- A
Linear regression
Why wrong: Linear regression predicts continuous values, not binary classification.
- B
Logistic regression
Logistic regression outputs probabilities for binary classification.
- C
K-means clustering
Why wrong: K-means is an unsupervised clustering algorithm, not for labeled data.
- D
Principal component analysis
Why wrong: PCA is a dimensionality reduction technique, not a classifier.
Question 313 of 1,000
Logistic Regression for Binary Classification
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?
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.
Answer choices
Why each option matters
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 the correct choice because it is specifically designed for binary classification tasks, such as predicting whether a customer will churn (yes/no). It models the probability of the binary outcome using a logistic (sigmoid) function, making it suitable for this supervised learning problem with a categorical target variable.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
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
Common exam trap: answer the scenario, not the keyword
CompTIA AI often tests the distinction between regression and classification algorithms, trapping candidates who confuse linear regression (continuous output) with logistic regression (binary output) due to the misleading similarity in names.
Detailed technical explanation
How to think about this question
Logistic regression uses the logistic function (sigmoid) to map a linear combination of input features to a probability between 0 and 1, with a decision threshold (commonly 0.5) to assign the class label. It is a generalized linear model (GLM) that assumes a binomial error distribution, and its coefficients are estimated via maximum likelihood estimation (MLE) rather than ordinary least squares. In practice, logistic regression is often used as a baseline for binary classification due to its interpretability and efficiency, even with mixed data types like account age, monthly charges, and support tickets.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
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
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FAQ
Questions learners often ask
What does this AI0-001 question test?
Machine Learning and Deep Learning — This question tests Machine Learning and Deep Learning — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Logistic regression — Logistic regression is the correct choice because it is specifically designed for binary classification tasks, such as predicting whether a customer will churn (yes/no). It models the probability of the binary outcome using a logistic (sigmoid) function, making it suitable for this supervised learning problem with a categorical target variable.
What should I do if I get this AI0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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Last reviewed: Jul 4, 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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