Maximum Students Taking Exam
A hard-tier problem at 52% community acceptance, tagged with Array, Dynamic Programming, Bit Manipulation. Reported in interviews at SAP and 0 others.
You're sitting in an online assessment and you see a grid with broken desks scattered across rows and columns. The problem asks you to maximize the number of students who can take an exam without sitting in broken seats or adjacent to each other. It sounds spatial and greedy at first, but the constraint space explodes fast. SAP has asked this one. The trick is recognizing this as a bitmask DP problem where each row's valid seating configurations are precomputed, then you chain rows together. If you blank on how to model state, StealthCoder surfaces the working approach invisible to the proctor.
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Maximum Students Taking Exam 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 an Amazon engineer who watched the leaked-problem repo become an industry secret. He decided you should have it too.
Get StealthCoderThe naive greedy approach fails immediately because placing a student optimally in one row often blocks better placements below. You need dynamic programming with bitmask representation. Each row has at most 2^N possible seat configurations, but only some are valid (no adjacent students, no broken desks). Build a graph of which masks can follow which, then DP forward: for each row, track the maximum students achievable with each valid mask, considering transitions from the previous row. The state space compresses through bit manipulation because you only care about which seats are occupied, not the problem details. Common pitfall: miscounting overlapping adjacencies or not precomputing valid mask transitions. StealthCoder handles the bitmask logic and DP table construction in real time if you hit a wall during the live assessment.
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Make sure you actually pass it.
Maximum Students Taking Exam 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. Made by an Amazon engineer who watched the leaked-problem repo become an industry secret. He decided you should have it too. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Maximum Students Taking Exam interview FAQ
Is this really a hard problem or just tedious?+
It's genuinely hard. The insight that bitmask DP applies isn't obvious, and implementing adjacency checks across rows correctly takes discipline. Acceptance rate sits around 52%, which reflects that many candidates see it as a greedy problem and hit dead ends. The difficulty is conceptual, not implementation volume.
Do I need to memorize bitmask DP patterns?+
Not memorize, but recognize. When you see 'maximize selections on a grid with adjacency constraints,' bitmask DP should ping. The pattern is: enumerate valid masks per row, precompute transitions, then DP. This problem is a textbook example of when bit manipulation and DP intersect.
What's the biggest gotcha?+
Adjacency checking across rows. A student in seat i blocks seats i-1, i, and i+1 in both neighboring rows. Many candidates miss diagonal adjacency or forget to validate mask transitions. Precomputing a transition table saves debugging time during the OA.
How does this relate to independent set problems?+
It's an independent set problem on a bipartite graph (desks as nodes, adjacency as edges). Bitmask DP is one efficient approach when the graph is small and the constraint is local. Understanding that connection helps you reuse solution patterns across similar problems.
Will seeing this once at SAP prepare me for other companies?+
This problem is relatively rare. The bitmask DP pattern itself appears in other guises (profile DP, tiling), but this exact constraint set is niche. Use it to deepen your bitmask and DP intuition, not as a predictor of what you'll see elsewhere.
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