Minimize Variation
Reported by candidates from Amazon's online assessment. Pattern, common pitfall, and the honest play if you blank under the timer.
Amazon's 'Minimize Variation' hit the OA circuit in June 2025, and it's a deceptively simple-sounding problem that trips up candidates who overthink it. You're given a set of values and need to reduce the spread or difference between them in some way, usually by selecting or modifying elements under a constraint. The catch is figuring out what 'minimize' actually means in the context of the input. StealthCoder will read the exact problem on screen and surface the pattern immediately if you blank during the live assessment.
Pattern and pitfall
This problem almost always boils down to either two-pointers (sort and pick a window) or greedy selection (always pick the locally optimal choice). The hidden trick: after sorting, the minimum variation often comes from a contiguous subarray, not a random pick. If you're allowed to remove or modify k elements, you're looking at a sliding window over sorted data. If you need to partition or select groups, think greedy: process in order and commit to a choice when the cost becomes unfavorable. The pitfall is trying to solve it with dynamic programming when a single pass works. StealthCoder catches this during the OA by showing you whether a linear or two-pointer scan is sufficient before you waste time coding a DP table.
If this hits your live OA and you blank, StealthCoder solves it in seconds, invisible to the proctor.
You can drill Minimize Variation cold, or you can hedge it. StealthCoder runs invisibly during screen share and surfaces a working solution in under 2 seconds. The proctor sees the IDE. They don't see what's behind it. Built by an Amazon engineer who would have shipped this the night before his JPMorgan OA if he'd had it.
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Amazon reuses patterns across OAs. Built by an Amazon engineer who would have shipped this the night before his JPMorgan OA if he'd had it. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Minimize Variation FAQ
Is this a 'sort first' problem?+
Almost always yes. Once sorted, the problem becomes clearer: you're looking for structure in the ordered sequence. If the goal is to minimize spread, you're picking a contiguous subarray. If it's about grouping, sorted order helps you see valid partitions immediately.
What if I can modify or remove elements?+
That's the constraint that changes the approach. If you can remove k elements, use a sliding window on the sorted array and find the subarray of length (n-k) with minimum difference. If you modify, think greedy: what's the cost-minimizing operation at each step?
How do I recognize the greedy choice?+
Look for a monotonic property: once a choice is made, you never revisit earlier options. If selecting element X rules out element Y forever, or if delaying a choice only increases cost, you're in greedy territory. Test with a small example to confirm.
Could this be a DP problem instead?+
Unlikely, but check if there's overlapping subproblems. If a smaller variation on a subset helps solve the full problem, DP might apply. Usually this problem doesn't need it. Greedy or two-pointers is faster and passes.
What's the common wrong answer?+
Trying to optimize every element individually instead of recognizing the global structure. Or picking random elements instead of realizing that after sorting, the answer lives in a contiguous window or follows a greedy commit pattern.