Kth Largest Element in an Array
A medium-tier problem at 68% community acceptance, tagged with Array, Divide and Conquer, Sorting. Reported in interviews at Pocket Gems and 40 others.
Kth Largest Element in an Array gets thrown at candidates from Meta, Spotify, eBay, and Walmart Labs with real regularity. It looks trivial on the surface, which is why people bomb it in live assessments. You see the problem, think "sort and grab the element," write the obvious solution, and then realize you've either nailed it or missed the optimization your interviewer was fishing for. The acceptance rate sits at 68%, which means a third of people who attempt this are getting it wrong under pressure. If this one hits your assessment and you blank on the optimal approach, StealthCoder surfaces a working solution invisible to the proctor.
Companies that ask "Kth Largest Element in an Array"
Kth Largest Element in 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 by an Amazon engineer who realized the OA tests how well you memorized 200 problems, not how well you code.
Get StealthCoderThe trick here isn't sorting. A full sort is O(n log n), and while it passes, it's the crutch answer. The real problem is asking for Quickselect, a O(n) average-case algorithm that partitions the array around a pivot without sorting everything. Most candidates either nail the sort solution or they don't know Quickselect exists and panic. A Heap approach also works and guarantees O(n log k) complexity, which beats sort if k is small. The pitfall: people write the sort, submit it, and think they're done. They are, technically, but they've left optimization points on the table. If you hit Quickselect during your live assessment and the implementation detail trips you up, StealthCoder runs invisibly and hands you the code.
Pattern tags
You know the problem.
Make sure you actually pass it.
Kth Largest Element in 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 by an Amazon engineer who realized the OA tests how well you memorized 200 problems, not how well you code. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Kth Largest Element in an Array interview FAQ
Is sorting really not good enough?+
Sorting works and passes most test cases. But Quickselect is O(n) average and O(n log n) worst, while sort is always O(n log n). At Meta and Spotify scale, interviewers know the difference and will ask why you didn't use it. Have both ready.
Why do so many people fail this at 68% acceptance?+
Edge cases and off-by-one errors. People forget k is 1-indexed, not 0-indexed. They also rush and don't handle duplicates or small arrays correctly. Slow down on this one.
Is Heap the right move if I'm unsure about Quickselect?+
Yes. A max heap with size k gives you O(n log k) and it's easier to code under pressure than Quickselect. eBay and ServiceNow both ask this, and a clean heap solution will pass their bars.
How do I pick between Quickselect and Heap in an interview?+
If k is close to n, Quickselect is better. If k is small, Heap wins. Mention both approaches when you walk through your thinking. Interviewers at eBay and Guidewire appreciate seeing the tradeoff analysis.
Does this problem come up at the companies listed?+
Frequently. Meta, Spotify, eBay, and Walmart Labs all report it. Pocket Gems and Verily ask it too. It's a filter problem, not a rare one, so if you're interviewing at those firms, drill this.
Want the actual problem statement? View "Kth Largest Element in an Array" on LeetCode →