Maximum Number of Points From Grid Queries
A hard-tier problem at 60% community acceptance, tagged with Array, Two Pointers, Breadth-First Search. Reported in interviews at J.P. Morgan and 0 others.
Maximum Number of Points From Grid Queries is a hard problem that combines sorting, spatial traversal, and dynamic connectivity in ways that don't telegraph themselves immediately. With an acceptance rate just below 60%, this problem has appeared in assessments for J.P. Morgan and tests your ability to recognize that greedy ordering plus union-find unlocks what looks like a brute-force grid search. If you hit this live and your first instinct is BFS or recursive backtracking, you'll time out. StealthCoder surfaces the correct approach in seconds, invisible to the proctor.
Companies that ask "Maximum Number of Points From Grid Queries"
Maximum Number of Points From Grid Queries 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. Built by an Amazon engineer who used it to pass JPMorgan's OA and system design loop.
Get StealthCoderThe trick is sorting queries by their point values in ascending order, then using union-find to build connectivity incrementally as you process each query's neighborhood. Most candidates default to BFS or DFS on the grid for each query independently, which is correct in logic but wrong in speed. The real insight is that by processing queries in order, you can reuse and build a single union-find structure rather than recomputing reachability from scratch each time. This is a rare problem where the sorting step is non-obvious and the data structure choice (union-find over a matrix) feels indirect. Pitfall: jumping to heap-based priority queue without recognizing that sorted iteration plus incremental union-find is the answer. StealthCoder hedges the moment you blank on whether sorting helps or you're unsure whether union-find even applies to grids.
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Maximum Number of Points From Grid Queries recycles across companies for a reason. It's hard-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. Built by an Amazon engineer who used it to pass JPMorgan's OA and system design loop. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Maximum Number of Points From Grid Queries interview FAQ
Why does sorting the queries matter here?+
Sorting by point values lets you process queries in ascending order and build connectivity incrementally. As you process each query, all previous queries (with lower values) have already been incorporated into your union-find structure. This avoids recomputing the same reachable cells repeatedly and collapses what looks like O(Q*N*M) into something tractable.
Is this problem still asked at major tech companies?+
Yes. J.P. Morgan has reportedly asked it. At hard difficulty with a sub-60% acceptance rate, it's a legitimate screen filter. It tests both algorithmic insight (recognizing the sorting + union-find pattern) and comfort with non-standard data structure applications.
What's the most common wrong approach?+
BFS or DFS from each query's starting cell, recomputed independently each time. This is algorithmically sound but times out. Candidates often don't recognize that union-find applies to grid reachability, or that sorting the input transforms the problem entirely.
How do I know when to use union-find on a matrix?+
When you need to track which cells are connected and that connectivity changes over time or depends on a parameter you can sort. Here, as you 'unlock' cells with higher point thresholds, union-find lets you merge components efficiently without recomputation.
Does this problem require Array, Two Pointers, BFS, and Union Find all at once?+
Union-find and sorting are core. BFS appears in some solutions for neighbor traversal. Two pointers is less central. The topic list flags the main tools, but the winning insight is recognizing which tool is critical (union-find) and which are supporting (sorting, neighbor iteration).
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