‚ฐ ์ฃผ์ฒด์„ฑ์„ +๋†’์—ฌ ์ค„ ์ˆ˜๋Š” ์žˆ์ง€๋งŒ ๊ทธ ์ž์ฒด๊ฐ€ ์ง€์„ฑ์ ์ธ ๋Œ€์ค‘์„ ๋งŒ๋“œ๋Š” ๊ฒƒ์€ ์•„๋‹ˆ๋‹ค. +๊ฐœ์ธ์€ ์ง€์„ฑ์  ์‚ฌ์œ ์˜ ์ฃผ์ฒด๊ฐ€ ๋˜์–ด์•ผ ํ•œ๋‹ค.","{'question': '๋‹ค์Œ ๊ธ€์—์„œ ํ•„์ž๊ฐ€ ๊ฐ•์กฐํ•˜๋Š” ํ˜„๋Œ€ ์‚ฌํšŒ์˜ ๋Œ€์ค‘์ด ๊ฐ€์ ธ์•ผ ํ•  ์ž์„ธ๋กœ ๊ฐ€์žฅ ์ ์ ˆํ•œ ๊ฒƒ์€?', 'choices': ['์ •๋ณด ๊ธฐ๊ธฐ์— ๋Œ€ํ•œ ๊ณผ๋„ํ•œ ์˜์กด์„ ๊ฒฝ๊ณ„ํ•œ๋‹ค.', '์ •๋ณด๋ฅผ ๋น„ํŒ์ ์œผ๋กœ ๋ถ„์„ํ•˜๊ณ  ํ‰๊ฐ€ํ•˜๋Š” ๋Šฅ๋ ฅ์„ ํ•จ์–‘ํ•œ๋‹ค.', '๋ฌธํ™”์˜ ์งˆ์ ์ €ํ•˜ ๋ฐฉ์ง€๋ฅผ ์œ„ํ•ด ์ง€๋‚˜์นœ ์ƒ์—…์„ฑ์„ ๊ฒฝ๊ณ„ํ•œ๋‹ค.', '๋ฌธํ™”์˜ ๋‹ค์–‘์„ฑ ์ œ๊ณ ๋ฅผ ์œ„ํ•ด ์ฝ˜ํ…์ธ  ์ƒ์‚ฐ์— ์ ๊ทน์ ์œผ๋กœ ์ฐธ์—ฌํ•œ๋‹ค.', 'ํ‘œํ˜„์˜ ์ž์œ ๋ฅผ ์ด์œ ๋กœ ํƒ€์ธ์˜ ๊ถŒ๋ฆฌ๋ฅผ ์นจํ•ดํ•˜์ง€ ์•Š๋„๋ก ์œ ์˜ํ•œ๋‹ค.'], 'answer': ''}",,2,2,True,[],2 +2025-SM-08," 1970๋…„๋Œ€์— โ–กโ–ก๊ตญ์ œ ์‹œ๋ฏผ ๋‹จ์ฒด๋Š” ๋ถ„์œ ๋ฅผ ๋งŒ๋“œ๋Š” โ—‹โ—‹๋‹ค๊ตญ์  ๊ธฐ์—…์— +๋Œ€ํ•œ ใ‰ ๋ถˆ๋งค ์šด๋™์„ ์ „๊ฐœํ•˜์˜€๋‹ค. ใ‰ก์ €๊ฐœ๋ฐœ๊ตญ์— ๋ถ„์œ ๋ฅผ ๋ฌด๋ฃŒ๋กœ ๋‚˜๋ˆ„์–ด ์ฃผ๋Š” +โ—‹โ—‹ +๋‹ค๊ตญ์  ๊ธฐ์—…์˜ ๊ณต๊ฒฉ์  ๋งˆ์ผ€ํŒ… +์œผ๋กœ ์ €๊ฐœ๋ฐœ๊ตญ์˜ ์˜์•„ ์‚ฌ๋ง๋ฅ ์ด ๊ธ‰๊ฒฉํžˆ +์ฆ๊ฐ€ํ•˜์˜€๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ์œ„์ƒ์ ์ธ ํ™˜๊ฒฝ์ด ๊ฐ–์ถ”์–ด์ง€์ง€ ์•Š์€ ์ €๊ฐœ๋ฐœ๊ตญ์—์„œ +์„ธ๊ท ์— ์˜ค์—ผ๋œ ๋ฌผ๊ณผ ์ –๋ณ‘์œผ๋กœ ์ธํ•ด ์„ค์‚ฌ์™€ ์—ด๋ณ‘์ด ๋ฐœ์ƒํ•ด ๋งŽ์€ ์•„๊ธฐ๊ฐ€ +์‚ฌ๋งํ•˜์˜€๋‹ค. โ—‹โ—‹๋‹ค๊ตญ์  ๊ธฐ์—…์— ์„ ์˜์˜ ์˜๋„๊ฐ€ ์žˆ์—ˆ์„์ง€๋ผ๋„, ๋ฌด๋ถ„๋ณ„ํ•œ +์‹œ์žฅ ํ™•๋Œ€๊ฐ€ ใ‰ข์ €๊ฐœ๋ฐœ๊ตญ์˜ ์˜์•„ ๊ฑด๊ฐ•์„ ์‹ฌ๊ฐํ•˜๊ฒŒ ์œ„ํ˜‘ํ•˜๋Š” ๊ฒฐ๊ณผ๋ฅผ +์ดˆ๋ž˜ํ•œ ๊ฒƒ +์ด๋‹ค. ์ด๋กœ ์ธํ•ด โ—‹โ—‹๋‹ค๊ตญ์  ๊ธฐ์—…์ด ์˜์•„ ์‚ฌ๋ง์— ๋Œ€ํ•ด ์ฑ…์ž„์„ +์ ธ์•ผ ํ•œ๋‹ค๋ฉฐ โ–กโ–ก๊ตญ์ œ ์‹œ๋ฏผ ๋‹จ์ฒด๊ฐ€ ์ „ ์„ธ๊ณ„์˜ ์†Œ๋น„์ž๋“ค๊ณผ ใ‰ฃ์ง‘๋‹จํ–‰๋™์„ +์‹œ์ž‘ํ–ˆ๋‹ค. ์ด๋ฅผ ๊ณ„๊ธฐ๋กœ ์—ฌ๋Ÿฌ ๊ตญ์ œ ์‹œ๋ฏผ ๋‹จ์ฒด๋Š” ๋‹ค๊ตญ์  ๊ธฐ์—…์˜ ์œค๋ฆฌ์  +์ฑ…์ž„์„ ์š”๊ตฌํ•˜๊ณ , ๋‚˜์•„๊ฐ€ ํ™˜๊ฒฝ ๋ฌธ์ œ, ์ž์› ๋ฌธ์ œ์™€ ๊ฐ™์€ ใ‰ค์ „ ์ง€๊ตฌ์  +์ˆ˜์ค€์˜ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•œ ํ™œ๋™ +์„ ์ง€์†์ ์œผ๋กœ ์ „๊ฐœํ•˜๊ณ  ์žˆ๋‹ค.","{'question': '๋ฐ‘์ค„ ์นœ ใ‰ ๏ฝžใ‰ค์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['ใ‰ ์€ ๊ธ‰๊ฒฉํ•œ ์‚ฌํšŒ ๋ณ€๋™์— ์ €ํ•ญํ•˜๊ธฐ์œ„ํ•ด ํŽผ์น˜๋Š” ์‚ฌํšŒ์šด๋™์ด๋‹ค.', 'ใ‰ก์€ ์‹๋Ÿ‰ ์ž์› ํ™•๋ณด๋ฅผ ์œ„ํ•œ ๊ตญ๊ฐ€ ๊ฐ„ ๊ฒฝ์Ÿ์ด ์ดˆ๋ž˜ํ•œ ๋ฌธ์ œ์ด๋‹ค.', 'ใ‰ข์€ โ—‹โ—‹๋‹ค๊ตญ์  ๊ธฐ์—…์˜ ์ด์œค ์ถ”๊ตฌ๋ฅผ ์ •๋‹นํ™”ํ•˜๋Š” ๊ทผ๊ฑฐ๊ฐ€ ๋œ๋‹ค.', 'ใ‰ฃ์€ ์‚ฌํšŒ ์ฒด์ œ์˜ ์ „๋ฉด์ ์ธ ๋ณ€ํ˜์„ ์ถ”๊ตฌํ•˜๋Š” ์‚ฌํšŒ ์šด๋™์ด๋‹ค.', 'ใ‰ค์€ ์„ธ๊ณ„ ์‹œ๋ฏผ ์˜์‹์„ ๋ฐ”ํƒ•์œผ๋กœ ํ•˜๋Š” ์กฐ์ง์ ์ธ ์‚ฌํšŒ ์šด๋™์ด๋‹ค.'], 'answer': ''}",,5,5,True,[],5 +2025-SM-09,"๊ต์‚ฌ: ๋ฌธํ™”์˜ ์†์„ฑ 5๊ฐ€์ง€๋ฅผ ๋ชจ๋‘ ๋ณ„๋กœ ์„œ๋กœ ๋‹ค๋ฅด๊ฒŒ ํ•œ ๊ฐ€์ง€์”ฉ ๋ฐฐ์ • +ํ•˜์˜€์Šต๋‹ˆ๋‹ค. ๊ฐ ๋ชจ๋‘ ์€ ๋ฐฐ์ •๋ฐ›์€ ์†์„ฑ์ด ๋ถ€๊ฐ๋œ ์‚ฌ๋ก€๋ฅผ ์›นํˆฐ ๋ฌธํ™”์—์„œ +์ฐพ์•„ ๋ฐœํ‘œํ•ด ๋ด…์‹œ๋‹ค. + <1 ๋ชจ๋‘ > ๋ถ€๋ชจ๊ฐ€ ์ž๋…€์—๊ฒŒ ์Šค๋งˆํŠธํฐ์„ ํ™œ์šฉํ•˜์—ฌ ์›นํˆฐ ์•ฑ์„ ์ด์šฉํ•˜๋Š” +๋ฐฉ๋ฒ•์„ ๋ฐฐ์šฐ๋Š” ๊ฒƒ์€ A๊ฐ€ ๋ถ€๊ฐ๋œ ์‚ฌ๋ก€์ž…๋‹ˆ๋‹ค. + <2 ๋ชจ๋‘ > ๋ถ€๋ชจ ์„ธ๋Œ€์—์„œ ์›นํˆฐ์„ ๋งŒํ™”๋ผ๊ณ  ๋ถ€๋ฅด๊ณ  ๋งŒํ™”๊ฐ€ ๋ณด๊ณ  ์‹ถ์„ ๋•Œ +๋งŒํ™”๋ฐฉ์„ ๋– ์˜ฌ๋ฆฌ๋Š” ๊ฒƒ์€ B๊ฐ€ ๋ถ€๊ฐ๋œ ์‚ฌ๋ก€์ž…๋‹ˆ๋‹ค. + <3 ๋ชจ๋‘ > ๋งŒํ™”์ฑ…์„ ๋ณด๋Š” ์‚ฌ๋žŒ์ด ์ค„์–ด๋“ค๊ณ  ํƒœ๋ธ”๋ฆฟ PC๋กœ ์›นํˆฐ์„ ๋ณด๋Š” +์‚ฌ๋žŒ์ด ๋Š˜์–ด๋‚œ ๊ฒƒ์€ C๊ฐ€ ๋ถ€๊ฐ๋œ ์‚ฌ๋ก€์ž…๋‹ˆ๋‹ค. + <4 ๋ชจ๋‘ > ๋ถ€๋ชจ ์„ธ๋Œ€์—์„œ ๋ˆˆ์œผ๋กœ๋งŒ ์ฆ๊ธฐ๋˜ ๋งŒํ™”์— ์Œ์„ฑ ์ง€์›, ๋ฐฐ๊ฒฝ ์Œ์•… +์žฌ์ƒ ๊ธฐ๋Šฅ ๋“ฑ์ด ์ถ”๊ฐ€๋œ ํ˜„์žฌ์˜ ์›นํˆฐ์€ D๊ฐ€๋ถ€๊ฐ๋œ ์‚ฌ๋ก€์ž…๋‹ˆ๋‹ค. +ํ•™์ƒ: ์„ ์ƒ๋‹˜, <2๋ชจ๋‘ >์˜ ๋ฐœํ‘œ ์‚ฌ๋ก€๋Š” D๊ฐ€ ๋ถ€๊ฐ๋œ ๊ฒƒ์ด ์•„๋‹๊นŒ์š”? +๊ต์‚ฌ:<2๋ชจ๋‘ >์˜ ์‚ฌ๋ก€๋Š” D๋กœ๋„ ์„ค๋ช…์ด ๊ฐ€๋Šฅํ•˜์ง€๋งŒ, ๋ถ€๋ชจ ์„ธ๋Œ€์—์„œ +๋งŒํ™”๋ฐฉ์„ ๋– ์˜ฌ๋ฆฐ๋‹ค๊ณ  ํ–ˆ๊ธฐ ๋•Œ๋ฌธ์— B๊ฐ€ ๋ถ€๊ฐ๋œ ๊ฒƒ์ด ๋งž์Šต๋‹ˆ๋‹ค. +<3 ๋ชจ๋‘ >๊ณผ <4๋ชจ๋‘ >์€ ๋ฐœํ‘œํ•œ ์‚ฌ๋ก€๊ฐ€ ์„œ๋กœ ๋ฐ”๋€Œ์–ด์•ผ ๊ฐ ๋ชจ๋‘ ์— +๋ฐฐ์ •๋œ ์†์„ฑ์ด ๋ถ€๊ฐ๋ฉ๋‹ˆ๋‹ค. <1๋ชจ๋‘ >์€ <5๋ชจ๋‘ >์— ๋ฐฐ์ •๋œ ์†์„ฑ์ด +๋ถ€๊ฐ๋œ ์‚ฌ๋ก€๋ฅผ ๋ฐœํ‘œํ–ˆ์–ด์š”. A๋ฅผ ๋ฐฐ์ •๋ฐ›์€ <1๋ชจ๋‘ >๊ณผ E๋ฅผ ๋ฐฐ์ •๋ฐ›์€ +<5๋ชจ๋‘ >์€ ๋‹ค์Œ ์‹œ๊ฐ„์— ๋ฐœํ‘œํ•ฉ์‹œ๋‹ค.","{'question': '๋‹ค์Œ ์ž๋ฃŒ์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€? (๋‹จ, A๏ฝžE๋Š” ๊ฐ๊ฐ ๊ณต์œ ์„ฑ, ๋ณ€๋™์„ฑ, ์ „์ฒด์„ฑ, ์ถ•์ ์„ฑ, ํ•™์Šต์„ฑ ์ค‘ ํ•˜๋‚˜์ž„.)', 'choices': ['๋ฌธํ™”๊ฐ€ ํ•œ ์‚ฌํšŒ ๊ตฌ์„ฑ์›์˜ ๊ณตํ†ต๋œ ์ƒํ™œ ์–‘์‹์ด๋ผ๋Š” ๊ฒƒ์„ ์˜๋ฏธํ•˜๋Š” ์†์„ฑ์€ B๊ฐ€ ์•„๋‹ˆ๋ผ A์ด๋‹ค.', '๋ฌธํ™”๊ฐ€ ์„ธ๋Œ€ ๊ฐ„ ์ „์Šน๋˜๋ฉฐ ๋”์šฑ ๋ฐœ์ „๋˜๊ณ  ํ’๋ถ€ํ•ด์ง€๋Š” ๊ฒƒ์„ ์˜๋ฏธํ•˜๋Š” ์†์„ฑ์€ C๊ฐ€ ์•„๋‹ˆ๋ผ D์ด๋‹ค.', '<1๋ชจ๋‘ >์— ๋ฐฐ์ •๋œ ์†์„ฑ์€ ๋ฌธํ™”์˜ ๊ฐ ์š”์†Œ๋“ค์ด ์ƒํ˜ธ ์œ ๊ธฐ์ ์œผ๋กœ ์—ฐ๊ฒฐ๋˜์–ด ์˜ํ–ฅ์„ ์ฃผ๊ณ ๋ฐ›๋Š” ๊ฒƒ์„ ์˜๋ฏธํ•œ๋‹ค.', '<2๋ชจ๋‘ >์— ๋ฐฐ์ •๋œ ์†์„ฑ์€ ๊ณต์œ ์„ฑ, <5๋ชจ๋‘ >์— ๋ฐฐ์ •๋œ ์†์„ฑ์€ ์ „์ฒด์„ฑ์ด๋‹ค.', '๋ฌธํ™”๊ฐ€ ์ƒ์ง•์„ ํ†ตํ•ด ํ›„์ฒœ์ ์œผ๋กœ ํ•™์Šต๋œ๋‹ค๋Š” ๊ฒƒ์„ ์˜๋ฏธํ•˜๋Š” ์†์„ฑ์€ <3 ๋ชจ๋‘ >์ด ์•„๋‹ˆ๋ผ <4๋ชจ๋‘ >์— ๋ฐฐ์ •๋˜์—ˆ๋‹ค.'], 'answer': ''}",,3,2,False,[],2 +2025-SM-11,"ใ‰ ๋Œ€๋ถ€๋ถ„์˜ ์‚ฌํšŒ์—๋Š” ๊ณ ์ธ(ๆ•…ไบบ)์„ ๋– ๋‚˜๋ณด๋‚ผ ๋•Œ ์น˜๋ฅด๋Š” ์˜๋ก€๊ฐ€ ์กด์žฌ +ํ•˜๋ฉฐ, ์„ธ๊ณ„ ๊ฐ์ง€์—๋Š” ๋‹ค์–‘ํ•œ ใ‰ก์žฅ๋ก€ ๋ฌธํ™”๊ฐ€ ์žˆ๋‹ค. โ—‹โ—‹์กฑ์€ ๊นŠ์€ ์‚ฐ์ด๋‚˜ +๋“ค๋…˜์—์„œ ์žˆ๋Š” ใ‰ข๋‚˜๋ฌด ์œ„์— ์‹œ์‹ ์„ ๋‘๋Š” ๋ฐฉ์‹์œผ๋กœ ์žฅ๋ก€๋ฅผ ์น˜๋ฅธ๋‹ค. ์–ด๋–ค +์‚ฌ๋žŒ๋“ค์€์ด๋Ÿฐ ๋ฐฉ์‹์„ ใ‰ฃ์ž์‹ ์˜ ๋ฌธํ™”๋ฅผ ๊ธฐ์ค€์œผ๋กœ ๋น„์ธ๊ฐ„์ ์ด๊ณ  ๊ธฐ์ดํ•œ +๊ด€์Šต์ด๋ผ ํ„ํ•˜ +ํ•œ๋‹ค. ํ•˜์ง€๋งŒ โ—‹โ—‹์กฑ์˜ ์žฅ๋ก€ ๋ฌธํ™”๋Š”, ์กฐ์ƒ์˜ ์ •๋ น์ด ํ›„์†์„ +์™ธ๋ถ€์˜ ์œ„ํ—˜์œผ๋กœ๋ถ€ํ„ฐ ๋ณดํ˜ธํ•ด ์ค€๋‹ค๋Š” ใ‰ค์ข…๊ต +์  ๋ฏฟ์Œ์˜ ๊ฒฐ๊ณผ๋ฌผ์ด๋‹ค. +์ด์ฒ˜๋Ÿผ ํ•ด๋‹น ์‚ฌํšŒ์˜ ๋งฅ๋ฝ์—์„œ ๊ฐ ๋ฌธํ™”๊ฐ€ ๊ฐ–๋Š” ๊ณ ์œ ํ•œ ์˜๋ฏธ๋ฅผ ํŒŒ์•…ํ•˜๋ ค๋ฉด +(๊ฐ€)ํ•˜๋Š” ํƒœ๋„๋ฅผ ์ง€๋…€์•ผ ํ•œ๋‹ค.","{'question': '๋‹ค์Œ ์ž๋ฃŒ์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['ใ‰ ์€ ๋ฌธํ™”์˜ ํŠน์ˆ˜์„ฑ์„ ๋‚˜ํƒ€๋‚ธ๋‹ค.', 'ใ‰ก์—์„œ โ€˜๋ฌธํ™”โ€™๋Š” ์ข์€ ์˜๋ฏธ์˜ ๋ฌธํ™”์ด๋‹ค.', 'ใ‰ข์€ ๋น„๋ฌผ์งˆ๋ฌธํ™”์—, ใ‰ค์€ ๋ฌผ์งˆ๋ฌธํ™”์— ํ•ด๋‹นํ•œ๋‹ค.', 'ใ‰ฃ๊ณผ ๊ฐ™์€ ํƒœ๋„๋Š” ๊ตญ์ˆ˜์ฃผ์˜๋กœ ๋ณ€์งˆ๋  ์ˆ˜ ์žˆ๋‹ค๋Š” ๋น„ํŒ์„ ๋ฐ›๋Š”๋‹ค.', '(๊ฐ€)์—๋Š” โ€˜์ž๊ธฐ ๋ฌธํ™”๋ฅผ ๋‚ฎ์ถ”๊ณ  ํƒ€ ๋ฌธํ™”์˜ ์šฐ์ˆ˜์„ฑ์„ ๋™๊ฒฝโ€™์ด ์ ์ ˆํ•˜๋‹ค.'], 'answer': ''}",,4,4,True,[],4 +2025-SM-12,"๊ณ„์‚ฐ์  ์‹ฌ์„ฑ์€ ๊ฐœ์ธ๋“ค์ด ์ผ์ƒ์—์„œ ๊ฒฐ๊ณผ๋ฅผ ์˜ˆ์ธกํ•˜๊ณ  ์ตœ์„ ์˜ ์ˆ˜๋‹จ์„ +์„ ํƒํ•˜์—ฌ ํ•ฉ๋ชฉ์ ์ ์œผ๋กœ ํ–‰๋™ํ•˜๋„๋ก ํ•œ๋‹ค. ๊ตญ๊ฐ€ ๊ด€๋ฃŒ์ œ์— ๊ธฐ๋ฐ˜์„ ๋‘” ํ–‰์ •๊ณผ +๋กœ๋งˆ๋ฒ•์— ๊ธฐ์ดˆํ•œ ๋ฒ•๋ฅ ์€ ์„œ๊ตฌ์ธ๋“ค๋กœ ํ•˜์—ฌ๊ธˆ ํ•ฉ๋ชฉ์ ์ ์œผ๋กœ ํ–‰๋™ํ•˜๋„๋ก +ํ•˜์˜€๋‹ค. ์ด๋ ‡๊ฒŒ ์„œ๊ตฌ ์‚ฌํšŒ์˜ ํ–‰์ •๊ณผ ๋ฒ•๋ฅ ์— ์˜ํ•ด ๋งŒ๋“ค์–ด์ง„ ๊ณ„์‚ฐ์  ์‹ฌ์„ฑ์€ +๊ทผ๋Œ€์  ๊ฒฝ์ œ ์„ฑ์žฅ์„ ์ด๋Œ์—ˆ๋‹ค.","{'question': '๋‹ค์Œ ๊ธ€์—์„œ ๊ฐœ์ธ๊ณผ ์‚ฌํšŒ์˜ ๊ด€๊ณ„๋ฅผ ๋ฐ”๋ผ๋ณด๋Š” ํ•„์ž์˜ ๊ด€์ ์— ๋Œ€ํ•œ ์˜ณ์€ ์„ค๋ช…๋งŒ์„ <๋ณด๊ธฐ>์—์„œ ๊ณ ๋ฅธ ๊ฒƒ์€?', 'choices': ['ใ„ฑ, ใ„ด', 'ใ„ฑ, ใ„ท', 'ใ„ด,ใ„ท', 'ใ„ด, ใ„น', 'ใ„ท, ใ„น'], 'answer': '', 'question_plus': 'ใ„ฑ. ์‚ฌํšŒ์— ์˜ํ•ด ๊ฐœ์ธ์€ ๊ตฌ์กฐํ™”๋œ ํ–‰๋™์„ ํ•œ๋‹ค๊ณ  ๋ณธ๋‹ค.\r\nใ„ด. ์‚ฌํšŒ์˜ ์†์„ฑ์€ ๊ฐœ์ธ์˜ ์†์„ฑ์— ์˜ํ•ด ๊ฒฐ์ •๋œ๋‹ค๊ณ  ๋ณธ๋‹ค.\r\nใ„ท. ์‚ฌํšŒ๋Š” ๊ฐœ์ธ ์™ธ๋ถ€์— ์กด์žฌํ•˜๋Š” ๋…๋ฆฝ์ ์ธ ์‹ค์ฒด๋ผ๊ณ  ๋ณธ๋‹ค.\r\nใ„น. ์‚ฌํšŒ๋Š” ๊ฐœ์ธ ์ด์ต์„ ์‹คํ˜„ํ•ด ์ฃผ๋Š” ๋„๊ตฌ์ผ ๋ฟ์ด๋ผ๊ณ  ๋ณธ๋‹ค.'}","ใ„ฑ. ์‚ฌํšŒ์— ์˜ํ•ด ๊ฐœ์ธ์€ ๊ตฌ์กฐํ™”๋œ ํ–‰๋™์„ ํ•œ๋‹ค๊ณ  ๋ณธ๋‹ค. +ใ„ด. ์‚ฌํšŒ์˜ ์†์„ฑ์€ ๊ฐœ์ธ์˜ ์†์„ฑ์— ์˜ํ•ด ๊ฒฐ์ •๋œ๋‹ค๊ณ  ๋ณธ๋‹ค. +ใ„ท. ์‚ฌํšŒ๋Š” ๊ฐœ์ธ ์™ธ๋ถ€์— ์กด์žฌํ•˜๋Š” ๋…๋ฆฝ์ ์ธ ์‹ค์ฒด๋ผ๊ณ  ๋ณธ๋‹ค. +ใ„น. ์‚ฌํšŒ๋Š” ๊ฐœ์ธ ์ด์ต์„ ์‹คํ˜„ํ•ด ์ฃผ๋Š” ๋„๊ตฌ์ผ ๋ฟ์ด๋ผ๊ณ  ๋ณธ๋‹ค.",2,2,True,[],2 +2025-SM-13,"โ—ฆ๊ฐ‘์€ ์ฒญ์†Œ๋…„์ด ํœด๋Œ€ ์ „ํ™”์— ๋ถ€์—ฌํ•˜๋Š” ์˜๋ฏธ๋ฅผ ํŒŒ์•…ํ•˜๊ธฐ ์œ„ํ•ด โ—‹โ—‹๊ณ ๋“ฑํ•™๊ต +ํ•™์ƒ์˜ ์ผ์ƒ์ƒํ™œ์„ ๊ด€์ฐฐํ•œ ์—ฐ๊ตฌ ๊ธฐ๊ด€์˜ ๋ณด๊ณ ์„œ๋ฅผ ๋ถ„์„ํ•จ. ์ดํ›„ ํœด๋Œ€ ์ „ํ™” +์˜์กด๋„๊ฐ€ ๋†’์€ ํ•™์ƒ๋“ค์—๊ฒŒ ์งˆ๋ฌธํ•˜์—ฌ ์‚ฌ์šฉ ์šฉ๋„์™€ ์ค‘๋… ์ฆ์ƒ ๋“ฑ์— ๋Œ€ํ•œ +์ด์•ผ๊ธฐ๋ฅผ ๊นŠ์ด ์žˆ๊ฒŒ ๋‚˜๋ˆ„๊ณ  ์ด ๊ณผ์ •์„ ๋…น์Œํ•จ. + โ—ฆ์„์€ ํŒฌ๋ค ๋ฌธํ™” ์—ฐ๊ตฌ๋ฅผ ์œ„ํ•ด โ˜†โ˜†์•ผ๊ตฌ๋‹จ์˜ ํŒฌํด๋Ÿฝ์— ๊ฐ€์ž…ํ•˜์—ฌ 6๊ฐœ์›”๊ฐ„ +ํšŒ์›๋“ค๊ณผ ๊ฒฝ๊ธฐ๋ฅผ ๊ด€๋žŒํ•˜๋ฉฐ ๊ทธ๋“ค์˜ ๋Œ€ํ™”์™€ ์‘์› ๋ชจ์Šต์„ ๊ธฐ๋กํ•จ. ์ดํ›„ +์•„์ด๋Œ ํŒฌํด๋Ÿฝ์˜ ์—ด์„ฑํŒฌ์„ ๋Œ€์ƒ์œผ๋กœ ๊ทธ๋“ค๋งŒ์˜ ์นœ๋ฐ€ํ•œ ๊ด€๊ณ„๋ฅผ ํ˜•์„ฑํ•œ +๊ฒฝํ—˜์„ ์ง์ ‘ ๋“ฃ๊ณ  ์‹ฌ์ธต์ ์ธ ์ž๋ฃŒ๋ฅผ ์–ป์Œ. + โ—ฆ๋ณ‘์€ ๋Œ€ํ•™์ƒ์˜ ์ •์น˜ ์„ฑํ–ฅ๊ณผ ์ •์น˜ ์ฐธ์—ฌ ์—ฐ๊ตฌ๋ฅผ ์œ„ํ•ด ๋Œ€ํ•™์ƒ 500๋ช…์„ +๋Œ€์ƒ์œผ๋กœ ๊ตฌ์กฐํ™”๋œ ๋ฌธํ•ญ์— ์‘๋‹ตํ•˜๋„๋ก ํ•จ. ๋˜ํ•œ ์„ ๊ฑฐ ๊ด€๋ จ ๊ธฐ๊ด€์ด +๋ฐœ๊ฐ„ํ•œ ๋Œ€ํ•™์ƒ ์ •์น˜ ์„ฑํ–ฅ ๋ฉด์ ‘ ์กฐ์‚ฌ ์ž๋ฃŒ์ง‘์„ ๋ถ„์„ํ•˜์—ฌ ๋Œ€ํ•™์ƒ์˜ ์ •์น˜ +์ฐธ์—ฌ ๊ณผ์ •์„ ์—ฐ๊ตฌํ•จ.","{'question': '๊ฐ‘๏ฝž๋ณ‘์ด ์‚ฌ์šฉํ•œ ์ž๋ฃŒ ์ˆ˜์ง‘ ๋ฐฉ๋ฒ•์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['๊ฐ‘๊ณผ ๋‹ฌ๋ฆฌ ์„์€ ํ‘œ์ค€ํ™”๋œ ๋„๊ตฌ๋กœ ๋Œ€๋Ÿ‰์˜ ์ž๋ฃŒ๋ฅผ ํš๋“ํ•˜๊ธฐ ์šฉ์ดํ•œ ์ž๋ฃŒ ์ˆ˜์ง‘ ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•˜์˜€๋‹ค.', '๋ณ‘๊ณผ ๋‹ฌ๋ฆฌ ๊ฐ‘์€ ์ธ์œ„์ ์œผ๋กœ ํ†ต์ œ๋œ ์ƒํ™ฉ์—์„œ ๋ณ€์ˆ˜์˜ ํšจ๊ณผ๋ฅผ ๊ด€์ฐฐํ•˜๋Š” ์ž๋ฃŒ ์ˆ˜์ง‘ ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•˜์˜€๋‹ค.', '๊ฐ‘๊ณผ ์„ ๋ชจ๋‘ ํ˜„์ง€์—์„œ ์—ฐ๊ตฌ ๋Œ€์ƒ์ž์™€ ํ•จ๊ป˜ ์ƒํ™œํ•˜๋ฉฐ ๊ด€์‹ฌ์„ ๊ฐ–๋Š” ์—ฐ๊ตฌ ํ˜„์ƒ์„ ๊ด€์ฐฐํ•˜๋Š” ์ž๋ฃŒ ์ˆ˜์ง‘ ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•˜์˜€๋‹ค.', '๊ฐ‘๊ณผ ๋ณ‘ ๋ชจ๋‘ ๊ธฐ์กด์˜ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋ฌผ์„ ์ž์‹ ์˜ ์—ฐ๊ตฌ์— ํ™œ์šฉํ•˜๋Š” ์ž๋ฃŒ ์ˆ˜์ง‘ ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•˜์˜€๋‹ค.', '์„๊ณผ ๋ณ‘ ๋ชจ๋‘ ์—ฐ๊ตฌ ๋Œ€์ƒ์ž์™€์˜ ์ •์„œ์  ๊ต๊ฐ ํ˜•์„ฑ์„ ์ค‘์‹œํ•˜๋Š” ์ž๋ฃŒ ์ˆ˜์ง‘ ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•˜์˜€๋‹ค.'], 'answer': ''}",,4,3,False,"['์—ฐ๊ตฌ์ž ๊ฐ‘์€ ์šฐ๋ฆฌ๋‚˜๋ผ ๋Œ€ํ•™์ƒ์˜ ใ‰ ๋Œ€ํ•™ ์ƒํ™œ์— ๋Œ€ํ•œ ๋งŒ์กฑ๋„์— ใ‰ก๋Œ€ํ•™ ๋‚ด ์‚ฌํšŒ์  ๊ด€๊ณ„์˜ ์ •๋„๊ฐ€ ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์•Œ์•„๋ณด๊ธฐ ์œ„ํ•œ ์—ฐ๊ตฌ๋ฅผ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ๊ฐ‘์€ ์šฐ์„  ๋ชจ๋“  ์กฐ๊ฑด์ด ๋™์ผํ•˜๋‹ค๋ฉด, ๋Œ€ํ•™ ๋‚ด ์‚ฌํšŒ์  ๊ด€๊ณ„์˜ ์ •๋„๊ฐ€ ๊ฐ•ํ•œ ํ•™์ƒ์ผ์ˆ˜๋ก ๋Œ€ํ•™ ์ƒํ™œ์— ๋Œ€ํ•œ ๋งŒ์กฑ๋„๊ฐ€ ๋†’์„ ๊ฒƒ์ด๋ผ๋Š” ๊ฐ€์„ค์„ ์„ธ์› ๋‹ค. ๊ฐ‘์€ ์„ค๋ฌธ์กฐ์‚ฌ์—์„œ ๋Œ€ํ•™ ๋‚ด ์‚ฌํšŒ์  ๊ด€๊ณ„์˜ ์ •๋„๋ฅผ ใ‰ข๊ณผ๊ฑฐ 6๊ฐœ์›”๊ฐ„ ๋™์•„๋ฆฌ ํ™œ๋™ ์ฐธ์—ฌ ํšŸ์ˆ˜๋กœ, ๋Œ€ํ•™ ์ƒํ™œ์— ๋Œ€ํ•œ ๋งŒ์กฑ๋„๋Š” 5์  ์ฒ™๋„๋ฅผ ์‚ฌ์šฉํ•œ ๋ฌธํ•ญ์œผ๋กœ ๊ฐ๊ฐ ์•Œ์•„๋ณด๊ธฐ๋กœ ํ•˜์˜€๋‹ค. ๊ฐ‘์€ ใ‰ฃโ—‹โ—‹๋Œ€ํ•™๊ต ํ•™๋ถ€์ƒ ์ค‘ ์„ฑ๋ณ„, ํ•™๋…„, ์ „๊ณต์„ ๊ณ ๋ คํ•ด ใ‰ค100๋ช…์˜ ํ•™๋ถ€์ƒ์„ ์ถ”์ถœํ•œ ํ›„ ์ด๋“ค์„ ๋Œ€์ƒ์œผ๋กœ ์ค€๋น„ํ•œ ์„ค๋ฌธ์ง€๋ฅผ ํ†ตํ•ด ์กฐ์‚ฌ๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ์กฐ์‚ฌ ์ˆ˜ํ–‰ ํ›„ ใ‰ฅ๋™์•„๋ฆฌ ํ™œ๋™์— ์ฐธ์—ฌํ•œ ์ ์ด ์—†๋Š” ํ•™์ƒ ์ง‘๋‹จ(์ง‘๋‹จ A)๊ณผ ใ‰ฆํ•œ ๋ฒˆ ์ด์ƒ ์ฐธ์—ฌํ•œ ํ•™์ƒ ์ง‘๋‹จ(์ง‘๋‹จ B)์œผ๋กœ ๊ตฌ๋ถ„ํ•˜์—ฌ ์ž๋ฃŒ๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋ฐ‘์ค„ ์นœ ใ‰ ๏ฝžใ‰ฆ์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€? Answer: ใ‰ก์€ ใ‰ข์œผ๋กœ ์กฐ์ž‘์  ์ •์˜ํ•˜์˜€๋‹ค.']",3 +2025-SM-17," โ–กโ–ก๊ตญ์€ ์†Œ์ˆ˜์ด์ง€๋งŒ ์ง€๋ฐฐ์ธต์„ ์ด๋ฃจ๋Š” A์กฑ๊ณผ ๋‹ค์ˆ˜์ด์ง€๋งŒ ์ง€๋ฐฐ๋ฅผ ๋ฐ›๋Š” +B์กฑ์œผ๋กœ ๊ตฌ์„ฑ๋˜์–ด ์žˆ์—ˆ๋‹ค. A์กฑ ์ถœ์‹  ์ง์—… ๊ตฐ์ธ์ธ ๊ฐ‘์€ใ‰ ์ž์‹ ์—๊ฒŒ ์ฃผ์–ด์ง„ +์—…๋ฌด ์ฒ˜๋ฆฌ๋ฅผ ์œ„ํ•ด ์ฒ ์ €ํžˆ ์ค€๋น„ํ•˜์—ฌ ์กฐ์ง์—์„œ์šฐ์ˆ˜ํ•œ ์„ฑ๊ณผ๋ฅผ ๋‚ด์—ˆ๋‹ค. ๋น ๋ฅธ +์ง„๊ธ‰์„ ํ•˜๋ฉฐ ์Šน์Šน์žฅ๊ตฌํ•˜๋˜ ๊ฐ‘์€ ํ›ˆ๋ จ ๋„์ค‘ ๋ถˆ์˜์˜ ์‚ฌ๊ณ ๋กœ ์žฅ์•  ํŒ์ •์„ +๋ฐ›์•„ ๋” ์ด์ƒ ๊ตฐ ์ƒํ™œ์„ ํ•  ์ˆ˜ ์—†์—ˆ๋‹ค. ์ดํ›„ ๋‹ค๋ฅธ ์ง์ข…์— ์ทจ์—…ํ•˜๋ ค ํ–ˆ์œผ๋‚˜ +์žฅ์• ์ธ์— ๋Œ€ํ•œ ์‚ฌํšŒ์  ํŽธ๊ฒฌ์œผ๋กœ์ธํ•ด ๋Š˜ ๊ฑฐ์ ˆ๋‹นํ–ˆ๋‹ค. ๊ฐ‘์ด ์ƒํ™œ์˜ ์–ด๋ ค์›€์„ +๊ฒช๋˜ ์ค‘ โ–กโ–ก๊ตญ์—์„œ ๋Œ€๋‹ค์ˆ˜๋ฅผ์ด๋ฃจ๋Š” B์กฑ์ด ๊ถŒ๋ ฅ์„ ์žฅ์•…ํ•˜๊ฒŒ ๋˜์—ˆ๋‹ค. B์กฑ์€ +๊ถŒ๋ ฅ์˜ ์ •ํ†ต์„ฑ์„ ํ™•๋ณดํ•˜๊ธฐ์œ„ํ•ด A์กฑ์—๊ฒŒ ์ธ์ข… ์ฐจ๋ณ„ ์ •์ฑ…์„ ์‹œํ–‰ํ•˜์˜€๋‹ค. +์ธ์ข… ์ฐจ๋ณ„๊นŒ์ง€ ๊ฒช์€ ๊ฐ‘์€ ใ‰กโ–กโ–ก๊ตญ์—์„œ ์ƒํ™œ์„ ๊ณ„์†ํ•ด์•ผ ํ• ์ง€ ์ฐจ๋ณ„์ด +์—†๋Š” ๋‹ค๋ฅธ ๋‚˜๋ผ๋กœ ์ด์ฃผํ•ด์•ผ +ํ• ์ง€ ๊ณ ๋ฏผ +ํ•˜์˜€๋‹ค.","{'question': '๋‹ค์Œ ์ž๋ฃŒ์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['ใ‰ ์€ ๊ฐ‘์˜ ์˜ˆ๊ธฐ ์‚ฌํšŒํ™”์ด๋‹ค.', 'ใ‰ก์€ ๊ฐ‘์˜ ์—ญํ•  ๊ฐˆ๋“ฑ์ด๋‹ค.', '๊ฐ‘์€ ์ƒ๋“์  ์š”์ธ๊ณผ ํ›„์ฒœ์  ์š”์ธ์— ๋”ฐ๋ฅธ ์ฐจ๋ณ„์„ ๋ชจ๋‘ ๊ฒฝํ—˜ํ•˜์˜€๋‹ค.', 'A์กฑ๊ณผ ๋‹ฌ๋ฆฌ B์กฑ์€ ์ˆ˜์ ์ธ ์—ด์„ธ๋กœ ์ธํ•ด ์ฐจ๋ณ„์„ ๋ฐ›์•˜๋‹ค.', 'B์กฑ๊ณผ ๋‹ฌ๋ฆฌ A์กฑ์€ ์‚ฌํšŒ์  ์†Œ์ˆ˜์ž ์šฐ๋Œ€ ์ •์ฑ…์œผ๋กœ ์—ญ์ฐจ๋ณ„์„ ๋ฐ›์•˜๋‹ค.'], 'answer': ''}",,3,3,True,[],3 +2025-SM-18,"์ธ๊ฐ„์ด ์ฐพ์•„๋‚ธ ๊ณผํ•™์  ์ง€์‹์€ ์ž์—ฐ์ด ๊ฐ€ํ•˜๋Š” ์ œ์•ฝ์œผ๋กœ ๋งŒ๋“ค์–ด์ง„ +์›์‹œ์ ์ธ ๋ฏธ์‹ ๊ณผ ์„ ์ž…๊ฒฌ, ์˜ค๋ฅ˜๋ฅผ ๊ทน๋ณตํ•˜๋Š” ๊ณผ์ •์—์„œ ์ถ•์ ๋˜๊ณ  ์ •ํ•ด์ง„ +ํ•˜๋‚˜์˜ ๋ฐฉํ–ฅ์„ ํ–ฅํ•ด ์ง„์ „ํ•˜๋ฉฐ ํ™•์žฅํ•œ๋‹ค. ๋ฌธ๋ช…์˜ ์ „๊ฐœ๋„ ๊ทผ๋Œ€ ๊ณผํ•™์˜ ์ด๋Ÿฌํ•œ +๊ณผ์ •๊ณผ ์œ ์‚ฌํ•˜๋‹ค.","{'question': '๋‹ค์Œ ๊ธ€์—์„œ ์‚ฌํšŒ ๋ณ€๋™์˜ ๋ฐฉํ–ฅ์„ ๋ฐ”๋ผ๋ณด๋Š” ํ•„์ž์˜ ๊ด€์ ์— ๋Œ€ํ•œ ์˜ณ์€ ์„ค๋ช…๋งŒ์„ <๋ณด๊ธฐ>์—์„œ ๊ณ ๋ฅธ ๊ฒƒ์€?', 'choices': ['ใ„ฑ, ใ„ด', 'ใ„ฑ, ใ„ท', 'ใ„ด,ใ„ท', 'ใ„ด, ใ„น', 'ใ„ท, ใ„น'], 'answer': '', 'question_plus': 'ใ„ฑ. ์„œ๊ตฌ ์ค‘์‹ฌ์  ์‚ฌ๊ณ ๋ผ๋Š” ๋น„ํŒ์„ ํ”ผํ•˜๊ธฐ ์–ด๋ ต๋‹ค.\r\nใ„ด. ์‚ฌํšŒ ๋ณ€๋™ ๋ฐฉํ–ฅ์„ ์˜ˆ์ธกํ•˜์—ฌ ๋Œ€์‘ํ•˜๊ธฐ ์–ด๋ ต๋‹ค.\r\nใ„ท. ์ง€์†์ ์œผ๋กœ ๋ฐœ์ „ํ•˜๋Š” ์‚ฌํšŒ๋ฅผ ์„ค๋ช…ํ•˜๊ธฐ ์šฉ์ดํ•˜๋‹ค.\r\nใ„น. ์ธ๋ฅ˜ ๋ฌธ๋ช…์˜ ํฅ๋ง์„ฑ์‡  ์—ญ์‚ฌ๋ฅผ ์„ค๋ช…ํ•˜๊ธฐ ์šฉ์ดํ•˜๋‹ค.'}","ใ„ฑ. ์„œ๊ตฌ ์ค‘์‹ฌ์  ์‚ฌ๊ณ ๋ผ๋Š” ๋น„ํŒ์„ ํ”ผํ•˜๊ธฐ ์–ด๋ ต๋‹ค. +ใ„ด. ์‚ฌํšŒ ๋ณ€๋™ ๋ฐฉํ–ฅ์„ ์˜ˆ์ธกํ•˜์—ฌ ๋Œ€์‘ํ•˜๊ธฐ ์–ด๋ ต๋‹ค. +ใ„ท. ์ง€์†์ ์œผ๋กœ ๋ฐœ์ „ํ•˜๋Š” ์‚ฌํšŒ๋ฅผ ์„ค๋ช…ํ•˜๊ธฐ ์šฉ์ดํ•˜๋‹ค. +ใ„น. ์ธ๋ฅ˜ ๋ฌธ๋ช…์˜ ํฅ๋ง์„ฑ์‡  ์—ญ์‚ฌ๋ฅผ ์„ค๋ช…ํ•˜๊ธฐ ์šฉ์ดํ•˜๋‹ค.",2,2,True,[],2 +2025-SY-01,"(๊ฐ€)์œค๋ฆฌํ•™์€ โ€˜์ข‹์Œโ€™, โ€˜์˜ณ์Œโ€™๊ณผ ๊ฐ™์€ ๋„๋•์  ์šฉ์–ด๋“ค์˜ ์˜๋ฏธ ๋ถ„์„๊ณผ +๋„๋•์  ์ถ”๋ก ์˜ ํƒ€๋‹น์„ฑ์„ ๊ฒ€์ฆํ•˜๊ธฐ ์œ„ํ•œ ๋…ผ๋ฆฌ์  ๋ถ„์„์— +์ฃผ๋œ ๊ด€์‹ฌ์„ ๋‘”๋‹ค. +(๋‚˜)์œค๋ฆฌํ•™์€ ๋„๋• ์ด๋ก ๊ณผ ์›๋ฆฌ๋ฅผ ์ ์šฉํ•˜์—ฌ, ์šฐ๋ฆฌ ์‚ถ์˜ ๋‹ค์–‘ํ•œ +์˜์—ญ์—์„œ ๋ฐœ์ƒํ•˜๋Š” ์œค๋ฆฌ์  ๋ฌธ์ œ๋“ค์— ๋Œ€ํ•œ ํ•ด๊ฒฐ ๋ฐฉ์•ˆ์„ +์ œ๊ณตํ•˜๋Š” ๋ฐ ์ฃผ๋œ ๊ด€์‹ฌ์„ ๋‘”๋‹ค.","{'question': '(๊ฐ€), (๋‚˜) ์œค๋ฆฌํ•™์˜ ํ•ต์‹ฌ ๊ณผ์ œ๋กœ ๊ฐ€์žฅ ์ ์ ˆํ•œ ๊ฒƒ์€?', 'choices': ['(๊ฐ€):๋‹ค์–‘ํ•œ ๋ฌธํ™”๊ถŒ์˜ ๊ด€ํ–‰์„ ๊ฐ€์น˜์ค‘๋ฆฝ์ ์œผ๋กœ ์„œ์ˆ ํ•˜๋Š” ๊ฒƒ์ด๋‹ค.', '(๊ฐ€):๋งˆ๋•…ํžˆ ์ถ”๊ตฌํ•ด์•ผ ํ•  ๋ฐ”๋žŒ์งํ•œ ์‚ถ์˜ ๋ชฉ์ ์„ ์ œ๊ณตํ•˜๋Š” ๊ฒƒ์ด๋‹ค.', '(๋‚˜):๋„๋• ์ด๋ก ์— ์‚ฌ์šฉ๋˜๋Š” ๋ช…์ œ์˜ ๋…ผ๋ฆฌ์  ๊ตฌ์กฐ๋ฅผ ๊ฒ€ํ† ํ•˜๋Š” ๊ฒƒ์ด๋‹ค.', '(๋‚˜):ํ˜„์‹ค์˜ ๋„๋• ๋ฌธ์ œ์— ๋Œ€ํ•œ ๊ตฌ์ฒด์  ํ•ด๋ฒ•์„ ๋ชจ์ƒ‰ํ•˜๋Š” ๊ฒƒ์ด๋‹ค.', '(๊ฐ€)์™€ (๋‚˜):๋ชจ๋“  ์‚ฌ๋žŒ์—๊ฒŒ ๋ณดํŽธํƒ€๋‹นํ•œ ๋„๋•๊ทœ๋ฒ”์„ ์ œ์‹œํ•˜๋Š” ๊ฒƒ์ด๋‹ค.'], 'answer': ''}",,4,4,True,[],4 +2025-SY-02,"๊ฐ‘:์ƒ๋ช… ๊ณผํ•™์ด ๋ฐœ๋‹ฌํ•จ์— ๋”ฐ๋ผ ๋‡Œ ์ž๊ทน๊ณผ ์•ฝ๋ฌผ์„ ํ†ตํ•ด ์ธ๊ฐ„์˜ +์งˆ๋ณ‘์„ ์น˜๋ฃŒํ•  ์ˆ˜ ์žˆ๊ฒŒ ๋˜์—ˆ๊ณ , ์ธ๊ฐ„์˜ ์ดํƒ€์‹ฌ์„ ํ–ฅ์ƒ +์‹œํ‚ค๋Š” ๊ฐ•ํ™”๋„ ๊ฐ€๋Šฅํ•ด์กŒ์Šต๋‹ˆ๋‹ค. +์„:๋™์˜ํ•ฉ๋‹ˆ๋‹ค. ์ธ๊ฐ„์˜ ์ดํƒ€์‹ฌ์„ ์ธ์œ„์  ์กฐ์ž‘์œผ๋กœ ๊ฐ•ํ™”ํ•จ +์œผ๋กœ์จ ์‚ฌํšŒ ์ด์ต์— ๊ธฐ์—ฌํ•˜๋Š” ๋„๋•์  ํ–‰๋™๋„ ์ฆ๊ฐ€์‹œํ‚ฌ ์ˆ˜ +์žˆ๊ฒŒ ๋˜์—ˆ์Šต๋‹ˆ๋‹ค. +๊ฐ‘:๋ฌผ๋ก  ์ดํƒ€์‹ฌ ๊ฐ•ํ™”์— ์˜ํ•ด ์‚ฌํšŒ ์ด์ต์— ๊ธฐ์—ฌํ•˜๋Š” ์นœ์‚ฌํšŒ์  +ํ–‰๋™์ด ์ฆ๊ฐ€ํ•˜๋Š” ๊ฒƒ์€ ์‚ฌ์‹ค์ž…๋‹ˆ๋‹ค. ํ•˜์ง€๋งŒ ๊ทธ๋Ÿฌํ•œ ํ–‰๋™์€ +์ž๊ทน์— ์˜ํ•œ ํƒ€์œจ์  ๋ฐ˜์‘์ผ ๋ฟ ๋„๋•์  ํ–‰๋™์€ ์•„๋‹™๋‹ˆ๋‹ค. +์„:์ดํƒ€์‹ฌ ๊ฐ•ํ™”๋กœ ์ธํ•ด ์ฆ๊ฐ€ํ•œ ์นœ์‚ฌํšŒ์  ํ–‰๋™์ด ์ž๊ทน์— ์˜ํ•œ +ํƒ€์œจ์  ๋ฐ˜์‘์ธ ๊ฒƒ์€ ๋งž์Šต๋‹ˆ๋‹ค. ํ•˜์ง€๋งŒ ๊ฒฐ๊ณผ์ ์œผ๋กœ ์‚ฌํšŒ +์ด์ต์„ ์ฆ์ง„ํ•˜๋ฏ€๋กœ ๊ทธ๋Ÿฌํ•œ ํ–‰๋™๋„ ๋„๋•์  ํ–‰๋™์ž…๋‹ˆ๋‹ค.","{'question': '๋‹ค์Œ ํ† ๋ก ์˜ ํ•ต์‹ฌ ์Ÿ์ ์œผ๋กœ ๊ฐ€์žฅ ์ ์ ˆํ•œ ๊ฒƒ์€? ', 'choices': ['์ƒ๋ช… ๊ณผํ•™์˜ ๋ฐœ๋‹ฌ์€ ์ธ๊ฐ„์˜ ์งˆ๋ณ‘ ์น˜๋ฃŒ์— ๊ธฐ์—ฌํ•˜๋Š”๊ฐ€?', '๊ฐ•ํ™”์— ์˜ํ•œ ์ธ๊ฐ„์˜ ์นœ์‚ฌํšŒ์  ํ–‰๋™์€ ๋„๋•์  ํ–‰๋™์ธ๊ฐ€?', '์ธ๊ฐ„์˜ ์ดํƒ€์‹ฌ์„ ์ธ์œ„์ ์œผ๋กœ ํ–ฅ์ƒ์‹œํ‚ค๋Š” ๋ฐฉ๋ฒ•์ด ์žˆ๋Š”๊ฐ€?', '๊ฐ•ํ™”์— ์˜ํ•œ ์ธ๊ฐ„์˜ ์นœ์‚ฌํšŒ์  ํ–‰๋™์€ ์‚ฌํšŒ ์ด์ต์— ๊ธฐ์—ฌํ•  ์ˆ˜ ์žˆ๋Š”๊ฐ€?', '๊ฐ•ํ™”์— ์˜ํ•œ ์ธ๊ฐ„์˜ ์นœ์‚ฌํšŒ์  ํ–‰๋™์€ ์ž๊ทน์— ์˜ํ•œ ํƒ€์œจ์  ๋ฐ˜์‘์ธ๊ฐ€?'], 'answer': ''}",,2,2,True,[],2 +2025-SY-04,"๊ฐ‘:ํƒœ์ดˆ์— ๋ฌด์–ธ๊ฐ€๊ฐ€ ์„ž์ด๊ณ  ๋ณ€ํ•˜์—ฌ ๊ธฐ(ๆฐฃ)๋ฅผ ์–ป์—ˆ๊ณ , ๊ธฐ๊ฐ€ +๋ณ€ํ•˜์—ฌ ํ˜•์ฒด๋ฅผ ๊ฐ–๊ฒŒ ๋˜์—ˆ์œผ๋ฉฐ, ํ˜•์ฒด๊ฐ€ ๋ณ€ํ•˜์—ฌ ์ƒ๋ช…์„ ์–ป๊ฒŒ +๋œ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๊ทธ ์ƒ๋ช…์ด ๋ณ€ํ•˜์—ฌ ์ฃฝ์Œ์— ์ด๋ฅธ๋‹ค. +์„:๋Š™์Œ[่€]๊ณผ ๋ณ‘๋“ฆ[็—…]๊ณผ ์ฃฝ์Œ[ๆญป]์„ ๋–จ์ณ๋‚ด์ง€ ๋ชปํ•˜๊ฒŒ ๋˜๋Š” +๊ฒƒ์€ ์„ธ ๊ฐ€์ง€์˜ ๋ฒ•(ๆณ•)์„ ๋Š์ง€ ๋ชปํ•˜๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ๊ทธ ์„ธ ๊ฐ€์ง€๋Š” +๋ฐ”๋กœ ํƒ์š•[่ฒช], ์„ฑ๋ƒ„[็ž‹], ์–ด๋ฆฌ์„์Œ[็™ก]์ด๋‹ค.","{'question': '๊ฐ‘, ์„ ์‚ฌ์ƒ๊ฐ€๋“ค์˜ ์ž…์žฅ์œผ๋กœ ๊ฐ€์žฅ ์ ์ ˆํ•œ ๊ฒƒ์€?', 'choices': ['๊ฐ‘:์ฃฝ์Œ์€ ๊ธฐ๊ฐ€ ํฉ์–ด์ง„ ๊ฒƒ์ด๋ฏ€๋กœ ์šด๋ช…์œผ๋กœ ๋ฐ›์•„๋“ค์—ฌ์„œ๋Š” ์•ˆ ๋œ๋‹ค.', '๊ฐ‘:์ฃฝ์Œ ์•ž์— ๋‘๋ ค์›€ ์—†์ด ์ดˆ์—ฐํ•ด์•ผ ์ธ๋ฅœ์˜ ๋„(้“)๋ฅผ ์™„์„ฑํ•  ์ˆ˜ ์žˆ๋‹ค.', '์„:์ฃฝ์Œ์˜ ์ฐธ๋ชจ์Šต์„ ์ž๊ฐํ•˜๋ฉด ์—…(ๆฅญ)์„ ์ง“์ง€ ์•Š๊ณ  ์œคํšŒํ•˜๊ฒŒ ๋œ๋‹ค', '์„:์—ด๋ฐ˜์— ์ด๋ฅด๊ธฐ ์œ„ํ•ด์„œ๋Š” ์‚ถ๊ณผ ์ฃฝ์Œ์˜ ์˜์กด๊ด€๊ณ„๋ฅผ ๋ถ€์ •ํ•ด์•ผ ํ•œ๋‹ค.', '๊ฐ‘๊ณผ ์„:๋„๋ฅผ ์–ป์Œ์œผ๋กœ์จ ์ƒ์‚ฌ(็”Ÿๆญป)์˜ ์–ฝ๋งค์ž„์—์„œ ๋ฒ—์–ด๋‚  ์ˆ˜ ์žˆ๋‹ค.'], 'answer': ''}",,5,2,False,[],5 +2025-SY-05,"์ข…๊ต์  ์ธ๊ฐ„์—๊ฒŒ ์ž์—ฐ์€ ๊ฒฐ์ฝ” ๋‹จ์ˆœํ•œ ์ž์—ฐ์ด ์•„๋‹ˆ๋‹ค. ๊ทธ๊ฒƒ์€ +ํ•ญ์ƒ ์ข…๊ต์  ์˜๋ฏธ๋กœ ์ถฉ๋งŒํ•ด ์žˆ๋‹ค. ์šฐ์ฃผ๋Š” ์‹ ๋“ค์˜ ์ฐฝ์กฐ๋ฌผ์ด๊ณ  +์„ธ๊ณ„๋Š” ์‹ ๋“ค์˜ ์†์œผ๋กœ ์™„์„ฑ๋œ ๊ฒƒ์ด์–ด์„œ ์„ฑ์Šค๋Ÿฌ์›€์œผ๋กœ ๊ฐ€๋“ ์ฐจ +์žˆ๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ์ด๋Š” ์˜ˆ๋ฅผ ๋“ค๋ฉด, ์‹ ์˜ ํ˜„์กด์— ์˜ํ•ด์„œ ์ •ํ™”๋œ +์žฅ์†Œ๋‚˜ ์‚ฌ๋ฌผ์— ๋จธ๋ฌด๋ฅด๋Š” ๊ฒฝ์šฐ์™€ ๊ฐ™์ด ์‹ ๋“ค๊ณผ ์ง์ ‘ ๊ต๋ฅ˜ํ•˜๋Š” +์‹ ์„ฑ์„ฑ๋งŒ์€ ์•„๋‹ˆ๋‹ค. ์‹ ๋“ค์€ ์„ธ๊ณ„์˜ ๊ตฌ์กฐ์™€ ์šฐ์ฃผ์  ํ˜„์ƒ์˜ ๊ตฌ์กฐ +๊ทธ ์ž์ฒด ์•ˆ์—์„œ ๋‹ค์–‘ํ•œ ์„ฑ(่–)์˜ ์–‘ํƒœ๋ฅผ ํ˜„ํ˜„(้กฏ็พ)ํ•œ๋‹ค.","{'question': '๋‹ค์Œ์„ ์ฃผ์žฅํ•œ ์‚ฌ์ƒ๊ฐ€์˜ ์ž…์žฅ์œผ๋กœ ๊ฐ€์žฅ ์ ์ ˆํ•œ ๊ฒƒ์€?', 'choices': ['์„ฑ์Šค๋Ÿฌ์›€์ด ๋“œ๋Ÿฌ๋‚˜๋Š” ์ž์—ฐ๊ณผ ์„ธ๊ณ„๊ฐ€ ์ดˆ์›”์  ์‹  ์ž์ฒด์ด๋‹ค.', '์ธ๊ฐ„์˜ ๋…ธ๋ ฅ ์—†์ด๋Š” ์„ธ๊ณ„ ์•ˆ์— ์„ฑ์Šค๋Ÿฌ์›€์ด ์กด์žฌํ•  ์ˆ˜ ์—†๋‹ค.', '์‹ ์ด ์ฐฝ์กฐํ•œ ์„ธ๊ณ„๋Š” ์‹ค์žฌํ•˜์ง€ ์•Š์ง€๋งŒ ์ผ์ •ํ•œ ๊ตฌ์กฐ๋ฅผ ์ง€๋‹Œ๋‹ค.', '์ข…๊ต์  ์ธ๊ฐ„์€ ์‹ ๊ณผ ์ง์ ‘ ๊ต๋ฅ˜ํ•จ์œผ๋กœ์จ๋งŒ ์„ฑ์Šค๋Ÿฌ์›€์„ ๋А๋‚€๋‹ค.', '์ข…๊ต์  ์ธ๊ฐ„์€ ์„ธ์†์  ๊ณต๊ฐ„์—์„œ๋„ ์„ฑ์Šค๋Ÿฌ์›€์„ ์ฒดํ—˜ํ•  ์ˆ˜ ์žˆ๋‹ค.'], 'answer': ''}",,5,5,True,[],5 +2025-SY-06,"ํ˜„๋Œ€ ๊ธฐ์ˆ ์ด ์ง€๊ตฌ ์ „์—ญ์„ ๋’ค๋ฎ๊ณ  ์žˆ์œผ๋ฉฐ ๊ทธ ๋ˆ„์ ๋œ ๊ฒฐ๊ณผ๊ฐ€ +๋ฏธ๋ž˜ ์„ธ๋Œ€์˜ ์ธ๋ฅ˜์—๊ฒŒ๋„ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋ฆฌ๋ผ๋Š” ์‚ฌ์‹ค์€ ๋ถ„๋ช…ํ•˜๋‹ค. +ํŠนํžˆ ์ฃผ๋ชฉํ•  ๊ฒƒ์€ ๋ฏธ๋ž˜ ์ง€๊ตฌ์™€ ๊ด€๋ จ๋œ ๋ฌธ์ œ๊ฐ€ ์šฐ๋ฆฌ์˜ ์ผ์ƒ์ ์ด๊ณ  +์‹ค์ฒœ์ ์ธ ๊ฒฐ๋‹จ์„ ์ด‰๊ตฌํ•œ๋‹ค๋Š” ์‚ฌ์‹ค, ๊ทธ๋ฆฌ๊ณ  ์ƒˆ๋กœ์šด ์œค๋ฆฌ๋ฅผ ์š”์ฒญ +ํ•œ๋‹ค๋Š” ์‚ฌ์‹ค์ด๋‹ค. ์ฑ…์ž„์€ ๋ฐ”๋กœ ์ด๋Ÿฌํ•œ ์ƒˆ๋กœ์šด ์‚ฌํƒœ๋ฅผ ์ค€๋น„ํ•˜๊ธฐ +์œ„ํ•ด ๋งˆ๋ จ๋œ ์œค๋ฆฌ์  ๋ฒ”์ฃผ๋ฅผ ์˜๋ฏธํ•œ๋‹ค","{'question': '๋‹ค์Œ์„ ์ฃผ์žฅํ•œ ์‚ฌ์ƒ๊ฐ€์˜ ์ž…์žฅ์œผ๋กœ ์ ์ ˆํ•œ ๊ฒƒ๋งŒ์„ <๋ณด๊ธฐ>์—์„œ\n๊ณ ๋ฅธ ๊ฒƒ์€?', 'choices': ['ใ„ฑ, ใ„ด', 'ใ„ฑ, ใ„ท', 'ใ„ด, ใ„ท', 'ใ„ด, ใ„น', 'ใ„ท, ใ„น'], 'answer': '', 'question_plus': 'ใ„ฑ.์ผ์ƒ์  ์ธ๊ฐ„๊ด€๊ณ„์—์„œ๋Š” ํ˜ธํ˜œ์  ์ฑ…์ž„์ด ์„ฑ๋ฆฝ๋˜์ง€ ์•Š๋Š”๋‹ค.\nใ„ด.์ธ๊ฐ„์˜ ์ฑ…์ž„ ๋ฒ”์œ„๋Š” ์ง€๊ตฌ ์ƒํƒœ๊ณ„ ์ „์ฒด๋ฅผ ํฌํ•จํ•ด์•ผ ํ•œ๋‹ค.\nใ„ท.์ž์—ฐ์— ๋Œ€ํ•œ ์ธ๊ฐ„์˜ ์˜๋ฌด๋Š” ์ธ๊ฐ„์— ๋Œ€ํ•œ ์ฑ…์ž„์„ ํ•จ์ถ•ํ•œ๋‹ค.\nใ„น.์„ ํ•œ ๊ฒฐ๊ณผ๊ฐ€ ์˜ˆ๊ฒฌ๋˜๋Š” ๊ธฐ์ˆ ๋งŒ์ด ๋„๋•์  ๊ฒ€ํ†  ๋Œ€์ƒ์—์„œ ์ œ์™ธ๋œ๋‹ค.'}","ใ„ฑ.์ผ์ƒ์  ์ธ๊ฐ„๊ด€๊ณ„์—์„œ๋Š” ํ˜ธํ˜œ์  ์ฑ…์ž„์ด ์„ฑ๋ฆฝ๋˜์ง€ ์•Š๋Š”๋‹ค. +ใ„ด.์ธ๊ฐ„์˜ ์ฑ…์ž„ ๋ฒ”์œ„๋Š” ์ง€๊ตฌ ์ƒํƒœ๊ณ„ ์ „์ฒด๋ฅผ ํฌํ•จํ•ด์•ผ ํ•œ๋‹ค. +ใ„ท.์ž์—ฐ์— ๋Œ€ํ•œ ์ธ๊ฐ„์˜ ์˜๋ฌด๋Š” ์ธ๊ฐ„์— ๋Œ€ํ•œ ์ฑ…์ž„์„ ํ•จ์ถ•ํ•œ๋‹ค. +ใ„น.์„ ํ•œ ๊ฒฐ๊ณผ๊ฐ€ ์˜ˆ๊ฒฌ๋˜๋Š” ๊ธฐ์ˆ ๋งŒ์ด ๋„๋•์  ๊ฒ€ํ†  ๋Œ€์ƒ์—์„œ ์ œ์™ธ๋œ๋‹ค.",3,3,True,[],3 +2025-SY-08,"๊ฐ‘:์‹ ์€ ์šฐ๋ฆฌ๋“ค ๊ฐ์ž๊ฐ€ ์ธ์ƒ์˜ ์˜จ๊ฐ– ํ™œ๋™์„ ํ•˜๋Š” ๊ฐ€์šด๋ฐ +๊ฐ์ž์˜ ๋ถ€๋ฅด์‹ฌ์„ ๊ธฐ์–ตํ•˜๊ณ  ์กด์ค‘ํ•  ๊ฒƒ์„ ๋ช…ํ•œ๋‹ค. ๊ทธ๋ฆฌ๊ณ  +๋ˆ„๊ตฌ๋„ ๊ฒฝ์†”ํ•˜๊ฒŒ ์ž๊ธฐ์˜ ํ•œ๊ณ„๋ฅผ ๋ฒ—์–ด๋‚˜์ง€ ์•Š๋„๋ก ๋‹ค์–‘ํ•œ +์ข…๋ฅ˜์˜ ์ƒํ™œ ์–‘์‹์„ ์†Œ๋ช…์ด๋ผ ์ด๋ฆ„ ๋ถ™์˜€๋‹ค. +์„:์„ ์™•(ๅ…ˆ็Ž‹)์€ ์‚ฌ๋žŒ๋“ค ์‚ฌ์ด์˜ ๋‹คํˆผ์œผ๋กœ ์ธํ•œ ํ˜ผ๋ž€์„ ์‹ซ์–ด +ํ•˜์˜€๊ธฐ ๋•Œ๋ฌธ์— ์˜ˆ(็ฆฎ)๋ฅผ ์ œ์ •ํ•ด ๋ถ„๋ณ„์˜ ๊ธฐ์ค€์œผ๋กœ ์‚ผ์•˜๋‹ค. +๊ทธ๋ฆฌํ•˜์—ฌ ์‚ฌ๋žŒ๋“ค์˜ ์š•๋ง์„ ์ถฉ์กฑ์‹œํ‚ค๊ณ  ๊ทธ๋“ค์ด ์›ํ•˜๋Š” ๊ฒƒ์„ +๊ณต๊ธ‰ํ•˜๊ฒŒ ํ•˜์—ฌ ๋ฌผ๊ฑด์ด ๋ถ€์กฑํ•˜์ง€ ์•Š๋„๋ก ํ•˜์˜€๋‹ค.","{'question': '๊ฐ‘, ์„ ์‚ฌ์ƒ๊ฐ€๋“ค์˜ ์ž…์žฅ์œผ๋กœ ์ ์ ˆํ•œ ๊ฒƒ๋งŒ์„ <๋ณด๊ธฐ>์—์„œ ๊ณ ๋ฅธ ๊ฒƒ์€?', 'choices': ['ใ„ฑ, ใ„ด', 'ใ„ฑ, ใ„ท', 'ใ„ด, ใ„ท', 'ใ„ด, ใ„น', 'ใ„ท, ใ„น'], 'answer': '', 'question_plus': '๊ฐ‘:์‹ ์€ ์šฐ๋ฆฌ๋“ค ๊ฐ์ž๊ฐ€ ์ธ์ƒ์˜ ์˜จ๊ฐ– ํ™œ๋™์„ ํ•˜๋Š” ๊ฐ€์šด๋ฐ\n๊ฐ์ž์˜ ๋ถ€๋ฅด์‹ฌ์„ ๊ธฐ์–ตํ•˜๊ณ  ์กด์ค‘ํ•  ๊ฒƒ์„ ๋ช…ํ•œ๋‹ค. ๊ทธ๋ฆฌ๊ณ \n๋ˆ„๊ตฌ๋„ ๊ฒฝ์†”ํ•˜๊ฒŒ ์ž๊ธฐ์˜ ํ•œ๊ณ„๋ฅผ ๋ฒ—์–ด๋‚˜์ง€ ์•Š๋„๋ก ๋‹ค์–‘ํ•œ\n์ข…๋ฅ˜์˜ ์ƒํ™œ ์–‘์‹์„ ์†Œ๋ช…์ด๋ผ ์ด๋ฆ„ ๋ถ™์˜€๋‹ค.\n์„:์„ ์™•(ๅ…ˆ็Ž‹)์€ ์‚ฌ๋žŒ๋“ค ์‚ฌ์ด์˜ ๋‹คํˆผ์œผ๋กœ ์ธํ•œ ํ˜ผ๋ž€์„ ์‹ซ์–ด\nํ•˜์˜€๊ธฐ ๋•Œ๋ฌธ์— ์˜ˆ(็ฆฎ)๋ฅผ ์ œ์ •ํ•ด ๋ถ„๋ณ„์˜ ๊ธฐ์ค€์œผ๋กœ ์‚ผ์•˜๋‹ค.\n๊ทธ๋ฆฌํ•˜์—ฌ ์‚ฌ๋žŒ๋“ค์˜ ์š•๋ง์„ ์ถฉ์กฑ์‹œํ‚ค๊ณ  ๊ทธ๋“ค์ด ์›ํ•˜๋Š” ๊ฒƒ์„\n๊ณต๊ธ‰ํ•˜๊ฒŒ ํ•˜์—ฌ ๋ฌผ๊ฑด์ด ๋ถ€์กฑํ•˜์ง€ ์•Š๋„๋ก ํ•˜์˜€๋‹ค.'}","๊ฐ‘:์‹ ์€ ์šฐ๋ฆฌ๋“ค ๊ฐ์ž๊ฐ€ ์ธ์ƒ์˜ ์˜จ๊ฐ– ํ™œ๋™์„ ํ•˜๋Š” ๊ฐ€์šด๋ฐ +๊ฐ์ž์˜ ๋ถ€๋ฅด์‹ฌ์„ ๊ธฐ์–ตํ•˜๊ณ  ์กด์ค‘ํ•  ๊ฒƒ์„ ๋ช…ํ•œ๋‹ค. ๊ทธ๋ฆฌ๊ณ  +๋ˆ„๊ตฌ๋„ ๊ฒฝ์†”ํ•˜๊ฒŒ ์ž๊ธฐ์˜ ํ•œ๊ณ„๋ฅผ ๋ฒ—์–ด๋‚˜์ง€ ์•Š๋„๋ก ๋‹ค์–‘ํ•œ +์ข…๋ฅ˜์˜ ์ƒํ™œ ์–‘์‹์„ ์†Œ๋ช…์ด๋ผ ์ด๋ฆ„ ๋ถ™์˜€๋‹ค. +์„:์„ ์™•(ๅ…ˆ็Ž‹)์€ ์‚ฌ๋žŒ๋“ค ์‚ฌ์ด์˜ ๋‹คํˆผ์œผ๋กœ ์ธํ•œ ํ˜ผ๋ž€์„ ์‹ซ์–ด +ํ•˜์˜€๊ธฐ ๋•Œ๋ฌธ์— ์˜ˆ(็ฆฎ)๋ฅผ ์ œ์ •ํ•ด ๋ถ„๋ณ„์˜ ๊ธฐ์ค€์œผ๋กœ ์‚ผ์•˜๋‹ค. +๊ทธ๋ฆฌํ•˜์—ฌ ์‚ฌ๋žŒ๋“ค์˜ ์š•๋ง์„ ์ถฉ์กฑ์‹œํ‚ค๊ณ  ๊ทธ๋“ค์ด ์›ํ•˜๋Š” ๊ฒƒ์„ +๊ณต๊ธ‰ํ•˜๊ฒŒ ํ•˜์—ฌ ๋ฌผ๊ฑด์ด ๋ถ€์กฑํ•˜์ง€ ์•Š๋„๋ก ํ•˜์˜€๋‹ค.",4,4,True,[],2 +2025-SY-10,"๊ฐ‘:์ž์—ฐ์€ ์‹ ์ด ์„ธ๊ณ„๋ฅผ ์ฐฝ์กฐํ•˜์—ฌ ๋‹ค์Šค๋ฆฌ๋Š” ๊ธฐ์˜ˆ์ด๋‹ค. ์ด ์ž์—ฐ์„ +์ธ๊ฐ„์˜ ๊ธฐ์˜ˆ๋กœ ๋ชจ๋ฐฉํ•˜์—ฌ ์ธ๊ณต์  ์ธ๊ฒฉ์„ ๋งŒ๋“ค์–ด ๋‚ผ ์ˆ˜ ์žˆ๋‹ค. +์ด๊ฒƒ์ด ๊ตญ๊ฐ€๋ผ๊ณ  ๋ถˆ๋ฆฌ๋Š” ์œ„๋Œ€ํ•œ ๋ฆฌ๋ฐ”์ด์–ด๋˜์ด๋‹ค. +์„:์‚ฌ๋žŒ๋“ค์ด ์‚ฌํšŒ์— ๋“ค์–ด๊ฐ€๋Š” ๋ชฉ์ ์€ ์žฌ์‚ฐ์„ ์•ˆ์ „ํ•˜๊ฒŒ ํ–ฅ์œ  +ํ•˜๋Š” ๊ฒƒ์ด๋ฉฐ, ์ด๋ฅผ ์œ„ํ•œ ์ฃผ์š”ํ•œ ์ˆ˜๋‹จ์ด ์‚ฌํšŒ์—์„œ ํ™•๋ฆฝ๋œ +๋ฒ•์ด๋‹ค. ์ตœ์ดˆ์˜ ์‹ค์ •๋ฒ•์€ ์ž…๋ฒ•๊ถŒ์„ ํ™•๋ฆฝํ•˜๋Š” ๊ฒƒ์ด๋‹ค.","{'question': '๊ฐ‘, ์„ ์‚ฌ์ƒ๊ฐ€๋“ค์˜ ์ž…์žฅ์œผ๋กœ ์ ์ ˆํ•œ ๊ฒƒ๋งŒ์„ <๋ณด๊ธฐ>์—์„œ ๊ณ ๋ฅธ\n๊ฒƒ์€?', 'choices': ['ใ„ฑ, ใ„ด', 'ใ„ฑ, ใ„ท', 'ใ„ด, ใ„ท', 'ใ„ด, ใ„น', 'ใ„ท, ใ„น'], 'answer': '', 'question_plus': 'ใ„ฑ.๊ฐ‘:์ ˆ๋Œ€ ๊ตฐ์ฃผ๋Š” ๋ชจ๋“  ์ธ๊ฐ„์˜ ์‚ฌํšŒ ๊ณ„์•ฝ ์ฒด๊ฒฐ๊ณผ ์ดํ–‰์„ ๊ฐ•์ œํ•œ๋‹ค.\nใ„ด.๊ฐ‘:์‚ฌํšŒ ๊ณ„์•ฝ ์ดํ›„์— ๊ตฐ์ฃผ์™€ ์‹œ๋ฏผ์€ ์ž์—ฐ๋ฒ•์„ ์ค€์ˆ˜ํ•ด์•ผ ํ•œ๋‹ค.\nใ„ท.์„:์ž์—ฐ ์ƒํƒœ๋Š” ์–ด๋– ํ•œ ๋ถˆํ‰๋“ฑ๋„ ์—†๋Š” ๋Œ€์ฒด๋กœ ํ‰ํ™”๋กœ์šด\n์ƒํƒœ์ด๋‹ค.\nใ„น.๊ฐ‘๊ณผ ์„:์ž์—ฐ ์ƒํƒœ์˜ ๋ชจ๋“  ์ธ๊ฐ„์€ ๋™์ผํ•œ ์ž์—ฐ๊ถŒ์„ ๊ฐ€์ง„๋‹ค.'}","ใ„ฑ.๊ฐ‘:์ ˆ๋Œ€ ๊ตฐ์ฃผ๋Š” ๋ชจ๋“  ์ธ๊ฐ„์˜ ์‚ฌํšŒ ๊ณ„์•ฝ ์ฒด๊ฒฐ๊ณผ ์ดํ–‰์„ ๊ฐ•์ œํ•œ๋‹ค. +ใ„ด.๊ฐ‘:์‚ฌํšŒ ๊ณ„์•ฝ ์ดํ›„์— ๊ตฐ์ฃผ์™€ ์‹œ๋ฏผ์€ ์ž์—ฐ๋ฒ•์„ ์ค€์ˆ˜ํ•ด์•ผ ํ•œ๋‹ค. +ใ„ท.์„:์ž์—ฐ ์ƒํƒœ๋Š” ์–ด๋– ํ•œ ๋ถˆํ‰๋“ฑ๋„ ์—†๋Š” ๋Œ€์ฒด๋กœ ํ‰ํ™”๋กœ์šด +์ƒํƒœ์ด๋‹ค. +ใ„น.๊ฐ‘๊ณผ ์„:์ž์—ฐ ์ƒํƒœ์˜ ๋ชจ๋“  ์ธ๊ฐ„์€ ๋™์ผํ•œ ์ž์—ฐ๊ถŒ์„ ๊ฐ€์ง„๋‹ค.",4,4,True,[],4 +2025-SY-11,"๊ฐ‘: ๋œป์„ ์–ป์œผ๋ฉด ๋ฐฑ์„ฑ๊ณผ ํ•จ๊ป˜ ๊ทธ ๋„(้“)๋ฅผ ํ–‰ํ•˜๊ณ , ๋œป์„ ์–ป์ง€ +๋ชปํ•˜๋ฉด ํ™€๋กœ ๊ทธ ๋„๋ฅผ ํ–‰ํ•œ๋‹ค. ๋ถ€๊ท€๊ฐ€ ๋งˆ์Œ์„ ์–ด์ง€๋Ÿฝํžˆ์ง€ +๋ชปํ•˜๊ณ , ๋นˆ์ฒœ์ด ํ–‰์œ„๋ฅผ ๋ฐ”๊พธ์ง€ ๋ชปํ•˜๋ฉฐ, ์œ„์„ธ์™€ ๋ฌด๋ ฅ์ด ์ง€์กฐ๋ฅผ +๊บพ์ง€ ๋ชปํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ์‚ฌ๋žŒ์„ ๋Œ€์žฅ๋ถ€๋ผ ํ•œ๋‹ค. +์„: ์˜ˆ(็ฆฎ)๋ผ๋Š” ๊ฒƒ์€ ์ง„์‹คํ•˜๊ณ  ์‹ ์‹คํ•œ ๋งˆ์Œ์ด ์–„ํŒํ•ด์ง„ ๊ฒฐ๊ณผ์ด๋ฉฐ +ํ˜ผ๋ž€์˜ ์›์ธ์ด๋‹ค. ์„ฃ๋ถ€๋ฅด๊ฒŒ ๋‚ด๋‹ค๋ณด๋Š” ๊ฒƒ์€ ๋„๊ฐ€ ๊พธ๋ฉฐ์ง„ +๊ฒƒ์ด์ž ์–ด๋ฆฌ์„์Œ์˜ ๋‹จ์ดˆ์ด๋‹ค. ๊ทธ๋Ÿฌ๋‹ˆ ๋Œ€์žฅ๋ถ€๋Š” ์ค‘ํ›„ํ•จ์— +์ฒ˜ํ•˜๋ฉฐ ์–„ํŒํ•œ ๊ณณ์— ๊ฑฐํ•˜์ง€ ์•Š๋Š”๋‹ค.","{'question': '๊ฐ‘, ์„ ์‚ฌ์ƒ๊ฐ€๋“ค์˜ ์ž…์žฅ์œผ๋กœ ์ ์ ˆํ•˜์ง€ ์•Š์€๊ฒƒ์€?', 'choices': ['๊ฐ‘: ์ˆ˜์˜ค(็พžๆƒก)์˜ ๋งˆ์Œ์€ ์˜๋กœ์šด ํ–‰์œ„๋ฅผ ๊พธ์ค€ํžˆ ์‹ค์ฒœํ•ด์•ผ๋งŒ ์ƒ๊ฒจ๋‚œ๋‹ค.', '๊ฐ‘: ์˜ค๋ฅœ(ไบ”ๅ€ซ)์˜ ์ฐธ๋œ ์‹ค์ฒœ์€ ๋ฐ˜๋“œ์‹œ ์ˆ˜๊ธฐ(ไฟฎๅทฑ)๊ฐ€ ๋ฐ”ํƒ•์ด ๋˜์–ด์•ผ ํ•œ๋‹ค.', '์„: ์ด์ƒ์ ์ธ ์ •์น˜๋Š” ์Šค์Šค๋กœ ๊ทธ๋Ÿฌํ•จ[่‡ช็„ถ]์˜ ์›๋ฆฌ์— ์–ด๊ธ‹๋‚˜์ง€ ์•Š๋Š”๋‹ค.', '์„: ์„ฑ์ธ(่–ไบบ)์˜ ๋„๋ฅผ ๋ณธ๋ฐ›์•„ ๊ฒธํ—ˆํ•˜๊ณ  ๋‹คํˆผ ์—†๋Š” ๋•์„ ์ง€๋…€์•ผ ํ•œ๋‹ค.', '๊ฐ‘๊ณผ ์„: ๋„๋ฅผ ๋”ฐ๋ฅด๋Š” ์‚ฌ๋žŒ์€ ๊ณง ๋ณธ์„ฑ์„ ๋”ฐ๋ฅด๋Š” ์‚ฌ๋žŒ์ด๋ผ ํ•  ์ˆ˜ ์žˆ๋‹ค.'], 'answer': ''}",,1,1,True,[],1 +2025-SY-12," โ€˜๋ชฉ์  ์—†๋Š” ํ•ฉ๋ชฉ์ ์„ฑโ€™์ด ๋˜์–ด ๋ฒ„๋ฆฐ ๊ณ„๋ชฝ์  ํ•ฉ๋ฆฌ์„ฑ์€ ์ž๋ณธ์ฃผ์˜ +๋Œ€์ค‘๋ฌธํ™”์—์„œ๋„ ๊ด€์ฐฐ๋œ๋‹ค. ๋Œ€์ค‘๋ฌธํ™”์˜ ์ง€๋ฐฐ์ž๋“ค์€ ๋Œ€์ค‘๋ฌธํ™”๊ฐ€ +์žฅ์‚ฌ์ผ ๋ฟ์ด๋ผ๋Š” ์‚ฌ์‹ค์„ ์ˆจ๊ธฐ์ง€ ์•Š๋Š”๋‹ค. ์˜คํžˆ๋ ค ๊ทธ๋“ค์€ ์ด ์‚ฌ์‹ค์„ +์ž์‹ ๋“ค์ด ๋งŒ๋“  ์ €์†ํ•œ ๋ฌธํ™” ์ƒํ’ˆ์„ ์ •๋‹นํ™”ํ•˜๋Š” ์ด๋ฐ์˜ฌ๋กœ๊ธฐ๋กœ +ํ™œ์šฉํ•œ๋‹ค. ์†Œ๋น„์ž๋“ค์€ ์ž์‹ ๋“ค์˜ ์š•๊ตฌ์— ๋งž๊ฒŒ ๊ทธ ์œ ํ˜•์ด ๊ทœ๊ฒฉํ™”๋œ +๋Œ€๋Ÿ‰์ƒ์‚ฐ๋ฌผ์„ ๋ณ„ ์ €ํ•ญ ์—†์ด ์Šค์Šค๋กœ ๋ฐ›์•„๋“ค์ด๊ฒŒ ๋œ๋‹ค. ๋ฌธํ™” ์‚ฐ์—…์˜ +๊ธฐ์ˆ ์ด ์‚ฌํšŒ์— ๋Œ€ํ•œ ๊ถŒ๋ ฅ์„ ํš๋“ํ•  ์ˆ˜ ์žˆ๋Š” ๊ธฐ๋ฐ˜์€ ๋ฐ”๋กœ ๊ฒฝ์ œ์  +๊ฐ•์ž์˜ ๊ถŒ๋ ฅ์ด๋ผ๋Š” ๊ฒƒ์€ ์—ฌ๊ธฐ์„œ ์–ธ๊ธ‰๋˜์ง€ ์•Š๋Š”๋‹ค. ๋‹ค์–‘ํ•œ ๋ฌธํ™” +์ƒํ’ˆ์ด ๋Œ€์ค‘์—๊ฒŒ ์ œ๊ณต๋˜์ง€๋งŒ ์ด๋Š” ๋Œ€๋Ÿ‰์ƒ์‚ฐ ๋ฒ•์น™์„ ๋” ์™„๋ฒฝํ•˜๊ฒŒ +์‹คํ˜„ํ•  ๋ฟ์ด๋‹ค.","{'question': '๋‹ค์Œ์„ ์ฃผ์žฅํ•œ ์‚ฌ์ƒ๊ฐ€์˜ ์ž…์žฅ์œผ๋กœ ๊ฐ€์žฅ ์ ์ ˆํ•œ ๊ฒƒ์€?', 'choices': ['๋ฌธํ™” ์‚ฐ์—…์€ ๋Œ€์ค‘๋ฌธํ™”๊ฐ€ ์ƒํ’ˆ์— ๋ถˆ๊ณผํ•˜๋‹ค๋Š” ์‚ฌ์‹ค์„์€ํํ•œ๋‹ค.', '๋ฌธํ™” ์ƒํ’ˆ์— ๋Œ€ํ•œ ๋Œ€์ค‘์˜ ์„ ํ˜ธ๋Š” ์ƒ์—…์  ์ „๋žต์— ๋Œ€ํ•œ์ˆœ์‘์ด๋‹ค.', '๋ฌธํ™” ์ƒํ’ˆ ์†Œ๋น„์ž๋Š” ํ•ฉ๋ฆฌ์ ์œผ๋กœ ๋Œ€์ค‘๋ฌธํ™”๋ฅผ ์ง€๋ฐฐํ•˜๋Š”์ฃผ์ฒด์ด๋‹ค.', '๋ฌธํ™” ์ƒํ’ˆ์— ๋Œ€ํ•œ ๋‹ค์–‘ํ•œ ์ˆ˜์š”๋Š” ํ‘œ์ค€ํ™”๋œ ์†Œ๋น„ ์–‘์‹๊ณผ ์ƒ์ถฉํ•œ๋‹ค.', '๋ฌธํ™” ์ƒํ’ˆ ์†Œ๋น„์ž๋Š” ๋Œ€์ค‘๋ฌธํ™”์˜ ๋ณธ์งˆ์„ ๊ฐ„ํŒŒํ•˜๋Š” ํ•ฉ๋ฆฌ์„ฑ์„๋ฐœํœ˜ํ•œ๋‹ค.'], 'answer': ''}",,2,2,True,[],2 +2025-SY-15,"๊ฐ‘: ์›์ดˆ์  ์ž…์žฅ์€ ๊ทธ ์ž…์žฅ์—์„œ ๋„๋‹ฌ๋œ ๊ธฐ๋ณธ์  ํ•ฉ์˜๊ฐ€ ๊ณต์ •ํ•จ์„ +๋ณด์žฅํ•ด์ฃผ๋Š” ์ ์ ˆํ•œ ์ตœ์ดˆ ์ƒํƒœ์ด๋‹ค. ๋ฐ”๋กœ ์ด ๋•Œ๋ฌธ์— ๊ณต์ • +์œผ๋กœ์„œ์˜ ์ •์˜๋ž€ ๋ช…์นญ์ด ์ƒ๊ฒจ๋‚œ ๊ฒƒ์ด๋‹ค. +์„: ๊ตญ๊ฐ€์— ๊ด€ํ•œ ์šฐ๋ฆฌ์˜ ๊ฒฐ๋ก ์— ๋”ฐ๋ฅด๋ฉด ๊ฐ•์š”, ์ ˆ๋„, ์‚ฌ๊ธฐ ๋“ฑ์œผ๋กœ +๋ถ€ํ„ฐ์˜ ๋ณดํ˜ธ์™€ ๊ฐ™์€ ์ตœ์†Œํ•œ์˜ ๊ธฐ๋Šฅ์— ๊ทธ ์—ญํ• ์ด ๊ตญํ•œ๋œ +์ตœ์†Œ๊ตญ๊ฐ€๋งŒ์ด ๋„๋•์ ์œผ๋กœ ์ •๋‹นํ™”๋œ๋‹ค.","{'question': '๊ฐ‘, ์„ ์‚ฌ์ƒ๊ฐ€๋“ค์˜ ์ž…์žฅ์œผ๋กœ ์ ์ ˆํ•œ ๊ฒƒ๋งŒ์„ <๋ณด๊ธฐ>์—์„œ ๊ณ ๋ฅธ ๊ฒƒ์€?', 'choices': ['ใ„ฑ,ใ„ด', 'ใ„ฑ,ใ„ท', 'ใ„ด,ใ„ท', 'ใ„ด,ใ„น', 'ใ„ท, ใ„น'], 'answer': '', 'question_plus': 'ใ„ฑ. ๊ฐ‘: ๋ฌด์ง€์˜ ๋ฒ ์ผ ์† ๊ฐœ์ธ์€ ์ž์œ ๋กญ๊ณ  ํ‰๋“ฑํ•œ ์ธ๊ฒฉ์ฒด์ด๋‹ค.