Start by restating the problem in your own words and asking clarifying questions to confirm assumptions and constraints. Then outline your approach, including the algorithm and data structures, before writing any code. While coding, verbalize your thoughts, and after writing, walk through edge cases and test your solution with examples.
Pro tip: Demonstrate a systematic approach to edge cases by categorizing them (e.g., empty input, single element, large input, duplicates) and explicitly testing each category. This shows thoroughness and reduces the chance of missing critical cases.
Restate the problem in your own words and ask clarifying questions about input types, constraints, expected output, and edge cases. Confirm your understanding with the interviewer.
Outline your approach, including the algorithm and data structures you'll use. Discuss time and space complexity, and consider alternative solutions. Get buy-in before coding.
Write clean, modular code while explaining each step. Use meaningful variable names and handle edge cases as you go. Keep the interviewer engaged by verbalizing your thought process.
Walk through your code with a normal case and several edge cases. Check for off-by-one errors, null inputs, and other pitfalls. If needed, debug and refine your solution.
If time permits, discuss potential optimizations or trade-offs. Summarize your solution and its complexity, and invite feedback from the interviewer.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I made a bet that trees wouldn't come up based on what I'd seen from this company online.
Clarify the problem, then walk through a recursive or iterative tree traversal solution, explaining time and space complexity. Optimize by considering iterative approaches or pruning to handle large inputs typical in Walmart's scale.
Pro tip: Always discuss edge cases like empty trees, skewed trees, and duplicate values, and mention how you'd test your solution with unit tests. This shows production-level thinking beyond just solving the algorithm.
Ask questions to confirm input format, output expectations, constraints (e.g., tree size, node values), and edge cases. This ensures you solve the right problem and demonstrates thoroughness.
Propose a brute-force solution first, then optimize. For tree problems, consider DFS (recursive/iterative) or BFS, and analyze trade-offs in time/space complexity.
Write clean, modular code with meaningful variable names. Explain your logic as you code, and handle edge cases explicitly.
Walk through a few test cases, including edge cases, to verify correctness. If time permits, discuss how you would write unit tests.
State the time and space complexity of your solution. If possible, suggest further optimizations or alternative approaches for scalability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.