The loan scenario is probably the most common one people run into.
Start by running the test suite to see the failures, then trace each failure to the relevant module using logs and code inspection. Prioritize fixes by impact and complexity, and verify each fix with targeted tests before running the full suite. Communicate your debugging process clearly, emphasizing root cause analysis and trade-offs.
Pro tip: Before diving into code, quickly scan the repo structure and README to understand the architecture and data flow; this often reveals common pitfalls like missing validation or incorrect API contracts. Also, use git bisect or recent commits to identify when bugs were introduced.
Run the test suite to identify all failing tests. Group failures by module and prioritize based on severity and dependencies (e.g., loan creation blocking other features).
For each failure, use logs, breakpoints, and code inspection to trace the error to its source. Check both frontend and backend, and verify API contracts and data validation.
Fix one issue at a time, starting with the most critical. After each fix, run the relevant tests to ensure the fix works and doesn't break other functionality.
Once all targeted tests pass, run the entire suite to catch regressions. If time permits, refactor for clarity or performance, but avoid over-engineering.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The undefined variables in the test file genuinely threw me.
Start by fixing the test runner by resolving undefined variables in the test URL file, then systematically debug each broken component of the password reset flow: code generation, expiry enforcement, and password persistence. For each issue, identify the root cause, implement a fix, and verify with tests, while considering edge cases and system design implications.
Pro tip: Demonstrate a test-driven approach by writing or fixing tests first to reproduce the bugs, then implement fixes and ensure all tests pass. Also, mention the importance of logging and monitoring to catch such issues in production.
Resolve undefined variables in the test URL file to ensure the test runner can execute. This allows you to run tests and validate fixes for the password reset flow.
Investigate why the verification code is never generated. Check the code generation logic, dependencies, and any error handling that might silently fail.
Examine the expiry logic: ensure timestamps are set correctly when codes are generated and validated against the current time. Consider clock skew and timezone issues.
Trace the password update flow to identify why the new password isn't saved. Check database transactions, ORM mappings, and error handling during persistence.
Run tests to confirm all fixes, add edge case tests (e.g., expired code, invalid code), and review for security best practices like rate limiting and secure code storage.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one's less about debugging and more about knowing where middleware lives in whatever stack they give you.
Start by clarifying requirements and constraints, then propose a layered architecture that separates rate limiting and blocklist concerns from core business logic. Walk through the design from data model to API enforcement, highlighting trade-offs and operational considerations. Conclude with a rollout and monitoring plan.
Pro tip: Emphasize idempotency and graceful degradation: rate limiting and blocklists should fail open or closed based on business impact, and you should discuss how to avoid blocking legitimate users during false positives.
Ask about scale, latency budgets, existing tech stack, and whether the blocklist is global or per-tenant. Confirm if rate limiting is per user, IP, or API key, and the desired enforcement point (gateway, middleware, service).
Choose appropriate storage for counters (e.g., Redis for rate limiting) and blocklist (e.g., database with caching). Discuss TTLs, eviction policies, and how to handle distributed consistency.
Describe where to intercept requests (e.g., API gateway, middleware) and how to apply rules without duplicating code. Cover algorithms like token bucket or sliding window for rate limiting, and exact-match or regex for blocklist.
Discuss trade-offs between accuracy and performance, e.g., approximate vs. exact counting, and how to handle race conditions. Cover failure modes: what happens if Redis is down? How to avoid blocking legitimate traffic?
Propose a phased rollout with feature flags, metrics (e.g., block rate, false positives), and alerting. Include a feedback loop to tune thresholds and update blocklists.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Six bugs across one scoring function sounds manageable until you realize the bugs compound each other.
Start by clarifying the expected behavior and identifying all inputs and outputs of the scoring service. Then systematically trace the scoring path, comparing each threshold and weight against the intended values, and use logging or unit tests to pinpoint discrepancies. Finally, propose a fix and a validation strategy to prevent similar issues.
Pro tip: Demonstrate a methodical approach by first reproducing the issue with a minimal test case, then use binary search or divide-and-conquer to isolate the faulty component. This shows efficiency and strong debugging skills.
Review the scoring service architecture, including inputs, outputs, and the role of each threshold and weight. Clarify the expected behavior and success criteria.
Create a minimal test case that triggers the incorrect scoring. Capture the actual output and compare it with the expected output to confirm the bug.
Trace the scoring path step by step, checking each threshold and weight. Use logging, breakpoints, or unit tests to identify which values are incorrect.
Correct the wrong values, then re-run tests to ensure the scoring matches expectations. Add regression tests to prevent future occurrences.
Suggest improvements such as configuration validation, automated tests for thresholds, or monitoring to catch similar issues early.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The framework lock-in is real and a little stressful.
Choose the framework you know best to minimize cognitive load, then systematically reproduce the failing tests, trace the bugs to their root causes, and fix them with minimal changes. Verify by running the full test suite and consider edge cases to ensure robustness.
Pro tip: Before fixing anything, read the test code to understand expected behavior and use the debugger or logging to pinpoint the exact failure points—this saves time and prevents guesswork.
Pick the framework you are most proficient in, considering your familiarity with its conventions, debugging tools, and testing setup. Avoid choosing based on popularity alone.
Execute the bundled tests to see which ones fail and capture the error messages. This gives you a clear starting point and confirms the bugs.
