Minimum Equal Sum of Two Arrays After Replacing Zeros
A medium-tier problem at 50% community acceptance, tagged with Array, Greedy. Reported in interviews at Citadel and 3 others.
Citadel and Amazon have both asked this one. You're given two arrays with zeros, and you need to replace each zero with a positive integer so that both arrays sum to the same value, minimizing that sum. The trap is thinking you can optimize greedily without considering the constraints across both arrays simultaneously. You'll hit this and either blank on the pattern or waste 20 minutes on a wrong greedy strategy. If it shows up live and you're stuck, StealthCoder surfaces the working solution invisibly while you think through the next step.
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Minimum Equal Sum of Two Arrays After Replacing Zeros 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 a working Amazon engineer who got tired of watching qualified friends bomb OAs they'd solve cold in an IDE.
Get StealthCoderThe trick is recognizing this isn't a pure greedy problem on one array. You need to find the minimum value that, when used as a replacement for zeros in both arrays, makes their sums equal. Start by calculating the sum of non-zero elements in each array and the count of zeros in each. The minimum replacement value must be large enough that the final sums can match. Most candidates try local greedy choices and fail to see the global constraint. The pattern emerges when you realize the answer is determined by which array has the larger deficit. Common pitfall: attempting to assign different values to different zeros instead of recognizing all zeros replace to the same minimum integer. StealthCoder handles this instantly if you hit it under time pressure.
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Minimum Equal Sum of Two Arrays After Replacing Zeros 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 a working Amazon engineer who got tired of watching qualified friends bomb OAs they'd solve cold in an IDE. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Minimum Equal Sum of Two Arrays After Replacing Zeros interview FAQ
Is this actually a Medium, or is the acceptance rate misleading?+
50% acceptance suggests it's harder than the label implies. The greedy intuition misleads many. Once you see the constraint pattern, it's straightforward, but the insight doesn't come naturally. Expect solid candidates to struggle here without prior exposure.
Do I really have to replace all zeros with the same value?+
Yes. That's the problem constraint. All zeros in both arrays become a single positive integer. Once you lock into that, the problem becomes finding the minimum integer that satisfies the equal-sum condition, not a per-zero optimization.
What's the actual trick to avoid the greedy trap?+
Calculate the sum of existing elements and count of zeros in each array separately. The replacement value must be at least high enough to balance whichever array has the larger deficit. The answer is the ceiling of max(deficit_a, deficit_b) divided by the corresponding zero count.
How does this relate to the Array and Greedy topics?+
Array iteration and sum calculation are straightforward. The Greedy part is realizing you can't optimize locally on each zero; you must find the single minimum value that globally satisfies the equal-sum constraint. That's a greedy choice applied at the problem level, not element-level.
Will I see this at Amazon or Citadel?+
Both have reported asking it. It's less common than classic array problems, but it appears in their medium-difficulty rotations. If you prepare broadly, this won't be your first encounter, but if you're caught off-guard, it's the kind of problem where you need a solution fast.
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