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Unsupervised Learning MCQ
10 Conceptual Questions with In-Depth Technical Explanations
Explore clustering, dimensionality reduction, association rules, and density estimation on unlabeled data.
Unsupervised Learning Questions
Select an answer to reveal detailed technical explanation and formulas.
2
Which of the following algorithms is primarily used for dimensionality reduction in unsupervised learning?
3
In Principal Component Analysis (PCA), how are the principal components ordered?
4
Which metric evaluates the quality of unsupervised clustering by measuring both cohesion and separation?
5
What type of unsupervised learning method is the Apriori algorithm known for?
6
What is the primary objective of t-SNE (t-Distributed Stochastic Neighbor Embedding)?
7
How does an Autoencoder perform unsupervised representation learning?
8
Which of the following is an unsupervised anomaly detection technique that isolates outliers by random recursive partitioning?
9
In Gaussian Mixture Models (GMM), what algorithm is used to estimate the mixture parameters without known labels?
10
Why is feature scaling crucial prior to running distance-based unsupervised algorithms like K-Means or PCA?
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About Unsupervised Learning Interview Questions & Quiz
These Unsupervised Learning multiple choice questions are curated to prepare engineers, researchers, and data science students for top technical interviews, machine learning certifications, and university exams.
Every MCQ incorporates core mathematical formulas, architectural tradeoffs, and practical algorithmic considerations. Practicing these questions guarantees a solid foundational and conceptual mastery of Unsupervised Learning.