Picked a recursive solution because it was the cleanest to write.
Start by restating the problem and clarifying constraints, then propose a simple brute-force solution before optimizing. Implement the optimal solution while explaining your reasoning, and finally walk through edge cases and test your code with examples.
Pro tip: Verbalize your thought process continuously, as Google evaluates problem-solving and communication equally. Before coding, explicitly state the time and space complexity of your approach and compare it to alternatives.
Restate the problem in your own words and ask clarifying questions about input size, constraints, and expected output. Confirm assumptions with the interviewer.
Discuss a brute-force solution and its complexity, then propose an optimized approach. Explain the trade-offs between different algorithms or data structures.
Write clean, modular code while explaining each step. Use meaningful variable names and handle edge cases as you go.
Walk through your code with a few test cases, including edge cases like empty input, single element, duplicates, or large values. Verify correctness and complexity.
If time permits, discuss potential improvements or alternative solutions. Summarize why your chosen approach is optimal for the given constraints.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Interviewer pushed back on my approach and suggested something more memory-efficient.
Start by clarifying the problem constraints and assumptions, then outline two distinct implementation approaches. Compare their time and space complexities, and discuss scenarios where each is preferable, emphasizing trade-offs and practical considerations.
Pro tip: Demonstrate awareness of real-world constraints like cache performance, memory hierarchy, and scalability; mention that asymptotic analysis alone may not capture practical performance differences.
Ask clarifying questions to understand input size, constraints, expected operations, and any special conditions. This ensures you address the correct problem and sets the stage for meaningful trade-off analysis.
Briefly describe two viable algorithms or data structures to solve the problem, highlighting their core mechanisms (e.g., hash map vs. sorting, iterative vs. recursive).
For each approach, derive the time and space complexity in big-O notation, considering best, average, and worst cases. Be precise about auxiliary space versus total space.
Compare the approaches: when would you choose one over the other? Consider factors like input size, memory limits, time constraints, and typical usage patterns.
Summarize which approach is generally preferable and under what conditions, showing balanced judgment and practical insight.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.