The original post references this as the second coding question and doesn't give much away about what it actually was.
Start by clarifying the problem constraints and edge cases, then propose a brute-force solution before optimizing. Implement the optimal solution with clean code, test it with examples, and analyze time and space complexity rigorously.
Pro tip: At Meta, interviewers value communication and iterative improvement: think aloud, explain trade-offs, and be open to hints. Always discuss how you would test your solution and handle edge cases.
Ask clarifying questions to confirm input/output formats, constraints, and edge cases. Restate the problem in your own words to ensure alignment.
Discuss a brute-force solution first, then identify inefficiencies and propose optimizations using appropriate data structures or algorithms.
Write clean, modular code with meaningful variable names. Explain your logic as you code and handle edge cases explicitly.
Walk through your code with a few test cases, including edge cases. If bugs are found, debug systematically and explain your fixes.
Derive the time and space complexity of your solution, explaining each component. Discuss potential improvements or trade-offs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.