Inspect the failing test cases and trace the code paths to identify the root causes. Use debugging tools, logs, or breakpoints to understand the incorrect behavior.
Apply targeted changes to correct the bugs without introducing unnecessary modifications. Ensure the fixes align with the framework's best practices.
Re-run the tests to confirm all pass. Additionally, consider edge cases or write quick sanity checks to ensure the fixes are robust and don't break other functionality.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by systematically reproducing the issue to isolate whether it's a frontend, backend, or database problem, then trace the request flow from API endpoint to persistence layer. Address the 401/403 handling by verifying authentication middleware and authorization checks, ensuring correct status codes and messages are returned. Finally, propose a fix with tests to prevent regression.
Pro tip: Demonstrate a bias for action by outlining immediate debugging steps (e.g., checking logs, using curl) while also considering long-term improvements like adding integration tests for auth scenarios. This shows you can balance urgency with quality, a key Amazon leadership principle.
Reproduce the issue consistently and determine if it's frontend (display), backend (persistence), or both. Check network requests, server logs, and database entries to narrow down the failure point.
Follow the request from the API endpoint through authentication, authorization, business logic, and database operations. Identify where the comment creation/update fails or where incorrect status codes are returned.
Correct the backend logic to properly save comments (e.g., missing database commit, incorrect query) and ensure the frontend correctly fetches and renders updated comments (e.g., cache invalidation, state update).
Ensure authentication middleware returns 401 for unauthenticated requests and authorization checks return 403 for unauthorized users, with clear, specific error messages. Verify these are consistently applied across all comment endpoints.
Write unit and integration tests covering comment CRUD operations and auth scenarios. Add logging and monitoring to catch similar issues early.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements and edge cases, then design a moderation service that integrates with the review submission flow. Focus on the data model for tracking violations and flagged status, and ensure atomicity and idempotency in the checks.
Pro tip: Emphasize that moderation should be a separate service to allow independent scaling and updates, and discuss how to handle false positives and appeals. Also, mention the importance of logging and monitoring for continuous improvement.
Ask questions to understand the scope: What defines 'bad content'? How are violated words determined? What is the expected traffic and latency? Are there existing systems to integrate with?
Define how to store user violation counts, flagged status, and records of violated words. Consider using a relational database with a users table and a violations table, or a NoSQL solution for scalability.
Outline the flow: on review submission, check if user is flagged; if so, block with 403. Otherwise, scan content for bad words; if found, increment violation count, record words, and if count reaches 3, set flagged state. Return 403 on any violation.
Use transactions or atomic operations to update violation counts and flagged status to avoid race conditions. Make the moderation check idempotent to handle retries safely.
Talk about scaling the moderation service, caching flagged users, and monitoring for false positives. Suggest logging violations for analysis and potential machine learning improvements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Six unit tests, pretty clear failure messages.
Start by systematically reproducing each broken operation (create, delete, update, add/remove movie) to identify the exact failure points. Then trace the code path for each operation, checking for missing HTTP status codes, absent existence checks, and database persistence issues. Finally, propose and implement fixes, ensuring each operation returns proper status codes, validates resource existence, and correctly saves data to the database.
Pro tip: Demonstrate a test-driven approach by writing unit/integration tests for each operation before fixing, which ensures all edge cases are covered and prevents regressions. Also, mention the importance of consistent error handling and logging for debugging in production.
Manually test each operation (create, delete, update list, add/remove movie) to observe the exact errors or incorrect behaviors. Document the expected vs. actual outcomes for each.
For each failure, trace the request through the controller, service, and repository layers to pinpoint missing status codes, missing existence checks, or missing database save calls.
Add appropriate HTTP status codes (e.g., 201 for creation, 204 for deletion, 404 for not found), existence checks before operations, and ensure entities are saved/updated in the database.
Create unit and integration tests for each operation to validate fixes and edge cases. Run the full test suite and manually verify the API endpoints.
Review the changes for consistency, add logging for critical operations, and consider potential concurrency or transaction issues to prevent future bugs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The AI limitation here actually changes how you work.
Start by clarifying the scenario: the AI assistant's limited visibility means you must reason about the bug using only the open file, so first identify what that file likely is (e.g., the home page component or recommendation service). Then systematically trace the data flow from that file outward, forming hypotheses about where the recommendation data could be lost or corrupted, and propose targeted diagnostics or fixes that can be validated without seeing other files.
Pro tip: Demonstrate that you can work effectively with incomplete information by explicitly stating your assumptions and how you would verify them, rather than guessing. Also, mention that you would use logging or feature flags to isolate the issue in production without needing to open other files.
Determine what the currently open file is (e.g., frontend component, API handler, service class) and its responsibility in the recommendation flow. This sets the boundaries of what you can directly inspect.
Follow how recommendation data is fetched, transformed, and rendered in this file. Look for obvious issues like null checks, error handling, or incorrect API calls.
Based on the file's interactions, hypothesize where the bug might originate outside the file (e.g., API response, database query, caching layer). Prioritize based on likelihood and impact.
Suggest concrete steps to confirm or rule out hypotheses, such as adding logging, writing unit tests, or using feature flags. If a fix is apparent in the file, propose it with justification.
Explain how you would validate the fix or diagnostic results, and what you would do if the bug persists (e.g., request access to other files, collaborate with teammates).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.