First, clarify the two parts and their relationship, then solve each part independently while considering how they might interact. For each part, identify the underlying classic algorithm, discuss trade-offs between approaches, and analyze time/space complexity. Finally, integrate the solutions if needed and test with edge cases.
Pro tip: Google interviewers value clear communication and structured problem-solving over rushing to code. Verbally walk through your thought process, ask clarifying questions, and explicitly state assumptions before diving into implementation.
Ask questions to fully understand the two parts, their inputs/outputs, constraints, and whether they are independent or connected. Confirm any assumptions with the interviewer.
Recognize which classic algorithm problems each part resembles (e.g., two-sum, merge intervals, BFS/DFS, dynamic programming). Explain the mapping to the interviewer.
Propose an approach for each part, starting with a brute-force solution and then optimizing. Discuss data structures and trade-offs (time vs. space).
For each solution, derive the time and space complexity in Big-O notation. Compare with alternatives and justify your choice.
If the parts interact, combine the solutions logically. Walk through examples, including edge cases (empty input, large input, duplicates), and verify correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.