How can unsupervised learning be used for anomaly detection?

By training a model on labeled examples of normal and anomalous data.

By clustering data into groups and then assigning labels to each group.

By reducing the number of features to make anomalies more visible.

By identifying data points that deviate significantly from the majority of the data.

A data scientist has a high-dimensional dataset and wants to visualize clusters. They first apply PCA to reduce dimensions to 50, then apply t-SNE to project to 2D. Why is this combination often used?

PCA reduces noise and speeds up t-SNE, which then reveals local cluster structure.

PCA is used to cluster the data, and t-SNE is used to label the clusters.

t-SNE preserves global variance, so PCA is needed to add local structure.

PCA is used to find outliers, and t-SNE is used to remove them.

A data scientist is analyzing a dataset of 10,000 customer records with 50 numerical features. They want to reduce the number of features while preserving as much variance as possible for a downstream clustering task. Which technique is most appropriate?

Principal Component Analysis (PCA)

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Domain 3Objective 2

Unsupervised Learning CT-AI Practice Questions (Page 3)

Part of the Domain 3: Machine Learning (ML) - Overview domain, which makes up ~15% of our current practice bank. ISTQB does not publish an official question count, but from its 60-minute exam (~25–40 total, ~4–6 in this domain), expect 1–1 from this objective — we provide 30 practice questions to prepare you well beyond it. (estimate)

30questions here

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6concepts

Questions 11–15

11foundation · easy
How can unsupervised learning be used for anomaly detection?
Select an answer first
12expert · hard
A data scientist has a high-dimensional dataset and wants to visualize clusters. They first apply PCA to reduce dimensions to 50, then apply t-SNE to project to 2D. Why is this combination often used?
Select an answer first
13application · medium
A marketing team wants to segment customers into distinct groups based on their purchasing behavior. They have data on customer demographics and purchase history but no predefined segments. They also want the segmentation to be interpretable, with a clear hierarchy of customer groups from broad to specific. Which clustering approach is most suitable?
Select an answer first
14application · medium
A researcher has a dataset of images with no labels and wants to group similar images together to discover categories. Which approach should they use?
Select an answer first
15application · medium
A data scientist is analyzing a dataset of 10,000 customer records with 50 numerical features. They want to reduce the number of features while preserving as much variance as possible for a downstream clustering task. Which technique is most appropriate?
Select an answer first

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Unsupervised Learning CT-AI Practice Questions (Page 3)

Questions 11–15

How can unsupervised learning be used for anomaly detection?

A data scientist has a high-dimensional dataset and wants to visualize clusters. They first apply PCA to reduce dimensions to 50, then apply t-SNE to project to 2D. Why is this combination often used?

A researcher has a dataset of images with no labels and wants to group similar images together to discover categories. Which approach should they use?

A data scientist is analyzing a dataset of 10,000 customer records with 50 numerical features. They want to reduce the number of features while preserving as much variance as possible for a downstream clustering task. Which technique is most appropriate?

Keep studying — Domain 3: Machine Learning (ML) - Overview