The recursive part wasn't hard to sketch out, but I kept second-guessing myself on the edge cases.
Start by clarifying the schema specification and edge cases (e.g., optional fields, type coercion, nested structures). Then design a recursive validator that checks required keys, types, and nested schemas, discussing trade-offs between strictness and flexibility. Finally, implement and test with representative examples, mentioning performance considerations for large data.
Pro tip: In ML systems, schema validation is often a bottleneck; propose optimizations like caching compiled schemas or using vectorized checks for arrays, and highlight how validation errors should be actionable for debugging data pipelines.
Ask the interviewer about the schema format, expected types, handling of extra keys, optional fields, and error reporting. Confirm whether nested objects and arrays need validation.
Outline a recursive function that takes a schema and data, checks required keys, validates types, and recurses into nested schemas. Consider using a declarative schema representation (e.g., dict of field: type).
Discuss handling of None, missing keys, type mismatches (e.g., int vs float), and nested structures. Decide on strict vs. lenient validation and how to report multiple errors.
Write clean, modular code with helper functions for type checking. Test with valid and invalid examples, including nested objects and arrays, and verify error messages.
Talk about performance (e.g., recursion depth, large data), extensibility (e.g., adding custom validators), and integration with ML pipelines (e.g., data ingestion).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.