Start by clarifying the problem constraints and edge cases, then discuss potential approaches with their trade-offs before coding. Implement the optimal solution with clean code, and test it with examples including edge cases.
Pro tip: At Amazon, interviewers value structured problem-solving and communication over just getting the right answer. Think aloud, explain your reasoning, and show how you would optimize and test your solution.
Ask clarifying questions to confirm input/output, constraints, and edge cases. Restate the problem in your own words to ensure alignment.
Discuss brute force and optimized solutions, analyzing time and space complexity. Choose the best approach based on constraints and explain why.
Write clean, modular code with meaningful variable names. Explain your logic as you code, and handle edge cases explicitly.
Walk through test cases, including normal, edge, and large inputs. Debug any issues and verify correctness and efficiency.
If time permits, discuss potential improvements or alternative solutions. Summarize the final approach and its complexity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Demonstrate a systematic approach to understanding the codebase: start with high-level architecture, then drill into the specific area relevant to the task. Emphasize how you leverage AI tools to accelerate comprehension while maintaining rigorous verification of generated code.
Pro tip: Show that you treat AI as a junior engineer: you review its suggestions critically, test edge cases, and never blindly trust output. This demonstrates both efficiency and engineering judgment.
Explore the repository structure, README, and key configuration files to understand the project's purpose, tech stack, and entry points. Identify the main modules and their responsibilities.
Locate the code related to the task by searching for keywords, following function calls, and reading tests. Understand the data flow and dependencies before making changes.
Use AI to explain unfamiliar code, suggest implementations, or generate boilerplate, but always validate its output against the codebase's patterns and run tests to ensure correctness.
Make small, focused changes and run existing tests frequently. Write new tests for your changes to catch regressions and ensure the solution meets requirements.
Clean up your code to match the project's style, add comments where necessary, and update documentation if applicable. Ensure your changes integrate seamlessly.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.