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Top K Frequent Elements at NVIDIA — Interview Guide

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

Updated 2026-09MediumHeaps & Hashing

The problem at NVIDIA

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

NVIDIA uses this problem to assess systems, performance, and parallel thinking. Expect NVIDIA-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 NVIDIA looks for

Low-level fluency — memory, caches, concurrency, SIMD.
Performance mindset — profile, optimize, and reason about bottlenecks.
Domain depth for the team (graphics, ML infra, autonomous systems).
Collaboration across hardware and software boundaries.

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