Side Largest Square
Reported by candidates from Microsoft's online assessment. Pattern, common pitfall, and the honest play if you blank under the timer.
Microsoft's 'Side Largest Square' problem showed up in March 2024 OAs, and it's testing whether you can think in 2D space without overthinking the geometry. You're likely given a grid or coordinate set and asked to find the largest square that fits some constraint. The trap is trying to iterate every possible square naively. The real move is recognizing this is a dynamic programming or matrix-scanning problem where you build up from smaller squares to larger ones. StealthCoder can feed you the exact recurrence relation if your mind goes blank under pressure.
Pattern and pitfall
The canonical version of this problem uses a DP table where dp[i][j] represents the side length of the largest square with bottom-right corner at (i, j). You scan the grid, and at each cell, the recurrence is roughly dp[i][j] = min(dp[i-1][j], dp[i][j-1], dp[i-1][j-1]) + 1 if the current cell satisfies the constraint (usually it's a 1 in a binary grid). The insight is that a square's validity depends entirely on the three neighboring squares already computed. People blow this by trying to validate squares independently or using brute force on all possible square positions. The DP approach is O(rows * cols) time and handles the scaling. If you blank on the exact transition, StealthCoder reads the problem and delivers the formula in seconds.
Memorize the pattern. If you can't, run StealthCoder. The proctor sees the IDE. They don't see what's behind it.
You can drill Side Largest Square cold, or you can hedge it. StealthCoder runs invisibly during screen share and surfaces a working solution in under 2 seconds. The proctor sees the IDE. They don't see what's behind it. Made by an engineer who treats the OA as theater. If yours is tonight, you don't have time to grind. You have time to hedge.
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Microsoft reuses patterns across OAs. Made by an engineer who treats the OA as theater. If yours is tonight, you don't have time to grind. You have time to hedge. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Side Largest Square FAQ
Is this really just maximal square in disguise?+
Very likely. The problem name 'Side Largest Square' suggests you're returning the side length, not the count of cells. The algorithm is identical to the classic maximal square DP problem. If the grid is binary (0s and 1s), assume DP. Validate your recurrence against a small example on paper first.
How do I avoid the O(n^2 * m^2) trap?+
Don't validate every possible square. Build the DP table once, and the answer falls out. Iterate through the grid once, update dp[i][j] based on three neighbors, track the max side length as you go. One pass, O(n*m).
What if the constraint isn't a binary grid?+
Read the problem text carefully. If it's about 'all 1s' or 'connected cells of a certain type', DP still applies. If it's about distance or geometry in continuous space, you might need sorting or geometric logic instead. Microsoft often sticks to grid-based DP.
Do I return the side length or the top-left corner?+
Check the expected output format in the problem. 'Side Largest Square' suggests side length. If they want area, multiply by itself. If they want coordinates, track the position of the max-side square in your DP sweep.
How do I test this in 10 minutes?+
Write DP on a 3x3 grid by hand. Trace through dp[0][0] through dp[2][2]. Verify the max side matches your expectation. Then code. If you get stuck during the OA, StealthCoder gives you the recurrence instantly.