What does Singular Value Decomposition (SVD) do to a matrix?

Sorts the rows of the matrix in descending order

Computes the eigenvalues and eigenvectors of the matrix

Calculates the inverse of the matrix

Factorizes it into three matrices: U, Σ, and Vᵀ

What is a key characteristic of hierarchical clustering compared to K-Means?

It requires the number of clusters to be specified in advance

It is faster than K-Means for large datasets

It always produces spherical clusters of similar size

It produces a tree-like structure of nested clusters called a dendrogram

Which of the following is a benefit of applying dimensionality reduction before training a machine learning model?

Eliminating the need for data normalization

Reducing the risk of overfitting by decreasing the number of features

Ensuring that all features are independent of each other

Guaranteeing that the model will achieve higher accuracy

CompTIADataX

Domain 3Objective 5

Unsupervised Learning DY0-001 Practice Questions (Page 2)

Part of the Machine learning domain, which accounts for 24% of the DY0-001 exam. CompTIA does not publish an official question count, but from its 165-minute exam (~65–110 total, ~16–26 in this domain), expect 3–5 from this objective — we provide 27 practice questions to prepare you well beyond it. (estimate)

27questions here

6free pages

7concepts

24%of the exam

Questions 6–10

6foundation · easy
What does Singular Value Decomposition (SVD) do to a matrix?
Select an answer first
7foundation · easy
What is a key characteristic of hierarchical clustering compared to K-Means?
Select an answer first
8application · medium
A research team is analyzing a small dataset of 50 animal species based on several morphological features. They want to see how species group together at different levels of similarity, from broad categories to fine distinctions. Which clustering method is most suitable?
Select an answer first
9foundation · easy
Which of the following is a benefit of applying dimensionality reduction before training a machine learning model?
Select an answer first
10expert · hard
A data scientist has a dataset with 1,000 features and wants to reduce dimensionality for a downstream classification task. They also need the reduced features to be interpretable in terms of the original features. Which approach is most appropriate?
Select an answer first

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Unsupervised Learning DY0-001 Practice Questions (Page 2)

Questions 6–10

What does Singular Value Decomposition (SVD) do to a matrix?

What is a key characteristic of hierarchical clustering compared to K-Means?

A research team is analyzing a small dataset of 50 animal species based on several morphological features. They want to see how species group together at different levels of similarity, from broad categories to fine distinctions. Which clustering method is most suitable?

Which of the following is a benefit of applying dimensionality reduction before training a machine learning model?

A data scientist has a dataset with 1,000 features and wants to reduce dimensionality for a downstream classification task. They also need the reduced features to be interpretable in terms of the original features. Which approach is most appropriate?

Keep studying — Machine learning