How do I approach Machine Learning interview questions?

Machine Learning questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master machine learning interviews.

What difficulty level is this interview question?

This is a medium difficulty Machine Learning question, commonly asked during Onsite rounds at Spotify.

What role is this question designed for?

This question is commonly asked for Machine Learning Engineer candidates at Spotify during technical interviews.

Compare Unsupervised Clustering Methods | Spotify Interview Question

Compare Unsupervised Clustering Methods

Last updated: Mar 29, 2026

Quick Overview

This question evaluates understanding of unsupervised clustering algorithms and related competencies such as algorithmic objectives, assumptions about cluster shape and scale, hyperparameter considerations, computational scalability, evaluation without labels, and preprocessing of high-dimensional or sparse features within the Machine Learning domain. It is commonly asked to assess an engineer's ability to compare centroid-based, hierarchical, density-based, Gaussian mixture, and spectral approaches, reason about trade-offs and common failure modes, and relate algorithm choice to data characteristics and resource constraints. The level of abstraction spans both conceptual understanding of model objectives and assumptions and practical application concerns such as complexity, hyperparameter selection, and preprocessing strategies.

Spotify

Mar 4, 2026, 12:00 AM

Machine Learning Engineer

Onsite

Machine Learning

27 solved

Explain several unsupervised clustering approaches and when you would use each one. At a minimum, compare centroid-based clustering, hierarchical clustering, density-based clustering, Gaussian mixture models, and spectral clustering.

For each method, discuss:

the core objective or intuition,
assumptions about cluster shape and scale,
important hyperparameters and how to choose them,
computational complexity and scalability,
strengths and common failure modes,
how to evaluate clustering quality when labels are unavailable,
how you would preprocess high-dimensional embeddings or sparse features before clustering.

Solution

Show

Comments (0)

Loading comments...

Browse More Questions

More Machine Learning•More Spotify•More Machine Learning Engineer•Spotify Machine Learning Engineer•Spotify Machine Learning•Machine Learning Engineer Machine Learning