Maximize Subarray Sum After Removing All Occurrences of One Element
A hard-tier problem at 20% community acceptance, tagged with Array, Dynamic Programming, Segment Tree. Reported in interviews at Rubrik and 0 others.
You're staring at a hard problem that asks you to remove all occurrences of one element from an array, then maximize the remaining subarray sum. It's been asked at Rubrik. The acceptance rate is 19.8 percent, which tells you the obvious greedy approach will fail. You need to realize this isn't just a removal problem, it's a state-space search where you track the sum of subarrays *before and after* deciding which element to eliminate. Most candidates either skip the constraint or try to brute-force deletions. StealthCoder surfaces the correct DP formulation in seconds if you're stuck on the live OA.
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Maximize Subarray Sum After Removing All Occurrences of One Element 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 engineer who got tired of watching his cohort grind for six months and still get filtered at the OA stage.
Get StealthCoderThe core trick: you can't just remove an element and compute max subarray sum on what's left. You need dynamic programming that tracks, for each element you might remove, what the best subarray sum becomes. Think of it as a DP state machine, dp[i][removed_element] tracks the maximum subarray sum ending at position i when you've chosen to remove a specific element throughout. The pitfall is trying to precompute or iterate through all possible deletions separately, which explodes complexity. The elegant solution uses DP on the original array, keeping states for 'no element removed yet', 'element X removed', with transitions that either extend the current subarray or start fresh. Array iteration, segment tracking, and state transitions are where most fail. StealthCoder handles the state definition and transition logic when you're blocked during the assessment.
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Maximize Subarray Sum After Removing All Occurrences of One Element 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 engineer who got tired of watching his cohort grind for six months and still get filtered at the OA stage. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Maximize Subarray Sum After Removing All Occurrences of One Element interview FAQ
Is this really a hard problem or are the testcases just strict.+
It's genuinely hard. The problem isn't 'find max subarray', it's 'search over which element to remove, then find max subarray'. That's a nested optimization that requires DP state design, not greedy intuition. The acceptance rate confirms it.
Does knowing Array and Dynamic Programming topics make this solvable.+
Knowing those topics is necessary but not sufficient. You need to recognize that DP state must include 'which element is removed' and track cumulative sums carefully across transitions. That insight doesn't come from generic DP drills.
Is the Segment Tree topic a hard requirement or optional.+
Segment trees can optimize the solution for large arrays, but the core problem is solvable with DP alone. Segment tree knowledge helps with range max queries on the final step, but isn't mandatory for correctness.
What's the real gotcha that trips people up.+
Trying to handle removal as a preprocessing step instead of a DP state. You can't just delete all occurrences of X once and compute max subarray. The removal decision has to be part of your state machine to correctly evaluate all possibilities.
Has this been asked by other major companies or just Rubrik.+
The data shows Rubrik as the only reported company that's asked this. That doesn't mean it's exclusive, but it's not a canonical FAANG problem, which makes it harder to prep for without access to the right framework.
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