First, clarify the problem constraints and edge cases, then discuss a brute-force solution before optimizing with appropriate data structures or algorithms. Focus on explaining your thought process, analyzing time and space complexity, and testing with examples.
Pro tip: Amazon values customer obsession and ownership; relate your solution to scalability and real-world impact, and always consider edge cases like empty inputs or large data.
Ask clarifying questions to confirm input/output format, constraints, and edge cases. Restate the problem in your own words to ensure alignment.
Start with a brute-force solution, then identify inefficiencies and propose optimizations using appropriate data structures or algorithms.
Select the optimal approach, explain your reasoning, and write clean, modular code with meaningful variable names.
Walk through test cases including edge cases, and verify correctness. Analyze time and space complexity.
Discuss potential improvements, trade-offs, and how the solution could scale or be adapted for different scenarios.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Treat the AI as a collaborative tool: first restate the problem and clarify constraints, then use AI to generate initial code or ideas, but critically review and optimize the solution for correctness, efficiency, and edge cases. Focus on demonstrating your problem-solving process and ability to leverage AI effectively while maintaining code quality.
Pro tip: Always test the AI-generated code with edge cases and explain your reasoning for any modifications; Amazon values ownership and high standards, so show that you don't blindly trust AI output.
Restate the problem in your own words, identify inputs/outputs, constraints, and edge cases. Ask clarifying questions if needed.
Use AI to brainstorm approaches or generate pseudocode, but evaluate trade-offs (time/space complexity, readability) and choose the best path.
Write or adapt code, leveraging AI for boilerplate or suggestions, but manually verify logic and handle edge cases.
Run through test cases, including edge cases, and debug any issues. Explain how you would test the solution.
Analyze time and space complexity, suggest improvements, and ensure code meets Amazon's quality standards.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.