\r\nใ„ด. ๊ฐ‘: ์›์ดˆ์  ์ž…์žฅ์˜ ๋‹น์‚ฌ์ž๋“ค์€ ์ƒํ˜ธ ์‹ ๋ขฐํ•  ์ˆ˜ ์žˆ๋Š” ์กด์žฌ๋“ค์ด๋‹ค.\r\nใ„ท. ์„: ์˜ค์ง ์ตœ์†Œ๊ตญ๊ฐ€์—์„œ๋งŒ ๊ฐœ์ธ์˜ ์†Œ์œ  ๊ถŒ๋ฆฌ๊ฐ€ ์กด์žฌํ•  ์ˆ˜ ์žˆ๋‹ค.\r\nใ„น. ๊ฐ‘๊ณผ ์„: ์ •์˜์˜ ์›์น™์ด ๋ณด์žฅํ•˜๋Š” ๊ธฐ๋ณธ์  ๊ถŒ๋ฆฌ๋Š” ์ œํ•œ๋ ์ˆ˜ ์—†๋‹ค.'}","ใ„ฑ. ๊ฐ‘: ๋ฌด์ง€์˜ ๋ฒ ์ผ ์† ๊ฐœ์ธ์€ ์ž์œ ๋กญ๊ณ  ํ‰๋“ฑํ•œ ์ธ๊ฒฉ์ฒด์ด๋‹ค. +ใ„ด. ๊ฐ‘: ์›์ดˆ์  ์ž…์žฅ์˜ ๋‹น์‚ฌ์ž๋“ค์€ ์ƒํ˜ธ ์‹ ๋ขฐํ•  ์ˆ˜ ์žˆ๋Š” ์กด์žฌ๋“ค์ด๋‹ค. +ใ„ท. ์„: ์˜ค์ง ์ตœ์†Œ๊ตญ๊ฐ€์—์„œ๋งŒ ๊ฐœ์ธ์˜ ์†Œ์œ  ๊ถŒ๋ฆฌ๊ฐ€ ์กด์žฌํ•  ์ˆ˜ ์žˆ๋‹ค. +ใ„น. ๊ฐ‘๊ณผ ์„: ์ •์˜์˜ ์›์น™์ด ๋ณด์žฅํ•˜๋Š” ๊ธฐ๋ณธ์  ๊ถŒ๋ฆฌ๋Š” ์ œํ•œ๋ ์ˆ˜ ์—†๋‹ค.",1,1,True,[],1 +2025-SY-17,"๊ทธ ์ž์ฒด๋กœ ๋†’์ด ํ‰๊ฐ€ํ•ด์•ผ ํ• , ๋” ์ด์ƒ์˜ ์˜๋„๊ฐ€ ์—†๋Š” ์„ ์˜์ง€ +๋ผ๋Š” ๊ฐœ๋…์€ ์ด๋ฏธ ์ž์—ฐ์ ์ธ ๊ฑด์ „ํ•œ ์ง€์„ฑ์— ๋‚ด์žฌํ•ด ์žˆ๊ณ , ๊ฐ€๋ฅด์น  +ํ•„์š”๋Š” ์—†์œผ๋ฉฐ ์˜คํžˆ๋ ค ๋‹จ์ง€ ๊ณ„๋ฐœ๋  ํ•„์š”๋งŒ ์žˆ๋Š” ๊ฒƒ์ด๋‹ค. + <๋ฌธ์ œ ์ƒํ™ฉ> +A๋Š” ์ข‹์•„ํ•˜๋Š” ๊ฒŒ์ž„ ์•„์ดํ…œ์„ ๊ตฌ์ž…ํ•˜๊ณ  ์‹ถ์ง€๋งŒ ์šฉ๋ˆ์ด ๋ถ€์กฑ +ํ•˜๋‹ค. A๋Š” ๊ฐš์ง€ ๋ชปํ•  ๊ฒƒ์„ ์•Œ๋ฉด์„œ๋„ ์นœ๊ตฌ์—๊ฒŒ โ€œ๊ผญ ๊ฐš์„๊ฒŒ!โ€๋ผ๊ณ  +์•ฝ์†ํ•˜๊ณ  ๋ˆ์„ ๋นŒ๋ ค์•ผ ํ• ์ง€ ๊ณ ๋ฏผํ•˜๊ณ  ์žˆ๋‹ค.","{'question': '๋‹ค์Œ ์‚ฌ์ƒ๊ฐ€์˜ ๊ด€์ ์—์„œ <๋ฌธ์ œ ์ƒํ™ฉ> ์† A์—๊ฒŒ ์ œ์‹œํ•  ์กฐ์–ธ์œผ๋กœ ๊ฐ€์žฅ ์ ์ ˆํ•œ ๊ฒƒ์€?', 'choices': ['์•ฝ์†์„ ์–ด๊ธด ์‚ฌ๋žŒ์€ ๋ชฉ์ ์œผ๋กœ ๋Œ€์šฐ๋ฐ›์•„์„œ๋Š” ์•ˆ ๋จ์„๋ช…์‹ฌํ•˜์„ธ์š”.', '์•ฝ์† ์ค€์ˆ˜์˜ ์˜๋ฌด๋Š” ์ž๊ธฐ ํ–‰๋ณต์— ๋Œ€ํ•œ ์—ด๋ง์— ๊ทผ๊ฑฐํ•จ์„๋ช…์‹ฌํ•˜์„ธ์š”.', '๊ฑฐ์ง“ ์•ฝ์†์€ ์นœ๊ตฌ์˜ ์ธ๊ฒฉ์„ ์กด๊ฒฝํ•˜๋Š” ๊ฒƒ์ด ์•„๋‹˜์„ ์œ ๋…ํ•˜์„ธ์š”.', '์•ฝ์†์€ ์นœ๊ตฌ์™€์˜ ๋ˆ๋…ํ•œ ์ •์„œ์  ์œ ๋Œ€๋ฅผ ์œ„ํ•ด ์ง€์ผœ์•ผ ํ•จ์„์œ ๋…ํ•˜์„ธ์š”.', '์นœ๊ตฌ์—๊ฒŒ ๋ฌดํ•ดํ•˜๋‹ค๋ฉด ๊ฑฐ์ง“ ์•ฝ์†๋„ ๋„๋•์ ์œผ๋กœ ์ •๋‹นํ™”๋จ์„์œ ๋…ํ•˜์„ธ์š”.'], 'answer': ''}",,3,3,True,[],3 +2025-SY-18,"์‹œ๋ฏผ ๋ถˆ๋ณต์ข…์„ ์ •๋‹นํ™”ํ•  ๋•Œ ์–ด๋–ค ๊ฐœ์ธ์  ๋„๋• ์›์น™์ด๋‚˜ +์ข…๊ต์  ๊ต์„ค์ด ์šฐ๋ฆฌ ์ฃผ์žฅ์„ ์ง€์ง€ํ•ด ์ค€๋‹ค๊ณ  ํ•ด์„œ ๊ทธ๊ฒƒ์— ์˜๊ฑฐ +ํ•ด์„œ๋Š” ์•ˆ ๋œ๋‹ค. ์‹œ๋ฏผ ๋ถˆ๋ณต์ข…์˜ ๊ทผ๊ฑฐ๊ฐ€ ์˜ค์ง ๊ฐœ์ธ์ด๋‚˜ ์ง‘๋‹จ์˜ +์ด์ต์—๋งŒ ๊ธฐ์ดˆํ•  ์ˆ˜ ์—†๋‹ค๋Š” ๊ฒƒ์€ ๋งํ•  ํ•„์š”๋„ ์—†๋‹ค. ๊ทธ ๋Œ€์‹  +์‹œ๋ฏผ ๋ถˆ๋ณต์ข…์€ ๊ณต๊ณต์  ์ •์˜๊ด€์— ์˜๊ฑฐํ•˜๊ฒŒ ๋œ๋‹ค.","{'question': '๋‹ค์Œ์„ ์ฃผ์žฅํ•œ ์‚ฌ์ƒ๊ฐ€์˜ ์ž…์žฅ์œผ๋กœ ๊ฐ€์žฅ ์ ์ ˆํ•œ ๊ฒƒ์€?', 'choices': ['์†Œ์ˆ˜์ž๊ฐ€ ์ง€๋‹Œ ์ •์˜๊ด€์€ ์‹œ๋ฏผ ๋ถˆ๋ณต์ข…์˜ ๊ทผ๊ฑฐ๊ฐ€ ๋  ์ˆ˜ ์—†๋‹ค.', '์ฐจ๋“ฑ์˜ ์›์น™์— ๊ทผ๊ฑฐํ•œ ๋ฒ•์€ ์‹œ๋ฏผ ๋ถˆ๋ณต์ข…์˜ ๋Œ€์ƒ์ด ๋  ์ˆ˜ ์žˆ๋‹ค.', '์ค€๋ฒ•์˜ ์˜๋ฌด๋Š” ๊ธฐ๋ณธ์  ์ž์œ ๋ฅผ ๋ฐฉ์–ดํ•  ๊ถŒ๋ฆฌ์™€ ์ƒ์ถฉํ•  ์ˆ˜ ์—†๋‹ค.', '์‹œ๋ฏผ ๋ถˆ๋ณต์ข…์˜ ๋Œ€์ƒ์€ ๊ธฐ๋ณธ์  ์ž์œ ์˜ ์‹ฌ๊ฐํ•œ ์œ„๋ฐ˜์— ๊ตญํ•œ๋œ๋‹ค.', '์–‘์‹ฌ์  ๊ฑฐ๋ถ€์— ๋Œ€ํ•œ ๊ตญ๊ฐ€์˜ ๊ทœ์ œ๋Š” ์‹œ๋ฏผ ๋ถˆ๋ณต์ข…์˜ ๋Œ€์ƒ์ด ๋  ์ˆ˜ ์—†๋‹ค.'], 'answer': ''}",,2,2,True,[],2 +2025-SY-19,"์„œ๋กœ์—๊ฒŒ ์˜ํ–ฅ์„ ๋ผ์น˜๋Š” ์‚ฌ๋žŒ๋“ค์€ ์–ด๋–ค ๊ณต๋ฏผ์  ์ฒด์ œ์— ์†ํ•ด์•ผ +ํ•œ๋‹ค. ๊ทธ๋Ÿฐ ์ฒด์ œ์— ๊ท€์†๋  ์‚ฌ๋žŒ๋“ค์— ๊ด€๊ณ„๋˜๋Š” ๋ชจ๋“  ๋ฒ•๋ฅ ์ƒ์˜ +์ฒด์ œ๋Š” ๋‹ค์Œ ์ค‘ ํ•˜๋‚˜์ด๋‹ค. ์ฒซ์งธ, ํ•œ ๊ตญ๊ฐ€ ์•ˆ์—์„œ๋Š” ์‹œ๋ฏผ๋ฒ•์— +๋”ฐ๋ฅด๋Š” ์ฒด์ œ์ด๋ฉฐ ๋‘˜์งธ, ๊ตญ๊ฐ€ ๊ฐ„ ๊ด€๊ณ„์—์„œ ๊ตญ์ œ๋ฒ•์— ๋”ฐ๋ฅด๋Š” ์ฒด์ œ +์ด๊ณ  ์…‹์งธ, ์‚ฌ๋žŒ์ด๋‚˜ ๊ตญ๊ฐ€๊ฐ€ ์„œ๋กœ ์˜ํ–ฅ์„ ์ค„ ์ˆ˜ ์žˆ๋Š” ๊ด€๊ณ„์— +์žˆ์œผ๋ฉด์„œ ๋ณดํŽธ ์ƒํƒœ์˜ ์‹œ๋ฏผ์œผ๋กœ ๊ณ ๋ ค๋˜๋Š” ํ•œ, ์„ธ๊ณ„ ์‹œ๋ฏผ๋ฒ•์— +๋”ฐ๋ฅด๋Š” ์ฒด์ œ์ด๋‹ค. ์ด๋Ÿฌํ•œ ๋ถ„๋ฅ˜๋Š” ์˜์›ํ•œ ํ‰ํ™” ์ด๋…์— ๊ฑธ๋งž์€ +ํ•„์—ฐ์ ์ธ ๊ฒƒ์ด๋‹ค.","{'question': '๋‹ค์Œ์„ ์ฃผ์žฅํ•œ ์‚ฌ์ƒ๊ฐ€์˜ ์ž…์žฅ์œผ๋กœ ๊ฐ€์žฅ ์ ์ ˆํ•œ ๊ฒƒ์€?', 'choices': ['์‹œ๋ฏผ๋ฒ• ์ฒด์ œ๊ฐ€ ํ•œ ๊ตญ๊ฐ€์˜ ๋Œ€๋‚ด์  ์ •์น˜ ์ œ๋„๋ฅผ ์ง€์ •ํ•  ์ˆ˜๋Š” ์—†๋‹ค.', '์„ธ๊ณ„ ์‹œ๋ฏผ๋ฒ•์€ ์ด๋ฐฉ์ธ์˜ ํ™˜๋Œ€๊ถŒ๊ณผ ์˜์†์  ์ฒด๋ฅ˜๊ถŒ์„ ๋ณด์žฅํ•œ๋‹ค.', '์ „์Ÿ ์ƒํƒœ ๊ทน๋ณต์„ ์œ„ํ•ด์„œ๋Š” ์ฃผ๊ถŒ์ด ๊ตญ์ œ ๊ตญ๊ฐ€๋กœ ๊ท€์†๋˜์–ด์•ผ ํ•œ๋‹ค.', '์˜์›ํ•œ ํ‰ํ™”๋ฅผ ์œ„ํ•ด ๊ตญ๊ฐ€๋Š” ์–ด๋– ํ•œ ๊ตญ์ฑ„๋„ ๋ฐœํ–‰ํ•ด์„œ๋Š” ์•ˆ ๋œ๋‹ค.', '๊ตญ๊ฐ€ ๊ฐ„ ์ ๋Œ€ ํ–‰์œ„๊ฐ€ ์ข…์‹๋˜์–ด์•ผ ์˜์›ํ•œ ํ‰ํ™” ์‹คํ˜„์ด ๊ฐ€๋Šฅํ•˜๋‹ค.'], 'answer': ''}",,5,5,True,[],5 +2025-SS-01,"์ด๊ฒƒ์€ ๋‚˜์ผ๊ฐ• ์œ ์—ญ ๋žŒ์„ธ์Šค 3์„ธ์˜ ๊ถ์ „ ์œ ์ ์—์„œ ๋ฐœ๊ตด๋œ ํƒ€์ผ +์กฐ๊ฐ๋“ค์ด๋‹ค. ์ด ์œ ๋ฌผ์—๋Š” ์ฃผ๋ณ€ ์ง€์—ญ์—์„œ ์‚ฌ๋กœ์žก์€ ํฌ๋กœ๋“ค์ด +ํ‘œํ˜„๋˜์–ด ์žˆ๋‹ค. ํŠนํžˆ ์ฒซ ๋ฒˆ์งธ ์ธ๋ฌผ์€ ์กฑ์žฅ์œผ๋กœ ์ถ”์ •๋˜๋ฉฐ, ํŒŒ๋ผ์˜ค๋Š” +์ด๋Ÿฌํ•œ ์žฅ์‹์„ ํ†ตํ•˜์—ฌ ๋Œ€๋‚ด์™ธ์ ์œผ๋กœ ์ž์‹ ์˜ ๊ถŒ๋ ฅ์„ ๊ณผ์‹œํ•˜๊ณ ์ž +ํ•˜์˜€๋‹ค.","{'question': '๋‹ค์Œ ์œ ๋ฌผ์„ ๋‚จ๊ธด ๊ณ ๋Œ€ ๋ฌธ๋ช…์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['์‚ฌ์ž์˜ ์„œ๋ฅผ ์ œ์ž‘ํ•˜์˜€๋‹ค.', '๊ธธ๊ฐ€๋ฉ”์‹œ ์„œ์‚ฌ์‹œ๋ฅผ ๋‚จ๊ฒผ๋‹ค.', '์˜ฌ๋ฆผํ”ผ์•„ ์ œ์ „์„ ๊ฐœ์ตœํ•˜์˜€๋‹ค.', '๊ฐ‘๊ณจ์— ์ ๋ณต์˜ ๋‚ด์šฉ์„ ๊ธฐ๋กํ•˜์˜€๋‹ค.', '๋ชจํ—จ์กฐ๋‹ค๋กœ์— ๊ณ„ํš๋„์‹œ๋ฅผ ๊ฑด์„คํ•˜์˜€๋‹ค.'], 'answer': ''}",,1,1,True,[],1 +2025-SS-02," (๊ฐ€) ์ค‘๊ตญ ํ™ฉ์ œ์˜ ์„œ์‹ ์— โ€œํ™ฉ์ œ๊ฐ€ ์™œํ™ฉ์—๊ฒŒ ์•ˆ๋ถ€๋ฅผ ๋ฌป๋Š”๋‹ค.โ€๋ผ๊ณ  +์ ํ˜€ ์žˆ๋˜ ๊ฒƒ์— ๋Œ€ํ•˜์—ฌ, ๊ตฐ์‹ ๋“ค์€ โ€œ์ด๊ฒƒ์€ ์ค‘๊ตญ์˜ ํ™ฉ์ œ๊ฐ€ +์ œํ›„ ์™•์—๊ฒŒ ์„œ์‹ ์„ ๋ณด๋‚ผ ๋•Œ์˜ ์˜ˆ์˜์ž…๋‹ˆ๋‹ค.โ€๋ผ ํ•˜์—ฌ ์ด์˜๋ฅผ +์ œ๊ธฐํ•˜์˜€๋‹ค. ํ•˜์ง€๋งŒ ์‡ผํ† ์ฟ  ํƒœ์ž๋Š” โ€œํ™ฉ(็š‡)์ด๋ž€ ๊ธ€์ž๋Š” +ํ•จ๋ถ€๋กœ ์‚ฌ์šฉํ•˜๋Š” ๊ธ€์ž๊ฐ€ ์•„๋‹ˆ๋‹ค.โ€๋ผ๊ณ  ๊ตฐ์‹ ๋“ค์˜ ์ฃผ์žฅ์„ +์ผ์ถ•ํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ค‘๊ตญ ์‚ฌ์‹ ๋“ค์—๊ฒŒ ๋‹ต์„œ๋ฅผ ๊ฑด๋„ค๊ณ  ํ›„ํ•œ +์ ‘๋Œ€๋ฅผ ํ•˜์—ฌ ๊ท€๊ตญ์‹œ์ผฐ๋‹ค. + (๋‚˜) ์Šค๊ฐ€์™€๋ผ๋…ธ ๋ฏธ์น˜์ž๋„ค๊ฐ€ ์ƒ์†Œํ•˜๊ธฐ๋ฅผ, โ€œ์ค‘๊ตญ์— ์žˆ๋Š” ์Šน๋ ค +์ธ„์นธ์ด ์ƒ์ธ ์™•๋ˆŒ์„ ํ†ตํ•ด ๋ณด๋‚ด์˜จ ๋ฌธ์„œ์— ์˜ํ•˜๋ฉด ์ง€๊ธˆ ์ค‘๊ตญ์€ +๋ฐ˜๋ž€์ด ๋๋‚œ ์ง€ ์–ผ๋งˆ ๋˜์ง€ ์•Š์•„ ์ˆ˜๋„ ์žฅ์•ˆ์ด ํ˜ผ๋ž€ํ•œ ๋ชจ์Šต์„ +๋ณด์ด๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ๋˜ ๋ฐ”๋‹ค๋ฅผ ๊ฑด๋„ˆ ์ค‘๊ตญ์œผ๋กœ ํ•ญํ•ดํ•˜๋Š” ๊ฒƒ์˜ +์–ด๋ ค์›€๊ณผ ํ•ด์ ์— ์˜ํ•œ ํ”ผํ•ด ๋“ฑ์„ ๊ณ ๋ คํ•  ๋•Œ, ์‚ฌ์‹  ํŒŒ๊ฒฌ์˜ +ํ•„์š” ์—ฌ๋ถ€์— ๋Œ€ํ•œ ์‹ ์ค‘ํ•œ ๋…ผ์˜๊ฐ€ ํ•„์š”ํ•˜๋‹ค๊ณ  ์ƒ๊ฐํ•ฉ๋‹ˆ๋‹ค.โ€ +๋ผ๊ณ  ํ•˜์˜€๋‹ค. ์–ผ๋งˆ ํ›„ ์ค‘๊ตญ ๋ฌธ๋ฌผ ์ˆ˜์šฉ์„ ์œ„ํ•œ ์‚ฌ์‹  ํŒŒ๊ฒฌ์ด +์ค‘์ง€๋˜์—ˆ๋‹ค.","{'question': '(๊ฐ€), (๋‚˜) ์‹œ๊ธฐ ์‚ฌ์ด์— ์žˆ์—ˆ๋˜ ์‚ฌ์‹ค๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['๋ฅ˜ํ๊ฐ€ ๋ณ‘ํ•ฉ๋˜์—ˆ๋‹ค.', '๊ณ ์‚ฌ๊ธฐ๊ฐ€ ํŽธ์ฐฌ๋˜์—ˆ๋‹ค.', 'ํ•ด์ฒด์‹ ์„œ๊ฐ€ ๊ฐ„ํ–‰๋˜์—ˆ๋‹ค.', '๋™์œ  ์šด๋™์ด ์ „๊ฐœ๋˜์—ˆ๋‹ค.', '์‚ฐํ‚จ์ฝ”ํƒ€์ด ์ œ๋„๊ฐ€ ์‹œํ–‰๋˜์—ˆ๋‹ค.'], 'answer': ''}",,2,3,False,[],3 +2025-SS-04,"์ง€๋„๋Š” ํฌ์„ธ๋ฅดํฌ์„ธ์Šค ๊ตฐ๋Œ€์˜ ์ง„๊ฒฉ๋กœ์™€ ์ฃผ์š” +๊ฒฉ์ „์ง€๋ฅผ ๋‚˜ํƒ€๋‚ธ ๊ฒƒ์ด๋‹ค. ํ…Œ๋ฅด๋ชจํ•„๋ ˆ ์ „ํˆฌ์—์„œ +ํฌ์„ธ๋ฅดํฌ์„ธ์Šค ๊ตฐ๋Œ€๋Š” ์ŠคํŒŒ๋ฅดํƒ€์˜ ์™• ๋ ˆ์˜ค๋‹ˆ๋‹ค์Šค๊ฐ€ +์ด๋„๋Š” 300์ธ๋Œ€์™€ ๋งˆ์ฃผํ•˜์˜€๋‹ค. ๋ ˆ์˜ค๋‹ˆ๋‹ค์Šค์˜ +๊ตฐ๋Œ€๋ฅผ ์ œ์™ธํ•œ ๋‹ค๋ฅธ ํŽ ๋กœํฐ๋„ค์†Œ์Šค๊ตฐ์€ ๊ฒ์„ ๋จน๊ณ  +ํŽ ๋กœํฐ๋„ค์†Œ์Šค๋กœ ์ฒ ์ˆ˜ํ•˜์—ฌ ์ง€ํ˜‘์„ ๋ฐฉ์–ดํ•ด์•ผ ํ•œ๋‹ค๊ณ  +์ฃผ์žฅํ•˜์˜€๋‹ค. โ€ฆ(์ค‘๋žต)โ€ฆ ํฌ์„ธ๋ฅดํฌ์„ธ์Šค์˜ ๊ตฐ๋Œ€๋Š” +ํ…Œ๋ฅด๋ชจํ•„๋ ˆ ์ „ํˆฌ์—์„œ ์Šน๋ฆฌํ•˜์˜€์ง€๋งŒ ์ดํ›„ ๋ฒŒ์–ด์ง„ +์‚ด๋ผ๋ฏธ์Šค ํ•ด์ „์—์„œ ๋Œ€ํŒจํ•œ ํ›„ ๋ฌผ๋Ÿฌ๊ฐ”๋‹ค.","{'question': '๋‹ค์Œ์„ ํ†ตํ•ด ์•Œ ์ˆ˜ ์žˆ๋Š” ์ „์Ÿ์˜ ๊ฒฐ๊ณผ๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['์†”๋ก ์ด ๊ฐœํ˜์„ ๋‹จํ–‰ํ•˜์˜€๋‹ค.', '์•„ํ…Œ๋„ค๊ฐ€ ๋ธ๋กœ์Šค ๋™๋งน์˜ ๋งน์ฃผ๊ฐ€ ๋˜์—ˆ๋‹ค.', '์ŠคํŒŒ๋ฅดํƒ€๊ฐ€ ๊ทธ๋ฆฌ์Šค์˜ ํŒจ๊ถŒ์„ ์žฅ์•…ํ•˜์˜€๋‹ค.', 'ํŽ˜์ด์‹œ์ŠคํŠธ๋ผํ† ์Šค๊ฐ€ ์ฐธ์ฃผ์ •์„ ์‹ค์‹œํ•˜์˜€๋‹ค.', 'ํด๋ ˆ์ด์Šคํ…Œ๋„ค์Šค๊ฐ€ ๋„ํŽธ ์ถ”๋ฐฉ์ œ๋ฅผ ๋งˆ๋ จํ•˜์˜€๋‹ค.'], 'answer': ''}",,2,2,True,"['๊ธฐ์›์ „ 480๋…„ ๋‹ค๋ฆฌ์šฐ์Šค์˜ ์•„๋“ค์ด์ž ํ›„๊ณ„์ž์ธ ์„ธ๋ฅดํฌ์Šค๋Š” ์ด ํŒจ๋ฐฐ๋ฅผ ๋ณต์ˆ˜ํ•˜๊ณ  ๊ทธ๋ฆฌ์Šค์˜ ๋ชจ๋“  ๋„์‹œ ๊ตญ๊ฐ€๋ฅผ ๊ตด๋ณต์‹œํ‚ค๊ธฐ ์œ„ํ•ด ๊ทธ๋ฆฌ์Šค์— ๋Œ€ํ•œ ์ž์‹ ์˜ ์นจ๊ณต์„ ๊ฐœ์‹œํ–ˆ๋‹ค. ๊ทธ๋Š” ๋ถ์ชฝ์—์„œ ์œก์ง€๋กœ ์นจ๊ณตํ•  ๊ตฐ๋Œ€๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ํ›จ์”ฌ ๋” ํฐ ํ•จ๋Œ€๋ฅผ ์กฐ๋ฆฝํ–ˆ๋‹ค. ์ด ์œ„๊ธฐ ์‹œ๊ธฐ์— ๊ทธ๋ฆฌ์Šค ๋„์‹œ๊ตญ๊ฐ€๋“ค์€ ๋Œ€๋ถ€๋ถ„ ๋™๋งน๊ตญ์œผ๋กœ ๋‹จ๊ฒฐํ•˜๊ธฐ๋กœ ๊ฒฐ์ •ํ•˜๊ณ  ํ†ต์ƒ์ ์œผ๋กœ ํ—ฌ๋ฆฌ์นธ ๋™๋งน์ด๋ผ๊ณ  ๋ถˆ๋ฆฌ๋Š” ๊ฒƒ์„ ๊ฒฐ์„ฑํ•˜์˜€๋‹ค. ์ŠคํŒŒ๋ฅดํƒ€๋Š” ๊ตฐ๋Œ€๋ฅผ ์ง€ํœ˜ํ–ˆ๊ณ  ์•„ํ…Œ๋„ค๋Š” ํ•จ๋Œ€๋ฅผ ์ง€ํœ˜ํ–ˆ๋‹ค. ๋Œ€๋ถ€๋ถ„ ์ŠคํŒŒ๋ฅดํƒ€์ธ์ธ ๋Œ€๊ทœ๋ชจ ์œก์ƒ๊ตฐ ์†Œ๊ทœ๋ชจ ๋ฌด๋ฆฌ๋Š” ๊ทธ๋ฆฌ์Šค ๋ถ๋™๋ถ€์˜ ์‚ฐ๊ณผ ๋ฐ”๋‹ค ์‚ฌ์ด์˜ ์ข์€ ๊ณ ๊ฐœ์ธ ์จ๋ชจํ•„๋ผ์— ๊ธฐ์„ธ๋ฅผ ์„ธ์šฐ๊ธฐ๋กœ ํ–ˆ๋‹ค. ๊ทธ๋“ค์˜ ๋ชฉํ‘œ๋Š” ์ž์‹ ๋“ค๋ณด๋‹ค ์ˆ˜์ ์œผ๋กœ ํ›จ์”ฌ ๋งŽ์€ ์นจ๋žตํ•˜๋Š” ํŽ˜๋ฅด์‹œ์•„๊ตฐ์„ ๋ฌผ๋ฆฌ์น˜๋Š” ๊ฒƒ์ด ์•„๋‹ˆ๋ผ ๋‚˜๋จธ์ง€ ์„ธ๋ ฅ์ด ๋ฐฉ์–ด๋ ฅ์„ ์กฐ์งํ•  ์ˆ˜ ์žˆ๋„๋ก ๊ทธ๋“ค์„ ์ง€์—ฐ์‹œํ‚ค๋Š” ๊ฒƒ์ด์—ˆ๋‹ค. ๋ฉฐ์น  ๋™์•ˆ ๊ทธ๋“ค์˜ ์™• ๋ ˆ์˜ค๋‹ˆ๋‹ค์Šค๊ฐ€ ์ด๋„๋Š” ์†Œ๊ทœ๋ชจ ์ŠคํŒŒ๋ฅดํƒ€ ๊ตฐ๋Œ€๋Š” ์—„์ฒญ๋‚˜๊ฒŒ ์šฐ์›”ํ•œ ํŽ˜๋ฅด์‹œ์•„ ๊ตฐ๋Œ€๋ฅผ ์„ฑ๊ณต์ ์œผ๋กœ ํ‡ด์ถœ์‹œ์ผฐ๋Š”๋ฐ, ๊ทธ๋ฆฌ์Šค์˜ ํ•œ ๋ฐฐ์‹ ์ž๊ฐ€ ํŽ˜๋ฅด์‹œ์•„์ธ๋“ค์—๊ฒŒ ๊ทธ๋“ค์ด ์ŠคํŒŒ๋ฅดํƒ€์ธ์„ ๋‘˜๋Ÿฌ ๋‘˜๋Ÿฌ๋ณผ ์ˆ˜ ์žˆ๋Š” ๋˜ ๋‹ค๋ฅธ ์‚ฐ ๊ณ ๊ฐœ๋ฅผ ์•Œ๋ฆด ๋•Œ๊นŒ์ง€์˜€๋‹ค. ์ŠคํŒŒ๋ฅดํƒ€ ๊ตฐ๋Œ€๋Š” ์ฃฝ์„ ๋•Œ๊นŒ์ง€ ์‹ธ์›Œ ๊ทธ๋ฆฌ์Šค์ธ๋“ค์—๊ฒŒ ์ „ํˆฌ๋ฅผ ๊ณ„์†ํ•˜๊ณ  ํ—ฌ๋ ˆ๋‹ˆ์–ธ ๋™๋งน์„ ํ•จ๊ป˜ ์žก์œผ๋ผ๊ณ  ์˜๊ฐ์„ ์ฃผ์—ˆ๋‹ค.']",2 +2025-SS-05,"์„ฑ์ง€ ํšŒ๋ณต์„ ๋ช…๋ถ„์œผ๋กœ ๋‚ด๊ฑด ํฌ๋ฆฌ์ŠคํŠธ๊ต ๊ตฐ๋Œ€๋Š” ๋‹ˆ์ผ€์•„๋ฅผ ์ ๋ นํ•œ +์ดํ›„ ์†Œ์•„์‹œ์•„๋ฅผ ๊ฒฝ์œ ํ•˜์—ฌ ๋ฌด์Šฌ๋ฆผ ๊ตฐ๋Œ€์˜ ๋ฐฉ์–ด๋ฅผ ๋šซ๊ณ  ์‹œ๋ฆฌ์•„์— +์ง„์ถœํ•˜์˜€๋‹ค. ๊ทธ๋“ค์€ ๋จผ์ € ์„ฑ์ง€๋กœ ํ–ฅํ•˜๋Š” ๊ธธ๋ชฉ์˜ ์š”์ถฉ์ง€์ธ ์•ˆํ‹ฐ์˜คํฌ๋ฅผ +์ ๋ นํ•˜๊ณ  ์˜ˆ๋ฃจ์‚ด๋ ˜๋งˆ์ € ํ•จ๋ฝ์‹œํ‚ค๋Š” ๋ฐ ์„ฑ๊ณตํ•˜์˜€๋‹ค. โ€ฆ(์ค‘๋žต)โ€ฆ +์‹œ๋ฆฌ์•„ ์ง€๋ฐฉ์„ ๊ฑฐ์ณ ๋™์ชฝ์œผ๋กœ ํƒˆ์ถœํ•œ ํ”ผ๋‚œ๋ฏผ๋“ค์€ (๊ฐ€)์˜ +์นผ๋ฆฌํ”„๊ฐ€ ๋จธ๋ฌด๋ฅด๊ณ  ์žˆ๋Š” ์ˆ˜๋„ ๋ฐ”๊ทธ๋‹ค๋“œ์— ์ด๋ฅด๋Ÿฌ ํฌ๋ฆฌ์ŠคํŠธ๊ต๋„๋“ค์ด +์˜ˆ๋ฃจ์‚ด๋ ˜์—์„œ ์žํ–‰ํ•œ ์‚ด์ธ๊ณผ ์•ฝํƒˆ์˜ ์†Œ์‹์„ ์ „ํ•˜์˜€๋‹ค.","{'question': '(๊ฐ€) ์™•์กฐ์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['์ˆ ํƒ„์˜ ์นญํ˜ธ๋ฅผ ๋ฐ›์•˜๋‹ค.', 'ํƒˆ๋ผ์Šค ์ „ํˆฌ์—์„œ ์Šน๋ฆฌํ•˜์˜€๋‹ค.', '์ด๋ฒ ๋ฆฌ์•„๋ฐ˜๋„๊นŒ์ง€ ์ง„์ถœํ•˜์˜€๋‹ค.', '์•„ํ”„๊ฐ„์กฑ์˜ ์นจ์ž…์œผ๋กœ ์‡ ํ‡ดํ•˜์˜€๋‹ค.', '์‚ฌ์‚ฐ ์™•์กฐ ํŽ˜๋ฅด์‹œ์•„๋ฅผ ์ •๋ณตํ•˜์˜€๋‹ค.'], 'answer': ''}",,2,4,False,"['์‹ญ์ž๊ตฐ ์ œ1๊ธฐ๋Š” ๋งˆ์นจ๋‚ด 1099๋…„ ์—ฌ๋ฆ„์— ์˜ˆ๋ฃจ์‚ด๋ ˜์— ๋„์ฐฉํ–ˆ๋‹ค. ์‹ญ์ž๊ตฐ์€ ๋„์‹œ๋ฅผ ๊ณต๊ฒฉํ•˜๊ธฐ ์ „์— ๊ธˆ์‹ํ•˜๊ณ  ์ˆœ๋ก€์ž๋กœ ์„ฑ๋ฒฝ์„ ๋Œ์•„๋‹ค๋‹ˆ๋ฉฐ ์ˆœ๋ก€์™€ ๋ฌด๋ ฅ ์ถฉ๋Œ์ด ์–ด์šฐ๋Ÿฌ์ง„ ๋ชจ์Šต์„ ๋ณด์—ฌ์ฃผ๋Š” ํ–‰์œ„์˜€๋‹ค. ์‹ญ์ž๊ตฐ์€ ์ด์–ด ๋„์‹œ๋ฅผ ์ ๋ นํ–ˆ๊ณ , ๋ฌด์Šฌ๋ฆผ๊ณผ ๊ธฐ๋…๊ต๋„ ๋ชจ๋‘์—๊ฒŒ ์ถฉ๊ฒฉ์„ ์ฃผ๋Š” ํ–‰๋™์œผ๋กœ ๋ฌด์Šฌ๋ฆผ๊ณผ ์œ ๋Œ€์ธ ์ฃผ๋ฏผ๋“ค์„ ํ•™์‚ดํ–ˆ๋‹ค. ์‹ญ์ž๊ตฐ์€ ๊ทธ ํ›„ ์ด ์ง€์—ญ์˜ ๋‹ค๋ฅธ ์ค‘์š”ํ•œ ๋„์‹œ๋ฅผ ์ ๋ นํ•˜๊ณ , ํ†ต์ œ๊ถŒ์„ ํ™•๋ณดํ•˜๊ธฐ ์œ„ํ•ด ์—๋ฐ์‚ฌ ์นด์šดํ‹ฐ, ์•ˆํ‹ฐ์˜คํ‚ค์•„ ๊ณต์ž‘, ํŠธ๋ฆฌํด๋ฆฌ ์นด์šดํ‹ฐ, ์˜ˆ๋ฃจ์‚ด๋ ˜ ์™•๊ตญ ๋“ฑ 4๊ฐœ์˜ ์‹ญ์ž๊ตฐ ๊ตญ๊ฐ€๋ฅผ ์„ค๋ฆฝํ–ˆ๋‹ค. ์ด๋“ค ์‹ญ์ž๊ตฐ ๊ตญ๊ฐ€๋“ค์€ ํ”„๋ž‘์Šค์ธ๋“ค์— ์˜ํ•ด ์™ธ์ง€์ธ(๋ฌธ์ž ""ํ•ด์™ธ"")์ด๋ผ๊ณ  ๋ถˆ๋ฆฌ๊ธฐ๋„ ํ•˜์˜€๊ณ , ์˜ˆ๋ฃจ์‚ด๋ ˜์„ ๊ทธ๋“ค์˜ ์ˆ˜๋„๋กœ ์ฃผ์žฅํ•˜๊ธฐ๋„ ํ•˜์˜€๋‹ค(๊ทธ๋ฆผ 13.20). ๋ชจ๋“  ์‹ญ์ž๊ตฐ ์ค‘ ์œ ์ผํ•˜๊ฒŒ ๋ชฉ์ ์„ ๋‹ฌ์„ฑํ•œ ๊ฒƒ์€ ์ด์˜€๋‹ค.', '์‹ญ์ž๊ตฐ ์™•๊ตญ์€ ๋Œ€๋ถ€๋ถ„ ์š”์ƒˆํ™” ๋œ ๋„์‹œ๋‚˜ ์„ฑ์— ํ•œ์ •๋œ ์†Œ์ˆ˜ ์ •๋ณต์ž๋“ค์˜ ์™•๊ตญ์ด์—ˆ๋‹ค. ๊ทธ๋ฆฌํ•˜์—ฌ ์ด์Šค๋ผ์—˜ ๋•…์€ ๊ธฐ๋…๊ต์˜ ์ง€๋ฐฐ ์•„๋ž˜ ๋“ค์–ด๊ฐ”์ง€๋งŒ ๊ธฐ๋…๊ต ๊ตญ๊ฐ€๊ฐ€ ๋˜์ง€๋Š” ์•Š์•˜๋‹ค. ์‹ญ์ž๊ตฐ์ด ์œ ๋Ÿฝ์—์„œ ์„ฑ์ง€๊นŒ์ง€์˜ ๊ตํ†ต๋กœ๋ฅผ ์—ด์–ด ์ˆœ๋ก€์˜ ๊ธธ์ด ๋Œ€์ค‘ํ™”๋˜๊ณ  ์ธ๊ธฐ๋ฅผ ์–ป์ž, ๋™์‹œ์— ๋Š˜์–ด๋‚˜๋Š” ์œ ํƒœ์ธ๋“ค์€ ๊ทธ๋“ค์˜ ์˜› ๊ณ ํ–ฅ์œผ๋กœ ๋Œ์•„๊ฐ€๊ธฐ๋ฅผ ๊ฐˆ๊ตฌํ•˜์˜€๋‹ค. ๋‹น์‹œ์˜ ๊ธฐ๋ก์— ์˜ํ•˜๋ฉด ํ”„๋ž‘์Šค์™€ ์˜๊ตญ์œผ๋กœ๋ถ€ํ„ฐ ๋ฌด๋ฆฌ๋ฅผ ์ง€์–ด์˜จ 300๋ช…์˜ ๋ž๋น„๋“ค ์ค‘ ๋ช‡๋ช‡์€ ์•…๊ณ ์—, ๋‚˜๋จธ์ง€๋Š” ์˜ˆ๋ฃจ์‚ด๋ ˜์— ์ •์ฐฉํ•˜์˜€๋‹ค. ์‹ญ์ž๊ตฐ์ด ์ฟ ๋ฅด๋“œ ์‚ด๋ผ๋”˜์—๊ฒŒ ๊ฒฉํŒŒ ๋‹นํ•˜์ž(1187๋…„), ์œ ํƒœ์ธ๋“ค์€ ์˜ˆ๋ฃจ์‚ด๋ ˜์— ์ •์ฐฉํ•  ์ˆ˜ ์žˆ๋Š” ๊ถŒ๋ฆฌ๋ฅผ ๋น„๋กฏํ•˜์—ฌ ์–ด๋А ์ •๋„ ์ž์œ ๋ฅผ ๋˜์ฐพ์•˜์ง€๋งŒ, ๊ทธ๋“ค์˜ ๊ฑฐ์ฃผ๋Š” ์š”์ƒˆํ™” ๋œ ๋ช‡๋ช‡ ์„ฑ์— ํ•œ์ •๋˜์—ˆ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ์ด์ง‘ํŠธ์—์„œ ๊ถŒ๋ ฅ์„ ์žก์€ ์ด์Šฌ๋žŒ ๊ตฐ์˜ ์—˜๋ฆฌํŠธ ๊ณ„๊ธ‰์ธ ๋ง˜๋ฃจํฌ์—๊ฒŒ ํŒจํ•˜์ž ์ด ๋•…์˜ ์‹ญ์ž๊ตฐ ์ง€๋ฐฐ๋Š” ์ข…๋ง์„ ๋งž๊ฒŒ ๋˜์—ˆ๋‹ค. ๋ง˜๋ฃจํฌ์˜ ์ง€๋ฐฐ์•„๋ž˜ ์ด ๋•…์€ ๋‹ค๋งˆ์Šค์ฟ ์Šค์˜ ์ง€๋ฐฐ๋ฅผ ๋ฐ›๋Š” ๋ฒฝ์ง€๊ฐ€ ๋˜์—ˆ๋‹ค. ์•…๊ณ  , ์šฅ๋ฐ”, ๊ทธ ๋ฐ–์˜ ํ•ญ๊ตฌ๋“ค์€ ์ƒˆ๋กœ์šด ์‹ญ์ž๊ตฐ์˜ ์นจ์ž…์œผ๋กœ ํŒŒ๊ดด๋˜๊ณ  ๋ฌด์—ญ์€ ๋‹จ์ ˆ๋˜์—ˆ๋‹ค. ์ค‘์„ธ ๋ง์—๋Š” ์ด ๋‚˜๋ผ ๋„์‹œ์˜ ์ค‘์‹ฌ์€ ํŒŒ๊ดด๋˜๊ณ  ์˜ˆ๋ฃจ์‚ด๋ ˜์˜ ๋Œ€๋ถ€๋ถ„์€ ํ™ฉํํ•ด์กŒ์œผ๋ฉฐ, ์œ ํƒœ์ธ๋“ค์€ ๊ถํ•ํ•ด์กŒ๋‹ค. ๋ง˜๋ฃจํฌ์˜ ์‡ ํ‡ด๊ธฐ๋Š” ์ •์น˜์ , ๊ฒฝ์ œ์ ์ธ ๊ฒฉ๋™๊ณผ ์งˆ๋ณ‘, ํƒ์š•์Šค๋Ÿฌ์šด ์ž๋“ค์˜ ์นจ์ž…๊ณผ ์ง€์ง„ ๋“ฑ์œผ๋กœ ์•”์šธํ•œ ์‹œ๊ธฐ์—ˆ๋‹ค. ๋‹ค์Œ์˜ 4์„ธ๊ธฐ ๋™์•ˆ ์ด์Šค๋ผ์—˜ ๋•…์€ ์˜ค์Šค๋งŒ ์ œ๊ตญ์— ์˜ํ•ด ์ฝ˜์Šคํƒ„ํ‹ฐ๋…ธํ”Œ์˜ ์ง€๋ฐฐ๋ฅผ ๋ฐ›์•˜๋‹ค. ์ด์Šค๋ผ์—˜ ๋•…์€ ๋„ค ๋ถ€๋ถ„์œผ๋กœ ๋ถ„ํ• ๋˜๊ณ  ํ–‰์ •์ ์œผ๋กœ๋Š” ๋‹ค๋งˆ์Šค์ฟ ์Šค์˜ ํ•œ ์ง€๋ฐฉ์œผ๋กœ ์˜ˆ์†๋˜์—ˆ๋‹ค', '1187๋…„ ์ „์„ค์ ์ธ ์ง€๋„์ž ์‚ด๋ผ๋”˜์˜ ํ†ต์น˜ํ•˜์— ๋ฌด์Šฌ๋ฆผ ์„ธ๋ ฅ์ด ๋„์‹œ๋ฅผ ๋˜์ฐพ์•˜๋‹ค. ๋ผ์ด์–ธํ•˜ํŠธ์ธ ์˜๊ตญ ์™• ๋ฆฌ์ฒ˜๋“œ 1์„ธ๊ฐ€ ๋‹ค๋ฅธ ์‚ฌ๋žŒ๋“ค๊ณผ ํ•จ๊ป˜ ๋˜ ๋‹ค๋ฅธ ํ–‰๋™์„ ์ทจํ•˜๋ฉด์„œ ์œ ๋Ÿฝ์œผ๋กœ๋ถ€ํ„ฐ์˜ ๋ฐ˜์‘์€ ๋นจ๋ž๋‹ค. ์„ฑ์ง€์ „์„ ์œ„ํ•œ ์ „ํˆฌ๋Š” ์‹ญ์ž๊ตฐ์ด 1291๋…„ ์•„ํฌ๋ ˆ(ํ˜„์žฌ์˜ ์ด์Šค๋ผ์—˜)์—์„œ ์ง€์ค‘ํ•ด ๊ฑฐ์ ์„ ์žƒ๊ณ  ๋ช‡ ๋…„ ํ›„ ๋งˆ์ง€๋ง‰ ๊ธฐ๋…๊ต์ธ๋“ค์ด ์ด ์ง€์—ญ์„ ๋– ๋‚  ๋•Œ๊นŒ์ง€ ๋๋‚˜์ง€ ์•Š์•˜๋‹ค.']",4 +2025-SS-06,"์ด ์™•์กฐ +๋Š” ํ˜ธ์ โ€ค์กฐ์„ธ ๋Œ€์žฅ์— ๋”ฐ๋ผ ์„ธ๊ธˆ์„ ๋ถ€๊ณผํ•˜๊ณ  ์ด์žฅํ˜ธ์™€ +๊ฐ‘์ˆ˜ํ˜ธ๊ฐ€ ์ง•์ˆ˜์™€ ๋‚ฉ์ž…์„ ์ฑ…์ž„์ง€๋Š” ์„ธ์ œ๋ฅผ ์ถ”์ง„ํ•˜์˜€๋‹ค. ๊ทธ๋Ÿฐ๋ฐ +์„ธ๊ธˆ ํ•ญ๋ชฉ์ด ๋ฌด์›์น™์ ์œผ๋กœ ๋Š˜์–ด๋‚˜ ๋ณต์žกํ•ด์ง€๊ณ  ๋ฐฑ์„ฑ๋“ค์ด ์„ธ๊ธˆ์„ +์ฒด๋‚ฉํ•˜๊ฑฐ๋‚˜ ํšŒํ”ผํ•˜๋Š” ์ผ์ด ๋งŽ์•„์กŒ๋‹ค. ์ด์— ์ฃผโ€คํ˜„์˜ ์žก๋‹คํ•œ +ํ•ญ๋ชฉ์˜ ์„ธ๊ธˆ์„ ์ง€์„ธ์™€ ์ •์„ธ๋กœ ํ†ตํ•ฉํ•˜์—ฌ ์ง€์„ธ๋Š” ๋ณด์œ  ํ† ์ง€์— ๋”ฐ๋ผ, +์ •์„ธ๋Š” ์„ฑ๋…„ ๋‚จ์ž์˜ ์ˆ˜์— ๋”ฐ๋ผ ์€์œผ๋กœ ํ• ๋‹นํ•ด ๋‚ฉ๋ถ€ํ•˜๋„๋ก ํ•˜๋Š” +์„ธ์ œ๋ฅผ ์‹œํ–‰ํ•˜์˜€๋‹ค. ์ด ์„ธ์ œ๋Š” ๊ฐ€์ •, ์œต๊ฒฝ ์—ฐ๊ฐ„ ๋ถ€๋ถ„์ ์œผ๋กœ +์‹œํ–‰๋˜๋‹ค๊ฐ€ ๋งŒ๋ ฅ ์—ฐ๊ฐ„ ๋‚ด๊ฐ ์ˆ˜๋ณด ๋Œ€ํ•™์‚ฌ์˜ ์ฃผ๋„๋กœ ์ „๊ตญ์ ์œผ๋กœ +ํ™•๋Œ€ ์‹ค์‹œ๋˜์—ˆ๋‹ค.","{'question': '๋ฐ‘์ค„ ์นœ โ€˜์ด ์™•์กฐโ€™์˜ ๊ฒฝ์ œ ์ƒํ™ฉ์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['์ง€์ •์€์ œ๊ฐ€ ์‹ค์‹œ๋˜์—ˆ๋‹ค.', '๋ฐ˜๋Ÿ‰์ „์œผ๋กœ ํ™”ํ๊ฐ€ ํ†ต์ผ๋˜์—ˆ๋‹ค.', '๊ท ์ˆ˜๋ฒ•๊ณผ ํ‰์ค€๋ฒ•์ด ์‹œํ–‰๋˜์—ˆ๋‹ค.', '์˜์ œ๊ฑฐ ๋“ฑ ๋Œ€์šดํ•˜๊ฐ€ ๊ฑด์„ค๋˜์—ˆ๋‹ค.', '๋ถ๋กœ๋‚จ์™œ๋กœ ์ธํ•ด ์žฌ์ •๋‚œ์ด ๊ฐ€์ค‘๋˜์—ˆ๋‹ค.'], 'answer': ''}",,5,5,True,"['์กฐ์„  ํ›„๊ธฐ ์ง€์—ญ๋ณ„๋กœ ์ด์•ก์„ ์ •ํ•ด ์ˆ˜์ทจํ•˜๋Š” ๋ถ€์„ธ ์ œ๋„. ์กฐ์„  ํ›„๊ธฐ ์กฐ์„ธ ์ˆ˜์ทจ๊ฐ€ ๋ถˆ์•ˆ์ •ํ•ด์ง€๋ฉด์„œ ๊ตญ๊ฐ€์—์„œ๋Š” ์•ˆ์ •์ ์œผ๋กœ ์„ธ๊ธˆ์„ ํ™•๋ณดํ•˜๊ธฐ ์œ„ํ•ด ์„ธ๊ธˆ์˜ ์ด์•ก์„ ๋ฏธ๋ฆฌ ์ •ํ•˜๊ณ  ๊ฐ ๊ตฐํ˜„์— ํ• ๋‹นํ•˜๋Š” ์กฐ์„ธ ์ˆ˜์ทจ ๋ฐฉ์‹์„ ์ฑ„ํƒํ•˜์˜€๋‹ค. ์„ธ๊ธˆ์˜ ๋ถ€๊ณผ ๊ธฐ์ค€์ด ๋˜๋Š” ํ† ์ง€๋‚˜ ์ธ์ •์˜ ๊ทœ๋ชจ๊ฐ€ ์ •๋ถ€์˜ ์žฌ์ •์„ ์ถ•์†Œ์‹œํ‚ฌ ๋งŒํผ ์ค„์–ด๋“ค์—ˆ๊ธฐ ๋•Œ๋ฌธ์ด์—ˆ๋‹ค. ํ† ์ง€์˜ ๊ฒฝ์šฐ ์ „์ฒด ์ˆ˜์„ธ ๊ทœ๋ชจ๋ฅผ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋˜ ์–‘์ „ ์‚ฌ์—…์ด ์˜ค๋žซ๋™์•ˆ ์‹ค์‹œ๋˜์ง€ ์•Š์•˜๊ณ , ํ™•๋ณด๋œ ํ† ์ง€์—์„œ๋„ ๊ถ๋ฐฉ์ „(ๅฎฎๆˆฟ็”ฐ), ๊ด€๋‘”์ „(ๅฎ˜ๅฑฏ็”ฐ)๊ณผ ๊ฐ™์ด ๋ฉด์„ธ ํ˜œํƒ์„ ๋ฐ›๋Š” ํ† ์ง€๊ฐ€ ๋Š˜๋ฉด์„œ ๊ณผ์„ธํ•  ์ˆ˜ ์žˆ๋Š” ๋Œ€์ƒ์ด ์ค„์–ด๋“ค์—ˆ๋‹ค. ์ด์— ์ •๋ถ€๋Š” ๊ณผ์„ธ ๊ทœ๋ชจ๊ฐ€ ์ถ•์†Œ๋˜๋Š” ๊ฒƒ์„ ๋ฐฉ์ง€ํ•˜๊ณ  ๊ณผ์„ธ ๋Œ€์ƒ์„ ํ™•๋ณดํ•˜๋Š” ๋น„์šฉ์„ ์ ˆ๊ฐํ•˜๊ณ ์ž ์ด์•ก์„ ๋ฏธ๋ฆฌ ์ •ํ•˜๊ณ  ์ด์— ํ•ด๋‹นํ•˜๋Š” ์„ธ๊ธˆ์„ ์ง€๋ฐฉ์—์„œ ์ž์ฒด์ ์œผ๋กœ ๊ฑฐ๋‘์–ด ์ƒ๋‚ฉํ•˜๋Š” ์ด์•ก์ œ๋ฅผ 18์„ธ๊ธฐ ํ›„๋ฐ˜๋ถ€ํ„ฐ ์ฑ„ํƒํ•˜๊ฒŒ ๋œ๋‹ค. ์ด์•ก์ œ๋Š” ์ „์„ธ(็”ฐ็จ…), ๋Œ€๋™์„ธ(ๅคงๅŒ็จ…) ๋“ฑ ํ† ์ง€๋ฅผ ๊ธฐ์ค€์œผ๋กœ ๋ถ€๊ณผ๋˜๋Š” ์„ธ๊ธˆ๊ณผ ์ธ์‹ (ไบบ่บซ)์— ๋ถ€๊ณผ๋˜๋Š” ์„ธ๊ธˆ์— ๋Œ€๋ถ€๋ถ„ ์ ์šฉ๋˜์—ˆ๊ณ  ํ™˜๊ณก๊นŒ์ง€ ํ™•๋Œ€๋˜์—ˆ๋‹ค. ๊ณต๋™๋‚ฉ์ ์ธ ์ด์•ก์ œ๊ฐ€ ์ ์šฉ๋  ๊ฒฝ์šฐ ์„ธ๊ธˆ์˜ ๋ถ€๊ณผ์™€ ์ง•์ˆ˜๋Š” ๊ณต๋™ ์ฑ…์ž„์œผ๋กœ ์ „๊ฐ€๋˜์—ˆ๋‹ค. ๋”ฐ๋ผ์„œ ์—ญ์„ ๋ถ€๋‹ดํ•˜๋˜ ๋ถ€๋‹ด์ž๊ฐ€ ๋‚ฉ์„ธ๋ฅผ ๋ฒ„ํ‹ฐ์ง€ ๋ชปํ•˜๊ณ  ๋„๋ง๊ฐ€๊ฑฐ๋‚˜ ์ง€์—ญ ๋‚ด์—์„œ ์„ธ๋ ฅ์ด ์žˆ๋˜ ํ† ํ˜ธ๋‚˜ ์–‘๋ฐ˜๋“ค์ด ๋‚ฉ์„ธ๋ฅผ ๊ฑฐ๋ถ€ํ•˜๋Š” ๊ฒฝ์šฐ ๋ถ€๋‹ด์€ ๋‚˜๋จธ์ง€ ๋ฐฑ์„ฑ๋“ค์—๊ฒŒ ์ „๊ฐ€๋  ์ˆ˜๋ฐ–์— ์—†์—ˆ๋‹ค. ๋”ฐ๋ผ์„œ ๋ฐฑ์„ฑ๋“ค์€ ์ด์•ก์„ ์ •ํ•ด ์„ธ์•ก์„ ๋ถ€๋‹ดํ•˜๋Š” ๋ถ€๊ณผ ๋ฐฉ์‹์— ์ ์‘ํ•˜๊ธฐ ์œ„ํ•ด ๊ณ„(ๅฅ‘)๋ฅผ ๊ฒฐ์„ฑํ•˜์—ฌ ๊ณต๋™์œผ๋กœ ๋Œ€๋น„ํ•˜์˜€๋‹ค. ์กฐ์„  ํ›„๊ธฐ ์ด์•ก์ œ ์ˆ˜์ทจ ๋ฐฉ์‹์€ ๊ด€์˜ ์ž…์žฅ์—์„œ๋Š” ์„ธ๊ธˆ์„ ์•ˆ์ •์ ์œผ๋กœ ํ™•๋ณดํ•  ์ˆ˜ ์žˆ๋Š” ์žฅ์ ์ด ์žˆ์—ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋ฐฑ์„ฑ๋“ค์—๊ฒŒ๋Š” ๋ถ€๋‹ด์ด ๊ฐ€์ค‘๋˜๋Š” ๋ฐฉ์‹์ด ๋˜์—ˆ์œผ๋ฏ€๋กœ 19์„ธ๊ธฐ์— ๋ฐœ์ƒํ•œ ๋ฏผ๋ž€์˜ ์›์ธ์ด ๋˜์—ˆ๋‹ค.']",1 +2025-SS-08," (๊ฐ€)์˜ ์นด๋“œํ”ผ์„ธ์Šค 2์„ธ๋Š” ํŽ€์ž๋ธŒ ์ง€๋ฐฉ์„ ์žฅ์•…ํ•˜๊ณ  ๋™์„œ +๊ต๋ฅ˜์˜ ๊ธฐ๋ฐ˜์„ ํ™•๋ฆฝํ•˜์˜€๋‹ค. ๊ทธ์˜ ์น˜์„ธ์— (๊ฐ€) +ํ•œํŽธ (๊ฐ€) +์€/๋Š” ์„œ๋กœ๋Š” +ํŒŒ๋ฅดํ‹ฐ์•„์™€ ๋กœ๋งˆ ์ œ๊ตญ, ๋™์œผ๋กœ๋Š” ์ค‘์•™์•„์‹œ์•„๋ฅผ ๊ฑฐ์ณ ํ›„ํ•œ๊นŒ์ง€ +์ด์–ด์ง€๋Š” ๋™์„œ ๊ต๋ฅ˜์˜ ํ†ต๋กœ ์—ญํ• ์„ ํ•˜๋ฉฐ ํฌ๊ฒŒ ๋ฐœ์ „ํ•˜์˜€๋‹ค. +์€/๋Š” ๋น„๋‹จ๊ธธ ๊ต์—ญ์„ ์žฅ์•…ํ•˜๊ธฐ ์œ„ํ•ด์„œ์—ญ์œผ๋กœ +๊ตฐ๋Œ€๋ฅผ ํŒŒ๊ฒฌํ•œ ํ›„ํ•œ๊ณผ ๋ช‡ ์ฐจ๋ก€ ๊ตฐ์‚ฌ์ ์œผ๋กœ ์ถฉ๋Œํ•˜๊ธฐ๋„ํ•˜์˜€๋‹ค.","{'question': '(๊ฐ€) ์™•์กฐ์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['์—ํ”„ํƒˆ์˜ ์นจ์ž…์œผ๋กœ ์‡ ํ‡ดํ•˜์˜€๋‹ค.', '์•„์†Œ์นด์™• ๋•Œ ์ „์„ฑ๊ธฐ๋ฅผ ๋งž์ดํ•˜์˜€๋‹ค.', '๊ฐ€์ฆˆ๋‹ˆ ์™•์กฐ์˜ ๊ตฐ๋Œ€๋ฅผ ๊ฒฉํ‡ดํ•˜์˜€๋‹ค.', '์ด๋ž€ ๊ณ„ํ†ต์˜ ๋ฏผ์กฑ์— ์˜ํ•ด ์„ธ์›Œ์กŒ๋‹ค.', '๋งˆ๋ผํƒ€ ๋™๋งน์˜ ๋ฐ˜๋ž€์œผ๋กœ ์œ„๊ธฐ๋ฅผ ๊ฒช์—ˆ๋‹ค.'], 'answer': ''}",,4,4,True,[],4 +2025-SS-10,"(๊ฐ€)์˜ ์ˆ ํƒ„์ด ์™„์ถฉ ์ง€๋Œ€์— ์œ„์น˜ํ•œ ๋‘˜์นด๋””๋ฅด ์™•์กฐ๋ฅผ ๊ณต๊ฒฉ +ํ•˜์ž์ด์— ์œ„ํ˜‘์„ ๋А๋‚€ ๋ง˜๋ฃจํฌ ์™•์กฐ์˜ ์ˆ ํƒ„์€ (๋‚˜) +์—ฐํ•ฉ ์„ธ๋ ฅ์„ ๊ตฌ์ถ•ํ•˜์˜€๋‹ค. ๋‹น์‹œ (๋‚˜) +์™€/๊ณผ +์€/๋Š” (๊ฐ€)์™€/๊ณผ +์„œ์ชฝ์œผ๋กœ ๊ตญ๊ฒฝ์„ ์ ‘ํ•˜๋ฉฐ ๋ฉ”์†Œํฌํƒ€๋ฏธ์•„ ์ง€๋ฐฉ์˜ ์ง€๋ฐฐ๊ถŒ์„ ๋†“๊ณ  +๋Œ€๋ฆฝํ•˜๊ณ  ์žˆ์—ˆ๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ์ด๋Ÿฌํ•œ ์ƒํ™ฉ์—์„œ (๊ฐ€)์˜ ์ˆ ํƒ„์€ +ํฌ๋ณ‘๊ณผ ์†Œ์ด ๋ถ€๋Œ€๋ฅผ ์ด๋Œ๊ณ  ์›์ •์— ๋‚˜์„œ ๊ธฐ๋ณ‘๋Œ€๋ฅผ ์•ž์„ธ์šด +๋ง˜๋ฃจํฌ ์™•์กฐ์˜ ๊ตฐ๋Œ€๋ฅผ ๊ฒฉํŒŒํ•˜๊ณ  ๋‹ค๋งˆ์Šค์ฟ ์Šค์— ์ž…์„ฑํ•˜์˜€๋‹ค. +๋’ค์ด์–ด์นด์ด๋กœ๋ฅผ ์ •๋ณตํ•˜์—ฌ ๋ถ์•„ํ”„๋ฆฌ์นด ์ผ๋Œ€๊นŒ์ง€ ์ง„์ถœํ•˜์˜€๋‹ค.","{'question': '(๊ฐ€), (๋‚˜) ์™•์กฐ์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['(๊ฐ€)-ํฌ๋ฆผ ์ „์Ÿ์—์„œ ํŒจ๋ฐฐํ•˜์˜€๋‹ค.', '(๊ฐ€)-ํ‹ฐ๋ฌด๋ฅด ์™•์กฐ๋ฅผ ๋ฉธ๋ง์‹œ์ผฐ๋‹ค.', '(๋‚˜)-๋ชฝ๊ณจ์˜ ์นจ์ž…์œผ๋กœ ๋ฉธ๋งํ•˜์˜€๋‹ค.', '(๋‚˜)-์ด์ŠคํŒŒํ•œ์„ ์ˆ˜๋„๋กœ ์ •ํ•˜์˜€๋‹ค.', '(๋‚˜)-์•„์ด๋ฐ”ํฌ๊ฐ€ ๋ธ๋ฆฌ๋ฅผ ์ ๋ นํ•˜์˜€๋‹ค.'], 'answer': ''}",,4,2,False,[],2 +2025-SS-12,"์šฐ๋ฆฌ๋Š” ์šฐ๋ฆฌ ๋‚˜๋ผ๋ฅผ ์˜ˆ์†ํ•˜๋ ค๊ณ  ํ•˜๋Š” ์ ์— ๋งž์„œ 3๋…„ ๋™์•ˆ +์œ„๋Œ€ํ•œ ํˆฌ์Ÿ์„ ํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ์ž”์ธํ•œ ์ ์€ ๋๊นŒ์ง€ ๋ฐœ์•…ํ•˜๊ณ  +์žˆ์œผ๋‚˜, ์šฐ๋ฆฌ์˜ ์šฉ๊ฐํ•œ ๊ตฐ๋Œ€๊ฐ€ ์˜๊ด‘์Šค๋Ÿฌ์šด ํ˜‘์ƒ๊ตญ๊ณผ ํ•จ๊ป˜ ์ ์„ +๋ฌด์ฐŒ๋ฅผ ์‹œ๊ฐ„์ด ๋‹ค๊ฐ€์˜ค๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ตœ๊ทผ ๊ตญ๋‚ด์—์„œ +๋ฐœ์ƒํ•œ ์†Œ์š” ์‚ฌํƒœ๊ฐ€ ์ด ๊ฒฐ์—ฐํ•œ ์‹ธ์›€์„ ์„ฑ๊ณต์ ์œผ๋กœ ์ง€์†ํ•ด ๋‚˜๊ฐ€๊ธฐ +์–ด๋ ต๊ฒŒ ๋งŒ๋“ญ๋‹ˆ๋‹ค. ์ด ๊ฒฐ์ •์ ์ธ ์‹œ๊ธฐ์— ๊ตญ๋ฏผ์ด ๋‹จ๊ฒฐํ•˜๊ณ  ๋ชจ๋“  +ํž˜์„ ๋ชจ์•„ ์ „์Ÿ์—์„œ ์‹ ์†ํ•œ ์Šน๋ฆฌ๋ฅผ ๊ฑฐ๋‘๋Š” ๊ฒƒ์ด ์šฐ๋ฆฌ์˜ ์˜๋ฌด๋ผ๊ณ  +์ƒ๊ฐํ•ฉ๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ด์œ ๋กœ ์ €๋Š” ๋‘๋งˆ์™€ ํ˜‘์˜ํ•˜์— ๊ถŒ์ขŒ์—์„œ +๋‚ด๋ ค์˜ค๊ณ ์ž ํ•ฉ๋‹ˆ๋‹ค.","{'question': '๋ฐ‘์ค„ ์นœ โ€˜์ „์Ÿโ€™์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['ํฌ์ธ ๋จธ์Šค ์กฐ์•ฝ ์ฒด๊ฒฐ๋กœ ์ข…๊ฒฐ๋˜์—ˆ๋‹ค.', '๋น„์‹œ ์ •๋ถ€๊ฐ€ ์ˆ˜๋ฆฝ๋˜๋Š” ๋ฐฐ๊ฒฝ์ด ๋˜์—ˆ๋‹ค.', '์‹ ์„ฑ ๋กœ๋งˆ ์ œ๊ตญ์˜ ํ•ด์ฒด์— ์˜ํ–ฅ์„ ์ฃผ์—ˆ๋‹ค.', 'ํ•„๋ฆฌํ•€์— ๋Œ€ํ•œ ์ง€๋ฐฐ๊ถŒ์„ ๋‘๊ณ  ๋Œ€๋ฆฝํ•˜์˜€๋‹ค.', '์˜ค์ŠคํŠธ๋ฆฌ์•„โ€คํ—๊ฐ€๋ฆฌ ์ œ๊ตญ์ด ๋ถ•๊ดด๋˜๋Š” ๊ฒฐ๊ณผ๋ฅผ ๊ฐ€์ ธ์™”๋‹ค.'], 'answer': ''}",,5,1,False,[],1 +2025-SS-13,"๋ฌด์—ญ ํ™œ๋™์ด ํ™•๋Œ€๋˜๋ฉด์„œ ์„œ์–‘ ์ƒ์ธ๋“ค์ด ์œ ๋ฆฌํ•œ ๋ฌด์—ญ ์กฐ๊ฑด์„ +์ฐพ์•„ ์ง€์ •๋œ ๋‚จ์ชฝ์˜ ํ•ญ๊ตฌ๋ฅผ ๋ฒ—์–ด๋‚˜ ๋‹๋ณด ๋“ฑ์œผ๋กœ ๋ถ์ƒํ•˜๋Š” ๊ฒฝ์šฐ๊ฐ€ +๋งŽ์•„์กŒ๋‹ค. ํ™ฉ์ œ๋Š” ํšจ์œจ์ ์œผ๋กœ ๋ฌด์—ญ์„ ํ†ต์ œํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ๋ฏผ์ ˆ +์ด๋… ์–‘์‘๊ฑฐ์˜ ๊ฑด์˜๋ฅผ ๋ฐ›์•„๋“ค์—ฌ ์„œ์–‘ ์ƒ์ธ๊ณผ์˜ ๋ฌด์—ญ์„ ๊ด‘์ €์šฐ +ํ•œ ๊ณณ์œผ๋กœ ์ œํ•œํ•˜์˜€๋‹ค. ์˜๊ตญ์ธ ์ œ์ž„์Šค ํ”Œ๋ฆฐํŠธ๊ฐ€ ํ™ฉ์ œ์—๊ฒŒ ๊ด‘๋‘ฅ์„ฑ +ํ•ด๊ด€์˜ ๋ถ€ํŒจ์™€ ๋ถˆ๋Ÿ‰ํ•œ ํƒœ๋„๋ฅผ ๊ณ ๋ฐœํ•˜๊ณ  ๋ถ์ชฝ ์—ฐ์•ˆ์˜ ๋” ๋งŽ์€ +ํ•ญ๊ตฌ์—์„œ ์„œ์–‘ ์ƒ์ธ์ด ๋ฌด์—ญ ํ™œ๋™์„ ํ•  ์ˆ˜ ์žˆ๋„๋ก ํ—ˆ์šฉํ•ด ์ค„ ๊ฒƒ์„ +์š”๊ตฌํ•˜์˜€์ง€๋งŒ ํ™ฉ์ œ๋Š” ์ด๋ฅผ ํ—ˆ๋ฝํ•˜์ง€ ์•Š์•˜๋‹ค. ์ด๋Ÿฌํ•œ ์ƒํ™ฉ์—์„œ +์„œ์–‘์ƒ์ธ๊ณผ์˜ ๋ฌด์—ญ์„ ๊ด€๋ฆฌํ•˜๊ธฐ ์œ„ํ•ด (๊ฐ€)","{'question': '(๊ฐ€)์— ๋“ค์–ด๊ฐˆ ๋‚ด์šฉ์œผ๋กœ ๊ฐ€์žฅ ์ ์ ˆํ•œ ๊ฒƒ์€?', 'choices': ['๊ณตํ–‰์„ ์šด์˜ํ•˜์˜€๋‹ค.', '์‹œ๋ฐ•์‚ฌ๋ฅผ ์„ค์น˜ํ•˜์˜€๋‹ค.', 'ํšŒ๊ด€, ๊ณต์†Œ๋ฅผ ๊ฑด๋ฆฝํ•˜์˜€๋‹ค.', '์‹œ์—ญ๋ฒ• ๋“ฑ ์‹ ๋ฒ•์„ ๋‹จํ–‰ํ•˜์˜€๋‹ค.', '์ƒ‰๋ชฉ์ธ์„ ์žฌ์ • ๊ด€๋ฃŒ๋กœ ๋“ฑ์šฉํ•˜์˜€๋‹ค.'], 'answer': ''}",,1,3,False,[],1 +2025-SS-14,"์–ด๋–ค ์ด๋“ค์€ ๋ถˆ์‹ ์— ์‚ฌ๋กœ์žกํ˜€ ์ด๋ ‡๊ฒŒ ๋งํ•ฉ๋‹ˆ๋‹ค. โ€˜์ œ๊ตญ, ๊ทธ๊ฒƒ์€ +๊ณง ์ „์Ÿ์ž…๋‹ˆ๋‹ค.โ€™ํ•˜์ง€๋งŒ ๋‚˜๋Š” ์ด๋ ‡๊ฒŒ ๋งํ•ฉ๋‹ˆ๋‹ค. โ€˜์ œ๊ตญ, ๊ทธ๊ฒƒ์€ +๊ณง ํ‰ํ™”์ž…๋‹ˆ๋‹ค.โ€™์™œ๋ƒํ•˜๋ฉด ์šฐ๋ฆฌ ๋‚˜๋ผ๊ฐ€ ์ œ๊ตญ์„ ์›ํ•˜๊ธฐ ๋•Œ๋ฌธ์ž…๋‹ˆ๋‹ค. +๋‚˜๋Š” (๊ฐ€)์ฒ˜๋Ÿผ ์‹ค๋กœ ์ด๋ฃจ์–ด ๋‚ด์•ผ ํ•  ์ •๋ณต์ด ์ˆ˜๋‘๋ฃฉํ•˜๋‹ค๋Š” +์ ์„ ์ธ์ •ํ•ฉ๋‹ˆ๋‹ค. ๋‚˜๋Š” (๊ฐ€)์ฒ˜๋Ÿผ ๋ฐ˜๋Œ€ํŒŒ๋“ค์„ ์„ค๋“ํ•˜์—ฌ ํ™”ํ•ฉ +์œผ๋กœ์ด๋Œ๊ณ  ์ •๋ณต์„ ํ†ตํ•ด ํ˜๋ช…์˜ ์ด๋…์„ ์ „ํŒŒํ•˜๊ณ  ์‹ถ์Šต๋‹ˆ๋‹ค. +์šฐ๋ฆฌ๋Š” 20์—ฌ ๋…„ ์ „ ์šฐ๋ฆฌ์˜ ์‹๋ฏผ์ง€๊ฐ€ ๋œ ์•Œ์ œ๋ฆฌ๋ฅผ ์šฐ๋ฆฌ ๋‚˜๋ผ์— +๋™ํ™”์‹œ์ผœ์•ผ ํ•ฉ๋‹ˆ๋‹ค. ๋ฐ˜์„ธ๊ธฐ ์ „ (๊ฐ€)์ด/๊ฐ€ ์„ธ์› ๋˜ ์ œ์ •์„ +๋‹ค์‹œ ์„ธ์›Œ์•ผ ํ•ฉ๋‹ˆ๋‹ค. ๋ฐ”๋กœ ์ด๊ฒƒ์ด ๋‚ด๊ฐ€ ์ดํ•ดํ•˜๋Š” ์ œ๊ตญ์˜ ๋ชจ์Šต์ด๋ฉฐ, +๋‚ด๊ฐ€ ๊ฟˆ๊พธ๋Š” ์ •๋ณต์ž…๋‹ˆ๋‹ค. ๋‚˜๋ฅผ ์ง€์ง€ํ•˜๋ฉฐ ๋‚˜์™€ ๊ฐ™์ด ์šฐ๋ฆฌ ์กฐ๊ตญ์˜ +ํ–‰๋ณต์„ ์—ผ์›ํ•˜๋Š” ์—ฌ๋Ÿฌ๋ถ„, ์—ฌ๋Ÿฌ๋ถ„์€ ๋ฐ”๋กœ ๋‚˜์˜ ๋ณ‘์‚ฌ๋“ค์ž…๋‹ˆ๋‹ค.-๊ณตํ™”๊ตญ ๋Œ€ํ†ต๋ น์˜ ์—ฐ์„ค-","{'question': '(๊ฐ€) ์ธ๋ฌผ์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['ํ†ต๋ น ์ •๋ถ€๋ฅผ ์ˆ˜๋ฆฝํ•˜์˜€๋‹ค.', '์ฒ ํ˜ˆ ์ •์ฑ…์„ ์ถ”์ง„ํ•˜์˜€๋‹ค.', '7์›” ํ˜๋ช…์œผ๋กœ ์ฆ‰์œ„ํ•˜์˜€๋‹ค.', '๋ฐ์นด๋ธŒ๋ฆฌ์ŠคํŠธ์˜ ๋ด‰๊ธฐ๋ฅผ ์ง„์••ํ•˜์˜€๋‹ค.', '๊ณตํ™”์ •์„ ์ˆ˜๋ฆฝํ•˜๊ณ  ํŒŒ๋ฆฌ ์ฝ”๋ฎŒ์„ ์ง„์••ํ•˜์˜€๋‹ค.'], 'answer': ''}",,1,5,False,[],5 +2025-SS-16,"์˜๊ตญ (๊ฐ€)์— ๊ณ ์šฉ๋œ ์šฉ๋ณ‘๋“ค์—๊ฒŒ ์ƒˆ๋กœ ์ง€๊ธ‰๋œ ์—”ํ•„๋“œ +์†Œ์ด์˜ ํƒ„์•ฝํ†ต์—๋Š” ์†Œ๊ธฐ๋ฆ„๊ณผ ๋ผ์ง€๊ธฐ๋ฆ„์ด ๋ฐœ๋ผ์ ธ ์žˆ๋‹ค๋Š” ์†Œ๋ฌธ์ด +๋Œ์•˜๋‹ค. ๋˜ํ•œ ์˜๊ตญ์ด ์ธ๋„์˜ ์ „ํ†ต์ธ ์นด์ŠคํŠธ๋ฅผ ํŒŒ๊ดดํ•˜๊ณ  ์šฉ๋ณ‘ +๋“ค์„ ํฌ๋ฆฌ์ŠคํŠธ๊ต๋กœ ๊ฐ•์ œ ๊ฐœ์ข…์‹œํ‚ค๋ ค ํ•œ๋‹ค๋Š” ๋ง๊นŒ์ง€ ํผ์กŒ๋‹ค. +โ€ฆ(์ค‘๋žต)โ€ฆ๋ถˆ๋งŒ์€ ํ™•์‚ฐ๋˜๊ณ  ์žˆ์—ˆ๊ณ , ์˜๊ตญ์ธ๋“ค๋งŒ ํƒ„์•ฝํ†ต์„ ์‚ฌ์šฉ +ํ•˜๋„๋ก ์ œํ•œํ•˜๋Š” ๋ช…๋ น์€ ์˜คํžˆ๋ ค ์šฉ๋ณ‘๋“ค์˜ ์˜์‹ฌ์„ ๋”์šฑ ํ‚ค์› ๋‹ค. +โ€ฆ(์ค‘๋žต)โ€ฆ๊ทธ ๊ฒฐ๊ณผ ๊ฑฐ์„ผ ํญํ’์ฒ˜๋Ÿผ ๋ฌด์žฅ๋ด‰๊ธฐ๊ฐ€ ์ผ์–ด๋‚ฌ๋‹ค.