Got 14 out of 15 test cases which felt decent.
Start by clarifying the problem constraints and edge cases, then outline a brute-force solution before optimizing with appropriate data structures or algorithms. Focus on demonstrating clean code, efficient time/space complexity, and thorough testing with examples.
Pro tip: Amazon values customer obsession and ownership; as you code, verbalize your thought process and trade-offs, and proactively test your solution with edge cases to show you think like a bar raiser.
Ask clarifying questions to confirm input/output formats, constraints, and edge cases. Restate the problem in your own words to ensure alignment.
Discuss a brute-force solution first, then analyze its inefficiencies. Propose an optimized approach using suitable data structures or algorithms, explaining trade-offs.
Outline the steps of your chosen algorithm, then write clean, modular code with meaningful variable names. Comment on key logic as you go.
Walk through your code with a sample input, then test edge cases (empty input, large values, duplicates). Fix any bugs and verify correctness.
State the time and space complexity of your solution, and discuss potential improvements or alternative approaches if time permits.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one was rough, not even because of the problem itself but the platform kept spitting out the same output on repeat for almost 30 minutes.
Start by clarifying the problem requirements and constraints, then outline a brute-force solution before optimizing. Focus on algorithmic efficiency and discuss trade-offs between time and space complexity, especially for AI-related data processing.
Pro tip: Demonstrate Amazon Leadership Principles by explicitly discussing how your solution scales and handles edge cases, and always test with provided examples plus your own.
Ask clarifying questions to confirm input/output formats, constraints, and edge cases. Restate the problem in your own words to ensure alignment.
Discuss a brute-force solution first, then propose an optimized algorithm. Explain your thought process and consider multiple approaches.
Compare time and space complexity of different solutions. Discuss scalability and potential bottlenecks, especially for AI workloads.
Write clean, modular code with meaningful variable names. Test with sample inputs, edge cases, and validate against expected outputs.
Refactor if needed, check for off-by-one errors, and discuss possible further optimizations or alternative data structures.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.