Got the right approach and explained the complexity fine at the end, which felt good.
First, clarify the problem requirements and constraints, then discuss a brute-force solution before optimizing. Implement the optimal solution with clean code, and finally analyze time and space complexity, explaining trade-offs.
Pro tip: Always communicate your thought process and consider edge cases; interviewers value problem-solving skills and clarity over just getting the right answer.
Ask clarifying questions to confirm input/output formats, constraints, and edge cases. Restate the problem in your own words to ensure alignment.
Discuss a naive solution first, then propose an optimized approach, explaining the reasoning and trade-offs. Consider multiple data structures and algorithms.
Write clean, modular code with meaningful variable names. Verbally explain each step as you code to demonstrate understanding.
Walk through the code with a few test cases, including edge cases. Debug any issues and verify correctness.
Derive the time and space complexity using Big-O notation. Explain how the complexity scales with input size and any potential optimizations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.