Which assumption is central to linear regression?

The target variable must be binary

Features must be independent of the target

The relationship between features and target is linear

In logistic regression, what does the model output before applying a decision threshold?

A continuous value that can be negative

A pharmacy chain wants to find combinations of over-the-counter medications that are frequently purchased together to optimize shelf placement. They run Apriori and find a rule {pain reliever} → {sleep aid} with high confidence but low lift. The business team is confused about why the rule is not useful despite high confidence. Which explanation is correct?

High confidence with low lift means the rule is not much better than random chance, because sleep aids are already very popular overall

The rule is invalid because confidence and lift cannot be high and low simultaneously

Low lift means the support of the rule is too low to be reliable

High confidence is always more important than lift for business decisions

CompTIADataX

Domain 3Objective 2

Supervised Learning DY0-001 Practice Questions (Page 1)

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 1–5

1foundation · easy
Which assumption is central to linear regression?
Select an answer first
2foundation · easy
In logistic regression, what does the model output before applying a decision threshold?
Select an answer first
3expert · hard
A pharmacy chain wants to find combinations of over-the-counter medications that are frequently purchased together to optimize shelf placement. They run Apriori and find a rule {pain reliever} → {sleep aid} with high confidence but low lift. The business team is confused about why the rule is not useful despite high confidence. Which explanation is correct?
Select an answer first
4foundation · easy
A bank wants to predict whether a loan applicant will default (yes/no) based on credit score and income. Which algorithm is most appropriate?
Select an answer first
5foundation · easy
What function does logistic regression use to map a linear combination of features to a probability?
Select an answer first

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Supervised Learning DY0-001 Practice Questions (Page 1)

Questions 1–5

Which assumption is central to linear regression?

In logistic regression, what does the model output before applying a decision threshold?

A bank wants to predict whether a loan applicant will default (yes/no) based on credit score and income. Which algorithm is most appropriate?

What function does logistic regression use to map a linear combination of features to a probability?

Keep studying — Machine learning