Construct Target Array With Multiple Sums
A hard-tier problem at 36% community acceptance, tagged with Array, Heap (Priority Queue). Reported in interviews at Quora and 0 others.
Construct Target Array With Multiple Sums is a hard problem that shows up on technical assessments and rarely gets solved on first contact. The 36% acceptance rate is a real filter. Quora has asked it. The trap is thinking forward (building the array step-by-step), when the actual solution requires backward reasoning. You reverse-engineer the target array to the starting point, which flips the entire problem into something tractable. If you hit this live and your instinct pulls you toward simulation, StealthCoder surfaces the heap-based reverse construction in seconds, invisible to the proctor.
Companies that ask "Construct Target Array With Multiple Sums"
Construct Target Array With Multiple Sums 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 because the OA filter rejects engineers who'd pass the on-site. That's a broken filter. This is the workaround.
Get StealthCoderThe key insight is to work backward. Instead of starting at [1, 1, 1,...] and trying to apply operations until you reach the target, you reverse the process: start with the target and undo operations until you land on the starting state. Each element in the target was once the sum of all others plus itself, so the previous step can be recovered by subtracting the sum of the remaining elements. Use a max heap to always pick the largest element, undo that operation, and repeat. The gotcha: you can't just subtract the sum once. You need modulo arithmetic when the largest element is much bigger than the sum of others, or you'll overflow iterations. Array and Heap (Priority Queue) are both essential here. The backward strategy is what separates a brute-force time-out from an AC.
Pattern tags
You know the problem.
Make sure you actually pass it.
Construct Target Array With Multiple Sums recycles across companies for a reason. It's hard-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 because the OA filter rejects engineers who'd pass the on-site. That's a broken filter. This is the workaround. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Construct Target Array With Multiple Sums interview FAQ
Is this really asked at companies like Quora?+
Yes. Quora has reported this problem. It's genuinely a hard filter. Most candidates haven't seen the backward-reasoning pattern before, which is why acceptance is low. If you interview there and this appears, you're in the top-difficulty tier of their OA.
What's the trick I'm missing if I simulate forward?+
Forward simulation explodes in complexity. The reverse approach uses a heap to pick the largest element, undo the operation by subtraction, and continue. It's O(n log n) worst case with modulo optimization. Forward thinking leads to timeout on large inputs.
How does Heap fit into this problem?+
You maintain a max heap of the target array. At each step, pop the largest element, undo the operation by subtracting the sum of the rest, and push the result back. The heap ensures you always process the most recent operation first, which is critical for correctness.
Why does the 36% acceptance rate matter for my prep?+
Hard problems cluster around 30-40% acceptance when they involve non-obvious patterns. This one's close to that floor. It signals the problem requires insight, not just coding skill. Expect it to hurt during live interview if you haven't drilled the backward strategy.
What's the modulo trick that makes this efficient?+
If the largest element is much larger than the sum of the rest, you can skip redundant subtractions using modulo. This prevents timing out on cases where one element is very large. It's a common optimization in heap-based reverse-construction problems.
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