-๋”” ์• ํ‹€๋žœํ‹ฑ-","{'question': '(๊ฐ€) ์กฐ์ง์— ๋Œ€ํ•œ ํƒ๊ตฌ ํ™œ๋™์œผ๋กœ ๊ฐ€์žฅ ์ ์ ˆํ•œ ๊ฒƒ์€?', 'choices': ['๋ฒต๊ณจ ๋ถ„ํ• ๋ น์˜ ์˜ํ–ฅ์„ ์ฐพ์•„๋ณธ๋‹ค.', '๋กค๋Ÿฟ๋ฒ• ์ œ์ •์˜ ๊ฒฐ๊ณผ๋ฅผ ํŒŒ์•…ํ•œ๋‹ค.', 'ํŒŒ์‡ผ๋‹ค ์‚ฌ๊ฑด์˜ ๋ฐฐ๊ฒฝ์„ ์กฐ์‚ฌํ•œ๋‹ค.', '์นด์Šˆ๋ฏธ๋ฅด ๋ถ„์Ÿ์˜ ์ฃผ์ฒด๋ฅผ ์•Œ์•„๋ณธ๋‹ค.', '๋ณด์Šคํ„ด ์ฐจ ์‚ฌ๊ฑด์˜ ์ „๊ฐœ ๊ณผ์ •์„ ์‚ดํŽด๋ณธ๋‹ค.'], 'answer': ''}",,5,2,False,"['์žฅ์‹œ๊ฐ„ ๋™์•ˆ ์•„์˜ค๋˜ ์˜๊ตญ์ธ์— ๋Œ€ํ•œ ๋ถ„๋…ธ๋Š” ์žฅ๋ณ‘๋“ค์ด ์ ์žฌํ•˜๊ธฐ ์ „์— ๋œฏ์–ด ๋ฌผ์–ด์•ผ ํ•˜๋Š” ์œคํ™œ ์นดํŠธ๋ฆฌ์ง€๋ฅผ ์‚ฌ์šฉํ•œ ์—”ํ•„๋“œ ๋ผ์ดํ”Œ์˜ ๋„์ž…์— ๋”ฐ๋ผ ํญ๋ ฅ์œผ๋กœ ์ „ํ™˜๋˜์—ˆ๋‹ค. ์œคํ™œ์œ ๋Š” ์‡ ๊ณ ๊ธฐ๋‚˜ ๋ผ์ง€ ์ง€๋ฐฉ์ด์—ˆ์ง€๋งŒ, ๋ฌด์Šฌ๋ฆผ๋“ค์—๊ฒŒ๋Š” ๋ผ์ง€๊ณ ๊ธฐ๊ฐ€ ๊ธˆ๊ธฐ์ด๊ณ , ํžŒ๋‘๊ต๋„๋“ค์€ ์†Œ๊ฐ€ ์‹ ์„ฑํ•˜๋‹ค๊ณ  ๋ฏฟ๋Š”๋‹ค. ์˜๊ตญ์€ ๋‹ค๋ฅธ ์œคํ™œ์œ ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ๋ฌธ์ œ๋ฅผ ์ˆ˜์ •ํ•˜๋ ค๊ณ  ์‹œ๋„ํ–ˆ์ง€๋งŒ ์ธ๋„ ๋ณ‘๋ ฅ์€ ์—ฌ์ „ํžˆ ์˜์‹ฌ์„ ํ’ˆ๊ณ  ์žˆ์—ˆ๊ณ  ์ผ๋ถ€๋Š” ์˜๊ตญ์ด ์„ธํฌ์ด๋ฅผ ๋ฌด์ž๋ ฅํ™”ํ•˜๊ธฐ ์œ„ํ•ด ์˜๋„์ ์œผ๋กœ ๊ณต๊ฒฉ ์žฌ๋ฃŒ๋กœ ๋งŒ๋“  ์œคํ™œ์œ ๋ฅผ ์‚ฌ์šฉํ–ˆ๋‹ค๊ณ  ๋ฏฟ๊ธฐ ์‹œ์ž‘ํ–ˆ๋‹ค. 1857๋…„ 3์›”, ๊ณ ์œ„ ๋ธŒ๋ผ๋ฏผ๊ณ„ ์‹ ๋ถ„ ์ถœ์‹  ํžŒ๋‘๊ต ์‹ ๋„์ธ ๋ง๊ฐˆํŒ๋ฐ๋ผ๋Š” ์„ธํฌ์ด๊ฐ€ ์˜๊ตญ ์žฅ๊ต 2๋ช…์„ ๊ณต๊ฒฉํ–ˆ๋‹ค. ์˜๊ตญ์€ ๊ทธ๋ฅผ ์ฒ˜ํ˜•ํ–ˆ๊ณ , ์ธ๋„๋Š” ๋Œ€์ค‘์˜ ๋ฐ˜๋ž€์œผ๋กœ ๋ถˆ๊ฑฐ์กŒ๋‹ค.']",2 +2025-SS-19,"์—ฌ๋Ÿฌ๋ถ„, ์šฐ๋ฆฌ๋Š” ๋ชน์‹œ ์–ด๋ ค์šด ์ƒํ™ฉ์—์„œ๋„ ํ˜๋ช… 24์ฃผ๋…„์„ +๊ธฐ๋…ํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. (๊ฐ€)์€/๋Š” ์šฐ๋ฆฌ ๋‚˜๋ผ๋ฅผ ์นจ๋žตํ•˜์ง€ +์•Š๊ฒ ๋‹ค๋Š” ํ˜‘์•ฝ์„ ์–ด๊ธฐ๊ณ  ์ „์Ÿ์„ ๋„๋ฐœํ•˜์˜€์Šต๋‹ˆ๋‹ค. ์šฐ๋ฆฌ ๋‚˜๋ผ๋Š” +์ผ์‹œ์ ์ด๊ธด ํ•˜์ง€๋งŒ ๋งŽ์€ ์ง€์—ญ์„ ์žƒ์—ˆ์Šต๋‹ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์šฐ๋ฆฌ ๊ตฐ๋Œ€๋Š” +์ ์˜ ๊ณต๊ฒฉ์„ ๋ง‰์•„ ๋‚ด๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ์šฐ๋ฆฌ ๋‚˜๋ผ๊ฐ€ ์ง€๊ธˆ๋ณด๋‹ค ๋” ์–ด๋ ค์šด +์ƒํ™ฉ์ผ ๋•Œ๋„ ์žˆ์—ˆ์Šต๋‹ˆ๋‹ค. ํ˜๋ช… 1์ฃผ๋…„์„ ๊ธฐ๋…ํ–ˆ์„ ๋•Œ๋ฅผ ์ƒ๊ฐํ•ด +๋ณด์‹ญ์‹œ์˜ค. ๊ทธ๋•Œ ์šฐ๋ฆฌ ๊ตญํ† ์˜ ์ƒ๋‹น ๋ถ€๋ถ„์ด๋‹ค๋ฅธ ๋‚˜๋ผ์— ์žฅ์•…๋˜์–ด +์žˆ์ง€ ์•Š์•˜์Šต๋‹ˆ๊นŒ! ์ค‘์•™์•„์‹œ์•„, ์šฐ๋ž„ ๋“ฑ์„ ์ผ์‹œ์ ์œผ๋กœ ์ƒ์‹คํ•˜์˜€์ง€๋งŒ +๊ฒฐ๊ตญ ์Šน๋ฆฌํ•˜์—ฌ ์ˆ˜๋ณตํ•˜์˜€์Šต๋‹ˆ๋‹ค.","{'question': '(๊ฐ€) ๊ตญ๊ฐ€์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['๊ตญ์ œ ์—ฐ๋งน์—์„œ ํƒˆํ‡ดํ•˜์˜€๋‹ค.', '๋ฏธ์–€๋งˆ๋ฅผ ์‹๋ฏผ์ง€๋กœ ์‚ผ์•˜๋‹ค.', '์•„๋„์™€ ์ „ํˆฌ์—์„œ ์Šน๋ฆฌํ•˜์˜€๋‹ค.', '์ธ๋„์ฐจ์ด๋‚˜ ์—ฐ๋ฐฉ์„ ์กฐ์งํ•˜์˜€๋‹ค.', '์‹ ๊ฒฝ์ œ ์ •์ฑ…[NEP]์„ ์ถ”์ง„ํ•˜์˜€๋‹ค.'], 'answer': ''}",,1,1,True,[],1 +2025-YS-01,"์Šค์Šน๋‹˜, ์‚ฌ๋žŒ์˜ ํƒ€๊ณ ๋‚œ ๋ณธ์„ฑ์€ ์„ ํ•œ๊ฐ€์š”? ์•„๋‹ˆ๋ผ๋„ค. ์‚ฌ๋žŒ์€ ํƒœ์–ด๋‚  ๋•Œ๋ถ€ํ„ฐ ์ด๊ธฐ์  ์š•๋ง์„ ์ง€๋‹ˆ๋ฏ€๋กœ ๋‹คํˆผ์ด ์ผ์–ด๋‚œ๋‹ค๋„ค. ๊ทธ๋Ÿฌ๋ฉด ๊ทธ๋Ÿฌํ•œ ๋‹คํˆผ์„ ์–ด๋–ป๊ฒŒ ์—†์•จ ์ˆ˜ ์žˆ์„๊นŒ์š”? ํ˜„๋ช…ํ•œ ๊ตฐ์ฃผ๊ฐ€ ์˜ˆ(็ฆฎ)๋ฅผ ๋งŒ๋“ค์–ด์„œ ๋‹ค์Šค๋ฆฌ๋ฉด ์ด๋Ÿฐ ๋‹คํˆผ์„ ์—†์•จ ์ˆ˜ ์žˆ๋‹ค๋„ค.","{'question': '๋‹ค์Œ ๊ฐ€์ƒ ๋Œ€ํ™”์˜ ์Šค์Šน์ด ๊ฐ•์กฐํ•˜๋Š” ์‚ถ์˜ ํƒœ๋„๋กœ ๊ฐ€์žฅ ์ ์ ˆํ•œ ๊ฒƒ์€?', 'choices': ['์ธ์œ„์ ์ธ ๋…ธ๋ ฅ์œผ๋กœ ๋ณธ์„ฑ์„ ๋ณ€ํ™”์‹œ์ผœ ์‚ฌํšŒ ๊ทœ๋ฒ”์„ ๋‚ด๋ฉดํ™”ํ•ด์•ผ ํ•œ๋‹ค.', '๋งŒ๋ฌผ์˜ ๊ทผ๋ณธ ์›๋ฆฌ์ธ ๋„๋ฅผ ๋”ฐ๋ผ ๋ฌด์œ„์ž์—ฐ์˜ ์‚ถ์„ ์˜์œ„ํ•ด์•ผ ํ•œ๋‹ค.', '์‚ฌํšŒ์  ํ˜ผ๋ž€์„ ๋ง‰๊ธฐ ์œ„ํ•ด ์„œ๋กœ ์ฐจ๋ณ„ ์—†์ด ์‚ฌ๋ž‘ํ•ด์•ผ[ๅ…ผๆ„›] ํ•œ๋‹ค.', '์—ด๋ฐ˜์— ๋„๋‹ฌํ•˜๊ธฐ ์œ„ํ•ด์„œ ์พŒ๋ฝ๊ณผ ๊ณ ํ†ต์˜ ์–‘๊ทน๋‹จ์„ ๋– ๋‚˜์•ผ ํ•œ๋‹ค.', '์žƒ์–ด๋ฒ„๋ฆฐ ๋งˆ์Œ์„ ๋˜์ฐพ์•„[ๆฑ‚ๆ”พๅฟƒ] ๋ณธ์„ฑ์„ ์ž˜ ๊ธธ๋Ÿฌ ๋‚˜๊ฐ€์•ผ ํ•œ๋‹ค.'], 'answer': ''}",,1,1,True,"['๊ทธ๋Ÿฌ๋ฏ€๋กœ ๋ฐฐ์›Œ์„œ ๋˜๋Š” ๊ฒƒ์€ ์œ„(็ˆฒ)์ด๋‹ค. ๋‘˜์งธ, ๋งน์ž๋Š” ์ธ์„ฑ์ด ์„ ํ•˜๋ฏ€๋กœ ์‚ฌ๋žŒ์ด ์•…์„ ํ•˜๋Š” ๊ฒƒ์€ ์„ฑ์„ ์ƒ์‹คํ–ˆ๊ธฐ ๋•Œ๋ฌธ์ด๋ผ๊ณ  ํ–ˆ๋Š”๋ฐ ๋งŒ์•ฝ ์„ฑ์ด ์„ ์ด๋ผ๋ฉด ๊ฒฐ์ฝ” ์ƒ์‹คํ•˜์ง€ ์•Š์„ ๊ฒƒ์ด๋‹ค. ์ƒ์‹คํ•œ๋‹ค๋Š” ๊ฒƒ์€ ์„ฑ์ด ์•…์ž„์„ ์ฆ๋ช…ํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ์…‹์งธ, ๋„๋•(้“ๅพท)์€ ๊ฐ•ํ•œ ์˜์ง€๋ ฅ(ๆ„ๅฟ—ๅŠ›)์œผ๋กœ ๊ฐ•์ œ๋ฅผ ํ•ด์•ผ ์ด๋ฃจ์–ด์ง€๋Š” ๊ฒƒ์ด๋ฏ€๋กœ ๊ทธ๊ฒƒ์€ ์œ„์ด๋‹ค. ๋„ท์งธ, ์‚ฌ๋žŒ์€ ํ•ญ์ƒ ์ž๊ธฐ์˜ ๊ฒฐ์ ์„ ๋ณด์ถฉํ•˜๋ ค๊ณ  ํ•œ๋‹ค. ์ด์™€ ๊ฐ™์ด ๋‘ ์„ฑ์ธ์˜ ์ฃผ์žฅํ•˜๋Š” ํ•™์„ค(ๅญธ่ชช)์ด ๊ทธ ๋ฐฉํ–ฅ์€ ๋‹ค๋ฅด๋‹ค๊ณ  ํ•  ์ˆ˜ ์žˆ์œผ๋‚˜ ์›๋ž˜์˜ ๋ชฉํ‘œ(็›ฎๆจ™)์ธ ์ธ๊ฐ„์€ ํƒœ์–ด๋‚  ๋•Œ๋ถ€ํ„ฐ ์ธ๊ฐ„ ๊ณ ์œ ์˜ ๋ณธ์งˆ(ๆœฌ่ณช)๋กœ์จ ์„ ํ•˜๊ฒŒ ํƒœ์–ด๋‚ฌ๋‹ค๋Š” ๊ฒƒ์ด๋ฏ€๋กœ, ํ›„์ฒœ์ (ๅพŒๅคฉ็š„)์œผ๋กœ ์ธ๊ฐ„์˜ ๊ฒฝํ—˜์— ์˜ํ•˜์—ฌ ์ขŒ์šฐ๋˜๋Š” ํƒœ์–ด๋‚˜์„œ๋ถ€ํ„ฐ ํ™˜๊ฒฝ์— ๋”ฐ๋ผ ์ขŒ์šฐ ๋œ๋‹ค๋Š” ๊ฒƒ์ด๋ฉฐ, ์ˆœ์ž(่€ๅญ)๋Š” ์‚ฌ๋žŒ์˜ ์„ฑ์€ ๋‚˜๋ฉด์„œ๋ถ€ํ„ฐ ์ด(ๅˆฉ)๋ฅผ ์ข‹์•„ํ•˜๋ฏ€๋กœ ์Ÿํƒˆ์ด ์ƒ๊ธฐ๊ณ  ์‚ฌ์–‘(่พญ่ฎ“)์ด ์—†์–ด์ง€๋ฉฐ ๋ฏธ์›Œํ•˜๋Š” ๋งˆ์Œ์ด ์žˆ์œผ๋ฏ€๋กœ ํ›„์ฒœ์ ์ธ ๊ฒฝํ—˜๊ณผ ์ˆ˜์–‘๊ณผ ๋…ธ๋ ฅ์— ์˜ํ•˜์—ฌ ์ธ๊ฐ„์€ ํ˜•์„ฑ๋˜๊ณ  ์˜ˆ์˜๋ฅผ ํ•™์Šตํ•˜๊ณ  ๋‚ด๋ฉดํ™”ํ•จ์œผ๋กœ์จ ๋„๋•์ (้“ๅพท็š„)์œผ๋กœ ์™„์„ฑ ๋  ์ˆ˜ ์žˆ๋‹ค๋Š” ์ฃผ์žฅ์œผ๋กœ ๋น„์ถ”์–ด ๋ณผ ๋•Œ ์ „์ž์ด๋“  ํ›„์ž์ด๋“  ํƒœ์–ด๋‚˜์„œ๋ถ€ํ„ฐ ๊ฒฝํ—˜(็ถ“้ฉ—)๊ณผ ์ˆ˜์–‘(ไฟฎ้คŠ)๊ณผ ๋…ธ๋ ฅ์ด ์žˆ์–ด์•ผ ํ•œ๋‹ค๋Š” ๊ฒƒ์€ ๊ณตํ†ต์ ์ด๋ผ ํ•  ์ˆ˜ ์žˆ๋‹ค. ์ „ํ•ญ์— ๋…ผ๊ฑฐ ํ•œ ๋ฐ”์™€ ๊ฐ™์ด ์„ฑ์„ (ๆ€งๅ–„)์ด๋“  ์„ฑ์•…(ๆ€งๆƒก)์ด๋“  ์ธ์„ฑ์€ ์–ด๋ฆด ๋•Œ๋ถ€ํ„ฐ ๋ฐ˜๋“œ์‹œ ๊ฒฝํ—˜๊ณผ ์ˆ˜์–‘์ด ์ด๋ฃจ์–ด์ ธ์•ผ ํ•˜๋Š” ๋ฐ๋Š” ์ด๋ก ์ด ์—†๋‹ค ํ•  ๊ฒƒ์ด๋‹ค. ์ด๊ฒƒ์€ ์ง€์—ญ(ๅœฐๅŸŸ)๊ณผ ๊ณ„์ธต์˜ ๊ตฌ๋ถ„์ด ์žˆ์„ ์ˆ˜ ์—†์œผ๋ฉฐ ๋™์ผํ•œ ์ง€์—ญ ๋‚ด ๊ฐ™์€ ๋ฌธํ™” ์ƒํ™œ๊ถŒ(็”Ÿๆดปๅœˆ) ๋‚ด์—์„œ๋Š” ๋ชจ๋‘ ๋‹ค ๊ฐ™์€ ์œ„์น˜์— ์žˆ๋Š” ๊ฒƒ์ด๋‹ค', '์‚ฌ๋žŒ์˜ ์„ฑ์€ ๋‚˜๋ฉด์„œ๋ถ€ํ„ฐ ์ด(ๅˆฉ)๋ฅผ ์ข‹์•„ํ•˜๋ฏ€๋กœ ์Ÿํƒˆ์ด ์ƒ๊ธฐ๊ณ  ์‚ฌ์–‘์ด ์—†์–ด์ง€๋ฉฐ, ์‚ฌ๋žŒ์€ ๋‚˜๋ฉด์„œ๋ถ€ํ„ฐ ๋ฏธ์›Œํ•˜๋Š” ๋งˆ์Œ์ด ์žˆ์œผ๋ฏ€๋กœ ์ž”์ (ๆฎ˜่ณŠ)์ด ์ƒ๊ธฐ๊ณ  ์ถฉ์‹ (ๅฟ ่‡ฃ)์ด ์—†์–ด์ง„๋‹ค๊ณ  ํ•œ๋‹ค. ๋˜ ์‚ฌ๋žŒ์€ ๋‚˜๋ฉด์„œ๋ถ€ํ„ฐ ์ด๋ชฉ(่€ณ็›ฎ)์˜ ์š•(ๆฌฒ)์ด ์žˆ์–ด ์„ฑ์ƒ‰(่ฒ่‰ฒ)์„ ์ข‹์•„ํ•˜๋ฏ€๋กœ ์Œ๋ž€์ด ์ƒ๊ธฐ๊ณ  ์˜ˆ์˜๋ฌธ๋ฆฌ(็ฆฎๅ„€ๆ–‡็†)๊ฐ€ ์—†์–ด์ง„๋‹ค๊ณ  ํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ์ˆœ์ž๋Š” ์ฒœ์„ฑ์— ๋”ฐ๋ฅด๋ฉด ์Ÿํƒˆ, ์ž”์ , ์Œ๋ž€ ๋“ฑ์ด ์ผ์–ด๋‚˜ ์‚ฌํ™”๊ฐ€ ํŽธํ—˜ ํŒจ๋ž€ํ•˜๊ฒŒ ๋˜๋ฏ€๋กœ ์„ฑ์ธ์ด โ€˜์‚ฌ๋ฒ•์ง€ํ™”(ๅธซๆณ•ไน‹ๅŒ–โ€™์™€ โ€˜์˜ˆ์˜์ง€๋„(็ฆฎ็พฉไน‹้“ )โ€™๋ฅผ ์ œ์ •ํ•˜์—ฌ ์ •๋ฆฌ์ •์น˜(ๆญฃ็†ๆ”ฟๆฒป), ์ฆ‰ ์„ ๋˜๊ฒŒ ํ•œ๋‹ค๊ณ  ์„ค๋ช… ํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ์„ฑ์•…(ๆ€งๆƒก)์˜ ์ฆํ—˜์œผ๋กœ์„œ ๋ช‡ ๊ฐ€์ง€๋ฅผ ๋“ค๊ณ  ์žˆ๋‹ค. ์ฒซ์งธ, ํ•™๋ฌธ์€ ์„ (ๅ–„์„) ์™„์„ฑํ•˜๋Š” ๊ฒƒ์ธ๋ฐ ๋งŒ์•ฝ ์„ฑ์ด ์„ ์ด๋ผ๋ฉด ๋ฐฐ์šธ ํ•„์š”๊ฐ€ ์—†์„ ๊ฒƒ์ด๋‹ค. ๊ทธ๋Ÿฌ๋ฏ€๋กœ ๋ฐฐ์›Œ์„œ ๋˜๋Š” ๊ฒƒ์€ ์œ„(็ˆฒ)์ด๋‹ค. ๋‘˜์งธ, ๋งน์ž๋Š” ์ธ์„ฑ์ด ์„ ํ•˜๋ฏ€๋กœ ์‚ฌ๋žŒ์ด ์•…์„ ํ•˜๋Š” ๊ฒƒ์€ ์„ฑ์„ ์ƒ์‹คํ–ˆ๊ธฐ ๋•Œ๋ฌธ์ด๋ผ๊ณ  ํ–ˆ๋Š”๋ฐ ๋งŒ์•ฝ ์„ฑ์ด ์„ ์ด๋ผ๋ฉด ๊ฒฐ์ฝ” ์ƒ์‹คํ•˜์ง€ ์•Š์„ ๊ฒƒ์ด๋‹ค. ์ƒ์‹คํ•œ๋‹ค๋Š” ๊ฒƒ์€ ์„ฑ์ด ์•…์ž„์„ ์ฆ๋ช…ํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ์…‹์งธ, ๋„๋•(้“ๅพท)์€ ๊ฐ•ํ•œ ์˜์ง€๋ ฅ(ๆ„ๅฟ—ๅŠ›)์œผ๋กœ ๊ฐ•์ œ๋ฅผ ํ•ด์•ผ ์ด๋ฃจ์–ด์ง€๋Š” ๊ฒƒ์ด๋ฏ€๋กœ ๊ทธ๊ฒƒ์€ ์œ„์ด๋‹ค. ๋„ท์งธ, ์‚ฌ๋žŒ์€ ํ•ญ์ƒ ์ž๊ธฐ์˜ ๊ฒฐ์ ์„ ๋ณด์ถฉํ•˜๋ ค๊ณ  ํ•œ๋‹ค']",1 +2025-YS-02,"(๊ฐ€) ๊ฐ‘ : ์ •๋ถ€์˜ ๊ธฐ๋Šฅ ํ™•๋Œ€๋Š” ํ˜„์žฌ ๊ฒฝ์ œ ํ˜•ํƒœ๋“ค์˜ ๊ทผ๋ณธ์  ํŒŒ๊ดด๋ฅผ ํ”ผํ• ์ˆ˜์žˆ๋Š”์‹คํ–‰์ˆ˜๋‹จ์ด๋‹ค. ์ •๋ถ€๋Š”๋„๋กœ๊ฑด์„ค, ์ฃผํƒ๊ฑด์ถ• ๋“ฑ์˜ ๊ณต๊ณต์‚ฌ์—…์„ ํ†ตํ•ด ์œ ํšจ ์ˆ˜์š”๋ฅผ ์ฐฝ์ถœํ•ด์•ผ ํ•œ๋‹ค. ์„ : ์ •๋ถ€์˜ ๊ฒฝ์ œ ๊ณ„ํš์€ ๊ฐœ์ธ์˜ ์ž์œ ๋ฅผ ์ฒ ์ €ํ•˜๊ฒŒ ํŒŒ๊ดดํ•œ๋‹ค. ๋ฐ˜๋ฉด์— ๊ฐœ์ธ๋“ค์˜ ๊ฐœ๋ณ„์  ๋…ธ๋ ฅ์„ ์กฐ์ •ํ•˜๋Š” ๋ฐฉ๋ฒ•์œผ๋กœ์„œ์˜ ์ž์œ  ๊ฒฝ์Ÿ์€ ์˜์‹์ ์ธ ์‚ฌํšŒ์  ํ†ต์ œ๋ฅผ ํ•„์š”๋กœ ํ•˜์ง€ ์•Š๋Š”๋‹ค. (๋‚˜) A: ๊ฐ‘์ด ์„์—๊ฒŒ ์ œ๊ธฐํ•  ์ˆ˜ ์žˆ๋Š” ๋น„ํŒ, B: ์„์ด ๊ฐ‘์—๊ฒŒ ์ œ๊ธฐํ•  ์ˆ˜ ์žˆ๋Š” ๋น„ํŒ","{'question': '(๊ฐ€)์˜ ์‚ฌํšŒ์‚ฌ์ƒ๊ฐ€ ๊ฐ‘, ์„์˜ ์ž…์žฅ์—์„œ ์„œ๋กœ์—๊ฒŒ ์ œ๊ธฐํ•  ์ˆ˜ ์žˆ๋Š” ๋น„ํŒ์„ (๋‚˜) ๊ทธ๋ฆผ์œผ๋กœ ํ‘œํ˜„ํ•  ๋•Œ, A, B์— ํ•ด๋‹นํ•˜๋Š” ๋‚ด์šฉ์œผ๋กœ ๊ฐ€์žฅ ์ ์ ˆํ•œ ๊ฒƒ์€?', 'choices': ['A : ์ž์œ ๋กœ์šด ๊ณ ์šฉ๊ณผ ํ•ด๊ณ ๋กœ ๊ธฐ์—…์ด ์ง€์† ์„ฑ์žฅํ•  ์ˆ˜ ์žˆ์Œ์„ ๊ฐ„๊ณผํ•œ๋‹ค.', 'A: ๋ฏผ์ฃผ์  ๋ฐฉ์‹์˜ ๊ตญ๊ฐ€ ๊ฒฝ์ œ ๊ณ„ํš์ด ๊ฐœ์ธ์˜ ์ž์œ ๋ฅผ ์–ต์••ํ•จ์„ ๊ฐ„๊ณผํ•œ๋‹ค.', 'A: ์œ ํšจ ์ˆ˜์š”์˜ ์ฐฝ์ถœ์„ ์œ„ํ•œ ํˆฌ์ž ์ •์ฑ…์„ ์„ธ์›Œ์•ผ ํ•จ์„ ๊ฐ„๊ณผํ•œ๋‹ค.', 'B : ์‚ฌ์  ์†Œ์œ ๊ถŒ๊ณผ ๊ฐœ์ธ์˜ ๊ฒฝ์ œ์  ์ž์œจ์„ฑ์„ ์ธ์ •ํ•ด์•ผ ํ•จ์„ ๊ฐ„๊ณผํ•œ๋‹ค.', 'B: ์‹œ์žฅ ๊ฒฝ์ œ์˜ ์›ํ™œํ•œ ์ž‘๋™์„ ์œ„ํ•œ ๋ฒ•์  ๊ทœ์ œ๊ฐ€ ํ—ˆ์šฉ๋จ์„ ๊ฐ„๊ณผํ•œ๋‹ค.'], 'answer': ''}",,3,3,True,[],2 +2025-YS-03,"๊ฐ‘ : ์ ˆ์ œ๋Š” ํ’ˆ์„ฑ์ ์ธ ๋•์œผ๋กœ, ์ง€๋‚˜์นจ๊ณผ ๋ชจ์ž๋žŒ์— ์˜ํ•ด ํŒŒ๊ดด๋˜๊ณ  ์ค‘์šฉ์— ์˜ํ•ด ๋ณด์กด๋œ๋‹ค. ์ ˆ์ œ ์žˆ๋Š” ์‚ฌ๋žŒ์€ ์˜ฌ๋ฐ”๋ฅธ ์ด์„ฑ์ด ๊ทœ์ •ํ•˜๋Š” ๋Œ€๋กœ ์ฆ๊ฑฐ์›€๋“ค์„ ์ข‹์•„ํ•˜๋Š” ์‚ฌ๋žŒ์ด๋‹ค. ์„: ์ ˆ์ œ๋Š” ์ด์„ฑ์  ์กด์žฌ์ž์—๊ฒŒ์„œ ๋ฐœ๊ฒฌ๋˜๋Š” ๋•์œผ๋กœ, ์พŒ๋ฝ์˜ ๋ฐ˜๋Œ€ํŽธ์— ์„œ ์žˆ๋‹ค. ์šฐ๋ฆฌ๋Š” ์‹ ๊ณผ ์ธ๊ฐ„์—๊ฒŒ ๊ณตํ†ต๋œ ์ด์„ฑ์— ๋”ฐ๋ผ ํ–‰๋™ํ•  ๋•Œ ๋‘๋ ค์›Œํ•  ๊ฒƒ๋„ ์—†๊ณ  ํ•ด๋ฅผ ์ž…์„ ๊ฒƒ๋„ ์—†๋‹ค.","{'question': '๊ฐ‘, ์„์€ ๊ณ ๋Œ€ ์„œ์–‘ ์‚ฌ์ƒ๊ฐ€๋“ค์ด๋‹ค. ๊ฐ‘์€ ๊ธ์ •, ์„์€ ๋ถ€์ •์˜ ๋Œ€๋‹ต์„ ํ•  ์งˆ๋ฌธ์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['๊ฐœ์ธ์˜ ์ฐธ๋œ ํ–‰๋ณต์€ ๋•์— ๋”ฐ๋ฅด๋Š” ์‚ถ์„ ํ†ตํ•ด ์‹คํ˜„๋  ์ˆ˜ ์žˆ๋Š”๊ฐ€?', '๋•์„ ๋ฐœํœ˜ํ•˜๋ฉด ์‚ฌ๊ฑด์„ ๋ฐ›์•„๋“ค์ด๋Š” ํƒœ๋„๊ฐ€ ๋ณ€ํ™”๋  ์ˆ˜ ์žˆ๋Š”๊ฐ€?', '๋•์žˆ๋Š” ์‚ถ์˜ ์‹คํ˜„์„ ์œ„ํ•œ ๋ชจ๋“  ์กฐ๊ฑด์€ ์ž๋ฐœ์„ฑ์˜ ์˜์—ญ ์•ˆ์— ์žˆ๋Š”๊ฐ€?', '์ด์„ฑ์˜ ๋ช…๋ น์— ๋”ฐ๋ผ ๋‘๋ ค์›€์˜ ๊ฐ์ •์„ ๊ฐ€์ ธ์•ผ ํ•˜๋Š” ๊ฒฝ์šฐ๊ฐ€ ์žˆ๋Š”๊ฐ€?', '๊ณต๋™์ฒด ์„ฑ์›์œผ๋กœ์„œ์˜ ์ธ๊ฐ„์€ ๊ณต๊ณต์˜ ์ด์ต์„ ์œ„ํ•ด ๋…ธ๋ ฅํ•ด์•ผ ํ•˜๋Š”๊ฐ€?'], 'answer': ''}",,4,4,True,"['์ด๋Š” ๊ทธ๋•Œ๊ทธ๋•Œ์˜ ์ƒ๊ฐ์ด๋‚˜ ๊ฐ์ •์— ๋”ฐ๋ผ ๋‚˜๋ถ€๋ผ๋Š” ๊ฒƒ์ด ์•„๋‹ˆ๋ผ ์ž์‹ ์˜ ์ƒ๊ฐ์ด๋‚˜ ๊ฐ์ •์„ ์ฃผ์ฒด์ ์œผ๋กœ ์กฐ์ ˆํ•œ๋‹ค๋Š” ๋œป์ด๋ฉฐ ์ด ๊ฐ™์€ ์ฃผ์ฒด์ ์ธ ์ž๊ธฐ์กฐ์ ˆ์„ ํ†ตํ•ด์„œ ์ ˆ์ œ๋„ ๊ฐ€๋Šฅํ•˜๊ฒŒ ๋œ๋‹ค. ์ž์ œ๋Š” ๋ถ„๋ช… ๋‹น์žฅ์—๋Š” ์ฆ๊ฑฐ์šด ์ผ์ด ์•„๋‹ˆ๋ฉฐ ๋•Œ๋กœ๋Š” ๊ณ ํ†ต์Šค๋Ÿฌ์šด ๊ฒƒ์ด๊ธด ํ•˜๋‚˜ ๊ฒฐ๊ตญ ์ž์ œ์‹ฌ์œผ๋กœ ํ›ˆ๋ จ๋œ ์‚ฌ๋žŒ์€ ํ‰ํ™”๋กญ๊ณ  ์˜ฌ๋ฐ”๋ฅธ, ๊ทธ๋ž˜์„œ ์ตœ๋Œ€๋กœ ์ž๊ธฐ์‹คํ˜„์„ ํ•  ์ˆ˜ ์žˆ๋Š” ์ธ์ƒ์˜ ์ˆ˜ํ™•์„ ๊ฑฐ๋‘๊ฒŒ ๋˜๋Š” ๊ฒƒ์ด๋‹ค. ์ ˆ์ œ(็ฏ€ๅˆถ)๋Š” ์ƒํ™œ์— ์žˆ์–ด์„œ ๊ท ํ˜•์„ ์œ ์ง€ํ•˜๋Š” ๊ฒƒ์„ ๋งํ•œ๋‹ค. ์ด๋Š” ์šฐ๋ฆฌ๊ฐ€ ํ•ญ์ƒ ๊ผญ ๊ฐ™์ด ํ–‰๋™ํ•ด์•ผ ํ•œ๋‹ค๊ฑฐ๋‚˜ ์–ธ์ œ๋‚˜ ์ธ์ƒ‰ํ•˜๊ฒŒ ๊ตด์–ด์•ผ ํ•œ๋‹ค๋Š” ๊ฒƒ์„ ๋œปํ•˜์ง€๋Š” ์•Š๋Š”๋‹ค. ์ ˆ์ œ๋ฅผ ํ•œ๋‹ค๋Š” ๊ฒƒ์€ ๊ณต๋ถ€๋„ ์ ์ ˆํ•˜๊ฒŒ, ๋…ธ๋Š” ๊ฒƒ, ์ผํ•˜๋Š” ๊ฒƒ, ์‰ฌ๋Š” ๊ฒƒ๋„ ์ ์ ˆํ•˜๊ฒŒ ํ•˜๋Š” ๊ฒƒ์„ ๋œปํ•œ๋‹ค. ์ ˆ์ œ๋ž€ ์ง€๋‚˜์น˜๊ธฐ ์ „์— ๋ฉˆ์ถ”๋Š” ๊ฒƒ์„ ๋งํ•œ๋‹ค. ๊ทธ๊ฒƒ์€ ์ž์ œํ•จ์œผ๋กœ์จ ์ง€๋‚˜์น˜์ง€ ์•Š์Œ์„ ๋งํ•œ๋‹ค. ๋ชจ์ž๋ผ๋Š” ๊ฒƒ๋„ ์ง€๋‚˜์น˜๋Š” ๊ฒƒ๊ณผ ๋งˆ์ฐฌ๊ฐ€์ง€๋กœ ์ ˆ์ œ๊ฐ€ ์•„๋‹ˆ๋‹ค. ๊ทธ๋ž˜์„œ ์ ˆ์ œ๋Š” ๊ณผ์œ ๋ถˆ๊ธ‰์ด ์—†๋Š” ์ค‘์šฉ์˜ ๋•์ด๋ผ๊ณ  ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋„ˆ๋ฌด ๋ง์ด ๋งŽ์œผ๋ฉด ์‚ฐ๋งŒํ•ด ๋ณด์ด๊ณ  ๋ง์ด ์ง€๋‚˜์น˜๊ฒŒ ์ ์œผ๋ฉด ์ง„์ •ํ•œ ๋œป์„ ๋ฌด์‹œ๋‹นํ•˜๊ฒŒ ๋œ๋‹ค. ์ ˆ์ œ๋Š” ๋์—†๋Š” ์š•๋ง์œผ๋กœ๋ถ€ํ„ฐ ์šฐ๋ฆฌ๋ฅผ ์ง€์ผœ์ฃผ๋Š” ์†Œ์ค‘ํ•œ ๋•๋ชฉ์ด ์•„๋‹ ์ˆ˜ ์—†๋‹ค. ์šฐ๋ฆฌ๊ฐ€ ์ž์ œํ•  ๊ฒฝ์šฐ์—๋Š” ์šฐ๋ฆฌ ์Šค์Šค๋กœ ํ–‰๋™์„ ํ†ต์ œํ•˜๋Š” ๊นŒ๋‹ญ์— ๋‚จ์˜ ๊ฐ„์„ญ์„ ๋ฐ›์„ ํ•„์š”๊ฐ€ ์—†์–ด์ง„๋‹ค. ๊ทธ๋Ÿฐ ๊นŒ๋‹ญ์— ์ž์ œ๋Š” ์šฐ๋ฆฌ์—๊ฒŒ ์ž์œ ๋ฅผ ๊ฐ€์ ธ๋‹ค์ค€๋‹ค๊ณ  ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ ์ž์ œ๋ฅผ ํ•  ์ˆ˜ ์žˆ์„ ๊ฒฝ์šฐ ํšจ์œจ์ ์œผ๋กœ ์ฒ˜๋ฆฌํ•  ์ˆ˜ ์žˆ์–ด ๋งค์‚ฌ์— ๋Š‘์žฅ๋ถ€๋ฆฌ๊ฑฐ๋‚˜ ๊พธ๋ฌผ๊ฑฐ๋ฆด ํ•„์š”๊ฐ€ ์—†์–ด์ง„๋‹ค. ์ž์ œ์‹ฌ์ด ๊ฒฐ์—ฌ๋œ ์‚ฌ๋žŒ์€ ์ž์‹ ์˜ ๊ฐ์ •์„ ํ†ต์ œํ•  ์ˆ˜๊ฐ€ ์—†๋‹ค. ๊ทธ๋ž˜์„œ ์ž์‹ ์˜ ์ฃผ๋ณ€์‚ฌ๋žŒ๋“ค์ด ์ƒ์ฒ˜๋ฅผ ๋ฐ›๊ฒŒ ๋˜๋ฉฐ ์ด๋Š” ๊ฒฐ๊ตญ ์ž์‹ ์—๊ฒŒ๋„ ์ด๋กœ์šธ ์ˆ˜๊ฐ€ ์—†๋‹ค. ์ž์ œ์‹ฌ์„ ๊ฐ–๊ณ  ํ–‰๋™ํ•  ๊ฒฝ์šฐ ์•„๋ฌด๋„ ๊ฐ์‹œํ•˜๊ฑฐ๋‚˜ ํ†ต์ œํ•  ํ•„์š”๊ฐ€ ์—†์–ด์ง„๋‹ค. ์™œ๋ƒํ•˜๋ฉด ์šฐ๋ฆฌ๊ฐ€ ์Šค์Šค๋กœ ๊ฐ์‹œํ•˜๊ณ  ํ†ต์ œํ•˜๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค']",1 +2025-YS-04,๊ฐ‘: ์–ด๋–ค์‚ฌ๋žŒ์ด์ข‹์€์‚ฌ๋žŒ์ธ์ด์œ ๋Š”๊ทธ๊ฐ€์ ์ ๋‚˜์•„์ง€๋Š”์ชฝ์œผ๋กœ ์›€์ง์ด๊ณ  ์žˆ๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ํ˜„์žฌ๋ฅผ ๋ณ€ํ™”์‹œํ‚ค๋Š” ๋Šฅ๋™์ ์ธ ๊ณผ์ •์œผ๋กœ์„œ ์„ฑ์žฅ ์ž์ฒด๊ฐ€ ์œ ์ผํ•œ ๋„๋•์  ๋ชฉ์ ์ด๋‹ค. ์„: ์–ด๋–ค ์‚ฌ๋žŒ์ด ๋น„๊ฒํ•œ ์ž์ด๊ฑฐ๋‚˜ ์˜์›…์ธ ์ด์œ ๋Š” ๊ทธ๊ฐ€ ์Šค์Šค๋กœ๋ฅผ ๊ทธ๋ ‡๊ฒŒ ๋งŒ๋“ค์—ˆ๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ์ธ๊ฐ„์ด ์ •์˜๋˜๋Š” ๊ฒƒ์€ ์ธ๊ฐ„์ด ์„ธ๊ณ„ ์†์— ์‹ค์กดํ•œ ์ดํ›„์˜ ์ผ์ด๋‹ค.,"{'question': 'ํ˜„๋Œ€ ์„œ์–‘ ์‚ฌ์ƒ๊ฐ€ ๊ฐ‘, ์„์˜ ์ž…์žฅ์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['๊ฐ‘: ๋ฌธ์ œ ์ƒํ™ฉ์„ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ์ ˆ๋Œ€์  ์ง€์‹์„ ์ ์šฉํ•ด์•ผ ํ•œ๋‹ค.', '๊ฐ‘ : ํ–‰์œ„๊ฐ€ ์ง€๋‹ˆ๋Š” ์œ ์šฉ์„ฑ ๊ฐ€์น˜๋Š” ํ–‰๋ณต์ด๋ผ๋Š” ์ตœ๊ณ ์„ ์— ๋”ฐ๋ผ ๊ฒฐ์ •๋œ๋‹ค.', '์„: ์ธ๊ฐ„ ๋ณธ์งˆ์„ ๊ฒฐ์ •ํ•˜๋Š” ์™ธ์  ์กด์žฌ๋ฅผ ํ†ตํ•ด ์‹ค์กด์„ ์ž๊ฐํ•ด์•ผ ํ•œ๋‹ค.', '์„: ์‹ค์กด์  ๋ถˆ์•ˆ ๊ทน๋ณต์„ ์œ„ํ•ด ๊ด€์Šต์  ๊ทœ๋ฒ”์„ ๊ธฐ์ค€์œผ๋กœ ์‚ผ์•„์•ผ ํ•œ๋‹ค.', '๊ฐ‘๊ณผ ์„: ํ˜„์‹ค์˜ ์‚ฌํšŒ์  ๋ฌธ์ œ ํ•ด๊ฒฐ์— ๋Šฅ๋™์ ์œผ๋กœ ์ฐธ์—ฌํ•ด์•ผ ํ•œ๋‹ค.'], 'answer': ''}",,5,5,True,[],5 +2025-YS-05,"์‹ ์€ ๊ณง ์ž์—ฐ์ด๋‹ค. ๊ฐœ๋ณ„์  ์กด์žฌ๋Š” ์‹ ์˜ ์†์„ฑ์„ ์–ด๋–ค ์ผ์ •ํ•œ ๋ฐฉ์‹์œผ๋กœ ํ‘œํ˜„ํ•˜๋Š” ์–‘ํƒœ์ด๋‹ค. ์ •์„œ๋Š” ์‹ ์ฒด์˜ ํ™œ๋™ ๋Šฅ๋ ฅ์„ ์ฆ๋Œ€ ์‹œํ‚ค๊ฑฐ๋‚˜ ๊ฐ์†Œ์‹œํ‚ค๋Š” ์‹ ์ฒด์˜ ๋ณ€์šฉ์ธ ๋™์‹œ์— ๊ทธ๋Ÿฌํ•œ ๋ณ€์šฉ์˜ ๊ด€๋… ์ด๋‹ค. ๊ทธ๋Ÿฌ๋ฏ€๋กœ ์šฐ๋ฆฌ๊ฐ€ ๊ทธ๋Ÿฌํ•œ ๋ณ€์šฉ์˜ ํƒ€๋‹นํ•œ ์›์ธ์ด ๋  ์ˆ˜ ์žˆ๋‹ค๋ฉด ๋‚˜๋Š” ๊ทธ ์ •์„œ๋ฅผ ๋Šฅ๋™์ด๋ผ๊ณ  ์ดํ•ดํ•˜๊ณ , ๊ทธ๋ ‡์ง€ ์•Š๋‹ค๋ฉด ์ˆ˜๋™์ด๋ผ๊ณ  ์ดํ•ดํ•œ๋‹ค.","{'question': '๋‹ค์Œ์„ ์ฃผ์žฅํ•œ ๊ทผ๋Œ€ ์„œ์–‘ ์‚ฌ์ƒ๊ฐ€์˜ ์ž…์žฅ์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['์‹ ์€ ์œ ์ผํ•œ ์‹ค์ฒด๋กœ์„œ ๋ชจ๋“  ์ž์—ฐ ์„ธ๊ณ„์˜ ์•ˆ๊ณผ ๋ฐ–์— ์กด์žฌํ•œ๋‹ค.', '์พŒ๋ฝ์„ ์–ต์ œํ•ด์•ผ๋งŒ ์ด์„ฑ์˜ ํž˜์œผ๋กœ ๋น„๋กœ์†Œ ์ง€๋ณต์— ๋„๋‹ฌํ•  ์ˆ˜ ์žˆ๋‹ค.', '์ž์—ฐ์€ ์ธ๊ณผ์  ๊ด€๊ณ„๋ฅผ ๋„˜์–ด์„  ๋ชฉ์ ์˜ ํ•„์—ฐ์„ฑ์œผ๋กœ ์ •ํ•ด์ ธ ์žˆ๋‹ค.', '์ •๋…์— ์†๋ฐ•๋˜์ง€ ์•Š์€ ์‚ฌ๋žŒ๋„ ์›์ธ์— ๋Œ€ํ•œ ๋ช…ํ™•ํ•œ ๊ด€๋…์— ์˜์กดํ•œ๋‹ค.', '์„ ์€ ์–‘ํƒœ๊ฐ€ ์ง€๋‹Œ ํŠน์„ฑ์œผ๋กœ์„œ ์ž๊ธฐ ๋ณด์กด์„ ์œ„ํ•œ ํ™œ๋™ ๋Šฅ๋ ฅ ๊ทธ ์ž์ฒด์ด๋‹ค.'], 'answer': ''}",,4,1,False,[],4 +2025-YS-06,"(๊ฐ€) ๊ฐ‘: ์พŒ๋ฝ ๊ทธ ์ž์ฒด๋Š” ๋‚˜์˜์ง€ ์•Š๋‹ค. ํ•˜์ง€๋งŒ ์šฐ๋ฆฌ๊ฐ€ ๋ชจ๋“  ์พŒ๋ฝ์„ ์„ ํƒํ•˜๋Š” ๊ฒƒ๋„ ํ•ญ์ƒ ๊ณ ํ†ต์„ ํ”ผํ•˜๋Š” ๊ฒƒ๋„ ์•„๋‹ˆ๋‹ค. ๊ณ ํ†ต์˜ ์ œ๊ฑฐ๋ฅผ ๋„˜์–ด์„œ๋Š” ์พŒ๋ฝ์€ ์กด์žฌํ•˜์ง€ ์•Š๋Š”๋‹ค. ์„: ์พŒ๋ฝ์€ ๊ทธ ์ž์ฒด๋กœ ์ข‹์€ ๊ฒƒ์ด๋ฉฐ ๊ณ ํ†ต์€ ๊ทธ ์ž์ฒด๋กœ ๋‚˜์œ ๊ฒƒ์ด๋‹ค. ์ž…๋ฒ•์ž๋Š” ์พŒ๋ฝ๊ณผ ๊ณ ํ†ต์˜ ๊ฐ€์น˜๋ฅผ ์ธก์ •ํ•  ๋•Œ ๊ทธ๊ฒƒ์˜ ๊ฐ•๋„, ์ง€์†์„ฑ, ๋ฒ”์œ„ ๋“ฑ์„ ๊ณ ๋ คํ•ด์•ผ ํ•œ๋‹ค. ๋ณ‘ : ์พŒ๋ฝ์€ ๋ชฉ์ ์œผ๋กœ์„œ ๋ฐ”๋žŒ์งํ•œ ์œ ์ผํ•œ ๊ฒƒ์ด๋‹ค. ์šฐ๋ฆฌ๋Š” ๊ฐ€๋Šฅํ•œ ํ•œ ๊ณ ํ†ต์ด ์—†๊ณ  ์งˆ์ ์œผ๋กœ๋‚˜ ์–‘์ ์œผ๋กœ๋‚˜ ์ตœ๋Œ€ํ•œ์˜ ์พŒ๋ฝ์„ ๋งŒ๋ฝํ•  ์ˆ˜ ์žˆ๋Š” ์ƒํƒœ์— ์ด๋ฅด๋Ÿฌ์•ผ ํ•œ๋‹ค. (๋‚˜) A: ๊ฐ‘๋งŒ์˜ ์ž…์žฅ, B: ๊ฐ‘๊ณผ ์„๋งŒ์˜ ๊ณตํ†ต ์ž…์žฅ, C: ์„๊ณผ ๋ณ‘๋งŒ์˜ ๊ณตํ†ต ์ž…์žฅ, D: ๊ฐ‘๊ณผ ์„๊ณผ ๋ณ‘์˜ ๊ณตํ†ต ์ž…์žฅ","{'question': '(๊ฐ€)์˜ ๊ณ ๋Œ€ ์„œ์–‘ ์‚ฌ์ƒ๊ฐ€ ๊ฐ‘, ๊ทผ๋Œ€ ์„œ์–‘ ์‚ฌ์ƒ๊ฐ€ ์„๊ณผ ๋ณ‘์˜ ์ž…์žฅ์„ (๋‚˜) ๊ทธ๋ฆผ์œผ๋กœ ํ‘œํ˜„ํ•  ๋•Œ, A~D์— ํ•ด๋‹นํ•˜๋Š” ์ง„์ˆ ๋กœ ์ ์ ˆํ•œ ๊ฒƒ๋งŒ์„ <๋ณด๊ธฐ>์—์„œ ๊ณ ๋ฅธ ๊ฒƒ์€?', 'choices': ['ใ„ฑ,ใ„ด', 'ใ„ฑ,ใ„ท', 'ใ„ด,ใ„ท', 'ใ„ด,ใ„น', 'ใ„ท,ใ„น'], 'answer': '', 'question_plus': 'แ„€. A: ์‚ฌ๋ ค ๊นŠ์Œ๊ณผ ์ •์˜๋Š” ๊ถ๊ทน์ ์œผ๋กœ ์พŒ๋ฝ์— ์˜ํ•ด ๊ฐ€์น˜๊ฐ€ ๊ฒฐ์ •๋œ๋‹ค. แ„‚. B : ํ–‰์œ„์˜ ์„ ์•…์„ ํ‰๊ฐ€ํ•˜๊ธฐ ์œ„ํ•œ ๋ณดํŽธ์  ๊ธฐ์ค€์ด ์กด์žฌํ•œ๋‹ค. แ„ƒ. C: ๋„๋•์  ์ด์ƒ ์‹คํ˜„์„ ์œ„ํ•ด ๊ณต์ ์‚ฌํšŒ์— ๊ด€์‹ฌ์„ ๊ฐ€์ ธ์•ผ ํ•œ๋‹ค. แ„…. D: ์ž๊ธฐ ์ž์‹ ์˜ ๊ณ ํ†ต์— ๋Œ€ํ•œ ๊ฒฝํ—˜์ด ํ–‰์œ„์˜ ๋™๊ธฐ๋ฅผ ์œ ๋ฐœํ•œ๋‹ค.'}",แ„€. A: ์‚ฌ๋ ค ๊นŠ์Œ๊ณผ ์ •์˜๋Š” ๊ถ๊ทน์ ์œผ๋กœ ์พŒ๋ฝ์— ์˜ํ•ด ๊ฐ€์น˜๊ฐ€ ๊ฒฐ์ •๋œ๋‹ค. แ„‚. B : ํ–‰์œ„์˜ ์„ ์•…์„ ํ‰๊ฐ€ํ•˜๊ธฐ ์œ„ํ•œ ๋ณดํŽธ์  ๊ธฐ์ค€์ด ์กด์žฌํ•œ๋‹ค. แ„ƒ. C: ๋„๋•์  ์ด์ƒ ์‹คํ˜„์„ ์œ„ํ•ด ๊ณต์ ์‚ฌํšŒ์— ๊ด€์‹ฌ์„ ๊ฐ€์ ธ์•ผ ํ•œ๋‹ค. แ„…. D: ์ž๊ธฐ ์ž์‹ ์˜ ๊ณ ํ†ต์— ๋Œ€ํ•œ ๊ฒฝํ—˜์ด ํ–‰์œ„์˜ ๋™๊ธฐ๋ฅผ ์œ ๋ฐœํ•œ๋‹ค.,5,4,False,[],3 +2025-YS-07,"๊ฐ‘ : ์ด(็†)๊ฐ€ ๋ฐœํ•˜์—ฌ ์‚ฌ๋‹จ์ด ๋˜๋Š”๋ฐ, ์—ฌ๊ธฐ์„œ ๋ฐœํ•˜๋Š” ๋ฐ”ํƒ•[่ณ‡]์€ ๊ธฐ(ๆฐฃ)์ด์ง€๋งŒ ์‹ค์ œ ๊ทธ๋ ‡๊ฒŒ ๋ฐœํ•˜๊ฒŒ ํ•˜๋Š” ๊ฒƒ์€ ์ด์ด๋‹ค. ์ด๊ฒƒ์€ ๋งˆ์น˜ ์‚ฌ๋žŒ์ด ๋ง[้ฆฌ]์„ ์ด๋„๋Š” ์ฃผ์ธ์ด๋ฏ€๋กœ, ๊ฐ€๋Š” ๊ฒƒ์€ ๋ง์ด์ง€๋งŒ ๋ง์„ ๊ฐ€๊ฒŒ ํ•˜๋Š” ๊ฒƒ์€ ์‚ฌ๋žŒ์ธ ๊ฒƒ๊ณผ ๊ฐ™๋‹ค. ์„: ์‚ฌ๋žŒ์€ ๋ง์ด ์•„๋‹ˆ๋ฉด ์ถœ์ž…ํ•˜์ง€ ๋ชปํ•˜๊ณ  ๋ง์€ ์‚ฌ๋žŒ์ด ์•„๋‹ˆ๋ฉด ๊ถค๋„๋ฅผ ์žƒ๋Š” ๊ฒƒ์ฒ˜๋Ÿผ, ์ด์™€ ๊ธฐ๋Š” ์„œ๋กœ ๋–จ์–ด์งˆ ์ˆ˜ ์—†๋‹ค. ๋‹ค๋งŒ ์„ฑ(ๆ€ง)์ด ๋ฐœํ•œ ์ดํ›„์— ์ด๋ฅผ ์œ„์ฃผ๋กœ, ๊ธฐ๋ฅผ ์œ„์ฃผ๋กœ ๋งํ•˜๋Š” ๊ฒƒ์— ์ฐจ์ด๊ฐ€ ์žˆ๋”๋ผ๋„ ์ด์™€ ๊ธฐ๊ฐ€ ์„œ๋กœ ๋ฐœํ•˜๋Š” ๊ฒƒ์€ ์•„๋‹ˆ๋‹ค.","{'question': 'ํ•œ๊ตญ ์œ ๊ต ์‚ฌ์ƒ๊ฐ€ ๊ฐ‘, ์„์˜ ์ž…์žฅ์œผ๋กœ ์˜ณ์€ ๊ฒƒ๋งŒ์„ <๋ณด๊ธฐ>์—์„œ ์žˆ๋Š” ๋Œ€๋กœ ๊ณ ๋ฅธ ๊ฒƒ์€?', 'choices': ['ใ„ฑ,ใ„ด', 'ใ„ฑ,ใ„ท', 'ใ„ด,ใ„น', 'ใ„ฑ,ใ„ท,ใ„น', 'ใ„ด,ใ„ท,ใ„น'], 'answer': '', 'question_plus': 'แ„€. ๊ฐ‘ : ์ •(ๆƒ…)์€ ๊ทธ ์—ฐ์›[ๆ‰€ๅพžไพ†]์— ๋”ฐ๋ผ ์ด๋‚˜ ๊ธฐ๋กœ๋งŒ ์กด์žฌํ•œ๋‹ค. แ„‚. ๊ฐ‘: ์น ์ •์€ ๊ธฐ๊ฐ€ ์ด์˜ ์ฃผ์žฌ๋ฅผ ๋ฒ—์–ด๋‚˜๋ฉด ๊ฒฐ์ฝ” ์„ ์ด ๋  ์ˆ˜ ์—†๋‹ค. แ„ƒ. ์„: ์‚ฌ๋‹จ์€ ๋ฐ˜๋“œ์‹œ ์„ฑ์ด ๋ฐœํ•ด์•ผ๋งŒ ๋“œ๋Ÿฌ๋‚˜๋Š” ๋งˆ์Œ์˜ ์ž‘์šฉ[็”จ]์ด๋‹ค. แ„…. ๊ฐ‘๊ณผ ์„: ์ด๋Š” ๊ธฐ๊ฐ€ ๋ฐœํ•˜๊ฒŒ ํ•˜๋Š” ์›์ธ๊ณผ ๊ทผ๊ฑฐ[ๆ‰€ไปฅ็„ถ]๊ฐ€ ๋œ๋‹ค.'}",แ„€. ๊ฐ‘ : ์ •(ๆƒ…)์€ ๊ทธ ์—ฐ์›[ๆ‰€ๅพžไพ†]์— ๋”ฐ๋ผ ์ด๋‚˜ ๊ธฐ๋กœ๋งŒ ์กด์žฌํ•œ๋‹ค. แ„‚. ๊ฐ‘: ์น ์ •์€ ๊ธฐ๊ฐ€ ์ด์˜ ์ฃผ์žฌ๋ฅผ ๋ฒ—์–ด๋‚˜๋ฉด ๊ฒฐ์ฝ” ์„ ์ด ๋  ์ˆ˜ ์—†๋‹ค. แ„ƒ. ์„: ์‚ฌ๋‹จ์€ ๋ฐ˜๋“œ์‹œ ์„ฑ์ด ๋ฐœํ•ด์•ผ๋งŒ ๋“œ๋Ÿฌ๋‚˜๋Š” ๋งˆ์Œ์˜ ์ž‘์šฉ[็”จ]์ด๋‹ค. แ„…. ๊ฐ‘๊ณผ ์„: ์ด๋Š” ๊ธฐ๊ฐ€ ๋ฐœํ•˜๊ฒŒ ํ•˜๋Š” ์›์ธ๊ณผ ๊ทผ๊ฑฐ[ๆ‰€ไปฅ็„ถ]๊ฐ€ ๋œ๋‹ค.,5,3,False,[],3 +2025-YS-08,๋ฏผ์ฃผ์ฃผ์˜๋Š” ์–ด์›์ƒ '๊ตญ๋ฏผ'๊ณผ 'ํ†ต์น˜'์˜ ํ•ฉ์„ฑ์–ด๋กœ ๊ตญ๋ฏผ์˜ ์ง€๋ฐฐ๋ฅผ ์˜๋ฏธํ•ฉ๋‹ˆ๋‹ค. ํ•˜์ง€๋งŒ ๊ตญ๋ฏผ๊ณผ ํ†ต์น˜๋ผ๋Š” ๋‹จ์–ด๊ฐ€ ๊ฐ–๋Š” ์˜๋ฏธ๊ฐ€ ๋ฌด์—‡์ด๋“ ์ง€ ๊ฐ„์— ๋ฏผ์ฃผ์ฃผ์˜๋Š” ๊ตญ๋ฏผ์ด ์‹ค์ œ๋กœ ์ง€๋ฐฐํ•œ๋‹ค๋Š” ๊ฒƒ์„ ๋œปํ•˜์ง€ ์•Š์Šต๋‹ˆ๋‹ค. ๋˜ํ•œ ๋ชจ๋“  ๊ตญ๋ฏผ์ด ๋™์˜ํ•  ์ˆ˜ ์žˆ๋Š” ๊ณต๋™์‹ ๊ณผ ๊ฐ™์€ ๊ฒƒ๋„ ์กด์žฌํ•˜์ง€ ์•Š์Šต๋‹ˆ๋‹ค. ๋”ฐ๋ผ์„œ ๋ฏผ์ฃผ์ฃผ์˜๋Š” ์ •์น˜์  ๊ฒฐ์ •์— ๋„๋‹ฌํ•˜๊ธฐ ์œ„ํ•œ ์ œ๋„์  ์žฅ์น˜์— ๋ถˆ๊ณผํ•ฉ๋‹ˆ๋‹ค. ์ •์น˜ ์—˜๋ฆฌํŠธ๋“ค์€ ๊ตญ๋ฏผ์˜ ํ‘œ๋ฅผ ์–ป๊ธฐ ์œ„ํ•œ ๊ฒฝ์Ÿ์„ ํ†ตํ•ด ๊ถŒ๋ ฅ์„ ํš๋“ํ•ด์•ผ ํ•ฉ๋‹ˆ๋‹ค. ์—ฌ๊ธฐ์„œ ๊ตญ๋ฏผ์˜ ์ฃผ๋œ ์—ญํ• ์€ ์ง์ ‘์ ์œผ๋กœ ๋˜๋Š” ์ค‘๊ฐ„ ๊ธฐ๊ตฌ๋ฅผ ํ†ตํ•˜์—ฌ ์ •๋ถ€๋ฅผ ํƒ„์ƒ์‹œํ‚ค๋Š” ๊ฒƒ์— ๋จธ๋ฌผ๋Ÿฌ์•ผ ํ•ฉ๋‹ˆ๋‹ค.,"{'question': '๊ทธ๋ฆผ์˜ ๊ฐ•์—ฐ์ž๊ฐ€ ์ง€์ง€ํ•  ์ž…์žฅ์œผ๋กœ ๊ฐ€์žฅ ์ ์ ˆํ•œ ๊ฒƒ์€?', 'choices': ['๊ตญ๋ฏผ์€ ๋Œ€ํ‘œ์ž์—๊ฒŒ ๊ถŒ๋ ฅ์˜ ์ •๋‹น์„ฑ์„ ๋ถ€์—ฌํ•˜๋Š” ์ฃผ๊ถŒ์„ ํ–‰์‚ฌํ•œ๋‹ค.', '๋ฏผ์ฃผ์ฃผ์˜๋Š” ์ •์น˜์  ์ ˆ์ฐจ๋ฅผ ๋œปํ•˜๋Š” ๊ฒƒ์ด ์•„๋‹ˆ๋ผ ๊ทธ ์ž์ฒด๊ฐ€ ๋ชฉ์ ์ด๋‹ค.', '์ •์น˜์  ์˜์‚ฌ ๊ฒฐ์ •์—์„œ ์ผ๋ฐ˜ ์‹œ๋ฏผ์€ ์†Œ์ˆ˜ ์ •์น˜์ธ๋ณด๋‹ค ํ•ฉ๋ฆฌ์ ์ด๋‹ค.', '๊ตญ๊ฐ€ ๊ตฌ์„ฑ์› ๋ชจ๋‘์˜ ์˜์ง€๊ฐ€ ๋ฐ˜์˜๋œ ์œ ์ผํ•œ ๊ณต๋™์„ ์ด ์กด์žฌํ•œ๋‹ค.', '์ •์น˜ ์—˜๋ฆฌํŠธ๋Š” ๊ตญ๋ฏผ ์—ฌ๋ก ์œผ๋กœ ํ˜•์„ฑ๋œ ์ •์ฑ…์˜ ์ง‘ํ–‰์ž ์—ญํ• ๋งŒ์„ ํ•œ๋‹ค.'], 'answer': ''}",,1,1,True,"['๋ฏผ์ฃผ์ฃผ์˜(democracy)๋ผ๋Š” ๋ง์€ ๊ทธ๋ฆฌ์Šค์–ด democratia์—์„œ ๊ธฐ์›ํ•˜๋Š”๋ฐ โ€˜๋ฏผ์ค‘โ€™์ด๋ผ๋Š” ๋œป์˜ demos์™€ โ€˜ํ†ต์น˜โ€™๋ผ๋Š” ๋œป์˜ kratos๊ฐ€ ํ•ฉ์ณ์ง„ ๋ง์ด์—์š”. ๊ฒฐ๊ตญ ๋ฏผ์ฃผ์ฃผ์˜๋Š” โ€˜๋ฏผ์ค‘์— ์˜ํ•œ ํ†ต์น˜โ€™๋ผ๋Š” ๋œป์ธ๋ฐ 1์ธ ํ˜น์€ ์†Œ์ˆ˜์˜ ๋ช‡ ์‚ฌ๋žŒ์— ์˜ํ•ด ์ด๋ฃจ์–ด์ง€๋Š” ํ†ต์น˜๊ฐ€ ์•„๋‹ˆ๋ผ ์ผ๋ฐ˜ ๊ตญ๋ฏผ๋“ค์ด ๋ชจ๋‘ ์ฐธ์—ฌํ•˜์—ฌ ์Šค์Šค๋กœ๋ฅผ ํ†ต์น˜ํ•˜๋Š” ๊ฒƒ์„ ๋งํ•ฉ๋‹ˆ๋‹ค. ๋ฏธ๊ตญ์˜ ์œ ๋ช…ํ•œ 16๋Œ€ ๋Œ€ํ†ต๋ น ์•„๋ธŒ๋ผํ•จ ๋ง์ปจ์€ 1863๋…„ 11์›” 19์ผ ๊ฒŒํ‹ฐ์Šค๋ฒ„๊ทธ ์—ฐ์„ค์—์„œ โ€˜๊ตญ๋ฏผ์˜(of the people), ๊ตญ๋ฏผ์— ์˜ํ•œ(by the people), ๊ตญ๋ฏผ์„ ์œ„ํ•œ(for the people) ์ •์น˜โ€™ ๋ผ๋Š” ์œ ๋ช…ํ•œ ๋ง๋กœ ๋ฏผ์ฃผ์ฃผ์˜๋ฅผ ์ •์˜ํ•˜๊ธฐ๋„ ํ–ˆ์ง€์š”. ๋ฏผ์ฃผ์ฃผ์˜์˜ ์ข…๋ฅ˜ ๋ฏผ์ฃผ์ฃผ์˜๋Š” ์ง์ ‘๋ฏผ์ฃผ์ฃผ์˜์™€ ๊ฐ„์ ‘๋ฏผ์ฃผ์ฃผ์˜ ๋‘ ๊ฐ€์ง€๊ฐ€ ์žˆ์–ด์š”. ์ง์ ‘๋ฏผ์ฃผ์ฃผ์˜๋Š” ๋ง ๊ทธ๋Œ€๋กœ ์‚ฌํšŒ ๊ตฌ์„ฑ์›๋“ค์ด ์ง์ ‘ ๋ชจ์—ฌ ์˜๊ฒฌ์„ ๋‚˜๋ˆ„๊ณ  ์ •์ฑ…์„ ๊ฒฐ์ •ํ•˜๋Š” ๋ฏผ์ฃผ์ฃผ์˜๋ฅผ ๋œปํ•ฉ๋‹ˆ๋‹ค. ๊ณ ๋Œ€ ๊ทธ๋ฆฌ์Šค์—์„œ๋Š” ์ง์ ‘๋ฏผ์ฃผ์ฃผ์˜๋กœ ๊ตญ๊ฐ€์˜ ์ผ๋“ค์— ๋Œ€ํ•ด ๊ฒฐ์ •์„ ํ–ˆ์ง€์š”. ํ•˜์ง€๋งŒ ๊ตญ๊ฐ€์˜ ์˜ํ† ๊ฐ€ ๋„“์–ด์ง€๊ณ , ์ธ๊ตฌ๊ฐ€ ๋Š˜์–ด๋‚˜๋ฉด์„œ ๋ชจ๋“  ๊ตญ๋ฏผ์ด ํ•œ์ž๋ฆฌ์— ๋ชจ์ด๋Š” ๊ฒƒ์€ ๋ถˆ๊ฐ€๋Šฅํ•ด์กŒ์–ด์š”. ๊ทธ๋ž˜์„œ ์‚ฌ๋žŒ๋“ค์€ ์ž๊ธฐ๋“ค์˜ ์˜๊ฒฌ๊ณผ ์ด์ต์„ ์ž˜ ๋ฐ˜์˜ํ•ด ์ค„ ๋Œ€ํ‘œ๋ฅผ ๋ฝ‘๊ธฐ ์‹œ์ž‘ํ–ˆ์ฃ . ์ด๋ ‡๊ฒŒ ๋Œ€ํ‘œ๋ฅผ ํ†ตํ•ด ๊ฐ„์ ‘์ ์œผ๋กœ ์ •์น˜์— ์ฐธ์—ฌํ•˜๋Š” ๊ฒƒ์„ ๊ฐ„์ ‘๋ฏผ์ฃผ์ฃผ์˜๋ผ๊ณ  ํ•ฉ๋‹ˆ๋‹ค. ๊ฐ„์ ‘๋ฏผ์ฃผ์ฃผ์˜๋Š” ๋‹ค๋ฅธ ๋ง๋กœ ๋Œ€์˜๋ฏผ์ฃผ์ฃผ์˜ ๋˜๋Š” ์˜ํšŒ๋ฏผ์ฃผ์ฃผ์˜๋กœ ๋ถ€๋ฅด๊ธฐ๋„ ํ•œ๋‹ต๋‹ˆ๋‹ค. ๋ฏผ์ฃผ์ฃผ์˜์˜ ์ •์‹  ๋ฏผ์ฃผ์ฃผ์˜์—์„œ ์กด์ค‘๋˜๋Š” ์ •์‹ ์€ ๋ฌด์—‡์ผ๊นŒ์š”. ๋ฐ”๋กœ โ€˜์ธ๊ฐ„์— ๋Œ€ํ•œ ์กด์ค‘โ€™, โ€˜์ž์œ โ€™, โ€˜ํ‰๋“ฑโ€™ ์„ธ ๊ฐ€์ง€๋ž๋‹ˆ๋‹ค. ๋ฏผ์ฃผ์ฃผ์˜์˜ ์ •์‹  ์ค‘์—์„œ๋„ ๊ฐ€์žฅ ์ค‘์š”ํ•œ ๊ฒƒ์€ โ€˜์ธ๊ฐ„์— ๋Œ€ํ•œ ์กด์ค‘โ€™์ž…๋‹ˆ๋‹ค. ๋ชจ๋“  ์ธ๊ฐ„์ด ํƒœ์–ด๋‚  ๋•Œ๋ถ€ํ„ฐ ๊ฐ€์ง€๊ณ  ์žˆ๋Š” ์ธ๊ฐ„์˜ ์กด์—„์„ฑ์„ ์ธ์ •ํ•ด์•ผ ํ•œ๋‹ค๋Š” ๋œป์ด์—์š”. ๊ทธ๋ž˜์„œ ์–ด๋– ํ•œ ๊ฒฝ์šฐ๋ผ๋„ ์ธ๊ฐ„์˜ ์กด์—„์„ฑ์ด ์šฐ์„ ์ ์œผ๋กœ ์กด์ค‘๋˜๊ณ  ๋ณดํ˜ธ๋ฐ›์„ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค', '๋…์žฌ์˜ ๋œป์€ ""ํ™€๋กœ(็จ) ์žฌ๋‹จ(่ฃ)ํ•œ๋‹ค""๋Š” ๋œป์ด๋ผ ํ•œ๋‹ค. ""์ผ์ธ, ๋˜๋Š” ์ผ์ •ํ•œ ์ง‘๋‹จ""์ด ๋งˆ์Œ๋Œ€๋กœ ๊ฐ€์œ„์งˆํ•˜๋“ฏ ์ง€๋ฐฐ(ๆ”ฏ้…)ํ•œ๋‹ค๋Š” ๋œป์ด ๋‹ด๊ฒจ์žˆ๋‹ค. ๊ตญ๋ฏผ์ด ์ฃผ์ธ(ไธปไบบ)๋˜๊ธฐ ์–ด๋ ต๋‹ค. ๋ฏผ์ฃผ์ฃผ์˜ ์ œ๋„(ๅˆถๅบฆ) ํ•˜์—์„œ ๊ตญ๋ฏผ์„ ๋Œ€์‹ ํ•˜์—ฌ ๊ตญ์‚ฌ(ๅœ‹ไบ‹)๋ฅผ ๋งก์€ ์‚ฌ๋žŒ๋“ค์ด ๋” ์˜๋ฆฌ(๏ฆฌๆ‚ง)ํ•˜๋‹ค ํ•ด์„œ ์ฃผ์ธํ–‰์„ธ(ไธปไบบ่กŒไธ–)๋ผ๋„ ํ•œ๋‹ค๋ฉด ์ฐธ๋‹ด(ๆ…˜ๆ†บ)ํ•œ ์‚ฌ๊ฑด์ด ๋œ๋‹ค. โ€˜์ค‘์šฐ์ •์น˜(่ก†ๆ„šๆ”ฟๆฒป)โ€™๋ž€ โ€˜์ผ๋ฐ˜ ์‚ฌ๋žŒ๋“ค์ด ์–ด๋ฆฌ์„๊ธฐ ๋•Œ๋ฌธ์—, ๋˜‘๋˜‘ํ•œ ์‚ฌ๋žŒ๋“ค์ด ์œ„์ž„(ๅง”ไปป)์„ ๋ฐ›์•„ ๋Œ€์‹ (ไปฃ่บซ) ์ •์น˜๋ฅผ ํ•ด์•ผ ํ•œ๋‹คโ€™๋ผ๋Š” ๋œป์ด๋‹ค. ๊ตญ๊ฐ€์  ๋˜๋Š” ์ง€๊ตฌ์  ์˜์‚ฌ๊ฒฐ์ •(ๆ„ๆ€ๆฑบๅฎš)์„ ํ•  ๊ฒฝ์šฐ, ์˜์‚ฌ์†Œํ†ต(ๆ„ๆ€็–้€š)์ด ์–ด๋ ต๊ธฐ ๋•Œ๋ฌธ์—, ํ”ผ์ง€๋ฐฐ์ž(่ขซๆ”ฏ้…่€…)์ธ ์ง€๊ตฌ์ธ์„ ๋ฉธ์‹œ(่”‘่ฆ–)ํ•œ๋‹ค๋ฉด ์˜ณ์ง€ ์•Š๋‹ค. ๋ฏผ์ฃผ์ฃผ์˜ ์ œ๋„์˜ ๋Œ€๋ช…์ œ(ๅคงๅ‘ฝ้กŒ) โ€œ๋ฏผ์ฃผ์ฃผ์˜๋Š” ๊ตญ๋ฏผ(ๅœ‹ๆฐ‘)์„ ์œ„ํ•œ, ๊ตญ๋ฏผ(ๅœ‹ๆฐ‘)์— ์˜ํ•œ, ๊ตญ๋ฏผ(ๅœ‹ๆฐ‘)์˜ ์ •์น˜โ€๊ฐ€ ๋งž๋‹ค. ์ •์น˜ ํ™œ๋™์ด ์ฐจ๋‹จ(้ฎๆ–ท)๋˜๊ณ , ๋ฒ•๊ณผ ์ œ๋„๊ฐ€ ๋ฐ˜๊ตญ๋ฏผ์ (ๅๅœ‹ๆฐ‘็š„)์ด๋ผ๋ฉด ๊ตญ๋ฏผ ์ฐธ์—ฌ๊ฐ€ ๋‹น์—ฐ(็•ถ็„ถ)ํ•˜๊ฒŒ ์‹ค์งˆ์ (ๅฏฆ่ณช็š„)์œผ๋กœ ์š”๊ตฌ(่ฆๆฑ‚)๋œ๋‹ค. ์ •์น˜๊ฐ€, ํŒจ๊ถŒ ์ถฉ๋Œ๋กœ ์ธํ•ด์„œ, ์ธ๋ฅ˜(ไบบ้กž) ์ƒ์กด(็”Ÿๅญ˜)์„ ์œ„ํ˜‘(ๅจ่„…)ํ•˜๋Š” ์ง€๊ตฌ(ๅœฐ็ƒ) ํŒŒ๊ดด(็ ดๅฃž) ๋“ฑ๊ณผ ๊ฐ™์€ ์œ„๊ธฐ(ๅฑๆฉŸ)์˜ ๋ฌธ์ œ๋ฅผ ์™ธ๋ฉดํ•œ๋‹ค๋ฉด, ์ธ๋ฅ˜ ์ฐธ์—ฌ๊ฐ€ ์ ˆ์‹ค(ๅˆ‡ๅฏฆ)ํ•˜๊ฒŒ ์š”๊ตฌ๋œ๋‹ค. ํ˜„๋Œ€ ์‚ฌํšŒ์˜ โ€˜๋Œ€์˜์ •์น˜(ไปฃ่ญฐๆ”ฟๆฒป)โ€™๋ž€, ๊ตญ๋ฏผ์ด ์œ„์ž„ํ•˜์—ฌ, ์ •์น˜๋ฅผ ๋Œ€์‹ (ไปฃ่บซ) ๋งก๊ฒจ ๋†“์•˜๋‹ค๋Š” ์˜๋ฏธ๊ฐ€ ํฌ๊ธฐ ๋•Œ๋ฌธ์—, ๊ตญ๋ฏผ์„ ์™ธ๋ฉด(ๅค–้ข) ๋ฐฉ์น˜(ๆ”พ็ฝฎ)ํ•˜๊ณ  ์‚ฌ์ต(็ง็›Š)์„ ์ทจํ•˜๋ฉฐ ๋งˆ์Œ๋Œ€๋กœ ํ•˜๋ผ๋Š” ์˜๋ฏธ(ๆ„ๅ‘ณ)๋Š” ์ ˆ๋Œ€(็ตถไปฃ) ์•„๋‹ˆ๋‹ค', '๋ฏผ์ฃผ์ฃผ์˜์—์„œ ๋‹น์„ ์ž๋Š” ๋‹ค๋ฅธ ์„ ํƒ๋ณด๋‹ค ๋” ๋งŽ์€ ์œ ๊ถŒ์ž๊ฐ€ ํˆฌํ‘œ๋ฅผ ์„ ํƒํ•˜๊ณ  ๊ทธ ํ›„๋ณด์—๊ฒŒ ํˆฌํ‘œํ•˜๊ธฐ ๋•Œ๋ฌธ์— ์Šน๋ฆฌํ•œ๋‹ค. ๋ฏผ์ฃผ์ฃผ์˜์˜ ์ •์˜๋Š” ๋ฐ”๋กœ ๋ฏผ์ฃผ์ฃผ์˜๊ฐ€ ๊ตญ๋ฏผ์ด ์ž์‹ ์˜ ์ง€๋„์ž๋ฅผ ์„ ํƒํ•  ์ˆ˜ ์žˆ๋Š” ๋Šฅ๋ ฅ ๋˜๋Š” ๊ฒฝ์šฐ์— ๋”ฐ๋ผ ๊ทธ๋“ค์ด ์ฑ„ํƒํ•  ์ •์ฑ…์„ ์„ ํƒํ•  ์ˆ˜ ์žˆ๋Š” ์ •๋ถ€ ํ˜•ํƒœ๋ผ๋Š” ๊ฒƒ์ด๋‹ค.']",1 +2025-YS-09,"๊ฐ‘ : ํ˜ธ์—ฐ์ง€๊ธฐ(ๆตฉ็„ถไน‹ๆฐฃ)๋Š” ์ง€๊ทนํžˆ ํฌ๊ณ  ๊ฐ•ํ•˜๋ฉฐ, ๋„(้“)์™€ ์˜(็พฉ)๋ฅผ ์ง์œผ๋กœ ์‚ผ๋Š”๋‹ค. ์ด๊ฒƒ์€ ์˜๊ฐ€ ์Œ“์—ฌ์„œ[้›†็พฉ] ์ด๋ฃจ์–ด์ง€๋Š” ๊ฒƒ์ด์ง€ ํ•œ ๋ฒˆ์˜ ์˜๋กœ์šด ํ–‰๋™์œผ๋กœ ์ƒ๊ฒจ๋‚˜๋Š” ๊ฒƒ์ด ์•„๋‹ˆ๋‹ค. ์„ : ์ขŒ๋ง(ๅๅฟ˜)์€ ์ž์‹ ์˜ ์œก์‹ ๊ณผ ์ด๋ช…(่ฐๆ˜Ž)์„ ๋ชจ๋‘ ๋ฌด๋„ˆ๋œจ๋ ค์„œ ๊ทธ ํ˜•์ฒด์™€ ์ง€์‹์„ ์™„์ „ํžˆ ๋– ๋‚˜๋Š” ๊ฒƒ์ด๋‹ค. ๊ทธ๋ฆฌํ•˜์—ฌ ๋„์™€ ํ•˜๋‚˜๋กœ ์œตํ•ฉ๋œ ์ƒํƒœ์ด๋‹ค.","{'question': '๊ณ ๋Œ€ ๋™์–‘ ์‚ฌ์ƒ๊ฐ€ ๊ฐ‘, ์„์˜ ์ž…์žฅ์œผ๋กœ ์ ์ ˆํ•œ ๊ฒƒ๋งŒ์„ <๋ณด๊ธฐ>์—์„œ ์žˆ๋Š” ๋Œ€๋กœ ๊ณ ๋ฅธ ๊ฒƒ์€? ', 'choices': ['ใ„ฑ,ใ„ด', 'ใ„ด,ใ„ท', 'ใ„ท,ใ„น', 'ใ„ฑ,ใ„ด,ใ„น', 'ใ„ฑ,ใ„ท,ใ„น'], 'answer': '', 'question_plus': 'แ„€. ๊ฐ‘ : ๋Œ€์ธ(ๅคงไบบ)๋„ ์‚ถ์ด ๊ถํ•ํ•ด์ง€๋ฉด ์–‘์ง€(่‰ฏ็Ÿฅ)๋ฅผ ์žƒ๊ฒŒ ๋œ๋‹ค. แ„‚. ๊ฐ‘: ์ฒœ๋ช…์„ ๋ฐ›์€ ๊ตฐ์ฃผ๋Š” ๋ฏผ์‹ฌ์„ ์žƒ์–ด๋„ ๊ฒฐ์ฝ” ์ซ“์•„๋‚ด์„œ๋Š” ์•ˆ ๋œ๋‹ค. แ„ƒ. ์„ : ๋ˆ„๊ตฌ๋‚˜ ๋งˆ์Œ์„ ํ…… ๋น„์šฐ๋ฉด[ๅฟƒ้ฝ‹] ์„ฑ์ธ(่–ไบบ)์ด ๋  ์ˆ˜ ์žˆ๋‹ค. แ„…. ๊ฐ‘๊ณผ ์„ : ๋ณธ์„ฑ์„ ๋ณด์กดํ•˜๋ ค๋ฉด ์‚ฌ์‚ฌ๋กœ์šด ์š•์‹ฌ์„ ์ค„์—ฌ์•ผ ํ•œ๋‹ค.'}",แ„€. ๊ฐ‘ : ๋Œ€์ธ(ๅคงไบบ)๋„ ์‚ถ์ด ๊ถํ•ํ•ด์ง€๋ฉด ์–‘์ง€(่‰ฏ็Ÿฅ)๋ฅผ ์žƒ๊ฒŒ ๋œ๋‹ค. แ„‚. ๊ฐ‘: ์ฒœ๋ช…์„ ๋ฐ›์€ ๊ตฐ์ฃผ๋Š” ๋ฏผ์‹ฌ์„ ์žƒ์–ด๋„ ๊ฒฐ์ฝ” ์ซ“์•„๋‚ด์„œ๋Š” ์•ˆ ๋œ๋‹ค. แ„ƒ. ์„ : ๋ˆ„๊ตฌ๋‚˜ ๋งˆ์Œ์„ ํ…… ๋น„์šฐ๋ฉด[ๅฟƒ้ฝ‹] ์„ฑ์ธ(่–ไบบ)์ด ๋  ์ˆ˜ ์žˆ๋‹ค. แ„…. ๊ฐ‘๊ณผ ์„ : ๋ณธ์„ฑ์„ ๋ณด์กดํ•˜๋ ค๋ฉด ์‚ฌ์‚ฌ๋กœ์šด ์š•์‹ฌ์„ ์ค„์—ฌ์•ผ ํ•œ๋‹ค.,3,4,False,[],3 +2025-YS-10,๊ฐ‘ : ์šฐ๋ฆฌ๋Š” ์ž์œ ๋ฅผ ์ ˆ๋Œ€์  ์˜๋ฏธ์—์„œ ๋ฌด์ œํ•œ์ ์œผ๋กœ ๋ˆ„๋ฆด ์ˆ˜ ์—†๋‹ค. ์ž์œ ๋ฅผ ๋ˆ„๋ฆฌ๊ธฐ ์œ„ํ•ด์„œ๋Š” ์ผ์ • ๋ถ€๋ถ„์„ ํฌ๊ธฐํ•ด์•ผ ํ•œ๋‹ค. ์ด๋•Œ ์šฐ๋ฆฌ๊ฐ€ ์˜นํ˜ธํ•ด์•ผ ํ•  ์ž์œ ๋Š” ์–ธ์ œ๋‚˜ โ€˜โˆผ๋กœ๋ถ€ํ„ฐ์˜ ์ž์œ โ€™์ด๋‹ค. ์„ : ์šฐ๋ฆฌ์˜ ๊ถŒ๋ฆฌ๋Š” ์—ญ์‚ฌ์ ์ธ ๊ฒƒ์ด์ง€ ์ž์—ฐ์ ์ธ ๊ฒƒ์ด ์•„๋‹ˆ๊ธฐ์— ๊ด€์Šต๊ณผ ๋ฒ•์ด ์ธ์ •ํ•  ๋•Œ๋งŒ ๊ถŒ๋ฆฌ๊ฐ€ ๋œ๋‹ค. ๋ฒ•์˜ ์ง€๋ฐฐํ•˜์—์„œ๋Š” ๋ˆ„๊ตฌ๋„ ์ž์˜์  ์˜์‚ฌ๋ฅผ ํƒ€์ธ์—๊ฒŒ ๊ฐ•์š”ํ•  ์ˆ˜ ์—†๊ฒŒ ๋œ๋‹ค.,"{'question': '์‚ฌํšŒ ์‚ฌ์ƒ๊ฐ€ ๊ฐ‘, ์„์ด ๊ณตํ†ต์œผ๋กœ ์ง€์ง€ํ•  ์ž…์žฅ์œผ๋กœ ๊ฐ€์žฅ ์ ์ ˆํ•œ ๊ฒƒ์€?', 'choices': ['๊ฐœ์ธ์ด ์ง€๋‹Œ ์ฒœ๋ถ€์ ์ธ ๊ถŒ๋ฆฌ๋ฅผ ๋ณด์žฅํ•˜๊ธฐ ์œ„ํ•œ ์ œ๋„๊ฐ€ ํ•„์š”ํ•˜๋‹ค.', '๋น„์ž์˜์  ๊ถŒ๋ ฅ์˜ ์กด์žฌ๋Š” ์–ธ์ œ๋‚˜ ๊ฐœ์ธ์˜ ์ž์œ ๋ฅผ ์นจํ•ดํ•˜๊ฒŒ ๋œ๋‹ค.', '๊ฐœ์ธ์˜ ์ž์œ ๋Š” ํƒ€์ธ์— ์˜ํ•œ ๊ฐ„์„ญ์˜ ๋ถ€์žฌ๋งŒ์œผ๋กœ๋„ ์‹คํ˜„๋  ์ˆ˜ ์žˆ๋‹ค.', '๋ฒ•์˜ ์ง€๋ฐฐํ•˜์— ๊ฐœ์ธ์˜ ํ–‰์œ„๋ฅผ ์ œ์•ฝํ•˜๋Š” ๊ฐœ์ž…์ด ํ—ˆ์šฉ๋  ์ˆ˜ ์žˆ๋‹ค.', '๊ฐœ์ธ์˜ ์ •์น˜์  ์ž์œ ๋ฅผ ์œ„ํ•ด ์ •ํ˜•ํ™”๋œ ์‚ถ์˜ ๋ฐฉ์‹์„ ๊ฐ•์กฐํ•ด์•ผ ํ•œ๋‹ค.'], 'answer': ''}",,4,4,True,[],4 +2025-YS-11,"๊ฐ‘ : ์ธ๊ฐ„์€ ์ž์—ฐ์˜ ์‚ฌ์šฉ์ž ๋ฐ ํ•ด์„์ž์ด๋‹ค. ์ธ๊ฐ„์€ ์ž์—ฐ์˜ ์งˆ์„œ์— ๋Œ€ํ•ด ์‹ค์ œ๋กœ ๊ด€์ฐฐํ•˜๊ณ  ๊ณ ์ฐฐํ•œ ๋งŒํผ๋งŒ ๋ฌด์—‡์ธ๊ฐ€๋ฅผ ํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ ์ดํ•ดํ•  ์ˆ˜ ์žˆ๋‹ค. ์„ : ๊ณต๊ฐ์€ ๋„๋•์  ๊ตฌ๋ณ„์˜ ์›์ฒœ์ด๋‹ค. ํ•˜์ง€๋งŒ ์šฐ๋ฆฌ๋Š” ์ž์‹ ๊ณผ ๊ฑฐ๋ฆฌ๊ฐ€ ๋จผ ์‚ฌ๋žŒ๋ณด๋‹ค ๊ฐ€๊นŒ์šด ์‚ฌ๋žŒ, ๋‚ฏ์„  ์‚ฌ๋žŒ๋ณด๋‹ค ์นœ์ˆ™ํ•œ ์‚ฌ๋žŒ์—๊ฒŒ ๋”์šฑ ์ž˜ ๊ณต๊ฐํ•œ๋‹ค. ๊ทธ๋ž˜์„œ ๊ณตํ‰ํ•œ ๊ด€์ฐฐ์ž์˜ ๊ด€์ ์ด ์š”๊ตฌ๋œ๋‹ค.","{'question': '๊ทผ๋Œ€ ์„œ์–‘ ์‚ฌ์ƒ๊ฐ€ ๊ฐ‘, ์„์˜ ์ž…์žฅ์œผ๋กœ ์ ์ ˆํ•œ ๊ฒƒ๋งŒ์„ <๋ณด๊ธฐ>์—์„œ ๊ณ ๋ฅธ ๊ฒƒ์€?', 'choices': ['ใ„ฑ,ใ„ด', 'ใ„ฑ,ใ„ท', 'ใ„ด,ใ„ท', 'ใ„ด,ใ„น', 'ใ„ท,ใ„น'], 'answer': '', 'question_plus': 'แ„€. ๊ฐ‘: ์ผ๋ฐ˜์  ์ง„๋ฆฌ๋Š” ์ถ”๋ก  ์—†์ด ๊ฒฝํ—˜์—์„œ ๋„์ถœ๋˜์–ด์•ผ ํ•œ๋‹ค. แ„‚. ์„ : ๊ณต๊ฐ๋„ ํŽธํ–ฅ๋  ์ˆ˜ ์žˆ์œผ๋ฏ€๋กœ ๋ฐ˜์„ฑ๊ณผ ๊ต์ •์„ ํ•„์š”๋กœ ํ•œ๋‹ค. แ„ƒ. ์„: ์ด์„ฑ์€ ๊ฐ์ •๊ณผ ๋ณ‘ํ–‰ํ•˜์—ฌ ์ •๋…์„ ์‹คํ–‰ํ•˜๋Š” ์˜์ง€๋ฅผ ์ด‰๋ฐœํ•œ๋‹ค. แ„…. ๊ฐ‘๊ณผ ์„: ์ง€์‹์˜ ํ™•์žฅ์—๋Š” ์ž์—ฐ์— ๋Œ€ํ•œ ๊ด€์ฐฐ์ด๋‚˜ ์‹คํ—˜์ด ์š”๊ตฌ๋œ๋‹ค.'}",แ„€. ๊ฐ‘: ์ผ๋ฐ˜์  ์ง„๋ฆฌ๋Š” ์ถ”๋ก  ์—†์ด ๊ฒฝํ—˜์—์„œ ๋„์ถœ๋˜์–ด์•ผ ํ•œ๋‹ค. แ„‚. ์„ : ๊ณต๊ฐ๋„ ํŽธํ–ฅ๋  ์ˆ˜ ์žˆ์œผ๋ฏ€๋กœ ๋ฐ˜์„ฑ๊ณผ ๊ต์ •์„ ํ•„์š”๋กœ ํ•œ๋‹ค. แ„ƒ. ์„: ์ด์„ฑ์€ ๊ฐ์ •๊ณผ ๋ณ‘ํ–‰ํ•˜์—ฌ ์ •๋…์„ ์‹คํ–‰ํ•˜๋Š” ์˜์ง€๋ฅผ ์ด‰๋ฐœํ•œ๋‹ค. แ„…. ๊ฐ‘๊ณผ ์„: ์ง€์‹์˜ ํ™•์žฅ์—๋Š” ์ž์—ฐ์— ๋Œ€ํ•œ ๊ด€์ฐฐ์ด๋‚˜ ์‹คํ—˜์ด ์š”๊ตฌ๋œ๋‹ค.,4,4,True,[],4 +2025-YS-12,"๊ฐ‘: ํ‰ํ™”๋Š”๊ตญ๊ฐ€๊ฐ„์˜๊ณ„์•ฝ์—†์ด๋Š”๊ตฌ์ถ•๋ ์ˆ˜๋„์—†๊ณ ๋ณด์žฅ๋  ์ˆ˜๋„ ์—†๋‹ค. ์˜์›ํ•œ ํ‰ํ™”๋ฅผ ์œ„ํ•ด์„œ๋Š” ํŠน๋ณ„ํ•œ ์ข…๋ฅ˜์˜ ์—ฐ๋งน์ด ํ•„์š”ํ•˜๋‹ค. ํ‰ํ™” ์กฐ์•ฝ์— ์˜ํ•ด ์ „์Ÿ์€ ์ผ์‹œ์ ์œผ๋กœ ์ค‘๋‹จ๋  ์ˆ˜ ์žˆ์ง€๋งŒ ์ „์Ÿ ์ƒํƒœ๊ฐ€ ์˜์›ํžˆ ์ข…์‹๋˜์ง€๋Š” ์•Š๋Š”๋‹ค. ์„ : ํ‰ํ™”๋Š” ์–ด๋– ํ•œ ํญ๋ ฅ๋„ ์—†๋Š” ์ƒํƒœ์ด๋ฉฐ ๋น„ํญ๋ ฅ์  ๋ฐฉ์‹์œผ๋กœ ์‹คํ˜„ํ•ด ๋‚˜๊ฐ€์•ผ ํ•œ๋‹ค. ํญ๋ ฅ์„ ์˜ˆ๋ฐฉํ•˜๊ณ  ์ œ๊ฑฐํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์ง์ ‘์  ํญ๋ ฅ, ๊ตฌ์กฐ์  ํญ๋ ฅ, ๊ทธ๋ฆฌ๊ณ  ๋ฌธํ™”์  ํญ๋ ฅ์— ๋Œ€ํ•œ ์ •ํ™•ํ•œ ์ง„๋‹จ๊ณผ ์ฒ˜๋ฐฉ์ด ํ•„์š”ํ•˜๋‹ค.","{'question': '์‚ฌํšŒ์‚ฌ์ƒ๊ฐ€ ๊ฐ‘, ์„์˜ ์ž…์žฅ์œผ๋กœ ์ ์ ˆํ•œ ๊ฒƒ๋งŒ์„ <๋ณด๊ธฐ>์—์„œ ์žˆ๋Š” ๋Œ€๋กœ ๊ณ ๋ฅธ ๊ฒƒ์€?', 'choices': ['ใ„ฑ,ใ„ด', 'ใ„ฑ,ใ„ท', 'ใ„ด,ใ„น', 'ใ„ฑ,ใ„ท,ใ„น', 'ใ„ด,ใ„ท,ใ„น'], 'answer': '', 'question_plus': 'แ„€. ๊ฐ‘: ๊ฐ ๊ตญ๊ฐ€๋Š” ์ž์œ  ๋ณด์žฅ์„ ์œ„ํ•ด ํ‰ํ™” ์—ฐ๋งน์— ์ฃผ๊ถŒ์„ ์œ„์ž„ํ•ด์•ผ ํ•œ๋‹ค. แ„‚. ์„: ๊ฒฝ์ œ์  ๋ถˆํ‰๋“ฑ์— ์˜ํ•œ ์ธ๊ฐ„ ์†Œ์™ธ๋„ ํ‰ํ™” ์‹คํ˜„์„ ๋ฐฉํ•ดํ•œ๋‹ค. แ„ƒ. ์„: ํญ๋ ฅ ์ฃผ์ฒด์˜ ์˜๋„์„ฑ์ด ์—†๋Š” ๊ฒฝ์šฐ์—๋Š” ํญ๋ ฅ์ด ์„ฑ๋ฆฝ๋˜์ง€ ์•Š๋Š”๋‹ค. แ„…. ๊ฐ‘๊ณผ ์„: ํ‰ํ™”๋ฅผ ์œ„ํ•œ ์˜ˆ๋ฐฉ์  ์ฒ˜๋ฐฉ ์—†์ด ์ง„์ •ํ•œ ํ‰ํ™”๋Š” ๋ถˆ๊ฐ€๋Šฅํ•˜๋‹ค.'}",แ„€. ๊ฐ‘: ๊ฐ ๊ตญ๊ฐ€๋Š” ์ž์œ  ๋ณด์žฅ์„ ์œ„ํ•ด ํ‰ํ™” ์—ฐ๋งน์— ์ฃผ๊ถŒ์„ ์œ„์ž„ํ•ด์•ผ ํ•œ๋‹ค. แ„‚. ์„: ๊ฒฝ์ œ์  ๋ถˆํ‰๋“ฑ์— ์˜ํ•œ ์ธ๊ฐ„ ์†Œ์™ธ๋„ ํ‰ํ™” ์‹คํ˜„์„ ๋ฐฉํ•ดํ•œ๋‹ค. แ„ƒ. ์„: ํญ๋ ฅ ์ฃผ์ฒด์˜ ์˜๋„์„ฑ์ด ์—†๋Š” ๊ฒฝ์šฐ์—๋Š” ํญ๋ ฅ์ด ์„ฑ๋ฆฝ๋˜์ง€ ์•Š๋Š”๋‹ค. แ„…. ๊ฐ‘๊ณผ ์„: ํ‰ํ™”๋ฅผ ์œ„ํ•œ ์˜ˆ๋ฐฉ์  ์ฒ˜๋ฐฉ ์—†์ด ์ง„์ •ํ•œ ํ‰ํ™”๋Š” ๋ถˆ๊ฐ€๋Šฅํ•˜๋‹ค.,3,1,False,[],1 +2025-YS-13,"์‹ (่‡ฃ)์ด ์ž„๊ธˆ๋‹˜๊ป˜ ์‚ผ๊ฐ€ ์•„๋ขฐ์˜ต๋‹ˆ๋‹ค. ๋งˆ์Œ์† ์ธก์€์ง€์‹ฌ์„ ์ด๋Œ์–ด ๊ธฐ๋ฅด๋ฉด ์–ด์ง„ ์ •์น˜๋ฅผ ํ–‰ํ•  ์ˆ˜ ์žˆ์œผ๋‹ˆ, ์ธก์€์ง€์‹ฌ์ด ์–ด์ง„ ์ •์น˜์˜ ์‹œ์ž‘์ด ์•„๋‹ˆ๊ฒ ์Šต๋‹ˆ๊นŒ? ์ด๋ฅผ ์‹ค์— ๋น„์œ ํ•˜๋ฉด ์ธก์€์ง€์‹ฌ์€ ์‹ค๋ญ‰์น˜์™€ ๊ฐ™๊ณ  ์ด๊ฒƒ์„ ํ’€์–ด๋‚ด๋ฉด ํšจ์ œ(ๅญๆ‚Œ)๋ฅผ ํ•  ์ˆ˜ ์žˆ์œผ๋‹ˆ, ์–ด๋А ๊ฒƒ์ด ๊ทผ๋ณธ์ด๊ณ  ๋ง๋‹จ์ด๊ฒ ์Šต๋‹ˆ๊นŒ? ๋งน์ž๊ฐ€ ์ง์ ‘ ์‚ฌ๋‹จ์„ ""๋ถˆ์ด ์ฒ˜์Œ ํƒ€์˜ค๋ฅด๊ณ  ์ƒ˜๋ฌผ์ด ์ฒ˜์Œ ์†Ÿ์•„๋‚˜๋Š” ๊ฒƒ๊ณผ ๊ฐ™๋‹ค.""