Path With Minimum Effort
A medium-tier problem at 61% community acceptance, tagged with Array, Binary Search, Depth-First Search. Reported in interviews at Cohesity and 1 others.
Path With Minimum Effort is a graph traversal problem disguised as a matrix navigation challenge. You're given a 2D grid of heights and need to find a path from top-left to bottom-right that minimizes the maximum elevation difference between any two adjacent cells. It's asked at Cohesity and Snowflake, and the 61% acceptance rate masks a common pitfall: greedy fails here. The trick is treating it as a shortest-path problem where the "distance" isn't steps, it's the worst climb you have to make. If you hit this live and freeze on the approach, StealthCoder surfaces a working solution in seconds, invisible to the proctor.
Companies that ask "Path With Minimum Effort"
Path With Minimum Effort is the kind of problem that decides whether you pass. StealthCoder reads the problem on screen and surfaces a working solution in under 2 seconds. Invisible to screen share. The proctor sees nothing. Made by a working FAANG engineer who treats the OA the way companies treat hiring: as a game with rules you should know.
Get StealthCoderMost candidates try greedy or naive DFS and either TLE or return suboptimal paths. The real solution reframes the problem: use Dijkstra's algorithm or binary search plus BFS/DFS to find the path where the maximum absolute difference between adjacent heights is minimized. Priority queue (heap) approach is most straightforward: always explore the neighbor reachable with the smallest maximum effort so far. Union Find also works if you binary search on the answer and check connectivity. The topics list hints at all valid approaches: Heap solves it directly, Binary Search pairs with DFS/BFS, Union Find pairs with binary search. Most will attempt the heap solution because it's cleaner to reason about. When you're coding live and the basic traversal isn't cutting it, the shift to "effort as the cost metric" is where most blank. StealthCoder bridges that gap by showing you the pattern instantly.
Pattern tags
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Path With Minimum Effort recycles across companies for a reason. It's medium-tier, and most candidates blank under the timer. StealthCoder is the hedge: an AI overlay invisible during screen share. It reads the problem and surfaces a working solution in under 2 seconds. Made by a working FAANG engineer who treats the OA the way companies treat hiring: as a game with rules you should know. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Path With Minimum Effort interview FAQ
Is this really a medium, or is the acceptance rate misleading?+
61% acceptance is honest. The problem itself is medium-level once you see it's a shortest-path variant. Most failures come from submitting greedy or exhaustive DFS before recognizing the pattern. Once you know Dijkstra maps here as the natural fit, it's straightforward to code.
Can you solve this without a heap or priority queue?+
Yes. Binary search on the answer (the maximum effort), then use DFS or BFS to check if a path exists within that effort threshold. Binary search + DFS is slightly slower but avoids heap overhead. Both land within time limits on typical constraints.
Why does greedy fail?+
Greedy picks the lowest-effort next step locally, but that can trap you in a high-effort region. The globally optimal path sometimes requires a slightly higher local step to unlock a much easier overall route. Dijkstra explores all neighbors by cumulative effort, not local effort.
How does Union Find apply here?+
Binary search the effort threshold, then use Union Find to check if top-left and bottom-right become connected when you only include edges with effort below the threshold. Fewer candidates reach this, but it's valid and sometimes cleaner than Dijkstra on paper.
Do Cohesity and Snowflake ask this the same way?+
Input data doesn't specify variations, so assume the standard LeetCode version. Both companies report asking it, so the core algorithm is what matters. Your implementation and debugging speed on the live OA is the differentiator.
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