The base problem took maybe 10 minutes, just iterate and do a dict lookup.
Start by clarifying the input format and fee table structure, then propose a straightforward solution using a hash map for the fee lookup. For follow-ups, discuss streaming with incremental aggregation, per-merchant grouping using a dictionary, and multi-currency normalization by converting amounts to a common currency using exchange rates.
Pro tip: Mention that in a real system, fee rates might change over time, so you'd need to version the fee table or include effective dates. Also, consider edge cases like negative amounts (refunds) and rounding rules for currency conversion.
Ask about the data format, fee table structure, and whether transactions are processed in batches or streams. Confirm if multi-currency involves fixed or dynamic exchange rates.
Use a hash map to store fee rates keyed by (status, type). Iterate through transactions, look up the fee rate, and accumulate the total fee. Handle missing entries by contributing zero.
For streaming, maintain a running total and update it as each transaction arrives. If per-merchant aggregation is needed, keep a dictionary mapping merchant IDs to their totals.
Convert each transaction amount to a common currency using exchange rates before applying the fee. Ensure consistent rounding and consider using decimal arithmetic to avoid floating-point errors.
Talk about time/space complexity, potential bottlenecks (e.g., frequent currency conversions), and how to handle late-arriving data or out-of-order events in streaming.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.