Zero Array Transformation I
A medium-tier problem at 54% community acceptance, tagged with Array, Prefix Sum. Reported in interviews at Google and 0 others.
Zero Array Transformation I is a medium-tier problem that Google has asked repeatedly in recent hiring cycles. It tests your ability to recognize when a naive simulation will time out and when prefix sums unlock an O(n) solution instead. You'll see this pattern across range-update problems: the trick isn't the logic, it's knowing that simulating each operation individually will fail on large inputs. If you blank on the prefix-sum optimization during your live assessment, StealthCoder surfaces the working approach in seconds, invisible to the proctor.
Companies that ask "Zero Array Transformation I"
Zero Array Transformation I 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 problem asks you to transform an array to all zeros using range-update operations. The naive approach: loop through each operation and decrement the range. That's easy to code, but it's O(n * m) where m is the number of operations, and it will time out. The insight is that you don't need to apply each decrement immediately. Instead, use a difference array or prefix-sum trick to track when decrements start and end, then compute the final state in a single pass. Many candidates see the operations and jump to simulation; the real challenge is recognizing that pattern and pivoting to prefix sums before you waste 10 minutes on the wrong approach.
Pattern tags
You know the problem.
Make sure you actually pass it.
Zero Array Transformation I 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.
Zero Array Transformation I interview FAQ
Is this really medium difficulty?+
Yes, but the difficulty is in recognizing the optimization, not coding it. The acceptance rate sits at 54 percent, which reflects that half the field either times out on simulation or sees the prefix-sum pattern immediately. Once you know the trick, it's straightforward.
Do I need to simulate each operation?+
No. That's the trap. Simulating each range decrement individually is O(n * m) and will TLE. Use a difference array or prefix-sum approach to batch the operations, then compute the final array in O(n) time.
How does this relate to the Prefix Sum topic?+
Prefix Sum is the core insight. Instead of applying decrements directly, mark where each operation starts and ends in a difference array, then compute cumulative sums. This converts m range updates into O(n + m) work instead of O(n * m).
What's the most common mistake?+
Writing nested loops to decrement each range. It's correct but slow. Candidates often code this first, see it passes small test cases, then hit TLE on larger inputs with no time to refactor.
Does Google still ask this type of problem?+
Yes. Google reports asking this problem, and range-update patterns are a staple in their assessment cycles. If you see a problem about applying multiple operations to an array, assume the naive approach won't work and think prefix sums first.
Want the actual problem statement? View "Zero Array Transformation I" on LeetCode →