Got through the core logic fine but the last two problems ran out of time.
Start by clarifying the data model: transactions have amounts, timestamps, and IDs; expected balances are per account. Then design a reconciliation algorithm that aggregates transactions per account and compares to expected balances, handling edge cases like missing transactions, duplicates, and timing differences. Discuss scalability and data integrity, and propose a system that can run in batch or real-time.
Pro tip: Emphasize idempotency and auditability: in financial systems, reconciliation must be repeatable and produce an audit trail. Mention that discrepancies should be categorized (e.g., missing, extra, amount mismatch) to aid resolution.
Ask about the structure of transactions and expected balances, including identifiers, timestamps, currencies, and whether balances are per account or global. Clarify if reconciliation is real-time or batch, and what constitutes a discrepancy.
Propose aggregating transactions by account and summing amounts, then comparing to expected balances. Handle edge cases: missing transactions, duplicates, timing differences, and currency conversion. Consider using a hash map for O(n) efficiency.
Discuss how to handle large volumes: use streaming or batch processing, partition by account, and leverage distributed systems if needed. Mention time and space complexity, and potential bottlenecks.
Incorporate idempotency, validation, and audit logging. Define how to report discrepancies: categorize them (e.g., missing, extra, amount mismatch) and provide actionable output.
Outline unit tests for edge cases, integration tests with sample data, and monitoring for reconciliation failures. Suggest metrics like discrepancy rate and processing time.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.