A data scientist has a dataset with 50,000 records and 100 features. They need to cluster the data, but they are concerned about the curse of dimensionality and want to reduce the feature space before clustering. They also need the clustering to be reproducible and scalable. Which approach is most appropriate?

Apply PCA to reduce to 10 components, then run K-Means with k=5

Apply SVD to reduce to 10 components, then run hierarchical clustering

Apply hierarchical clustering on the original features

Run K-Means directly on the original 100 features

Which of the following is a common real-world application of Singular Value Decomposition (SVD)?

Classifying images using a decision tree

Forecasting stock prices using ARIMA models

Recommender systems, such as predicting user ratings for movies

Detecting outliers in a dataset using isolation forests

What is the primary purpose of dimensionality reduction in data analysis?

To reduce the number of features while preserving the essential information

To remove all features that are correlated with the target variable

To ensure all features have the same scale

To increase the number of features to improve model accuracy

In which of the following scenarios is SVD most likely to be applied?

Compressing images by approximating the pixel matrix with a lower rank

Predicting whether a loan applicant will default

Clustering customers into segments based on purchase history

Detecting fraudulent credit card transactions in real time

CompTIADataX

Domain 3Objective 5

Unsupervised Learning DY0-001 Practice Questions (Page 5)

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 21–25

21expert · hard
A data scientist has a dataset with 50,000 records and 100 features. They need to cluster the data, but they are concerned about the curse of dimensionality and want to reduce the feature space before clustering. They also need the clustering to be reproducible and scalable. Which approach is most appropriate?
Select an answer first
22foundation · easy
Which of the following is a common real-world application of Singular Value Decomposition (SVD)?
Select an answer first
23foundation · easy
What is the primary purpose of dimensionality reduction in data analysis?
Select an answer first
24foundation · easy
In which of the following scenarios is SVD most likely to be applied?
Select an answer first
25application · medium
A logistics company wants to group its delivery locations into zones to optimize routes. They have GPS coordinates for 10,000 delivery points and need a method that is fast and can handle this volume. Which clustering algorithm is most appropriate?
Select an answer first

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Unsupervised Learning DY0-001 Practice Questions (Page 5)

Questions 21–25

Which of the following is a common real-world application of Singular Value Decomposition (SVD)?

What is the primary purpose of dimensionality reduction in data analysis?

In which of the following scenarios is SVD most likely to be applied?

A logistics company wants to group its delivery locations into zones to optimize routes. They have GPS coordinates for 10,000 delivery points and need a method that is fast and can handle this volume. Which clustering algorithm is most appropriate?

Keep studying — Machine learning