My approach moved exactly one step per call and passed two test cases, but the large data cases all timed out.
Clarify the problem constraints and the API's behavior, then design a search strategy that efficiently covers the grid within the attempt limit. Consider systematic traversal patterns (e.g., spiral, zigzag, or binary search) and discuss trade-offs between coverage and attempts.
Pro tip: Always ask about the distribution of compromised servers and whether the function provides feedback (e.g., distance or direction hints) to tailor your search. Also, mention that you would write unit tests for edge cases like 1x1 grids or maximum attempts.
Ask questions to understand the grid dimensions, the function's return values, and what 'compromised' means. Confirm the maximum attempts and whether there are multiple compromised servers.
Model the grid as a graph or matrix and identify possible search patterns. Consider if the function gives any hints (e.g., 'hotter/colder') to guide the search.
Select an algorithm that balances coverage and attempts, such as binary search on rows/columns, spiral traversal, or random sampling with replacement. Justify your choice based on constraints.
Calculate worst-case attempts and time complexity. Discuss if the algorithm guarantees finding the server within the limit or if it's probabilistic.
Compare alternative approaches (e.g., exhaustive vs. heuristic) and mention edge cases like empty grid, server at corners, or attempts exhausted.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.