Twenty minutes of clarifying questions and I still couldn't pin down what the functional requirements were.
Start by explicitly stating your assumptions about the system's purpose, scale, and constraints, then propose a high-level design and iterate based on feedback. Treat the vagueness as an opportunity to demonstrate structured thinking and proactive clarification.
Pro tip: Even if the interviewer doesn't answer your clarifying questions, verbalize your assumptions and proceed—this shows you can make progress under ambiguity, a key trait for senior engineers.
Ask targeted questions about functional and non-functional requirements, but be prepared to state your own assumptions if answers are vague. Focus on scale, latency, consistency, and key features.
Sketch the main components (e.g., clients, load balancers, services, databases, caches) and how they interact. Keep it abstract first, then dive deeper.
Choose 1-2 components to detail based on the assumed requirements (e.g., data storage, scaling strategy). Discuss trade-offs and alternatives.
Identify potential bottlenecks (e.g., database, network) and explain how to mitigate them (e.g., sharding, caching, async processing). Discuss CAP theorem trade-offs if relevant.
Recap the design, highlighting how it meets the assumed requirements. Invite feedback and be ready to adjust based on interviewer input.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The actual underlying problem was basic, just aggregation with a map.
First, clarify the obscure built-in's behavior and edge cases, then outline a plan to read the file line by line, build a map for aggregation, and compute the final result. Emphasize clean code, error handling, and discussing trade-offs like memory usage versus simplicity.
Pro tip: Mention that you'd verify the obscure built-in's documentation and test it with a small sample before scaling, showing you prioritize correctness over cleverness. Also, discuss how you'd handle large files by streaming rather than loading everything into memory.
Ask about file size, format, expected output, and any performance constraints. Confirm the obscure built-in's purpose and limitations.
Decide on the map structure (e.g., hash map) and the aggregation function (sum, count, average). Consider edge cases like missing keys or malformed lines.
Use the obscure built-in to read the file, parse each line, and update the map. Handle exceptions and ensure resource cleanup.
Iterate over the map to produce the final aggregated result, formatting it as required. Discuss time and space complexity.
Walk through test cases (empty file, large file, invalid data) and suggest optimizations like using a more efficient data structure or parallel processing if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.