A data scientist is building a recommender system for a movie streaming service. They have a user-movie rating matrix that is very sparse. They want to use SVD for collaborative filtering, but they are concerned about the missing values. Which approach is most appropriate?

Apply SVD directly to the sparse matrix

Use K-Means to cluster users and then predict ratings

Use PCA to reduce the user features

Use SVD after imputing missing values with the mean rating

In K-Means clustering, what does the 'K' represent?

The number of features in the dataset

The number of iterations the algorithm will run

The number of data points in the dataset

The number of clusters to partition the data into

Which of the following is a typical application of clustering in a business context?

Identifying which products are frequently purchased together

Predicting the exact sales amount for the next quarter

Classifying emails as spam or not spam

Segmenting customers into distinct groups for targeted marketing

In PCA, the first principal component is the direction that:

Has the smallest eigenvalue in the covariance matrix

Maximizes the variance of the projected data

Minimizes the variance of the projected data

In agglomerative hierarchical clustering, the algorithm starts with which of the following?

A predefined number of clusters

Each data point as its own cluster

A random set of cluster centroids

CompTIADataX

Domain 3Objective 5

Unsupervised Learning DY0-001 Practice Questions (Page 3)

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 11–15

11expert · hard
A data scientist is building a recommender system for a movie streaming service. They have a user-movie rating matrix that is very sparse. They want to use SVD for collaborative filtering, but they are concerned about the missing values. Which approach is most appropriate?
Select an answer first
12foundation · easy
In K-Means clustering, what does the 'K' represent?
Select an answer first
13foundation · easy
Which of the following is a typical application of clustering in a business context?
Select an answer first
14foundation · easy
In PCA, the first principal component is the direction that:
Select an answer first
15foundation · easy
In agglomerative hierarchical clustering, the algorithm starts with which of the following?
Select an answer first

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