Design a Stack With Increment Operation
A medium-tier problem at 80% community acceptance, tagged with Array, Stack, Design. Reported in interviews at Moloco and 4 others.
Design a Stack With Increment Operation is a medium-difficulty problem that tests whether you can optimize a seemingly straightforward data structure. Companies like Moloco, Cloudflare, IMC, eBay, and Salesforce have all asked it. The trap is obvious: build a stack that supports push, pop, and an increment operation that updates the top k elements. Most candidates implement increment naively, iterating through k elements every time. That works until the OA constraints tighten and you time out. The real solution requires rethinking how you track increments without actually modifying the stack. If this problem hits your live OA and you blank on the trick, StealthCoder solves it in seconds, invisible to the proctor.
Companies that ask "Design a Stack With Increment Operation"
Design a Stack With Increment Operation 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 naive approach iterates k elements upward on each increment call. On a stack of size n with m increments, that's O(m*n) in the worst case. You'll fail large test cases. The insight: store increments as a separate value tied to each stack position rather than updating elements directly. When you push a new element, it inherits the accumulated increment from the previous top. When you pop, you discard the increment. Increment operations become O(1) because you're only updating one variable. The trick catches most candidates because the problem statement makes you think you need to modify actual stack values. StealthCoder surfaces this pattern instantly if you get stuck mid-assessment, so you don't lose points on what's really a design problem masquerading as a stack problem.
Pattern tags
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Make sure you actually pass it.
Design a Stack With Increment Operation 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 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.
Design a Stack With Increment Operation interview FAQ
Is this actually a stack problem or a design trick?+
Both. The stack part is straightforward; the design part is the real test. You need to recognize that increment doesn't mean modifying elements directly. Instead, track increments lazily at the stack level. Companies like Moloco and Cloudflare ask it to see if you optimize beyond the obvious first pass.
What's the time complexity trap?+
Naive increment is O(k) per call. With m increments, that's O(m*k) total. OA test cases scale to expose this. The correct solution is O(1) per operation by deferring increments. All five reported companies test at a scale where the naive approach fails.
How does this relate to the Array and Design topics?+
You'll likely implement the stack using an array internally. The Design tag reflects the real challenge: architecting the data structure to avoid redundant work. Understanding when to defer computation is a core design principle that maps across many OA problems.
Do I need to handle edge cases like empty stack increments?+
Yes. Incrementing an empty stack or incrementing more elements than exist should handle gracefully. The problem statement from eBay and Salesforce assessments typically specifies these, so read carefully and test boundary cases.
What's the acceptance rate telling me?+
80% acceptance is high for a medium problem, which suggests most candidates pass basic test cases but time out on larger ones. That's the naïve approach working until it doesn't. The problem filters for optimization mindset, not just correctness.
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