Start by clarifying the problem constraints and edge cases, then outline a brute-force solution before optimizing. Focus on writing clean, efficient code with proper variable names and comments, and test with sample inputs to catch bugs early.
Pro tip: In timed assessments, prioritize passing all test cases over writing the most elegant solution. Use a systematic approach: solve, test, then optimize if time permits.
Read the problem statement carefully, identify input/output formats, constraints, and edge cases. Ask clarifying questions if allowed.
Choose an appropriate data structure and algorithm. Start with a simple brute-force solution, then consider optimizations based on time/space complexity.
Write clean, modular code with meaningful variable names. Handle edge cases and use comments to explain logic.
Run through sample inputs and edge cases manually or with a debugger. Check for off-by-one errors and boundary conditions.
If the solution works but is inefficient, analyze time/space complexity and optimize. Ensure changes don't break existing functionality.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This section tripped me up more than the straight coding problem.
Start by reproducing the failing tests and understanding the expected behavior. Use AI-assisted tooling to analyze the test failures, suggest potential fixes, and validate them. Then, iteratively refine the solution, ensuring the fix is correct and doesn't introduce regressions.
Pro tip: Demonstrate how you balance AI suggestions with your own judgment—highlight a case where you rejected an AI suggestion because it didn't align with the codebase's patterns or performance requirements.
Run the failing tests to see the errors and stack traces. Read the test code and the corresponding production code to understand what the test expects and why it might be failing.
Use AI-assisted tools (e.g., GitHub Copilot, Amazon CodeWhisperer) to analyze the failure, suggest root causes, and propose potential fixes. Review the suggestions critically.
Apply the most promising fix, either from AI or your own analysis. Run the specific test to see if it passes, then run the full test suite to check for regressions.
If the fix doesn't work or introduces new failures, iterate by refining your approach, possibly using AI again with more context. Ensure the final solution is clean and maintainable.
Document the root cause and the fix for future reference. Reflect on how AI assisted and where human judgment was crucial, to improve future debugging processes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Treat the assessment as a structured opportunity to demonstrate Amazon's Leadership Principles, especially Customer Obsession and Ownership. For email scenarios, use a clear, concise format that addresses the issue, proposes a solution, and shows bias for action. Maintain a professional tone that reflects Amazon's writing culture.
Pro tip: Amazon values data-driven decisions and customer impact; in email responses, quantify the problem and solution where possible, and always tie back to the customer. Also, proofread carefully—Amazon's writing culture demands clarity and precision.
Read the email scenario carefully to identify the sender's intent, the underlying issue, and any urgency. Determine which Leadership Principles are most relevant (e.g., Customer Obsession, Ownership, Bias for Action).
Use a clear email format: greeting, acknowledgment, concise explanation, proposed solution or next steps, and a professional closing. Keep it brief but complete.
Incorporate specific principles into your response. For example, show Customer Obsession by prioritizing customer impact, or Ownership by taking responsibility and proposing a fix.
Check for clarity, tone, and grammar. Ensure your response is actionable and aligns with Amazon's writing style: direct, data-oriented, and customer-centric.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.