Came up with two approaches but one had a small bug I only noticed after the fact.
Start by clarifying the problem constraints and edge cases, then outline a brute-force solution before optimizing. For the second solution, choose a fundamentally different algorithmic paradigm (e.g., iterative vs. recursive, or different data structure) and compare trade-offs in time/space complexity.
Pro tip: At Apple, interviewers value clean, production-ready code with meaningful variable names and modular functions. Always discuss how your solution would scale and handle real-world inputs, not just pass test cases.
Restate the problem in your own words, ask about input size, constraints, and edge cases. Confirm expected output format and any assumptions.
Think aloud about possible solutions, starting with a brute-force method. Identify bottlenecks and consider optimizations using different data structures or algorithms.
Write clean, well-structured code for your chosen approach. Explain your reasoning as you code, and handle edge cases explicitly.
Propose a distinct alternative solution, such as using a different algorithmic paradigm or trading time for space. Implement it and highlight key differences.
Compare both solutions in terms of time/space complexity, readability, and scalability. Discuss when each might be preferred in practice.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.