A retail analyst runs the Apriori algorithm on transaction data and finds a rule {diapers} → {beer} with support=0.02, confidence=0.80, and lift=1.5. The business team asks what lift=1.5 means. Which interpretation is correct?

Customers who buy diapers are 1.5 times more likely to buy beer than the overall probability of buying beer

The rule is statistically significant at the 1.5% level

80% of all transactions contain both diapers and beer

Diapers and beer are negatively correlated

A team is building a recommendation system to suggest products to users based on their purchase history. They have a sparse matrix of user-item interactions (most entries are zero). They are considering KNN (user-based) and Naive Bayes. Which challenge is most significant for KNN in this sparse high-dimensional setting?

KNN will overfit because it has no training phase

KNN's distance calculations become unreliable because most users share few or no purchased items, making similarity measures meaningless

KNN requires the data to be normally distributed

KNN cannot handle categorical data, so it is unsuitable for purchase history

In a Naive Bayes text classifier, what do the features typically represent?

Word frequencies or presence indicators

What is the key assumption of the Naive Bayes classifier?

The target variable is continuous

Features are normally distributed

Features are conditionally independent given the class label

CompTIADataX

Domain 3Objective 2

Supervised 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 31 practice questions to prepare you well beyond it. (estimate)

31questions here

7free pages

10concepts

24%of the exam

Questions 21–25

21application · medium
A retail analyst runs the Apriori algorithm on transaction data and finds a rule {diapers} → {beer} with support=0.02, confidence=0.80, and lift=1.5. The business team asks what lift=1.5 means. Which interpretation is correct?
Select an answer first
22expert · hard
A team is building a recommendation system to suggest products to users based on their purchase history. They have a sparse matrix of user-item interactions (most entries are zero). They are considering KNN (user-based) and Naive Bayes. Which challenge is most significant for KNN in this sparse high-dimensional setting?
Select an answer first
23application · medium
A spam filter team is building a classifier to distinguish spam from legitimate email. They have a large corpus of labeled emails and want a model that trains quickly, handles high-dimensional text features (bag-of-words), and is robust to the fact that many words are irrelevant. The team does not need probability calibration—only the final class label. Which algorithm is the most natural fit?
Select an answer first
24foundation · easy
In a Naive Bayes text classifier, what do the features typically represent?
Select an answer first
25foundation · easy
What is the key assumption of the Naive Bayes classifier?
Select an answer first

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Supervised Learning DY0-001 Practice Questions (Page 5)

Questions 21–25

A retail analyst runs the Apriori algorithm on transaction data and finds a rule {diapers} → {beer} with support=0.02, confidence=0.80, and lift=1.5. The business team asks what lift=1.5 means. Which interpretation is correct?

In a Naive Bayes text classifier, what do the features typically represent?

What is the key assumption of the Naive Bayes classifier?

Keep studying — Machine learning