Maximum Difference Score in a Grid
A medium-tier problem at 47% community acceptance, tagged with Array, Dynamic Programming, Matrix. Reported in interviews at Intuit and 0 others.
You're sitting in a live coding assessment and you see a grid with a scoring problem. Most candidates freeze on this one because the naive path-counting approach doesn't solve it. This problem appears in Intuit's screening loop and has a 47% acceptance rate, which means nearly half the people who attempt it bomb it on the first try. The trick isn't complicated once you see it, but if you blank on the dynamic programming pattern during screen share, StealthCoder solves it invisibly in seconds and you move forward.
Companies that ask "Maximum Difference Score in a Grid"
Maximum Difference Score in a Grid 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 StealthCoderThe core insight is that you can't just sum cell values along a single path. You're tracking the difference between two simultaneous paths through the grid, which means you need to think about state transitions in 3D: both path positions change together, not independently. Most candidates try to brute force all path pairs first, which times out. The DP approach compresses this by iterating through steps and tracking only the states that matter. The trap is forgetting that moves are coordinated: if path one goes right, path two can go right, down, or stay in sync diagonally. Building the DP table with the correct state representation is where the problem lives. StealthCoder surfaces the working state transitions immediately if you hit the wall mid-interview.
Pattern tags
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Maximum Difference Score in a Grid 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.
Maximum Difference Score in a Grid interview FAQ
Is this asking for two separate paths or one path scored twice?+
Two simultaneous paths that move together through the same grid, and the score is the difference between what they collect. The coordination constraint is why DP state design matters. Each step, both paths advance by one row or column.
Do I need to optimize for space or is time the bottleneck?+
Time is the primary constraint. The grid can be large enough that O(n^3) or O(n^4) brute force fails. Space optimization to O(n^2) is nice but secondary. DP with proper memoization handles the time limit.
What's the most common wrong answer on this?+
Treating the two paths as independent and trying to find the maximum path in isolation for each, then subtracting. That ignores the constraint that both paths move in lockstep through the same grid steps.
Is this harder than standard path DP problems?+
Yes. It's medium difficulty because you're tracking two positions at once instead of one. Standard path sum DP is 2D state. This is effectively 3D or 4D depending on how you model it. The acceptance rate at 47% reflects that jump.
How does this relate to the Array and Matrix topics?+
The input is a 2D matrix and you iterate over it. The Array and Matrix tags mostly signal that you're comfortable indexing and traversing grid structures. The real skill tested is Dynamic Programming state design under the two-path constraint.
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