๋ผ๊ณ  ํ’€์ดํ–ˆ์œผ๋‹ˆ, '๋‹จ(็ซฏ)'์ด '์‹œ์ž‘[ๅง‹]'์ž„์ด ๋ถ„๋ช…ํ•ฉ๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋œป์„ ์ƒ๊ฐํ•˜์‹œ์–ด ๋ถ€๋”” ์–ด์ง„ ์ •์น˜๋ฅผ ๋ฒ ํ’€์–ด ์ฃผ์‹œ๊ธธ ๊ฐ„์ฒญํ•˜์˜ต๋‹ˆ๋‹ค.","{'question': '๋‹ค์Œ ๊ฐ€์ƒ ํŽธ์ง€๋ฅผ ์“ด ํ•œ๊ตญ ์œ ๊ต ์‚ฌ์ƒ๊ฐ€์˜ ์ž…์žฅ์œผ๋กœ ๊ฐ€์žฅ ์ ์ ˆํ•œ ๊ฒƒ์€?', 'choices': ['๋ถ€๋ชจ์—๊ฒŒ ํšจ๋„ํ•˜๊ณ  ํ˜•์„ ๊ณต๊ฒฝํ•˜๋Š” ๋•์ด ์ธก์€์ง€์‹ฌ์˜ ๊ทผ๋ณธ์ด๋‹ค.', 'ํ˜•๊ตฌ(ๅฝข่ป€)์˜ ๊ธฐํ˜ธ์—๋Š” ์–ด๋– ํ•œ ๋„๋•์  ์š•๊ตฌ๋„ ๋‚ด์žฌ๋˜์–ด ์žˆ์ง€ ์•Š๋‹ค.', '์ธ๊ฐ„๋งŒ์ด ์ง€๋‹Œ ์„ฑ(ๆ€ง)์€ ๊ธฐ์งˆ์— ๋”ฐ๋ผ ์„ ์•…์˜ ํ–ฅ๋ฐฉ์ด ๋ณ€ํ•  ์ˆ˜ ์žˆ๋‹ค.', '์„ ์„ ์ข‹์•„ํ•˜๋Š” ๋งˆ์Œ์€ ์ธ๊ฐ„์—๊ฒŒ ์ž์œ  ์˜์ง€๋กœ ๋ถ€์—ฌ๋œ ์ฒœ๋ช…(ๅคฉๅ‘ฝ)์ด๋‹ค.', '์‹œ๋น„์ง€์‹ฌ์„ ์‹œ์ž‘์œผ๋กœ ์‚ผ์•„ ํ™•์ถฉํ•˜๋ฉด ์„ฑ์œผ๋กœ์„œ ์ง€(ๆ™บ)๋ฅผ ํ˜•์„ฑํ•  ์ˆ˜ ์žˆ๋‹ค.'], 'answer': ''}",,2,5,False,"['โ€˜์„ โ€™์€ โ€˜์–ธ(่จ€)โ€™๊ณผ โ€˜์–‘(็พŠ)โ€™์ด ํ•ฉํ•˜์—ฌ ๋œ ๊ธ€์ž์ด๋‹ค. ์„ค๋ฌธํ•ด์ž(่ชชๆ–‡่งฃๅญ—)์—์„œ๋Š” ๊ทธ๊ฒƒ์„ โ€˜์˜(็พฉ)๋‚˜ ๋ฏธ(็พŽ)์™€ ๊ฐ™์€ ๋œปโ€™์œผ๋กœ ๋ณด๊ณ  ์žˆ๋‹ค. ๋งน์ž์˜ ์„ฑ์„ ์„ค์— ์žˆ์–ด์„œ ์„ฑ์„ ์ด๋ผ ํ•จ์€ ์ธ๊ฐ„ ๊ณ ์œ ์˜ ๋ณธ์งˆ๋กœ์„œ ์ธ์„ฑ(ไบบๆ€ง)์ด ์„ ํ•˜๋‹ค๋Š” ๊ฒƒ์ด๋ฉฐ ํ›„์ฒœ์ ์œผ๋กœ ์ธ๊ฐ„์˜ ๊ฒฝํ—˜์— ์˜ํ•˜์—ฌ ์ขŒ์šฐ ๋˜๋Š” ๋ฐ”์˜ ์„ฑ์„ ์ด๋ผ๋Š” ๋œป์ด ์•„๋‹ˆ๋‹ค. ๊ทธ๋Ÿฌ๋ฏ€๋กœ ์ด ์„ ์€ ์œค๋ฆฌ์ (ๅ€ซ็†็š„) ์˜๋ฏธ์— ์žˆ์–ด์„œ๋‚˜ ์‹ฌ๋ฆฌ์ ์ธ ์ž‘์šฉ์— ์žˆ์–ด์„œ ์ž‘์šฉ๋˜๋Š” ์„ ์ด๋ผ๊ธฐ๋ณด๋‹ค ๋„๋• ๊ธฐ๋Šฅ(้“ๅพทๅฏ่ƒฝ)์˜ ๊ทธ๊ฑฐ๋กœ์„œ์˜ ์„ฑ์ด๋ฉฐ ์„ ์ด๋‹ค. ์ธก์€์˜ ๋งˆ์Œ์€ ์ด(ไป)์˜ ๋‹จ(็ซฏ)์ด๊ณ  ์ˆ˜์˜ค์˜ ๋งˆ์Œ์€ ์˜(็พฉ)์˜ ๋‹จ์ด๊ณ  ์‚ฌ์–‘์˜ ๋งˆ์Œ์€ ์˜ˆ(็ฆฎ)์˜ ๋‹จ์ด๊ณ  ์‹œ๋น„์˜ ๋งˆ์Œ์€ ์ง€(ๆ™บ)์˜ ๋‹จ์ด๋‹ค. ์‚ฌ๋žŒ์€ ๋ˆ„๊ตฌ์—๊ฒŒ๋‚˜ ๋‹ค ์ด ์‚ฌ๋‹จ(ๅ››็ซฏ)์ด ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋ฏ€๋กœ ์‚ฌ๋žŒ์€ ๋‹ค ์„ ํ•œ ์„ฑ์„ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. ๊ทธ๋Š” โ€˜์„ฑ(่ช )์€ ํ•˜๋Š˜์˜ ๋„(้“)์ด๊ณ , ์„ฑ์„ ์ƒ๊ฐํ•˜๋Š” ๊ฒƒ์€ ์‚ฌ๋žŒ์˜ ๋„์ด๋‹ค.โ€™๋ผ๊ณ  ํ–ˆ๋Š”๋ฐ, ์ด๊ฒƒ์€ ์ค‘์šฉ(ไธญๅบธ)์—์„œโ€˜์„ฑ์€ ํ•˜๋Š˜์˜๋„์š”, ์„ฑํ•˜๋Š” ๊ฒƒ์€ ์‚ฌ๋žŒ์˜ ๋„์ด๋‹ค.โ€™๋ผ๊ณ  ํ•œ ๊ฒƒ๊ณผ ๊ฐ™์ด ์‚ฌ๋žŒ์˜ ๋ณธ์„ฑ์€ ์„ฑ(่ช )์œผ๋กœ์„œ์˜ ์„ ์ด๋ผ๊ณ  ๋‹จ์–ธํ•œ ๊ฒƒ์ด๋‹ค. ์ˆœ์ž(่€ๅญ)๋Š” ์ธ๊ฐ„์˜ ๋ณธ์„ฑ์€ ์•…ํ•˜๋‹ค ๊ณ  ํ–ˆ๋‹ค. ์„ฑ์•…(ๆ€งๆƒก)์—์„œ โ€˜์‚ฌ๋žŒ์˜ ์„ฑ(ๆ€ง)์€ ์•…์ด๋‹ˆ, ๊ทธ ์„ (ๅ–„)์ด๋ž€ ๊ฒƒ์€ ์œ„(็ˆฒ)์ด๋‹ค.โ€™๋ผ๊ณ  ํ•˜์˜€๋‹ค. ์—ฌ๊ธฐ์„œ โ€˜์œ„โ€™๋ž€ ์ธ์œ„๋ฅผ ์˜๋ฏธํ•˜๋ฉฐ, ์ˆœ์ž๋Š” ์„ฑ์ด๋ž€ ์ฒœ์„ฑ(ๅคฉๆ€ง)์œผ๋กœ ์‚ฌ๋žŒ์ด ๋ฐฐ์šธ ์ˆ˜๋„ ์—†๊ณ , ์ผ์‚ผ์„(ไบ‹) ์ˆ˜๋„ ์—†๋Š” ๊ฒƒ์ด๋ฉด์„œ ์‚ฌ๋žŒ์— ์žˆ๋Š” ๊ฒƒ์ด๋ฉฐ, ๋˜ํ•œ ๋ฐฐ์›Œ์„œ ๋Šฅ(่ƒฝ)ํ•˜๊ณ  ์ผ์‚ผ์•„์„œ ์ด๋ฃฐ ์ˆ˜ ์žˆ๋Š” ๊ฒƒ์œผ๋กœ์„œ ์‚ฌ๋žŒ์—๊ฒŒ ์žˆ๋Š” ๊ฒƒ์ด ์œ„๋ผ๊ณ  ํ•˜์˜€๋‹ค. ์‚ฌ๋žŒ์˜ ์„ฑ์€ ๋‚˜๋ฉด์„œ๋ถ€ํ„ฐ ์ด(ๅˆฉ)๋ฅผ ์ข‹์•„ํ•˜๋ฏ€๋กœ ์Ÿํƒˆ์ด ์ƒ๊ธฐ๊ณ  ์‚ฌ์–‘์ด ์—†์–ด์ง€๋ฉฐ, ์‚ฌ๋žŒ์€ ๋‚˜๋ฉด์„œ๋ถ€ํ„ฐ ๋ฏธ์›Œํ•˜๋Š” ๋งˆ์Œ์ด ์žˆ์œผ๋ฏ€๋กœ ์ž”์ (ๆฎ˜่ณŠ)์ด ์ƒ๊ธฐ๊ณ  ์ถฉ์‹ (ๅฟ ่‡ฃ)์ด ์—†์–ด์ง„๋‹ค๊ณ  ํ•œ๋‹ค', '๋ฐ”๋กœ ์ด๋Ÿฐ ๋งˆ์Œ ๋•Œ๋ฌธ์— ์‚ฌ๋žŒ๋“ค์€ ์•„์ด๋ฅผ ๊ตฌํ•˜๋ ค๊ณ  ํ•œ๋‹ค. ๋งน์ž๋Š” ์‚ฌ๋žŒ๋“ค์—๊ฒŒ ๋„ค ๊ฐ€์ง€ ์ˆœ์ˆ˜ํ•œ ๋งˆ์Œ์ด ์žˆ๋‹ค๊ณ  ํ•œ๋‹ค. โ€œ์ธก์€ํ•ดํ•˜๋Š” ๋งˆ์Œ์€ ์ธ(ไป)์˜ ์‹ค๋งˆ๋ฆฌ์ด๊ณ , ๋ถ€๋„๋Ÿฌ์›Œํ•˜๋Š” ๋งˆ์Œ์€ ์˜(็พฉ)์˜ ์‹ค๋งˆ๋ฆฌ์ด๋ฉฐ, ์‚ฌ์–‘ํ•˜๋Š” ๋งˆ์Œ์€ ์˜ˆ(๏ฆถ)์˜ ์‹ค๋งˆ๋ฆฌ์ด๊ณ , ์˜ณ๊ณ  ๊ทธ๋ฆ„์„ ๊ฐ€๋ฆฌ๋Š” ๋งˆ์Œ์€ ์ง€(็Ÿฅ)์˜ ์‹ค๋งˆ๋ฆฌ๋‹ค. ์‚ฌ๋žŒ์ด ๋„ค ๊ฐ€์ง€ ์‹ค๋งˆ๋ฆฌ๋ฅผ ์ง€๋‹ˆ๊ณ  ์žˆ๋Š” ๊ฒƒ์€ ๊ทธ๋“ค์ด ํŒ”๋‹ค๋ฆฌ๋ฅผ ๊ฐ€์ง„ ๊ฒƒ๊ณผ ๊ฐ™๋‹ค.โ€ ์ด๊ฒƒ์ด ๋งน์ž๊ฐ€ ๋งํ•œ โ€˜์‚ฌ๋‹จ(ๅ››็ซฏ)โ€™, ์ฆ‰ ์ธ๊ฐ„์˜ ๋ณธ์„ฑ์—์„œ ๋‚˜์˜ค๋Š” ๋„ค ๊ฐ€์ง€ ๋งˆ์Œ์ด๋‹ค. ๊ทธ๋Ÿฐ๋ฐ ์™œ ์‚ฌ๋žŒ๋“ค์€ ์‹ค์ œ๋กœ๋Š” ๋ชจ๋‘ ์ฐฉํ•˜์ง€ ์•Š์„๊นŒ? ๋งน์ž๋Š” ๋น„์œ ๋ฅผ ๋“ค์–ด ์„ค๋ช…ํ•œ๋‹ค. ์šฐ์‚ฐ(็‰›ๅฑฑ)์˜ ๋‚˜๋ฌด๋Š” ์•„๋ฆ„๋‹ค์› ๋‹ค. ๊ทธ๋Ÿฐ๋ฐ ์šฐ์‚ฐ์€ ํฐ ๋‚˜๋ผ์˜ ๊ต์™ธ์— ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ๋„๋ผ๋กœ ๊ทธ ๋‚˜๋ฌด๋“ค์„ ์ฐ์–ด ๋ƒˆ์œผ๋‹ˆ ์•„๋ฆ„๋‹ค์›Œ์งˆ ์ˆ˜ ์žˆ๊ฒ ๋Š”๊ฐ€? ๋ฐค๋‚ฎ์œผ๋กœ ์ž๋ผ๊ณ , ๋น„์™€ ์ด์Šฌ์˜ ์œคํƒํ•จ์„ ๋ฐ›์•„ ์‹น์ด ๋‹๋Š” ์ผ์ด ์—†์ง€ ์•Š์•˜์ง€๋งŒ ์†Œ์™€ ์–‘์„ ๋Œ์–ด๋‹ค ์ž๋ผ๋Š” ์กฑ์กฑ ๋จน์ด๊ณค ํ–ˆ๋‹ค. ๊ทธ๋ž˜์„œ ์ €๋ ‡๊ฒŒ ๋ฐ‹๋ฐ‹ํ•œ ์‚ฐ์ด ๋˜์—ˆ๋‹ค. ์‚ฌ๋žŒ๋“ค์€ ๊ทธ ๋ฐ‹๋ฐ‹ํ•œ ๊ฒƒ์„ ๋ณด๊ณ  ๊ฑฐ๊ธฐ์—๋Š” ์žฌ๋ชฉ์ด ์žˆ์–ด๋ณธ ์ผ์ด ์—†์—ˆ๋‹ค๊ณ  ์ƒ๊ฐํ•œ๋‹ค. ๊ทธ๊ฒƒ์ด ์–ด์ฐŒ ์‚ฐ์˜ ๋ณธ์„ฑ์ด๊ฒ ๋Š”๊ฐ€? ์‚ฌ๋žŒ์— ๋“ค์–ด ์žˆ๋Š” ๋ณธ์„ฑ์ด ์ธ์˜๋ฅผ ๋”ฐ๋ฅด๋Š” ๋งˆ์Œ์ด ์—†๊ฒ ๋Š”๊ฐ€? ๋ณธ๋ž˜์˜ ๋งˆ์Œ์„ ๋ฒ ์–ด๋ฒ„๋ฆฌ๋Š” ์ผ์€ ๋„๋ผ๋กœ ๋‚˜๋ฌด๋ฅผ ๋‹ค๋ฃจ๋Š” ๊ฒƒ๊ณผ ๊ฐ™๋‹ค. ๋งค์ผ๋งค์ผ ์ฐ์–ด๋‚ด๋Š”๋ฐ ์–ด์ฐŒ ์•„๋ฆ„๋‹ค์›Œ์ง€๊ฒ ๋Š”๊ฐ€? ์‚ฌ๋žŒ์˜ ๋งˆ์Œ๊ณผ ํ–‰๋™์ด ์•…ํ•ด์ง„ ์ด์œ ๋Š” ๋„๋ผ๋กœ ์‚ฐ์˜ ๋‚˜๋ฌด๋ฅผ ์ฐ์–ด๋ฒ„๋ฆฌ๋“ฏ ์‚ฌ๋žŒ๋“ค ์Šค์Šค๋กœ๊ฐ€ ๊ทธ๋ ‡๊ฒŒ ํ•˜๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ์‚ฌ๋žŒ๋“ค์ด ์Šค์Šค๋กœ ์ธ๊ฐ„ ๋ณธ์„ฑ์„ ์ฐพ์œผ๋ ค ํ•œ๋‹ค๋ฉด ๋ชจ๋‘๋Š” ๋‹ค์‹œ ์ฐฉํ•˜๊ฒŒ ๋  ์ˆ˜ ์žˆ๋‹ค. ๋งน์ž์˜ ์šฉ๊ธฐ, ๊ธฐ์ƒ์€ ๋ณธ๋ž˜ ์„ ํ•œ ๊ฒƒ์„ ์ž˜ ๊ธฐ๋ฅธ ๊ฒฐ๊ณผ๋‹ค. ๋งน์ž์˜ ๊ด€์‹ฌ์€ ๋ฐ”๋ฅธ ์ •์น˜์— ์žˆ๋‹ค. ์–ด๋–ป๊ฒŒ ๋ฐ”๋ฅธ ์ •์น˜๋ฅผ ํ•  ์ˆ˜ ์žˆ๋Š”๊ฐ€? ๋งน์ž๋Š” ์ œ๋‚˜๋ผ ์„ ์™•์˜ ์ผํ™”๋ฅผ ์†Œ๊ฐœํ•œ๋‹ค']",5 +2025-YS-14,"๋ชจ๋“  ์‚ฌ๋žŒ์€ ์„ ์ด๋ผ๋Š” ๊ฐœ๋…์„ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค. ์‹ค์ฒœ ์ด์„ฑ์— ๋ถ€์—ฌ๋œ ์ œ1 ์›๋ฆฌ๋Š” ์„ ์ด๋ผ๋Š” ๊ฐœ๋… ์œ„์— ์„ธ์›Œ์กŒ๋‹ค. โ€˜์„ ์€ ๋ชจ๋“  ๊ฒƒ์ด ์ถ”๊ตฌํ•˜๋Š” ๊ฒƒ์ด๋‹ค.โ€™๊ฐ€ ๊ทธ ์›๋ฆฌ์ด๋‹ค. ๋”ฐ๋ผ์„œ ์ž์—ฐ๋ฒ•์˜ ์ œ1 ๊ณ„๋ช…์€ โ€˜์„ ์€ ํ–‰ํ•˜๊ณ  ์ถ”๊ตฌํ•ด์•ผ ํ•˜๋Š” ๊ฒƒ์ด๋ฉฐ, ์•…์€ ํ”ผํ•ด์•ผ ํ•˜๋Š” ๊ฒƒ์ด๋‹ค.โ€™ ๋ผ๋Š” ๊ฒƒ์ด๋‹ค.","{'question': '๋‹ค์Œ์„ ์ฃผ์žฅํ•œ ์ค‘์„ธ ์„œ์–‘ ์‚ฌ์ƒ๊ฐ€์˜ ์ž…์žฅ์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['์ธ๊ฐ„์˜ ์ด์„ฑ์  ๋ณธ์„ฑ์—๋Š” ์‹ ์˜ ์˜๋„๋กœ ํ–ฅํ•˜๋Š” ์„ ํ•œ ์„ฑํ–ฅ์ด ์—†๋‹ค.', '์ธ๊ฐ„์˜ ๋„๋•์  ์˜๋ฌด๋Š” ์ธ๊ฐ„ ๋ณธ์„ฑ์— ๋ถ€์—ฌ๋œ ์›๋ฆฌ์— ๊ธฐ์ดˆํ•ด์•ผ ํ•œ๋‹ค.', '์ธ๊ฐ„์˜ ์ž๊ธฐ ๋ณด์กด์„ ์œ„ํ•œ ๋…ธ๋ ฅ ๊ทธ ์ž์ฒด๋Š” ์„ ๋„ ์•„๋‹ˆ๊ณ  ์•…๋„ ์•„๋‹ˆ๋‹ค.', '์ž์—ฐ๋ฒ•์— ๊ทผ๊ฑฐํ•œ ์‹ค์ •๋ฒ•์„ ์ค€์ˆ˜ํ•˜๊ธฐ๋งŒ ํ•˜๋ฉด ์ฐธ๋œ ํ–‰๋ณต์— ๋„๋‹ฌํ•œ๋‹ค.', '์ธ๊ฐ„๋ฒ•์ด ์ž์—ฐ๋ฒ•์— ์œ„๋ฐฐ๋˜์–ด๋„ ๊ทธ๋Ÿฌํ•œ ๋ฒ•์€ ์˜์›๋ฒ•์— ๋ถ€ํ•ฉํ•  ์ˆ˜ ์žˆ๋‹ค.'], 'answer': ''}",,2,2,True,"['๊ทธ๋Ÿฌ๋ฏ€๋กœ ๋ฐฐ์›Œ์„œ ๋˜๋Š” ๊ฒƒ์€ ์œ„(็ˆฒ)์ด๋‹ค. ๋‘˜์งธ, ๋งน์ž๋Š” ์ธ์„ฑ์ด ์„ ํ•˜๋ฏ€๋กœ ์‚ฌ๋žŒ์ด ์•…์„ ํ•˜๋Š” ๊ฒƒ์€ ์„ฑ์„ ์ƒ์‹คํ–ˆ๊ธฐ ๋•Œ๋ฌธ์ด๋ผ๊ณ  ํ–ˆ๋Š”๋ฐ ๋งŒ์•ฝ ์„ฑ์ด ์„ ์ด๋ผ๋ฉด ๊ฒฐ์ฝ” ์ƒ์‹คํ•˜์ง€ ์•Š์„ ๊ฒƒ์ด๋‹ค. ์ƒ์‹คํ•œ๋‹ค๋Š” ๊ฒƒ์€ ์„ฑ์ด ์•…์ž„์„ ์ฆ๋ช…ํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ์…‹์งธ, ๋„๋•(้“ๅพท)์€ ๊ฐ•ํ•œ ์˜์ง€๋ ฅ(ๆ„ๅฟ—ๅŠ›)์œผ๋กœ ๊ฐ•์ œ๋ฅผ ํ•ด์•ผ ์ด๋ฃจ์–ด์ง€๋Š” ๊ฒƒ์ด๋ฏ€๋กœ ๊ทธ๊ฒƒ์€ ์œ„์ด๋‹ค. ๋„ท์งธ, ์‚ฌ๋žŒ์€ ํ•ญ์ƒ ์ž๊ธฐ์˜ ๊ฒฐ์ ์„ ๋ณด์ถฉํ•˜๋ ค๊ณ  ํ•œ๋‹ค. ์ด์™€ ๊ฐ™์ด ๋‘ ์„ฑ์ธ์˜ ์ฃผ์žฅํ•˜๋Š” ํ•™์„ค(ๅญธ่ชช)์ด ๊ทธ ๋ฐฉํ–ฅ์€ ๋‹ค๋ฅด๋‹ค๊ณ  ํ•  ์ˆ˜ ์žˆ์œผ๋‚˜ ์›๋ž˜์˜ ๋ชฉํ‘œ(็›ฎๆจ™)์ธ ์ธ๊ฐ„์€ ํƒœ์–ด๋‚  ๋•Œ๋ถ€ํ„ฐ ์ธ๊ฐ„ ๊ณ ์œ ์˜ ๋ณธ์งˆ(ๆœฌ่ณช)๋กœ์จ ์„ ํ•˜๊ฒŒ ํƒœ์–ด๋‚ฌ๋‹ค๋Š” ๊ฒƒ์ด๋ฏ€๋กœ, ํ›„์ฒœ์ (ๅพŒๅคฉ็š„)์œผ๋กœ ์ธ๊ฐ„์˜ ๊ฒฝํ—˜์— ์˜ํ•˜์—ฌ ์ขŒ์šฐ๋˜๋Š” ํƒœ์–ด๋‚˜์„œ๋ถ€ํ„ฐ ํ™˜๊ฒฝ์— ๋”ฐ๋ผ ์ขŒ์šฐ ๋œ๋‹ค๋Š” ๊ฒƒ์ด๋ฉฐ, ์ˆœ์ž(่€ๅญ)๋Š” ์‚ฌ๋žŒ์˜ ์„ฑ์€ ๋‚˜๋ฉด์„œ๋ถ€ํ„ฐ ์ด(ๅˆฉ)๋ฅผ ์ข‹์•„ํ•˜๋ฏ€๋กœ ์Ÿํƒˆ์ด ์ƒ๊ธฐ๊ณ  ์‚ฌ์–‘(่พญ่ฎ“)์ด ์—†์–ด์ง€๋ฉฐ ๋ฏธ์›Œํ•˜๋Š” ๋งˆ์Œ์ด ์žˆ์œผ๋ฏ€๋กœ ํ›„์ฒœ์ ์ธ ๊ฒฝํ—˜๊ณผ ์ˆ˜์–‘๊ณผ ๋…ธ๋ ฅ์— ์˜ํ•˜์—ฌ ์ธ๊ฐ„์€ ํ˜•์„ฑ๋˜๊ณ  ์˜ˆ์˜๋ฅผ ํ•™์Šตํ•˜๊ณ  ๋‚ด๋ฉดํ™”ํ•จ์œผ๋กœ์จ ๋„๋•์ (้“ๅพท็š„)์œผ๋กœ ์™„์„ฑ ๋  ์ˆ˜ ์žˆ๋‹ค๋Š” ์ฃผ์žฅ์œผ๋กœ ๋น„์ถ”์–ด ๋ณผ ๋•Œ ์ „์ž์ด๋“  ํ›„์ž์ด๋“  ํƒœ์–ด๋‚˜์„œ๋ถ€ํ„ฐ ๊ฒฝํ—˜(็ถ“้ฉ—)๊ณผ ์ˆ˜์–‘(ไฟฎ้คŠ)๊ณผ ๋…ธ๋ ฅ์ด ์žˆ์–ด์•ผ ํ•œ๋‹ค๋Š” ๊ฒƒ์€ ๊ณตํ†ต์ ์ด๋ผ ํ•  ์ˆ˜ ์žˆ๋‹ค. ์ „ํ•ญ์— ๋…ผ๊ฑฐ ํ•œ ๋ฐ”์™€ ๊ฐ™์ด ์„ฑ์„ (ๆ€งๅ–„)์ด๋“  ์„ฑ์•…(ๆ€งๆƒก)์ด๋“  ์ธ์„ฑ์€ ์–ด๋ฆด ๋•Œ๋ถ€ํ„ฐ ๋ฐ˜๋“œ์‹œ ๊ฒฝํ—˜๊ณผ ์ˆ˜์–‘์ด ์ด๋ฃจ์–ด์ ธ์•ผ ํ•˜๋Š” ๋ฐ๋Š” ์ด๋ก ์ด ์—†๋‹ค ํ•  ๊ฒƒ์ด๋‹ค. ์ด๊ฒƒ์€ ์ง€์—ญ(ๅœฐๅŸŸ)๊ณผ ๊ณ„์ธต์˜ ๊ตฌ๋ถ„์ด ์žˆ์„ ์ˆ˜ ์—†์œผ๋ฉฐ ๋™์ผํ•œ ์ง€์—ญ ๋‚ด ๊ฐ™์€ ๋ฌธํ™” ์ƒํ™œ๊ถŒ(็”Ÿๆดปๅœˆ) ๋‚ด์—์„œ๋Š” ๋ชจ๋‘ ๋‹ค ๊ฐ™์€ ์œ„์น˜์— ์žˆ๋Š” ๊ฒƒ์ด๋‹ค', '์ž์—ฐ ์ƒํƒœ๋Š” ๋ชจ๋“  ์‚ฌ๋žŒ์—๊ฒŒ ์ง€๋ฐฐํ•  ์˜๋ฌด๊ฐ€ ์žˆ๋Š” ์ž์—ฐ์˜ ๋ฒ•์น™์„ ๊ฐ€์ง€๊ณ  ์žˆ์œผ๋ฉฐ, ๊ทธ ๋ฒ•์น™์ธ ์ด์„ฑ์€ ๋ชจ๋“  ์ธ๋ฅ˜์—๊ฒŒ ๋ชจ๋“  ํ‰๋“ฑํ•˜๊ณ  ๋…๋ฆฝ์ ์ด๊ธฐ ๋•Œ๋ฌธ์— ์•„๋ฌด๋„ ์ž์‹ ์˜ ์ƒ๋ช…, ๊ฑด๊ฐ•, ์ž์œ  ๋˜๋Š” ์†Œ์œ ์—์„œ ๋‹ค๋ฅธ ์‚ฌ๋žŒ์„ ํ•ดํ•ด์„œ๋Š” ์•ˆ ๋œ๋‹ค๋Š” ๊ฒƒ์„ ๊ฐ€๋ฅด์นœ๋‹ค. ์ธ๊ฐ„์€ ๋งŒ๋Šฅํ•˜๊ณ  ๋ฌดํ•œํžˆ ์ง€ํ˜œ๋กœ์šด ์ฐฝ์กฐ์ž์˜ ์ผ๊พผ์ด๊ธฐ ๋•Œ๋ฌธ์—, ๊ทธ์˜ ๋ช…๋ น์— ์˜ํ•ด ์„ธ๊ณ„์— ๋ณด๋‚ด์ง„ ํ•œ ์ฃผ๊ถŒํ•œ ์ฃผ์ธ์˜ ๋ชจ๋“  ์ข…๊ณผ ๊ทธ์˜ ์‚ฌ์—…์— ๋Œ€ํ•ด, ๊ทธ๋“ค์€ ๊ทธ์˜ ์žฌ์‚ฐ์ด๋ฉฐ, ๊ทธ ์ผ๊พผ์˜ ์ผ๊พผ์€ ์„œ๋กœ์˜ ์พŒ๋ฝ์ด ์•„๋‹ˆ๋ผ ๊ทธ์˜ ์พŒ๋ฝ ๋™์•ˆ ์ง€์†๋˜๋„๋ก ๋งŒ๋“ค์–ด์กŒ์œผ๋ฉฐ, ๊ฐ™์€ ํŠน์„ฑ์„ ๊ฐ–์ถ”๊ณ , ์ž์—ฐ์˜ ํ•œ ๊ณต๋™์ฒด์—์„œ ๋ชจ๋‘๋ฅผ. ๋ˆ„๊ตฌ๋“ ์ง€ ์ž๊ธฐ ์ž์‹ ์„ ๋ณด์กดํ•  ์˜๋ฌด๊ฐ€ ์žˆ๊ณ , ์ž์‹ ์˜ ์ง€์œ„๋ฅผ ๊ณ ์˜๋กœ ํฌ๊ธฐํ•˜์ง€ ์•Š์„ ๊ฒƒ์ด๋ฏ€๋กœ, ๊ทธ์™€ ์œ ์‚ฌํ•œ ์ด์œ ๋กœ ์ž์‹ ์˜ ๋ณด์กด์ด ๊ฒฝ์Ÿ์—์„œ ์˜ค์ง€ ์•Š์„ ๋•Œ, ๊ทธ๋Š” ๊ฐ€๋Šฅํ•œ ํ•œ ๋‚˜๋จธ์ง€ ์ธ๋ฅ˜๋ฅผ ๋ณด์กดํ•ด์•ผ ํ•˜๋ฉฐ, ๋ฒ”๋ฒ•์ž์—๊ฒŒ ์ •์˜๋ฅผ ํ–‰ํ•˜๊ธฐ ์œ„ํ•œ ๊ฒƒ์ด ์•„๋‹ˆ๋ผ๋ฉด ์ƒ๋ช…์„ ๋นผ์•—๊ฑฐ๋‚˜ ์†์ƒ์‹œํ‚ฌ ์ˆ˜ ์—†๋‹ค.๋‚˜ ๋‹ค๋ฅธ ์‚ฌ๋žŒ์˜ ์ƒ๋ช…, ์ž์œ , ๊ฑด๊ฐ•, ํŒ”๋‹ค๋ฆฌ ๋˜๋Š” ์žฌํ™”์˜ ๋ณด์กด์„ ์œ„ํ•œ ๊ฒƒ.']",2 +2025-YS-15,"๊ฐ‘: ์น˜์ง€(่‡ด็Ÿฅ)๋ž€ ๋‚ด ๋งˆ์Œ์˜ ์–‘์ง€(่‰ฏ็Ÿฅ)๋ฅผ ๋๊นŒ์ง€ ํ™•์ถฉํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ๋งŒ์•ฝ ์น˜์ง€ํ•˜์ง€ ๋ชปํ•˜๋ฉด ์•…์„ ๋ฏธ์›Œํ•ด์•ผ ํ•จ์„ ์•Œ๋ฉด์„œ๋„ ๋ฏธ์›Œํ•จ์„ ํ–‰๋™์œผ๋กœ ์‹ค์ฒœํ•˜์ง€ ๋ชปํ•˜๊ฒŒ ๋œ๋‹ค. ์„ : ์น˜์ง€๋ž€ ๋ชจ๋“  ์‚ฌ๋ฌผ ๊ฐ๊ฐ์˜ ์ด์น˜[็†]๋ฅผ ์•„๋Š” ๊ฒƒ์ด๋‹ค. ๊ทธ๋Ÿฌ๋ฏ€๋กœ ์น˜์ง€ํ•˜๋ฉด ์•Œ์ง€ ๋ชปํ•˜๋Š” ๋ฐ”๊ฐ€ ์—†์œผ๋‹ˆ, ๊ทธ ๋ถˆ์„ (ไธๅ–„)์„ ํ–‰ํ•ด์„œ๋Š” ์•ˆ ๋œ๋‹ค๋Š” ๊ฒƒ์„ ์•Œ๊ฒŒ ๋œ๋‹ค.","{'question': '์ค‘๊ตญ ์œ ๊ต ์‚ฌ์ƒ๊ฐ€ ๊ฐ‘, ์„์˜ ์ž…์žฅ์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['๊ฐ‘: ๋งˆ์Œ์˜ ์ฒœ๋ฆฌ๋ฅผ ๊นจ๋‹ฌ์•„ ๊ฐ๊ด€์  ์‚ฌ๋ฌผ์ด ์ง€๋‹Œ ์ด์น˜๋ฅผ ์•Œ ์ˆ˜ ์žˆ๋‹ค.', '๊ฐ‘ : ๋งˆ์Œ์„ ๋ฐ”๋กœ ์žก์œผ๋ฉด[ๆ ผ็‰ฉ] ์–‘์ง€๋Š” ๋ณ„๋„์˜ ํ•™์Šต ์—†์ด ํš๋“๋œ๋‹ค.', '์„ : ์„ฑ์„ (ๆ€งๅ–„)์€ ์ธก์€์ง€์‹ฌ์˜ ์‹ค๋งˆ๋ฆฌ[็ท–]์ธ ์ธ(ไป)์„ ํ†ตํ•ด ํ™•์ฆ๋œ๋‹ค.', '์„ : ๊ฐœ๋ณ„์ ์ธ ๋„๋• ์‹ค์ฒœ๋ณด๋‹ค ๊ถ๋ฆฌ(็ชฎ็†)๋ฅผ ํ†ตํ•œ ์•Ž์„ ์ค‘์‹œํ•ด์•ผ ํ•œ๋‹ค.', '๊ฐ‘๊ณผ ์„ : ์ง„์ •ํ•œ ์•Ž[็œž็Ÿฅ]์„ ์‹คํ˜„ํ•˜๋Š” ์ˆ˜์–‘์ธ ์น˜์ง€๋ฅผ ์ด๋ฃจ์–ด์•ผ ํ•œ๋‹ค.'], 'answer': ''}",,5,5,True,[],5 +2025-YS-16,๊ฐ‘ : ๋ชจ๋“  ๊ฒƒ์€ ์˜ค์ง ์‹์ผ ๋ฟ[ๅ”ฏ่ญ˜]์ด๋‹ค. โ€˜์‹โ€™์—์„œ ๋– ๋‚˜์ง€ ์•Š๋Š” ๋ฒ•(ๆณ•)์€ ๋ถ€์ •๋˜์ง€ ์•Š๊ธฐ ๋•Œ๋ฌธ์— โ€˜์˜ค์งโ€™์ด๋ผ๊ณ  ๋งํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ๋”ฐ๋ผ์„œ ๋ชจ๋“  ๋ฒ•์€ ์–‘๊ทน๋‹จ์„ ๋ฒ—์–ด๋‚˜ ์ค‘๋„(ไธญ้“)์— ๋ถ€ํ•ฉํ•œ๋‹ค. ์„: ๋ชจ๋“  ๊ฒƒ์ด ๊ณต(็ฉบ)ํ•˜์ง€ ์•Š๋‹ค๋ฉด ์ƒ๊ฒจ๋‚จ๋„ ์—†์–ด์ง๋„ ์—†๋‹ค. ์ธ(ๅ› )๊ณผ ์—ฐ(็ทฃ)์—์„œ ์ƒ๊ฒจ๋‚˜๋Š” ์ผ์ฒด ๋ฒ•์€ ๊ณต์ด๊ณ  ๊ฐ€๋ช…(ๅ‡ๅ)์ด๋ฉฐ ์ค‘๋„์— ๋ถ€ํ•ฉํ•œ๋‹ค.,"{'question': '๋™์–‘ ๋ถˆ๊ต ์‚ฌ์ƒ๊ฐ€ ๊ฐ‘, ์„์˜ ์ž…์žฅ์œผ๋กœ ์ ์ ˆํ•œ ๊ฒƒ๋งŒ์„ <๋ณด๊ธฐ>์—์„œ ์žˆ๋Š” ๋Œ€๋กœ ๊ณ ๋ฅธ ๊ฒƒ์€?', 'choices': ['ใ„ฑ,ใ„ด', 'ใ„ฑ,ใ„น', 'ใ„ด,ใ„ท', 'ใ„ฑ,ใ„ท,ใ„น', 'ใ„ด,ใ„ท,ใ„น'], 'answer': '', 'question_plus': 'แ„€. ๊ฐ‘: ์˜์›๋ถˆ๋ณ€์˜ ๋‚˜๋ฅผ ์ƒ์ •ํ•˜๋Š” ์ž์•„์˜์‹์€ ์ž์„ฑ(่‡ชๆ€ง)์„ ์ง€๋‹Œ๋‹ค. แ„‚. ๊ฐ‘: ์„ธ์†์˜ ์˜ค์—ผ๋œ ์˜์‹์„ ์ „ํ™˜ํ•˜์—ฌ ๋ง‘์€ ์ง€ํ˜œ๋ฅผ ์–ป์–ด์•ผ ํ•œ๋‹ค. แ„ƒ. ์„ : ์ธ์—ฐ์˜ ํ™”ํ•ฉ์— ์˜ํ•ด ์ƒ๊ฒจ๋‚œ ์‚ฌ๋ฌผ์€ ์ž„์‹œ์ ์œผ๋กœ ์กด์žฌํ•œ๋‹ค. แ„…. ๊ฐ‘๊ณผ ์„: ๊ณต์— ๋Œ€ํ•œ ์˜ฌ๋ฐ”๋ฅธ ์ธ์‹์„ ํ†ตํ•ด ์ค‘๋„๋ฅผ ์ง€ํ–ฅํ•ด์•ผ ํ•œ๋‹ค.'}",แ„€. ๊ฐ‘: ์˜์›๋ถˆ๋ณ€์˜ ๋‚˜๋ฅผ ์ƒ์ •ํ•˜๋Š” ์ž์•„์˜์‹์€ ์ž์„ฑ(่‡ชๆ€ง)์„ ์ง€๋‹Œ๋‹ค. แ„‚. ๊ฐ‘: ์„ธ์†์˜ ์˜ค์—ผ๋œ ์˜์‹์„ ์ „ํ™˜ํ•˜์—ฌ ๋ง‘์€ ์ง€ํ˜œ๋ฅผ ์–ป์–ด์•ผ ํ•œ๋‹ค. แ„ƒ. ์„ : ์ธ์—ฐ์˜ ํ™”ํ•ฉ์— ์˜ํ•ด ์ƒ๊ฒจ๋‚œ ์‚ฌ๋ฌผ์€ ์ž„์‹œ์ ์œผ๋กœ ์กด์žฌํ•œ๋‹ค. แ„…. ๊ฐ‘๊ณผ ์„: ๊ณต์— ๋Œ€ํ•œ ์˜ฌ๋ฐ”๋ฅธ ์ธ์‹์„ ํ†ตํ•ด ์ค‘๋„๋ฅผ ์ง€ํ–ฅํ•ด์•ผ ํ•œ๋‹ค.,5,5,True,[],5 +2025-YS-17,์ธ๊ฐ„์€ ์˜ˆ์ง€ ์„ธ๊ณ„์˜ ์„ฑ์›์ธ ๋™์‹œ์— ๊ฐ์„ฑ ์„ธ๊ณ„์˜ ์„ฑ์›์ด๊ธฐ๋„ ํ•˜๋‹ค. ์˜ˆ์ง€ ์„ธ๊ณ„์— ์†ํ•˜๋Š” ์ด์„ฑ์  ์กด์žฌ๋กœ์„œ์˜ ์ธ๊ฐ„์€ ์ž๊ธฐ ์˜์ง€์˜ ์›์ธ์„ฑ์„ ์ž์œ ์˜ ์ด๋… ์•„๋ž˜ ๋†“์—ฌ ์žˆ๋Š” ๊ฒƒ์œผ๋กœ๋ฐ–์—๋Š” ๋‹ฌ๋ฆฌ ์ƒ๊ฐํ•  ์ˆ˜ ์—†๋‹ค. ์ด๋ ‡๊ฒŒ ์ž์œ ์˜ ์ด๋…์ด ๋‚˜๋ฅผ ์˜ˆ์ง€ ์„ธ๊ณ„์˜ ๊ตฌ์„ฑ์›์œผ๋กœ ๋งŒ๋“ฆ์œผ๋กœ์จ ์ •์–ธ ๋ช…๋ น์ด ๊ฐ€๋Šฅํ•ด์ง„๋‹ค.,"{'question': '๋‹ค์Œ์„ ์ฃผ์žฅํ•œ ๊ทผ๋Œ€ ์„œ์–‘ ์‚ฌ์ƒ๊ฐ€์˜ ์ž…์žฅ์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['์ž์œ  ์˜์ง€๋Š” ๋„๋• ๋ฒ•์น™์— ์†๋ฐ•๋œ ์˜์ง€์™€๋Š” ๋‹ค๋ฅธ ์ข…๋ฅ˜์˜ ๊ฒƒ์ด๋‹ค.', '์ž๊ธฐ ํ–‰๋ณต์˜ ์ถ”๊ตฌ๋Š” ์ง์ ‘์  ์˜๋ฌด๋Š” ์•„๋‹ˆ์ง€๋งŒ ์„ ์˜์ง€๋ฅผ ์ฆ๋Œ€ํ•œ๋‹ค.', '๋„๋• ๋ฒ•์น™์— ์ ํ•ฉํ•œ ์ค€์น™์€ ์˜์š• ๋Œ€์ƒ์„ ์œ„ํ•ด์„œ ๋ณดํŽธํ™”๋œ ๊ฒƒ์ด๋‹ค.', '์ด์„ฑ์„ ์ง€๋‹Œ ์™„์ „ํ•œ ์กด์žฌ์—๊ฒŒ๋„ ์ •์–ธ ๋ช…๋ น์ด ๋ถ€๊ณผ๋  ์ˆ˜๋ฐ–์— ์—†๋‹ค.', '์˜์ง€์˜ ์ž์œจ์€ ๊ฐ์„ฑ ์„ธ๊ณ„์˜ ์–ด๋– ํ•œ ์›์ธ์— ์˜ํ•ด์„œ๋„ ๊ฒฐ์ •๋˜์ง€ ์•Š๋Š”๋‹ค.'], 'answer': ''}",,5,5,True,[],5 +2025-YS-18,"(๊ฐ€) ์„œ์–‘ ์™ธ์ [ๆด‹่ณŠ]์„ ์น˜์ž๋Š” ๊ฒƒ์€ ์šฐ๋ฆฌ๋‚˜๋ผ ์ž…์žฅ์— ์„  ์‚ฌ๋žŒ ์ด๊ณ , ์„œ์–‘ ์™ธ์ ๊ณผ ํ™”์นœํ•˜์ž๋Š” ๊ฒƒ์€ ๊ทธ๋“ค์˜ ์ž…์žฅ์— ์„  ์‚ฌ๋žŒ ์ด๋‹ค. ์ „์ž๋Š” ๋‚˜๋ผ ์•ˆ ๋ฌธ๋ฌผ์ œ๋„๋ฅผ ๋ณด์ „ํ•  ์ˆ˜ ์žˆ์ง€๋งŒ, ํ›„์ž๋Š” ์‚ฌ๋žŒ๋“ค์„ ์ง์Šน์˜ ์˜์—ญ์— ๋ฐ€์–ด ๋„ฃ์„ ๊ฒƒ์ด๋‹ค. (๋‚˜) ์šฐ๋ฆฌ์˜ ๋„(้“)๋Š” ํ•œ์šธ๋‹˜์˜ ๋งˆ์Œ์„ ์ง€ํ‚ค๊ณ  ํ•œ์šธ๋‹˜์˜ ๊ธฐ์šด์„ ๋ฐ”๋ฅด๊ฒŒ ํ•˜๋Š” ๊ฒƒ[ๅฎˆๅฟƒๆญฃๆฐฃ]์ด๋‹ค. ํ•œ์šธ๋‹˜์˜ ์„ฑํ’ˆ์„ ๋”ฐ๋ฅด๊ณ  ํ•œ์šธ๋‹˜์˜ ๊ฐ€๋ฅด์นจ์„ ๋ฐ›์œผ๋ฉด ์ž์—ฐ์Šค๋Ÿฌ์šด ๊ฐ€์šด๋ฐ ๋ณ€ํ™”๊ฐ€ ๋‚˜ํƒ€๋‚  ๊ฒƒ[็„ก็ˆฒ่€ŒๅŒ–]์ด๋‹ค.","{'question': '๊ทผ๋Œ€ ํ•œ๊ตญ ์‚ฌ์ƒ (๊ฐ€), (๋‚˜)์˜ ์ž…์žฅ์œผ๋กœ ๊ฐ€์žฅ ์ ์ ˆํ•œ ๊ฒƒ์€?', 'choices': ['(๊ฐ€): ๋ฏผ์กฑ์„ ์ค‘์‹ฌ์œผ๋กœ ์ƒˆ๋กœ์šด ์ •์ฒด์„ฑ์„ ์„ธ์›Œ ํ‰๋“ฑ ์‚ฌํšŒ๋ฅผ ๊ตฌํ˜„ํ•ด์•ผ ํ•œ๋‹ค.', '(๊ฐ€): ์„œ์–‘์˜ ๋ฌผ๊ฑด์€ ํ’์†์„ ํ•ด์น˜๋ฏ€๋กœ ์„œ์–‘๊ณผ์˜ ๊ต์—ญ์„ ๋‹จ์ ˆํ•ด์•ผ ํ•œ๋‹ค.', '(๋‚˜): ํ˜„์„ธ์˜ ๊ณ ํ†ต์„ ์ธ๋‚ดํ•˜๊ณ  ์‚ฌํ›„(ๆญปๅพŒ)์˜ ๊ทน๋ฝ์„ธ๊ณ„๋ฅผ ๊ธฐ๋‹ค๋ ค์•ผ ํ•œ๋‹ค.', '(๋‚˜) : ๋ณด๊ตญ์•ˆ๋ฏผ์„ ์œ„ํ•ด ๋™์–‘์˜ ํ•™๋ฌธ๊ณผ ์„œ์–‘์˜ ์ข…๊ต๋ฅผ ์œตํ•ฉํ•ด์•ผ ํ•œ๋‹ค.', '(๊ฐ€)์™€ (๋‚˜): ์„ฑ๋ฆฌํ•™์  ์‹ ๋ถ„ ์งˆ์„œ์™€ ๊ทœ๋ฒ”์„ ์ง€ํ‚ค๊ธฐ ์œ„ํ•ด ๋…ธ๋ ฅํ•ด์•ผ ํ•œ๋‹ค.'], 'answer': ''}",,2,5,False,[],5 +2025-YS-19,"(๊ฐ€) ๊ฐ‘: ๊ตญ๊ฐ€๋Š” ๋ชจ๋“  ์‚ฌ๋žŒ๋“ค์ด ์ง€๋‹Œ ๊ถŒ๋ ฅ๊ณผ ํž˜์„ ํ•œ ์‚ฌ๋žŒ ๋˜๋Š” ํ•˜๋‚˜์˜ ํ•ฉ์˜์ฒด์— ์–‘๋„ํ•˜์—ฌ ์„ธ์šด ์ธ๊ฒฉ์ด๋‹ค. ์ธ๊ฐ„์€ ๊ตญ๊ฐ€๋ฅผ ํ†ตํ•ด์„œ๋งŒ ์ „์Ÿ ์ƒํƒœ์—์„œ ๋ฒ—์–ด๋‚  ์ˆ˜ ์žˆ๋‹ค. ์„: ๊ตญ๊ฐ€๋Š” ์ง€๋ฐฐ ๊ณ„๊ธ‰์ด ์ž์‹ ๋“ค์˜ ๊ณต๋™ ์ดํ•ด๋ฅผ ๊ด€์ฒ ํ•˜๋Š” ์ •์น˜์  ํ˜•ํƒœ์ด๋‹ค. ํ”„๋กค๋ ˆํƒ€๋ฆฌ์•„๋Š” ํ˜๋ช…์„ ํ†ตํ•ด ๊ธฐ์กด ์งˆ์„œ๋ฅผ ๋ฌด๋„ˆ๋œจ๋ ค์•ผ๋งŒ ์ธ๊ฐ„๋‹ต๊ฒŒ ์‚ด ์ˆ˜ ์žˆ๋‹ค. (๋‚˜) ์‚ฌ์ƒ๊ฐ€ ๊ฐ‘, ์„์˜ ์ž…์žฅ์„ ํƒ๊ตฌํ•œ๋‹ค -> A ์งˆ๋ฌธ์— '์˜ˆ'์ด๊ณ  B ์งˆ๋ฌธ์— '์˜ˆ'์ด๋ฉด ๊ฐ‘์˜ ์ž…์žฅ์ด๋‹ค. A ์งˆ๋ฌธ์— '์•„๋‹ˆ์š”'์ด๊ณ  C ์งˆ๋ฌธ์— '์˜ˆ'์ด๋ฉด ์„์˜ ์ž…์žฅ์ด๋‹ค.","{'question': '(๊ฐ€)์˜ ์‚ฌํšŒ์‚ฌ์ƒ๊ฐ€ ๊ฐ‘, ์„์˜ ์ž…์žฅ์„ (๋‚˜) ๊ทธ๋ฆผ์œผ๋กœ ํƒ๊ตฌํ•˜๊ณ ์ž ํ•  ๋•Œ, A~C์— ๋“ค์–ด๊ฐˆ ์ ์ ˆํ•œ ์งˆ๋ฌธ๋งŒ์„ <๋ณด๊ธฐ>์—์„œ ์žˆ๋Š” ๋Œ€๋กœ ๊ณ ๋ฅธ ๊ฒƒ์€?', 'choices': ['ใ„ฑ,ใ„ด', 'ใ„ฑ,ใ„น', 'ใ„ท,ใ„น', 'ใ„ฑ,ใ„ด,ใ„ท', 'ใ„ด,ใ„ท,ใ„น'], 'answer': '', 'question_plus': 'แ„€. A: ๊ตญ๊ฐ€๋Š” ์ „์ฒด ๊ตฌ์„ฑ์›์˜ ํ‰ํ™”์™€ ์•ˆ์ „์„ ํ™•๋ฆฝํ•˜๊ธฐ ์œ„ํ•œ ๋…ธ๋ ฅ์œผ๋กœ ์ถœํ˜„ํ•˜๋Š”๊ฐ€?, แ„‚. B : ์ •์˜์˜ ์‹คํ˜„์€ ๋™์˜์— ์˜ํ•ด ์„ค๋ฆฝ๋œ ์ฃผ๊ถŒ์— ๊ทผ๊ฑฐํ•˜๋Š”๊ฐ€?, แ„ƒ. B : ์‹œ๋ฏผ์€ ์ฃผ๊ถŒ์ž์˜ ํ–‰์œ„๋ฅผ ์ž์‹ ์˜ ํ–‰์œ„๋กœ ์ธ์ •ํ•ด์•ผ ํ•˜๋Š”๊ฐ€? แ„…. C: ๊ตญ๊ฐ€๋Š” ์ธ๊ฐ„์˜ ์ž์•„์‹คํ˜„์— ๊ธฐ๋ฐ˜์ด ๋˜๋Š” ์ •์น˜์  ์ค‘๋ฆฝ ์กฐ์ง์ธ๊ฐ€?'}","แ„€. A: ๊ตญ๊ฐ€๋Š” ์ „์ฒด ๊ตฌ์„ฑ์›์˜ ํ‰ํ™”์™€ ์•ˆ์ „์„ ํ™•๋ฆฝํ•˜๊ธฐ ์œ„ํ•œ ๋…ธ๋ ฅ์œผ๋กœ ์ถœํ˜„ํ•˜๋Š”๊ฐ€?, แ„‚. B : ์ •์˜์˜ ์‹คํ˜„์€ ๋™์˜์— ์˜ํ•ด ์„ค๋ฆฝ๋œ ์ฃผ๊ถŒ์— ๊ทผ๊ฑฐํ•˜๋Š”๊ฐ€?, แ„ƒ. B : ์‹œ๋ฏผ์€ ์ฃผ๊ถŒ์ž์˜ ํ–‰์œ„๋ฅผ ์ž์‹ ์˜ ํ–‰์œ„๋กœ ์ธ์ •ํ•ด์•ผ ํ•˜๋Š”๊ฐ€? แ„…. C: ๊ตญ๊ฐ€๋Š” ์ธ๊ฐ„์˜ ์ž์•„์‹คํ˜„์— ๊ธฐ๋ฐ˜์ด ๋˜๋Š” ์ •์น˜์  ์ค‘๋ฆฝ ์กฐ์ง์ธ๊ฐ€?",4,4,True,[],1 +2025-YS-20,"๊ฐ‘ : ๋ชจ๋“  ๋ฒ•(ๆณ•)์ด ๋น„์–ด ๊ณ ์š”ํ•œ ๊ฒƒ์ด ์—ฌ๋ž˜์˜ ์ขŒ(ๅ)์ด๊ณ , ์ƒ๊ฒจ๋‚จ์ด ์—†๊ณ  ์†Œ๋ฉธํ•จ๋„ ์—†๋Š” ๊ฒƒ์ด ์—ฌ๋ž˜์˜ ์„ (็ฆช)์ด๋‹ค. ๊ทผ๋ณธ ๋ฐ”ํƒ•์ด ์ฒญ์ •ํ•˜๊ณ  ๊นจ๋‹ฌ์Œ์˜ ๋ณธ์ฒด๊ฐ€ ๋šœ๋ ท์ด ๋ฐ์Œ์„ ๋ณด๊ฒŒ ๋˜๋ฉด, ์ด๊ฒƒ์ด ๊ณง ๊ฒฌ์„ฑ์„ฑ๋ถˆ(่ฆ‹ๆ€งๆˆไฝ›)์ด๋ฉฐ ์—ฌ๋ž˜์˜ ์ง€๊ฒฌ(็Ÿฅ่ฆ‹)์ด๋‹ค. ์„ : ํ•œ๋งˆ์Œ[ไธ€ๅฟƒ]์€ ๋ถ€์ฒ˜๊ฐ€ ์ฒด๋“ํ•œ ๊ฒƒ์ด๋ฏ€๋กœ ์ด ๋งˆ์Œ์„ ์ผ์ปฌ์–ด ๋ถˆ์„ฑ(ไฝ›ๆ€ง)์ด๋ผ ๋ถ€๋ฅธ๋‹ค. ์ด๋Š” ์—ฌ๋Ÿฌ ๊ฒฝ์ „๋“ค์˜ ๋ถ€๋ถ„์„ ํ†ต๊ด„ ํ•˜๋Š”๋ฐ, ๋งˆ์น˜ ๋ชจ๋“  ๋ฌผ์ค„๊ธฐ๊ฐ€ ๋ฐ”๋‹ค๋กœ ๋ชจ์ด๋“ฏ ํ•œ๋ง›[ไธ€ๅ‘ณ]์œผ๋กœ ๋Œ์•„๊ฐ€๊ฒŒ ํ•œ๋‹ค. X: ๊ต์™ธ๋ณ„์ „(ๆ•Žๅค–ๅˆฅๅ‚ณ)์„ ์ค‘์‹œํ•˜๋Š” ์ •๋„, Y: ๋ˆ์˜ค๋ˆ์ˆ˜(้ “ๆ‚Ÿ้ “ไฟฎ)๋ฅผ ์ค‘์‹œํ•˜๋Š” ์ •๋„, Z: ํ™”์Ÿ์„ ํ†ตํ•œ ๊ฒฝ์ „ ํ•ด์„์„ ์ค‘์‹œํ•˜๋Š” ์ •๋„, ใ„ฑ: X ๋‚ฎ์Œ, Y ๋‚ฎ์Œ, Z ๋†’์Œ, ใ„ด: X ๋†’์Œ, Y ๋†’์Œ, Z ๋†’์Œ, ใ„ท: X ๋†’์Œ, Y ๋‚ฎ์Œ, Z ๋‚ฎ์Œ, ใ„น: X ๋‚ฎ์Œ, Y ๋‚ฎ์Œ, Z ๋‚ฎ์Œ, ใ…: X ๋‚ฎ์Œ, Y ๋†’์Œ, Z ๋‚ฎ์Œ","{'question': '๊ฐ‘์€ ์ค‘๊ตญ ๋ถˆ๊ต ์‚ฌ์ƒ๊ฐ€, ์„์€ ํ•œ๊ตญ ๋ถˆ๊ต ์‚ฌ์ƒ๊ฐ€์ด๋‹ค. ๊ฐ‘์˜ ์ž…์žฅ์— ๋น„ํ•ด ์„์˜ ์ž…์žฅ์ด ๊ฐ–๋Š” ์ƒ๋Œ€์  ํŠน์ง•์„ ๊ทธ๋ฆผ์˜ แ„€~แ„† ์ค‘์—์„œ ๊ณ ๋ฅธ ๊ฒƒ์€?', 'choices': ['ใ„ฑ', 'ใ„ด', 'ใ„ท', 'ใ„น', 'ใ…'], 'answer': ''}",,1,2,False,[],5 +2025-JB-02,"ํ˜•๋ฒŒ์€ ๋ฒ”์ฃ„์— ๋Œ€ํ•œ ๋ฒ•๋ฅ  ํšจ๊ณผ๋กœ ๊ตญ๊ฐ€๊ฐ€ ๋ฒ”์ฃ„์ธ์—๊ฒŒ ์ฑ…์ž„์„ ๊ธฐ์ดˆ๋กœ +๋ถ€๊ณผํ•˜๋Š” ๋ฒ•์ต์˜ ๋ฐ•ํƒˆ์„ ์˜๋ฏธํ•œ๋‹ค. ํ˜•๋ฒŒ๊ถŒ์˜ ์ฃผ์ฒด๋Š” ๊ตญ๊ฐ€์ด์ง€๋งŒ ์ž…๋ฒ•์ž๊ฐ€ +๋ฒ”์ฃ„์˜ ์œ ํ˜• ๋ฐ ํ˜•๋ฒŒ์˜ ์ข…๋ฅ˜์™€ ์ •๋„๋ฅผ ๋ฏธ๋ฆฌ ์„ฑ๋ฌธ์˜ ๋ฒ•๋ฅ ๋กœ ๊ทœ์ •ํ•ด์•ผ, +์ด์— ๊ทผ๊ฑฐํ•œ ๊ตญ๊ฐ€์˜ ํ˜•๋ฒŒ๊ถŒ ํ–‰์‚ฌ๋Š” ์ •๋‹นํ™”๋  ์ˆ˜ ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ž…๋ฒ•์ž๊ฐ€ +์„ ํƒํ•œ ํ˜•๋ฒŒ์ด ๊ตฌ์„ฑ ์š”๊ฑด์— ๊ธฐ์ˆ ๋œ ๋ถˆ๋ฒ•์˜ ๋‚ด์šฉ์— ์ƒ์‘ํ•˜์ง€ ์•Š๊ฑฐ๋‚˜ +ํ–‰์œ„์ž์˜ ์ฑ…์ž„์— ์ƒ์‘ํ•˜์ง€ ์•Š์„ ์ •๋„๋กœ ๊ณผ๋„ํ•œ ๊ฒฝ์šฐ์—, ์ด๋Š” ๋ฒ•๋ฅ ์˜ ๋‚ด์šฉ์ด +์ •์˜์— ํ•ฉ์น˜๋  ๊ฒƒ์„ ์š”๊ตฌํ•˜๋Š” A์— ๋”ฐ๋ผ ์šฉ์ธ๋  ์ˆ˜ ์—†๋‹ค. ์ด๋Ÿฌํ•œ ํ˜•๋ฒŒ +๊ทœ์ •์€ ๋ฒ•๋ฅ ์˜ ์™ธ๊ด€์„๋ ๊ณ  ์žˆ์œผ๋‚˜ โ€˜๋ฒ•๋ฅ ์  ๋ถˆ๋ฒ•โ€™์— ํ•ด๋‹นํ•˜๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค.","{'question': '๋ฒ•์น˜์ฃผ์˜์˜ ์œ ํ˜• A์— ๋Œ€ํ•œ ์˜ณ์€ ์„ค๋ช…๋งŒ์„ <๋ณด๊ธฐ>์—์„œ ๊ณ ๋ฅธ ๊ฒƒ์€?', 'choices': ['ใ„ฑ,ใ„ด', 'ใ„ฑ,ใ„ท', 'ใ„ด,ใ„ท', 'ใ„ด,ใ„น', 'ใ„ท, ใ„น'], 'answer': '', 'question_plus': 'ใ„ฑ. ์ฃ„ํ˜• ๋ฒ•์ •์ฃผ์˜์˜ ๋‚ด์šฉ์ธ ์ ์ •์„ฑ์˜ ์›์น™์„ ๊ฐ•์กฐํ•˜๋Š” ๊ทผ๊ฑฐ๊ฐ€ ๋œ๋‹ค.\r\nใ„ด.์ž…๋ฒ•์ž์˜ ์ž์˜๋กœ๋ถ€ํ„ฐ ๊ฐœ์ธ์˜ ์ž์œ ์™€ ๊ถŒ๋ฆฌ๋ฅผ ๋ณด์žฅํ•  ๊ฒƒ์„ \r\n์š”์ฒญํ•œ๋‹ค.\r\nใ„ท. ์ •์˜์— ๋ถ€ํ•ฉํ•˜๋Š” ๊ฒฝ์šฐ๋ผ๋ฉด ๋ฒ•๋ฅ ์— ๊ทผ๊ฑฐํ•˜์ง€ ์•Š์€ ๊ตญ๊ฐ€ ๊ถŒ๋ ฅ\r\nํ–‰์‚ฌ๋„ ์ •๋‹นํ™”๋œ๋‹ค๊ณ  ๋ณธ๋‹ค.\r\nใ„น. ํ•ฉ๋ฒ•์  ์ ˆ์ฐจ๋ฅผ ๊ฑฐ์ณ ์ œ์ •๋œ ๋ฒ•๋ฅ ์ด ๊ตญ๊ฐ€์˜ ํญ๋ ฅ์„ ์ •๋‹นํ™”ํ•˜๋Š”\r\n๋„๊ตฌ๋กœ ์•…์šฉ๋  ์ˆ˜ ์žˆ์Œ์„ ๊ฐ„๊ณผํ•œ๋‹ค.'}","ใ„ฑ. ์ฃ„ํ˜• ๋ฒ•์ •์ฃผ์˜์˜ ๋‚ด์šฉ์ธ ์ ์ •์„ฑ์˜ ์›์น™์„ ๊ฐ•์กฐํ•˜๋Š” ๊ทผ๊ฑฐ๊ฐ€ ๋œ๋‹ค. +ใ„ด.์ž…๋ฒ•์ž์˜ ์ž์˜๋กœ๋ถ€ํ„ฐ ๊ฐœ์ธ์˜ ์ž์œ ์™€ ๊ถŒ๋ฆฌ๋ฅผ ๋ณด์žฅํ•  ๊ฒƒ์„ +์š”์ฒญํ•œ๋‹ค. +ใ„ท. ์ •์˜์— ๋ถ€ํ•ฉํ•˜๋Š” ๊ฒฝ์šฐ๋ผ๋ฉด ๋ฒ•๋ฅ ์— ๊ทผ๊ฑฐํ•˜์ง€ ์•Š์€ ๊ตญ๊ฐ€ ๊ถŒ๋ ฅ +ํ–‰์‚ฌ๋„ ์ •๋‹นํ™”๋œ๋‹ค๊ณ  ๋ณธ๋‹ค. +ใ„น. ํ•ฉ๋ฒ•์  ์ ˆ์ฐจ๋ฅผ ๊ฑฐ์ณ ์ œ์ •๋œ ๋ฒ•๋ฅ ์ด ๊ตญ๊ฐ€์˜ ํญ๋ ฅ์„ ์ •๋‹นํ™”ํ•˜๋Š” +๋„๊ตฌ๋กœ ์•…์šฉ๋  ์ˆ˜ ์žˆ์Œ์„ ๊ฐ„๊ณผํ•œ๋‹ค.",1,1,True,[],1 +2025-JB-03," A์˜ ์นจํ•ด ์—ฌ๋ถ€๋Š” ๊ถŒ๋ฆฌ๋ฅผ ์ฃผ์žฅํ•˜๋Š” ์ฃผ์ฒด, ๊ตญ๊ฐ€, ๊ทธ๋ฆฌ๊ณ  ๋น„๊ต์˜ ๋Œ€์ƒ์ด ๋˜๋Š” +๋˜ ๋‹ค๋ฅธ ์ฃผ์ฒด์™€์˜ ๊ด€๊ณ„์—์„œ ํŒ๋‹จ๋œ๋‹ค. A๋ฅผ ์นจํ•ด๋‹นํ•œ ์ฃผ์ฒด๊ฐ€ ์ฃผ์žฅํ•˜๋Š” +๊ตญ๊ฐ€ ์ž‘์šฉ์˜ ์œ„ํ—Œ์„ฑ์€ ๋ณธ์งˆ์ ์œผ๋กœ ๊ฐ™์€ ๊ฒƒ์„ ์ž์˜์ ์œผ๋กœ ๋‹ค๋ฅด๊ฒŒ, ๋ณธ์งˆ์ ์œผ๋กœ +๋‹ค๋ฅธ ๊ฒƒ์„ ์ž์˜์ ์œผ๋กœ ๊ฐ™๊ฒŒ ์ทจ๊ธ‰ํ•˜๋Š”์ง€ ์—ฌ๋ถ€์— ์žˆ๋‹ค. ํ•œํŽธ, ๊ธฐ๋ณธ๊ถŒ ์นจํ•ด ์‹œ +๊ตญ๊ฐ€์— ๋Œ€ํ•˜์—ฌ ๊ตฌ์ œ๋ฅผ ์š”๊ตฌํ•  ์ˆ˜ ์žˆ๋Š” ์ˆ˜๋‹จ์  ๊ถŒ๋ฆฌ์ธ B๋Š” ๊ตญ๊ฐ€์˜ ํ–‰์œ„๋‚˜ ์กฐ๋ ฅ์„ +ํ•„์š”๋กœ ํ•˜๊ธฐ์— ์ž…๋ฒ•์ž์— ์˜ํ•ด ๋ฒ•๋ฅ ๋กœ ํ–‰์‚ฌ ์ ˆ์ฐจ๊ฐ€ ๊ตฌ์ฒดํ™”๋  ๊ฒƒ์ด ์š”๊ตฌ๋œ๋‹ค.","{'question': '๊ธฐ๋ณธ๊ถŒ ์œ ํ˜• A, B์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['A๋Š” ๋‹ค๋ฅธ ๊ธฐ๋ณธ๊ถŒ ๋ณด์žฅ์˜ ์ „์ œ๊ฐ€ ๋˜๋Š” ๊ถŒ๋ฆฌ์ด๋‹ค.', 'B๋Š”๊ตญ๊ฐ€๋กœ๋ถ€ํ„ฐ์˜ ๊ฐ„์„ญ์ด๋‚˜ ์นจํ•ด๋ฅผ ๋ฐฐ์ œํ•˜๋Š” ๋ฐฉ์–ด์  ๊ถŒ๋ฆฌ์ด๋‹ค.', 'A์™€ ๋‹ฌ๋ฆฌ B๋Š” ํ—Œ๋ฒ•์— ์—ด๊ฑฐ๋˜์ง€ ์•Š์•„๋„ ๋ณด์žฅ๋˜๋Š” ๊ถŒ๋ฆฌ๋กœ ํฌ๊ด„์„ฑ์„ ๊ฐ€์ง„๋‹ค.', 'B์™€๋‹ฌ๋ฆฌ A๋Š” ๊ธฐ๋ณธ๊ถŒ ๋ณด์žฅ์„ ์œ„ํ•œ ๊ธฐ๋ณธ๊ถŒ์œผ๋กœ ์ ˆ์ฐจ์  ๊ถŒ๋ฆฌ์ด๋‹ค.', 'A์™€ B ๋ชจ๋‘ ์˜ํšŒ๊ฐ€ ์ œ์ •ํ•œ ๋ฒ•๋ฅ ๋กœ๋„ ์ œํ•œํ•  ์ˆ˜ ์—†๋Š” ๊ถŒ๋ฆฌ์ด๋‹ค.'], 'answer': ''}",,1,1,True,[],1 +2025-JB-04,"A๋Š” ๊ตญ๊ฐ€ ์˜์‚ฌ๋ฅผ ์ตœ์ข…์ ์œผ๋กœ ๊ฒฐ์ •ํ•˜๋Š” ์ตœ๊ณ ์˜ ๊ถŒ๋ ฅ์ด ๊ตญ๋ฏผ์—๊ฒŒ ์žˆ๋‹ค๋Š” +์›๋ฆฌ๋ผ๋Š” ์ ์—์„œ ๋ฏผ์ฃผ์ฃผ์˜๋กœ ๊ท€๊ฒฐ๋œ๋‹ค. ์ด๋Ÿฌํ•œ ๋ฏผ์ฃผ์ฃผ์˜๊ฐ€ ์ž์œ ์ฃผ์˜์™€ +๊ฒฐํ•ฉ๋˜๋ฉด์„œ ์šฐ๋ฆฌ ํ—Œ๋ฒ•์˜ ๋˜ ๋‹ค๋ฅธ ๊ธฐ๋ณธ ์›๋ฆฌ๋กœ ์ž๋ฆฌ ์žก๊ณ  ์žˆ๋‹ค. ํ•œํŽธ, +์ž์œ ๋ฐฉ์ž„์  ์‹œ์žฅ ๊ฒฝ์ œ ์งˆ์„œ๊ฐ€ ์ดˆ๋ž˜ํ•œ ๋ถˆํ‰๋“ฑ ๋ฌธ์ œ๋ฅผ ๊ตญ๊ฐ€์˜ ์ ๊ทน์  ์—ญํ• ์„ +ํ†ตํ•˜์—ฌ ํ•ด๊ฒฐํ•˜๋ ค๋Š” ๋…ธ๋ ฅ์œผ๋กœ ๋“ฑ์žฅํ•œ B๋Š” ๊ตญ๋ฏผ์˜ ์ธ๊ฐ„๋‹ค์šด ์ƒํ™œ์„ ๋ณด์žฅํ•˜๊ธฐ +์œ„ํ•œ ๊ตญ๊ฐ€์˜ ์ง€์›๊ณผ ๋ฐฐ๋ ค๋ฅผ ์ •๋‹นํ™”ํ•˜๋Š” ๊ทผ๊ฑฐ๊ฐ€ ๋œ๋‹ค.","{'question': '์šฐ๋ฆฌ๋‚˜๋ผ ํ—Œ๋ฒ•์˜ ๊ธฐ๋ณธ ์›๋ฆฌ A, B์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ๊ฐ€์žฅ ์ ์ ˆํ•œ ๊ฒƒ์€?', 'choices': ['A๋Š” ์ƒํ˜ธ์ฃผ์˜ ์›์น™์— ๋”ฐ๋ผ ์™ธ๊ตญ์ธ์˜ ์ง€์œ„๋ฅผ ๋ณด์žฅํ•˜๋Š” ๊ทผ๊ฑฐ๊ฐ€ ๋œ๋‹ค.', '๊ตญ๋ฏผ ํˆฌํ‘œ๋ฅผ ํ†ตํ•ด ๊ตญ๋ฏผ์ด ์ง์ ‘ ๊ตญ๊ฐ€ ์•ˆ์œ„์— ๊ด€ํ•œ ์ค‘์š” ์ •์ฑ…์„ ๊ฒฐ์ •ํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•˜๋Š” ๊ฒƒ์€ A์˜ ์‹คํ˜„ ๋ฐฉ์•ˆ์ด๋‹ค.', 'B๋Š” ๋‚จ๋ถ ๋ถ„๋‹จ์˜ ์ƒํ™ฉ์„ ๋ฐ˜์˜ํ•œ ์šฐ๋ฆฌ๋‚˜๋ผ ํ—Œ๋ฒ• ํŠน์œ ์˜ ์›๋ฆฌ์ด๋‹ค.', 'A์™€ ๋‹ฌ๋ฆฌ B๋Š” ๊ทผ๋Œ€ ์ž…ํ—Œ์ฃผ์˜ ํ—Œ๋ฒ•์—์„œ๋ถ€ํ„ฐ ๊ฐ•์กฐ๋˜๊ธฐ ์‹œ์ž‘ํ•œ ์›๋ฆฌ์ด๋‹ค.', '๊ตญ๊ฐ€๊ฐ€ ์ ์ •ํ•œ ์†Œ๋“์˜ ๋ถ„๋ฐฐ๋ฅผ ์œ ์ง€ํ•˜๊ธฐ ์œ„ํ•ด ๊ฒฝ์ œ ์งˆ์„œ์— ๊ด€ํ•œ ๊ทœ์ œ์™€ ์กฐ์ •์„ ํ•˜๋Š” ๊ฒƒ์€ B๊ฐ€ ์•„๋‹Œ A์˜ ์‹คํ˜„ ๋ฐฉ์•ˆ์ด๋‹ค.'], 'answer': ''}",,2,2,True,"['์ œ34์กฐโ‘ ๋ชจ๋“  ๊ตญ๋ฏผ์€ ์ธ๊ฐ„๋‹ค์šด ์ƒํ™œ์„ ํ•  ๊ถŒ๋ฆฌ๋ฅผ ๊ฐ€์ง„๋‹ค.\n์ œ119์กฐโ‘ก๊ตญ๊ฐ€๋Š” ๊ท ํ˜•์žˆ๋Š” ๊ตญ๋ฏผ๊ฒฝ์ œ์˜ ์„ฑ์žฅ ๋ฐ ์•ˆ์ •๊ณผ ์ ์ •ํ•œ ์†Œ๋“์˜ ๋ถ„๋ฐฐ๋ฅผ ์œ ์ง€ํ•˜๊ณ , ์‹œ์žฅ์˜ ์ง€๋ฐฐ์™€ ๊ฒฝ์ œ๋ ฅ์˜ ๋‚จ์šฉ์„ ๋ฐฉ์ง€ํ•˜๋ฉฐ, ๊ฒฝ์ œ์ฃผ์ฒด๊ฐ„์˜ ์กฐํ™”๋ฅผ ํ†ตํ•œ ๊ฒฝ์ œ์˜ ๋ฏผ์ฃผํ™”๋ฅผ ์œ„ํ•˜์—ฌ ๊ฒฝ์ œ์— ๊ด€ํ•œ ๊ทœ์ œ์™€ ์กฐ์ •์„ ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋‹ค์Œ ํ—Œ๋ฒ• ์กฐํ•ญ์ด ๊ณตํ†ต์ ์œผ๋กœ ์ถ”๊ตฌํ•˜๋Š” ํ—Œ๋ฒ•์˜ ๊ธฐ๋ณธ ์›๋ฆฌ์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์ง€ ์•Š์€ ๊ฒƒ์€? Answer: ๊ถŒ๋ ฅ๋ถ„๋ฆฝ๊ณผ ์ ๋ฒ•์ ˆ์ฐจ ์›๋ฆฌ์— ์˜ํ•ด ์‹คํ˜„๋œ๋‹ค.', '๋ฏผ์ฃผ์ฃผ์˜์˜ ์ •์‹  ๋ฏผ์ฃผ์ฃผ์˜์—์„œ ์กด์ค‘๋˜๋Š” ์ •์‹ ์€ ๋ฌด์—‡์ผ๊นŒ์š”. ๋ฐ”๋กœ โ€˜์ธ๊ฐ„์— ๋Œ€ํ•œ ์กด์ค‘โ€™, โ€˜์ž์œ โ€™, โ€˜ํ‰๋“ฑโ€™ ์„ธ ๊ฐ€์ง€๋ž๋‹ˆ๋‹ค. ๋ฏผ์ฃผ์ฃผ์˜์˜ ์ •์‹  ์ค‘์—์„œ๋„ ๊ฐ€์žฅ ์ค‘์š”ํ•œ ๊ฒƒ์€ โ€˜์ธ๊ฐ„์— ๋Œ€ํ•œ ์กด์ค‘โ€™์ž…๋‹ˆ๋‹ค. ๋ชจ๋“  ์ธ๊ฐ„์ด ํƒœ์–ด๋‚  ๋•Œ๋ถ€ํ„ฐ ๊ฐ€์ง€๊ณ  ์žˆ๋Š” ์ธ๊ฐ„์˜ ์กด์—„์„ฑ์„ ์ธ์ •ํ•ด์•ผ ํ•œ๋‹ค๋Š” ๋œป์ด์—์š”. ๊ทธ๋ž˜์„œ ์–ด๋– ํ•œ ๊ฒฝ์šฐ๋ผ๋„ ์ธ๊ฐ„์˜ ์กด์—„์„ฑ์ด ์šฐ์„ ์ ์œผ๋กœ ์กด์ค‘๋˜๊ณ  ๋ณดํ˜ธ๋ฐ›์„ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ์šฐ๋ฆฌ๋‚˜๋ผ ํ—Œ๋ฒ•์—์„œ๋„ โ€˜๋ชจ๋“  ๊ตญ๋ฏผ์€ ์ธ๊ฐ„์œผ๋กœ์„œ์˜ ์กด์—„๊ณผ ๊ฐ€์น˜๋ฅผ ๊ฐ€์ง€๋ฉฐ, ํ–‰๋ณต์„ ์ถ”๊ตฌํ•  ๊ถŒ๋ฆฌ๋ฅผ ๊ฐ€์ง„๋‹ค.โ€™ ๊ณ  ๋งํ•˜๊ณ  ์žˆ์ง€์š”. โ€˜์ž์œ โ€™๋Š” ์†Œ๊ทน์ ์ธ ์˜๋ฏธ์—์„œ ์™ธ๋ถ€๋กœ๋ถ€ํ„ฐ์˜ ์œ„ํ˜‘๊ณผ ๊ฐ•์ œ์—์„œ ํ•ด๋ฐฉ๋˜๋Š” ๊ฒƒ์ด๊ณ , ์ ๊ทน์ ์ธ ์˜๋ฏธ์—์„œ ์ž๊ธฐ ์Šค์Šค๋กœ ์„ ํƒํ•˜๊ณ  ์˜๊ฒฌ์„ ๋ฐœํ‘œํ•  ๊ธฐํšŒ๊ฐ€ ํ—ˆ์šฉ๋˜๋Š” ๊ฒƒ์„ ๋งํ•ด์š”. ๊ทธ๋Ÿฌ๋‚˜ ์ง„์ •ํ•œ ์ž์œ ๋Š” ๋‹ค๋ฅธ ์‚ฌ๋žŒ์—๊ฒŒ ํ”ผํ•ด๋ฅผ ์ฃผ์ง€ ์•Š์•„์•ผ ํ•˜๊ณ  ์ž์‹ ์˜ ํ–‰๋™์— ์ฑ…์ž„์„ ์งˆ ์ˆ˜ ์žˆ์–ด์•ผ ํ•œ๋‹ต๋‹ˆ๋‹ค. โ€˜ํ‰๋“ฑโ€™์€ ๋ชจ๋“  ์‚ฌ๋žŒ๋“ค์ด ์–ด๋– ํ•œ ์ผ์„ ํ•  ๋•Œ ์ฐจ๋ณ„์„ ๋ฐ›์ง€ ์•Š๋Š” ๊ฒƒ์„ ๋œปํ•ฉ๋‹ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋ฏผ์ฃผ์ฃผ์˜์—์„œ ๋งํ•˜๋Š” ํ‰๋“ฑ์€ ๊ฐœ์ธ์˜ ์žฌ๋Šฅ๊ณผ ๋Šฅ๋ ฅ์˜ ์ฐจ์ด๊นŒ์ง€ ํ‰๋“ฑํ•˜๋‹ค๋Š” ๋œป์ด ์•„๋‹ˆ๋ผ ๋ฒ• ์•ž์—์„œ ํ‰๋“ฑ, ๋˜‘๊ฐ™์ด ํˆฌํ‘œํ•  ๊ถŒ๋ฆฌ, ์ธ๊ฐ„์˜ ์กด์—„์„ฑ์— ๋Œ€ํ•œ ํ‰๋“ฑ์„ ๋งํ•˜๋Š” ๊ฒƒ์ด์—์š”. ๋ฏผ์ฃผ์ฃผ์˜์˜ ์›๋ฆฌ ๋ฏผ์ฃผ์ฃผ์˜๋ฅผ ๋’ท๋ฐ›์นจํ•˜๋Š” ์›๋ฆฌ๋กœ๋Š” ๊ตญ๋ฏผ์ฃผ๊ถŒ, ๊ถŒ๋ ฅ๋ถ„๋ฆฝ, ์ž…ํ—Œ์ฃผ์˜, ๋‹ค์ˆ˜๊ฒฐ์ฃผ์˜๊ฐ€์žˆ์–ด์š”. ๊ตญ๋ฏผ์ฃผ๊ถŒ์€ ๊ตญ๊ฐ€์˜ ์˜์‚ฌ๋ฅผ ์ตœ์ข…์ ์œผ๋กœ ๊ฒฐ์ •ํ•  ์ˆ˜ ์žˆ๋Š” ํž˜์ด ๋ฐ”๋กœ ๊ตญ๋ฏผ์—๊ฒŒ ์žˆ๋‹ค๋Š” ๋œป์ด์—์š”. ์šฐ๋ฆฌ๋‚˜๋ผ ํ—Œ๋ฒ• ์ œ1์กฐ ์ œ2ํ•ญ์—์„œ๋„ โ€˜๋Œ€ํ•œ๋ฏผ๊ตญ์˜ ์ฃผ๊ถŒ์€ ๊ตญ๋ฏผ์—๊ฒŒ ์žˆ๊ณ , ๋ชจ๋“  ๊ถŒ๋ ฅ์€ ๊ตญ๋ฏผ์œผ๋กœ๋ถ€ํ„ฐ ๋‚˜์˜จ๋‹ค.โ€™ ๋ผ๊ณ  ํ•˜๊ณ  ์žˆ์ง€์š”. ๊ถŒ๋ ฅ๋ถ„๋ฆฝ์€ ๋…์žฌ๋ฅผ ๋ง‰๊ธฐ ์œ„ํ•ด ๊ตญ๊ฐ€์˜ ๊ถŒ๋ ฅ์„ ์—ฌ๋Ÿฌ ๊ฐœ๋กœ ๋‚˜๋ˆ„์–ด ๋†“์€ ๊ฒƒ์„ ๋งํ•ด์š”', '๊ตญ๋ฏผ์ฃผ๊ถŒ์€ ๊ตญ๊ฐ€์˜ ์˜์‚ฌ๋ฅผ ์ตœ์ข…์ ์œผ๋กœ ๊ฒฐ์ •ํ•  ์ˆ˜ ์žˆ๋Š” ํž˜์ด ๋ฐ”๋กœ ๊ตญ๋ฏผ์—๊ฒŒ ์žˆ๋‹ค๋Š” ๋œป์ด์—์š”. ์šฐ๋ฆฌ๋‚˜๋ผ ํ—Œ๋ฒ• ์ œ1์กฐ ์ œ2ํ•ญ์—์„œ๋„ โ€˜๋Œ€ํ•œ๋ฏผ๊ตญ์˜ ์ฃผ๊ถŒ์€ ๊ตญ๋ฏผ์—๊ฒŒ ์žˆ๊ณ , ๋ชจ๋“  ๊ถŒ๋ ฅ์€ ๊ตญ๋ฏผ์œผ๋กœ๋ถ€ํ„ฐ ๋‚˜์˜จ๋‹ค.