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 writing clean, efficient code and testing with examples to ensure correctness.
Pro tip: Amazon values customer obsession and ownership; during coding, verbalize your thought process and trade-offs, and proactively discuss how your solution scales and handles edge cases.
Restate the problem in your own words and ask clarifying questions about input size, constraints, and expected output. Confirm edge cases like empty input or large values.
Discuss a brute-force solution first, then analyze its time and space complexity. Propose optimizations using appropriate data structures or algorithmic paradigms (e.g., two pointers, hash maps, dynamic programming).
Outline the steps of your chosen approach, then write clean, modular code with meaningful variable names. Comment on key parts and handle edge cases explicitly.
Walk through your code with a few test cases, including edge cases. If time permits, dry-run or trace the execution to catch off-by-one errors or logical flaws.
State the final time and space complexity, and discuss potential improvements or alternative solutions. Mention any trade-offs made.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by understanding the code's intended behavior and identifying the bug through systematic testing and reasoning. Then, explain the root cause, propose a fix, and verify it with edge cases. Communicate your thought process clearly, as Amazon values structured problem-solving and ownership.
Pro tip: Verbally walk through your debugging steps as if you're pair programming; this demonstrates collaboration and makes your reasoning transparent to the interviewer. Also, always consider edge cases and test your fix mentally before declaring it correct.
Read the code carefully, identify inputs, outputs, and the intended functionality. Clarify any ambiguities with the interviewer.
Trace through the code with sample inputs to observe incorrect behavior. Note where the output deviates from expectations.
Use debugging techniques like print statements, breakpoints, or logical reasoning to isolate the faulty line or logic. Consider common pitfalls like off-by-one errors, null pointers, or incorrect conditionals.
Explain the fix clearly, ensuring it addresses the root cause without introducing new issues. Modify the code accordingly.
Run through test cases, including edge cases, to confirm the bug is resolved and the code works as intended. Discuss any trade-offs or alternative solutions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.