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Start by clarifying the core requirements and constraints, then design a minimal but extensible data model using hash maps and balanced trees to support timestamped operations and historical queries. Walk through each operation with its data structures, return values, and time/space complexity, and finish with a testing plan that covers merge and activity edge cases.
Pro tip: Emphasize immutability and versioning for historical queries—use persistent data structures or event sourcing to avoid mutating past states, which also simplifies merge logic and testing.
Ask about expected scale, concurrency, timestamp granularity, and whether operations must be atomic. Confirm the exact semantics of two-phase accept and merge (e.g., what happens to pending transfers).
Propose a global timestamped event log or versioned state. Use a hash map for account lookup, a balanced BST or skip list for timestamp-ordered balances, and a pending transfers map for two-phase accept.
For each operation (create, deposit, pay, transfer, accept, merge, getBalance), specify inputs, outputs, error codes for invalid cases (e.g., insufficient funds, account not found, duplicate accept), and time/space complexity.
List unit tests for each operation, integration tests for merge and two-phase transfers, and edge cases: merging accounts with pending transfers, querying balance before/after merge, concurrent operations, and timestamp collisions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the data model and constraints (e.g., number of accounts, operations per timestamp, update frequency). Then propose an efficient solution using a hash map to aggregate activity per account at timestamp t, followed by a selection algorithm (e.g., min-heap or quickselect) to find the top k. Discuss trade-offs between time and space, and consider real-time vs. batch processing.
Pro tip: Mention that if queries are frequent for the same timestamp, precomputing and caching the top k per timestamp can be beneficial, but be mindful of memory and update costs. Also, highlight that the choice of algorithm depends on k relative to the number of active accounts.
Ask about the scale (number of accounts, operations per timestamp), whether operations are streaming or batch, and if multiple queries for the same timestamp occur. This determines the optimal approach.
Use a hash map to accumulate the sum of absolute operation amounts for each account that has operations exactly at timestamp t. This takes O(m) time where m is the number of operations at t.
Use a min-heap of size k to find the k accounts with highest activity in O(m log k) time, or quickselect for O(m) average time. Discuss trade-offs: heap is simpler and works well for small k; quickselect is faster but modifies the array.
If the same timestamp is queried often, consider precomputing and storing the top k for that timestamp. Alternatively, maintain a global data structure (e.g., balanced BST) that supports dynamic updates and top-k queries, but note the overhead.
Address memory vs. speed, handling ties, accounts with zero activity, and the impact of k being close to the number of active accounts. Also mention concurrency if operations are being added in real-time.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Easier part of the question but I still fumbled the merge case.
Start by clarifying that the exact return values depend on the API contract and error-handling philosophy—whether to return error codes, exceptions, or result objects. Then propose a consistent, predictable scheme for each invalid input scenario, justifying your choices with principles like idempotency, clarity, and client usability. Finally, discuss trade-offs and how you would document and test these behaviors.
Pro tip: Show that you think about error handling from the client's perspective: consistent, actionable errors reduce integration friction and support burden. Mention that you'd align with existing API conventions (e.g., HTTP status codes, gRPC status codes) to avoid surprising consumers.
Ask whether the API uses exceptions, error codes, or result types, and whether it follows REST, gRPC, or another style. This determines the shape of return values.
For depositing to a non-existent account, propose a clear error (e.g., 404 Not Found or AccountNotFound). For insufficient funds, suggest a specific error (e.g., 400 Bad Request or InsufficientFunds). For querying a merged account after merge, decide whether to return the merged account's state or an error (e.g., 410 Gone).
Use the same error format across operations and consider idempotency keys for deposits to avoid duplicate errors on retries. For merged accounts, define a deterministic rule (e.g., always return the surviving account).
Explain why you chose specific codes or exceptions: e.g., 404 vs 400 for non-existent account, or returning merged data vs error for historical queries. Discuss impact on clients and debugging.
Emphasize that these behaviors must be documented in the API spec and covered by unit and integration tests to prevent regressions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I structured this as a few scenario groups: basic lifecycle tests, then merge edge cases (re-creation after merge, querying the gap period, dst balance continuity), then activity boundary tests (same-timestamp multi-operation sums, merged accounts excluded from top_activity).
Start by clarifying the merge and activity edge cases in the banking service, then outline a structured unit testing plan that covers each case with specific test scenarios. Emphasize isolation, mocking, and edge case coverage while discussing trade-offs and adaptability.
Pro tip: Demonstrate maturity by discussing not just how to test but also why certain edge cases are critical for financial correctness and how you prioritize them based on risk.
Ask clarifying questions to understand the merge and activity edge cases, such as concurrent merges, partial failures, and activity logging during merges. Identify the specific behaviors that need testing.
Outline a unit testing strategy that isolates the merge and activity logic using mocks for dependencies like databases or external services. Plan for both positive and negative test cases.
List specific test scenarios for merge edge cases (e.g., merging accounts with overlapping transactions, merging during an active transaction) and activity edge cases (e.g., logging activities during merge, handling missing activity data).
Describe how you would implement these tests using a unit testing framework, ensuring they are fast, repeatable, and integrated into CI/CD. Mention techniques like parameterized tests for multiple edge cases.
Explain how you would review test coverage, possibly using mutation testing or code coverage tools, and iterate to cover newly discovered edge cases or changing requirements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.