โ€™ ๋ผ๊ณ  ํ•˜๊ณ  ์žˆ์ง€์š”. ๊ถŒ๋ ฅ๋ถ„๋ฆฝ์€ ๋…์žฌ๋ฅผ ๋ง‰๊ธฐ ์œ„ํ•ด ๊ตญ๊ฐ€์˜ ๊ถŒ๋ ฅ์„ ์—ฌ๋Ÿฌ ๊ฐœ๋กœ ๋‚˜๋ˆ„์–ด ๋†“์€ ๊ฒƒ์„ ๋งํ•ด์š”. ๋ฒ•์„ ์ œ์ •ํ•˜๋Š” ์ž…๋ฒ•๋ถ€, ๋ฒ•์„ ์ง‘ํ–‰ํ•˜๋Š” ํ–‰์ •๋ถ€, ๋ฒ•์„ ์ ์šฉํ•˜๋Š” ์‚ฌ๋ฒ•๋ถ€๋กœ ๋‚˜๋ˆ„์–ด ์„ธ ๊ธฐ๊ด€์ด ์„œ๋กœ ๊ฒฌ์ œํ•˜๋ฉด์„œ ์–ด๋А ํ•˜๋‚˜๊ฐ€ ๊ถŒ๋ ฅ์„ ๋งˆ์Œ๋Œ€๋กœ ํ–‰์‚ฌํ•˜์ง€ ๋ชปํ•˜๋„๋ก ํ•˜๋Š” ๊ฒƒ์„ ๋งํ•ฉ๋‹ˆ๋‹ค. ์ž…ํ—Œ์ฃผ์˜๋Š” ๊ตญ๊ฐ€์—์„œ ์ œ์ •ํ•œ ํ—Œ๋ฒ•์— ๋”ฐ๋ผ ์ •์น˜๋ฅผ ํ•˜๋Š” ๊ฒƒ์„ ๋งํ•ด์š”. ํ—Œ๋ฒ•์€ ๊ตญ๋ฏผ์˜ ์กด์—„์„ฑ์„ ๋ณดํ˜ธํ•˜๋Š” ๋‚ด์šฉ์„ ๋‹ด๊ณ  ์žˆ์–ด์•ผ ํ•ฉ๋‹ˆ๋‹ค. ๋‹ค์ˆ˜๊ฒฐ์ฃผ์˜๋Š” ๋‹ค์ˆ˜์˜ ํŒ๋‹จ์— ๋”ฐ๋ผ ์ •์ฑ…์ด ๊ฒฐ์ •๋˜๋Š” ๊ฒƒ์„ ๋งํ•ด์š”. ๋‹ค์ˆ˜์˜ ํŒ๋‹จ์ด ์†Œ์ˆ˜์˜ ํŒ๋‹จ๋ณด๋‹ค ์‹ค์ˆ˜ํ•  ๊ฐ€๋Šฅ์„ฑ์ด ๋‚ฎ๊ณ , ๋” ๋งŽ์€ ์‚ฌ๋žŒ๋“ค์ด ์›ํ•˜๋Š” ๊ฒƒ์ด ๋” ์ข‹๋‹ค๊ณ  ๋ณด๊ธฐ ๋•Œ๋ฌธ์ด์ง€์š”. ๊ทธ๋Ÿฌ๋‚˜ ์ง„์ •ํ•œ ๋ฏผ์ฃผ์ฃผ์˜๋ฅผ ์œ„ํ•ด์„œ๋Š” ๋ฐ˜๋“œ์‹œ ์ž์œ ๋กœ์šด ํ† ๋ก ์ด ๋ณด์žฅ๋˜์–ด์•ผ ํ•˜๊ณ , ํƒ€ํ˜‘ํ•˜๊ณ  ์–‘๋ณดํ•˜๋Š” ํƒœ๋„๊ฐ€ ํ•„์š”ํ•ฉ๋‹ˆ๋‹ค.']",2 +2025-JB-06," A,B, C๋Š” ๋ชจ๋‘ (๊ฐ€)๋Š” ์ ์—์„œ ์ •์น˜ ๊ณผ์ •์— ์˜ํ–ฅ๋ ฅ์„ ํ–‰์‚ฌํ•˜๋Š” ์ฃผ์ฒด๋กœ +๋ณผ ์ˆ˜ ์žˆ์ง€๋งŒ, A๋Š” (๋‚˜)๋Š” ์ ์—์„œ B, C์™€ ๊ตฌ๋ณ„๋˜๋Š” ํŠน์ง•์„ ๊ฐ–๋Š”๋‹ค. +์†Œ์† ์ง‘๋‹จ์˜ ํŠน์ˆ˜ํ•œ ์ดํ•ด๊ด€๊ณ„๋ฅผ ๊ด€์ฒ ํ•˜๊ธฐ ์œ„ํ•ด ์ •์น˜ ํ™œ๋™์„ ํ•˜๋Š” B์™€ ๊ณต์ต ์ถ”๊ตฌ๋ฅผ +๋ชฉ์ ์œผ๋กœ ๊ณต๊ณต์˜ ์ดํ•ด๊ด€๊ณ„๊ฐ€ ๊ฑธ๋ฆฐ ์Ÿ์ ์„ ์ œ๊ธฐํ•˜๊ณ  ํ•ด๊ฒฐ์„ ์š”๊ตฌํ•˜๋Š” C๋Š” ๋ชจ๋‘ +์ •์ฑ… ๊ฒฐ์ • ๊ณผ์ •์—์„œ ํ•ด๋‹น ์ง‘๋‹จ์˜ ์š”๊ตฌ๊ฐ€ ๊ด€์ฒ ๋˜๊ธฐ๋ฅผ ์›ํ•˜์ง€๋งŒ, B์™€ C๋Š” ๋ชจ๋‘ +์ •์น˜๊ถŒ๋ ฅ์„ ๋ชฉํ‘œ๋กœ ํ•˜๋Š” ์ง‘๋‹จ์€ ์•„๋‹ˆ๋‹ค. ํ•˜์ง€๋งŒ A๋Š” ๋‹จ์ˆœํžˆ ์š”๊ตฌ์˜ ์ „๋‹ฌ์— +๊ทธ์น˜์ง€ ์•Š๊ณ  ๊ทธ ๊ตฌ์„ฑ์›์ด ๊ณต์œ ํ•˜๋Š” ๊ณตํ†ต์˜ ์ •์น˜์  ๊ฐ€์น˜์— ๊ธฐ๋ฐ˜ํ•˜์—ฌ ์ •์น˜๊ถŒ๋ ฅ์„ +ํš๋“ํ•ด์•ผ ํ•˜๋Š” ์ด์œ ๋ฅผ ์ œ์‹œํ•จ์œผ๋กœ์จ ์œ ๊ถŒ์ž๋ฅผ ์„ค๋“ํ•ด์•ผ ํ•œ๋‹ค.","{'question': '๋‹ค์Œ ์ž๋ฃŒ์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€? (๋‹จ, A๏ฝžC๋Š” ๊ฐ๊ฐ ์ •๋‹น, ์ด์ต ์ง‘๋‹จ, ์‹œ๋ฏผ ๋‹จ์ฒด ์ค‘ ํ•˜๋‚˜์ž„.)', 'choices': ['A๋Š” ์ •์น˜์  ํ˜„์•ˆ์— ๊ด€ํ•œ ์ •๋ณด๋ฅผ ์ œ๊ณตํ•˜๋Š” ์ •์น˜ ์‚ฌํšŒํ™” ๊ธฐ๋Šฅ์„ ํ•œ๋‹ค.', 'B๋Š” ํ–‰์ •๋ถ€์™€ ์˜ํšŒ๋ฅผ ๋งค๊ฐœํ•˜๊ธฐ ์œ„ํ•ด ๊ต์„ญ ๋‹จ์ฒด๋ฅผ ๊ตฌ์„ฑํ•œ๋‹ค.', 'C๋Š” ๊ตญ๊ฐ€ ์ˆ˜์ค€์˜ ์ •์ฑ…์  ๋Œ€์•ˆ์„ ์ œ์‹œํ•˜๋Š” ์ •์ฑ… ๊ฒฐ์ • ๊ธฐ๊ตฌ์ด๋‹ค.', '(๊ฐ€)์—๋Š” โ€˜๊ณต์ง ์„ ๊ฑฐ๋ฅผ ํ†ตํ•ด ์ •์น˜์  ์ฑ…์ž„์„ ์ง„๋‹คโ€™๊ฐ€ ๋“ค์–ด๊ฐˆ ์ˆ˜ ์žˆ๋‹ค.', '(๋‚˜)์—๋Š” โ€˜์‚ฌํšŒ์  ์Ÿ์ ์— ๋Œ€ํ•œ ์—ฌ๋ก ์„ ํ˜•์„ฑํ•œ๋‹คโ€™๊ฐ€ ๋“ค์–ด๊ฐˆ ์ˆ˜ ์žˆ๋‹ค.'], 'answer': ''}",,1,1,True,[],4 +2025-JB-09,"โ—‹โ—‹์ฃ„๋กœ ๊ธฐ์†Œ๋œ ๊ฐ‘์€ ๋ฒŒ๊ธˆํ˜•์„ ์„ ๊ณ ๋ฐ›๊ณ  ์ƒ๊ณ ํ•˜์˜€์œผ๋‚˜, +A๊ฐ€ ์ด๋ฅผ ๊ธฐ๊ฐํ•˜์—ฌ ํŒ๊ฒฐ์ด ํ™•์ •๋จ์— ๋”ฐ๋ผ โ–กโ–ก๋ฒ• ์กฐํ•ญ์— ๋”ฐ๋ฅธ +์‹ ์ƒ ์ •๋ณด ๋“ฑ๋ก ๋Œ€์ƒ์ž๊ฐ€ ๋˜์—ˆ๋‹ค. ์ด์— ๋‹ค๋ฅธ ๊ตฌ์ œ ์ ˆ์ฐจ๋ฅผ ๊ฑฐ์น  ์ˆ˜ +์—†์—ˆ๋˜ ๊ฐ‘์€ โ–กโ–ก๋ฒ• ์กฐํ•ญ์ด ์ž์‹ ์˜ ๊ธฐ๋ณธ๊ถŒ์„ ์ง์ ‘ ์นจํ•ดํ•œ๋‹ค๊ณ  +์ฃผ์žฅํ•˜๋ฉด์„œ B์— ํ—Œ๋ฒ• ์†Œ์› ์‹ฌํŒ์„ ์ฒญ๊ตฌํ•˜์˜€๋‹ค. ์ด์— ๋Œ€ํ•ด B๋Š” +ํ•ด๋‹น ์กฐํ•ญ์ด ๊ฐ‘์˜ ๊ธฐ๋ณธ๊ถŒ์„ ์นจํ•ดํ•˜์ง€ ์•Š๋Š”๋‹ค๊ณ  ํŒ๋‹จํ•˜์˜€๋‹ค. +ํ•œํŽธ, โ–ณโ–ณ์ฃ„๋กœ ๊ธฐ์†Œ๋œ ์„์€ ์ง•์—ญํ˜•์„ ์„ ๊ณ ๋ฐ›๊ณ  ํŒ๊ฒฐ์ด ํ™•์ •๋˜์–ด +โ–กโ–ก๋ฒ• ์กฐํ•ญ์— ๋”ฐ๋ผ ์‹ ์ƒ ์ •๋ณด ๋“ฑ๋ก ๋Œ€์ƒ์ž ๋ฐ โ—‡โ—‡๋ฒ• ์กฐํ•ญ์— +๋”ฐ๋ผ ๋””์—”์—์ด(DNA)๊ฐ์‹ ์‹œ๋ฃŒ ์ฑ„์ทจ ๋Œ€์ƒ์ž๊ฐ€ ๋˜์—ˆ๋‹ค. ์ด์— +๊ฒ€์‚ฌ๋Š” ์„์˜ ๋””์—”์—์ด(DNA)๊ฐ์‹ ์‹œ๋ฃŒ๋ฅผ ์ฑ„์ทจํ•˜๋„๋ก ํ•˜๋Š” ์ฒ˜๋ถ„์„ +ํ•˜์˜€๋‹ค. ์„์€ ์ด์— ๋ถˆ๋ณตํ•˜์—ฌ C์— ๊ทธ ์ฒ˜๋ถ„์˜ ์ทจ์†Œ๋ฅผ ์ฒญ๊ตฌํ•˜๊ณ , +๋‹นํ•ด ์‚ฌ๊ฑด์— ์ ์šฉ๋˜๋Š” โ—‡โ—‡๋ฒ• ์กฐํ•ญ์— ๋Œ€ํ•˜์—ฌ ์œ„ํ—Œ ๋ฒ•๋ฅ  ์‹ฌํŒ์„ +์ œ์ฒญํ•ด ์ค„ ๊ฒƒ์„ C์— ์‹ ์ฒญํ•˜์˜€๋‹ค. C๊ฐ€ ์„์˜ ์ œ์ฒญ ์‹ ์ฒญ์„ ๊ธฐ๊ฐ +ํ•˜์ž ์„์€ โ—‡โ—‡๋ฒ• ์กฐํ•ญ์ด ์ž์‹ ์˜ ์‹ ์ฒด์˜ ์ž์œ ๋ฅผ ์นจํ•ดํ•œ๋‹ค๊ณ  +์ฃผ์žฅํ•˜๋ฉฐ B์— ํ—Œ๋ฒ• ์†Œ์› ์‹ฌํŒ์„ ์ฒญ๊ตฌํ•˜์˜€๋‹ค. ์ด์— ๋Œ€ํ•ด B๋Š” +โ—‡โ—‡๋ฒ• ์กฐํ•ญ์ด ํ—Œ๋ฒ•์— ์œ„๋ฐ˜๋˜์ง€ ์•Š๋Š”๋‹ค๊ณ  ํŒ๋‹จํ•˜์˜€๋‹ค.","{'question': '๋‹ค์Œ ์ž๋ฃŒ์— ๋Œ€ํ•œ ๋ฒ•์  ํŒ๋‹จ์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['A์˜ ๊ธฐ๊ฐ ๊ฒฐ์ •์— ๋ถˆ๋ณตํ•˜๋Š” ๊ฒฝ์šฐ C์— ์ƒ์†Œํ•˜์—ฌ ์žฌํŒ์„ ๋ฐ›์„ ์ˆ˜ ์žˆ๋‹ค.', '๊ฐ‘์€ A์˜ ๊ธฐ๊ฐ ๊ฒฐ์ •์— ๋Œ€ํ•˜์—ฌ B์— ํ—Œ๋ฒ• ์†Œ์› ์‹ฌํŒ์„ ์ฒญ๊ตฌํ•˜์˜€๋‹ค.', 'C๋Š”โ–กโ–ก๋ฒ• ์กฐํ•ญ์ด ์„์˜ ์‹ ์ฒด์˜ ์ž์œ ๋ฅผ ์นจํ•ดํ•œ๋‹ค๊ณ  ํŒ๋‹จํ•˜์˜€๋‹ค.', '๊ฐ‘๊ณผ ๋‹ฌ๋ฆฌ ์„์€ โ–กโ–ก๋ฒ• ์กฐํ•ญ์— ๋”ฐ๋ฅธ ์‹ ์ƒ ์ •๋ณด ๋“ฑ๋ก ๋Œ€์ƒ์ž๊ฐ€ ๋˜์ง€ ์•Š์•˜๋‹ค.', '์„์ด ์ฒญ๊ตฌํ•œ ํ—Œ๋ฒ• ์†Œ์› ์‹ฌํŒ๊ณผ ๋‹ฌ๋ฆฌ ๊ฐ‘์ด ์ฒญ๊ตฌํ•œ ํ—Œ๋ฒ• ์†Œ์› ์‹ฌํŒ์€ ๊ถŒ๋ฆฌ ๊ตฌ์ œํ˜• ํ—Œ๋ฒ• ์†Œ์› ์‹ฌํŒ์ด๋‹ค.'], 'answer': ''}",,5,2,False,[],2 +2025-JB-10,"๊ฐ‘์€ โ—‹โ—‹์Šคํ‚ค์žฅ์„ ์ž„์ฐจํ•˜์—ฌ ์ง์ ‘ ์ ์œ ํ•˜๋ฉด์„œ ์šด์˜ํ•˜๊ณ  ์žˆ๋‹ค. +โ—‹โ—‹์Šคํ‚ค์žฅ ์Šฌ๋กœํ”„์—๋Š” ์„ค์น˜์ƒ ํ•˜์ž๋กœ ๋ฐฐ์ˆ˜๊ด€์ด ํŠ€์–ด๋‚˜์™€ +์žˆ์—ˆ๋Š”๋ฐ, ์Šคํ‚ค๋ฅผ ํƒ€๊ณ  ๋‚ด๋ ค์˜ค๋˜ ์„์€ ์ด ๋ฐฐ์ˆ˜๊ด€์— ๊ฑธ๋ ค ๋‹ค๋ฆฌ๊ฐ€ +๋ถ€๋Ÿฌ์กŒ๋‹ค. ๊ฐ‘์˜ ์ง์› ๋ณ‘์€ ๊ฐ‘์˜ ์ง€์‹œ์— ๋”ฐ๋ผ ์ฐจ๋Ÿ‰์„ ์šด์ „ํ•˜์—ฌ +์„์„ ๋ณ‘์›์— ๋ฐ๋ฆฌ๊ณ  ๊ฐ€๋˜ ์ค‘ ์ค‘์•™์„ ์„ ์นจ๋ฒ”ํ•˜์˜€๊ณ , ์ •์ด ์šด์ „ํ•˜๋˜ +ํŠธ๋Ÿญ๊ณผ ์ถฉ๋Œํ•˜์˜€๋‹ค. ์ด ์‚ฌ๊ณ ๋กœ ์„์ด ์ฐจ๋Ÿ‰ ๋ฐ–์œผ๋กœ ํŠ•๊ฒจ ๋‚˜๊ฐ€ +ํฌ๊ฒŒ ๋‹ค์ณ ์„ ๋‹ฌ๊ฐ„ ์ž…์› ์น˜๋ฃŒ๋ฅผ ๋ฐ›๊ฒŒ ๋˜์—ˆ๊ณ , ์ •์‹ ์ ์œผ๋กœ๋„ ํฐ +๊ณ ํ†ต์„ ๊ฒช์—ˆ๋‹ค. ํ•œํŽธ ์ด ์‚ฌ๊ณ  ๋‹น์‹œ ์ •์€ ํ™”๋ฌผ์นธ์— ๋ผ์ง€๋“ค์„ ์‹ฃ๊ณ  +๋ผ์ง€ ์†Œ์œ ์ž์—๊ฒŒ ์šด์†กํ•˜๋˜ ์ค‘์ด์—ˆ๋Š”๋ฐ, ์‚ฌ๊ณ ๋กœ ํŠธ๋Ÿญ์ด ๋ฉˆ์ถ˜ ์‚ฌ์ด +ํ™”๋ฌผ์นธ์— ์‹ค๋ ค ์žˆ๋˜ ๋ผ์ง€๋“ค์ด ํƒˆ์ถœํ•˜์—ฌ ๋„๋กœ ์ธ๊ทผ์— ์žˆ๋Š” ๋ฌด๊ฐ€ +์†Œ์œ ํ•œ ๋ฐฐ์ถ”๋ฐญ์„ ๋งˆ๊ตฌ ํ—ค์ง‘์–ด ๋ฌด์—๊ฒŒ ์žฌ์‚ฐ์ƒ ํ”ผํ•ด๋ฅผ ์ž…ํ˜”๋‹ค.","{'question': '๋‹ค์Œ ์‚ฌ๋ก€์— ๋Œ€ํ•œ ๋ฒ•์  ํŒ๋‹จ์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['๊ฐ‘์€ ๋ฐฐ์ˆ˜๊ด€์œผ๋กœ ์ธํ•œ ์†ํ•ด ๋ฐฉ์ง€์— ํ•„์š”ํ•œ ์ฃผ์˜๋ฅผ ๋‹คํ•˜์˜€๋”๋ผ๋„ ์„์— ๋Œ€ํ•˜์—ฌ ๊ณต์ž‘๋ฌผ ์ ์œ ์ž๋กœ์„œ ํŠน์ˆ˜ ๋ถˆ๋ฒ• ํ–‰์œ„ ์ฑ…์ž„์„ ์ง„๋‹ค.', '๊ฐ‘์ด ์„์—๊ฒŒ ์น˜๋ฃŒ๋น„ ์ƒ๋‹น์˜ ์†ํ•ด๋ฅผ ๋ฐฐ์ƒํ•˜์˜€๋‹ค๋ฉด ๊ฐ‘์€ ์„์—๊ฒŒ ์ •์‹ ์  ๊ณ ํ†ต์— ๋”ฐ๋ฅธ ์œ„์ž๋ฃŒ๋Š” ๋ฐฐ์ƒํ•˜์ง€ ์•Š์•„๋„ ๋œ๋‹ค.', '์„์— ๋Œ€ํ•˜์—ฌ ๋ณ‘์˜ ์‚ฌ์šฉ์ž๋กœ์„œ ๊ฐ‘์˜ ํŠน์ˆ˜ ๋ถˆ๋ฒ• ํ–‰์œ„ ์ฑ…์ž„์ด ์ธ์ • ๋˜๋”๋ผ๋„ ๋ณ‘์€ ์„์— ๋Œ€ํ•œ ์ผ๋ฐ˜ ๋ถˆ๋ฒ• ํ–‰์œ„ ์ฑ…์ž„์„ ๋ฉดํ•  ์ˆ˜ ์—†๋‹ค.', '์ •์ด ๋™๋ฌผ์˜ ์ข…๋ฅ˜์™€ ์„ฑ์งˆ์— ๋”ฐ๋ผ ๋ณด๊ด€์— ์ƒ๋‹นํ•œ ์ฃผ์˜๋ฅผ ๊ธฐ์šธ์˜€์Œ์„ ์ฆ๋ช…ํ•˜๋”๋ผ๋„ ๋ฌด์— ๋Œ€ํ•œ ์ •์˜ ์†ํ•ด ๋ฐฐ์ƒ ์ฑ…์ž„์€ ๋ฉด์ œ๋˜์ง€ ์•Š๋Š”๋‹ค.', '๋ฌด์— ๋Œ€ํ•œ ์ •์˜ ์†ํ•ด ๋ฐฐ์ƒ ์ฑ…์ž„์ด ์ธ์ •๋˜๋Š” ๊ฒฝ์šฐ ํŠน๋ณ„ํ•œ ์‚ฌ์ •์ด ์—†๋Š” ํ•œ ์ •์€ ๋ฌด๊ฐ€ ์†Œ์œ ํ•œ ๋ฐฐ์ถ”๋ฐญ์„ ์›์ƒ์œผ๋กœ ํšŒ๋ณต์‹œ์ผœ์•ผ ํ•œ๋‹ค.'], 'answer': ''}",,3,3,True,[],3 +2025-JB-11,"๊ต์‚ฌ: A๊ตญ์—์„œ ์‹œํ–‰๋˜๊ณ  ์žˆ๋Š” โ–ณโ–ณ๋ฒ•๊ณผ ๊ทธ ์ ์šฉ ์‚ฌ๋ก€์— ๋Œ€ํ•˜์—ฌ ์šฐ๋ฆฌ๋‚˜๋ผ +ํ˜•๋ฒ•์— ๊ทผ๊ฑฐํ•˜์—ฌ ๋ฒ•์  ํŒ๋‹จ์„ ํ•ด ๋ณด์„ธ์š”. + A๊ตญ์—์„œ๋Š” ํ—ˆ์œ„ ์˜์ƒ๋ฌผ์˜ ์ œ์ž‘๊ณผ ์œ ํฌ ๋“ฑ์ด ์‚ฌํšŒ ๋ฌธ์ œ๋กœ ๋“ฑ์žฅํ•˜์˜€์ง€๋งŒ +์ด๋ฅผ ์ฒ˜๋ฒŒํ•  ์ˆ˜ ์žˆ๋Š” ๋ฒ•๋ฅ ์ด ์—†์—ˆ๋‹ค. ์ด์— A๊ตญ ์˜ํšŒ๋Š” ์ฒ˜์Œ์œผ๋กœ ํ—ˆ์œ„ +์˜์ƒ๋ฌผ์˜ ์ œ์ž‘๊ณผ ์œ ํฌ๋ฅผ ์ฒ˜๋ฒŒํ•˜๊ธฐ ์œ„ํ•ด 2020๋…„ 6์›” โ–ณโ–ณ๋ฒ•์„ ์ œ์ •ํ•˜์˜€๋‹ค. + โ—‹2020๋…„ 6์›” ์‹œํ–‰๋œ โ–ณโ–ณ๋ฒ• ์ œ14์กฐ ์ œ1ํ•ญ์€ โ€˜์œ ํฌํ•  ๋ชฉ์ ์œผ๋กœ ์‚ฌ๋žŒ์˜ ์‹ ์ฒด๋ฅผ +๋Œ€์ƒ์œผ๋กœ ํ•œ ํ—ˆ์œ„ ์˜์ƒ๋ฌผ์„ ์ œ์ž‘ํ•œ ์ž๋Š” 1๋…„ ์ดํ•˜์˜ ๊ธˆ๊ณ ์— ์ฒ˜ํ•œ๋‹ค.โ€™๊ณ  ๊ทœ์ • +ํ•˜์˜€๋‹ค. + โ—‹2021๋…„ 6์›” โ–ณโ–ณ๋ฒ• ์ œ14์กฐ ์ œ1ํ•ญ์€ ํ˜•๋ฒŒ ๊ทœ์ •๋งŒ โ€˜2๋…„ ์ดํ•˜์˜ ์ง•์—ญ์— ์ฒ˜ํ•œ๋‹ค.โ€™๋กœ +๊ฐœ์ •โ€ค์‹œํ–‰๋˜์—ˆ๋‹ค. + โ—‹๋ณ‘์€ 2022๋…„ 10์›”์— ์‚ฌ๋žŒ์˜ ์‹ ์ฒด๋ฅผ ๋Œ€์ƒ์œผ๋กœ ํ•œ ํ—ˆ์œ„ ์˜์ƒ๋ฌผ์„ ์ €์žฅํ•˜์—ฌ +์‹œ์ฒญํ•œ ์‚ฌ์‹ค๋กœ 2023๋…„ 1์›”์— ๊ธฐ์†Œ๋˜์–ด ์žฌํŒ ์ค‘์ด๋‹ค. + โ—‹2022๋…„ 7์›” โ–ณโ–ณ๋ฒ• ์ œ14์กฐ ์ œ2ํ•ญ โ€˜์‚ฌ๋žŒ์˜ ์‹ ์ฒด๋ฅผ ๋Œ€์ƒ์œผ๋กœ ํ•œ ํ—ˆ์œ„ ์˜์ƒ๋ฌผ์„ +์ €์žฅโ€ค์‹œ์ฒญํ•œ ์ž๋Š” 2์ฒœ๋งŒ ์› ์ดํ•˜์˜ ๋ฒŒ๊ธˆ์— ์ฒ˜ํ•œ๋‹ค.โ€™๋Š” ์กฐํ•ญ์ด ์‹ ์„คโ€ค์‹œํ–‰ +๋˜์—ˆ๋‹ค. + [โ–ณโ–ณ๋ฒ•์˜ ์ ์šฉ ์‚ฌ๋ก€] + โ—‹๊ฐ‘์€ 2020๋…„ 8์›”์— ์‚ฌ๋žŒ์˜ ์‹ ์ฒด๋ฅผ ๋Œ€์ƒ์œผ๋กœ ํ•œ ํ—ˆ์œ„ ์˜์ƒ๋ฌผ์„ ์ œ์ž‘ํ•˜์—ฌ +์œ ํฌํ•œ ์‚ฌ์‹ค๋กœ 2021๋…„ 9์›”์— ๊ธฐ์†Œ๋˜์–ด ์žฌํŒ ์ค‘์ด๋‹ค. + โ—‹์„์€ 2021๋…„ 12์›”์— ์‚ฌ๋žŒ์˜ ์‹ ์ฒด๋ฅผ ๋Œ€์ƒ์œผ๋กœ ํ•œ ํ—ˆ์œ„ ์˜์ƒ๋ฌผ์„ ์ œ์ž‘ํ•˜์—ฌ +์œ ํฌํ•œ ์‚ฌ์‹ค๋กœ 2022๋…„ 2์›”์— ๊ธฐ์†Œ๋˜์–ด ์žฌํŒ ์ค‘์ด๋‹ค. +ํ•™์ƒ: +(๊ฐ€)","{'question': '๋‹ค์Œ ์ž๋ฃŒ์˜ (๊ฐ€)์— ๋“ค์–ด๊ฐˆ ์ˆ˜ ์žˆ๋Š” ์ง„์ˆ ๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['โ–ณโ–ณ๋ฒ•์˜ ๊ธฐ๋Šฅ์€ ์ผ๋ฐ˜ ๊ตญ๋ฏผ์˜ ์ž์œ ์™€ ๊ถŒ๋ฆฌ ๋ณด์žฅ์ด ์•„๋‹Œ ์‚ฌํšŒ ์งˆ์„œ ์œ ์ง€์— ์žˆ์Šต๋‹ˆ๋‹ค.', '๋ฒ•์›์€ ๊ฐ‘์—๊ฒŒ ๊ฐœ์ • ์ „ โ–ณโ–ณ๋ฒ• ์ œ14์กฐ ์ œ1ํ•ญ์„ ์ ์šฉํ•˜์—ฌ ์ •ํ•ด์ง„ ๋…ธ์—ญ์— ๋ณต๋ฌดํ•ด์•ผ ํ•˜๋Š” ํ˜•๋ฒŒ์„ ์„ ๊ณ ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.', '์„์˜ ํ–‰์œ„์— ๋Œ€ํ•˜์—ฌ ๊ฐœ์ • ์ „ โ–ณโ–ณ๋ฒ•์ œ14์กฐ ์ œ1ํ•ญ์„ ์ ์šฉํ•˜๋Š” ๊ฒƒ์€ ์ฃ„ํ˜• ๋ฒ•์ •์ฃผ์˜์— ์œ„๋ฐฐ๋ฉ๋‹ˆ๋‹ค.', '๋ณ‘์˜ ํ–‰์œ„์— ๋Œ€ํ•˜์—ฌ โ–ณโ–ณ๋ฒ• ์ œ14์กฐ ์ œ2ํ•ญ์„ ์ ์šฉํ•˜๋Š” ๊ฒƒ์€ ์ฃ„ํ˜• ๋ฒ•์ •์ฃผ์˜์— ์œ„๋ฐฐ๋ฉ๋‹ˆ๋‹ค.', 'โ–ณโ–ณ๋ฒ•์ œ14์กฐ ์ œ2ํ•ญ์—๋Š” ๋ฒ”์ฃ„ ํ–‰์œ„์— ์ œ๊ณตํ•œ ๋ฌผ๊ฑด์„ ๊ตญ๊ณ ์— ๊ท€์†์‹œํ‚ฌ ์ˆ˜ ์žˆ๋Š” ํ˜•๋ฒŒ์ด ๊ทœ์ •๋˜์–ด ์žˆ์Šต๋‹ˆ๋‹ค.'], 'answer': ''}",,3,4,False,[],3 +2025-JB-12,"๊ฐ‘์˜ ๋ถ€๋ชจ A, B๊ฐ€ ์ดํ˜ผํ•˜๋ฉด์„œ A๊ฐ€ ๊ฐ‘์˜ ๋‹จ๋… ์นœ๊ถŒ์ž๋กœ ์ง€์ •๋˜์—ˆ๋‹ค. +๊ฐ‘์€ ๋ฌด์šฉ ๊ณต์—ฐ์„ ์œ„ํ•˜์—ฌ ์„๊ณผ 100๋งŒ ์›์— ์˜์ƒ ์ œ์ž‘ ๊ณ„์•ฝ์„ ์ฒด๊ฒฐํ•˜์˜€๋‹ค. +์„์€ ๊ณ„์•ฝ ๋‹น์ผ์—๋Š” ๊ฐ‘์ด ๋ฏธ์„ฑ๋…„์ž์ž„์„ ๋ชฐ๋ž์œผ๋‚˜ ๋‹ค์Œ ๋‚  ๊ฐ‘์ด ๋ฏธ์„ฑ๋…„์ž์ž„์„ +์•Œ๊ฒŒ ๋˜์—ˆ๋‹ค. ์ผ์ฃผ์ผ ํ›„ ๊ฐ‘์€ 19์„ธ ์ƒ์ผ์— ์นœ๊ตฌ์˜ ๊ณต์—ฐ์„ ๋ณด๋Ÿฌ ๊ฐ”๋‹ค๊ฐ€ +์„์—๊ฒŒ ์ œ์ž‘์„ ๋งก๊ธด ์˜์ƒ๊ณผ ๋™์ผํ•œ ์˜์ƒ์„ 90๋งŒ ์›์— ์ œ์ž‘ํ•ด ์ค€๋‹ค๋Š” ๋ณ‘์„ +์†Œ๊ฐœ๋ฐ›์•˜๋‹ค. ๊ฐ‘์€ ๋ณ‘์—๊ฒŒ 80๋งŒ ์›์— ์˜์ƒ์„ ์ œ์ž‘ํ•ด ์ค„ ์ˆ˜ ์žˆ๋Š”์ง€ ๋ฌผ์—ˆ๊ณ , +๋ณ‘์ด ์•Œ๊ฒ ๋‹ค๊ณ  ํ•˜์—ฌ ๊ฐ‘๊ณผ ๋ณ‘์€ 80๋งŒ ์›์— ์˜์ƒ ์ œ์ž‘์„ ํ•˜๋Š” ๋ฐ์— ํ•ฉ์˜ํ•˜๊ณ  +๋‹ค์Œ ๋‚  ๊ณ„์•ฝ์„œ๋ฅผ ์ž‘์„ฑํ•˜์˜€๋‹ค. ์ดํ›„ ์„์€ ๊ฐ‘์˜ ์˜์ƒ ์ œ์ž‘์„ ์™„๋ฃŒํ•œ ์ƒํƒœ์—์„œ +๊ฐ‘๊ณผ ๋ณ‘์˜ ๊ณ„์•ฝ ์ฒด๊ฒฐ ์‚ฌ์‹ค์„ ์•Œ๊ฒŒ ๋˜์—ˆ๊ณ , ์ œ์ž‘ํ•œ ์˜์ƒ์„ ๊ฐ‘์—๊ฒŒ ์ธ๋„ํ•˜์˜€๋‹ค. +ํ•œํŽธ A, B๋Š” ๊ฐ‘๊ณผ ์„์ด ์ฒด๊ฒฐํ•œ ๊ณ„์•ฝ๊ณผ ๊ฐ‘๊ณผ ๋ณ‘์ด ์ฒด๊ฒฐํ•œ ๊ณ„์•ฝ์— ๊ด€ํ•˜์—ฌ +์•Œ์ง€ ๋ชปํ•˜๋‹ค๊ฐ€ ์„์ด ๊ฐ‘์—๊ฒŒ ์˜์ƒ์„ ์ธ๋„ํ•œ ๋‚  ์ด ๊ณ„์•ฝ๋“ค์— ๊ด€ํ•˜์—ฌ ์•Œ๊ฒŒ ๋˜์—ˆ๋‹ค.","{'question': '๋‹ค์Œ ์‚ฌ๋ก€์— ๋Œ€ํ•œ ๋ฒ•์  ํŒ๋‹จ์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['์„์€ ๊ฐ‘์ด ๋ฏธ์„ฑ๋…„์ž์ž„์„ ์•Œ๊ฒŒ ๋˜์—ˆ์„ ๋‹น์‹œ ๊ฐ‘๊ณผ์˜ ์˜์ƒ ์ œ์ž‘ ๊ณ„์•ฝ์„ ์ฒ ํšŒํ•  ์ˆ˜ ์—†๋‹ค.', '์„์€ B์—๊ฒŒ ๊ฐ‘๊ณผ์˜ ์˜์ƒ ์ œ์ž‘ ๊ณ„์•ฝ์˜ ์ทจ์†Œ ์—ฌ๋ถ€์— ๋Œ€ํ•œ ํ™•๋‹ต์„ ์ด‰๊ตฌํ•  ์ˆ˜ ์—†๋‹ค.', '๋ณ‘์˜ ์ฒญ์•ฝ๊ณผ ๊ฐ‘์˜ ์Šน๋‚™์ด ํ•ฉ์น˜๋œ ๋•Œ ๊ฐ‘๊ณผ ๋ณ‘์˜ ์˜์ƒ ์ œ์ž‘ ๊ณ„์•ฝ์ด ์„ฑ๋ฆฝํ•˜์˜€๋‹ค.', 'A๋Š” ์„์ด ์ œ์ž‘ํ•œ ์˜์ƒ์„ ๊ฐ‘์—๊ฒŒ ์ธ๋„ํ•œ ๋•Œ์— ๊ฐ‘๊ณผ ๋ณ‘์˜ ์˜์ƒ ์ œ์ž‘ ๊ณ„์•ฝ์„ ์ทจ์†Œํ•  ์ˆ˜ ์žˆ๋‹ค.', '์„์ด ์ œ์ž‘ํ•œ ์˜์ƒ์„ ์ธ๋„๋ฐ›์€ ๊ฐ‘์ด ๊ทธ ๋Œ€๊ธˆ์„ ์ง€๊ธ‰ํ•˜์ง€ ์•Š๋”๋ผ๋„ ๊ฐ‘์—๊ฒŒ ๊ณ„์•ฝ์ƒ ์˜๋ฌด ์œ„๋ฐ˜์— ๋”ฐ๋ฅธ ์ฑ…์ž„์€ ์„ฑ๋ฆฝํ•˜์ง€ ์•Š๋Š”๋‹ค.'], 'answer': ''}",,2,3,False,[],3 +2025-JB-13,"๋ฒ•๋ฅ ํ˜ผ ๊ด€๊ณ„์— ์žˆ๋Š” ๊ฐ‘๊ณผ ์„ ์‚ฌ์ด์—์„œ A๊ฐ€ ํƒœ์–ด๋‚œ ํ›„ B๊ฐ€ ์ ๋ฒ•ํ•œ ์ ˆ์ฐจ๋ฅผ +๊ฑฐ์ณ ๊ฐ‘๊ณผ ์„์˜ ์นœ์–‘์ž๋กœ ์ž…์–‘๋˜์—ˆ๋‹ค. ๊ทธ ํ•ด์— ๊ฐ‘์€ ์œ ์–ธ ์—†์ด ์‚ฌ๋งํ•˜์˜€๋‹ค. +ํ•œํŽธ ๋ณ‘์€ ์ •๊ณผ์˜ ์‚ฌ์ด์—์„œ ์ž๋…€ C๋ฅผ ๋‚ณ์•˜์œผ๋‚˜ ๊ฒฝ์ œ์  ๋ฌธ์ œ๋กœ ์ž์ฃผ +๋‹คํˆฌ์—ˆ๋‹ค. ๊ฒฐ๊ตญ ๋ณ‘๊ณผ ์ •์€ ๋ฒ•์›์— ์ดํ˜ผ ์˜์‚ฌ ํ™•์ธ์˜ ์‹ ์ฒญ์„ ํ•˜์—ฌ ์ดํ˜ผ์— +์ด๋ฅด๋ €๊ณ , ๋ณ‘์ด ๋‹จ๋…์œผ๋กœ C์— ๋Œ€ํ•œ ์นœ๊ถŒ์ž ๋ฐ ์–‘์œก๊ถŒ์ž๊ฐ€ ๋˜์—ˆ๋‹ค. ์ดํ›„ +๋ณ‘์€ ์„๊ณผ ๋ฒ•๋ฅ ํ˜ผ์„ ํ•˜์˜€๊ณ , ์„์ด ์ ๋ฒ•ํ•œ ์ ˆ์ฐจ๋ฅผ ๊ฑฐ์ณ C๋ฅผ ์นœ์–‘์ž ์•„๋‹Œ +์–‘์ž๋กœ ์ž…์–‘ํ•˜์˜€๋‹ค. ๊ทธ ํ›„ ๋ณ‘์ด ๊ฐ‘์ž‘์Šค๋Ÿฐ ์‚ฌ๊ณ ๋กœ ์œ ์–ธ ์—†์ด ์‚ฌ๋งํ•˜์˜€๊ณ , +1๋…„ ํ›„ C๋„ ์ดˆ๋“ฑํ•™๊ต ์ž…ํ•™์„ ์•ž๋‘” ์ƒํƒœ์—์„œ ๊ฐ‘์ž‘์Šค๋Ÿฐ ์‚ฌ๊ณ ๋กœ ์‚ฌ๋งํ•˜์˜€๋‹ค.","{'question': '๋‹ค์Œ ์‚ฌ๋ก€์— ๋Œ€ํ•œ ๋ฒ•์  ํŒ๋‹จ์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['๊ฐ‘์˜ ์‚ฌ๋ง ์‹œ ์„๊ณผ A๋Š” ๊ฐ‘์˜ ์ƒ์†์ธ์ด์ง€๋งŒ B๋Š” ๊ฐ‘์˜ ์ƒ์†์ธ์ด ์•„๋‹ˆ๋‹ค.', '๋ณ‘๊ณผ ์ •์ด ๋ฒ•์›์œผ๋กœ๋ถ€ํ„ฐ ์ดํ˜ผ ์˜์‚ฌ ํ™•์ธ์„ ๋ฐ›์€ ๋•Œ๋ถ€ํ„ฐ ์ •์€ C์— ๋Œ€ํ•˜์—ฌ ๋ฉด์ ‘ ๊ต์„ญ๊ถŒ์„ ๊ฐ–๋Š”๋‹ค.', '๋ณ‘์˜ ์‚ฌ๋ง ์‹œ B๋Š” A์˜ ์นœ์กฑ์ด์ง€๋งŒ C๋Š” A์˜ ์นœ์กฑ์ด ์•„๋‹ˆ๋‹ค', '๋ณ‘์˜ ์‚ฌ๋ง ์‹œ C๋Š” ๋ณ‘์˜ ์ƒ์†์ธ์ด์ง€๋งŒ A์™€ B๋Š” ๋ณ‘์˜ ์ƒ์†์ธ์ด ์•„๋‹ˆ๋‹ค', 'C์˜์‚ฌ๋ง ์‹œ์ •์€ C์˜ ์ƒ์†์ธ์ด ์•„๋‹ˆ๋‹ค.'], 'answer': ''}",,4,4,True,[],4 +2025-JB-14,"๊ฐ‘(18์„ธ)์€ ๋ˆ์„ ๋‚ผ ์˜์‚ฌ๊ฐ€ ์—†์Œ์—๋„ A๊ฐ€ ์šด์˜ํ•˜๋Š” ์‹๋‹น์—์„œ ์Œ์‹์„ +์ฃผ๋ฌธํ•˜์—ฌ ๋จน๊ณ  ๋„์ฃผํ•˜์˜€๋‹ค. A๋Š” ์‹๋‹น ์•ž์„ ์ง€๋‚˜๊ฐ€๋˜ ์„์„ ๊ฐ‘์œผ๋กœ ์˜ค์ธํ•˜๊ณ  +โ€œ๊ณ„์‚ฐํ•˜๊ณ  ๊ฐ€์•ผ์ง€.โ€๋ผ๊ณ  ํ•˜๋ฉฐ ์„์˜ ๋ฉฑ์‚ด์„ ์žก๊ณ  ์‹๋‹น์œผ๋กœ ๋Œ๊ณ  ์™”๋‹ค. +๊ฒฝ์ฐฐ์ด ์ˆ˜์‚ฌ๋ฅผ ํ†ตํ•˜์—ฌ A์˜ ์‹๋‹น์—์„œ ์Œ์‹์„ ๋จน์€ ์‚ฌ๋žŒ์€ ์„์ด ์•„๋‹ˆ๋ผ๋Š” +์‚ฌ์‹ค์„ ๋ฐํ˜€๋ƒˆ์ง€๋งŒ, ์ˆ˜์‚ฌ ๊ณผ์ •์—์„œ ์„์€ ์ •์‹ ์ ์œผ๋กœ ํฐ ๊ณ ํ†ต์„ ๋ฐ›์•˜๋‹ค. +๊ฒ€์‚ฌ๋Š” A์— ๋Œ€ํ•˜์—ฌ ํญํ–‰ ํ˜์˜๋Š” ์ธ์ •๋˜์ง€๋งŒ ์ •์ƒ์„ ์ฐธ์ž‘ํ•˜์—ฌ ๊ธฐ์†Œ ์œ ์˜ˆ +์ฒ˜๋ถ„์„ ํ•˜์˜€๋‹ค. ์ดํ›„ ๊ฒฝ์ฐฐ์€ ๋™์ข…์˜ ๋ฒ”์ฃ„๋กœ ์ˆ˜์‚ฌ ๋Œ€์ƒ์ด์—ˆ๋˜ ๊ฐ‘์„ ์ฒดํฌ +ํ•˜์˜€๊ณ , ๊ฒ€์‚ฌ๋Š” ๊ตฌ์† ์ƒํƒœ์ธ ๊ฐ‘์„ ๊ธฐ์†Œํ•˜์˜€๋‹ค. 1์‹ฌ ๋ฒ•์›์ด ๊ฐ‘์—๊ฒŒ ์‚ฌ๊ธฐ์ฃ„๋กœ +์ง•์—ญ 6์›”์— ์ง‘ํ–‰ ์œ ์˜ˆ 1๋…„์„ ์„ ๊ณ ํ•˜์˜€๋‹ค. ์ด์— ๊ฒ€์‚ฌ๋Š” ํ•ญ์†Œ๋ฅผ ํ•˜์˜€์ง€๋งŒ +๊ฐ‘์€ ํ•ญ์†Œ๋ฅผ ํฌ๊ธฐํ•˜์˜€๋‹ค.","{'question': '๋‹ค์Œ ์‚ฌ๋ก€์— ๋Œ€ํ•œ ๋ฒ•์  ํŒ๋‹จ์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['์„์˜ ์ •์‹ ์  ํ”ผํ•ด์— ๋Œ€ํ•˜์—ฌ ๊ตญ๊ฐ€๋Š” ๋ฒ”์ฃ„ ํ”ผํ•ด์ž ๊ตฌ์กฐ๊ธˆ์„ ์ง€๊ธ‰ ํ•ด์•ผ ํ•œ๋‹ค.', '๊ฒ€์‚ฌ๋Š” A์˜ ํ–‰์œ„๊ฐ€ ์ž๊ตฌ ํ–‰์œ„์— ํ•ด๋‹นํ•œ๋‹ค๊ณ  ํŒ๋‹จํ•˜์˜€๋‹ค.', '๊ฐ‘์€ ๊ธฐ์†Œ ์ „ ๊ฒ€์‚ฌ์—๊ฒŒ ๊ตฌ์† ์ ๋ถ€ ์‹ฌ์‚ฌ๋ฅผ ์ฒญ๊ตฌํ•˜์—ฌ ์„๋ฐฉ๋  ์ˆ˜ ์žˆ๋‹ค.', '1์‹ฌ ๋ฒ•์›์€ ๊ฐ‘์—๊ฒŒ ํ˜• ์„ ๊ณ ์˜ ์ทจ์†Œ ์—†์ด 1๋…„์ด ๊ฒฝ๊ณผํ•œ ๋•Œ์—๋Š” ๊ณต์†Œ ์ œ๊ธฐ๊ฐ€ ์—†์—ˆ๋˜ ๊ฒƒ์œผ๋กœ ๊ฐ„์ฃผ๋˜๋Š” ํŒ๊ฒฐ์„ ์„ ๊ณ ํ–ˆ๋‹ค.', '๊ฐ‘์ด ํ•ญ์†Œ๋ฅผ ํฌ๊ธฐํ•˜์˜€๋”๋ผ๋„ ๊ฐ‘์—๊ฒŒ๋Š” ๋‹นํ•ด ์‚ฌ๊ฑด์˜ ํ•ญ์†Œ์‹ฌ ๊ณ„์† ์ค‘ ๋ฌด์ฃ„ ์ถ”์ •์˜ ์›์น™์ด ์ ์šฉ๋œ๋‹ค.'], 'answer': ''}",,5,5,True,[],5 +2025-JB-15,"์ง€๋ฐฉ ์ž์น˜๋ฒ•์€ โ€˜์ฃผ๋ฏผ์—๊ฒŒ ๊ณผ๋„ํ•œ ๋ถ€๋‹ด์„ ์ฃผ๊ฑฐ๋‚˜ ์ค‘๋Œ€ํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” +์ง€๋ฐฉ ์ž์น˜ ๋‹จ์ฒด์˜ ์ฃผ์š” ๊ฒฐ์ • ์‚ฌํ•ญโ€™์„ ์ฃผ๋ฏผ ํˆฌํ‘œ์˜ ๋Œ€์ƒ์œผ๋กœ ๊ทœ์ •ํ•˜๊ณ  ์žˆ๋‹ค. +์ข…์ „์—๋Š” ์ง€๋ฐฉ ์ž์น˜ ๋‹จ์ฒด์˜ ์ฃผ์š” ๊ฒฐ์ • ์‚ฌํ•ญ ์ค‘ A์˜ ์˜๊ฒฐ๋กœ ์ œ์ •ํ•œ ์กฐ๋ก€๋กœ +์ •ํ•˜๋Š” ์‚ฌํ•ญ์„ ์ฃผ๋ฏผ ํˆฌํ‘œ์˜ ๋Œ€์ƒ์œผ๋กœ ํ•˜์˜€์ง€๋งŒ, ์ฃผ๋ฏผ ํˆฌํ‘œ๋ฒ• ๊ฐœ์ •์— ๋”ฐ๋ผ +์กฐ๋ก€๋กœ ์ •ํ•˜์ง€ ์•Š์•„๋„ ์ง€๋ฐฉ ์ž์น˜๋ฒ•์— ๊ทœ์ •๋œ ์‚ฌํ•ญ์— ํ•ด๋‹นํ•˜๋ฉด ์ฃผ๋ฏผ ํˆฌํ‘œ์˜ +๋Œ€์ƒ์ด ๋  ์ˆ˜ ์žˆ๋‹ค. ์ด๋Š” ์ง€๋ฐฉ ์ž์น˜ ๋‹จ์ฒด๋ฅผ ๋Œ€ํ‘œํ•˜๋Š” B๊ฐ€ ๊ถŒํ•œ์„ ๊ฐ€์ง€๊ณ  +๊ฒฐ์ •ํ•  ์ˆ˜ ์žˆ๋Š” ์‚ฌํ•ญ์„ ์ฃผ๋ฏผ ํˆฌํ‘œ์— ๋ถ€์น  ์ˆ˜ ์žˆ๋„๋ก ํ•˜์—ฌ ์ง€๋ฐฉ ์ž์น˜ ํ–‰์ •์˜ +์ฑ…์ž„์„ฑ์„ ์ œ๊ณ ํ•˜๊ธฐ ์œ„ํ•œ ๊ฒƒ์œผ๋กœ ํŒ๋‹จ๋œ๋‹ค.","{'question': '์šฐ๋ฆฌ๋‚˜๋ผ ์ง€๋ฐฉ ์ž์น˜ ๋‹จ์ฒด์˜ ๊ธฐ๊ด€ A, B์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€? (๋‹จ, A, B๋Š” ๊ฐ๊ฐ ์ง€๋ฐฉ ์ž์น˜ ๋‹จ์ฒด์˜ ์žฅ, ์ง€๋ฐฉ ์˜ํšŒ ์ค‘ ํ•˜๋‚˜์ž„.)', 'choices': ['A๋Š” ์ฃผ๋ฏผ ํˆฌํ‘œ๋ฒ• ๊ฐœ์ • ๋ฐ ํ์ง€์— ๋Œ€ํ•œ ์˜๊ฒฐ๊ถŒ์„ ๊ฐ€์ง„๋‹ค.', 'B๋Š” ๋ฒ•๋ น ์œ„๋ฐ˜์„ ์ด์œ ๋กœ๋Š” ์ฃผ๋ฏผ ์†Œํ™˜์˜ ๋Œ€์ƒ์ด ๋  ์ˆ˜ ์—†๋‹ค.', 'B๋Š” ํ•ด๋‹น ์ง€๋ฐฉ ์ž์น˜ ๋‹จ์ฒด์˜ ์‚ฌ๋ฌด์™€ ๋ฒ•๋ น์— ๋”ฐ๋ผ B์—๊ฒŒ ์œ„์ž„๋œ ์‚ฌ๋ฌด๋ฅผ ๊ด€๋ฆฌํ•˜๊ณ  ์ง‘ํ–‰ํ•œ๋‹ค.', 'B๋Š” A๊ฐ€ ํŽธ์„ฑํ•œ ์ง€๋ฐฉ ์ž์น˜ ๋‹จ์ฒด์˜ ์˜ˆ์‚ฐ์•ˆ์— ๋Œ€ํ•ด ์‹ฌ์˜โ€คํ™•์ •ํ•œ๋‹ค.', '์ง€๋ฐฉ ์ž์น˜ ๋‹จ์ฒด์˜ ํ–‰์ • ์‚ฌ๋ฌด ๊ฐ์‚ฌ๊ถŒ์€ A์— ๋Œ€ํ•œ B์˜ ๊ฒฌ์ œ ์ˆ˜๋‹จ์— ํ•ด๋‹นํ•œ๋‹ค.'], 'answer': ''}",,3,4,False,"['๋”ฐ๋ผ์„œ ์ง€๋ฐฉ์˜ํšŒ๋Š” ์ง€์—ญ ์ฃผ๋ฏผ๋“ค์ด ์ฐธ์—ฌํ•œ ์„ ๊ฑฐ๋ฅผ ํ†ตํ•ด ์„ ์ถœ๋œ ๋Œ€ํ‘œ์ž๋“ค์ด ๋ชจ์ธ ๊ธฐ๊ด€์ด๋ผ๋Š” ์ง€์œ„๋ฅผ ๊ฐ–๋Š”๋‹ค. - ์ง€๋ฐฉ์˜ํšŒ๋Š” ์ง€๋ฐฉ์ž์น˜๋‹จ์ฒด์˜ ์ •์ฑ…์— ๊ด€ํ•œ ์ค‘์š” ์‚ฌํ•ญ์— ๋Œ€ํ•˜์—ฌ ์ง€๋ฐฉ์ž์น˜๋‹จ์ฒด์˜ ์˜์‚ฌ๋ฅผ ์ตœ์ข…์ ์œผ๋กœ ๊ฒฐ์ •ํ•˜๋Š” ์ง€๋ฐฉ์ž์น˜๋‹จ์ฒด์˜ ์˜๊ฒฐ ๊ธฐ๊ด€์ด๋ผ๋Š” ์ง€์œ„๋ฅผ ๊ฐ–๋Š”๋‹ค. - ์ง€๋ฐฉ์˜ํšŒ๋Š” ์ง€๋ฐฉ์ž์น˜๋‹จ์ฒด์˜ ๋ฒ•๋ น์ธ ์กฐ๋ก€๋ฅผ ์ œ์ •ํ•˜๊ฑฐ๋‚˜ ๊ฐœ์ •, ํ์ง€ ์—ฌ๋ถ€๋ฅผ ์˜๊ฒฐํ•˜๋Š” ์ž…๋ฒ• ๊ธฐ๊ด€์ด๋‹ค. ๋˜ํ•œ ์ง€๋ฐฉ์ž์น˜๋‹จ์ฒด์˜ ์˜ˆ์‚ฐ์•ˆ์„ ์‹ฌ์˜ยทํ™•์ •ํ•˜๊ณ  ๊ฒฐ์‚ฐ์„ ์Šน์ธํ•˜๋Š” ์•ˆ๊ฑด๊ณผ ๊ฐ™์ด ๋ฒ•๋ น ๋˜๋Š” ์กฐ๋ก€์— ๋”ฐ๋ผ ์ง€๋ฐฉ์˜ํšŒ์˜ ๊ถŒํ•œ์— ์†ํ•˜๋Š” ์ฃผ์š” ์ •์ฑ…์„ ์‹ฌ์˜ยท์˜๊ฒฐํ•œ๋‹ค. - ์ง€๋ฐฉ์˜ํšŒ๋Š” ์ง€๋ฐฉ์ž์น˜๋‹จ์ฒด์˜ ์ง‘ํ–‰ ๊ธฐ๊ด€์ธ ์ง€๋ฐฉ์ž์น˜๋‹จ์ฒด์žฅ๊ณผ๋Š” ์ƒํ˜ธ ๋…๋ฆฝ๊ณผ ์กด์ค‘, ๊ฒฌ์ œ์™€ ๊ท ํ˜•์˜ ์›๋ฆฌ์— ๋”ฐ๋ผ ์šด์˜๋œ๋‹ค. ใ€Š๋Œ€ํ•œ๋ฏผ๊ตญ ์ง€๋ฐฉ์ž์น˜๋ฒ•ใ€‹์€ ์ง€๋ฐฉ์ž์น˜๋‹จ์ฒด์žฅ๊ณผ ์ง€๋ฐฉ์˜ํšŒ ๊ฐ„์˜ ๊ด€๊ณ„๋ฅผ ๊ธฐ๊ด€๋Œ€๋ฆฝํ˜•์ด๋ผ๊ณ  ๊ทœ์ •ํ•˜๊ณ  ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ์ง€๋ฐฉ์ž์น˜๋‹จ์ฒด์˜ ์ง‘ํ–‰ ๊ธฐ๊ด€์ธ ์ง€๋ฐฉ์ž์น˜๋‹จ์ฒด์žฅ์€ ์ง€๋ฐฉ์˜ํšŒ์˜ ์ž์œจ์„ฑ์„ ์นจํ•ดํ•  ์ˆ˜ ์—†๊ณ  ์ง€๋ฐฉ์ž์น˜๋‹จ์ฒด์˜ ์˜๊ฒฐ ๊ธฐ๊ด€์ธ ์ง€๋ฐฉ์˜ํšŒ๋Š” ์ง€๋ฐฉ์ž์น˜๋‹จ์ฒด์žฅ์˜ ์ง‘ํ–‰ ๊ถŒํ•œ์— ๊ด€ํ•œ ๋ณธ์งˆ์ ์ธ ๋‚ด์šฉ์„ ์นจํ•ดํ•  ์ˆ˜ ์—†๋‹ค. - ์ง€๋ฐฉ์˜ํšŒ๋Š” ์ง€๋ฐฉ์ž์น˜๋‹จ์ฒด์˜ ์ง‘ํ–‰ ๊ธฐ๊ด€์ธ ์ง€๋ฐฉ์ž์น˜๋‹จ์ฒด์žฅ์„ ๊ฐ์‹œํ•˜๋Š” ๊ธฐ๊ด€์ด๋ผ๋Š” ์ง€์œ„๋ฅผ ๊ฐ–๋Š”๋‹ค. ๋Œ€ํ•œ๋ฏผ๊ตญ์˜ ์ง€๋ฐฉ์˜ํšŒ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™์€ ๊ถŒํ•œ์„ ๊ฐ–๊ณ  ์žˆ๋‹ค', '(๊ฐ€)์ด๊ฒƒ์€ ์ง€๋ฐฉ์ž์น˜๋‹จ์ฒด์˜ ์˜ˆ์‚ฐ ํŽธ์„ฑ ๊ถŒํ•œ์„ ์ฃผ๋ฏผ๊ณผ ๊ณต์œ ํ•˜์—ฌ ๊ณต๊ณต ์„œ๋น„์Šค๋‚˜ ํ–‰์ • ํ™œ๋™์— ๋Œ€ํ•œ ์ฃผ๋ฏผ์˜ ๋‹ค์–‘ํ•œ ์˜๊ฒฌ์„ ์˜ˆ์‚ฐ์— ๋ฐ˜์˜ํ•˜๋Š” ๊ฒƒ์ด๋‹ค.\n(๋‚˜)์ด๊ฒƒ์€ ์ง€๋ฐฉ์ž์น˜๋‹จ์ฒด์™€ ๊ทธ ์žฅ์˜ ๊ถŒํ•œ์— ์†ํ•˜๋Š” ์‚ฌ๋ฌด์˜ ์ฒ˜๋ฆฌ๊ฐ€ ๋ฒ•๋ น์— ์œ„๋ฐ˜๋˜๊ฑฐ๋‚˜ ๊ณต์ต์„ ํ˜„์ €ํžˆ ํ•ด์นœ๋‹ค๊ณ  ์ธ์ •๋˜๋ฉด ์ผ์ • ์ˆ˜ ์ด์ƒ์˜ ์ฃผ๋ฏผ์ด ์—ฐ๋Œ€ ์„œ๋ช…ํ•˜์—ฌ ์ง์ ‘ ๊ฐ์‚ฌ๋ฅผ ์ฒญ๊ตฌํ•˜๋Š” ๊ฒƒ์ด๋‹ค. (๊ฐ€), (๋‚˜)์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์ง€ ์•Š์€ ๊ฒƒ์€? Answer: (๋‚˜)๊ฐ€ ์ด๋ฃจ์–ด์ง€๋ฉด ์ง€๋ฐฉ์ž์น˜๋‹จ์ฒด์žฅ์˜ ๊ถŒํ•œ์ด ์ •์ง€๋œ๋‹ค.', '- ์˜๊ฒฐ๊ถŒ - ์ง€๋ฐฉ์ž์น˜๋‹จ์ฒด์˜ ๋ฒ•๋ น์ธ ์กฐ๋ก€๋ฅผ ์ œ์ •ํ•˜๊ฑฐ๋‚˜ ๊ฐœ์ •, ํ์ง€ ์—ฌ๋ถ€๋ฅผ ์˜๊ฒฐํ•จ - ์ง€๋ฐฉ์ž์น˜๋‹จ์ฒด์˜ ์˜ˆ์‚ฐ์•ˆ์„ ์‹ฌ์˜ยทํ™•์ •ํ•˜๊ณ  ๊ฒฐ์‚ฐ์„ ์Šน์ธํ•˜๋Š” ์•ˆ๊ฑด๊ณผ ๊ฐ™์ด ๋ฒ•๋ น ๋˜๋Š” ์กฐ๋ก€์— ๋”ฐ๋ผ ์ง€๋ฐฉ์˜ํšŒ์˜ ๊ถŒํ•œ์— ์†ํ•˜๋Š” ์ฃผ์š” ์ •์ฑ…์„ ์‹ฌ์˜ยท์˜๊ฒฐํ•จ - ใ€Š๋Œ€ํ•œ๋ฏผ๊ตญ ์ง€๋ฐฉ์ž์น˜๋ฒ•ใ€‹ ์ œ47์กฐ ์ œ1ํ•ญ์— ๋ช…์‹œ๋œ ํ•„์š”์  ์˜๊ฒฐ ์‚ฌํ•ญ - ์กฐ๋ก€์˜ ์ œ์ •ยท๊ฐœ์ • ๋ฐ ํ์ง€ - ์˜ˆ์‚ฐ์˜ ์‹ฌ์˜ยทํ™•์ • - ๊ฒฐ์‚ฐ์˜ ์Šน์ธ - ๋ฒ•๋ น์— ๊ทœ์ •๋œ ๊ฒƒ์„ ์ œ์™ธํ•œ ์‚ฌ์šฉ๋ฃŒยท์ˆ˜์ˆ˜๋ฃŒยท๋ถ„๋‹ด๊ธˆยท์ง€๋ฐฉ์„ธ ๋˜๋Š” ๊ฐ€์ž…๊ธˆ์˜ ๋ถ€๊ณผ์™€ ์ง•์ˆ˜ - ๊ธฐ๊ธˆ์˜ ์„ค์น˜ยท์šด์šฉ - ๋Œ€ํ†ต๋ น๋ น์œผ๋กœ ์ •ํ•˜๋Š” ์ค‘์š” ์žฌ์‚ฐ์˜ ์ทจ๋“ยท์ฒ˜๋ถ„ - ๋Œ€ํ†ต๋ น๋ น์œผ๋กœ ์ •ํ•˜๋Š” ๊ณต๊ณต์‹œ์„ค์˜ ์„ค์น˜ยท์ฒ˜๋ถ„ - ๋ฒ•๋ น๊ณผ ์กฐ๋ก€์— ๊ทœ์ •๋œ ๊ฒƒ์„ ์ œ์™ธํ•œ ์˜ˆ์‚ฐ ์™ธ์˜ ์˜๋ฌด ๋ถ€๋‹ด์ด๋‚˜ ๊ถŒ๋ฆฌ์˜ ํฌ๊ธฐ - ์ฒญ์›์˜ ์ˆ˜๋ฆฌ์™€ ์ฒ˜๋ฆฌ - ์™ธ๊ตญ ์ง€๋ฐฉ์ž์น˜๋‹จ์ฒด์™€์˜ ๊ต๋ฅ˜ยทํ˜‘๋ ฅ - ์ง€๋ฐฉ์ž์น˜๋ฒ•๋ น์ด๋‚˜ ๊ธฐํƒ€ ๋ฒ•๋ น, ํ•ด๋‹น ์ง€๋ฐฉ์ž์น˜๋‹จ์ฒด์˜ ๊ถŒํ•œ์— ์†ํ•˜๋Š” ์˜๊ฒฐ ์‚ฌํ•ญ - ํ•ด๋‹น ์ง€๋ฐฉ์ž์น˜๋‹จ์ฒด์˜ ์กฐ๋ก€์— ๋”ฐ๋ผ ์ง€๋ฐฉ์˜ํšŒ์˜ ์˜๊ฒฐ์„ ๋ฐ›์•„์•ผ ํ•˜๋Š” ์‚ฌํ•ญ - ํ–‰์ •์‚ฌ๋ฌด๊ฐ์‚ฌ ๋ฐ ์กฐ์‚ฌ๊ถŒ - ํ–‰์ •์‚ฌ๋ฌด๊ฐ์‚ฌ: ์ง€๋ฐฉ์ž์น˜๋‹จ์ฒด์˜ ์‚ฌ๋ฌด ์ „๋ฐ˜์— ๊ด€ํ•œ ๊ฐ์‚ฌ (๋งค๋…„ 1ํšŒ ์ •๋ก€ํšŒ ๊ธฐ๊ฐ„ ์ค‘ 9์ผ ์ด๋‚ด์— ์ง„ํ–‰๋˜๋Š” ๋ฒ•์ • ๊ฐ์‚ฌ์ž„) - ํ–‰์ •์‚ฌ๋ฌด์กฐ์‚ฌ: ์ง€๋ฐฉ์ž์น˜๋‹จ์ฒด์˜ ํŠน์ • ์‚ฌ์•ˆ์— ๊ด€ํ•œ ์กฐ์‚ฌ (๋ณธํšŒ์˜๋ฅผ ํ†ตํ•ด ์ˆ˜์‹œ๋กœ ์ •ํ•ด์ง€๋ฉฐ ์žฌ์  ์˜์› 1/3 ์ด์ƒ์ด ๋ฌธ์„œ์— ์„œ๋ช…ํ•˜๊ณ  ๋ณธํšŒ์˜ ์˜๊ฒฐ์„ ํ†ตํ•ด ์ง„ํ–‰๋จ) - ์ž์œจ๊ถŒ (์ง€๋ฐฉ์˜ํšŒ๋Š” ๊ตญ๊ฐ€ ๊ธฐ๊ด€์ด๋‚˜ ์ง€๋ฐฉ์ž์น˜๋‹จ์ฒด์˜ ์ง‘ํ–‰ ๊ธฐ๊ด€์œผ๋กœ๋ถ€ํ„ฐ ์ผ์ •ํ•œ ๋ฒ”์œ„ ์•ˆ์—์„œ ์–ด๋– ํ•œ ๊ด€์—ฌ๋ฅผ ๋ฐ›์ง€ ์•Š์Œ) - ์ž„์‹œํšŒ ์†Œ์ง‘, ๊ฐœํšŒยทํœดํšŒยทํšŒ๊ธฐ ๊ฒฐ์ •์— ๊ด€ํ•œ ๊ถŒํ•œ ๋ถ€์—ฌ - ํšŒ์˜ ๊ทœ์น™์„ ๋น„๋กฏํ•œ ์šด์˜๊ณผ ๊ด€๋ จ๋œ ๊ทœ์น™์„ ์ œ์ •ํ•  ๊ถŒํ•œ ๋ถ€์—ฌ - ์˜์›์ด ์˜์žฅยท๋ถ€์˜์žฅยท์ž„์‹œ ์˜์žฅยท์ƒ์ž„์œ„์›ํšŒ ์œ„์›์žฅยทํŠน๋ณ„์œ„์›ํšŒ ์œ„์›์žฅ์„ ์„ ์ถœํ•˜๊ฑฐ๋‚˜ ๋ถˆ์‹ ์ž„ ์—ฌ๋ถ€๋ฅผ ์˜๊ฒฐํ•  ๊ถŒํ•œ ๋ถ€์—ฌ - ์˜์›์˜ ์ž๊ฒฉ, ์ง•๊ณ„ ๋“ฑ์„ ๊ฒฐ์ •ํ•˜๋Š” ๊ถŒํ•œ ๋ถ€์—ฌ - ์˜์›์ด ์œ„์›ํšŒ(์ƒ์ž„์œ„์›ํšŒยทํŠน๋ณ„์œ„์›ํšŒ)๋ฅผ ๊ตฌ์„ฑํ•˜๊ณ  ์˜์•ˆ์„ ๋ฐœ์˜ํ•  ๊ถŒํ•œ ๋ถ€์—ฌ - ๋‚ด๋ถ€ยท์™ธ๋ถ€ ๊ธฐ๊ด€ ๊ตฌ์„ฑ์„ ์œ„ํ•œ ์œ„์› ์„ ์ž„ยท์ถ”์ฒœ - ๊ฒฐ์‚ฐ๊ฒ€์‚ฌ์œ„์›์„ ์„ ์ž„ํ•จ - ์ง€๋ฐฉ์ž์น˜๋‹จ์ฒด์žฅ์˜ ์š”์ฒญ ๋˜๋Š” ๋ฒ•๋ นยท์กฐ๋ก€์— ๊ทœ์ •๋œ ์ ˆ์ฐจ์— ๋”ฐ๋ผ ์ง‘ํ–‰ ๊ธฐ๊ด€์˜ ๊ฐ์ข… ์œ„์›ํšŒ ์œ„์› ๋“ฑ์„ ์ถ”์ฒœํ•จ - ์ง€๋ฐฉ์ž์น˜๋‹จ์ฒด์žฅ์ด ์ง€๋ฐฉ์˜ํšŒ์˜ ๋™์˜๋ฅผ ํ†ตํ•ด ์œ„์ด‰ํ•˜๋Š” ์‹œ๋ฏผ๊ณ ์ถฉ์ฒ˜๋ฆฌ์œ„์›ํšŒ ์œ„์› ์ถ”์ฒœ - ์ฒญ์›์ˆ˜๋ฆฌ๊ถŒ: ์ง€๋ฐฉ์˜ํšŒ๋Š” ํ•ด๋‹น ์ง€์—ญ์˜ ์ฃผ๋ฏผ๋“ค๋กœ๋ถ€ํ„ฐ ์ œ์ถœ๋ฐ›์€ ์ฒญ์›์„ ์ˆ˜๋ฆฌํ•˜์—ฌ ์ฒ˜๋ฆฌํ•  ์ˆ˜ ์žˆ๋‹ค']",4 +2025-JB-17,"ํ‰์†Œ์— ์žฅ๋‚œ์ด ์‹ฌํ•œ ๊ฐ‘(18์„ธ)์€ ์นœ๊ตฌ ์„(18์„ธ)์˜ ํœด๋Œ€ ์ „ํ™”๋ฅผ ์‹ค์ˆ˜๋กœ +๊นจ๋œจ๋ ธ๋‹ค. ๊ทธ๋‚  ์ €๋… ๊ฐ‘์€ ๋ถ€๋ชจ์˜ ํ—ˆ๋ฝ์„ ๋ฐ›์•„ ์ž์‹ ์ด ํ‚ค์šฐ๋˜ ๊ฐœ๋ฅผ ๋ฐ๋ฆฌ๊ณ  +์‚ฐ์ฑ…ํ•˜๋˜ ์ค‘ ์„์„ ๋งŒ๋‚ฌ๋‹ค. ๊ฐ‘์€ ์žฅ๋‚œ์‚ผ์•„ ์ž์‹ ์˜ ๊ฐœ์—๊ฒŒ ์„์„ ํ–ฅํ•˜์—ฌ +โ€œ๋ฌผ์–ด!โ€๋ผ๊ณ  ๋ช…๋ นํ•˜์˜€๊ณ , ๋ชฉ์ค„์ด ํ’€๋ฆฐ ๊ฐ‘์˜ ๊ฐœ๊ฐ€ ์„์„ ๋ฌผ์–ด ์„์—๊ฒŒ ์ƒํ•ด๋ฅผ +์ž…ํ˜”๋‹ค. ์„์˜ ์œ„๊ธ‰ํ•œ ์ƒํ™ฉ์„ ๋ชฉ๊ฒฉํ•œ ๋ณ‘์€ ์„์„ ๊ตฌํ•˜๊ธฐ ์œ„ํ•ด ๊ฐ‘์˜ ๊ฐœ๋ฅผ +๋ฐœ๋กœ ์ฐผ๋‹ค. ๋™๋ฌผ ํ•™๋Œ€ ํ˜์˜๋กœ ์ˆ˜์‚ฌ๋ฅผ ๋ฐ›์€ ๋ณ‘์— ๋Œ€ํ•ด ๊ฒ€์‚ฌ๋Š” ์ •๋‹น๋ฐฉ์œ„๋ฅผ +์ด์œ ๋กœ ๋ถˆ๊ธฐ์†Œ ์ฒ˜๋ถ„์„ ํ•˜์˜€๋‹ค. ํœด๋Œ€ ์ „ํ™” ์†๊ดด ๋ฐ ์ƒํ•ด ํ˜์˜๋กœ ๋ถˆ๊ตฌ์† +์ƒํƒœ์—์„œ ์ˆ˜์‚ฌ๋ฅผ ๋ฐ›์€ ๊ฐ‘์— ๋Œ€ํ•ด ๊ฒ€์‚ฌ๋Š” ๊ณผ์‹ค์— ์˜ํ•œ ์†๊ดด๋Š” ๋ฒ• ๊ทœ์ •์ด ์—†์–ด +๋ฒ”์ฃ„๊ฐ€ ์„ฑ๋ฆฝํ•˜์ง€ ์•Š๋Š”๋‹ค๊ณ  ๋ถˆ๊ธฐ์†Œ ์ฒ˜๋ถ„์„ ํ•˜์˜€๊ณ , ์ƒํ•ด ํ˜์˜์— ๋Œ€ํ•ด์„œ๋Š” +์ •์ƒ์„ ์ฐธ์ž‘ํ•˜์—ฌ ๊ธฐ์†Œ ์œ ์˜ˆ ์ฒ˜๋ถ„์„ ํ•˜์˜€๋‹ค.","{'question': '๋‹ค์Œ ์‚ฌ๋ก€์— ๋Œ€ํ•œ ๋ฒ•์  ํŒ๋‹จ์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['๊ฐ‘์—๊ฒŒ ์ฑ…์ž„ ๋Šฅ๋ ฅ์ด ์ธ์ •๋˜๋”๋ผ๋„ ๊ฐ‘์˜ ๋ถ€๋ชจ๋Š” ์„์˜ ์ƒํ•ด์— ๋Œ€ํ•ด ๋ฒ•์ • ๊ฐ๋… ์˜๋ฌด์ž๋กœ์„œ ํŠน์ˆ˜ ๋ถˆ๋ฒ• ํ–‰์œ„ ์ฑ…์ž„์„ ์ง„๋‹ค.', '์„์€ ๋ฐฐ์ƒ ๋ช…๋ น ์ œ๋„๋ฅผ ํ†ตํ•˜์—ฌ ๊ฐ‘์—๊ฒŒ ์†ํ•ด๋ฅผ ๋ฐฐ์ƒ๋ฐ›์„ ์ˆ˜ ์žˆ๋‹ค.', '๊ฒ€์‚ฌ๋Š” ๊ฐ‘์ด ์„์˜ ํœด๋Œ€ ์ „ํ™”๋ฅผ ์†๊ดดํ•œ ํ–‰์œ„์— ๋Œ€ํ•˜์—ฌ ๊ตฌ์„ฑ ์š”๊ฑด์— ํ•ด๋‹นํ•˜์ง€ ์•Š๋Š” ๊ฒƒ์œผ๋กœ ํŒ๋‹จํ•˜์˜€๋‹ค.', '๊ฒ€์‚ฌ๋Š”๊ฐ‘์˜ ์ƒํ•ด ํ˜์˜์— ๋Œ€ํ•˜์—ฌ ๊ฐ๊ด€์  ๋ฒ•์งˆ์„œ ๊ด€์ ์—์„œ ์œ„๋ฐฐ ๋˜์ง€๋งŒ, ๋ฒ•์  ๋น„๋‚œ ๊ฐ€๋Šฅ์„ฑ์ด ์—†๋Š” ๊ฒƒ์œผ๋กœ ํŒ๋‹จํ•˜์˜€๋‹ค.', '๊ฒ€์‚ฌ๋Š”๋ณ‘์˜ ํ–‰์œ„์— ๋Œ€ํ•˜์—ฌ ํƒ€์ธ์˜ ๋ฒ•์ต์— ๋Œ€ํ•œ ํ˜„์žฌ์˜ ์œ„๋‚œ์„ ํ”ผํ•˜๊ธฐ ์œ„ํ•œ ํ–‰์œ„๋กœ ์ƒ๋‹นํ•œ ์ด์œ ๊ฐ€ ์žˆ๋‹ค๊ณ  ํŒ๋‹จํ•˜์˜€๋‹ค.'], 'answer': ''}",,3,5,False,"['<์‚ฌ ๋ก€>\nโ—‹14์„ธ์ธ ๊ฐ‘์€ ๋ฐฐ๊ณ ํ””์„ ์ฐธ์ง€ ๋ชปํ•˜๊ณ  ๋นต์ง‘์—์„œ ๋นต์„ ํ›”์ณค๋‹ค.\nโ—‹์„์€ ๋นš์„ ๊ฐš์ง€ ์•Š๊ณ  ํ•ด์™ธ๋กœ ๋„๋ง๊ฐ€๋Š” ์ฑ„๋ฌด์ž๋ฅผ ๊ณตํ•ญ์—์„œ ๊ฐ•์ œ๋กœ ๋ถ™์žก์•˜๋‹ค.\nโ—‹๋ณ‘์€ ๊ฐ‘์ž๊ธฐ ๋‚˜ํƒ€๋‚˜ ๋‹ฌ๋ ค๋“œ๋Š” ๋งน๊ฒฌ์„ ํ”ผํ•˜๊ธฐ ์œ„ํ•ด ๋Œ€๋ฌธ์ด ์—ด๋ฆฐ ๋‚จ์˜ ์ง‘์œผ๋กœ ๋“ค์–ด๊ฐ”๋‹ค.\nโ—‹์ •์€ ์นœ๊ตฌ์˜ ๊ฐ€๋ฐฉ์—์„œ ๋ˆ์„ ํ›”์ณค๋Š”๋ฐ ์นœ๊ตฌ๋Š” ๊ทธ ์‚ฌ์‹ค์„ ์•Œ์ง€ ๋ชปํ–ˆ๋‹ค. ๋‹ค์Œ <์‚ฌ๋ก€>์— ๋Œ€ํ•œ ๋ฒ•์  ํŒ๋‹จ์œผ๋กœ ์˜ณ์€ ๊ฒƒ๋งŒ์„ <๋ณด๊ธฐ>์—์„œ ๋ชจ๋‘ ๊ณ ๋ฅด๋ฉด? ใ„ฑ.๊ฐ‘์€ ์ฑ…์ž„๋Šฅ๋ ฅ์ด ์—†๋‹ค.\nใ„ด.์„์˜ ํ–‰์œ„๋Š” ์ž๊ตฌํ–‰์œ„์— ํ•ด๋‹นํ•˜์—ฌ ์œ„๋ฒ•์„ฑ์ด ์กฐ๊ฐ๋  ์ˆ˜ ์žˆ๋‹ค.\nใ„ท.๋ณ‘์˜ ํ–‰์œ„๋Š” ๊ธด๊ธ‰ํ”ผ๋‚œ์— ํ•ด๋‹นํ•˜์—ฌ ์œ„๋ฒ•์„ฑ์ด ์กฐ๊ฐ๋  ์ˆ˜ ์žˆ๋‹ค.\nใ„น.์ •์˜ ํ–‰์œ„๋Š” ์ ˆ๋„์ฃ„์˜ ๊ตฌ์„ฑ์š”๊ฑด์— ํ•ด๋‹นํ•˜์ง€ ์•Š๋Š”๋‹ค. Answer: ใ„ด, ใ„ท']",5 +2025-JB-19,"๊ตญ์ œ ์ •์น˜ ๋ถ„์„์˜ ํ•ต์‹ฌ ๋‹จ์œ„๋ฅผ ์ฃผ๊ถŒ ๊ตญ๊ฐ€์— ๋‘๊ณ  ์žˆ๋Š” A๋Š” ๊ตญ์ œ ์‚ฌํšŒ์—์„œ +๊ฐœ๋ณ„ ๊ตญ๊ฐ€์˜ ์ตœ์šฐ์„  ๋ชฉํ‘œ๊ฐ€ ์ž๊ตญ ์ด์ต ๊ทน๋Œ€ํ™”์— ์žˆ๊ณ , ๊ตญ์ œ๊ธฐ๊ตฌ๋Š” ์ฃผ๊ถŒ +๊ตญ๊ฐ€์˜ํ–‰์œ„๋ฅผ ์–ต์ œํ•  ์ˆ˜ ์—†๋‹ค๊ณ  ๋ณธ๋‹ค. ๋ฐ˜๋ฉด ๊ตญ์ œ ๊ด€๊ณ„๋ฅผ ์ธ๊ฐ„์˜ ์ด์„ฑ๊ณผ +๋„๋•์ด ์ž‘๋™ํ•˜๋Š” ์‚ฌํšŒ๋กœ ํŒŒ์•…ํ•˜๋Š” B๋Š” ๊ตญ์ œ๊ธฐ๊ตฌ๊ฐ€ ๊ตญ๊ฐ€ ๊ฐ„ ๊ฐˆ๋“ฑ์˜ ์œ„ํ—˜์„ฑ์„ +๋‚ฎ์ถ”๊ณ  ํ‰ํ™”์ ์ด๊ณ  ํ˜‘๋ ฅ์ ์ธ ๊ตญ์ œ ์‚ฌํšŒ ํ˜•์„ฑ์— ๊ฑด์„ค์ ์ธ ์—ญํ• ์„ ํ•œ๋‹ค๊ณ  ๋ณธ๋‹ค.","{'question': '๊ตญ์ œ ๊ด€๊ณ„๋ฅผ ๋ฐ”๋ผ๋ณด๋Š” ๊ด€์  A, B์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['A๋Š” ๊ตญ๊ฐ€ ๊ฐ„ ๊ฒฝ์Ÿ๋ณด๋‹ค ์ƒํ˜ธ ์˜์กด์  ๊ด€๊ณ„๋ฅผ ์ค‘์‹œํ•œ๋‹ค.', 'B๋Š” ํž˜์˜ ๋…ผ๋ฆฌ๊ฐ€ ์ง€๋ฐฐํ•˜๋Š” ๊ตญ์ œ ์‚ฌํšŒ์˜ ํ˜„์‹ค์„ ๊ฐ•์กฐํ•œ๋‹ค.', 'A์™€ ๋‹ฌ๋ฆฌ B๋Š” ๊ฐœ๋ณ„ ๊ตญ๊ฐ€์˜ ์ด์ต๊ณผ ๊ตญ์ œ ์‚ฌํšŒ ์ „์ฒด์˜ ์ด์ต์ด ์กฐํ™”๋ฅผ ์ด๋ฃฐ ์ˆ˜ ์žˆ๋‹ค๊ณ  ๋ณธ๋‹ค.', 'B์™€ ๋‹ฌ๋ฆฌ A๋Š” ํŠน์ • ๊ตญ๊ฐ€์˜ ์นจ๋žต ํ–‰์œ„์— ๋ชจ๋“  ๊ตญ๊ฐ€๋“ค์ด ๊ณต๋™์œผ๋กœ ๋Œ€์‘ํ•จ์œผ๋กœ์จ ๊ตญ์ œ ๋ถ„์Ÿ์ด ํ•ด๊ฒฐ๋  ์ˆ˜ ์žˆ๋‹ค๊ณ  ๋ณธ๋‹ค.', 'A์™€ B ๋ชจ๋‘ ๊ตญ์ œ ์‚ฌํšŒ์˜ ์ผ๋ฐ˜์  ๊ด€ํ–‰๊ณผ ๋ฒ•์  ํ™•์‹ ์— ๊ธฐ์ดˆํ•œ ๊ตญ์ œ ๊ทœ๋ฒ”์„ ํ†ตํ•ด ๊ตญ์ œ ์‚ฌํšŒ์˜ ๋ฌด์ •๋ถ€ ์ƒํƒœ๋ฅผ ๊ทน๋ณตํ•  ์ˆ˜ ์žˆ๋‹ค๊ณ  ๋ณธ๋‹ค.'], 'answer': ''}",,3,3,True,"['(๊ฐ€)๊ตญ๊ฐ€๋Š” ํž˜์„ ์ถ”๊ตฌํ•˜๋ฉฐ, ๊ตญ๊ฐ€๊ฐ€ ํž˜์„ ์ถ”๊ตฌํ•˜๋Š” ๋ฐ ์žˆ์–ด ๋ณดํŽธ์  ์œค๋ฆฌ๋Š” ์ค‘์š”ํ•œ ๊ด€์‹ฌ์˜ ๋Œ€์ƒ์ด ์•„๋‹ˆ๋ผ๊ณ  ๋ณธ๋‹ค.\n(๋‚˜)๊ตญ์ œ์‚ฌํšŒ๊ฐ€ ๋™๋ฌผ์˜ ์„ธ๊ณ„์ฒ˜๋Ÿผ ํž˜์ด ์ง€๋ฐฐํ•˜๋Š” ์„ธ๊ณ„๊ฐ€ ์•„๋‹ˆ๋ผ ์ธ๊ฐ„์˜ ์ด์„ฑ๊ณผ ์œค๋ฆฌ๊ฐ€ ์ž‘๋™ํ•˜๋Š” ์‚ฌํšŒ๋ผ๊ณ  ๋ณธ๋‹ค. ๊ตญ์ œ์‚ฌํšŒ๋ฅผ ๋ฐ”๋ผ๋ณด๋Š” ๋‹ค์Œ์˜ ๊ด€์ ์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์ง€ ์•Š์€ ๊ฒƒ์€? Answer: (๊ฐ€)๋Š” (๋‚˜)์˜ ๊ด€์ ๊ณผ ๋‹ฌ๋ฆฌ ๊ฒฝ์ œ, ํ™˜๊ฒฝ, ์ธ๊ถŒ ๋ฌธ์ œ๋„ ์ค‘์‹œํ•œ๋‹ค.']",3 +2025-history-01,"(๊ฐ€) ์‹œ๋Œ€ ์ƒํ™œ ์ฒดํ—˜ ์ถ•์ œ. ์ฃผ๋จน๋„๋ผ, ์Šด๋ฒ ์ฐŒ๋ฅด๊ฐœ ๋“ฑ ๋—ธ์„๊ธฐ๋ฅผ ์‚ฌ์šฉํ•˜๋˜ (๊ฐ€) ์‹œ๋Œ€์˜ ๋ชจ์Šต์„ ๋‹ค์–‘ํ•œ ํ”„๋กœ๊ทธ๋žจ์„ ํ†ตํ•ด ์ฒดํ—˜ํ•ด๋ณด์„ธ์š”.","{'question': '(๊ฐ€) ์‹œ๋Œ€์˜ ์‚ฌํšŒ ๋ชจ์Šต์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['์œจ๋ น์„ ๋ฐ˜ํฌํ•˜์˜€๋‹ค.', '์ฒ ์ œ ๋†๊ธฐ๊ตฌ๋ฅผ ๋ณด๊ธ‰ํ•˜์˜€๋‹ค.', '๋น„ํŒŒํ˜• ๋™๊ฒ€์„ ์ œ์ž‘ํ•˜์˜€๋‹ค.', '๊ณ ์ถ”, ์ธ์‚ผ ๋“ฑ ์ƒํ’ˆ ์ž‘๋ฌผ์„ ์žฌ๋ฐฐํ•˜์˜€๋‹ค.', '์‚ฌ๋ƒฅ๊ณผ ์ฑ„์ง‘์„ ํ•˜๋ฉฐ ์ด๋™ ์ƒํ™œ์„ ํ•˜์˜€๋‹ค.'], 'answer': ''}",,5,5,True,[],5 +2025-history-02,"์ด ์ฑ…์€ (๊ฐ€)์˜ ์ด์Šนํœด๊ฐ€ ์“ด ์—ญ์‚ฌ์‹œ(ๆญทๅฒ่ฉฉ)๋กœ ์ƒ, ํ•˜ ์–‘๊ถŒ์œผ๋กœ ๊ตฌ์„ฑ๋˜์–ด ์žˆ๋‹ค. ์ƒ๊ถŒ์€ ์ค‘๊ตญ์˜ ์—ญ์‚ฌ๋ฅผ ์‹ ํ™”์—์„œ๋ถ€ํ„ฐ ์›(ๅ…ƒ) ์™•์กฐ์˜ ์„ฑ๋ฆฝ๊นŒ์ง€, ํ•˜๊ถŒ์€ ์šฐ๋ฆฌ๋‚˜๋ผ์˜ ์—ญ์‚ฌ๋ฅผ ๋‹จ๊ตฐ๋ถ€ํ„ฐ (๊ฐ€) ์ถฉ๋ ฌ์™•๊นŒ์ง€ ์„œ์ˆ ํ•˜๊ณ  ์žˆ๋‹ค. ์ด ์ฑ…์€ ์šฐ๋ฆฌ ์—ญ์‚ฌ์˜ ์‹œ์ž‘์„ ๋‹จ๊ตฐ์œผ๋กœ ์„ค์ •ํ–ˆ๋‹ค๋Š” ์ ์—์„œ ๊ท€์ค‘ํ•œ ๋ฌธํ—Œ ์ž๋ฃŒ๋กœ ํ‰๊ฐ€๋˜๊ณ  ์žˆ๋‹ค.","{'question': '(๊ฐ€) ๊ตญ๊ฐ€์˜ ๋ฌธํ™”์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€? ', 'choices': ['๊ตญ์ž๊ฐ์ด ์„ค๋ฆฝ๋˜์—ˆ๋‹ค.', '๋ถํ•™๋ก ์ด ์ œ๊ธฐ๋˜์—ˆ๋‹ค.', '์œก์˜ ๊ณต์›์ด ์„ค์น˜๋˜์—ˆ๋‹ค.', '์กฐ์„ ์–ด ํ•™ํšŒ๊ฐ€ ๊ฒฐ์„ฑ๋˜์—ˆ๋‹ค.', 'ํ•œ๊ตญ๋…๋ฆฝ์šด๋™์ง€ํ˜ˆ์‚ฌ๊ฐ€ ํŽธ์ฐฌ๋˜์—ˆ๋‹ค'], 'answer': ''}",,1,2,False,"['๊ทธ๋Š” ๋‹น์‹œ ์‚ฌํšŒ์˜ ์—ฌ๋Ÿฌ ๋ชจ์ˆœ์„ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•ด ์ถฉ๋ ฌ์™•์˜ ์‹ค์ •๊ณผ ๊ถŒ์„ธ๊ฐ€๋“ค์„ ๋น„ํŒํ•˜๋‹ค๊ฐ€ ํŒŒ์ง๋˜์–ด ์€๋‘” ์ƒํ™œ์„ ํ•˜๊ธฐ๋„ ํ–ˆ๋Š”๋ฐ, ์ด ์ฑ…๋„ ์ด๋•Œ ์ €์ˆ ํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ์‹œ๋Œ€์  ๋ฐฐ๊ฒฝ์„ ๋ฐ”ํƒ•์œผ๋กœ ์ด์Šนํœด๋Š” ์ด ์„œ์‚ฌ์‹œ์—์„œ ์›์˜ ๊ฐ•๋Œ€ํ•จ์„ ์ธ์ •ํ•˜๋ฉด์„œ๋„ ๊ณ ๋ ค์˜ ๋…์ž์„ฑ๊ณผ ์—ญ์‚ฌ์  ์œ ๊ตฌํ•จ์„ ๊ฐ•์กฐํ•˜๊ณ  ์žˆ๋‹ค. ์ง€๋ฆฌ๊ธฐ์—์„œ ์š”๋™์„ ์ค‘๊ตญ๊ณผ ๊ตฌ๋ณ„๋˜๋Š” ๋ณ„์ฒœ์ง€๋ผ๊ณ  ์„œ์ˆ ํ•œ ๋‚ด์šฉ์ด๋‚˜, ์ค‘๊ตญ์˜ ์‹ ํ™” ์‹œ๋Œ€์— ๋น„๊ฒฌ๋˜๋Š” ์šฐ๋ฆฌ ์—ญ์‚ฌ๋กœ ๋‹จ๊ตฐ ์กฐ์„ ์„ ์Š์€ ๊ฒƒ ๋“ฑ์ด ์ด์— ํ•ด๋‹นํ•œ๋‹ค. ๋˜ํ•œ ๊ตญ๋‚ด์ ์œผ๋กœ๋Š” ๊ตญ๊ฐ€ ์งˆ์„œ์˜ ํšŒ๋ณต์„ ๊ธฐ์›ํ•˜๋ฉฐ, ์—ญ์‚ฌ์ ์œผ๋กœ๋Š” ์ •์น˜๋ฅผ ์ž˜๋ชปํ•œ ๊ตฐ์ฃผ์™€ ์‹ ํ•˜๋“ค์„ ๊ฑฐ๋ก ํ•˜๊ณ  ๊ตฐ์‹ ์ด ๊ฐ๊ฐ ๊ฐ–์ถ”์–ด์•ผ ํ•  ์œ ๊ต์  ์ •์น˜ ์ด๋…์„ ์ œ์‹œํ•˜์˜€๋‹ค. ์ค‘๊ตญ๊ณผ ์šฐ๋ฆฌ๋‚˜๋ผ์˜ ์ง€๋ฆฌ์ โ‹…๋ฌธํ™”์  ์ฐจ์ด๋ฅผ ๊ฐ•์กฐํ•˜๊ณ  ๋‹จ๊ตฐ ์กฐ์„ ์„ ํ•œ๊ตญ์‚ฌ์˜ ๊ธฐ์›์œผ๋กœ ์„ค์ •ํ•˜์˜€๋Š”๋ฐ, ์ด๋Š” ๋น„์Šทํ•œ ์‹œ๊ธฐ์— ์ €์ˆ ๋œ ใ€Ž์‚ผ๊ตญ์œ ์‚ฌ(ไธ‰ๅœ‹้บไบ‹)ใ€์™€ ์ƒํ†ตํ•˜๋Š” ๋ฌธ์ œ์˜์‹์—์„œ ๋น„๋กฏ๋˜์—ˆ๋‹ค๊ณ  ๋ณผ ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ ๋ฐœํ•ด๋ฅผ ๊ณ ๊ตฌ๋ ค์˜ ๊ณ„์Šน๊ตญ์œผ๋กœ ์ธ์ •ํ•˜๊ณ  ๊ณ ๋ ค ํƒœ์กฐ์—๊ฒŒ ๊ท€์ˆœํ•œ ์‚ฌ์‹ค ๋“ฑ์„ ์„œ์ˆ ํ•˜์—ฌ ๋ฐœํ•ด๋ฅผ ์šฐ๋ฆฌ ์—ญ์‚ฌ์˜ ํ•œ ๋ถ€๋ถ„์œผ๋กœ ํŽธ์ž…์‹œ์ผฐ๋‹ค. ์ด๋Ÿฌํ•œ ์ด์Šนํœด์˜ ์—ญ์‚ฌ ์„œ์ˆ ์€ ๋Œ€๋ชฝ ํ•ญ์Ÿ๊ณผ ์›์˜ ์ •์น˜์  ๊ฐ„์„ญ ์†์—์„œ ๊ณ ๋ ค์˜ ๋…์ž์„ฑ๊ณผ ์œ ๊ตฌ์„ฑ์„ ๊ฐ•์กฐํ•˜๋Š” ์—ญ์‚ฌ ์˜์‹์ด ์„ฑ์žฅํ–ˆ์Œ์„ ๋ณด์—ฌ ์ค€๋‹ค. ๋˜ํ•œ ์‚ฌํ•™์‚ฌ์  ์ธก๋ฉด์—์„œ๋Š” ์šฐ๋ฆฌ ์—ญ์‚ฌ์˜ ๊ธฐ์›์„ ์‚ผ๊ตญ ์‹œ๊ธฐ ์ด์ „์˜ ์ƒ๊ณ ์‚ฌ๋กœ ๋Œ์–ด์˜ฌ๋ ธ๋‹ค๊ณ  ํ‰๊ฐ€ํ•  ์ˆ˜ ์žˆ๋‹ค.', ""1287๋…„(์ถฉ๋ ฌ์™• 13)์— ์ด์Šนํœด(ๆŽๆ‰ฟไผ‘, 1224~1300)๊ฐ€ ์ง€์€ ์žฅํŽธ ์„œ์‚ฌ์‹œ. 5์–ธ, 7์–ธ ํ˜•์‹์œผ๋กœ ์ค‘๊ตญ๊ณผ ์šฐ๋ฆฌ๋‚˜๋ผ์˜ ์—ญ์‚ฌ๋ฅผ ๋‹ค๋ฃฌ ์žฅํŽธ ์„œ์‚ฌ์‹œ์ด๋ฉฐ, ์ƒโ‹…ํ•˜ 2๊ถŒ 1์ฑ…์œผ๋กœ ๋˜์–ด ์žˆ๋‹ค. ์ƒ๊ถŒ์€ ์ค‘๊ตญ ์—ญ์‚ฌ๋ฅผ ์„œ(ๅบ)์— ์ด์–ด ์‹ ํ™” ์‹œ๋Œ€๋ถ€ํ„ฐ ์›(ๅ…ƒ)์˜ ๋“ฑ์žฅ๊นŒ์ง€ ๋‹ค๋ฃจ๊ณ  ์žˆ๋‹ค. ํ•˜๊ถŒ์€ ์šฐ๋ฆฌ๋‚˜๋ผ ์—ญ์‚ฌ์— ๊ด€ํ•œ ๋‚ด์šฉ์œผ๋กœ โ€˜๋™๊ตญ๊ตฐ์™•๊ฐœ๊ตญ์—ฐ๋Œ€(ๆฑๅœ‹ๅ›็Ž‹้–‹ๅœ‹ๅนดไปฃ)'์™€ โ€˜๋ณธ์กฐ๊ตฐ์™•์„ธ๊ณ„์—ฐ๋Œ€(ๆœฌๆœๅ›็Ž‹ไธ–็ณปๅนดไปฃ)'์˜ 2๋ถ€๋กœ ๋‚˜๋ˆ„์–ด์ ธ ์žˆ๋‹ค. ๋™๊ตญ๊ตฐ์™•๊ฐœ๊ตญ์—ฐ๋Œ€์—๋Š” ์„œ(ๅบ)์— ์ด์–ด ์ง€๋ฆฌ๊ธฐ(ๅœฐ็†่จ˜), ๋‹จ๊ตฐ์˜ ์ „์กฐ์„ (ๅ‰ๆœ้ฎฎ), ํ›„์กฐ์„ (ๅพŒๆœ้ฎฎ), ์œ„๋งŒ(่ก›ๆปฟ), ์‚ผํ•œ(ไธ‰้Ÿ“), ์‹ ๋ผโ‹…๋ฐฑ์ œโ‹…๊ณ ๊ตฌ๋ ค์˜ 3๊ตญ๊ณผ ํ›„์‚ผ๊ตญ ๋ฐ ๋ฐœํ•ด(ๆธคๆตท)๊ฐ€ ๊ณ ๋ ค๋กœ ํ†ต์ผ๋˜๋Š” ๊ณผ์ •๊นŒ์ง€ ๋‹ค๋ฃจ์—ˆ๊ณ , ๋ณธ์กฐ๊ตฐ์™•์„ธ๊ณ„์—ฐ๋Œ€์—๋Š” ๊ณ ๋ ค ํƒœ์กฐ(ๅคช็ฅ–, ์žฌ์œ„ 918โˆผ943) ์„ธ๊ณ„ ์„คํ™”(ไธ–็ณป่ชช่ฉฑ)์—์„œ๋ถ€ํ„ฐ ๋‹น๋Œ€์ธ ์ถฉ๋ ฌ์™•(ๅฟ ็ƒˆ็Ž‹, ์žฌ์œ„ 1274~1308) ๋Œ€๊นŒ์ง€ ์Š์—ˆ๋‹ค. ์ด์Šนํœด๋Š” ๋ชฝ๊ณจ์˜ ์นจ์ž…๊ณผ ๊ณ ๋ ค ์กฐ์ •์˜ ๊ฐ•ํ™”๋„ ์ฒœ๋„, ์‚ผ๋ณ„์ดˆ์˜ ๋‚œ ๋“ฑ์„ ์ง์ ‘ ๊ฒช์—ˆ๋‹ค. 100์—ฌ ๋…„๊ฐ„ ์ง€์†๋˜๋˜ ๋ฌด์‹  ์ •๊ถŒ์ด ์ข…์‹๋˜๊ณ  ์›๊ณผ์˜ ํ™”์นœ์ด ์ด๋ฃจ์–ด์กŒ์ง€๋งŒ, ์›์˜ ๊ฐ„์„ญ์ด ์‹ฌํ•ด์ง€๋ฉด์„œ ๊ณ ๋ ค ์‚ฌํšŒ ๋‚ด๋ถ€์—๋Š” ๋˜ ๋‹ค๋ฅธ ๋ฌธ์ œ๋“ค์ด ๋ฐœ์ƒํ•˜์˜€๋‹ค. ๊ทธ๋Š” ๋‹น์‹œ ์‚ฌํšŒ์˜ ์—ฌ๋Ÿฌ ๋ชจ์ˆœ์„ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•ด ์ถฉ๋ ฌ์™•์˜ ์‹ค์ •๊ณผ ๊ถŒ์„ธ๊ฐ€๋“ค์„ ๋น„ํŒํ•˜๋‹ค๊ฐ€ ํŒŒ์ง๋˜์–ด ์€๋‘” ์ƒํ™œ์„ ํ•˜๊ธฐ๋„ ํ–ˆ๋Š”๋ฐ, ์ด ์ฑ…๋„ ์ด๋•Œ ์ €์ˆ ํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ์‹œ๋Œ€์  ๋ฐฐ๊ฒฝ์„ ๋ฐ”ํƒ•์œผ๋กœ ์ด์Šนํœด๋Š” ์ด ์„œ์‚ฌ์‹œ์—์„œ ์›์˜ ๊ฐ•๋Œ€ํ•จ์„ ์ธ์ •ํ•˜๋ฉด์„œ๋„ ๊ณ ๋ ค์˜ ๋…์ž์„ฑ๊ณผ ์—ญ์‚ฌ์  ์œ ๊ตฌํ•จ์„ ๊ฐ•์กฐํ•˜๊ณ  ์žˆ๋‹ค"", '์‹ (่‡ฃ)์ด ์ด ์ฑ…์„ ํŽธ์ˆ˜ํ•˜์—ฌ ๋ฐ”์น˜๋Š” ๊ฒƒ์€ โ€ฆ(์ค‘๋žต)โ€ฆ ์ค‘๊ตญ์€ ๋ฐ˜๊ณ ๋ถ€ํ„ฐ ๊ธˆ๊ตญ์— ์ด๋ฅด๊ธฐ๊นŒ์ง€, ๋™๊ตญ์€ ๋‹จ๊ตฐ์œผ๋กœ๋ถ€ํ„ฐ ๋ณธ์กฐ(ๆœฌๆœ)์— ์ด๋ฅด๊ธฐ๊นŒ์ง€ ์ฒ˜์Œ ์ผ์–ด๋‚˜๊ฒŒ ๋œ ๊ทผ์›์„ ๊ฐ„์ฑ…์—์„œ ๋‹ค ์ฐพ์•„๋ณด์•„ ๊ฐ™๊ณ  ๋‹ค๋ฅธ ๊ฒƒ์„ ๋น„๊ตํ•˜์—ฌ ์š”์ ์„ ์ทจํ•˜๊ณ  ์Š์กฐ๋ฆผ์— ๋”ฐ๋ผ ์žฅ์„ ์ด๋ฃจ์—ˆ์Šต๋‹ˆ๋‹ค. ๋ฐ‘์ค„ ์นœ โ€˜์ด ์ฑ…โ€™์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€? Answer: ์› ๊ฐ„์„ญ๊ธฐ์— ์ค‘๊ตญ๊ณผ ๊ตฌ๋ณ„๋˜๋Š” ์šฐ๋ฆฌ ์—ญ์‚ฌ์˜ ๋…์ž์„ฑ์„ ๊ฐ•์กฐํ•˜์˜€๋‹ค.']",2 +2025-history-03,"๊ฐœ๋กœ์™•์ด ๋ถ์œ„์— ์‚ฌ์‹ ์„ ๋ณด๋‚ด ํ‘œ(่กจ)๋ฅผ ์˜ฌ๋ ธ๋‹ค. โ€œ์šฐ๋ฆฌ์™€ (๊ฐ€) ์€/๋Š” ๋ชจ๋‘ ๋ถ€์—ฌ์—์„œ ๋‚˜์™”์œผ๋ฏ€๋กœ ์„œ๋กœ ์˜›์ •์„ ๊ตณ๊ฑดํžˆ ์กด์ค‘ํ•˜์˜€๋Š”๋ฐ, ๊ทธ๋“ค์˜ ์„ ์กฐ์ธ ์‡ (้‡—, ๊ณ ๊ตญ์›์™•)๊ฐ€ ์ด์›ƒ๊ณผ์˜ ์šฐํ˜ธ๋ฅผ ๊นจ๋ฒ„๋ฆฌ๊ณ  ์šฐ๋ฆฌ์˜ ๊ตญ๊ฒฝ์„ ์ง“๋ฐŸ์•˜์Šต๋‹ˆ๋‹ค. ์ด์— ์šฐ๋ฆฌ์˜ ์„ ์กฐ๊ป˜์„œ ํ‰์–‘์„ฑ์„ ๊ณต๊ฒฉํ•˜์—ฌ ์‡ ๋ฅผ ์ฃฝ์˜€์Šต๋‹ˆ๋‹ค. โ€ฆ (์ค‘๋žต) โ€ฆ ์ง€๊ธˆ (๊ฐ€) ์˜ ์—ฐ(็’‰, ์žฅ์ˆ˜์™•)์ด ์ฃ„๊ฐ€ ์žˆ์–ด ๋‚˜๋ผ๋Š” ์—‰๋ง์ด ๋˜์—ˆ๊ณ , ๋ฐฑ์„ฑ๋“ค์€ ์ด๋ฆฌ์ €๋ฆฌ ํฉ์–ด์ง€๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ์ด๋Š” ๋ฉธ๋ง์‹œํ‚ฌ ๊ธฐํšŒ์ด๋ฉฐ ํํ•˜์˜ ๋„์›€์„ ๋ฐ›์•„์•ผ ํ•  ๋•Œ์ž…๋‹ˆ๋‹ค.