Start by clarifying the CSV format, fee rules, and discount thresholds. Then outline a solution that parses each row, tracks per-buyer transaction counts to apply volume discounts, and computes fees using a provider-country rate table. Emphasize preserving input order and handling edge cases like malformed rows or missing data.
Pro tip: Mention that you would separate parsing, fee calculation, and discount logic into distinct functions or classes to make the code testable and maintainable, and discuss how you'd handle large inputs efficiently.
Ask about the CSV schema, fee rules per provider and country, discount thresholds, and how to handle invalid or missing data. Confirm that results must be in the original order.
Choose a CSV parser (or write a simple one) and define structures to hold transaction data and per-buyer counts. Consider using a dictionary keyed by buyer ID to track transaction counts.
For each transaction, look up the base fee by provider and country, then apply any volume discount based on the buyer's cumulative transaction count. Update the buyer's count after processing.
Process rows sequentially to maintain input order, and include error handling for malformed rows, unknown providers/countries, or missing fields.
Write unit tests for typical and edge cases, and discuss time/space complexity. Mention potential optimizations like streaming for large files.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.