The part nobody warned me about: the prompt transcript is graded.
Start by reproducing the failing scenario and tracing the transaction flow to pinpoint where the balance diverges, using the AI assistant to quickly navigate and analyze the code. Then, hypothesize the root cause (e.g., missing transaction boundaries, race conditions, or improper error handling) and verify with targeted tests. Finally, implement a fix that ensures atomicity and consistency, and validate with edge cases.
Pro tip: Demonstrate a systematic debugging process: articulate your hypotheses and how you'll test them before making changes. This shows you can leverage AI effectively without blindly trusting it, and that you understand the importance of transactional integrity in financial systems.
Run the failing scenario to observe the incorrect balance and gather details like input values, expected vs. actual outcomes, and any error logs. Use the AI assistant to quickly locate the relevant transaction code and understand the intended flow.
Trace the transaction execution path, focusing on database operations, transaction boundaries, and concurrency controls. Isolate the component where the balance becomes inconsistent, using logging or breakpoints.
Form a hypothesis about the root cause (e.g., missing transaction, race condition, incorrect isolation level, or error handling bug). Verify by writing a minimal test or using the AI to simulate the scenario and confirm the hypothesis.
Apply a fix that ensures atomicity and consistency, such as wrapping operations in a transaction, adding locking, or correcting error handling. Write tests to cover the original failure and edge cases like concurrent transfers.
Run the full test suite and the original scenario to confirm the fix. Reflect on whether similar issues could exist elsewhere and consider improvements like better monitoring or code review practices.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Felt like a gut-check on whether you actually understood the code or just got lucky with the fix.
First, demonstrate that you understand the existing codebase and the bug fix before proposing the new feature. Then, outline a step-by-step plan that integrates the feature without disrupting the fix, highlighting trade-offs and testing strategies. Finally, discuss how you would validate the feature and ensure it meets Uber's scalability and reliability standards.
Pro tip: Show that you prioritize code maintainability and minimal disruption: propose adding the feature behind a feature flag and include comprehensive tests to catch regressions. This demonstrates maturity and aligns with Uber's engineering culture of safe, incremental changes.
Review the codebase, the bug diagnosis, and the fix to ensure you fully grasp the current state and potential side effects.
Ask clarifying questions about the feature's expected behavior, performance requirements, and any constraints (e.g., backward compatibility, deadlines).
Propose a design that minimally impacts existing code, considering modularity, reusability, and potential trade-offs (e.g., complexity vs. performance).
Outline a plan to implement the feature in small, testable increments, including unit tests, integration tests, and possibly feature flags for safe rollout.
Describe how you would validate the feature in staging and monitor it in production, with rollback plans if issues arise.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.