What is the primary goal of clustering in unsupervised learning?

To predict a continuous outcome for each data point.

To reduce the number of features in the dataset.

To group similar data points together based on their features.

To assign data points to pre-existing labeled classes.

A fraud detection team has a dataset with many features and wants to use anomaly detection. They are concerned that high dimensionality may make distance-based anomaly detection less effective. Which approach is most appropriate to address this concern?

Increase the number of features to improve detection.

Apply PCA to reduce dimensions before running anomaly detection.

Use k-means clustering to find anomalies.

Use association rule mining to find unusual patterns.

A data science team is building a customer segmentation model. They have a dataset with many features, including some that are highly correlated. They plan to use k-means clustering. Which combination of preprocessing steps is most appropriate?

Apply t-SNE to reduce dimensions and then cluster on the t-SNE output.

Use raw features without any preprocessing to avoid information loss.

Use association rule mining to identify important features before clustering.

Apply PCA to reduce dimensionality and standardize the features before clustering.

A media streaming service wants to create user profiles based on viewing history to recommend content. They have no predefined user categories. Which unsupervised learning application is most appropriate?

PCA to reduce the number of users

Customer segmentation using clustering

Association rule mining to find frequently watched genres

Anomaly detection to find unusual viewing habits

Certified Tester AI Testing

Domain 3Objective 2

Unsupervised Learning CT-AI Practice Questions (Page 1)

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

6free pages

6concepts

Questions 1–5

1foundation · easy
What is the primary goal of clustering in unsupervised learning?
Select an answer first
2expert · hard
A fraud detection team has a dataset with many features and wants to use anomaly detection. They are concerned that high dimensionality may make distance-based anomaly detection less effective. Which approach is most appropriate to address this concern?
Select an answer first
3expert · hard
A data science team is building a customer segmentation model. They have a dataset with many features, including some that are highly correlated. They plan to use k-means clustering. Which combination of preprocessing steps is most appropriate?
Select an answer first
4application · medium
A media streaming service wants to create user profiles based on viewing history to recommend content. They have no predefined user categories. Which unsupervised learning application is most appropriate?
Select an answer first
5application · medium
A data science team is working with a high-dimensional dataset (e.g., 1,000 features) and wants to reduce the number of features to improve model training speed while retaining as much information as possible. They do not need to interpret the new features. Which technique is most appropriate?
Select an answer first

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