Counting Sort interview questions
6 counting sort problems tagged across recent interview reports. Drilled most heavily by tcs, accenture, and bytedance.
Counting Sort is a non-comparative sorting algorithm that trades comparison operations for frequency counting. It's optimal when the range of input values is small relative to the dataset size, typically integers in a known, bounded range. You'll encounter it across 6 distinct problems in interviews, from H-Index to Relative Sort Array. Companies like TCS, ByteDance, and De Shaw use it to filter candidates who understand when to break out of comparison-based sorting entirely. If you hit a constraint like "sort with integers in range 0 to K," Counting Sort is often the intended solution. StealthCoder solves these instantly when you're under pressure.
Most-asked counting sort problems
| # | Problem | Diff | # Companies | Pass % |
|---|---|---|---|---|
| 01 | H-Index | MEDIUM | 4 | 40% |
| 02 | Sort an Array | MEDIUM | 3 | 57% |
| 03 | Height Checker | EASY | 2 | 81% |
| 04 | How Many Numbers Are Smaller Than the Current Number | EASY | 2 | 87% |
| 05 | Make String Anti-palindrome | HARD | 1 | 42% |
| 06 | Relative Sort Array | EASY | 1 | 75% |
You can't drill every counting sort variant before the assessment. StealthCoder runs invisibly during screen share and solves whichever variant they throw at you. No browser extension. No detection signature. Built by an engineer who got tired of watching his cohort grind for six months and still get filtered at the OA stage.
Get StealthCoderRecognize Counting Sort when a problem asks you to sort or frequency-count a limited integer range without comparing elements directly. The pattern appears in three forms: direct integer sorting (Sort an Array), frequency-based rearrangement (Height Checker), and derived-metric problems (H-Index, How Many Numbers Are Smaller). The algorithm builds a count array, iterates through it to reconstruct sorted order, and runs in O(n + k) time where k is the range. Start with the simplest variant, pure integer sorting, then practice frequency aggregation (Relative Sort Array), then move to indirect applications like H-Index where you map values to counts. Most candidates skip this pattern and rely on comparison sorts; when a live OA restricts time and memory, you'll be grateful for the O(1) space option if you've drilled it. StealthCoder covers the variant you didn't practice: the moment you see range constraints, it spots Counting Sort and delivers the solution.
Companies that hire most on counting sort
6 counting sort problems.
You won't drill them all. Pass anyway.
Counting Sort is one of the patterns interviews actually filter on. Memorizing every variant in a week is a fantasy. StealthCoder is the hedge: an AI overlay invisible during screen share. It reads the problem and surfaces a working solution in under 2 seconds, no matter which counting sort flavor lands in your live OA. Built by an engineer who got tired of watching his cohort grind for six months and still get filtered at the OA stage. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Counting Sort interview FAQ
How many Counting Sort problems should I drill before an OA?+
All 6 in the problem set. The pattern has only a few shapes, direct sorting, frequency counting, and metric derivation. Once you've coded all variants, recognition becomes automatic. Most candidates only encounter 1 or 2 in their prep and freeze when a new angle appears live.
Which companies ask Counting Sort the most?+
TCS leads with 3 problems, followed by ByteDance, De Shaw, Nvidia, Infosys, Intuit, Hive, Accenture, and Zoox each with 2. If you're interviewing at any of these, Counting Sort is not optional, it's part of their screening bar.
How do I recognize a Counting Sort problem in real time?+
Look for these signals: integers with a known, small range; a constraint mentioning "0 to K" or "1 to N"; or a sorting requirement where comparison feels wasteful. Problems like H-Index and Height Checker disguise it, you have to map values to frequencies first, then reconstruct. If you see range limits, Counting Sort is worth considering before quicksort.
Can Counting Sort handle negative integers or floats?+
Standard Counting Sort only works with non-negative integers. For negatives, offset them into a positive range. Floats require bucketing or radix sort variants. Most interview problems stick to non-negative integers within a bounded range, which is why Counting Sort is tested.
Is Counting Sort faster than quicksort on interviews?+
Only if the range k is small. Counting Sort is O(n + k) time, O(k) space. Quicksort is O(n log n) average, O(1) space. For k much smaller than n log n, Counting Sort wins outright. Interviewers test it specifically to see if you can spot when a linear algorithm beats a comparison sort.