Part one went clean, dry run and everything.
Treat the three parts as a progressive refinement: first solve the simplest version with a brute-force or naive approach, then optimize using appropriate data structures, and finally handle edge cases and scalability. Communicate your thought process clearly at each stage, explaining trade-offs and why you're moving to the next level of complexity.
Pro tip: At Stripe, interviewers value clean, production-ready code and clear communication over rushing to the optimal solution. Start with a working solution, then iterate—this mirrors real-world engineering where you ship, measure, and improve.
Ask clarifying questions to ensure you understand the problem constraints, input/output formats, and what each part entails. Restate the problem in your own words to confirm alignment.
Implement a straightforward, correct solution for the first part, even if inefficient. Focus on correctness and clean code, and explain your approach before coding.
Analyze the time and space complexity of your Part 1 solution, then propose and implement an optimized version using appropriate data structures or algorithms. Discuss trade-offs.
Address the most complex part by considering edge cases, scalability, and potential real-world constraints. If needed, combine techniques from previous parts or introduce advanced concepts.
Walk through test cases, including edge cases, to verify correctness. Review code for readability, modularity, and potential improvements, and summarize the evolution of your solution.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.