โ€","{'question': '(๊ฐ€) ๊ตญ๊ฐ€์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€? ', 'choices': ['์กฐ์‚ฌ ์‹œ์ฐฐ๋‹จ์„ ํŒŒ๊ฒฌํ•˜์˜€๋‹ค.', '๋„๋ณ‘๋งˆ์‚ฌ๋ฅผ ์„ค์น˜ํ•˜์˜€๋‹ค.', '์ˆ˜์˜ ์นจ๋žต์„ ๋ง‰์•„ ๋ƒˆ๋‹ค.', '์ „๊ตญ์„ 8๋„๋กœ ๋‚˜๋ˆ„์—ˆ๋‹ค.', '๋Œ€๊ฐ€์•ผ๋ฅผ ์ •๋ณตํ•˜์˜€๋‹ค.'], 'answer': ''}",,3,1,False,"['๋ฐฑ์ œ ๊ฐœ๋กœ์™•์€ ์žฅ๊ธฐ์™€ ๋ฐ”๋‘‘์„ ์ข‹์•„ํ•˜์˜€๋Š”๋ฐ, ๋„๋ฆผ์ด ๊ณ ํ•˜๊ธฐ๋ฅผ โ€œ์ œ๊ฐ€ ์ Š์–ด์„œ๋ถ€ํ„ฐ ๋ฐ”๋‘‘์„ ๋ฐฐ์›Œ ๊ฝค ๋ฌ˜ํ•œ ์ˆ˜๋ฅผ ์•Œ๊ฒŒ ๋˜์—ˆ์œผ๋‹ˆ ๊ฐœ๋กœ์™•๊ป˜ ์•Œ๋ ค๋“œ๋ฆฌ๊ธฐ๋ฅผ ์›ํ•ฉ๋‹ˆ๋‹ค.โ€๋ผ๊ณ  ํ•˜์˜€๋‹ค. โ€ฆ(์ค‘๋žต)โ€ฆ ๊ฐœ๋กœ์™•์ด (๋„๋ฆผ์˜ ๋ง์„ ๋“ฃ๊ณ ) ๋‚˜๋ผ ์‚ฌ๋žŒ์„ ์ง•๋ฐœํ•˜์—ฌ ํ™์„ ์ช„์„œ ์„ฑ(ๅŸŽ)์„ ์Œ“๊ณ  ๊ทธ ์•ˆ์—๋Š” ๊ถ์‹ค, ๋ˆ„๊ฐ, ์ •์ž๋ฅผ ์ง€์œผ๋‹ˆ ๋ชจ๋‘๊ฐ€ ์›…์žฅํ•˜๊ณ  ํ™”๋ คํ•˜์˜€๋‹ค. ์ด๋กœ ๋ง๋ฏธ์•”์•„ ์ฐฝ๊ณ ๊ฐ€ ๋น„๊ณ  ๋ฐฑ์„ฑ์ด ๊ณค๊ถํ•˜๋‹ˆ, ๋‚˜๋ผ์˜ ์œ„ํƒœ๋กœ์›€์ด ์•Œ์„ ์Œ“์•„ ๋†“์€ ๊ฒƒ๋ณด๋‹ค ๋” ์‹ฌํ•˜๊ฒŒ ๋˜์—ˆ๋‹ค. ๊ทธ์ œ์•ผ ๋„๋ฆผ์ด ๋„๋ง์„ ์ณ ์™€์„œ ๊ทธ ์‹ค์ •์„ ๊ณ ํ•˜๋‹ˆ ์ด ์™•์ด ๊ธฐ๋ปํ•˜์—ฌ ๋ฐฑ์ œ๋ฅผ ์น˜๋ ค๊ณ  ์žฅ์ˆ˜์—๊ฒŒ ๊ตฐ์‚ฌ๋ฅผ ๋‚˜๋ˆ„์–ด ์ฃผ์—ˆ๋‹ค. ๏ผ์‚ผ๊ตญ์‚ฌ๊ธฐ๏ผ ๋ฐ‘์ค„ ์นœ โ€˜์ด ์™•โ€™์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€? Answer: ํ‰์–‘์œผ๋กœ ๋„์์„ ์ฒœ๋„ํ•˜์˜€๋‹ค.']",3 +2025-history-04,"๊ณ ๋ ค ๋ฌด์‹ ๋“ค์ด 100๋…„์— ๊ฑธ์ณ ๊ถŒ๋ ฅ์„ ์žฅ์•…ํ–ˆ๋˜ ์ด ์‹œ๊ธฐ์˜ ๋Œ€ํ‘œ์ ์ธ ์ธ๋ฌผ๊ณผ ํ™œ๋™์„ ์กฐ์‚ฌํ•ด์„œ ์˜ฌ๋ ค ์ฃผ์„ธ์š”. ์ •์ค‘๋ถ€. ์ด์˜๋ฐฉ๊ณผ ํ•จ๊ป˜ ์ •๋ณ€์„ ์ผ์œผ์ผœ ์ƒˆ๋กœ์šด ์™•์„ ์˜น๋ฆฝํ•˜์˜€์–ด์š”. ์ตœ์ถฉํ—Œ. ๋„๋ฐฉ์„ ๋” ํฐ ๊ทœ๋ชจ๋กœ ์žฌ๊ฑดํ•˜๊ณ , ๊ต์ •๋„๊ฐ์ด๋ผ๋Š” ๊ธฐ๊ตฌ๋ฅผ ์„ค์น˜ํ•˜์˜€์–ด์š”. ์ตœ์šฐ. ์ •๋ฐฉ์„ ํ†ตํ•ด ์ธ์‚ฌ๊ถŒ์„ ์žฅ์•…ํ•˜์˜€๊ณ , ๋ชฝ๊ณจ์˜ ์นจ๋žต์— ๋Œ€ํ•ญํ•˜์˜€์–ด์š”.","{'question': '๋ฐ‘์ค„ ์นœ โ€˜์ด ์‹œ๊ธฐโ€™์— ์žˆ์—ˆ๋˜ ์‚ฌ์‹ค๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['๊ณจํ’ˆ์ œ๊ฐ€ ์šด์˜๋˜์—ˆ๋‹ค', 'ํ•œ์ธ ์• ๊ตญ๋‹จ์ด ์กฐ์ง๋˜์—ˆ๋‹ค.', '์ˆ˜๋„๊ฐ€ ๊ฐ•ํ™”๋„๋กœ ์˜ฎ๊ฒจ์กŒ๋‹ค.', '๋ฐฑ๋‘์‚ฐ์ •๊ณ„๋น„๊ฐ€ ๊ฑด๋ฆฝ๋˜์—ˆ๋‹ค.', '์ด๋ฅธ๋ฐ” ๋‚จํ•œ ๋Œ€ํ† ๋ฒŒ ์ž‘์ „์ด ์ „๊ฐœ๋˜์—ˆ๋‹ค.'], 'answer': ''}",,3,1,False,"['๊ทธ๋Ÿฌ๋‚˜ ์‚ฌํšŒ ๊ฐœํ˜์ฑ…์€ ํ์ง€๋ถ€์ง€๋˜๊ณ , ๊ทธ๋Š” ์˜คํžˆ๋ ค ๋งŽ์€ ํ† ์ง€์™€ ๋…ธ๋น„๋ฅผ ์ฐจ์ง€ํ•˜๊ณ ์‚ฌ๋ณ‘์„ ์–‘์„ฑํ•˜์—ฌ ๊ถŒ๋ ฅ ์œ ์ง€์— ์น˜์ค‘ํ•˜์˜€๋‹ค. ์ตœ์ถฉํ—Œ์€ ์ตœ๊ณ  ์ง‘์ •๋ถ€์˜ ๊ตฌ์‹ค์„ ํ•˜๋Š”๊ต์ •๋„๊ฐ์„ ์„ค์น˜ํ•˜์—ฌ ๊ถŒ๋ ฅ์„ ํ–‰์‚ฌํ•˜์˜€๋‹ค. ๋˜,์‚ฌ๋ณ‘๊ธฐ๊ด€์ธ๋„๋ฐฉ์„ ์„ค์น˜ํ•˜์—ฌ ์‹ ๋ณ€์„ ๊ฒฝํ˜ธํ•˜์˜€๋‹ค.๋„๋ฐฉ์€์‚ผ๋ณ„์ดˆ์™€ ํ•จ๊ป˜ ์ตœ์”จ ์ •๊ถŒ์„ ์œ ์ง€ํ•˜๋Š” ๊ตฐ์‚ฌ์  ๊ธฐ๋ฐ˜์ด ๋˜์—ˆ๋‹ค. ์ตœ์ถฉํ—Œ์˜ ๋’ค๋ฅผ ์ด์€์ตœ์šฐ๋„๊ต์ •๋„๊ฐ์„ ํ†ตํ•˜์—ฌ ์ •์น˜ ๊ถŒ๋ ฅ์„ ํ–‰์‚ฌํ•˜์˜€๊ณ , ๋” ๋‚˜์•„๊ฐ€ ์ž๊ธฐ ์ง‘์—์ •๋ฐฉ์„ ์„ค์น˜ํ•˜์—ฌ ๋ชจ๋“  ๊ด€์ง์— ๋Œ€ํ•œ ์ธ์‚ฌ๊ถŒ์„ ์žฅ์•…ํ•˜์˜€๋‹ค. ์ •๊ตญ์ด ์•ˆ์ •๋˜๋ฉด์„œ์ตœ์šฐ๋Š” ๋ฌธํ•™์ ์ธ ์†Œ์–‘๊ณผ ํ•จ๊ป˜ ํ–‰์ • ์‹ค๋ฌด ๋Šฅ๋ ฅ์„ ๊ฐ–์ถ˜ ๋ฌธ์‹ ๋“ค์„ ๋“ฑ์šฉํ•˜์—ฌ ๊ณ ๋ฌธ ์—ญํ• ์„ ๋‹ด๋‹นํ•˜๊ฒŒ ํ•˜์˜€๋‹ค. ์ตœ์”จ์˜ ์ง‘๊ถŒ์œผ๋กœ ๋ฌด์‹  ์ •๊ถŒ์ด ์ •์น˜์ ์œผ๋กœ๋Š” ์•ˆ์ •๋˜์—ˆ์ง€๋งŒ, ๊ตญ๊ฐ€ ํ†ต์น˜ ์งˆ์„œ๋Š” ์˜คํžˆ๋ ค ์•ฝํ™”๋˜์—ˆ๋‹ค. ์ตœ์”จ ์ •๊ถŒ์€ ๊ถŒ๋ ฅ์˜ ์œ ์ง€์™€ ์ด๋ฅผ ์œ„ํ•œ ์ฒด์ œ์˜ ์ •๋น„์— ์ง‘์ฐฉํ–ˆ์„ ๋ฟ, ๊ตญ๊ฐ€์˜ ๋ฐœ์ „์ด๋‚˜ ๋ฐฑ์„ฑ์˜ ์•ˆ์ •์„ ์œ„ํ•œ ๋…ธ๋ ฅ์—๋Š” ์†Œํ™€ํ•˜์˜€๋‹ค. ๋ฌด์‹  ์ง‘๊ถŒ์ž์˜ ๋ณ€ํ™” ์ด์˜๋ฐฉโ†’ ์ •์ค‘๋ถ€ โ†’ ๊ฒฝ๋Œ€์Šน โ†’์ด์˜๋ฏผโ†’ ์ตœ์ถฉํ—Œ ์ค‘๋ฐฉ ์ตœ๊ณ ์œ„ ๋ฌด์‹ ๋“ค๋กœ ๊ตฌ์„ฑ๋œ ํšŒ์˜ ๊ธฐ๊ตฌ. ๋ฌด์‹ ์ •๋ณ€ ์งํ›„๋ถ€ํ„ฐ ์ตœ์ถฉํ—Œ ์ด ๊ถŒ๋ ฅ์„ ์žก์„ ๋•Œ๊นŒ์ง€ ์ตœ๊ณ  ๊ถŒ๋ ฅ ๊ธฐ๊ตฌ์˜€๋‹ค. ๊ต์ •๋„๊ฐ ์ตœ์”จ ์ •๊ถŒ์˜ ๋ฐ˜๋Œ€ ์„ธ๋ ฅ์„ ์ œ๊ฑฐํ•˜๊ณ , ๊ตญ์ •์„ ์ด๊ด„ํ•˜๋Š” ์ตœ๊ณ ์˜ ์ •์น˜ ๊ธฐ๊ตฌ ์‚ผ๋ณ„์ดˆ ์ตœ์šฐ ๊ฐ€ ์ง‘๊ถŒํ•˜๋ฉด์„œ ์„ค์น˜ํ•œ ์•ผ๋ณ„์ดˆ์—์„œ ๋ถ„๋ฆฌ๋œ ์ขŒ๋ณ„์ดˆ, ์šฐ๋ณ„์ดˆ์™€ ๋ชฝ๊ณจ์— ํฌ๋กœ๋กœ ์žกํ˜€๊ฐ”๋˜ ๋ณ‘์‚ฌ๋“ค๋กœ ์กฐ์ง๋œ ์‹ ์˜๊ตฐ์„ ๋งํ•œ๋‹ค.', '๋ฌ˜์ฒญ์˜์„œ๊ฒฝ ์ฒœ๋„ ์šด๋™์ดํ›„ ๋ฌธ๋ฒŒ ๊ท€์กฑ ์ง€๋ฐฐ ์ฒด์ œ์˜ ๋ชจ์ˆœ์€ ๋”์šฑ ๊นŠ์–ด์กŒ๋‹ค. ์ง€๋ฐฐ์ธต์€ ์ด์™€ ๊ฐ™์€ ์ƒํ™ฉ์— ํšจ๊ณผ์ ์œผ๋กœ ๋Œ€์‘ํ•˜์ง€ ๋ชปํ•œ ์ฑ„ ์ •์น˜์  ๋ถ„์—ด์„ ๊ฑฐ๋“ญํ•˜์˜€๋‹ค.์˜์ข…์—ญ์‹œ ์ธก๊ทผ ์„ธ๋ ฅ์„ ํ‚ค์šฐ๋ฉด์„œ ์ด๋“ค์— ์˜์กดํ•˜๊ณ  ํ–ฅ๋ฝ์— ๋น ์ง€๋Š” ๋“ฑ ์‹ค์ •์„ ๊ฑฐ๋“ญํ•˜์˜€๊ณ , ๋ฌธ์‹  ์šฐ๋Œ€์™€ ๋ฌด์‹  ์ฐจ๋ณ„์— ๋”ฐ๋ฅธ ๋ฌด์‹ ๋“ค์˜ ๋ถˆ๋งŒ์ด ์ปค์กŒ๋‹ค. ์—ฌ๊ธฐ์—๊ตฐ์ธ์ „์„ ์ œ๋Œ€๋กœ ์ง€๊ธ‰๋ฐ›์ง€ ๋ชปํ•œ ํ•˜๊ธ‰ ๊ตฐ์ธ๋“ค์˜ ๋ถˆ๋งŒ๋„ ๋†’์•„์กŒ๋‹ค. ์ด๋Ÿฌํ•œ ์ง€๋ฐฐ ์ฒด์ œ์˜ ๋ชจ์ˆœ์ด ํญ๋ฐœํ•œ ๊ฒƒ์ด๋ฌด์‹ ์ •๋ณ€์ด์—ˆ๋‹ค(1170). ์ •์ค‘๋ถ€, ์ด์˜๋ฐฉ ๋“ฑ ๋ฌด์‹ ๋“ค์€ ์ •๋ณ€์„ ์ผ์œผ์ผœ ๋‹ค์ˆ˜์˜ ๋ฌธ์‹ ์„ ์ฃฝ์ด๊ณ ์˜์ข…์„ ํํ•˜์—ฌ ๊ฑฐ์ œ๋„๋กœ ๊ท€์–‘ ๋ณด๋‚ธ ํ›„,๋ช…์ข…์„ ์„ธ์›Œ ์ •๊ถŒ์„ ์žฅ์•…ํ•˜์˜€๋‹ค. ๋ฌด์‹ ๋“ค์€์ค‘๋ฐฉ์„ ์ค‘์‹ฌ์œผ๋กœ ๊ถŒ๋ ฅ์„ ํ–‰์‚ฌํ•˜๋ฉด์„œ ์ฃผ์š” ๊ด€์ง์„ ๋…์ฐจ์ง€ํ•˜๊ณ  ํ† ์ง€์™€ ๋…ธ๋น„๋ฅผ ๋Š˜๋ ค ๋‚˜๊ฐ”์œผ๋ฉฐ, ์ €๋งˆ๋‹ค์‚ฌ๋ณ‘์„ ๊ธธ๋Ÿฌ ๊ถŒ๋ ฅ ์Ÿํƒˆ์ „์„ ๋ฒŒ์˜€๋‹ค. ์ตœ์ถฉํ—Œ์€ ์ •๊ถŒ์„ ์žก์ž, ๋ฌด์‹  ์ •๊ถŒ ์ดˆ๊ธฐ์˜ ํ˜ผ๋ž€์„ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ๋ด‰์‚ฌ 10์กฐ์™€ ๊ฐ™์€ ์‚ฌํšŒ ๊ฐœํ˜์ฑ…์„ ์ œ์‹œํ•˜๋Š” ํ•œํŽธ, ๋†๋ฏผ ํ•ญ์Ÿ์˜ ์ง„์••์—๋„ ์ ๊ทน์ ์œผ๋กœ ๋‚˜์„ฐ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์‚ฌํšŒ ๊ฐœํ˜์ฑ…์€ ํ์ง€๋ถ€์ง€๋˜๊ณ , ๊ทธ๋Š” ์˜คํžˆ๋ ค ๋งŽ์€ ํ† ์ง€์™€ ๋…ธ๋น„๋ฅผ ์ฐจ์ง€ํ•˜๊ณ ์‚ฌ๋ณ‘์„ ์–‘์„ฑํ•˜์—ฌ ๊ถŒ๋ ฅ ์œ ์ง€์— ์น˜์ค‘ํ•˜์˜€๋‹ค. ์ตœ์ถฉํ—Œ์€ ์ตœ๊ณ  ์ง‘์ •๋ถ€์˜ ๊ตฌ์‹ค์„ ํ•˜๋Š”๊ต์ •๋„๊ฐ์„ ์„ค์น˜ํ•˜์—ฌ ๊ถŒ๋ ฅ์„ ํ–‰์‚ฌํ•˜์˜€๋‹ค. ๋˜,์‚ฌ๋ณ‘๊ธฐ๊ด€์ธ๋„๋ฐฉ์„ ์„ค์น˜ํ•˜์—ฌ ์‹ ๋ณ€์„ ๊ฒฝํ˜ธํ•˜์˜€๋‹ค.๋„๋ฐฉ์€์‚ผ๋ณ„์ดˆ์™€ ํ•จ๊ป˜ ์ตœ์”จ ์ •๊ถŒ์„ ์œ ์ง€ํ•˜๋Š” ๊ตฐ์‚ฌ์  ๊ธฐ๋ฐ˜์ด ๋˜์—ˆ๋‹ค', '์ •๋น„๋œ ๊ณ ๋ ค์˜ ์ฒด์ œ๋Š” 12์„ธ๊ธฐ์— ๋“ค์–ด์„œ๋ฉด์„œ ๋ณ€ํ™”๋˜๊ธฐ ์‹œ์ž‘ํ–ˆ๋‹ค. ์šฐ์„  ์ •์น˜ ์ธก๋ฉด์—์„œ ์ •์น˜ ๊ถŒ๋ ฅ์„ ์žฅ์•…ํ•˜๊ณ  ์žˆ๋˜ ๋ฌธ๋ฒŒ ๊ท€์กฑ ๊ด€๋ฃŒ ์‚ฌ์ด์˜ ๊ท ํ˜•์ด ๊นจ์–ด์ ธ ๊ถŒ๋ ฅ๊ณผ ๊ฒฝ์ œ๋ ฅ์˜ ๋…์  ํ˜„์ƒ์ด ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๊ถŒ๋ ฅ ๋…์ ์— ๋ฐ˜๋ฐœํ•˜๋Š” ์‹ ์ง„ ๊ด€๋ฃŒ์˜ ๋Œ€ํ•ญ์œผ๋กœ ์ธํ•ด ์ด์ž๊ฒธ(ๆŽ่ณ‡่ฌ™)์˜ ๋‚œ๊ณผ ๋ฌ˜์ฒญ(ๅฆ™ๆทธ)์˜ ๋‚œ์ด ์ผ์–ด๋‚ฌ๋‹ค. ๋ฌธ๋ฒŒ ๊ท€์กฑ์ด ์ง€๋ฐฐํ•˜๋˜ ์‚ฌํšŒ๋Š” ์œ„๊ธฐ์— ์ฒ˜ํ–ˆ๊ณ , ๋งˆ์นจ๋‚ด ๋ฌด์ธ๋ž€(ๆญฆไบบไบ‚)์œผ๋กœ ๋ถ•๊ดด๋˜์—ˆ๋‹ค. 1170๋…„ ์ •์ค‘๋ถ€(้„ญไปฒๅคซ) ๋“ฑ์ด ์ฃผ๋„ํ•œ ๋ฌด์ธ๋ž€์€ ๋ฌด๋ฐ˜์— ๋Œ€ํ•œ ์ฐจ๋ณ„๊ณผ ๊ตฐ์ธ์˜ ๋ถˆ๋งŒ์ด ์›์ธ์ด ๋˜์–ด ์ผ์–ด๋‚œ ์ •๋ณ€์œผ๋กœ, ๊ณ ๋ ค ์‚ฌํšŒ๋ฅผ ์ƒˆ๋กœ์šด ๊ตญ๋ฉด์œผ๋กœ ์ „ํ™˜์‹œ์ผฐ๋‹ค (โ†’ ์ƒ‰์ธ : ์ •์ค‘๋ถ€์˜ ๋‚œ). ๊ถŒ๋ ฅ์„ ์žฅ์•…ํ•œ ๋ฌด์ธ๋“ค์€ ์ง‘๊ถŒ ๋ฌธ์‹ ๋“ค์„ ๋Œ€๊ฑฐ ์ˆ™์ฒญใ†์‚ดํ•ดํ•˜๊ณ , ์™•์˜ ํ๋ฆฝ์„ ๋งˆ์Œ๋Œ€๋กœ ํ–ˆ๋‹ค. ์ •์ค‘๋ถ€ใ†๊ฒฝ๋Œ€์Šน(ๆ…ถๅคงๅ‡)ใ†์ด์˜๋ฏผ(ๆŽ็พฉๆ—ผ)์— ์ด์–ด ์ง‘๊ถŒํ•œ ์ตœ์ถฉํ—Œ(ๅด”ๅฟ ็ป)์€ ๊ณผ๊ฐํ•œ ์ „์ œ ์ •์น˜๋กœ ์ •๊ถŒ์„ ์•ˆ์ •์‹œ์ผœ, ์ดํ›„ 4๋Œ€ 60๋…„๊ฐ„์˜ ์ตœ์”จ ์ง‘์ • ์‹œ๋Œ€๋ฅผ ์—ด์—ˆ๋‹ค. ๋ฌด์ธ ์ •๊ถŒ๊ธฐ์— ์™•์€ ์œ ๋ช…๋ฌด์‹คํ•ด์ง€๊ณ , ์ง‘๊ถŒ ๋ฌด์ธ์ด ์ •๋ฐฉ(ๆ”ฟๆˆฟ)ใ†๋„๋ฐฉ(้ƒฝๆˆฟ)ใ†๊ต์ •๋„๊ฐ(ๆ•Žๅฎš้ƒฝ็›ฃ)๊ณผ ๊ฐ™์€ ๊ธฐ๊ตฌ๋ฅผ ํ†ตํ•ด ๋ชจ๋“  ๊ถŒ๋ ฅ์„ ํ–‰์‚ฌํ–ˆ๋‹ค. ๋˜ํ•œ ๊ฒฝ์ œ ๊ธฐ๋ฐ˜์œผ๋กœ ๋Œ€ํ† ์ง€๋ฅผ ์†Œ์œ ํ•˜์—ฌ ์‚ฌํšŒ ๊ฒฝ์ œ์  ๋ชจ์ˆœ์„ ๊ฒฉํ™”์‹œ์ผฐ๋‹ค. ํ•œํŽธ ์‚ฌํšŒ ๊ฒฝ์ œ ์ธก๋ฉด์—์„œ๋Š” ๊ถŒ๋ ฅ ์ง‘์ค‘์— ๋”ฐ๋ฅธ ๊ฒฝ์ œ๋ ฅ์˜ ์ง‘์ค‘์„ ๊ฐ€์ ธ์™€ ํ† ์ง€์˜ ๊ฒธ๋ณ‘ ํ˜„์ƒ์ด ๋ฐœ์ƒํ–ˆ๊ณ , ๊ทธ ๊ฒฐ๊ณผ ์ „์‹œ๊ณผ ์ฒด์ œ๊ฐ€ ์œ„๊ธฐ์— ๋†“์ด๊ฒŒ ๋˜์—ˆ๋‹ค. ์‹ ๋ถ„์ œ๋„ ํ•ด์ดํ•ด์กŒ์œผ๋ฉฐ, ๋†๋ฏผ์— ๋Œ€ํ•œ ์ˆ˜์ทจ๋Š” ๋”์šฑ ๊ฐ€ํ˜นํ•ด์ ธ ํ† ์ง€๋กœ๋ถ€ํ„ฐ ์œ ๋ฆฌํ•˜๋Š” ๋†๋ฏผ์ด ์ฆ๊ฐ€ํ–ˆ๋‹ค']",1 +2025-history-05,์ด๊ฒƒ์€ ์˜์กฐ์— ์ด์–ด ์ฆ‰์œ„ํ•œ ์ด ์™•์ด ์‚ฌ์šฉํ•œ ์žฅ์„œ์ธ ์ค‘ 2๊ฐœ์ด๋‹ค. ์žฅ์„œ์ธ์€ ์ž์‹ ์ด ์†Œ์žฅํ•œ ์„œ์ฑ…์— ์ฐ๋Š” ๋„์žฅ์„ ์ผ์ปซ๋Š”๋‹ค. 'ํƒ•ํ‰ํ‰ํƒ•'์ด๋ผ๊ณ  ์ƒˆ๊ฒจ์ง„ ์ด ์žฅ์„œ์ธ๋“ค์€ ๋ถ•๋‹น ๊ฐ„์˜ ๋Œ€๋ฆฝ์„ ์™„ํ™”ํ•˜๊ณ ์ž ํ–ˆ๋˜ ์ด ์™•์˜ ํƒ•ํ‰ ์ •์ฑ… ์ถ”์ง„ ์˜์ง€๋ฅผ ์—ฟ๋ณผ ์ˆ˜ ์žˆ๋Š” ์ž๋ฃŒ์ด๋‹ค.,"{'question': '๋ฐ‘์ค„ ์นœ โ€˜์ด ์™•โ€™์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['๊ทœ์žฅ๊ฐ์„ ์œก์„ฑํ•˜์˜€๋‹ค.', 'ํ›„์‚ผ๊ตญ์„ ํ†ต์ผํ•˜์˜€๋‹ค.', '๋‹น๋ฐฑ์ „์„ ๋ฐœํ–‰ํ•˜์˜€๋‹ค.', '๊ฒฝ๊ตญ๋Œ€์ „์„ ๋ฐ˜ํฌํ•˜์˜€๋‹ค.', '์Œ์„ฑ์ด๊ด€๋ถ€๋ฅผ ํšŒ๋ณตํ•˜์˜€๋‹ค.'], 'answer': ''}",,1,1,True,[],1 +2025-history-06,"์ด ๋น„๊ฐ์—๋Š” ๊ตญ๊ฐ€์œ ์‚ฐ์œผ๋กœ ์ง€์ •๋œ ๊ฑด์›๋ฆ‰ ์‹ ๋„๋น„๊ฐ€ ์žˆ์Šต๋‹ˆ๋‹ค. ๊ฑด์›๋ฆ‰์€ ์กฐ์„ ์„ ๊ฑด๊ตญํ•œ (๊ฐ€)์˜ ๋Šฅ์œผ๋กœ, ์ด ๋น„์„์—๋Š” ๊ทธ๊ฐ€ ํ™๊ฑด์ ๊ณผ ์™œ๊ตฌ๋ฅผ ๊ฒฉํ‡ดํ•œ ์‚ฌ์‹ค, ๋ฐฐ๊ทน๋ ด, ์กฐ์ค€ ๋“ฑ์˜ ์ถ”๋Œ€๋ฅผ ๋ฐ›์•„ ์™•์œ„์— ์˜ค๋ฅธ ์‚ฌ์‹ค ๋“ฑ์ด ๊ธฐ๋ก๋˜์–ด ์žˆ์Šต๋‹ˆ๋‹ค.","{'question': '(๊ฐ€) ์ธ๋ฌผ์˜ ํ™œ๋™์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['๋…น์์„ ํ์ง€ํ•˜์˜€๋‹ค.', '๋ณ„๋ฌด๋ฐ˜์„ ํŽธ์„ฑํ•˜์˜€๋‹ค.', '๋Œ€๋™๋ฒ•์„ ์‹œํ–‰ํ•˜์˜€๋‹ค.', '์œ„ํ™”๋„ ํšŒ๊ตฐ์„ ๋‹จํ–‰ํ•˜์˜€๋‹ค.', 'ํ†ต๋ฆฌ๊ธฐ๋ฌด์•„๋ฌธ์„ ์„ค์น˜ํ•˜์˜€๋‹ค.'], 'answer': ''}",,4,4,True,"['๊ทธ๋Ÿฌ๋‚˜ ํƒœ์ข…์€ ์˜์˜์ • ํ•˜๋ฅœ(ๆฒณๅด™) ๋“ฑ์—๊ฒŒ ๋ช…ํ•˜์—ฌ ์›ํ‰, ๋ด‰์„ฑ, ํ–‰์ฃผ ๋“ฑ ๋‹ค์–‘ํ•œ ํ›„๋ณด์ง€๋ฅผ ๋‘˜๋Ÿฌ๋ณด๋„๋ก ํ•œ ๋์—, ์ตœ์ข…์ ์œผ๋กœ ๋Šฅ์˜ ์œ„์น˜๋ฅผ ์–‘์ฃผ ๊ฒ€์•”์œผ๋กœ ํ™•์ •ํ•˜์˜€๋‹ค. ์‹ ๋• ์™•ํ›„์˜ ์ •๋ฆ‰์€ ๋‹น์‹œ ๋„์„ฑ ๋ฐ–์œผ๋กœ ์˜ฎ๊ฒจ์ ธ ํ˜„์žฌ ์„ฑ๋ถ๋™์— ์žˆ๋‹ค. ๊ฑด์›๋ฆ‰์€ ์กฐ์„ ์„ ๊ฑด๊ตญํ•œ ๊ฐœ๊ตญ์กฐ์˜ ์™•๋ฆ‰์ด๋‹ˆ๋งŒํผ ์กฐ์„ ์—์„œ๋„ ํŠน๋ณ„ํ•˜๊ฒŒ ๊ด€๋ฆฌํ•˜์˜€๋‹ค. ์™•๋ฆ‰ ์กฐ์„ฑ ํ›„ ํƒœ์ข…์€ ์žฌ๊ถ(้ฝ‹ๅฎฎ)์— ๊ฐœ๊ฒฝ์‚ฌ(้–‹ๆ…ถๅฏบ)๋ผ๋Š” ์ด๋ฆ„์„ ๋‚ด๋ฆฌ๊ณ , ๋…ธ๋น„์™€ ํ† ์ง€ ๋ฐ ์ˆ˜ํ˜ธ๊ตฐ์„ ๋‘๋„๋ก ํ•˜์—ฌ ๊ด€๋ฆฌ์— ์†Œํ™€ํ•จ์ด ์—†๋„๋ก ํ•˜์˜€๋‹ค. ๊ฑด์›๋ฆ‰์€ ์ดํ›„ ์ˆ˜์ฐจ๋ก€์˜ ๋ณด์ˆ˜๋ฅผ ๊ฑฐ์ณค๋‹ค. ๊ฐ€์žฅ ์ฒ˜์Œ์œผ๋กœ ๋‚˜ํƒ€๋‚˜๋Š” ๋ณด์ˆ˜์— ๊ด€ํ•œ ๊ธฐ๋ก์€ ์„ธ์ข…(ไธ–ๅฎ—) ๋Œ€์ด๋‹ค. ์„ธ์ข…์€ ์‚ฐ๋ฆ‰ ์ˆ˜๋ฆฌ๋„๊ฐ์„ ์„ค์น˜ํ•ด ๊ฑด์›๋ฆ‰๊ณผ ์‹ ์˜ ์™•ํ›„์˜ ์ œ๋ฆ‰, ํƒœ์ข…์˜ ํ—Œ๋ฆ‰์„ ํ•จ๊ป˜ ์ˆ˜๋ฆฌํ•˜๋„๋ก ํ•˜์˜€๋‹ค. ๊ฑด์›๋ฆ‰์€ ์ž„์ง„ ์™œ๋ž€ ๋‹น์‹œ์—๋„ ํ›ผ์†๋˜์ง€ ์•Š์„ ์ •๋„๋กœ ์‹ ๋ นํ•จ์ด ๊นƒ๋“  ๊ณณ์ด๋ผ ํ•˜์—ฌ, ๋ณ„๋‹ค๋ฅธ ์ผ์ด ์•„๋‹ˆ๋ฉด ๋ณด์ˆ˜ ์ž‘์—…์„ ํ•˜๋Š” ๊ฒƒ๋„ ์‹ ์ค‘ํ•˜๊ฒŒ ๊ฒฐ์ •ํ•˜์˜€๋‹ค. 1690๋…„(์ˆ™์ข… 16)์—๋Š” ์‹ ๋„๋น„(็ฅž้“็ข‘)[์ฃฝ์€ ์‚ฌ๋žŒ์˜ ์—…์ ์„ ์นญ์†กํ•˜์—ฌ ์ƒˆ๊ธด ๋น„์„]๋ฅผ ๋ณดํ˜ธํ•˜๊ธฐ ์œ„ํ•ด ๋น„๊ฐ(็ข‘้–ฃ)์„ ์„ธ์šฐ๋„๋ก ํ•˜์˜€๋‹ค. ๊ฑด์›๋ฆ‰์˜ ์ •์ž๊ฐ ๋˜ํ•œ ์„ ์กฐ ๋Œ€์— ์ค‘์ˆ˜ํ•œ ๋ฐ” ์žˆ์œผ๋ฉฐ, ์˜์กฐ ๋Œ€์—๋„ ๋‹ค์‹œ ์ค‘์ˆ˜๋„๊ฐ์„ ์„ค์น˜ํ•˜์—ฌ ์ •์ž๊ฐ์„ ์ˆ˜๋ฆฌํ–ˆ๋‹ค. ๊ฑด์›๋ฆ‰์€ ์กฐ์„ ์—์„œ ์ฒ˜์Œ์œผ๋กœ ์กฐ์„ฑํ•œ ์™•๋ฆ‰์œผ๋กœ, ์กฐ์„  ์™•๋ฆ‰ ์ œ๋„์˜ ํ‘œ๋ณธ์ด๋ผ๊ณ  ํ•  ์ˆ˜ ์žˆ๋‹ค. ๊ธฐ๋ณธ์ ์œผ๋กœ๋Š” ๊ณ ๋ ค ๋ง ์™•๋ฆ‰์˜ ์–‘์‹์„ ๋”ฐ๋ž์œผ๋‚˜, ๋ด‰๋ถ„ ์ฃผ์œ„์— ๊ณก์žฅ(ๆ›ฒๅขป)[๋Šฅ, ์›, ๋ฌ˜ ๋”ฐ์œ„์˜ ๋ฌด๋ค ๋’ค์™€ ์ขŒ์šฐ์— ๋‘˜๋Ÿฌ์Œ“์€ ๋‚˜์ง€๋ง‰ํ•œ ๋‹ด]์„ ๋‘๋ฅด๋Š” ๋“ฑ ์„ธ๋ถ€์ ์ธ ์„๋ฌผ ๋ฐฐ์น˜์—์„œ ๊ณ ๋ ค ์‹œ๋Œ€ ์™•๋ฆ‰๊ณผ๋Š” ๋‹ค๋ฅธ ๋ณ€ํ™”๋ฅผ ๋ณด์—ฌ ์ฃผ๊ณ  ์žˆ๋‹ค', '**๊ตฌ๋ฆฌ ํƒœ์กฐ ๊ฑด์›๋ฆ‰ ์‹ ๋„๋น„**(ไน้‡Œ ๅคช็ฅ– ๅฅๅ…ƒ้™ต ็ฅž้“็ข‘)๋Š” ๊ฒฝ๊ธฐ๋„ ๊ตฌ๋ฆฌ์‹œ, ๊ฑด์›๋ฆ‰์— ์žˆ๋Š” ์กฐ์„ ์‹œ๋Œ€์˜ ๋น„์„์ด๋‹ค. 2013๋…„ 7์›” 16์ผ ๋Œ€ํ•œ๋ฏผ๊ตญ์˜ ๋ณด๋ฌผ ์ œ1803ํ˜ธ๋กœ ์ง€์ •๋˜์—ˆ๋‹ค. ## ๊ฐœ์š” ์ด ์‹ ๋„๋น„๋Š” 1409๋…„(ํƒœ์ข… 9)์— ์„ธ์šด ๊ฒƒ์ด๋‹ค. ์กฐ์„ ์„ ๊ฐœ๊ตญํ•œ ํƒœ์กฐ ์ด์„ฑ๊ณ„(ๆŽๆˆๆก‚, 1335\\~1408๋…„)์˜ ๊ฑด๊ตญ ๊ณผ์ •์„ ๋น„๋กฏํ•˜์—ฌ ์ƒ์• ์™€ ์—…์  ๋“ฑ์„ ์˜์›ํžˆ ๊ธฐ๋ฆฌ๊ณ ์ž ์ผ๋Œ€๊ธฐ๋ฅผ ์ง“๊ณ  ๋Œ์— ์ƒˆ๊ฒจ ์„ธ์šด ๋น„์ด๋‹ค. ์ด ์‹ ๋„๋น„๋Š” ์ด์ˆ˜(่žญ้ฆ–), ๋น„์‹ ๊ณผ ๊ท€๋ถ€(้พœ่ถบ)๊ฐ€ ์–‘ํ˜ธํ•˜๊ฒŒ ์ž˜ ๋ณด์กด๋˜์–ด ์žˆ์–ด ์กฐ์„  ์ดˆ๊ธฐ ์™•์˜ ์‹ ๋„๋น„๋Š” ๋ฌผ๋ก  ์—ฌํƒ€ ์‹ ๋„๋น„์˜ ์ „ํ˜•์œผ๋กœ ํ‰๊ฐ€๋œ๋‹ค. ๋‹น๋Œ€์˜ ์‹ ๋ง์žˆ๋Š” ๋ฌธ์‹ ์ด์ž ๋Œ€ํ•™์ž์˜€๋˜ ๊ถŒ๊ทผ(ๆฌŠ่ฟ‘, 1352\\~1409๋…„)์ด ๋น„๋ฌธ์„ ์ง“๊ณ , ๋ช…๋ฌธ์žฅ๊ฐ€์˜€๋˜ ๋ณ€๊ณ„๋Ÿ‰(ๅžๅญฃ่‰ฏ, 1369\\~1430๋…„)์ด ๋น„์Œ๊ธฐ(็ข‘้™ฐ่จ˜)๋ฅผ ์ง€์—ˆ์œผ๋ฉฐ, ๋ช…์‹  ์„œ์˜ˆ๊ฐ€์ธ ์ •๊ตฌ(้„ญ็Ÿฉ, 1350\\~1418๋…„)๊ฐ€ ์ „์•ก(็ฏ†้ก)์„ ์“ฐ๊ณ , ์กฐ์„  ์ดˆ๊ธฐ ๋ช…ํ•„์ธ ์„ฑ์„๋ฆฐ(ๆˆ็Ÿณ็’˜, 1338\\~1423๋…„)์ด ๋น„๋ฌธ ๊ธ€์”จ๋ฅผ ์ผ๋‹ค. ๋น„๋ก ๋น„์ขŒ ๋ถ€๋ถ„์ด ์ƒˆ๋กญ๊ฒŒ ๋งŒ๋“ค์–ด์กŒ์ง€๋งŒ ๊ณ ๋ ค์‹œ๋Œ€ ์„๋น„ ์กฐํ˜•์„ ํƒˆํ”ผํ•˜์—ฌ ์กฐ์„ ์‹œ๋Œ€ ๋“ค์–ด์™€ ์ƒˆ๋กญ๊ฒŒ ๋ช…๋‚˜๋ผ์˜ ์„๋น„์ „ํ†ต์„ ๋ฐ›์•„ ๋“ค์—ฌ ์„ธ์šด ๋น„๋กœ, ์กฐ์„ ์‹œ๋Œ€ ์„๋น„์˜ ๊ธฐ์ค€์ž‘์ด ๋˜๋Š” ํ•œํŽธ ์„œ์˜ˆ์‚ฌ๋ฅผ ๋น„๋กฏํ•œ ์—ญ์‚ฌยท๋ฌธํ™”์‚ฌ ์—ฐ๊ตฌ์ž๋ฃŒ๊ฐ€ ๋  ์ˆ˜ ์žˆ๋Š” ๋งค์šฐ ๊ท€์ค‘ํ•œ ๋ฌธํ™”์žฌ์ด๋‹ค. ## ๊ฐ™์ด ๋ณด๊ธฐ - ๊ฑด์›๋ฆ‰', ""์‹ ๋„๋น„์˜ ๋น„๋ฌธ์€ ๊ถŒ๊ทผ(ๆฌŠ่ฟ‘), ์Œ๊ธฐ(้™ฐ่จ˜)๋Š” ๋ณ€๊ณ„๋Ÿ‰(ๅžๅญฃ่‰ฏ)์ด ์ง€์—ˆ์œผ๋ฉฐ, ์ „์•ก(็ฏ†้ก)์€ ์ •๊ตฌ(้„ญ็Ÿฉ)๊ฐ€ ์“ฐ๊ณ , ๊ธ€์”จ๋Š” ์„ฑ์„๋ฆฐ(ๆˆ็Ÿณ็’˜)์ด ์ผ๋‹ค. ๋ฌ˜ํ‘œ๋Š” 1900๋…„์— ์„ธ์šด ๊ฒƒ์ด๋‹ค. ์ „๋ฉด๊ณผ ์Œ๊ธฐ ๋ชจ๋‘ ๊ณ ์ข…(้ซ˜ๅฎ—)์˜ ์–ดํ•„๋กœ ๋˜์–ด ์žˆ์œผ๋ฉฐ, ์ „๋ฉด์—๋Š” ์ „์„œ๋กœ '๋Œ€ํ•œ ๊ณ ํ™ฉ์ œ ๊ฑด์›๋ฆ‰(ๅคง้Ÿ“ ้ซ˜็š‡ๅธ ๅฅๅ…ƒ้™ต)'์ด๋ผ ์ ํ˜€ ์žˆ๋‹ค. ๋Œ€ํ•œ ์ œ๊ตญ ์„ ํฌ ์ดํ›„ 1899๋…„์— ๊ณ ์ข…์€ ํƒœ์กฐ๋ฅผ ๊ณ ํ™ฉ์ œ๋กœ ์ถ”์กดํ•˜๊ณ  1๋…„ ๋’ค ๋ฌ˜ํ‘œ๋ฅผ ์„ธ์› ๋Š”๋ฐ, ์ด๋Š” ๋Œ€ํ•œ ์ œ๊ตญ์˜ ๋…์ž์„ฑ์„ ๋ณด์—ฌ ์ฃผ๊ณ ์ž ํ•˜์˜€๋˜ ๊ณ ์ข…์˜ ์˜๋„๊ฐ€ ๋ฐ˜์˜๋œ ๊ฒƒ์ด๋ผ ํ‰๊ฐ€๋œ๋‹ค. ๊ฑด์›๋ฆ‰์ด ์†ํ•œ ๊ตฌ๋ฆฌ ๋™๊ตฌ๋ฆ‰ ์ „์ฒด๊ฐ€ 1970๋…„ 5์›” 26์ผ ์‚ฌ์  ์ œ193ํ˜ธ๋กœ ์ง€์ •๋˜์—ˆ๋‹ค. ๋˜ํ•œ 2009๋…„ 6์›”์—๋Š” ์กฐ์„  ์™•๋ฆ‰ ์ „์ฒด๊ฐ€ ๊ทธ ๋ฌธํ™”์  ๊ฐ€์น˜๋ฅผ ์ธ์ •๋ฐ›์•„ ์œ ๋„ค์Šค์ฝ” ์„ธ๊ณ„ ๋ฌธํ™”์œ ์‚ฐ์œผ๋กœ ์ง€์ •๋˜์—ˆ๋‹ค. 2011๋…„ 12์›”์—๋Š” ๊ฑด์›๋ฆ‰ ์ •์ž๊ฐ์ด ๋ณด๋ฌผ ์ œ1741ํ˜ธ๋กœ, 2013๋…„ 7์›”์—๋Š” ๊ฑด์›๋ฆ‰ ์‹ ๋„๋น„๊ฐ€ ๋ณด๋ฌผ ์ œ1803ํ˜ธ๋กœ ์ง€์ •๋œ ๋ฐ” ์žˆ๋‹ค. ํ˜„์žฌ ๊ฑด์›๋ฆ‰์€ ๋ฌธํ™”์žฌ์ฒญ ์กฐ์„  ์™•๋ฆ‰ ๊ด€๋ฆฌ์†Œ ๋™๋ถ€ ์ง€๊ตฌ ๊ด€๋ฆฌ์†Œ์—์„œ ๊ด€๋ฆฌํ•˜๊ณ  ์žˆ๋‹ค. ๊ฑด์›๋ฆ‰์€ ์„ธ๊ณ„ ๋ฌธํ™”์œ ์‚ฐ์œผ๋กœ ์ง€์ •๋œ ์กฐ์„  ์™•๋ฆ‰ ์ค‘์—์„œ๋„ ๊ทธ ํ‘œ๋ณธ์ด ๋˜๋Š” ํƒœ์กฐ ์ด์„ฑ๊ณ„์˜ ๋Šฅ์œผ๋กœ, ์ค‘์š”ํ•œ ์—ญ์‚ฌ์  ๊ฐ€์น˜๋ฅผ ์ง€๋‹ˆ๊ณ  ์žˆ๋‹ค. ์กฐ์„  ์™•์‹ค์˜ ๋ฌธํ™”์™€ ์™•๋ฆ‰ ์—ฐ๊ตฌ์—๋„ ์œ ์šฉํ•˜๊ฒŒ ์“ฐ์ผ ์ˆ˜ ์žˆ๋Š” ๊ท€์ค‘ํ•œ ์œ ์ ์ด๋‹ค.""]",4 +2025-history-07,ํ•œ์„ฑ์—์„œ ๋ฐ€๋ฆฐ ๊ธ‰๋ฃŒ๋ฅผ ๋ฐ›๋Š” ๊ณผ์ •์—์„œ ํ™”๊ฐ€ ๋‚œ ๊ตฐ์ธ๋“ค์ด ํฐ ๋ณ€๋ž€์„ ์ผ์œผ์ผฐ๋‹ค์ง€? ์ด ๋•Œ๋ฌธ์— ์ž„๊ธˆ๊ป˜์„œ ๋‚˜๋ž์ผ์„ ํฅ์„  ๋Œ€์›๊ตฐ์—๊ฒŒ ๋ฌผ์–ด ๊ฒฐ์ •ํ•˜๋ผ ๋ช…ํ•˜์…จ๋‹ค ๋“ค์—ˆ๋„ค. ์–ด์ฐŒ๋œ ์ผ์ธ๊ฐ€? ๋ง๋„ ๋ง๊ฒŒ. ๋ณ€๋ž€์„ ์ผ์œผํ‚จ ๊ตฐ์ธ๋“ค์ด ๋ณ„๊ธฐ๊ตฐ ๊ตฐ์ธ์˜ ์ง‘๊ณผ ์ผ๋ณธ ๊ณต์‚ฌ๊ด€์„ ๋ถ€์ˆ˜์—ˆ๋„ค. ์‹ฌ์ง€์–ด ๊ถ๊ถ์— ๋“ค์–ด๊ฐ€ ๋ฏผ๊ฒธํ˜ธ ๋“ฑ ์ •๋ถ€ ๊ณ ๊ด€์„ ์‚ดํ•ดํ•˜์˜€๋‹ค๋„ค. ์ด ์™€์ค‘์— ์šฐ๋ฆฌ ์ง‘๋„ ๋ถ€์„œ์ ธ ๋„๋ง์ณ ๋‚˜์™”๋„ค.,"{'question': '๋ฐ‘์ค„ ์นœ โ€˜๋ณ€๋ž€โ€™์˜ ์˜ํ–ฅ์œผ๋กœ ๊ฐ€์žฅ ์ ์ ˆํ•œ ๊ฒƒ์€?', 'choices': ['6โ€ค3 ์‹œ์œ„๊ฐ€ ์ „๊ฐœ๋˜์—ˆ๋‹ค', '์ˆ˜์„ ์‚ฌ ๊ฒฐ์‚ฌ๊ฐ€ ์ œ์ฐฝ๋˜์—ˆ๋‹ค.', '๋ฌผ์‚ฐ ์žฅ๋ ค ์šด๋™์ด ์ผ์–ด๋‚ฌ๋‹ค.', '๊ตญ๋ฏผ ๋Œ€ํ‘œ ํšŒ์˜๊ฐ€ ๊ฐœ์ตœ๋˜์—ˆ๋‹ค.', '์กฐ์ฒญ ์ƒ๋ฏผ ์ˆ˜๋ฅ™ ๋ฌด์—ญ ์žฅ์ •์ด ์ฒด๊ฒฐ๋˜์—ˆ๋‹ค.'], 'answer': ''}",,5,2,False,[],5 +2025-history-08,"์ผ๋ณธ์ด ์ œ1์ฐจ ํ•œ์ผ ํ˜‘์•ฝ์„ ์ฒด๊ฒฐํ•˜์—ฌ ๊ณ ๋ฌธ ์ •์น˜์˜ ์„œ๋ง‰์„ ์—ด๊ณ  ์žฌ์ •โ€ค์™ธ๊ต๋ฅผ ๊ฐ์‹œํ•˜๋”๋‹ˆ, ์ดํ›„์— (๊ฐ€) ์„/๋ฅผ ์ฒด๊ฒฐํ•ด์„œ ์™ธ๊ต๋ฅผ ์ฃผ๊ด€ํ•˜๊ฒŒ ๋˜์—ˆ๋‹ค. ์ด์— ์กฐ์„ ์˜ ์šฐ๊ตญ์ง€์‚ฌ๊ฐ€ ์‹œ๊ตญ์ด ๋‚ ๋กœ ๊ทธ๋ฆ‡๋˜๋Š” ๊ฒƒ์„ ๋ชฉ๋„ํ•˜๊ณ  ๋น„๋ถ„๊ฐ•๊ฐœํ•˜๋Š” ์‚ฌ์ด์— ๋ฏผ์˜ํ™˜์„ ๋น„๋กฏํ•œ ๊ฐ•์งํ•œ ์‹ ํ•˜๊ฐ€ ์ž๊ฒฐํ•˜๊ณ , ์ตœ์ตํ˜„๊ณผ ๊ฐ™์€ ์ ˆ์˜๋ฅผ ์ง€๋‹Œ ์‚ฌ๋žŒ์ด ๊ฑฐ๋ณ‘(ๆ“งๅ…ต)ํ•˜์—ฌ ์ฒœํ•˜๊ฐ€ ๋– ๋“ค์ฉํ•œ ์ค‘์— ์ผ๋Œ€ ํฐ์ผ์ด ๋Œ์—ฐํžˆ ์ผ์–ด๋‚˜๋‹ˆ, ์ฆ‰ ์ด์ƒ์„ค ๋“ฑ์˜ ํŠน์‚ฌ ์‚ฌ๊ฑด์ด๋‹ค","{'question': '(๊ฐ€)์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€? ', 'choices': ['๋ฒ ํŠธ๋‚จ ํŒŒ๋ณ‘์˜ ๋ฐฐ๊ฒฝ์ด ๋˜์—ˆ๋‹ค.', 'ํ†ต๊ฐ๋ถ€๊ฐ€ ์„ค์น˜๋˜๋Š” ๊ทผ๊ฑฐ๊ฐ€ ๋˜์—ˆ๋‹ค.', '๊ฐ•๋™ 6์ฃผ๋ฅผ ํ™•๋ณดํ•˜๋Š” ๊ณ„๊ธฐ๊ฐ€ ๋˜์—ˆ๋‹ค.', '์ตœํ˜œ๊ตญ ๋Œ€์šฐ๋ฅผ ์ฒ˜์Œ์œผ๋กœ ๊ทœ์ •ํ•˜์˜€๋‹ค.', '๋ถ๋ฒŒ๋ก ์ด ๋Œ€๋‘ํ•˜๋Š” ๋ฐ ์˜ํ–ฅ์„ ๋ผ์ณค๋‹ค.'], 'answer': ''}",,2,2,True,"['์ด๋“ค์€ 1904๋…„ 8์›” 22์ผ ์ฒด๊ฒฐ๋œ ์ œ1์ฐจ ํ•œ์ผ ํ˜‘์•ฝ์˜ ๊ณ ๋ฌธ ์šฉ๋น™์— ๊ด€ํ•œ ๊ทœ์ •์— ๋”ฐ๋ผ ํ•œ๊ตญ ์ •๋ถ€์— ๊ณ ๋ฌธ์œผ๋กœ ์ฐธ์—ฌํ–ˆ๊ฑฐ๋‚˜ ํ†ต๊ฐ๋ถ€์—์„œ ์žฌ์งํ–ˆ๋˜ ์ธ๋ฌผ๋“ค์ด์—ˆ๋‹ค. ์ง€๋ฐฉ์—์„œ๋„ ์‚ฌ๋ฌด๊ด€์„ ๋น„๋กฏํ•ด ์ฃผ์‚ฌ๊นŒ์ง€ ์ผ๋ณธ์ธ์ด ์ž„๋ช…๋˜๊ณ , ํ•œ๊ตญ์ธ๋“ค์˜ ๋ฐ˜์ผ ํ™œ๋™์„ ๋ง‰๊ธฐ ์œ„ํ•ด ์น˜์•ˆ ์—…๋ฌด๋ฅผ ๋‹ด๋‹นํ•˜๋Š” ๊ฒฝ๋ฌด๊ตญ์žฅ, ๊ฒฝ๋ฌด์‚ฌ, ๋ถ€๊ฒฝ๋ฌด์‚ฌ ๋“ฑ์—๋„ ์ผ๋ณธ์ธ์ด ์ž„๋ช…๋˜์—ˆ๋‹ค. ์ผ์ œ ํ†ต๊ฐ๋ถ€๋Š” ๊ธฐ์กด์˜ ๊ณ ๋ฌธ ์ •์น˜๋ฅผ ํ์ง€ํ•˜๊ณ  ์ฐจ๊ด€ ์ •์น˜๋ฅผ ์‹คํ–‰ํ•จ์œผ๋กœ์จ ๋Œ€ํ•œ์ œ๊ตญ์˜ ์™ธ๊ต์— ์ด์–ด ํ–‰์ •๊ณผ ์‚ฌ๋ฒ•์˜ ๋‚ด์ •๊นŒ์ง€ ์™„์ „ํžˆ ์žฅ์•…ํ•  ์ˆ˜ ์žˆ๊ฒŒ ๋˜์—ˆ๋‹ค.', '์ดํ›„๋ถ€ํ„ฐ๋Š” ๊ฐ‘์˜ค๊ฐœํ˜์œผ๋กœ ์กฐ์„ ์˜ ๊ฐœ๊ตญ ์ด๋ž˜์˜ ์ •์น˜์ œ๋„๋ฅผ ๊ฐœํ˜ํ•˜๋Š” ๋ฐ ๊นŠ์ด ๊ด€์—ฌํ•˜๊ฒŒ ๋˜์—ˆ๊ณ , ๋ช…์„ฑํ™ฉํ›„ ์‹œํ•ด(ๅผ‘ๅฎณ)์— ๊ฐ€๋‹ดํ•œ ์ดํ›„์—๋Š” ํ•œ๋•Œ ๋Ÿฌ์‹œ์•„ ์„ธ๋ ฅ์— ๋ฐ€๋ ค๋‚˜๊ธฐ๋„ ํ•˜์˜€์œผ๋‚˜, ๋Ÿฌยท์ผ์ „์Ÿ์— ์Šน๋ฆฌํ•˜์—ฌ ํ•œ๋ฐ˜๋„์—์„œ์˜ ์ •์น˜ยท๊ตฐ์‚ฌยท๊ฒฝ์ œ์ƒ์˜ ์šฐ์œ„(ๅ„ชไฝ)๋ฅผ ๊ตญ์ œ์ ์œผ๋กœ ์ธ์ •๋ฐ›์•˜๋‹ค. ์ผ๋ณธ์€ ๋Ÿฌ์‹œ์•„์™€ ์ „์Ÿ์ค‘์ด๋˜ 1904๋…„ 2์›” ํ•œ๊ตญ์ •๋ถ€๋ฅผ ๊ฐ•์••ํ•˜์—ฌ ํ•œ์ผ์˜์ •์„œ(้Ÿ“ๆ—ฅ่ญฐๆ”ฟๆ›ธ)๋ฅผ ์„ฑ๋ฆฝ์‹œ์ผœ ๋‚ด์ •๊ฐ„์„ญ์˜ ๋ฐœํŒ์„ ๋งŒ๋“ค๊ณ , ๊ฐ™์€ ํ•ด 8์›”์—๋Š” ์ œ1์ฐจ ํ•œ์ผํ˜‘์•ฝ์ธ โ€˜ํ•œ์ผ์™ธ๊ตญ์ธ๊ณ ๋ฌธ์šฉ๋น™์— ๊ด€ํ•œ ํ˜‘์ •โ€™์„ ์ฒด๊ฒฐํ•˜๊ฒŒ ํ•˜์—ฌ ๊ณ ๋ฌธ์ •์น˜๋ฅผ ์‹œ์ž‘ํ•˜๋ฉด์„œ ๋ณธ๊ฒฉ์ ์ธ ์‹๋ฏผ์ง€ํ™” ๊ณต์ž‘์— ๋“ค์–ด๊ฐ”๋‹ค. 1905๋…„ ์ œ2์ฐจ ํ•œ์ผํ˜‘์•ฝ์ธ ์„์‚ฌ์กฐ์•ฝ์„ ์ฒด๊ฒฐํ•˜์—ฌ ์ด์— ๋”ฐ๋ผ 1906๋…„ 2์›” ํ†ต๊ฐ๋ถ€๋ฅผ ์„ค์น˜ํ•˜๊ณ , ์ด๋ฅธ๋ฐ” ๋ณดํ˜ธ์ •์น˜๋ฅผ ํŽด ์™ธ๊ต๊ถŒ์„ ๋Œ€ํ–‰ํ•˜๋Š” ๋“ฑ ์‹ค์งˆ์ ์ธ ์ง€๋ฐฐ์— ๋“ค์–ด๊ฐ”๋‹ค. ์ด์–ด 1907๋…„ 7์›”์—๋Š” ํ—ค์ด๊ทธ ๋ฐ€์‚ฌ์‚ฌ๊ฑด์„ ๊ตฌ์‹ค๋กœ ๊ณ ์ข…ํ™ฉ์ œ๋ฅผ ๊ฐ•์ œ ํ‡ด์œ„์‹œํ‚ค๊ณ , ์ •๋ฏธ 7์กฐ์•ฝ(ไธๆœชไธƒๆข็ด„)์„ ๊ฐ•์ œํ•˜์—ฌ ํ†ต๊ฐ์ด ์ž…๋ฒ•ยท์‚ฌ๋ฒ•ยทํ–‰์ • ์ „๋ฐ˜์— ๊ฑธ์นœ ํ†ต์น˜๊ถŒ์„ ์ „๋‹จ(ๅฐˆๆ–ท)ํ•˜๋„๋ก ํ•˜์˜€์œผ๋ฉฐ, ํ•œ๊ตญ์ธ ๋Œ€์‹ (ๅคง่‡ฃ) ๋ฐ‘์— ์‹ค๊ถŒ์„ ์žฅ์•…ํ•˜๊ฒŒ ํ•˜๋Š” ์ผ๋ณธ์ธ ์ฐจ๊ด€์„ ๋‘๋Š” ์ฐจ๊ด€์ •์น˜๋ฅผ ์‹คํ˜„ํ•˜์˜€๋‹ค. ๋˜ํ•œ โ€˜ํ•œ๊ตญ ์‚ฌ๋ฒ• ๋ฐ ๊ฐ์˜ฅ์‚ฌ๋ฌด์œ„ํƒ์— ๊ด€ํ•œ ๊ฐ์„œโ€™๋ฅผ ํ†ตํ•ด ํ•œ๊ตญ์˜ ์‚ฌ๋ฒ•๊ถŒ์„ ํƒˆ์ทจํ•˜์˜€์œผ๋ฉฐ, ์ด์–ด ํ•œ๊ตญ๊ตฐ๋Œ€๋ฅผ ํ•ด์‚ฐํ•˜์˜€๊ณ  ํ•œ์ผ๊ฒฝ์ฐฐ๊ด€์„ ํ†ตํ•ฉํ•˜์—ฌ ํ•œ๊ตญ ๊ฒฝ์ฐฐ๊ด€์„ ์ผ๋ณธ ๊ด€ํ—Œ์˜ ์ง€ํœ˜๊ฐ๋…ํ•˜์— ๋‘์—ˆ๋‹ค. ๋งˆ์ง€๋ง‰ ๋‹จ๊ณ„๋กœ 1910๋…„ 8์›” 22์ผ ํ•ฉ๋ณ‘์กฐ์•ฝ์˜ ์ฒด๊ฒฐ์„ ๊ฐ•ํ–‰ํ•จ์œผ๋กœ์จ ์ผ์ œ์˜ ํ•œ๊ตญ์‹๋ฏผํ™” ์นจ๋žต์€ ์™„์„ฑ๋˜์—ˆ๋‹ค']",2 +2025-history-09,"์กฐ์„  ํ•™์ƒ ์ฒญ๋…„ ๋Œ€์ค‘์ด์—ฌ ๊ถ๊ธฐํ•˜๋ผ! ์ œ๊ตญ์ฃผ์˜์  ์นจ๋žต์— ์ €ํ•ญํ•˜๋Š” ์˜์ง€๋ฅผ ๊ฐ€์ง€๊ณ  ๊ด‘์ฃผ ํ•™์ƒ์„ ์„ฑ์›ํ•˜์ž! ...(์ค‘๋žต)... ๊ทธ๋“ค์€ ๊ฒฝ์ฐฐ์„ ์ด๋™์›ํ•˜์—ฌ ๊ด‘์ฃผ ์กฐ์„  ํ•™์ƒ ๋™์ง€๋“ค์—๊ฒŒ ์‡ ๊ณ ๋ž‘์„ ๊ฑธ๊ณ  ๋ง์•˜๋‹ค. ๊ทธ๋Œ€๋“ค์ด์—ฌ! ๊ถ๊ธฐํ•˜๋ผ! ์กฐ์„  ํ•™์ƒ์˜ ์ด์ต๊ณผ ์•ฝ์†Œ ๋ฏผ์กฑ์˜ ์Šน๋ฆฌ๋ฅผ ์œ„ํ•œ ํ•ญ์Ÿ์— ๊ณตํ—Œํ•˜๋ผ. ...(์ค‘๋žต)... ๊ตฌ์†๋œ ๊ด‘์ฃผ ์กฐ์„  ํ•™์ƒ์„ ์ฆ‰์‹œ ํƒˆํ™˜ํ•˜์ž. ์‹๋ฏผ์ง€ ๋…ธ์˜ˆ ๊ต์œก์— ๋ฐ˜๋Œ€ํ•˜์ž. [ํ•ด์„ค] ์ด ์ž๋ฃŒ๋Š” (๊ฐ€)์ด/๊ฐ€ ์ „๊ฐœ๋˜๋Š” ๊ณผ์ •์—์„œ ์„œ์šธ์— ์œ ํฌ๋œ ๊ฒฉ๋ฌธ์œผ๋กœ, ์ผ์ œ์— ๋งž์„œ ๊ถ๊ธฐํ•˜์ž๋Š” ๋‚ด์šฉ์ด ๋‹ด๊ฒผ๋‹ค. ์ด ์šด๋™์€ ํ•œ๊ตญ์ธ ํ•™์ƒ๊ณผ ์ผ๋ณธ์ธ ํ•™์ƒ ๊ฐ„์˜ ์ถฉ๋Œ์„ ๊ณ„๊ธฐ๋กœ ๊ด‘์ฃผ์—์„œ ์‹œ์ž‘๋˜์—ˆ๋‹ค. ์ผ์ œ๋Š” ์ง„์••์— ๋‚˜์„ฐ์ง€๋งŒ, ๋ฏผ์กฑ ์ฐจ๋ณ„ ๋Œ€์šฐ์— ํ•ญ๊ฑฐํ•œ ํ•™์ƒ๋“ค์˜ ์‹œ์œ„๋Š” ์ „๊ตญ ๊ฐ์ง€๋กœ ํ™•์‚ฐ๋˜์—ˆ๋‹ค.","{'question': '(๊ฐ€) ์šด๋™์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['3โ€ค1 ์šด๋™์— ์˜ํ–ฅ์„ ์ฃผ์—ˆ๋‹ค.', '์ˆœ์ข…์˜ ์žฅ๋ก€์ผ์„ ๊ธฐํ•ด ์ผ์–ด๋‚ฌ๋‹ค.', 'ํ•™์ƒ ๊น€์ฃผ์—ด์˜ ์‚ฌ๋ง์œผ๋กœ ๊ฒฉํ™”๋˜์—ˆ๋‹ค', '๊น€์˜ฅ๊ท  ๋“ฑ ๊ธ‰์ง„ ๊ฐœํ™”ํŒŒ๊ฐ€ ์ฃผ๋„ํ•˜์˜€๋‹ค.', '์‹ ๊ฐ„ํšŒ๊ฐ€ ์กฐ์‚ฌ๋‹จ์„ ํŒŒ๊ฒฌํ•˜์—ฌ ์ง€์›ํ•˜์˜€๋‹ค.'], 'answer': ''}",,5,5,True,"['1929๋…„์— ํ†ตํ•™ ์—ด์ฐจ๋ฅผ ์ด์šฉํ•˜๋˜ ํ•œ ์ผ๋ณธ์ธ ํ•™์ƒ์ด ํ•œ๊ตญ์ธ ์—ฌํ•™์ƒ์„ ํฌ๋กฑํ•œ ์‚ฌ๊ฑด์ด ์ผ์–ด๋‚ฌ๋‹ค. ์ด์— ๋ถ„๋…ธํ•œ ํ•œ๊ตญ์ธ ํ•™์ƒ์€ ์ผ๋ณธ์ธ ํ•™์ƒ์— ๋งž์„œ ์‹ธ์› ๋‹ค. ์ด๋•Œ ์ผ์ œ ๊ฒฝ์ฐฐ์€ ์ผ๋ณธ์ธ ํ•™์ƒ๋งŒ ๋‘๋‘”ํ•˜๊ณ  ๋‚˜์„ฐ๋‹ค. ๊ด‘์ฃผ์˜ ํ•™์ƒ๋“ค์€ ์ด์— ๋Œ€์‘ํ•ด ์‹œ์œ„๋ฅผ ๋ฒŒ์˜€๋‹ค. ์ผ์ œ์˜ ์ฐจ๋ณ„ ์ •์ฑ…์— ๋งž์„œ ์ผ์–ด๋‚œ ์ด ์šด๋™์€ ์ „๊ตญ์œผ๋กœ ํผ์กŒ๊ณ  ๊ณณ๊ณณ์—์„œ ๋™๋งน ํœดํ•™ ํˆฌ์Ÿ์ด ์—ฐ์ด์–ด ๋ฒŒ์–ด์กŒ๋‹ค. ๋ฐ‘์ค„ ์นœ โ€˜์ด ์šด๋™โ€™์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€? Answer: ์‹ ๊ฐ„ํšŒ๊ฐ€ ๊ทธ ์ง„์ƒ์„ ๊ทœ๋ช…ํ•˜๊ณ ์ž ์กฐ์‚ฌ๋‹จ์„ ํ˜„์ง€์— ํŒŒ๊ฒฌํ•˜์˜€๋‹ค.', '(๊ฐ€)์ˆœ์ข…์˜ ์ธ์‚ฐ์ผ์„ ๊ธฐํ•˜์—ฌ โ€˜๋™์–‘ ์ฒ™์‹ ์ฃผ์‹ํšŒ์‚ฌ๋ฅผ ์ฒ ํํ•˜๋ผ!โ€™, โ€˜์ผ๋ณธ์ธ ์ง€์ฃผ์—๊ฒŒ ์†Œ์ž‘๋ฃŒ๋ฅผ ๋ฐ”์น˜์ง€ ๋ง์ž!โ€™ ๋“ฑ์˜ ๊ฒฉ๋ฌธ์„ ๋‚ด๊ฑด ์šด๋™์ด ์ผ์–ด๋‚ฌ๋‹ค. (๋‚˜)๊ด‘์ฃผ์—์„œ ํ•œ๊ตญ์ธ ํ•™์ƒ๊ณผ ์ผ๋ณธ์ธ ํ•™์ƒ ์‚ฌ์ด์— ์ผ์–ด๋‚œ ์ถฉ๋Œ์„ ๊ณ„๊ธฐ๋กœ ํ•™์ƒ๋“ค์ด ์ด๊ถ๊ธฐํ•˜๋Š” ์šด๋™์ด ์ผ์–ด๋‚ฌ๋‹ค. (๊ฐ€)์™€ (๋‚˜) ์‚ฌ์ด์˜ ์‹œ๊ธฐ์— ์žˆ์—ˆ๋˜ ์‚ฌ์‹ค๋กœ ์˜ณ์€ ๊ฒƒ์€? Answer: ์‹ ๊ฐ„ํšŒ๊ฐ€ ์ฐฝ์„ค๋˜์—ˆ๋‹ค.', '**๊ด‘์ฃผ ํ•™์ƒ ๋…๋ฆฝ ์šด๋™**(ๅ…‰ๅทžๅญธ็”Ÿ็จ็ซ‹้‹ๅ‹•) ๋˜๋Š” **๊ด‘์ฃผ ํ•™์ƒ ํ•ญ์ผ ์šด๋™**(ๅ…‰ๅทžๅญธ็”ŸๆŠ—ๆ—ฅ้‹ๅ‹•)์€ 1929๋…„ 11์›” 3์ผ๋ถ€ํ„ฐ ์ „๋ผ๋‚จ๋„ ๊ด‘์ฃผ ์‹œ๋‚ด์—์„œ ์ผ์–ด๋‚œ ์ผ๋ณธ ํ•™์ƒ์˜ ํ•œ๊ตญ ์—ฌํ•™์ƒ ํฌ๋กฑ์œผ๋กœ ์ผ์–ด๋‚œ ํ•œ๊ตญ ํ•™์ƒ๊ณผ ์ผ๋ณธ ํ•™์ƒ ๊ฐ„ ์ถฉ๋Œ๊ณผ 11์›” 12์ผ ๊ด‘์ฃผ์ง€์—ญ ํ•™์ƒ ๋Œ€์‹œ์œ„ ์šด๋™์„ ๊ฑฐ์ณ, ํ•œํŽธ์œผ๋กœ๋Š” ํ˜ธ๋‚จ์ง€์—ญ ์ผ๋Œ€๋กœ ํ™•์‚ฐ๋˜๊ณ , ๋‹ค๋ฅธ ํ•œํŽธ์œผ๋กœ ์„œ์šธ์„ ๊ฑฐ์ณ์„œ ์ „๊ตญ ๊ฐ์ง€๋กœ ํ™•์‚ฐ๋œ ํ•ญ์ผ ์šด๋™์œผ๋กœ 1929๋…„ 11์›” ๋ง์—์„œ 1930๋…„ 3์›”์ด๋‚˜ 5์›”๊นŒ์ง€ ์ „๊ตญ์ ์œผ๋กœ ํ™•์‚ฐ๋œ ํ•™์ƒ๋…๋ฆฝ์šด๋™์„ ๋งํ•œ๋‹ค. **11ยท3 ๊ด‘์ฃผ ํ•™์ƒ ํ•ญ์ผ ์šด๋™**์ด๋ผ๊ณ ๋„ ํ•œ๋‹ค. 12์›”์—๋Š” ๊ฒฝ์„ฑ๊ณผ ํ‰์–‘, ํ•จ๊ฒฝ๋„ ๋“ฑ์ง€์™€ ๊ฐ™์€ ๊ตญ๋‚ด ์ง€์—ญ๊ณผ ๊ฐ„๋„, ๋ฏธ๊ตญ, ์ค‘๊ตญ, ์ผ๋ณธ ๋“ฑ์œผ๋กœ ํ™•์‚ฐ๋˜์—ˆ๊ณ , 1930๋…„ 5์›”๊นŒ์ง€ ์ „๊ตญ์ ์ธ ๋™๋งนํœดํ•™, ํ•™์ƒ ํ•ญ์ผ ์‹œ์œ„๋กœ ๋ณ€๋ชจ, ๋ฐœ์ „ํ–ˆ๋‹ค. 1929๋…„ ๋ฐœ์ƒํ•œ ๊ด‘์ฃผํ•™์ƒ๋…๋ฆฝ์šด๋™์€ 1919๋…„ 3ยท1 ์šด๋™ ์ดํ›„ ๊ตญ๋‚ด ์ตœ๋Œ€๊ทœ๋ชจ์˜ ๋Œ€์ค‘์  ํ•ญ์ผ ์šด๋™์œผ๋กœ ๊ผฝํžŒ๋‹ค. ๋Œ€ํ•œ๋ฏผ๊ตญ ์ •๋ถ€์—์„œ๋Š” ์ด ์šด๋™์„ ๊ธฐ๋…ํ•˜๊ธฐ ์œ„ํ•ด ํ•™์ƒ๋…๋ฆฝ์šด๋™ ๊ธฐ๋…์ผ์„ ์ œ์ •ํ–ˆ๋‹ค. ## ์šด๋™์˜ ๋ฐฐ๊ฒฝ ### ์ผ๋ณธ์˜ ์šฐ๋ฏผํ™” ์ •์ฑ…๊ณผ ์–ต์•• ๋‹น์‹œ ์—ด์ฐจ์—์„œ ์ผ๋ณธ์ธ ํ•™์ƒ๋“ค๊ณผ ์‹ธ์šฐ๋˜ ๋ฐ•์ค€์ฑ„ ๋‹น์‹œ ์ผ์ œ๋Š” ๋™์•„์‹œ์•„ ์ง„์ถœ์„ ์œ„ํ•œ 20๋…„๊ฐ„์˜ ํ•œ๋ฐ˜๋„ ์‹๋ฏผ์ง€๋ฐฐ๊ฐ€ ์•ˆ์ •์  ์ˆ˜์ค€์œผ๋กœ ํ™•๊ณ ํ•ด ์žˆ๋‹ค๊ณ  ํ™•์‹ ํ•˜๊ณ  ์žˆ์—ˆ๋‹ค. ์ด๋“ค์€ ์ค‘๊ตญ ๋ณธํ† ์— ๋Œ€ํ•œ ์•ผ์‹ฌ์„ ๋“œ๋Ÿฌ๋‚ด๋ฉด์„œ ๋™์•„์‹œ์•„ ์ „์—ญ์— ๋Œ€ํ•œ ์นจ๋žต์˜ ์•ผ์š•์„ ๋ถˆํƒœ์šฐ๊ณ  ์žˆ์—ˆ๋˜ ๊ฒƒ์ด๋‹ค']",5 +2025-history-10,์ž๋ฃŒ๋Š” ๊ฒฝ๋ณต๊ถ์—์„œ ์—ด๋ฆฐ โ€˜์‹œ์ •(ๅง‹ๆ”ฟ) 5๋…„ ๊ธฐ๋… ์กฐ์„  ๋ฌผ์‚ฐ ๊ณต์ง„ํšŒโ€™ ํ™๋ณด์ง€์˜ ์‚ฝํ™”์ด๋‹ค. ์ผ์ œ๋Š” ํ•œ๊ตญ์ธ์„ ๋ฌด๋ ฅ์œผ๋กœ ๊ตด๋ณต์‹œํ‚ค๊ณ ์ž ํ—Œ๋ณ‘ ๊ฒฝ์ฐฐ์ œ๋ฅผ ์‹œํ–‰ํ•˜๋˜ (๊ฐ€) ์‹œ๊ธฐ์— ์ด๋Ÿฌํ•œ ํ–‰์‚ฌ๋ฅผ ์—ด์–ด ์‹๋ฏผ ํ†ต์น˜๋ฅผ ๋ฏธํ™”ํ•˜๋ ค ํ•˜์˜€๋‹ค. ์‚ฝํ™”์—์„œ ๋ณผ ์ˆ˜ ์žˆ๋“ฏ์ด ์ผ์ œ๋Š” ๊ถ๊ถ ๋‚ด ์ˆ˜๋งŽ์€ ์ „๊ฐ๋“ค์„ ํŒŒ๊ดดํ•˜๊ณ  ๊ทธ ์ž๋ฆฌ์— ํ–‰์‚ฌ์žฅ์„ ์กฐ์„ฑํ•จ์œผ๋กœ์จ ์กฐ์„  ์™•์กฐ๋ฅผ ์ƒ์ง•ํ•˜๋Š” ๊ณต๊ฐ„์„ ์˜๋„์ ์œผ๋กœ ํ›ผ์†ํ•˜์˜€๋‹ค.,"{'question': '(๊ฐ€) ์‹œ๊ธฐ์— ์žˆ์—ˆ๋˜ ์‚ฌ์‹ค๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['์‚ผ์ฒญ ๊ต์œก๋Œ€๊ฐ€ ์šด์˜๋˜์—ˆ๋‹ค.', '์ „๋ฏผ๋ณ€์ •๋„๊ฐ์ด ์„ค์น˜๋˜์—ˆ๋‹ค.', 'ํ† ์ง€ ์กฐ์‚ฌ ์‚ฌ์—…์ด ์‹ค์‹œ๋˜์—ˆ๋‹ค.', '์ž„์ˆ  ๋†๋ฏผ ๋ด‰๊ธฐ๊ฐ€ ๋ฐœ์ƒํ•˜์˜€๋‹ค.', '์ง€๊ณ„์•„๋ฌธ์—์„œ ์ง€๊ณ„๊ฐ€ ๋ฐœ๊ธ‰๋˜์—ˆ๋‹ค.'], 'answer': ''}",,3,3,True,"['1915๋…„ ์ผ์ œ๊ฐ€ ํ•œ๊ตญ ๊ฐ•์  5์ฃผ๋…„์„ ๋งž์•„ ์ง€๋ฐฐ์˜ ์ •๋‹น์„ฑ์„ ๋ณด์—ฌ ์ฃผ๊ธฐ ์œ„ํ•ด ๊ฐœ์ตœํ•œ ํ–‰์‚ฌ. ์ผ์ œ๋Š” 1915๋…„ 9์›” 11์ผ๋ถ€ํ„ฐ 10์›” 31์ผ๊นŒ์ง€ ์กฐ์„  ๋ฌผ์‚ฐ ๊ณต์ง„ํšŒ๋ฅผ ๊ฐœ์ตœํ•˜์˜€๋‹ค. ์‚ฐ์—…์˜ ์ง„ํฅ๊ณผ ๋ฌธ๋ช…๊ฐœํ™”๋ฅผ ๋ช…๋ถ„์œผ๋กœ ๋‚ด์„ธ์› ์ง€๋งŒ ์‹๋ฏผ ์ง€๋ฐฐ 5๋…„ ๋™์•ˆ์˜ ์„ฑ๊ณผ๋ฅผ ํ•œ๊ตญ์ธ์—๊ฒŒ ํ™๋ณดํ•˜๊ณ  ๊ถ๊ทน์ ์œผ๋กœ๋Š” ์ผ๋ณธ์— ๋™ํ™”์‹œํ‚ค๋ ค๋Š” ์˜๋„๋กœ ์ถ”์ง„๋œ ํ–‰์‚ฌ์˜€๋‹ค. ์กฐ์„  ์ด๋…๋ถ€๋Š” 1913๋…„๋ถ€ํ„ฐ ์ค€๋น„์— ๋“ค์–ด๊ฐ€ ๊ธฐ๋ณธ ๊ณ„ํš์„ ์ˆ˜๋ฆฝํ•˜๊ณ , ์˜ˆ์‚ฐ์„ ํ™•๋ณดํ•˜์˜€๋‹ค. ์กฐ์„  ๋ฌผ์‚ฐ ๊ณต์ง„ํšŒ๊ฐ€ ๊ฐœ์ตœ๋  ์žฅ์†Œ๋กœ๋Š” ๊ฒฝ๋ณต๊ถ์ด ์„ ์ •๋˜์—ˆ๋‹ค. 1913๋…„ 9์›”๋ถ€ํ„ฐ ๋ฏธ์ˆ ๊ด€์„ ๋น„๋กฏํ•˜์—ฌ ์ œ1ํ˜ธ ์ง„์—ด๊ด€, ์ œ2ํ˜ธ ์ง„์—ด๊ด€ ๋“ฑ ๊ณต์ง„ํšŒ๋ฅผ ์œ„ํ•œ ๊ฐ์ข… ์‹œ์„ค๋ฌผ ๊ณต์‚ฌ๊ฐ€ ์‹œ์ž‘๋๋‹ค. ์กฐ์„  ๋ฌผ์‚ฐ ๊ณต์ง„ํšŒ๋ฅผ ์œ„ํ•œ ์‹œ์„ค๋ฌผ์„ ์„ค์น˜ํ•˜๊ธฐ ์œ„ํ•ด์„œ ๊ฒฝ๋ณต๊ถ์— ์œ„์น˜ํ•œ ๋งŽ์€ ์ „๊ฐ๋“ค์ด ์ด์ „๋˜๊ฑฐ๋‚˜ ํ›ผ์†๋˜์—ˆ๋‹ค. ๊ทผ์ •์ „, ๊ฒฝํšŒ๋ฃจ, ๊ตํƒœ์ „ ๋“ฑ ์ผ๋ถ€ ๊ฑด๋ฌผ์„ ์ œ์™ธํ•˜๊ณ  4์ฒœ์—ฌ ์นธ์˜ ๊ฑด๋ฌผ์ด ์ฒ ๊ฑฐ๋˜์—ˆ๋‹ค. ์กฐ์„  ์ด๋…๋ถ€๋Š” ์กฐ์„  ๋ฌผ์‚ฐ ๊ณต์ง„ํšŒ๋ฅผ ์ค€๋น„ํ•˜๋Š” ๊ณผ์ •์—์„œ ์˜› ๋Œ€ํ•œ์ œ๊ตญ์˜ ์ƒ์ง•์ธ ๊ถ๊ถ์˜ ์œ„์ƒ์„ ํฌ๊ฒŒ ์ถ•์†Œ์‹œํ‚จ ๊ฒƒ์ด๋‹ค. ์กฐ์„  ๋ฌผ์‚ฐ ๊ณต์ง„ํšŒ์— ์ง„์—ด๋œ ๋ฌผํ’ˆ์€ ์ด 40,444์ ์— ๋‹ฌํ–ˆ๋‹ค. ๋Œ€๋ถ€๋ถ„ ์ผ์ œ ๊ฐ•์  5๋…„๊ฐ„์˜ ํ†ต์น˜ ์„ฑ์ ์„ ๋ฏธํ™”ํ•˜๋Š” ๊ฐ์ข… ์ƒํ’ˆ, ์‹œ์„ค๋ฌผ, ํ†ต๊ณ„์ž๋ฃŒ ๋“ฑ์ด์—ˆ๋‹ค. ์—ฌ๊ธฐ์— ๋”ํ•ด์„œ ํ•œ๊ตญ๊ณผ ๊ฑฐ๋ž˜๊ฐ€ ํ™œ๋ฐœํ•œ ๋„์ฟ„, ์˜ค์‚ฌ์นด, ๋‚˜๊ฐ€์‚ฌํ‚ค ๋“ฑ์—์„œ ์ƒ์‚ฐ๋œ ์ผ๋ณธ์˜ ์ƒํ’ˆ๋“ค๋„ ์ „์‹œ ํŒ๋งค๋˜์—ˆ๋‹ค. ์กฐ์„  ๋ฌผ์‚ฐ ๊ณต์ง„ํšŒ๋Š” ์‹๋ฏผ ํ†ต์น˜์˜ ์„ฑ๊ณผ๋ฅผ ์„ ์ „ํ•˜๋Š” ๊ณต๊ฐ„์ด์ž ์ผ๋ณธ ์ƒํ’ˆ์— ๋Œ€ํ•œ ํ™๋ณด์˜ ์žฅ์œผ๋กœ๋„ ํ™œ์šฉ๋˜์—ˆ๋˜ ๊ฒƒ์ด๋‹ค. ์กฐ์„  ์ด๋…๋ถ€๋Š” ์กฐ์„  ๋ฌผ์‚ฐ ๊ณต์ง„ํšŒ์— ๋งŽ์€ ํ•œ๊ตญ์ธ๋“ค์„ ๋™์›ํ•˜๊ธฐ ์œ„ํ•ด ๋…ธ๋ ฅํ•˜์˜€๋‹ค', '๋ช‡ ์‚ฌ๋žŒ์„ ์œ„ํ•ด ์ฐธ์œผ๋กœ ๋งŽ์€ ์‚ฌ๋žŒ๋“ค์ด ๋ด‰์‚ฌํ–ˆ์ง€์š”. ์ด๋ ‡๊ฒŒ ์ด์ฒœ์—ฌ ๋ช…์— ๊ฐ€๊นŒ์šด ์‚ฌ๋žŒ๋“ค์ด ๋จน๊ณ  ์ž๊ณ , ๋“œ๋‚˜๋“ค๋ฉฐ ํ™œ๋™ํ–ˆ์–ด์š”. ์‹ฌ์ง€์–ด ํ™”์žฅ์‹ค๋„ 23๊ฐœ ์ •๋„๊ฐ€ ์žˆ์—ˆ๋‹ค๊ณ  ํ•˜๋‹ˆ, ๊ฒฝ๋ณต๊ถ์˜ ๊ทœ๋ชจ๋ฅผ ์ง์ž‘ํ•  ์ˆ˜ ์žˆ๊ฒ ์ง€์š”. ๊ทธ๊ฒƒ์€ ์ผ์ œ๊ฐ€ ์šฐ๋ฆฌ ๋‚˜๋ผ์˜ ๊ถŒ์œ„๋ฅผ ์ง“๋ฐŸ๊ธฐ ์œ„ํ•ด ์™•์กฐ๊ตญ๊ฐ€์˜ ์ƒ์ง•์ธ ๊ถ๊ถ์„ ์˜๋„์ ์œผ๋กœ ํ›ผ์†ํ•˜๋ฉด์„œ ์‹œ์ž‘๋˜์—ˆ์–ด์š”. ๊ฒฝ๋ณต๊ถ์€ ์™•์กฐ๊ตญ๊ฐ€ ์กฐ์„ ์˜ ์ƒ์ง•์œผ๋กœ ์ •์น˜ยทํ–‰์ •์˜ ์ค‘์‹ฌ์ง€์ด๊ณ , ๋ฐฑ์„ฑ๋“ค์—๊ฒŒ๋„ ๋‚˜๋ผ์˜ ์œ„์‹ ์ด๊ณ  ๋‹จ๊ฒฐ์˜ ๊ตฌ์‹ฌ์ด์—ˆ๋Š”๋ฐ, ๊ทธ ๊ถŒ์œ„๋ฅผ ๋ฌด๋„ˆ๋œจ๋ฆฌ๋ ค๊ณ ํ•œ ๊ฒƒ์ด์—ˆ์ง€์š”. ๋งŽ์€ ๋ถ€์† ๊ฑด๋ฌผ์„ ์ผ๋ณธ์ธ์—๊ฒŒ ํŒ”๊ฑฐ๋‚˜ ์ผ๋ณธ์˜ ๊ด€๊ณต์„œ๋กœ ์‚ฌ์šฉํ–ˆ์–ด์š”. ์‹ฌ์ง€์–ด ๋ถ€์ˆ˜๊ฑฐ๋‚˜ ์˜ฎ๊ฒจ์„œ ์›๋ž˜์˜ ๊ฒฝ๊ด€์„ ๋‹ค ํŒŒ๊ดดํ–ˆ์ง€์š”. 