This one took me a minute to even parse what they were asking.
First, parse the Markdown into a linear sequence of tokens (headers, paragraphs, code blocks, etc.) while tracking the active header hierarchy. Then, greedily accumulate tokens into chunks up to the size limit, and whenever a chunk starts mid-section, prepend the current header stack to that chunk. Finally, handle edge cases like oversized atomic blocks and ensure the header prefix doesn't cause the chunk to exceed the limit.
Pro tip: Mention that you would treat headers as a stack and that you'd write tests for edge cases like a single code block larger than the limit, deeply nested headers, and headers with no content. Also, discuss the trade-off between strict size enforcement and preserving Markdown semantics.
Convert the Markdown into a list of tokens (headers, paragraphs, lists, code blocks, etc.) while maintaining the active header stack. Use a Markdown parser or write a simple line-based parser that recognizes headers and block boundaries.
Decide on a greedy approach: accumulate tokens until adding the next would exceed the size limit. If a single token exceeds the limit, decide whether to split it (if possible) or allow it as an oversized chunk. Keep track of the current header stack at the start of each chunk.
For each chunk, if it does not start at the beginning of a section (i.e., the first token is not a header that resets the stack), prepend the active header stack (all parent headers) as Markdown lines. Ensure the prepended headers plus content fit within the limit; if not, adjust by moving content to the next chunk or splitting further.
Address cases like oversized code blocks, headers with no content, and deeply nested headers. Validate that each chunk is valid Markdown and that the concatenation of chunks (ignoring added headers) reconstructs the original document.
Discuss time and space complexity (O(n) where n is document length), and trade-offs between strict size adherence and preserving semantic structure. Mention potential optimizations like streaming or two-pass approaches.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.