AI0-001 Machine Learning and Deep Learning Practice Question
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?
⚠ Common exam trap
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.
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.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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Linear regression
Why it's wrong here
Linear regression predicts a continuous numeric value, so its output cannot represent a binary churn label. It is tempting because it is a supervised model using the same historical features, and would be correct for forecasting a quantity such as expected monthly revenue.
- ✓
Logistic regression
Why this is correct
Logistic regression estimates the probability of a binary outcome via a sigmoid function, outputting a class label for churn or no churn. This satisfies the stem's constraint of a binary target variable, unlike linear regression, which predicts continuous values.
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K-means clustering
Why it's wrong here
K-means assigns unlabelled points to clusters and outputs no target prediction, so it cannot classify churn against a binary label. It is tempting because it groups customers by similarity, and would be correct for segmenting a customer base without predefined outcomes.
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Principal component analysis
Why it's wrong here
Principal component analysis reduces dimensionality by projecting features onto components, producing no binary prediction. It is tempting as preprocessing that can precede modelling, and would be correct for compressing correlated features before training a separate classifier.
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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.