1915๋…„์—๋Š” ๊ถ ์•ˆ์—์„œ ์กฐ์„ ๋ฌผ์‚ฐ๊ณต์ง„ํšŒ๋ฅผ ์—ฐ๋‹ค๋ฉฐ ์ „๊ฐ๋“ค์„ ๋ถ€์ˆ˜๊ณ  ์„œ์–‘์‹ ๊ฑด๋ฌผ์„ ์„ธ์› ์–ด์š”. ๊ฑด๋ฌผ์ด ์žˆ๋˜ ์ž๋ฆฌ์—๋Š” ์ „์— ์—†๋˜ ์ž”๋””๋ฐญ์„ ๋งŒ๋“ค์—ˆ์–ด์š”. ์ง€๊ธˆ ๊ฒฝ๋ณต๊ถ ์•ˆ์— ์žˆ๋Š” ๋„“์€ ์ž”๋””๋ฐญ์€ ๊ทธ๋ ‡๊ฒŒ ๋งŒ๋“ค์–ด์ง„ ๊ฒƒ์ด์ง€์š”. ์—ฌ๋Ÿฌ๋ถ„์ด ๋‹ค ์•„๋Š” ์กฐ์„ ์ด๋…๋ถ€๋Š” ๊ทธ ๋Œ€ํ‘œ์ ์ธ ์˜ˆ์˜ˆ์š”. ๊ฒฐ๊ตญ, ์ฃผ์š” ๊ฑด๋ฌผ ๋ช‡ ๊ฐœ๋งŒ ๋‚จ์•„์„œ ์ง€๊ธˆ ๋ชจ์Šต์ด ๋œ ๊ฒƒ์ด์ง€์š”. ๊ฒฝ๋ณต๊ถ์€ ์กฐ์„ ์‹œ๋Œ€์˜ ์ •์น˜, ๊ฒฝ์ œ, ๋ฌธํ™”์˜ ์ค‘์‹ฌ์ง€๋กœ์„œ ๋‹น์‹œ์˜ ์—ญ์‚ฌ์™€ ์‚ฌ์ƒ, ๋ฌธํ™”๋ฅผ ๋ฐฐ์šธ ์ˆ˜ ์žˆ๋Š” ์ข‹์€ ์—ญ์‚ฌ๊ด€์ด์—์š”. ์š”์ฆ˜ ๊ฒฝ๋ณต๊ถ ๋ณต์›์„ ํ•˜๊ณ  ์žˆ๋Š”๋ฐ, ๋ณต์›์„ ํ•œ๋‹ค๋ฉด ์˜›๋‚  ๋ชจ์Šต์„ ๋” ์ž˜ ์•Œ ์ˆ˜ ์žˆ์„ ๊ฑฐ์˜ˆ์š”.', 'ํ•œ๊ตญ์„ ์‹๋ฏผ์ง€๋กœ ์‚ผ์€ ์ผ์ œ๋Š” ํ—Œ๋ณ‘์—๊ฒŒ ๊ฒฝ์ฐฐ ์—…๋ฌด๋ฅผ ๋ถ€์—ฌํ•œ ํ—Œ๋ณ‘ ๊ฒฝ์ฐฐ์ œ๋ฅผ ์‹œํ–‰ํ–ˆ๋‹ค. ํ—Œ๋ณ‘ ๊ฒฝ์ฐฐ์€ ์ •์‹ ์žฌํŒ ์—†์ด ํ•œ๊ตญ์ธ์—๊ฒŒ ๋ฒŒ๊ธˆ ๋“ฑ์˜ ์ฒ˜๋ฒŒ์„ ๊ฐ€ํ•˜๊ฑฐ๋‚˜ ํƒœํ˜•์— ์ฒ˜ํ•  ์ˆ˜๋„ ์žˆ์—ˆ๋‹ค. ํ•œ๊ตญ์ธ์€ ์ด์ฒ˜๋Ÿผ ๊ฐ•์••์ ์ธ ์ง€๋ฐฐ์— ์ €ํ•ญํ•ด 3โ€ค1 ์šด๋™์„ ์ผ์œผ์ผฐ์œผ๋ฉฐ, ์ผ์ œ๋Š” ์ด๋ฅผ ๊ณ„๊ธฐ๋กœ ์ง€๋ฐฐ ์ •์ฑ…์„ ์ „ํ™˜ํ–ˆ๋‹ค. ์ผ์ œ๊ฐ€ ํ•œ๊ตญ์„ ๋ณ‘ํ•ฉํ•œ ์งํ›„๋ถ€ํ„ฐ 3โ€ค1 ์šด๋™์ด ๋ฒŒ์–ด์ง„ ๋•Œ๊นŒ์ง€๋ฅผ (๊ฐ€) ์‹œ๊ธฐ๋ผ๊ณ  ๋ถ€๋ฅธ๋‹ค. (๊ฐ€) ์‹œ๊ธฐ์— ์žˆ์—ˆ๋˜ ์‚ฌ์‹ค๋กœ ์˜ณ์€ ๊ฒƒ์€? Answer: ํ† ์ง€ ์กฐ์‚ฌ๋ น์ด ๊ณตํฌ๋˜์—ˆ๋‹ค.']",3 +2025-history-11,"(๊ฐ€) ์™•์ด ์ข…๋ฌ˜์— ๋‚˜์•„๊ฐ€ ๋‹ค์Œ๊ณผ ๊ฐ™์ด ๊ณ ํ•˜์˜€๋‹ค. โ€œโ€ฆ (์ค‘๋žต) โ€ฆ ์ง์€ ํ™๋ฒ” 14์กฐ๋ฅผ ํ•˜๋Š˜์— ๊ณ„์‹  ์„ ๋Œ€ ์™•๋“ค์˜ ์‹ ๋ น๊ป˜ ๊ณ ํ•˜๋ฉด์„œ ๊ทธ๋“ค์ด ๋‚จ๊ธฐ์‹  ์—…์ ์„ ์šฐ๋Ÿฌ๋Ÿฌ ๋Šฅํžˆ ๊ณต์ ์„ ์ด๋ฃฉํ•˜๊ณ  ๊ฐํžˆ ์–ด๊ธฐ์ง€ ์•Š์„ ๊ฒƒ์ด๋‹ˆ, ๋ฐ์€ ์‹ ๋ น๊ป˜์„œ๋Š” ๊ตฝ์–ด ์‚ดํ”ผ์‹œ๊ธฐ ๋ฐ”๋ž๋‹ˆ๋‹ค.โ€ (๋‚˜) ์™•์ด ์˜์‹ฌ๊ณผ ๋‘๋ ค์›€์„ ์ด๊ธฐ์ง€ ๋ชปํ•˜๊ณ  ์ƒˆ๋ฒฝ์— ๊ถ๋…€๊ฐ€ ํƒ€๋Š” ๊ฐ€๋งˆ๋ฅผ ์ด์šฉํ•˜์—ฌ ๋ชฐ๋ž˜ ๊ฑด์ถ˜๋ฌธ์„ ๋‚˜์™€ ๋Ÿฌ์‹œ์•„ ๊ณต์‚ฌ๊ด€์œผ๋กœ ๊ฑฐ์ฒ˜๋ฅผ ์˜ฎ๊ฒผ๋‹ค. โ€ฆ (์ค‘๋žต) โ€ฆ ๊ถ๊ถ ๋‚ด์—์„œ๋Š” ์•Œ์•„์ฐจ๋ฆฌ์ง€ ๋ชปํ–ˆ๊ณ , ๋‚ด๊ฐ ๋˜ํ•œ ์•Œ์ง€ ๋ชปํ–ˆ๋‹ค. ์™•์ด ๋Ÿฌ์‹œ์•„ ๊ณต์‚ฌ๊ด€์— ๋„์ฐฉํ•œ ๋•Œ๋Š” ๋Œ€๋žต ์˜ค์ „ 7์‹œ 20๋ถ„์ฏค์ด์—ˆ๋‹ค.","{'question': '(๊ฐ€), (๋‚˜) ์‹œ๊ธฐ ์‚ฌ์ด์— ์žˆ์—ˆ๋˜ ์‚ฌ์‹ค๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['์˜๊ตญ์ด ๊ฑฐ๋ฌธ๋„๋ฅผ ๋ถˆ๋ฒ• ์ ๋ นํ•˜์˜€๋‹ค.', '๋Œ€ํ•œ๊ตญ ๊ตญ์ œ๊ฐ€ ์ œ์ •๋˜์—ˆ๋‹ค.', '์ •๋ฌ˜ํ˜ธ๋ž€์ด ๋ฐœ์ƒํ•˜์˜€๋‹ค.', '์‚ผ๋ณ„์ดˆ๊ฐ€ ๋ด‰๊ธฐํ•˜์˜€๋‹ค.', '์„๋ฏธ์‚ฌ๋ณ€์ด ์ผ์–ด๋‚ฌ๋‹ค.'], 'answer': ''}",,5,2,False,"['1895๋…„ ์„๋ฏธ์‚ฌ๋ณ€์œผ๋กœ ์กฐ์„  ๊ตญ๋ฏผ์˜ ๋Œ€์ผ ๊ฐ์ •์ด ๊ทน๋„๋กœ ์•…ํ™”ํ•˜๊ณ  ๊ฐ์ง€์—์„œ ์˜๋ณ‘์ด ์ผ์–ด๋‚˜ ์ „๊ตญ์ด ์†Œ๋ž€ํ•ด์ง€์ž ๋Ÿฌ์‹œ์•„ ๊ณต์‚ฌ ๋ฒ ๋ฒ ๋ฅด๋Š” ๊ณต์‚ฌ๊ด€ ๋ณดํ˜ธ๋ผ๋Š” ๋ช…๋ชฉ์œผ๋กœ ์ˆ˜๋ณ‘(ๆฐดๅ…ต) ๋ฐฑ๋ช…์„ ์„œ์šธ๋กœ ๋ฐ๋ ค์™”๋‹ค. ์ด์— ์นœ๋ŸฌํŒŒ์ธ ์ด๋ฒ”์ง„ ๋“ฑ์€ ๋ฒ ๋ฒ ๋ฅด์™€ ๊ณต๋ชจํ•˜์—ฌ ๊ฑด์–‘ 1๋…„(1896๋…„) 2์›” 11์ผ์— ๊ตญ์™•์˜ ๊ฑฐ์ฒ˜๋ฅผ ๊ถ๊ถ๋กœ๋ถ€ํ„ฐ ์ง€๊ธˆ์˜ ์„œ์šธํŠน๋ณ„์‹œ ์ค‘๊ตฌ ์ •๋™์ธ ์ •๋™(่ฒžๆดž)์— ์žˆ๋Š” ๋Ÿฌ์‹œ์•„ ๊ณต์‚ฌ๊ด€์œผ๋กœ ์˜ฎ๊ฒผ๋‹ค.[3] ๊ณ ์ข…์€ ์˜ฎ๊ธด ๋‹น์ผ ๋‚ด๊ฐ์ด๋ฆฌ๋Œ€์‹  ๊น€ํ™์ง‘์„ ๋น„๋กฏํ•˜์—ฌ, ๊น€์œค์‹, ์œ ๊ธธ์ค€, ์–ด์œค์ค‘, ์กฐํฌ์—ฐ, ์žฅ๋ฐ•, ์ •๋ณ‘ํ•˜, ๊น€์ข…ํ•œ, ํ—ˆ์ง„, ์ด๋ฒ”๋ž˜, ์ด์ง„ํ˜ธ๋ฅผ ๋ฉด์งํ•˜๊ณ , ์œ ๊ธธ์ค€ ๋“ฑ์„ ์ฒดํฌํ•˜๋„๋ก ๋ช…ํ•˜์˜€๋‹ค. ์ด์–ด ๊น€๋ณ‘์‹œ๋ฅผ ๋‚ด๊ฐ์ด๋ฆฌ๋Œ€์‹ ์— ๋ช…ํ•˜๋Š” ๋“ฑ ๋‚ด๊ฐ ์ธ์‚ฌ๋ฅผ ์ƒˆ๋กœ ํ•˜์˜€๋‹ค. ์ด๋‚  ๊น€ํ™์ง‘๊ณผ ์ •๋ณ‘ํ•˜๊ฐ€ ๋ฐฑ์„ฑ๋“ค์—๊ฒŒ ์‚ดํ•ด๋˜์—ˆ๋‹ค[4]. ์–ด์œค์ค‘ ๋˜ํ•œ ์‚ดํ•ด๋˜์—ˆ๊ณ , ์œ ๊ธธ์ค€ยท์กฐํฌ์—ฐ ๋“ฑ์€ ์ผ๋ณธ์œผ๋กœ ๋ง๋ช…ํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ , ์ด๋ฒ”์ง„ยท์ด์™„์šฉ ๋“ฑ์˜ ์นœ๋Ÿฌ ๋‚ด๊ฐ์ด ์กฐ์ง๋˜์—ˆ๋‹ค.[3] ๊ทธ๋Ÿฌ๋‚˜ ๋Ÿฌ์‹œ์•„ ์ œ๊ตญ์€ 1896๋…„ 5์›” ๋‹ˆ์ฝœ๋ผ์ด 2์„ธ์˜ ํ™ฉ์ œ ๋Œ€๊ด€์‹ ์ดํ›„์— ์ผ๋ณธ ์ œ๊ตญ๊ณผ ๊ฐ€๊นŒ์›Œ์ง€๋ฉฐ, ์•ผ๋งˆ๊ฐ€ํƒ€-๋กœ๋ฐ”๋…ธํ”„ ํ˜‘์ •์„ ๋งบ๋Š”๋‹ค. ๋˜ํ•œ ๋Ÿฌ์‹œ์•„ ์ œ๊ตญ์€ ๊ฒฝ์›๊ณผ ๊ฒฝ์„ฑ์˜ ์ฑ„๊ตด๊ถŒ๊ณผ ์••๋ก๊ฐ•, ๋‘๋งŒ๊ฐ• ๋ฐ ์šธ๋ฆ‰๋„์˜ ์ฑ„๋ฒŒ๊ถŒ๊ณผ ๊ฐ™์€ ๊ฐ์ข… ์ด๊ถŒ์„ ์š”๊ตฌํ•˜์˜€๋‹ค. ์ด์— 1897๋…„ 2์›” 18์ผ, ๊ถ์œผ๋กœ ๋Œ์•„๊ฐˆ ๊ฒƒ์„ ๋ช…ํ•œ ๊ณ ์ข…์€ ์ดํ‹€ ๋’ค์ธ 2์›” 20์ผ์— ๋•์ˆ˜๊ถ์œผ๋กœ ํ™˜๊ถํ•˜์˜€๋‹ค. ์ด๋•Œ, ๊ณ ์ข…์ด ๋‹ค๋ฅธ ๋‚˜๋ผ์˜ ๊ณต๊ด€์— ํ”ผ์‹ ํ•˜์—ฌ ๋‹ค๋ฅธ ๋‚˜๋ผ ๊ตฐ๋Œ€์˜ ๋ณดํ˜ธ๋ฅผ ๋ฐ›๊ณ  ์žˆ์œผ๋‹ˆ, ์กฐ์„ ์˜ ์ž์ฃผ๊ถŒ์ด ์‹ฌํ•˜๊ฒŒ ํ›ผ์†๋œ๋‹ค', '์ด์— 1897๋…„ 2์›” 18์ผ, ๊ถ์œผ๋กœ ๋Œ์•„๊ฐˆ ๊ฒƒ์„ ๋ช…ํ•œ ๊ณ ์ข…์€ ์ดํ‹€ ๋’ค์ธ 2์›” 20์ผ์— ๋•์ˆ˜๊ถ์œผ๋กœ ํ™˜๊ถํ•˜์˜€๋‹ค. ์ด๋•Œ, ๊ณ ์ข…์ด ๋‹ค๋ฅธ ๋‚˜๋ผ์˜ ๊ณต๊ด€์— ํ”ผ์‹ ํ•˜์—ฌ ๋‹ค๋ฅธ ๋‚˜๋ผ ๊ตฐ๋Œ€์˜ ๋ณดํ˜ธ๋ฅผ ๋ฐ›๊ณ  ์žˆ์œผ๋‹ˆ, ์กฐ์„ ์˜ ์ž์ฃผ๊ถŒ์ด ์‹ฌํ•˜๊ฒŒ ํ›ผ์†๋œ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๊ทธ ์ดํ›„ ๋Ÿฌ์‹œ์•„์˜ ๊ฐ„์„ญ์ด ์‹ฌํ•ด์ง€๊ฒŒ ๋œ๋‹ค. ๊ตญ์™•์ด ๋Ÿฌ์‹œ์•„ ๊ณต์‚ฌ๊ด€์— ์ฒด๋ฅ˜ํ•˜๊ณ  ์žˆ๋Š” 1๋…„ ๋™์•ˆ ๋ชจ๋“  ์ •์น˜๋Š” ๋Ÿฌ์‹œ์•„์˜ ์ˆ˜์ค‘์— ์žˆ์—ˆ์œผ๋ฉฐ, ๋‹น์‹œ ํƒ์ง€๋ถ€ ๊ณ ๋ฌธ ์•Œ๋ ‰์„ธ์˜ˆํ”„(Alexeev)๋Š” ์‚ฌ์‹ค์ƒ ์žฌ๋ฌด์žฅ๊ด€์ด๋‚˜ ๋งˆ์ฐฌ๊ฐ€์ง€์˜€๋‹ค. ํ•œํŽธ, ์•„๊ด€ํŒŒ์ฒœ ์ดํ›„ ๋งŽ์€ ์ด๊ถŒ์ด ๋Ÿฌ์‹œ์•„๋ฅผ ์œ„์‹œํ•œ ์—ด๊ฐ•์˜ ์†์— ๋„˜์–ด๊ฐ€ ๋ฒ„๋ ธ๋‹ค.[3] ๋Ÿฌ์‹œ์•„๊ณต์‚ฌ๊ด€์œผ๋กœ ์˜ฎ๊ธด ํ›„์— ์™•์€ ๋น„๋กœ์†Œ ๊ตฐ์ฃผ๊ถŒ์„ ํšŒ๋ณตํ•œ๋‹ค. ์ด์ „๊นŒ์ง„ ์ผ๋ณธ์ด ์ผ๋ณธ์‹ ์ œ๋Œ€๋กœ ๋‚ด๊ฐ์ด๋ผ๋Š” ๊ฒƒ์„ ๋งŒ๋“ค์–ด ์นœ์ผ์  ์ธ์‚ฌ๋ฅผ ์ด๋ฆฌ๋Œ€์‹ ์ด ๋˜๋„๋ก ํ•˜์—ฌ ๊ทธ๊ฐ€ ์ผ๋ณธ๊ณต์‚ฌ๊ด€์˜ ์ง€์‹œ๋ฅผ ๋ฐ›์•„ ๊ตญ์‚ฌ๋ฅผ ๊ฒฐ์ •ํ•˜๋„๋ก ํ–ˆ๋‹ค. ์™•์€ 1๋…„ ๋™์•ˆ ๋Ÿฌ์‹œ์•„๊ณต์‚ฌ๊ด€์— ๋จธ๋ฌผ๋ฉด์„œ ๋นผ์•—๊ฒผ๋˜ ๊ตฐ์ฃผ๊ถŒ์„ ํšŒ๋ณตํ•˜๊ณ  ์™•์ •์„ ์›์ƒ์œผ๋กœ ๋Œ๋ ธ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๋‚˜๋ผ๋ฅผ ํ•œ ๋“ฑ๊ธ‰ ์Šน๊ฒฉ์‹œ์ผœ ์ œ๊ตญ์œผ๋กœ์„œ ์žฌ์ถœ๋ฐœํ•˜๋Š” ๊ธฐํšŒ๋ฅผ ๋งŒ๋“ค์—ˆ๋‹ค. ํ™ฉ์ œ๋Š” ๊ด‘๋ฌด๋ผ๋Š” ์—ฐํ˜ธ๋ฅผ ์‚ฌ์šฉํ•˜๊ณ  ๊ทผ๋Œ€ํ™”์‚ฌ์—…์„ ์ ๊ทน์ ์œผ๋กœ ์ถ”์ง„ํ•ด ๋‚˜๊ฐ”๋‹ค.[5]']",2 +2025-history-12,๋ฒ ๋ธ(1872๋…„~1909๋…„) ๋ฒ ๋ธ์€ ์˜๊ตญ ์‹ ๋ฌธ '๋ฐ์ผ๋ฆฌ ํฌ๋กœ๋‹ˆํด'์˜ ํŠน๋ณ„ ํ†ต์‹ ์›์œผ๋กœ ๋Ÿฌ์ผ ์ „์Ÿ์„ ์ทจ์žฌํ•˜๊ธฐ ์œ„ํ•ด ํ•œ๊ตญ์— ์˜จ ์ดํ›„ ์–‘๊ธฐํƒ๊ณผ ํ•จ๊ป˜ (๊ฐ€)์„/๋ฅผ ์ฐฝ๊ฐ„ํ•˜์˜€๋‹ค. ์˜๊ตญ์ธ์ธ ๊ทธ๊ฐ€ ๋ฐœํ–‰์ธ์œผ๋กœ ์ฐธ์—ฌํ•˜์˜€๊ธฐ ๋•Œ๋ฌธ์— (๊ฐ€)์€/๋Š” ์ƒ๋Œ€์ ์œผ๋กœ ์ผ์ œ์˜ ๊ฒ€์—ด์—์„œ ์ž์œ ๋กœ์›Œ ์˜๋ณ‘ ์šด๋™์„ ๋ณด๋„ํ•˜๊ณ  ์ผ์ œ์˜ ๊ตญ๊ถŒ ์นจํƒˆ์„ ๋น„ํŒํ•˜๋Š” ๊ธฐ์‚ฌ๋ฅผ ๋งŽ์ด ์‹ค์„ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ํ•˜์ง€๋งŒ ๊ทธ๋Š” ์ผ์ œ์˜ ์ง‘์š”ํ•œ ๊ณ„๋žต ์†์— ์žฌํŒ์— ํšŒ๋ถ€๋˜๋Š” ๋“ฑ ๊ณ ์ดˆ๋ฅผ ๊ฒช๋‹ค๊ฐ€ 1909๋…„ ์ˆจ์„ ๊ฑฐ๋‘์—ˆ๋‹ค.,"{'question': '(๊ฐ€) ์‹ ๋ฌธ์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['๋ฐ•๋ฌธ๊ตญ์—์„œ ๋ฐœ๊ฐ„๋˜์—ˆ๋‹ค.', '๋ธŒ๋‚˜๋กœ๋“œ ์šด๋™์„ ์ฃผ๋„ํ•˜์˜€๋‹ค.', 'YH ๋ฌด์—ญ ์‚ฌ๊ฑด์„ ๋ณด๋„ํ•˜์˜€๋‹ค.', '๊ตญ์ฑ„ ๋ณด์ƒ ์šด๋™์„ ์ง€์›ํ•˜์˜€๋‹ค.', '๋…๋ฆฝ๋ฌธ ๊ฑด๋ฆฝ์— ์˜ํ–ฅ์„ ์ฃผ์—ˆ๋‹ค.'], 'answer': ''}",,4,4,True,"['๋Œ€ํ•œ๋งค์ผ์‹ ๋ณด๋Š” ์„์‚ฌ์กฐ์•ฝ์˜ ๋ฌดํšจ๋ฅผ ์ฃผ์žฅํ•˜๊ณ , ๊ณ ์ข… ์˜ ์นœ์„œ๋ฅผ ๊ฒŒ์žฌํ•˜๋Š” ๋“ฑ ํ•ญ์ผ ์–ธ๋ก  ํ™œ๋™์„ ํ™œ๋ฐœํ•˜๊ฒŒ ์ „๊ฐœํ•˜์˜€๋‹ค. ๋˜, 1907๋…„์—๋Š” ๊ตญ์ฑ„ ๋ณด์ƒ ์šด๋™ ์ด ์ผ์–ด๋‚˜์ž, ์ด๋ฅผ ์ ๊ทน์ ์œผ๋กœ ํ›„์›ํ•˜์˜€๋‹ค. ์ผ์ œ๊ฐ€ ํƒ„์••ํ•˜๋Š”๋ฐ๋„ ๋Œ€ํ•œ๋งค์ผ์‹ ๋ณด๊ฐ€ ํ•ญ์ผ ์šด๋™์„ ํŽผ์น  ์ˆ˜ ์žˆ์—ˆ๋˜ ๊ฒƒ์€ ์˜๊ตญ์ธ ๋ฒ ๋ธ ์ด ์‚ฌ์žฅ์ด์—ˆ๊ธฐ ๋•Œ๋ฌธ์ด์—ˆ๋‹ค. ์ด์—, ํ†ต๊ฐ๋ถ€ ๋Š” 1908๋…„ 5์›”์— ์˜๊ตญ ์ƒํ•˜์ด ๊ณ ๋“ฑ ๋ฒ•์›์— ๋ฒ ๋ธ ์„ ์ œ์†Œํ•˜์—ฌ 3์ฃผ๊ฐ„์˜ ๊ธˆ๊ณ  ์ƒํ™œ์„ ํ•˜๊ฒŒ ํ•˜์˜€๋‹ค. ๋ฒ ๋ธ ์€ ์„œ์šธ๋กœ ๋Œ์•„์˜จ ํ›„์—๋„ ํ•ญ์ผ ์–ธ๋ก  ํ™œ๋™์„ ํŽผ์น˜๋‹ค๊ฐ€ 1909๋…„ 5์›” 1์ผ์— ์‹ฌ์žฅ๋ณ‘์œผ๋กœ ๋ณ‘์‚ฌํ•˜์—ฌ ์„œ์šธ ์–‘ํ™”์ง„ ์™ธ๊ตญ์ธ ๋ฌ˜์ง€์— ๋ฌปํ˜”๋‹ค.']",5 +2025-history-13,์ง€๋„์— ํ‘œ์‹œ๋œ (๊ฐ€) ์ง€์—ญ์€ 19์„ธ๊ธฐ ํ›„๋ฐ˜๋ถ€ํ„ฐ ๋งŽ์€ ํ•œ์ธ์ด ์ด์ฃผํ•œ ๊ณณ์ž…๋‹ˆ๋‹ค. ์ด ์ง€์—ญ์— ์‹ ํ•œ์ดŒ์ด ๊ฑด์„ค๋˜์—ˆ๊ณ  ์ด๋™ํœ˜ ๋“ฑ์ด ์ค‘์‹ฌ์ด ๋˜์–ด ๋Œ€ํ•œ ๊ด‘๋ณต๊ตฐ ์ •๋ถ€๋ฅผ ์ˆ˜๋ฆฝํ•˜์˜€์Šต๋‹ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ 1937๋…„ ์†Œ๋ จ์€ ์ด ์ง€์—ญ์˜ ์ˆ˜๋งŽ์€ ํ•œ์ธ์„ ์ค‘์•™์•„์‹œ์•„๋กœ ์ด์ฃผ์‹œ์ผฐ์Šต๋‹ˆ๋‹ค.,"{'question': '(๊ฐ€) ์ง€์—ญ์—์„œ ์žˆ์—ˆ๋˜ ์‚ฌ์‹ค๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['๊ถŒ์—…ํšŒ๊ฐ€ ์กฐ์ง๋˜์—ˆ๋‹ค.', '์กฐ์„  ํ˜•ํ‰์‚ฌ๊ฐ€ ๊ฒฐ์„ฑ๋˜์—ˆ๋‹ค.', '์‹ ํฅ ๊ฐ•์Šต์†Œ๊ฐ€ ์„ค๋ฆฝ๋˜์—ˆ๋‹ค.', '์ฒญ์‚ฐ๋ฆฌ ์ „ํˆฌ๊ฐ€ ์ „๊ฐœ๋˜์—ˆ๋‹ค.', '์œค๋ด‰๊ธธ์˜ ํ›™์ปค์šฐ ๊ณต์› ์˜๊ฑฐ๊ฐ€ ์ผ์–ด๋‚ฌ๋‹ค.'], 'answer': ''}",,1,2,False,"['17์„ธ๊ธฐ ์ดˆ๋ฐ˜๋ถ€ํ„ฐ ์กฐ์„ ์ธ์ด ํ˜„์žฌ์˜ ๋žด์˜ค๋‹์„ฑ์— ์ •์ฐฉํ•˜๊ธฐ๋„ ํ–ˆ๋‹ค๋Š” ์„ค์ด ์žˆ์œผ๋‚˜[1], ๋ณธ๊ฒฉ์ ์ธ ์ด์ฃผ๋Š” ๋Œ€๋žต 19์„ธ๊ธฐ ์ดˆ๋ถ€ํ„ฐ ์‹œ์ž‘๋œ ๊ฒƒ์œผ๋กœ ์—ฌ๊ฒจ์ง„๋‹ค. ๋‹น์‹œ ๋งŒ์ฃผ๋ฅผ ์กฐ์ƒ์˜ ๋•…์œผ๋กœ์„œ ์„ฑ์—ญํ™”ํ•˜์—ฌ ๋งŒ์ฃผ์กฑ ์ด์™ธ์˜ ์ด์ฃผ๋ฅผ ๊ธˆ์ง€ํ•˜๋˜ ์ฒญ๋‚˜๋ผ์˜ ๋ด‰๊ธˆ(ๅฐ็ฆ)๋ น์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ , ์„ธ๋„์ •์น˜๋ฅผ ๋น„๋กฏํ•œ ๊ตญ๋‚ด์˜ ์—ฌ๋Ÿฌ ํ˜ผ๋ž€์œผ๋กœ ์ƒํ™œ์ด ์–ด๋ ค์›Œ์ง„ ์กฐ์„ ์ธ๋“ค์€ ๋‘๋งŒ๊ฐ•์„ ๊ฑด๋„ˆ ์กฐ์„ ์˜ ๊ด€๊ถŒ์ด ๋ฏธ์น˜์ง€ ์•Š๋Š” ๊ฐ„๋„ ์ง€์—ญ์— ์ •์ฐฉํ•˜๊ธฐ ์‹œ์ž‘ํ–ˆ๋‹ค. ์ด๋“ค์€ ์ฃผ๋กœ ํ™”์ „์œผ๋กœ ๋ฐญ์„ ์ผ๊ตฌ๊ฑฐ๋‚˜ ์ธ์‚ผ ๋“ฑ์„ ์ฑ„์ง‘ํ•˜์˜€๋‹ค. ํŠนํžˆ 1869๋…„์—๋Š” ํ•จ๊ฒฝ๋„์— ๋Œ€๊ทœ๋ชจ ๊ธฐ๊ทผ์ด ๋ฐœ์ƒํ•˜์—ฌ ๋งŽ์€ ์กฐ์„ ์ธ๋“ค์ด ๊ฑด๋„ˆ์˜ค๊ฒŒ ๋˜์—ˆ๊ณ  ์ด๋กœ ์ธํ•ด ์ฒญ๋‚˜๋ผ์™€ ์กฐ์„  ์‚ฌ์ด์— ๊ตญ๊ฒฝ ๋ถ„์Ÿ์ด ์ž์ฃผ ๋ฐœ์ƒํ•˜๊ธฐ๋„ ํ•˜์˜€๋‹ค. ์กฐ์„ ์—์„œ๋Š” ๋‘๋งŒ๊ฐ• ๊ฑด๋„ˆํŽธ์˜ ์กฐ์„ ์ธ ๊ฑฐ์ฃผ์ง€๋ฅผ ๋ถ๊ฐ„๋„๋ผ๊ณ  ๋ถˆ๋ €์œผ๋ฉฐ ์••๋ก๊ฐ• ๊ฑด๋„ˆํŽธ์˜ ์กฐ์„ ์ธ ๊ฑฐ์ฃผ์ง€๋ฅผ ์„œ๊ฐ„๋„๋ผ๊ณ  ๋ถˆ๋ €๋‹ค. ์กฐ์„ ์ธ๋“ค์ด ์ค‘๊ตญ ๋™๋ถ์œผ๋กœ ์œ ์ž…ํ•˜๋Š” ํ˜„์ƒ์€ 1885๋…„ ์ฒญ๋‚˜๋ผ ์ •๋ถ€๊ฐ€ ๋™๋ถ ์ด๋ฏผ ๊ธˆ์ง€๋ น์„ ์ฒ ํํ•˜๋ฉด์„œ ํฌ๊ฒŒ ์ฆ๊ฐ€ํ•˜๊ฒŒ ๋œ๋‹ค. 1885๋…„๋ถ€ํ„ฐ 1910๋…„ ํ•œ์ผ๋ณ‘ํ•ฉ ์ด์ „๊นŒ์ง€ ์ค‘๊ตญ์œผ๋กœ ์ด์ฃผํ•œ ์กฐ์„ ์ธ์€ 26๋งŒ ๋ช…์œผ๋กœ ์ถ”์ •๋œ๋‹ค. ์ผ์ œ๊ฐ•์ ๊ธฐ ๋™์•ˆ 1910๋…„๋ถ€ํ„ฐ 1928๋…„๊นŒ์ง€ 48๋งŒ ๋ช…์— ์ด๋ฅด๋Š” ์กฐ์„ ์ธ์ด ์ด์ฃผํ•˜์˜€์„ ์ •๋„๋กœ ์กฐ์„ ์ธ ์ด์ฃผ๋ฏผ์˜ ์ˆ˜๊ฐ€ ๊ธ‰์ฆํ•˜์˜€๋‹ค. ๊ตญ๊ฒฝ์„ ๋„˜์€ ์กฐ์„ ์ธ๋“ค ์ค‘์—๋Š” ๋†๋ฏผ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ, ์ผ์ œ์˜ ํƒ„์••์„ ํ”ผํ•˜์—ฌ ํ•ญ์ผ์šด๋™์„ ์ „๊ฐœํ•˜๊ณ ์ž ํ•˜๋Š” ๋ง๋ช… ๋…๋ฆฝ์šด๋™๊ฐ€๋“ค๋„ ๋งŽ์•˜๋‹ค. 1920๋…„ 6์›”์—๋Š” ๋Œ€ํ•œ๋…๋ฆฝ๊ตฐ์ด ์ผ๋ณธ๊ตฐ์„ ์ƒ๋Œ€๋กœ ํฌ๊ฒŒ ์Šน๋ฆฌํ•œ ์ฒญ์‚ฐ๋ฆฌ ์ „ํˆฌ, ๋ด‰์˜ค๋™ ์ „ํˆฌ๊ฐ€ ๊ฐ„๋„ ์ง€์—ญ์—์„œ ์ „๊ฐœ๋˜๊ธฐ๋„ ํ•˜์˜€๋‹ค']",1 +2025-history-14,"์ด ์ง€๋„๋Š” 1872๋…„ ์ œ์ž‘๋œ ๊ฒฝ์ƒ๋„ ์‚ฐ์ฒญํ˜„ ์ง€๋„์˜ ์ผ๋ถ€์ด๋‹ค. ์—ฌ๊ธฐ์—๋Š” ๋‹น์‹œ ์‚ฌํšŒ์  ์ƒํ™ฉ์„ ์•Œ ์ˆ˜ ์žˆ๋Š” ๋‹ค์–‘ํ•œ ์ •๋ณด๋“ค์ด ๋‚˜ํƒ€๋‚˜ ์žˆ๋Š”๋ฐ, ๊ทธ์ค‘ ํ•˜๋‚˜๊ฐ€ ๊ด€์•„ ๋ถ€๊ทผ์— ๊ฑด๋ฆฝ๋œ ์ฒ™ํ™”๋น„์ด๋‹ค. ์ฒ™ํ™”๋น„๋Š” ๋ฏธ๊ตญ์˜ ๊ตฐ๋Œ€๊ฐ€ ๊ฐ•ํ™”๋„๋ฅผ ์นจ์ž…ํ•œ ์ด ์‚ฌ๊ฑด ์ดํ›„ ์ „๊ตญ ๊ฐ์ง€์— ๊ฑด๋ฆฝ๋œ ๊ฒƒ์œผ๋กœ, ๋‹น์‹œ ์กฐ์„  ์ •๋ถ€์˜ ํ†ต์ƒ ์ˆ˜๊ต ๊ฑฐ๋ถ€ ์˜์ง€๋ฅผ ํ™•์ธํ•  ์ˆ˜ ์žˆ๋‹ค","{'question': '๋ฐ‘์ค„ ์นœ โ€˜์ด ์‚ฌ๊ฑดโ€™์— ๋Œ€ํ•œ ํƒ๊ตฌ ํ™œ๋™์œผ๋กœ ๊ฐ€์žฅ ์ ์ ˆํ•œ ๊ฒƒ์€?', 'choices': ['์ •๋™ํ–‰์„ฑ์ด ์„ค์น˜๋œ ์›์ธ์„ ์กฐ์‚ฌํ•œ๋‹ค.', '์›์‚ฐ ์ดํŒŒ์—…์˜ ์ „๊ฐœ ๊ณผ์ •์„ ์‚ดํŽด๋ณธ๋‹ค.', '์ œ๋„ˆ๋Ÿด์…”๋จผํ˜ธ ์‚ฌ๊ฑด์˜ ์˜ํ–ฅ์„ ์•Œ์•„๋ณธ๋‹ค.', '๋ฏผ๋ฆฝ ๋Œ€ํ•™ ์„ค๋ฆฝ ์šด๋™์˜ ๋ฐฐ๊ฒฝ์„ ๋ถ„์„ํ•œ๋‹ค', '3โ€ค1๋ฏผ์ฃผ ๊ตฌ๊ตญ ์„ ์–ธ์˜ ๋‚ด์šฉ์„ ์ฐพ์•„๋ณธ๋‹ค'], 'answer': ''}",,3,3,True,[],3 +2025-history-15,โ—ฆ ๋Œ€ํ•œ๋ฏผ๊ตญ ์ž„์‹œ ์ •๋ถ€๋Š” ์˜๊ตญ๊ตฐ์˜ ์š”๊ตฌ์— ์‘ํ•˜์—ฌ (๊ฐ€) ์˜ ๋Œ€์› ์ผ๋ถ€๋ฅผ ์ธ๋„์— ํŒŒ๊ฒฌํ•˜์˜€๋‹ค. ์—ฐํ•ฉ๊ตญ์ด ์ด ๊ตฐ๋Œ€์— ๊ณต์ž‘ ์ž„๋ฌด๋ฅผ ๋งก๊ธด ๊ฒƒ์€ 1940๋…„ ์ฐฝ์„ค ์ด๋ž˜ ์Œ“์€ ํ›ˆ๋ จ์˜ ์„ฑ๊ณผ๋ฅผ ์ธ์ •ํ•œ ๊ฒƒ์ด๋‹ค. ์กฐ์„  ๋ฏผ์กฑ์˜ ๋Šฅ๋ ฅ์„ ๋ฐœํœ˜ํ•˜์—ฌ ์ผ๋ณธ ์ œ๊ตญ์ฃผ์˜๋ฅผ ๋ฐ•๋ฉธํ•˜๋Š” ๊ฒƒ์€ ์—ฐํ•ฉ๊ตญ์˜ ๋ชฉ์ ์ธ ๋™์‹œ์— ์šฐ๋ฆฌ์˜ ์ฑ…์ž„์ด๋‹ค. โ—ฆ ์ผ๋ณธ๊ตฐ์ด ์ธ๋„์˜ ์ž„ํŒ” ์ธ๊ทผ๊นŒ์ง€ ์ ‘๊ทผํ•˜์˜€๋‹ค. ์ด์— ์šฐ๋ฆฌ (๊ฐ€) ๊ณต์ž‘๋Œ€ ๋Œ€์›๋“ค์€ ๊ทธ๋“ค์—๊ฒŒ ๊ฐ€๊นŒ์ด ๋‹ค๊ฐ€๊ฐ€ ์ผ๋ณธ์–ด๋กœ ๋ฐฉ์†กํ•˜์˜€์œผ๋ฉฐ ์„ ์ „๋ฌธ์„ ์‚ดํฌํ•˜์˜€๋‹ค. ๋˜ํ•œ ๋Œ€์›๋“ค์€ ์ผ๋ณธ๊ตฐ ๋ฌธ์„œ๋ฅผ ๋ฒˆ์—ญํ•˜๊ณ  ํฌ๋กœ๋ฅผ ์‹ฌ๋ฌธํ•˜๋ฉด์„œ ์˜๊ตญ๊ตฐ๊ณผ ํ•จ๊ป˜ ์ž‘์ „์„ ์ „๊ฐœํ•˜์˜€๋‹ค.,"{'question': '(๊ฐ€) ๊ตฐ์‚ฌ ์กฐ์ง์— ๋Œ€ํ•œ ์„ค๋ช…์œผ๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['๋ด‰์˜ค๋™ ์ „ํˆฌ์— ์ฐธ์—ฌํ•˜์˜€๋‹ค.', '๊ตญ๋‚ด ์ง„๊ณต ์ž‘์ „์„ ๊ณ„ํšํ•˜์˜€๋‹ค.', '์–‘์„ธ๋ด‰์˜ ์ง€ํœ˜ํ•˜์— ํ™œ๋™ํ•˜์˜€๋‹ค.', '๊ณ ์ข…์˜ ๋ฐ€๋ช…์„ ๋ฐ›์•„ ์กฐ์ง๋˜์—ˆ๋‹ค', '์ž์œ ์‹œ ์ฐธ๋ณ€์œผ๋กœ ํ”ผํ•ด๋ฅผ ์ž…์—ˆ๋‹ค.'], 'answer': ''}",,2,2,True,"['๊ทธ ํ›„ ์ผ์ œ๊ฐ€ ํƒœํ‰์–‘์ „์Ÿ์„ ์ผ์œผํ‚ค์ž ๋Œ€ํ•œ๋ฏผ๊ตญ ์ž„์‹œ์ •๋ถ€๋Š” 1941๋…„ 12์›” 9์ผ ์ •์‹์œผ๋กœ ์ผ๋ณธ๊ณผ ๋…์ผ์— ๋Œ€ํ•ด ์„ ์ „ํฌ๊ณ ๋ฅผ ํ•˜์˜€๋‹ค. ์ด๋กœ์จ ๊ด‘๋ณต๊ตฐ์€ ๋Œ€ํ•œ๋ฏผ๊ตญ ์ž„์‹œ์ •๋ถ€์˜ ๊ตญ๊ตฐ์œผ๋กœ์„œ ์ค‘๊ตญ์€ ๋ฌผ๋ก , ์˜๊ตญยท๋ฏธ๊ตญ ๋“ฑ ์—ฐํ•ฉ๊ตฐ๊ณผ ์–ด๊นจ๋ฅผ ๋‚˜๋ž€ํžˆ ํ•˜๊ณ  ์ค‘๊ตญ์ „์„ ๊ณผ ๋ฏธ์–€๋งˆ์ „์„  ๋“ฑ์—์„œ ๋Œ€์ผ์ „์Ÿ์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ํŠนํžˆ 1943๋…„ 6์›”๋ถ€ํ„ฐ ์ธ๋„ยท๋ฒ„๋งˆ ์ „์„ ์— ๊ด‘๋ณต๊ตฐ์ด ํˆฌ์ž…๋ผ ์ผ๋ณธ๊ตฐ์— ๊ฐ•์ œ ์ง•์ง‘๋œ ์กฐ์„  ์ถœ์‹  ์žฅ๋ณ‘๋“ค์— ๋Œ€ํ•œ ์„ ๋ฌด๊ณต์ž‘์—์„œ ์˜๊ตญ์ธก์— ์ ˆ๋Œ€์ ์ธ ๋„์›€์„ ์ฃผ์—ˆ๋‹ค. 1944๋…„์—๋Š” ์ค‘๊ตญ๊ณผ ์ƒˆ๋กœ์šด ๊ตฐ์‚ฌํ˜‘์ •์„ ์ฒด๊ฒฐ, ์šฐ๋ฆฌ ๊ด‘๋ณต๊ตฐ์˜ ๋…์ž์ ์ธ ๊ตฐ์‚ฌํ–‰๋™๊ถŒ์„ ํ™•๋ณดํ•˜์˜€์œผ๋ฉฐ, 1945๋…„์—๋Š” ๊ตญ๋‚ด ์ง„์ž…์ž‘์ „์˜ ์ผํ™˜์œผ๋กœ ๊ตญ๋‚ด ์ •์ง„๊ตฐ(ๅœ‹ๅ…งๆŒบ้€ฒ่ป) ์ด์ง€ํœ˜๋ถ€๊ฐ€ ์„ค๋ฆฝ๋˜์—ˆ๋‹ค. ์ด๋Š” ๋ฏธ๊ตญ์ด 1945๋…„ 5์›” ์ˆ˜๋ฆฝํ•œ โ€˜์ผ๋ณธ๊ณผ ํ•œ๊ตญ์— ๋Œ€ํ•œ ์นจ๊ณต ๋ฐ ์ ๋ น์„ ์œ„ํ•œ ์ „๋žต ๊ณ„ํšโ€™์— ์˜ํ•ด ๊ด‘๋ณต๊ตฐ์ด ๋ฏธ๊ตฐ์˜ OSS๋ถ€๋Œ€์™€ ํ•ฉ๋™์œผ๋กœ ๊ตญ๋‚ด์— ์ง„์ž…, ์ผ๋ณธ๊ตฐ์„ ๋ฌด์žฅํ•ด์ œ์‹œํ‚ค๋ ค๋˜ ๊ณ„ํš์ด์—ˆ๋‹ค. ์ด ์ž‘์ „์— ์˜ํ•ด ๊ด‘๋ณต๊ตฐ์ด ๊ตญ๋‚ด ์ •์ง„๊ตฐ์œผ๋กœ์„œ ์ œ๋ฐ˜ ํ›ˆ๋ จ์„ ๋งˆ์น˜๊ณ  ๋ฐœ์ง„ ๋ช…๋ น๋งŒ์„ ๊ธฐ๋‹ค๋ฆฌ๊ณ  ์žˆ๋˜ ์ฐจ์— ์•ˆํƒ€๊น๊ฒŒ๋„ ์ผ์ œ๊ฐ€ ์—ฐํ•ฉ๊ตฐ์—๊ฒŒ ๋ฌด์กฐ๊ฑด ํ•ญ๋ณตํ•จ์œผ๋กœ์จ ์ขŒ์ ˆ๋˜๊ณ  ๋ง์•˜๋‹ค. ํ•˜์ง€๋งŒ ๋ฌด๋ ค 50๋…„์— ๊ฑธ์นœ ์žฅ๊ธฐ ๋Œ€์ผ์ „์Ÿ์„ ๋ˆ์งˆ๊ธฐ๊ฒŒ ์ˆ˜ํ–‰ํ•จ์œผ๋กœ์จ ๊ด‘๋ณต ํ›„ ๋Œ€ํ•œ๋ฏผ๊ตญ๊ณผ ์šฐ๋ฆฌ ๊ตญ๊ตฐ์˜ ์ •ํ†ต์„ฑ์„ ํ™•๊ณ ํ•œ ๋ฐ˜์„ ์œ„์— ์˜ฌ๋ ค๋†“์„ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋ฐ”๋กœ ๊ทธ๋Ÿฐ ์ ์—์„œ ๊ตญ๊ฐ€์™€ ๋ฏผ์กฑ์„ ์ˆ˜ํ˜ธํ•˜๋Š” ์šฐ๋ฆฌ ๊ตฐ(่ป)์€ ๋Œ€ํ•œ์ œ๊ตญ ๊ตญ๊ตฐ โ†’ ์˜๋ณ‘ โ†’ ๋…๋ฆฝ๊ตฐ โ†’ ๊ด‘๋ณต๊ตฐ โ†’ ๋Œ€ํ•œ๋ฏผ๊ตญ ๊ตญ๊ตฐ์œผ๋กœ ์ด์–ด์ง€๋Š” ์—ญ์‚ฌ์  ์˜์˜๋ฅผ ๊ฐ–๊ฒŒ ๋˜์—ˆ๋‹ค']",5 +2025-history-16,"์ด ์‚ฌ์ง„์€ ์ผ์ œ๊ฐ€ ํƒ‘๊ณจ ๊ณต์›์˜ ๋ฌธ์„ ๋œฏ์–ด๋‚ด๋Š” ์žฅ๋ฉด์ด๋‹ค. ์ผ์ œ๋Š” ์ค‘์ผ ์ „์Ÿ์„ ์ผ์œผํ‚ค๊ณ  ์นจ๋žต ์ „์Ÿ์„ ํ™•๋Œ€ํ•˜๋˜ ์ด ์‹œ๊ธฐ์— ์‚ฌ์ง„๊ณผ ๊ฐ™์ด ๊ธˆ์† ์†Œ์žฌ๋กœ ์ œ์ž‘๋œ ๋ฌธ๊ณผ ํ˜„ํŒ, ์šธํƒ€๋ฆฌ ๋“ฑ๋„ ๊ฐ€์ ธ๊ฐ€ ๊ตฐ์ˆ˜ ๋ฌผ์ž๋กœ ํ™œ์šฉํ•˜์˜€๋‹ค. ๋˜ํ•œ ์• ๊ตญ๋ฐ˜๊ณผ ๊ฐ™์€ ์กฐ์ง์„ ํ™œ์šฉํ•˜์—ฌ ๊ธˆ์†๋ฅ˜์— ๋Œ€ํ•œ ๊ณต์ถœ์„ ์‹ค์‹œํ•˜์˜€๋‹ค.","{'question': '๋ฐ‘์ค„ ์นœ โ€˜์ด ์‹œ๊ธฐโ€™์— ๋ณผ ์ˆ˜ ์žˆ๋Š” ๋ชจ์Šต์œผ๋กœ ๊ฐ€์žฅ ์ ์ ˆํ•œ ๊ฒƒ์€?', 'choices': ['๋น„๋ณ€์‚ฌ์—์„œ ํšŒ์˜ํ•˜๋Š” ๊ด€๋ฆฌ', '์‹ ์‚ฌ ์ฐธ๋ฐฐ์— ๋™์›๋˜๋Š” ํ•™์ƒ', 'ํ™๊ฒฝ๋ž˜์˜ ๋‚œ์— ์ฐธ์—ฌํ•˜๋Š” ๋†๋ฏผ', '13๋„ ์ฐฝ์˜๊ตฐ์„ ์ด๋„๋Š” ์˜๋ณ‘์žฅ', '์ „ํƒœ์ผ ๋ถ„์‹  ์‚ฌ๊ฑด์„ ์ทจ์žฌํ•˜๋Š” ๊ธฐ์ž'], 'answer': ''}",,2,2,True,[],2 +2025-history-17,"ํ–‰๋™ํ•˜๋Š” ๊ตญ๋ฏผ ์†์— ๋ฐ•์ข…์ฒ ์€ ๋ถ€ํ™œํ•œ๋‹ค! ๊ตญ๋ฏผ ์—ฌ๋Ÿฌ๋ถ„! 4ยท13 ํ˜ธํ—Œ ์กฐ์น˜๋Š” ์šฐ๋ฆฌ ๊ตญ๋ฏผ๋“ค์˜ ๋ฏผ์ฃผํ™” ์—ด๋ง์˜ ์ฃฝ์Œ์ด์—ˆ์Šต๋‹ˆ๋‹ค. ...(์ค‘๋žต)... ํ˜ธํ—Œ์œผ๋กœ ๋Œ์•„์„œ๋Š” ์ž๋“ค์ด ๊ตฐ๋ฆผํ•˜๊ณ  ์žˆ๋Š” ํ•œ, ๋ฐ•์ข…์ฒ  ๊ตฐ์˜ ์˜ํ˜ผ์€ ํŽธ์•ˆํžˆ ์ž ๋“ค์ง€ ๋ชปํ•  ๊ฒƒ์ž…๋‹ˆ๋‹ค. ์˜ค๋กœ์ง€ ์Šค์Šค๋กœ ์ž์‹ ์˜ ๋ฏผ์ฃผ ๊ถŒ๋ฆฌ๋ฅผ ์Ÿ์ทจํ•˜๊ณ ์ž ํ•˜๋Š” ๊ตญ๋ฏผ ์—ฌ๋Ÿฌ๋ถ„์˜ ํ–‰๋™ ์†์—์„œ ๋ฐ• ๊ตฐ์€ ๋˜์‚ด์•„๋‚  ๊ฒƒ์ž…๋‹ˆ๋‹ค. ๋ฏผ์ฃผํ™” ์—†์ด ์˜ฌ๋ฆผํ”ฝ ์—†์Šต๋‹ˆ๋‹ค. ๋‚˜์•„๊ฐ‘์‹œ๋‹ค. ๋ฏผ์ฃผ ํ—Œ๋ฒ• ์Ÿ์ทจ๋ฅผ ์œ„ํ•ด, ์ด ๋•…์˜ ๋ฏผ์ฃผํ™”๋ฅผ ์œ„ํ•ด ํ•จ๊ป˜ ํ–‰๋™ํ•ฉ์‹œ๋‹ค.","{'question': '๋‹ค์Œ ์ž๋ฃŒ์— ๋‚˜ํƒ€๋‚œ ๋ฏผ์ฃผํ™” ์šด๋™์˜ ๊ฒฐ๊ณผ๋กœ ์˜ณ์€ ๊ฒƒ์€? ', 'choices': ['๊ตฐ๊ตญ๊ธฐ๋ฌด์ฒ˜๊ฐ€ ์„ค์น˜๋˜์—ˆ๋‹ค.', '์น˜์•ˆ ์œ ์ง€๋ฒ•์ด ๊ณตํฌ๋˜์—ˆ๋‹ค.', '๋‚ด๊ฐ ์ฑ…์ž„์ œ ์ •๋ถ€๊ฐ€ ์ถœ๋ฒ”ํ•˜์˜€๋‹ค.', '๋Œ€ํ†ต๋ น ์ง์„ ์ œ ๊ฐœํ—Œ์ด ์ด๋ฃจ์–ด์กŒ๋‹ค.', 'ํ†ต์ผ ์ฃผ์ฒด ๊ตญ๋ฏผ ํšŒ์˜๊ฐ€ ๊ตฌ์„ฑ๋˜์—ˆ๋‹ค.'], 'answer': ''}",,4,4,True,"['์ „๋‘ํ™˜ ์ •๋ถ€์˜ ๊ฐ•์••์ ์ธ ํ†ต์น˜ํ•˜์—์„œ๋„ ๊ณ„์†๋œ ๋ฏผ์ฃผํ™” ์š”๊ตฌ๋Š”, 1987๋…„ ๋ฐ•์ข…์ฒ  ๊ณ ๋ฌธ ์‚ฌ๋ง ์‚ฌ๊ฑด๊ณผ 4โ‹…13 ํ˜ธํ—Œ ์กฐ์น˜๋ฅผ ๊ณ„๊ธฐ๋กœ 6์›” ๋ฏผ์ฃผ ํ•ญ์Ÿ์œผ๋กœ ๋ฐœ์ „ํ•˜์˜€๋‹ค. ์ง์„ ์ œ ๊ฐœํ—Œ์„ ๋น„๋กฏํ•˜์—ฌ ๊ด‘๋ฒ”์œ„ํ•œ ๋ฏผ์ฃผํ™”๋ฅผ ์š”๊ตฌํ•˜๋Š” ์‹œ์œ„๋Š” ์ „๊ตญ์ ์œผ๋กœ ํ™•๋Œ€๋˜์—ˆ๋‹ค. ๊ฒฐ๊ตญ, ์ •๋ถ€๋Š” ๊ตญ๋ฏผ์˜ ๋ฏผ์ฃผํ™” ์š”๊ตฌ๋ฅผ ์ˆ˜์šฉํ•˜์—ฌ 6โ‹…29 ๋ฏผ์ฃผํ™” ์„ ์–ธ์„ ๋ฐœํ‘œํ•˜์˜€๊ณ , ์—ฌ์•ผ ํ•ฉ์˜์— ์˜ํ•ด 5๋…„ ๋‹จ์ž„์˜ ๋Œ€ํ†ต๋ น ์ง์„ ์ œ๋ฅผ ๊ณจ์ž๋กœ ํ•˜๋Š” ์ƒˆ ํ—Œ๋ฒ•์ด ๋งˆ๋ จ๋˜์—ˆ๋‹ค. ์ด์— ๋”ฐ๋ผ ์น˜๋Ÿฌ์ง„ ๋Œ€ํ†ต๋ น ์„ ๊ฑฐ์—์„œ๋Š” ์•ผ๊ถŒ์˜ ํ›„๋ณด ๋‹จ์ผํ™” ์‹คํŒจ๋กœ ์‹ ๊ตฐ๋ถ€ ์ถœ์‹ ์˜ ๋…ธํƒœ์šฐ๊ฐ€ ๋‹น์„ ๋˜์—ˆ๋‹ค. ๋…ธํƒœ์šฐ ์ •๋ถ€๋Š” ๋™์œ ๋Ÿฝ ๊ณต์‚ฐ์ฃผ์˜ ๊ตญ๊ฐ€ ๋ฐ ์†Œ๋ จ, ์ค‘๊ตญ๊ณผ ์™ธ๊ต ๊ด€๊ณ„๋ฅผ ์ˆ˜๋ฆฝํ•˜๋Š” ๋ถ๋ฐฉ ์ •์ฑ…์„ ์ถ”์ง„ํ•˜์˜€๊ณ , ์œ ์—”์— ๋‚จ๋ถํ•œ์ด ํ•จ๊ป˜ ๊ฐ€์ž…ํ•˜๋Š” ๋“ฑ ์„ฑ๊ณผ๋ฅผ ์˜ฌ๋ ธ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋…ธํƒœ์šฐ ์ •๋ถ€๋Š” ์ ๊ทน์ ์ธ ๊ฐœํ˜์„ ์ถ”์ง„ํ•˜์ง€ ๋ชปํ•œ ์ฑ„ ํ†ต์น˜ ๊ธฐ๊ฐ„ ์ค‘์— ๋ฐœ์ƒํ•œ ๋ถ€์ •๊ณผ ๋น„๋ฆฌ๋กœ ๊ตญ๋ฏผ์  ์ง€์ง€๋ฅผ ์ œ๋Œ€๋กœ ์–ป์ง€ ๋ชปํ•˜์˜€๋‹ค. 1993๋…„์— ์„ฑ๋ฆฝ๋œ ๊น€์˜์‚ผ ์ •๋ถ€๋Š” ๊ณต์ง์ž์˜ ์žฌ์‚ฐ ๋“ฑ๋ก๊ณผ ๊ธˆ์œต ์‹ค๋ช…์ œ๋“ฑ์„ ๋ฒ•์ œํ™”ํ•˜์—ฌ ๋ถ€์ •๋ถ€ํŒจ ์ฒ™๊ฒฐ์— ๋…ธ๋ ฅํ•˜์˜€๋‹ค. ๋˜, โ€˜์—ญ์‚ฌ ๋ฐ”๋กœ์„ธ์šฐ๊ธฐโ€™์˜ ์ผํ™˜์œผ๋กœ ์‹ ๊ตฐ๋ถ€ ์„ธ๋ ฅ์„ ๋ฒ•์ •์— ์„ธ์šฐ๊ณ , 5โ‹…16 ๊ตฐ์‚ฌ ์ •๋ณ€ ํ›„ ์ค‘๋‹จ๋˜์—ˆ๋˜ ์ง€๋ฐฉ ์ž์น˜์ œ๋ฅผ ์ „๋ฉด์ ์œผ๋กœ ์‹ค์‹œํ•˜์˜€๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ง‘๊ถŒ ๋ง๊ธฐ์— ๊ตญ์ œ ๊ฒฝ์ œ ์—ฌ๊ฑด์˜ ์•…ํ™”์™€ ์™ธํ™˜ ๋ถ€์กฑ์œผ๋กœ ์ธํ•˜์—ฌ ๊ฒฝ์ œ์  ์œ„๊ธฐ๋ฅผ ๊ฒช์—ˆ๋‹ค. 1998๋…„์— ์„ฑ๋ฆฝ๋œ ๊น€๋Œ€์ค‘ ์ •๋ถ€๋Š” ์™ธํ™˜ ์œ„๊ธฐ ๊ทน๋ณต ๋ฐ ๋ฏผ์ฃผ์ฃผ์˜์™€ ์‹œ์žฅ ๊ฒฝ์ œ์˜ ๋ณ‘ํ–‰ ๋ฐœ์ „์„ ์ฒœ๋ช…ํ•˜์˜€๊ณ , ๋‚จ๋ถ ํ‰ํ™” ์ •์ฐฉ์„ ์œ„ํ•œ ์ ๊ทน์ ์ธ ๋Œ€๋ถ ์ •์ฑ…์„ ์ถ”์ง„ํ•˜์˜€๋‹ค']",4 +2025-history-18,"3๋‹น ํ•ฉ๋‹น ๋ฐœํ‘œ. 19โ–ณโ–ณ๋…„ 1์›” 22์ผ์€ '3๋‹น ํ•ฉ๋‹น'์ด ๋ฐœํ‘œ๋œ ๋‚ ์ด๋‹ค. ์ œ13๋Œ€ ๊ตญํšŒ ์˜์› ์„ ๊ฑฐ์—์„œ ์•ผ๋‹น์˜ ์˜์„์ˆ˜๊ฐ€ ์ง‘๊ถŒ ์—ฌ๋‹น๋ณด๋‹ค ๋งŽ์€ ์ด๋ฅธ๋ฐ” '์—ฌ์†Œ์•ผ๋Œ€' ๊ตญ๋ฉด์ด ์กฐ์„ฑ๋˜์—ˆ๋‹ค. ํ•˜์ง€๋งŒ ๊ตญ์ •์˜ ์ฃผ๋„๊ถŒ์„ ์ฅ๋ ค๋Š” ์—ฌ๋‹น์ธ ๋ฏผ์ฃผ ์ •์˜๋‹น์ด ํ†ต์ผ ๋ฏผ์ฃผ๋‹น, ์‹ ๋ฏผ์ฃผ ๊ณตํ™”๋‹น ๋“ฑ ๋‘ ์•ผ๋‹น๊ณผ ํ†ตํ•ฉํ•˜์—ฌ ๊ณผ๋ฐ˜์ˆ˜ ์ด์ƒ์˜ ์˜์„์„ ์ฐจ์ง€ํ•˜๋Š” ๋ฏผ์ฃผ ์ž์œ ๋‹น์„ ์ฐฝ๋‹นํ•˜์˜€๋‹ค.","{'question': '๋‹ค์Œ ์‚ฌ๊ฑด์ด ์ผ์–ด๋‚œ ์ •๋ถ€ ์‹œ๊ธฐ์— ์žˆ์—ˆ๋˜ ์‚ฌ์‹ค๋กœ ์˜ณ์€ ๊ฒƒ์€?', 'choices': ['์šด์š”ํ˜ธ ์‚ฌ๊ฑด์ด ์ผ์–ด๋‚ฌ๋‹ค.', '์ขŒ์šฐ ํ•ฉ์ž‘ ์œ„์›ํšŒ๊ฐ€ ๊ฒฐ์„ฑ๋˜์—ˆ๋‹ค.', '๋‚จ๋ถ ๊ธฐ๋ณธ ํ•ฉ์˜์„œ๊ฐ€ ์ฑ„ํƒ๋˜์—ˆ๋‹ค.', '๊ตญ๊ฐ€ ์žฌ๊ฑด ์ตœ๊ณ  ํšŒ์˜๊ฐ€ ์กฐ์ง๋˜์—ˆ๋‹ค.', '6โ€ค15๋‚จ๋ถ ๊ณต๋™ ์„ ์–ธ์ด ๋ฐœํ‘œ๋˜์—ˆ๋‹ค.'], 'answer': ''}",,3,3,True,[],2 +2025-history-19,"์–ด์ œ 21์ผ ๊น€๊ทœ์‹์€ ์ถœ๋ฐœ์— ์•ž์„œ ์ด๋ฒˆ ๋ถํ–‰(ๅŒ—่กŒ)์— ๋Œ€ํ•œ ์†Œ๊ฒฌ์„ ๋‹ค์Œ๊ณผ ๊ฐ™์ด ํ”ผ๋ ฅํ•˜์˜€๋‹ค. โ€œ๋‚˜์™€ ๊น€๊ตฌ ์„ ์ƒ์€ ์šฐ๋ฆฌ์˜ ์†์œผ๋กœ์จ ์กฐ๊ตญ์„ ํ†ต์ผ์‹œ์ผœ์•ผ ํ•œ๋‹ค๋Š” ๋ฐ์„œ ๋‚จ๋ถ ํ˜‘์ƒ์„ ์ œ์•ˆํ•˜์˜€๋˜ ๊ฒƒ์ด๋‹ค. โ€ฆ (์ค‘๋žต) โ€ฆ ์šฐ๋ฆฌ๋Š” ์•ˆ์œผ๋กœ ๋ฏผ์กฑ์˜ ํ†ต์ผ์„ ์„ฑ์ทจ์‹œํ‚ค๊ณ , ๋ฐ–์œผ๋กœ ์—ฐํ•ฉ๊ตญ์˜ ํ˜‘์กฐ๋ฅผ ํ†ตํ•˜์—ฌ ์šฐ๋ฆฌ์˜ ์ž์ฃผ๋…๋ฆฝ์„ ์ด๋ฃจ๊ธฐ ์œ„ํ•˜์—ฌ ๋‹ค์Œ๊ณผ ๊ฐ™์€ ์›์น™์„ ์ œ์‹œํ•  ์˜ˆ์ •์ด๋‹ค. 1. ์–ด๋– ํ•œ ํ˜•ํƒœ์˜ ๋…์žฌ ์ •์น˜๋ผ๋„ ์ด๋ฅผ ๋ฐฐ๊ฒฉํ•˜๊ณ  ์ง„์ •ํ•œ ๋ฏผ์ฃผ์ฃผ์˜ ๊ตญ๊ฐ€๋ฅผ ๊ฑด๋ฆฝํ•  ๊ฒƒ. โ€ฆ (์ค‘๋žต) โ€ฆ 5. ๋ฏธโ€ค์†Œ ์–‘๊ตฐ์˜ ์กฐ์†ํ•œ ์ฒ ์ˆ˜์— ๊ด€ํ•ด์„œ๋Š” ์šฐ์„  ์–‘๊ตฐ ๋‹น๊ตญ์ด ์ฒ ์ˆ˜ ์กฐ๊ฑด ๋ฐ ๊ธฐ์ผ ๋“ฑ์„ ํ˜‘์ •ํ•˜์—ฌ ๊ณตํฌํ•˜๋ผ๊ณ  ์ฃผ์žฅํ•  ๊ฒƒ.โ€ ์กฐ์„  ๊ฑด๊ตญ ๋™๋งน ์กฐ์ง - (๊ฐ€) - 8ยท15 ๊ด‘๋ณต - (๋‚˜) - ์ œํ—Œ ๊ตญํšŒ ์ถœ๋ฒ” - (๋‹ค) - ์ •์ „(ํœด์ „) ํ˜‘์ • ์ฒด๊ฒฐ - (๋ผ) - 5ยท16 ๊ตฐ์‚ฌ ์ •๋ณ€ - (๋งˆ) - 3์„  ๊ฐœํ—Œ","{'question': '๋‹ค์Œ ์ƒํ™ฉ์ด ๋‚˜ํƒ€๋‚œ ์‹œ๊ธฐ๋ฅผ ์—ฐํ‘œ์—์„œ ์˜ณ๊ฒŒ ๊ณ ๋ฅธ ๊ฒƒ์€?', 'choices': ['(๊ฐ€)', '(๋‚˜)', '(๋‹ค)', '(๋ผ)', '(๋งˆ)'], 'answer': ''}",,2,3,False,['(๊ฐ€)์ขŒ์šฐํ•ฉ์ž‘ 7์›์น™์ด ๋ฐœํ‘œ๋˜์—ˆ๋‹ค. (๋‚˜)์กฐ์„  ๊ฑด๊ตญ ์ค€๋น„ ์œ„์›ํšŒ๊ฐ€ ๊ฒฐ์„ฑ๋˜์—ˆ๋‹ค. (๋‹ค)๋ชจ์Šคํฌ๋ฐ” 3๊ตญ ์™ธ์ƒ ํšŒ์˜๊ฐ€ ๊ฐœ์ตœ๋˜์—ˆ๋‹ค. (๋ผ)๊น€๊ตฌ์™€ ๊น€๊ทœ์‹์ด ๋‚จ๋ถํ˜‘์ƒ์„ ์ œ์˜ํ•˜์˜€๋‹ค. (๊ฐ€)๏ฝž(๋ผ)๋ฅผ ์‹œ๊ธฐ์ˆœ์œผ๋กœ ๋ฐ”๋ฅด๊ฒŒ ๋‚˜์—ดํ•œ ๊ฒƒ์€? Answer: (๋‚˜)โ†’(๋‹ค)โ†’(๊ฐ€)โ†’(๋ผ)'],1 +2025-history-20,"์ €๋Š” ๊ฒฝ์ œ ๋Œ€๊ตญ์œผ๋กœ ๋ฐœ์ „ํ•ด ๊ฐ€๋Š” ํ•œ๊ตญ์ด '๋‚˜์˜ ๊ณ ํ–ฅ'์ด๋ผ๋Š” ์ž๋ถ€์‹ฌ์„ ๊ฐ„์งํ•˜์—ฌ ์™”์Šต๋‹ˆ๋‹ค. ๋œป๋ฐ–์—๋„ ๊ณ ๊ตญ์ด ๊ฒฝ์ œ ์œ„๊ธฐ์— ์ฒ˜ํ–ˆ๋‹ค๋Š” ์†Œ์‹์„ ๋“ฃ๊ณ  ํฌ๊ฒŒ ๋†€๋ž์Šต๋‹ˆ๋‹ค. ๋œป๋ฐ–์—๋„ ๊ณ ๊ตญ์ด ๊ฒฝ์ œ ์œ„๊ธฐ์— ์ฒ˜ํ–ˆ๋‹ค๋Š” ์†Œ์‹์„ ๋“ฃ๊ณ  ํฌ๊ฒŒ ๋†€๋ž์Šต๋‹ˆ๋‹ค. ์–ด๋А๋ง ํ•ด๊ฐ€ ๋ฐ”๋€Œ๋ฉด์„œ '์•„์ด์— ์—ํ”„(IMF)' ํ•œํŒŒ๋กœ ๊ฝ๊ฝ ์–ผ์–ด๋ถ™์—ˆ๋˜ ๊ณ ๊ตญ์˜ ๊ฒฝ์ œ ์ƒํ™ฉ์ด ์กฐ๊ธˆ์”ฉ ํ’€๋ ค ๊ฐ€๊ณ  ์žˆ๋‹ค๋Š” ๋ณด๋„๊ฐ€ ๋ฌด์„  ์ „ํŒŒ๋ฅผ ํ†ตํ•˜์—ฌ ์ด๊ณณ ๋จธ๋‚˜๋จผ ๋งŒ์ฃผ ๋•…๊นŒ์ง€ ์ „ํ•ด์ ธ ์˜ค๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ...(์ค‘๋žต)... ์ €๋Š” ํ•ด์™ธ ๋™ํฌ์˜ ์ผ์›์œผ๋กœ์„œ, ๋”์šฑ์ด ์กฐ๊ตญ ๋…๋ฆฝ์„ ์œ„ํ•˜์—ฌ ์ผ์ œ์™€ ์šฉ๊ฐํžˆ ์‹ธ์šฐ๋‹ค ์ˆœ๊ตญํ•˜์‹  ๋…๋ฆฝ์šด๋™๊ฐ€์˜ ํ›„์†์œผ๋กœ์„œ ๊ณ ๊ตญ์˜ ๊ฒฝ์ œ ์œ„๊ธฐ์— ์กฐ๊ทธ๋งˆํ•œ ๋„์›€์ด๋ผ๋„ ๋˜๊ธฐ๋ฅผ ๋ฐ”๋ผ๋ฉด์„œ ๋ฏธํ™” 30๋‹ฌ๋Ÿฌ๋ฅผ ๋ณด๋‚ด์˜ค๋‹ˆ ๋ฐ˜๊ฐ€์ด ๋ฐ›์•„ ์ฃผ์‹ญ์‹œ์˜ค. -1998๋…„ 1์›”, ์ค‘๊ตญ ํ—ค์ด๋กฑ์žฅ์„ฑ์—์„œ-","{'question': '๋‹ค์Œ ์ž๋ฃŒ๋ฅผ ํ™œ์šฉํ•œ ํƒ๊ตฌ ์ฃผ์ œ๋กœ ๊ฐ€์žฅ ์ ์ ˆํ•œ ๊ฒƒ์€?', 'choices': ['์™ธํ™˜ ์œ„๊ธฐ์˜ ๊ทน๋ณต ๋…ธ๋ ฅ', '์•”ํƒœ๋„ ์†Œ์ž‘ ์Ÿ์˜์˜ ์ „๊ฐœ', 'ํ™”ํ ์ •๋ฆฌ ์‚ฌ์—… ์‹ค์‹œ์˜ ์˜ํ–ฅ', '๋™์–‘ ์ฒ™์‹ ์ฃผ์‹ํšŒ์‚ฌ์˜ ์„ค๋ฆฝ ๋ชฉ์ ', '์ œ1์ฐจ ๊ฒฝ์ œ ๊ฐœ๋ฐœ 5๊ฐœ๋…„ ๊ณ„ํš์˜ ์ถ”์ง„ ๋ฐฐ๊ฒฝ'], 'answer': ''}",,1,1,True,[],1 diff --git a/streamlit/assets/score.csv b/streamlit/assets/score.csv new file mode 100644 index 0000000..bb7df36 --- /dev/null +++ b/streamlit/assets/score.csv @@ -0,0 +1,9 @@ +๊ณผ๋ชฉ,์ด ๋ฌธ์ œ ์ˆ˜,SOTA ์ •๋‹ต๋ฅ  +๊ตญ์–ด,45,88.89 +ํ•œ๊ตญ์‚ฌ,20,65.0 +์ •์น˜์™€ ๋ฒ•,13,61.54 +์‚ฌํšŒ ๋ฌธํ™”,12,66.67 +์œค๋ฆฌ์™€ ์‚ฌ์ƒ,20,60.0 +๋™์•„์‹œ์•„์‚ฌ,11,45.45 +์ƒํ™œ๊ณผ ์œค๋ฆฌ,13,92.31 +์„ธ๊ณ„์‚ฌ,12,41.67 diff --git a/streamlit/assets/score.jpg b/streamlit/assets/score.jpg new file mode 100644 index 0000000..dccdc17 Binary files /dev/null and b/streamlit/assets/score.jpg differ diff --git a/streamlit/assets/tutorial.png b/streamlit/assets/tutorial.png new file mode 100644 index 0000000..1ab6db1 Binary files /dev/null and b/streamlit/assets/tutorial.png differ diff --git a/streamlit/home.py b/streamlit/home.py new file mode 100644 index 0000000..50b42dc --- /dev/null +++ b/streamlit/home.py @@ -0,0 +1,32 @@ +from PIL import Image +import streamlit as st + +st.set_page_config( + page_title='Generation for NLP - Korean SAT', + layout="wide", + initial_sidebar_state="expanded", +) + +st.sidebar.page_link("home.py", label="๐Ÿ  Home") +st.sidebar.page_link("pages/architecture.py", label="๐Ÿข Architecture") +st.sidebar.page_link("pages/demo.py", label="๐Ÿค– Demo") + +st.header("Lv.2 Generation for NLP - 2025ํ•™๋…„๋„ ์ˆ˜๋Šฅ with RAG") + +st.subheader("๊ฐœ์š”") +img = Image.open("assets/tutorial.png").resize((900, 300)) +st.image(img) + +st.markdown("##### ๋ชฉํ‘œ") +st.markdown("์ž‘์€ ๊ทœ๋ชจ์˜ ๋ชจ๋ธ์„ ์‚ฌ์šฉํ•˜์—ฌ ์ˆ˜๋Šฅ ๋ฌธ์ œ๋ฅผ ์ •ํ™•ํ•˜๊ฒŒ ํ’€ ์ˆ˜ ์žˆ๋Š” AI ๋ชจ๋ธ ๊ฐœ๋ฐœ") + +st.markdown("##### ํ‰๊ฐ€ ์ง€ํ‘œ") +st.markdown("Accuracy = ๋ชจ๋ธ์ด ๋งž์ถ˜ ๋ฌธ์ œ์˜ ์ˆ˜ / ์ „์ฒด ๋ฌธ์ œ์˜ ์ˆ˜") + +st.markdown("##### ๋ฐ์ดํ„ฐ") +st.markdown("""**์ž…๋ ฅ:** ์ˆ˜๋Šฅ ๊ตญ์–ด์™€ ์‚ฌํšŒ ๊ณผ๋ชฉ์˜ ์ง€๋ฌธ ํ˜•ํƒœ, `id`, `paragraph`, `problems(question, choices, answer)`, `question_plus` + **์ถœ๋ ฅ:** ์ฃผ์–ด์ง„ ์„ ํƒ์ง€(choices) ์ค‘์—์„œ ์ •๋‹ต์— ํ•ด๋‹นํ•˜๋Š” ๋ฒˆํ˜ธ ์ถœ๋ ฅ""") + +st.markdown("##### ์˜์˜") +st.markdown("""1. ์ˆ˜๋Šฅํ˜• ๋ฌธ์ œ๋ฅผ ์ •ํ™•ํžˆ ํ’€์–ด๋ƒ„์œผ๋กœ์จ ์ž์—ฐ์–ด ์ดํ•ด๋Š” ๋ฌผ๋ก  ๋ชจ๋ธ์˜ ๋งฅ๋ฝ ํŒŒ์•…, ๋…ผ๋ฆฌ์  ์ถ”๋ก , ์ •๋ณด์˜ ์ข…ํ•ฉ ๋Šฅ๋ ฅ์„ ๋ณด๋‹ค ๋ณตํ•ฉ์ ์œผ๋กœ ํ‰๊ฐ€ํ•˜๊ณ  ํ–ฅ์ƒ์‹œํ‚จ๋‹ค. +2. ๋ณด๋‹ค ์ž‘์€ ๋ชจ๋ธ๋กœ ๋ณธ ํ”„๋กœ์ ํŠธ๋ฅผ ์ˆ˜ํ–‰ํ•˜๋ฉฐ ์ž์›์˜ ํšจ์œจ์„ฑ, ์‹ค์šฉ์„ฑ์„ ๋‹ฌ์„ฑํ•˜๋ฉฐ ํ™•์žฅ ๊ฐ€๋Šฅ์„ฑ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ๋‹ค.""") diff --git a/streamlit/pages/architecture.py b/streamlit/pages/architecture.py new file mode 100644 index 0000000..9871d0c --- /dev/null +++ b/streamlit/pages/architecture.py @@ -0,0 +1,21 @@ +from PIL import Image +import streamlit as st + +st.set_page_config( + page_title='Generation for NLP - Korean SAT', + layout="wide", + initial_sidebar_state="expanded", +) + +st.sidebar.page_link("home.py", label="๐Ÿ  Home") +st.sidebar.page_link("pages/architecture.py", label="๐Ÿข Architecture") +st.sidebar.page_link("pages/demo.py", label="๐Ÿค– Demo") + + +st.header("Lv.2 Generation for NLP - 2025ํ•™๋…„๋„ ์ˆ˜๋Šฅ with RAG") + +st.subheader("๊ตฌ์กฐ๋„") +# st.markdown("""**Retriever:** Sparse & Dense Retriever โ†’ Re-Ranker +# **Reader:** `itsmenlp/unsloth_qwen_2.5_32B_bnb_4bit_finetuned`""") +arc_img = Image.open("assets/architecture.png") +st.image(arc_img) diff --git a/streamlit/pages/demo.py b/streamlit/pages/demo.py new file mode 100644 index 0000000..22f1abb --- /dev/null +++ b/streamlit/pages/demo.py @@ -0,0 +1,130 @@ +import time +import pandas as pd +from PIL import Image +import streamlit as st +from ast import literal_eval + +st.set_page_config( + page_title='Generation for NLP - Korean SAT', + layout="wide", + initial_sidebar_state="expanded", +) + +st.sidebar.page_link("home.py", label="๐Ÿ  Home") +st.sidebar.page_link("pages/architecture.py", label="๐Ÿข Architecture") +st.sidebar.page_link("pages/demo.py", label="๐Ÿค– Demo") + +KEYS = ["2025-korean-01", "2025-history-03", "2025-history-13"] +for key in KEYS: + if key not in st.session_state: + st.session_state[key] = False + +ksat_df = pd.read_csv("assets/ksat_dataset.csv") + +st.header("Lv.2 Generation for NLP - 2025ํ•™๋…„๋„ ์ˆ˜๋Šฅ with RAG") +st.subheader("2025ํ•™๋…„๋„ ์ˆ˜๋Šฅ ๊ตญ์–ด, ์‚ฌํšŒ ์˜์—ญ ๋ฌธ์ œ ํ’€์ด") + +def ksat_demo(ksat_id): + target = ksat_df.loc[ksat_df["id"]==ksat_id] + references = literal_eval(target["reference"].values[0]) + + st.markdown( + f""" +

