Sort an Array
A medium-tier problem at 57% community acceptance, tagged with Array, Divide and Conquer, Sorting. Reported in interviews at Hive and 2 others.
Sort an Array is a medium-difficulty problem that appears in assessments for Hive, Infosys, and TCS. With an acceptance rate around 56%, it catches candidates who assume the built-in sort is always fair game or who can't articulate why a specific algorithm matters. The problem forces you to implement or defend a sorting strategy, not just call Arrays.sort(). If you hit this in your live OA and freeze on which algorithm to choose, StealthCoder surfaces a working solution in seconds, invisible to the proctor.
Companies that ask "Sort an Array"
Sort an Array 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 trick isn't math. It's recognizing that different constraints demand different algorithms. Merge Sort guarantees O(n log n) worst-case and is stable. Quicksort is faster in practice but can degrade. Heap Sort uses O(1) extra space. Radix, Bucket, and Counting Sort exploit the range or distribution of input data for linear time. Most candidates default to Merge Sort, which is safe but not always optimal. The real test is whether you can justify your choice and code it under pressure. Many OAs disable built-in sort entirely or require you to implement it from scratch. If you haven't drilled all seven sorting approaches, StealthCoder running invisibly during screen share gives you the working code and reasoning when your memory blanks.
Pattern tags
You know the problem.
Make sure you actually pass it.
Sort an Array 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. 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.
Sort an Array interview FAQ
What's the difference between Merge Sort and Quicksort for this problem?+
Merge Sort guarantees O(n log n) time and is stable, making it safer for live assessments. Quicksort is faster on average but can degrade to O(n^2) in worst case. If the problem doesn't specify constraints on pivot selection, Merge Sort is the lower-risk pick.
When should I use Radix or Counting Sort instead?+
Use these when the input range is small or known. Radix Sort runs in O(nk) where k is digit count. Counting Sort is O(n+k) but requires extra space. If the problem explicitly mentions numeric range or repeated values, these beat comparison sorts.
Is built-in sort allowed in the assessment?+
Most problem statements say you can't use it, or they're testing whether you know why not. Always assume you'll need to implement your own. If the platform allows it, ask via the chat. Better safe than disqualified.
Why is this problem asked at companies like Hive, Infosys, and TCS?+
It separates candidates who memorized sort names from those who understand trade-offs. These companies test system design thinking early. Sorting appears everywhere in backend code. Getting it right, fast, under pressure matters.
How do I avoid off-by-one errors when coding Merge Sort live?+
Use concrete indices and draw a small example on paper before writing. Test your merge logic separately. Many candidates get the partition right but fail on the merge step. Practice coding it twice before your OA to build muscle memory.
Want the actual problem statement? View "Sort an Array" on LeetCode →