Quick Answer
Logistic regression is the correct choice because customer churn is a binary outcome—either a customer churns or they do not—and logistic regression is specifically designed for binary classification tasks. Unlike linear regression, which predicts continuous values, logistic regression models the probability of an event using a sigmoid function to map any input to a value between 0 and 1, making it ideal for yes/no predictions even when you have a mix of categorical features like region and plan type alongside continuous features like usage and tenure. On the CompTIA Data+ DA0-001 exam, this question tests your ability to match regression types to the nature of the target variable; a common trap is choosing linear regression because you see numeric predictors, but remember that linear regression predicts numbers, not categories. A quick memory tip: think of the word “logistic” as containing “log” for log-odds and “istic” for “is it?”—if you’re asking “is it churn or not?” the answer is logistic regression.
This DA0-001 practice question tests your understanding of analyzing and modeling data. 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 analyst wants to predict customer churn based on categorical features like region and plan type, and continuous features like usage and tenure. Which regression type should be used?
Logistic regression
Logistic regression is used for binary classification, suitable for churn prediction.
Ridge regression
Why wrong: Ridge regression is a regularized linear regression, still for continuous outcomes.
Linear regression
Why wrong: Linear regression predicts a continuous outcome, not binary.
Lasso regression
Why wrong: Lasso regression is also for continuous outcomes with feature selection.
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 the correct choice because the target variable, customer churn, is binary (churn vs. no churn). Logistic regression models the probability of a binary outcome using a sigmoid function, making it suitable for classification tasks with both categorical and continuous predictors.
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.
Logistic regression
Why this is correct
Logistic regression is used for binary classification, suitable for churn prediction.
Related concept
Read the scenario before looking for a memorised answer.
Ridge regression
Why it's wrong here
Ridge regression is a regularized linear regression, still for continuous outcomes.
Linear regression
Why it's wrong here
Linear regression predicts a continuous outcome, not binary.
Lasso regression
Why it's wrong here
Lasso regression is also for continuous outcomes with feature selection.
Common exam traps
CompTIA often tests the misconception that 'regression' in the option name implies it is only for continuous outcomes, leading candidates to overlook logistic regression as a valid classification technique.
Detailed technical explanation
Logistic regression uses the logit link function to transform the linear combination of predictors into a probability between 0 and 1. The model estimates coefficients via maximum likelihood estimation (MLE) rather than ordinary least squares, and it assumes a binomial distribution of the error term. In practice, categorical features like region and plan type must be one-hot encoded before fitting, and continuous features like usage and tenure are standardized to improve convergence.
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 DA0-001 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
Analyzing and Modeling Data — This question tests Analyzing and Modeling Data — Read the scenario before looking for a memorised answer..
The correct answer is: Logistic regression — Logistic regression is the correct choice because the target variable, customer churn, is binary (churn vs. no churn). Logistic regression models the probability of a binary outcome using a sigmoid function, making it suitable for classification tasks with both categorical and continuous predictors.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
This DA0-001 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 DA0-001 exam.
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