+
+

์ •๋‹ต

+

{target["answer_true"].values[0]}

+
+ """, + unsafe_allow_html=True, + ) + + st.write(" ") + if st.button("๋‹จ์ผ ๋ชจ๋ธ ๊ฒฐ๊ณผ", key=ksat_id+"-only"): + st.session_state[ksat_id] = True + if st.session_state[ksat_id]: + time.sleep(2) + st.markdown( + f""" +
+

๋‹จ์ผ ๋ชจ๋ธ ์˜ˆ์ธก ๊ฒฐ๊ณผ

+

{target["answer_pred"].values[0]}

+
+ """, + unsafe_allow_html=True + ) + st.write(" ") + + if ksat_id != KEYS[0]: + if st.button("RAG ๊ฒฐ๊ณผ", key=ksat_id+"-rag"): + time.sleep(3) + + st.markdown( + f""" +
+

์ฐธ๊ณ  ๋ฌธ์„œ

+
+ """, + unsafe_allow_html=True + ) + + for i, reference in enumerate(references): + st.markdown( + f""" +
+

{i+1}๋ฒˆ์งธ
{reference}

+
+ """, + unsafe_allow_html=True + ) + + st.markdown( + f""" +
+

RAG ์˜ˆ์ธก ๊ฒฐ๊ณผ

+

{target["answer_rag"].values[0]}

+
+ """, + unsafe_allow_html=True + ) + +score_tab, korean_tab, social_tab1, social_tab2 = st.tabs(["๐ŸŽฏ ์ ์ˆ˜", "๐Ÿ“– ๊ตญ์–ด", "๐Ÿ“ฐ ํ•œ๊ตญ์‚ฌ 1", "๐Ÿ“ฐ ํ•œ๊ตญ์‚ฌ 2"]) +with score_tab: + st.subheader("์ฑ„์  ๊ฒฐ๊ณผ") + # score_df = pd.read_csv("assets/score.csv").set_index("๊ณผ๋ชฉ") + # st.dataframe(score_df, width=400) + score_img = Image.open("assets/score.jpg").resize((1100, 350)) + st.image(score_img) + +with korean_tab: + st.subheader("๊ตญ์–ด") + + kr_col1, kr_col2 = st.columns(2) + with kr_col1: + img = Image.open(f"assets/{KEYS[0]}.png").resize((380, 900)) + st.image(img) + + with kr_col2: + ksat_demo(KEYS[0]) + +with social_tab1: + st.subheader("ํ•œ๊ตญ์‚ฌ - ์˜ˆ์‹œ 1") + + so1_col1, so1_col2 = st.columns(2) + with so1_col1: + img =Image.open(f"assets/{KEYS[1]}.png").resize((450, 400)) + st.image(img) + + with so1_col2: + ksat_demo(KEYS[1]) + +with social_tab2: + st.subheader("ํ•œ๊ตญ์‚ฌ - ์˜ˆ์‹œ 2") + + so2_col1, so2_col2 = st.columns(2) + with so2_col1: + img =Image.open(f"assets/{KEYS[2]}.png").resize((450, 400)) + st.image(img) + + with so2_col2: + ksat_demo(KEYS[2]) \ No newline at end of file diff --git a/wandb/.gitkeep b/wandb/.gitkeep new file mode 100644 index 0000000..e69de29