Mark Elements on Array by Performing Queries
A medium-tier problem at 48% community acceptance, tagged with Array, Hash Table, Sorting. Reported in interviews at Barclays and 1 others.
Mark Elements on Array by Performing Queries is a medium-difficulty problem sitting at just under 48% acceptance. It's asked at Barclays and Samsung. You're given an array and a series of queries that mark elements; the trick is handling the marking operation efficiently without TLE-ing on naive simulation. This is the kind of problem where the obvious approach looks clean but runs into timeout hell on large test cases. If you hit this live and don't see the optimization path, StealthCoder will surface a working solution invisible to the proctor.
Companies that ask "Mark Elements on Array by Performing Queries"
Mark Elements on Array by Performing Queries 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 core pattern: you can't just iterate and mark every element for every query. You need to sidestep redundant work. The winning approach uses a pointer or hash table to skip elements you've already processed, or a heap to track unvisited indices efficiently. Common fail point is writing a clean O(n * m) nested loop solution that passes examples but TLEs on the actual test suite. Array, Hash Table, and Sorting are table stakes; Heap (Priority Queue) is the real accelerator because it lets you jump directly to the next unmarked element instead of scanning. The simulation itself is simple, but the data structure choice determines whether you pass or timeout. This is exactly the problem where StealthCoder's real-time solution delivery prevents you from burning minutes on a suboptimal implementation during the assessment.
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Mark Elements on Array by Performing Queries 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 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.
Mark Elements on Array by Performing Queries interview FAQ
Is this problem actually asked at FAANG and big tech?+
It appears at Barclays and Samsung in reported data. Mid-tier interview problem, not headline-grabbing. You'll see it more in backend and infrastructure roles where simulation and efficient array traversal matter.
What makes this hard if the algorithm is simple?+
The algorithm is trivial, but the implementation isn't. A straightforward loop passes small examples and fails silently on larger test cases. You need to track which elements are marked and skip them, not rescan the whole array each time. That's where Heap or Hash Table efficiency counts.
Should I use a Heap or Hash Table?+
Heap (Priority Queue) is stronger if you need to find the next unmarked element fast. Hash Table works if you're checking membership and skipping marked indices. Heap gives you O(log n) pops; Hash Table gives you O(1) lookups. Problem specifics determine the winner.
How hard is 48% acceptance really?+
It's medium. Most candidates nail the simulation logic but trip on the efficiency layer. If you see the optimization early (skip marked elements), you'll solve it in under 15 minutes. If you don't, you'll rewrite twice.
What topics should I study before tackling this?+
Array basics and Sorting are mandatory. Hash Table for membership checks. Heap (Priority Queue) if the marking count is high and you need fast next-unvisited lookups. Simulation is just careful index tracking and state updates.
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