The mismatch between the problem description and the actual code threw me off more than the problem itself.
When you notice a mismatch between the problem description and the starter code, don't silently assume or ignore it. Politely point out the discrepancy to the interviewer, ask for clarification, and then proceed with a clear, mutually agreed-upon interpretation while explaining your reasoning.
Pro tip: Treat the mismatch as a test of your communication and adaptability—interviewers often introduce such inconsistencies intentionally to see how you handle ambiguity. By addressing it directly and professionally, you demonstrate the ownership and customer obsession Amazon values.
Carefully read the problem statement and compare it with the starter code. If you spot a mismatch, note it explicitly without making assumptions.
Politely ask the interviewer to clarify which version is correct or how they would like you to proceed. This shows attentiveness and avoids wasted effort.
Offer a reasonable interpretation or suggest adapting the code to match the problem. Explain your rationale and confirm with the interviewer before coding.
Once aligned, implement the solution confidently. If needed, document the assumption in comments or verbally reiterate it.
After solving, briefly mention how you would handle similar ambiguities in real projects, emphasizing communication and validation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the bug's symptoms and scope, then systematically trace the comment algorithm's logic, using the AI assistant to accelerate hypothesis testing and code comprehension. Balance speed with rigor by validating AI suggestions against logs, tests, and code, and discuss trade-offs between quick fixes and root-cause solutions.
Pro tip: Treat the AI assistant as a junior engineer: verify its suggestions with data and tests, and always explain why you accept or reject its advice. This shows you can leverage tools without blindly trusting them, a key trait at Amazon.
Ask questions to understand the expected vs. actual behavior, reproduction steps, and impact. Define what 'fixed' looks like.
Inspect logs, error messages, and relevant code paths. Use the AI assistant to summarize code or suggest potential causes, but validate each hypothesis with evidence.
Narrow down the faulty component by adding logging, writing unit tests, or using a debugger. Leverage the AI to generate test cases or explain unfamiliar code.
Apply the minimal fix that addresses the root cause, considering trade-offs like performance, maintainability, and side effects. Verify with tests and monitor.
Document the root cause, update tests, and suggest improvements to prevent similar issues. Discuss how AI could be used to catch such bugs earlier.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.