Start by clarifying the requirements and edge cases with the interviewer, then design a modular validator where each rule is a separate function. Implement the rules efficiently, considering data structures like sets for membership checks, and discuss trade-offs such as early exit versus collecting all errors.
Pro tip: Demonstrate production awareness by discussing how to handle large datasets (e.g., streaming validation) and how to make rules configurable rather than hardcoded, which is crucial for Stripe's dynamic KYC requirements.
Ask questions to understand the exact constraints: what defines an empty field, the specific length constraint, the forbidden words list, and the cross-column membership rule. Confirm expected output format (e.g., list of errors) and performance requirements.
Propose a design where each rule is a separate function or class, allowing easy addition or modification of rules. This separation of concerns makes the code testable and maintainable.
For each rule, choose appropriate data structures: e.g., sets for forbidden words and column word sets to achieve O(1) lookups. For the cross-column rule, precompute sets of words from the two columns and check each word in column 2.
Consider empty records, null values, case sensitivity, and whitespace. Decide whether to fail fast or collect all errors. Discuss how to report errors clearly (e.g., with record index and rule violated).
Talk about time/space complexity, and how the solution scales with large K. Mention potential optimizations like parallel processing or streaming validation, and trade-offs between strictness and flexibility.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through sets for O(1) lookup versus tries if you need prefix matching, and mentioned short-circuiting as soon as any rule fails so you're not doing unnecessary work.
Start by clarifying the current implementation and constraints (dictionary size, record volume, latency/throughput goals). Then propose targeted optimizations for word lookup (e.g., tries, hash maps, bloom filters), early termination (e.g., pruning, short-circuit evaluation), and scaling across many records (e.g., sharding, caching, parallelization). Finally, discuss trade-offs and how you would measure improvements.
Pro tip: Quantify the impact of each optimization with back-of-the-envelope estimates (e.g., memory, time complexity) and tie it to Stripe's scale and reliability requirements. Show you can prioritize based on effort vs. impact.
Ask about dictionary size, number of records, latency/throughput targets, memory limits, and whether updates are frequent. This ensures your optimizations are relevant.
Propose data structures like tries, hash maps, or bloom filters for O(1) or O(k) lookup. Consider memory vs. speed trade-offs and whether the dictionary fits in memory.
Suggest techniques like pruning search branches, short-circuit evaluation, or using heuristics to stop processing early when a match is impossible or sufficient.
Discuss sharding the dictionary, caching frequent lookups, parallelizing record processing, and using distributed systems if needed. Consider batch processing and streaming.
Compare time/space complexity, implementation complexity, and maintainability. Propose metrics (e.g., latency, throughput, memory) and A/B testing to validate improvements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.