I always try to restate the problem back before touching any code.
Walk through a structured process: first clarify inputs, outputs, and constraints; then discuss data structure choices with trade-offs; finally enumerate edge cases and outline a plan before coding. Emphasize communication and iterative refinement with the interviewer.
Pro tip: Verbalize your thought process and ask clarifying questions early—interviewers value how you think and collaborate more than rushing to code. At Amazon, tie your choices to customer impact and operational excellence.
Ask about input types, size, output format, and constraints (time/space). Confirm assumptions and restate the problem to ensure alignment.
Walk through simple and complex examples to validate understanding and uncover hidden requirements or patterns.
Discuss candidate data structures, comparing trade-offs (time, space, simplicity) and justify your choice based on the problem's needs.
Enumerate edge cases like empty inputs, duplicates, overflow, and invalid data. Explain how your solution handles them.
Present a high-level plan, get interviewer buy-in, then write clean code with meaningful names and comments.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one tripped me up more than the algo round.
Start by restating the problem and asking targeted questions about browser API constraints, expected inputs, and performance requirements. Then outline a modular solution with clear separation of concerns, and walk through edge cases and validation steps verbally, explaining your reasoning. Emphasize that you would test mentally and consider fallbacks for unsupported APIs.
Pro tip: Mention that you would write code that degrades gracefully and use feature detection (e.g., `if ('IntersectionObserver' in window)`) to handle browser inconsistencies, showing you think about real-world deployment. Also, explicitly state assumptions you make and invite the interviewer to correct them, demonstrating collaboration.
Ask questions to understand the exact problem, target browsers, performance expectations, and any API limitations. Confirm input/output formats and edge cases the interviewer cares about.
Sketch a high-level structure, breaking the problem into functions or components. Explain how you'll handle data flow, state, and interactions, and mention any browser APIs you plan to use.
Write the solution step by step, adding comments to explain your reasoning. Use descriptive variable names and keep functions small and focused.
Walk through the code with sample inputs, including edge cases like empty data, large inputs, or unsupported APIs. Explain how you'd handle errors and fallbacks.
After writing, review for potential bugs, readability, and performance. Suggest improvements or alternative approaches, showing you think critically about your own code.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.