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Top K Frequent Elements at Google — Interview Guide

How to approach Top K Frequent Elements in a Google interview: the optimal approach, what Google interviewers look for, and company-specific tips.

Updated 2026-09MediumHeaps & Hashing

The problem at Google

Given a non-empty array and an integer k, return the k most frequent elements. The answer is guaranteed unique.

Google uses this problem to assess algorithmic rigor and generalist problem-solving. Expect Google-specific follow-ups that tie the solution back to production reasoning and the company's engineering bar.

Key concepts

Count frequencies with a hash map in O(n).
A min-heap of size k keeps the top k by frequency in O(n log k).
Bucket sort by frequency achieves O(n) when values are bounded.

Approach

import heapq, collections
def top_k(nums, k):
    freq = collections.Counter(nums)
    return heapq.nlargest(k, freq.keys(), key=freq.get)

What Google looks for

Algorithmic fluency — the right data structure and the optimal complexity, fast.
Googleyness — comfort with ambiguity, intellectual curiosity, and a collaborative style.
Leadership and impact — especially at L4+; evidence of driving outcomes.
Communication — explaining your thinking as you solve.

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