The format changed to AI-assisted a few days before, so I went in expecting something completely different.
Start by clarifying the scope and constraints of the rule parser, then outline a modular design that can be built incrementally. Prioritize a working solution for the core parsing logic before handling edge cases, and communicate your progress and trade-offs clearly throughout.
Pro tip: Demonstrate strong time management by setting a timer for each sub-part and verbally noting when you're moving on, showing you can deliver under pressure. Also, mention how you would test each component early to catch issues before integration.
Ask questions to understand the rule format, expected input/output, performance requirements, and time limit. Confirm assumptions before diving in.
Break the parser into components: tokenizer, parser, evaluator. Sketch a simple design that allows incremental development and testing.
Focus on getting a basic version working for the most common cases. Use simple data structures and avoid over-engineering.
Once core works, address edge cases like nested rules, error handling, and performance. Discuss trade-offs if time is limited.
Write quick tests for each component and explain your reasoning and progress to the interviewer. Be transparent about what you would do with more time.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.