The missing product ID edge case is where I spent way too long deliberating.
Start by clarifying requirements: input types, expected output format, and error handling expectations. Then outline a solution that iterates through line items, looks up prices, accumulates totals, and handles missing product IDs gracefully (e.g., skip, default price, or raise error). Finally, discuss trade-offs and edge cases.
Pro tip: Demonstrate production awareness by suggesting logging missing product IDs and returning a structured error or warning, rather than silently failing. This shows you consider observability and user experience.
Ask about input validation, expected behavior for missing IDs, and output format (e.g., include missing items in breakdown?). Confirm whether to fail fast or continue processing.
Iterate over line items, look up each product ID in the price dictionary, multiply price by quantity, and accumulate total. Track per-item breakdown with product ID, quantity, unit price, and subtotal.
Decide on a strategy: skip item, use a default price (e.g., 0), or raise an error. Consider returning a list of missing IDs for visibility. Discuss trade-offs of each approach.
Write clean code with clear variable names. Test with empty list, zero quantities, negative quantities, duplicate product IDs, and missing IDs. Ensure floating-point precision is handled (e.g., use Decimal for currency).
Mention time complexity O(n) and space O(n) for breakdown. If this is part of a larger system, discuss caching, batch processing, or API error handling.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The duplicate product ID question tripped me up.
Start by clarifying the function's contract and error-handling expectations, then design a validation layer that checks each row for numeric, non-negative, finite values and reports all errors with row context. Handle empty input and duplicate product IDs explicitly, and discuss trade-offs between failing fast and collecting all errors.
Pro tip: Mention that you'd collect all validation errors into a structured list (e.g., row number, field, reason) rather than throwing on the first error, because that's what production systems like Stripe's API do to help users fix multiple issues at once.
Ask about the expected input format, whether validation should fail fast or aggregate errors, and how duplicate product IDs should be handled (reject, merge, or last-wins).
For each row, verify quantity and price are numeric, non-negative, and finite (reject NaN, Infinity, -Infinity). Use Number.isFinite() and type checks to avoid coercion pitfalls.
Return an empty result or appropriate message for empty input. For duplicate product IDs, decide on a policy and implement it with a Set or Map, producing a clear error if duplicates are invalid.
Include row index, field name, and the invalid value in each error message. Aggregate errors into a list so the caller can see all issues at once.
Explain why you chose aggregation vs. fail-fast, and outline unit tests for edge cases like empty input, NaN, Infinity, negative numbers, and duplicates.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements: what 'breakdown' refers to, the data structure, and the expected output format. Then, design a function that accepts an optional boolean sort parameter; when true, sort the breakdown items by per-item cost descending using a lambda key. Explicitly document rounding rules (e.g., round half up to 2 decimal places) and ensure the sorted output matches expected results by testing with sample data.
Pro tip: Mention that you would use a stable sort to preserve original order for equal costs, and that you'd add unit tests to verify rounding and sorting behavior, especially for edge cases like zero or negative costs.
Ask questions to understand the breakdown structure, the definition of per-item cost, and the expected output format. Confirm rounding rules and whether sorting should be ascending or descending.
Define a function that takes the breakdown and an optional boolean parameter (e.g., sort=False). When sort is True, apply sorting; otherwise, return the breakdown as-is.
Use a lambda function as the key to sort by per-item cost in descending order. For example: sorted(breakdown, key=lambda item: item['cost'], reverse=True).
Clearly state how per-item costs are rounded (e.g., round half up to 2 decimal places) and ensure the sorting uses the rounded values if rounding is applied before sorting.
Write test cases with sample breakdowns to verify that the sorted output matches expected results, including edge cases like equal costs and varying rounding scenarios.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.