Majority Element II
A medium-tier problem at 54% community acceptance, tagged with Array, Hash Table, Sorting. Reported in interviews at Zenefits and 4 others.
Majority Element II is a medium-difficulty array problem that appears in assessments at Zenefits, Darwinbox, Atlassian, Accenture, and Salesforce. The acceptance rate sits around 54%, which means half the candidates who attempt it don't solve it cleanly on their first try. The problem asks you to find all elements appearing more than n/3 times in an array, and the trick isn't obvious if you default to a hash table. Most candidates start with a straightforward counting approach, then hit the memory constraint or realize the problem is testing something deeper. If you blank on the optimal pattern during a live OA, StealthCoder surfaces a working solution in seconds, invisible to the proctor.
Companies that ask "Majority Element II"
Majority Element II 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 for the engineer who has done the work but might still blank with a webcam pointed at him.
Get StealthCoderThe naive approach counts frequencies in a hash table and returns all elements with count > n/3. It works, but the problem often expects you to solve it in O(1) space, which immediately disqualifies the hash table. The key insight is the majority voting pattern: since you're looking for elements appearing more than n/3 times, there can be at most two such elements. This constraint unlocks a tracking-based solution similar to Boyer-Moore majority voting, but adapted for multiple candidates. You maintain two potential majority elements and their counts, then do a second pass to verify which actually meet the threshold. Candidates miss this because it feels unintuitive and requires thinking about the math of the constraint before coding. The Counting and Sorting topics in the problem tag hint at alternative approaches, but the optimal path is the voting algorithm. When this problem appears in your live assessment and you're stuck on space optimization, StealthCoder gives you the verified solution without breaking your screen share.
Pattern tags
You know the problem.
Make sure you actually pass it.
Majority Element II 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 for the engineer who has done the work but might still blank with a webcam pointed at him. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Majority Element II interview FAQ
Is the hash table approach actually wrong on this problem?+
Hash table is correct and passes, but many interviewers at these companies ask follow-up constraints like O(1) space or O(n) time with constant extra space. The problem tests whether you know the voting algorithm exists and when to apply it, not just whether you can count.
How do I know there are at most two majority elements?+
If an element appears more than n/3 times and another also appears more than n/3 times, the sum is > 2n/3, leaving less than n/3 for all other elements combined. You can't fit three or more such elements. This math is the crux of the problem.
Is Majority Element II still heavily asked at FAANG?+
It appears regularly at Salesforce, Atlassian, and similar companies. The 54% acceptance rate suggests it's not oversaturated but remains a solid screen interview problem, especially when the interviewer pushes on space complexity.
What's the difference between this and the classic Majority Element problem?+
Classic Majority Element asks for one element appearing > n/2 times. This extends it to n/3, forcing you to track multiple candidates. The voting algorithm generalizes further, but n/3 is where the pattern becomes non-trivial and interviewer-friendly.
Do I really need to implement Boyer-Moore voting from scratch?+
No. You need to understand the pattern: track up to k-1 candidates when looking for elements > n/k, increment counts for matches, reset when a candidate reaches zero. The concept matters more than memorizing the exact implementation.
Want the actual problem statement? View "Majority Element II" on LeetCode →