Took me a minute to internalize the spec-driven part.
Start by clarifying the requirements and edge cases, then propose a clean, extensible design that separates the verification spec from the validation logic. Walk through a concrete example to demonstrate correctness, and discuss trade-offs like performance, error handling, and scalability.
Pro tip: Emphasize that the spec should be data-driven (e.g., JSON schema) so that new fields or rules can be added without code changes, and mention that you'd return structured errors with field-level details for debugging and user feedback.
Ask about the expected input size, whether the spec is static or dynamic, and what the output format should be (e.g., per-field pass/fail, error messages). Confirm if partial validation is needed or if it should short-circuit.
Define a VerificationSpec that maps field names to validation rules (e.g., required, type, format, enum). Define a RecordResult that contains a map of field names to validation results and an overall boolean.
Iterate over each record and each field in the spec, applying the corresponding validator. Collect results per field, and determine overall validity by checking if all required fields passed.
Consider missing fields, null values, type mismatches, and invalid formats. Decide whether to fail fast or accumulate all errors. Ensure the output includes clear error messages for each failed field.
Discuss time complexity (O(n*m) where n is number of records and m is number of fields) and space complexity. Mention potential optimizations like pre-compiling validators or parallel processing for large datasets.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.