Quick Answer
The answer is logistic regression, the correct algorithm for binary classification tasks like predicting customer churn. This model works by applying a logistic function to a linear combination of input features—such as account age, monthly charges, and support tickets—to output a probability between 0 and 1, which is then mapped to a binary outcome (churn or not). On the CompTIA AI+ AI0-001 exam, this question tests your ability to distinguish between supervised classification and other algorithm types; a common trap is confusing logistic regression with linear regression, which predicts continuous values, or selecting unsupervised methods like K-means or dimensionality reduction like PCA. To remember, think of the word “logistic” as containing “logic” for yes/no decisions—if the target is binary, logistic regression is the logical choice.
This AI0-001 practice question tests your understanding of machine learning and deep learning. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A data scientist needs to predict whether a customer will churn based on historical data containing features like account age, monthly charges, and support tickets. The target variable is binary (churn or not). Which type of machine learning algorithm should be used?
Linear regression
Why wrong: Linear regression predicts continuous values, not binary classification.
Logistic regression
Logistic regression outputs probabilities for binary classification.
K-means clustering
Why wrong: K-means is an unsupervised clustering algorithm, not for labeled data.
Principal component analysis
Why wrong: PCA is a dimensionality reduction technique, not a classifier.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Logistic regression
Logistic regression is a classification algorithm well-suited for binary outcomes. Linear regression is for continuous outputs, K-means is unsupervised, and PCA is dimensionality reduction.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Linear regression
Why it's wrong here
Linear regression predicts continuous values, not binary classification.
Logistic regression
Why this is correct
Logistic regression outputs probabilities for binary classification.
Related concept
Read the scenario before looking for a memorised answer.
K-means clustering
Why it's wrong here
K-means is an unsupervised clustering algorithm, not for labeled data.
Principal component analysis
Why it's wrong here
PCA is a dimensionality reduction technique, not a classifier.
Common exam traps
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A practitioner preparing for the AI0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Identify which AI0-001 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Machine Learning and Deep Learning — This question tests Machine Learning and Deep Learning — Read the scenario before looking for a memorised answer..
The correct answer is: Logistic regression — Logistic regression is a classification algorithm well-suited for binary outcomes. Linear regression is for continuous outputs, K-means is unsupervised, and PCA is dimensionality reduction.
Identify which AI0-001 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jun 23, 2026
This AI0-001 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI0-001 exam.
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