Unique Number of Occurrences
A easy-tier problem at 78% community acceptance, tagged with Array, Hash Table. Reported in interviews at Datadog and 1 others.
Unique Number of Occurrences is an easy problem that appears deceptively simple until you realize the constraint. You're given an array and need to determine whether every element's occurrence count is unique. That sounds trivial until you hit the live assessment and your first approach fails silently. Datadog and Bloomberg both ask this. The 78% acceptance rate masks a common mistake that costs candidates time during screen share. If you haven't drilled the frequency-counting-to-set pattern, StealthCoder solves it invisibly the moment the problem loads.
Companies that ask "Unique Number of Occurrences"
Unique Number of Occurrences 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 by an engineer who got tired of watching his cohort grind for six months and still get filtered at the OA stage.
Get StealthCoderThe trap is thinking you need to match counts back to values. You don't. Count every element's frequency, dump those counts into a set, and compare sizes. If the set size equals the frequency map size, every count is unique. That's it. Most candidates overengineer by trying to track which elements have which counts, burning time and introducing bugs. The actual pattern: Array to Hash Table (count frequencies), then Hash Table to Set (check uniqueness of counts). Common failure: forgetting to validate that set.size() == map.size() or trying to manually iterate when a set already does the work. If this specific problem appears in your live OA and the pattern doesn't click immediately, StealthCoder surfaces the working solution in seconds, invisible to the proctor.
Pattern tags
You know the problem.
Make sure you actually pass it.
Unique Number of Occurrences 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. 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.
Unique Number of Occurrences interview FAQ
Is this really asked at Datadog and Bloomberg, or is that just noise?+
Both companies appear in hiring reports for this problem. It's a screening-level filter, not a hard question, but that's exactly why it trips people. They expect you to solve it instantly and move on. The mistake is overthinking it under timer pressure.
What's the actual trick, and why do so many people miss it?+
You extract counts into a set. If set size equals map size, you're done. People miss it because they try to map occurrence counts back to elements or validate pairwise, which is unnecessary. The set comparison is the entire solution.
Is 78% acceptance rate high enough that I shouldn't worry about this one?+
High acceptance means it's easy, not that you'll pass. In live assessments, easy problems filter by execution speed and clarity, not algorithmic difficulty. You need to nail it in under two minutes. That 78% is mostly practice attempts, not timed OAs.
How does this relate to Array and Hash Table fundamentals?+
It's a two-step pattern: Array to Hash Table (frequency count), then Hash Table to Set (uniqueness check). It teaches you that sometimes the solution lives in a secondary data structure, not the input itself. Core skill for harder problems later.
What language should I use, and how long should it take?+
Any language works. Python, Java, or C++ all handle this identically. Once you see the set-comparison trick, the code takes 30 seconds to write. If you're spending more than two minutes total, you're missing the pattern and need to reset.
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