Maximal Score After Applying K Operations
A medium-tier problem at 64% community acceptance, tagged with Array, Greedy, Heap (Priority Queue). Reported in interviews at McKinsey and 0 others.
Maximal Score After Applying K Operations is a medium-difficulty problem that shows up in live coding assessments, particularly at firms like McKinsey. You're given an array and must apply exactly K operations to maximize your final score. The catch: the greedy move isn't always obvious, and many candidates blow past the optimal strategy on first read. The trick involves recognizing which elements to prioritize, which is where the right data structure becomes essential. If you hit this problem cold in your assessment and freeze on the approach, StealthCoder surfaces the working solution in seconds, invisible to the proctor.
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Maximal Score After Applying K Operations 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 a senior engineer who knows the OA is theater. This is the script.
Get StealthCoderThis problem lives in the greedy space, but greedy here means 'always pick the maximum value available after each operation,' which immediately points to a heap. The naive approach tries to manually track the best candidate each time, but with K operations stacking up, that's too slow. You'll want a max heap (priority queue) to keep the next-best score choice always at the top. The gotcha: each operation typically transforms an element or reveals a new value, so you can't just pre-sort and iterate. Build your heap from the initial array, then simulate K pops and pushes based on the operation rules. Most candidates either skip the heap entirely or misunderstand what gets added back after each operation. Acceptance rate sits at roughly 64 percent, which suggests the pattern clicks for those who've seen heap-based greedy before, but trips up those trying to optimize prematurely.
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Maximal Score After Applying K Operations 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. Built by a senior engineer who knows the OA is theater. This is the script. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Maximal Score After Applying K Operations interview FAQ
Is this problem actually medium, or does it feel harder?+
It reads medium because the greedy intuition is sound, but execution is strict. You must use a heap correctly and track state across K iterations. The 64 percent acceptance rate confirms it's not easy; many solutions time out or miss edge cases around the operation logic itself.
Why can't I just sort once and pick the top K values?+
Because each operation transforms the array or reveals new values. You can't pre-compute the final K best scores. After each pick, the landscape changes. A heap lets you always grab the current maximum without re-sorting, which is the real win.
What's the most common mistake on this one?+
Forgetting to push the transformed element back into the heap after an operation. Candidates pop the max, compute the new score, but then lose track of that value, breaking the state for the next iteration.
Is this still asked at big firms like McKinsey?+
Yes. McKinsey explicitly reports this problem, and it fits their style: algorithmic but not exotic, testable in 30-40 minutes, and it separates candidates who know heap patterns from those who don't.
How do I prepare for the heap + operation combo?+
Practice problems that simulate K operations with state changes: simulate K rounds, use a heap to track the best choice each round, and always re-insert the modified element. Dry-run on paper first to nail the operation logic before coding.
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