Needed a hint or two to get to the optimal but did get there.
Start by clarifying the problem and walking through a simple example. Then present a brute force solution with its time and space complexity, and finally derive an optimized solution by identifying inefficiencies and applying appropriate data structures or algorithmic techniques. Compare the complexities and discuss trade-offs.
Pro tip: Always communicate your thought process clearly and ask clarifying questions before diving into solutions. Google values problem-solving skills and collaboration as much as the final answer.
Restate the problem in your own words, ask clarifying questions about input constraints, edge cases, and expected output. Confirm assumptions with the interviewer.
Describe a straightforward, naive approach that solves the problem, even if inefficient. Analyze its time and space complexity.
Point out the bottlenecks in the brute force solution, such as repeated computations or unnecessary iterations, and discuss what causes the inefficiency.
Propose an optimized solution using appropriate data structures (e.g., hash maps, heaps) or algorithmic paradigms (e.g., dynamic programming, two pointers). Explain how it improves upon the brute force.
Compare the time and space complexities of both solutions, discuss trade-offs, and consider edge cases. If time permits, outline how you would test the solution.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was the best interview of the whole process.
For each problem, start by clarifying requirements and edge cases, then discuss a brute-force solution before optimizing. Implement clean, modular code with clear variable names, and after coding, walk through test cases and analyze time/space complexity. If time permits, mention alternative approaches and trade-offs.
Pro tip: Treat the interview as a collaborative problem-solving session: think aloud, ask clarifying questions, and proactively discuss trade-offs. This demonstrates strong communication and problem-solving skills, which are highly valued at Google.
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 bottlenecks and propose optimized approaches. Compare time and space complexities of each.
Write clean, modular code with meaningful variable names. Explain your thought process as you code and handle edge cases explicitly.
Walk through your code with sample inputs, including edge cases. Verify correctness and analyze the final time and space complexity.
If time allows, discuss potential improvements or alternative solutions. Mention trade-offs and ask for feedback.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Got the core solution working and all the test cases passed, but then there was this edge case involving a while loop and I put it in the wrong place.
Acknowledge the incomplete edge case handling, then systematically walk through the edge cases you missed and how you would address them. Emphasize your debugging process and the importance of thorough testing, showing that you learn from the experience. Conclude with how you would prevent similar issues in the future.
Pro tip: Interviewers value candidates who can critically evaluate their own work and improve; openly discussing your mistake and the steps you took to fix it demonstrates maturity and a growth mindset.
Briefly admit that your initial solution missed some edge cases, showing self-awareness without being defensive.
List the specific edge cases you overlooked (e.g., empty input, single element, duplicates, overflow) and explain why they are important.
Describe how you discovered the missing edge cases, such as through testing, code review, or thinking about boundary conditions.
Outline how you would modify your code to handle each edge case, ensuring the solution remains efficient and correct.
Discuss strategies to avoid similar oversights, like writing unit tests first, using test-driven development, or conducting thorough edge case analysis.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.