Start by clarifying the problem, constraints, and edge cases, then discuss a brute-force solution before optimizing with appropriate data structures and algorithms. Think aloud to demonstrate your problem-solving process, and write clean, modular code with meaningful variable names. Test your solution with examples and consider time/space complexity trade-offs.
Pro tip: At Amazon, interviewers value the Leadership Principles, especially Customer Obsession and Ownership. Show how your solution is scalable, efficient, and considers real-world constraints like large inputs or distributed systems.
Ask clarifying questions to ensure you fully understand the problem, including input/output formats, constraints, and edge cases. Restate the problem in your own words to confirm.
Discuss a brute-force solution first, then analyze its time and space complexity. Propose optimizations using appropriate data structures (e.g., hash maps, heaps, trees) and algorithms (e.g., sorting, dynamic programming, graph traversal).
Outline your chosen approach step-by-step, then write clean, modular code. Use meaningful variable names and handle edge cases. Think aloud as you code to make your thought process clear.
Walk through your code with a few test cases, including edge cases. Identify and fix any bugs. Discuss potential improvements or alternative solutions.
State the time and space complexity of your solution. Discuss trade-offs and whether further optimization is possible or necessary.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.