This is the one that's been keeping me up.
Treat the DSA problem as a collaborative exercise: restate the problem, clarify constraints, and discuss trade-offs before coding. Walk through a brute-force solution, then optimize using appropriate data structures and algorithms, explaining your reasoning at each step. Finally, analyze time and space complexity and test with edge cases.
Pro tip: Even in an HR round, Amazon values data-driven decision making, so quantify the efficiency gains of your optimized solution (e.g., 'This reduces time from O(n²) to O(n log n)'). Also, tie your approach to Amazon's Leadership Principles like Customer Obsession (solving the customer's problem efficiently) and Dive Deep (understanding underlying complexities).
Restate the problem in your own words and ask clarifying questions about input size, constraints, and expected output. Confirm assumptions with the interviewer.
Start with a brute-force solution, then propose an optimized approach using suitable data structures or algorithms. Explain the trade-offs between them.
Write clean, modular code while verbally explaining each step. Use meaningful variable names and handle edge cases.
State the time and space complexity of your solution and compare it to the brute-force approach. Justify why it's optimal.
Walk through test cases, including edge cases, to verify correctness. Discuss potential improvements or alternative solutions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.