A data analyst wants to visualize high-dimensional data in a 2D scatter plot to identify potential clusters. They want the plot to preserve the global structure of the data as much as possible. Which technique should they use?

Hierarchical clustering with 2 clusters

PCA to project onto the first two principal components

What does Principal Component Analysis (PCA) do to the original features?

Selects a subset of the original features that are most important

Clusters the features into groups based on similarity

Ranks the original features by their variance and keeps the top ones

Transforms them into a new set of uncorrelated variables called principal components

A data scientist has a dataset with 500 features and wants to build a classification model. They are concerned about overfitting due to the high dimensionality. Which approach should they take?

Apply PCA to reduce the feature space before modeling

Use SVD to increase the number of features

Use hierarchical clustering to group features

Use K-Means to cluster the features

Which step is part of the K-Means clustering algorithm's iterative process?

Computing the covariance matrix to find principal components

Assigning each data point to the nearest cluster centroid

Building a dendrogram to visualize cluster hierarchy

Selecting support vectors to define decision boundaries

CompTIADataX

Domain 3Objective 5

Unsupervised Learning DY0-001 Practice Questions (Page 1)

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 1–5

1application · medium
A data analyst wants to visualize high-dimensional data in a 2D scatter plot to identify potential clusters. They want the plot to preserve the global structure of the data as much as possible. Which technique should they use?
Select an answer first
2foundation · easy
What does Principal Component Analysis (PCA) do to the original features?
Select an answer first
3application · medium
A data scientist has a dataset with 500 features and wants to build a classification model. They are concerned about overfitting due to the high dimensionality. Which approach should they take?
Select an answer first
4foundation · easy
Which step is part of the K-Means clustering algorithm's iterative process?
Select an answer first
5foundation · easy
A data scientist is analyzing customer purchase histories to identify natural groups of similar shoppers without using pre-existing labels. Which unsupervised learning technique is most appropriate for this task?
Select an answer first

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