Complete computer science general concepts section

computer-science/general-concepts/answers/marianne.md

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+What is the difference between synchronous and asynchronous processing? What is a Promise in JS? How are promises handled in JS? How do other languages (go, python, etc) handle asynchronous function calls?
+    A.  Synchronous code runs line by line - each step waits for the previous to finish. Asynchronous code can start a task and move on, handling the result later when it's ready. In JS, a Promise is an object representing a value that will exist in the future; you handle it with .then()/.catch() or async/await. Go uses goroutines (lightweight threads) and channels; Python uses asyncio with async/await syntax similar to JS.
+What is recursion? When would you use it?
+    A.  Recursion is when a function calls itself, breaking a problem into smaller versions of the same problem until it hits a base case. It's useful for tree traversal, nested structures, or problems naturally defined recursively (like factorial, Fibonacci, or searching through file directories).
+What is the difference between parallel and concurrent processing?
+    A.  Concurrent means multiple tasks are in progress at the same time, but not necessarily executing simultaneously - they might be interleaved (like one pauses while another runs). Parallel means multiple tasks are literally running at the same instant on different CPU cores. Concurrency is about structure; parallelism is about execution.
+What is an anonymous or lambda function? Why would you ever use this?
+    A.  A function without a name, defined inline. You use them for short, one-off operations - like a callback you'll only use once, or passing a quick transformation to .map() or .filter(). It keeps code concise when naming the function would add clutter without clarity.
+Why would you choose one language over another? Can you give an example scenario and trade off two languages?
+    A.  You choose based on the problem, ecosystem, performance needs, and team familiarity. Example: for a quick data analysis script, Python wins - tons of libraries (pandas, numpy), fast to write, readable. For a high-performance backend handling millions of requests, Go might be better - compiles to fast binaries, great concurrency model, but less mature data science ecosystem. Tradeoff is speed of development vs runtime performance.