This one took me a few minutes to even parse what was being asked.
Start by clarifying requirements and edge cases, then outline a pipeline: normalize names via alias map, aggregate votes per routing number across sources, resolve conflicts using majority vote with source priority tiebreaker, and generate a conflict report. Emphasize data structures (hash maps, priority queues) and discuss trade-offs like handling ties, scalability, and data quality.
Pro tip: Proactively discuss how you would handle ties that persist after majority vote and source priority, such as falling back to the most recent source or flagging for manual review, showing you think about real-world data ambiguity.
Ask about alias map format, source priority definition, tiebreaker rules, and expected output format. Confirm handling of missing data, case sensitivity, and performance constraints.
Use a hash map to apply aliases and normalize names. Aggregate votes per routing number across sources, storing counts per canonical bank and tracking source priority for tiebreaking.
For each routing number, select the bank with the most votes. If tied, use source priority (e.g., higher priority source wins) or a predefined tiebreaker like lexicographic order.
For routing numbers with multiple distinct banks, record the vote distribution and the final decision. Include details like which sources voted for which bank.
Talk about time/space complexity, handling large datasets (streaming vs. in-memory), and potential improvements like caching or parallel processing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.