I went with highest-bonus wins and documented it as the policy, which felt cleaner than first-match since first-match is basically arbitrary depending on list order.
Start by clarifying the requirements: how bonuses are configured, whether they stack, and what the expected behavior is for overlapping windows. Then propose a data model and algorithm that resolves overlaps deterministically, such as selecting the highest applicable bonus or the most specific window, and discuss trade-offs like performance, maintainability, and edge cases.
Pro tip: Mention that you would make the overlap resolution rule configurable or at least clearly documented, because pricing rules often change and hardcoding a single strategy can lead to costly refactors.
Ask about the bonus configuration format, whether bonuses can stack, and what the desired behavior is for overlapping windows (e.g., highest bonus, first match, sum). Confirm if windows are inclusive/exclusive and if time zones matter.
Represent each bonus window with start time, end time, bonus amount/rate, and priority. Consider storing them in a list sorted by start time or in an interval tree for efficient lookup.
Choose a deterministic rule: e.g., apply the bonus with the highest priority, or the one with the highest value, or the most specific (shortest) window. Document it clearly and make it configurable if possible.
Given a delivery time, find all windows containing that time, then apply the resolution rule to select the bonus. Ensure the base pay calculation remains unchanged and the bonus is added correctly.
Talk about performance (e.g., O(n) scan vs. interval tree), handling of boundary times, overlapping windows with equal priority, and how to test the logic thoroughly.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where I actually felt more comfortable.
Start by clarifying the business context and requirements: pay accuracy is critical, so invalid input must be handled carefully to avoid incorrect payments. Then propose a tiered validation strategy that rejects clearly invalid inputs, falls back to safe defaults where appropriate, and computes partial pay only when the missing data doesn't affect the core calculation. Emphasize logging, monitoring, and user feedback to handle edge cases gracefully.
Pro tip: Frame your answer around the principle of 'fail fast, but fail safe'—reject invalid inputs early with clear error messages, but design fallbacks for non-critical fields to avoid blocking payments. Also, mention that you'd collaborate with product and legal to define the policy, since pay calculations have compliance implications.
Ask about the business impact of incorrect pay, regulatory requirements, and whether partial pay is acceptable. Understand the data sources and typical failure modes.
Classify inputs as critical (e.g., pay rate, hours) vs. non-critical (e.g., optional modifiers). Specify validation for each field: type, range, format, and cross-field consistency.
For critical invalid inputs, reject with a clear error. For non-critical, use safe defaults or omit. For partial data, compute pay only if the missing fields don't affect the core calculation; otherwise, flag for manual review.
Log all validation failures with context, monitor rates of invalid inputs, and alert on spikes. This helps identify upstream issues and improve data quality.
Return descriptive error messages to the caller, and where possible, suggest corrections. For partial pay, indicate what was missing and how to resolve it.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.