Start by clarifying the problem and identifying the two conditions. Solve the simpler case first, then show how the harder case can be transformed into the simpler one, and implement the solution with clean code and complexity analysis.
Pro tip: Explicitly state the reduction: 'The harder case can be reduced to the simpler one by...' This shows you can decompose problems and recognize patterns, which is highly valued at Google.
Restate the problem in your own words, ask clarifying questions about input/output, constraints, and edge cases. Identify the two conditions and confirm which is simpler.
Develop an algorithm for the simpler condition, considering brute force and optimal approaches. Analyze time and space complexity.
Explain how the harder condition can be transformed or reduced to the simpler one. This may involve preprocessing, transforming inputs, or using a different perspective.
Write clean, modular code that handles both cases. Test with examples, edge cases, and discuss potential bugs.
Provide overall time and space complexity. Discuss possible optimizations or trade-offs, and mention any alternative approaches.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the problem, constraints, and edge cases, then propose a brute-force solution and optimize it using appropriate data structures and algorithms. After implementing and testing the base solution, analyze the follow-up variant to identify how it differs, and adapt your approach by reusing components or extending the algorithm while discussing trade-offs.
Pro tip: Verbalize your thought process clearly and engage the interviewer as a collaborator—ask clarifying questions and discuss alternative approaches before coding. This demonstrates strong communication and problem-solving skills, which are highly valued at Google.
Restate the problem in your own words, ask clarifying questions about input/output, constraints, and edge cases. Confirm your understanding with the interviewer before proceeding.
Discuss a brute-force solution and its complexity, then propose optimized approaches using suitable data structures or algorithms. Compare trade-offs and select the best one with interviewer buy-in.
Write clean, modular code for the chosen approach. Walk through your code with a sample test case, checking edge cases and fixing any issues.
Carefully read or listen to the follow-up variant. Identify how it differs from the original (e.g., different constraints, additional operations) and determine what changes are needed.
Modify your solution to handle the follow-up, reusing existing code where possible. Discuss the impact on time/space complexity and test the updated solution.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.