Top K Frequent Elements
A medium-tier problem at 65% community acceptance, tagged with Array, Hash Table, Divide and Conquer. Reported in interviews at Avito and 42 others.
Top K Frequent Elements shows up everywhere: Apple, Meta, Tesla, Yelp, and 39 other companies pull it regularly. The problem sounds simple, find the K most frequent elements in an array, but the performance expectations separate the candidates who bomb from the ones who move forward. You either know the trick or you don't. If you blank on the optimal approach during the OA, StealthCoder surfaces a working solution in seconds while the proctor sees nothing on your screen.
Companies that ask "Top K Frequent Elements"
Top K Frequent Elements 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 trap is using a max heap or sorting by frequency, which works but burns time and doesn't impress. The real move is bucket sort or quickselect. Bucket sort is cleaner: count frequencies with a hash table, then create buckets indexed by frequency count (index 0 to n), dump elements into buckets, and walk backward. Quickselect is faster on average but harder to code under pressure. Most candidates fail because they either overthink the data structure or miss that frequency itself becomes the indexing key. When you're 20 minutes in and the heap solution is slow, you'll wish you'd practiced bucket sort. StealthCoder handles both patterns; if the interview time is tight and you're stuck between approaches, it unblocks you fast.
Pattern tags
You know the problem.
Make sure you actually pass it.
Top K Frequent Elements 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.
Top K Frequent Elements interview FAQ
Is bucket sort really faster than a heap here?+
Bucket sort is O(n) time, heap is O(n log k). For large n and small k, bucket sort dominates. It's also simpler to code correctly. The bucket index is the frequency count itself, so no complex comparisons needed. Heaps are safer if k is close to n, but bucket sort is the flex move.
Why do Apple and Meta ask this so much?+
It tests hash table fundamentals, frequency counting, and knowledge of multiple data structures in one problem. Quickselect reveals algorithm breadth. It's not about domain-specific knowledge; it's about whether you can pick the right tool for the job and code it clean under time pressure.
What's the quickselect trap?+
Quickselect is O(n) average but O(n squared) worst case. Partition logic is easy to mess up in a live setting. You have to partition around a pivot index in the frequency array, not the original array. Most people mix that up and fail the examples. Bucket sort avoids this whole class of bug.
How does this relate to Heap and Bucket Sort as topics?+
This problem is the canonical drill for both. Heap solution uses a min-heap of size k. Bucket sort solution uses frequency as the bucket index. Both are correct; bucket sort is preferred because it's faster and the insight is cleaner. Companies ask this to see which approach you default to.
Can I just use sorting and skip the fancy stuff?+
You can sort by frequency in O(n log n) time and it will pass. But interviewers at Yelp, Tesla, and Meta will probe: why not faster. They're testing whether you know bucket sort or quickselect exist. Sorting is safe if time is short, but it won't close the loop on a strong hire signal.
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