Crowdstrike·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Jul 2026

Summary

CrowdStrike technical phone screen, looks like they went straight for a medium-hard graph problem. Nothing behavioral, just code.

Questions Asked (1)

Given an m x n grid of integers, find a path from the top-left to the bottom-right cell (moving in 4 directions) that maximizes the minimum value encountered along the path. Return that maximum achievable minimum value.

Algorithms & Data Structures

Author's notes

I knew the brute force immediately but it was obviously too slow.

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AI HintsAI Generated

Suggested Approach

Reframe the problem as finding the maximum threshold T such that there exists a path from start to end where all cells have values >= T. Use binary search on T and for each T, perform BFS/DFS to check connectivity. Alternatively, use a max-heap (priority queue) to always expand the cell with the highest minimum value so far, similar to Dijkstra's algorithm.

Pro tip: Clarify with the interviewer whether diagonal moves are allowed (they are not, per the problem) and discuss the trade-offs between binary search + BFS (O(mn log(maxVal))) and heap-based approach (O(mn log(mn))). Mention that the heap approach can be more efficient when the value range is large.

1. Understand the problem and constraints

Restate the problem: find a path from (0,0) to (m-1,n-1) moving up/down/left/right that maximizes the minimum value along the path. Note that the answer is the maximum possible minimum value.

2. Choose an approach

Decide between binary search on the answer with BFS/DFS validation, or a max-heap (Dijkstra-like) approach. Explain the reasoning for your choice based on constraints.

3. Implement the algorithm

For binary search: define low and high bounds, and for each mid, check if a path exists using only cells >= mid. For heap: initialize with start cell, maintain a min-heap of (-minValue, row, col), and update the answer when reaching the end.

4. Analyze complexity and edge cases

Discuss time and space complexity. Handle edge cases: single cell grid, all cells same value, negative values, large grids.

5. Test with examples

Walk through a small example to verify correctness, e.g., grid = [[5,4,5],[1,2,6],[7,4,6]] should return 4.

Key Points to Mention

Binary search on the answer: monotonic property (if a path exists for T, it exists for any T' < T).
BFS/DFS for connectivity check: only traverse cells with value >= T.
Max-heap (priority queue) approach: similar to Dijkstra's algorithm, where the 'distance' is the minimum value along the path.
Time complexity: O(mn log(maxVal)) for binary search + BFS, O(mn log(mn)) for heap approach.
Space complexity: O(mn) for visited array or heap.
Edge cases: single cell, all cells same, negative values, large grids.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.