Maximize Sum Of Array After K Negations
A easy-tier problem at 52% community acceptance, tagged with Array, Greedy, Sorting. Reported in interviews at Druva and 0 others.
You're looking at a problem that shows up on Druva's assessment and has a 52% acceptance rate, which is deceptive. The title sounds simple: negate up to K elements to maximize the sum. Most candidates read it, think greedy (flip the negatives), and miss the twist. That twist costs them. If you hit this live and blank on the trick, StealthCoder surfaces the solution invisible to the proctor. The problem tests whether you can think past the obvious greedy choice and adapt when the negatives run out.
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Maximize Sum Of Array After K Negations 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 greedy approach is correct, but incomplete. You sort the array, then negate the K most negative numbers to maximize the sum. The catch: what if K is larger than the count of negative numbers? You'll have negations left over. At that point, you don't negate more negatives randomly. Instead, you flip the smallest absolute value an odd number of times (once if K is odd, zero times if K is even after consuming all negatives). Most first-time solvers either miss the second phase or implement it incorrectly. The problem sits at intersection of Greedy and Array manipulation, and it requires you to think in two stages. StealthCoder catches the logic break and runs through the sorting and conditional flip in seconds during your OA.
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Maximize Sum Of Array After K Negations recycles across companies for a reason. It's easy-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.
Maximize Sum Of Array After K Negations interview FAQ
Is this problem actually easy or is the 52% rate misleading?+
The acceptance rate suggests candidates are getting tripped up. The problem IS algorithmically simple once you see both stages, but the twist about leftover negations catches people off guard in a live assessment. It's easy once you drill it, hard the first time.
What's the trick that makes this different from a basic sort-and-flip?+
After you negate all negative numbers using available K, you might have negations left. You can't just negate random elements again. You flip the smallest absolute value an odd number of times to minimize damage. That's the non-obvious step most candidates miss.
How does Greedy apply here?+
Greedy says: always negate the most negative (largest absolute value) first. That maximizes each step. But when negatives run out, greedy flips to a different goal: minimize the final damage by flipping the smallest absolute value, not a random one.
Do I need to actually modify the array or just calculate the sum?+
You don't modify the original array. You sort a copy, negate elements in place during logic, and return the sum. Some solutions use a heap for the second phase to avoid re-scanning for minimum absolute value, which is cleaner.
Is this still asked at other companies beyond Druva?+
Data shows Druva in the reports, but the pattern (greedy with a conditional second phase) is common in array assessments across tech interviews. It's worth knowing even if your target company isn't listed here.
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