The justify-before-coding part tripped me up more than the actual implementation.
Start by clarifying requirements and defining a rule schema that separates rule evaluation logic from the data it operates on, then justify a return type that provides both the violating records and the reasons for violation. Design the engine around a registry of rule evaluators so new rule types can be added without modifying core evaluation code, and implement a clean evaluate_rules function with extensibility hooks for a future rule-creation API.
Pro tip: Explicitly discuss how you would version and validate rules created via the future API, and mention idempotency and auditability—these are critical for financial systems like expense/trip compliance at Rippling.
Ask about rule types, data volume, latency needs, and whether rules are static or dynamic. Confirm that the return type must support both violation detection and downstream actions like notifications or blocking.
Define a rule as a predicate over an expense/trip record with metadata (id, type, severity). Justify returning a list of violation objects containing the record, the rule that failed, and a human-readable reason, rather than just the records.
Use a registry or strategy pattern where each rule type maps to an evaluator function. Ensure the core evaluate_rules function iterates rules and delegates to evaluators, so new rule types can be added without changing the engine.
Write pseudocode or actual code for evaluate_rules, handling edge cases like empty rules or malformed records. Discuss performance (e.g., short-circuiting, batching) and how the design supports a future rule-creation API.
Explain how the rule schema and registry enable a CRUD API for rules, including validation, versioning, and testing. Mention observability (logging violations) and security (rule injection prevention).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.