The tree part was fine, knocked it out in maybe 20 minutes.
First, solve the tree problem by clarifying requirements, discussing approaches, and writing clean code with edge cases. Then, shift to the handoff process: describe how you would test, document, and communicate your solution to ensure it's understandable and maintainable for your manager and team.
Pro tip: Emphasize that you treat your manager as a stakeholder: provide a concise summary of the problem, your solution's trade-offs, and potential risks, so they can advocate for your work.
Ask clarifying questions about the tree (e.g., binary? balanced? input size?) and outline your approach, including time/space complexity, before coding.
Write clean, modular code and test with edge cases (empty tree, single node, skewed tree) to ensure correctness.
Self-review for readability, naming, and efficiency; consider if a simpler or more optimal solution exists.
Document the solution: add comments, a brief README, and note any assumptions, trade-offs, and potential improvements.
Summarize the problem, solution, and trade-offs; highlight testing done and ask for feedback or next steps.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Got lucky here because I'd seen something similar before and jumped straight to the optimal approach.
First, clarify the problem constraints and edge cases, then outline an efficient algorithm with clear time and space complexity. For the follow-up, discuss how you would extend the solution to handle additional requirements, focusing on scalability and maintainability.
Pro tip: Demonstrate proactive thinking by suggesting test cases and potential optimizations before being asked. This shows you consider real-world scenarios and code quality.
Restate the problem in your own words and ask clarifying questions about input size, constraints, and expected output. This ensures you and the interviewer are aligned.
Propose a high-level approach, such as dynamic programming or graph traversal, and justify why it's suitable. Discuss trade-offs between different solutions.
Clearly state the time and space complexity of your solution. If possible, mention how it compares to naive approaches.
Write clean, modular code and walk through a few test cases, including edge cases. Explain your reasoning as you code.
For the follow-up, describe how you would modify the solution to handle new constraints, such as larger inputs or additional features. Discuss potential optimizations and trade-offs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.