DA0-002 Data Analysis Practice Question
A data analyst is evaluating a classification model that predicts customer churn. The model has an accuracy of 95%, but the analyst suspects it is not performing well on the minority class (churners). The dataset is highly imbalanced, with only 5% churners. Which metric should the analyst prioritize to assess the model's ability to correctly identify churners?
⚠ Common exam trap
The trap here is assuming high accuracy indicates good performance, ignoring the class imbalance and the need to evaluate minority class detection.
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
✓
Recall
Recall is the appropriate metric because it quantifies how many actual churners the model successfully identifies. In imbalanced settings, accuracy and specificity can be high even when the model fails to detect the minority class. Precision is also important but secondary when the goal is to capture as many churners as possible.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Recall
Why this is correct
Recall measures the proportion of actual churners that were correctly identified. In an imbalanced dataset where churners are rare, high accuracy can be misleading because the model may simply predict the majority class. Recall directly assesses the model's sensitivity to the minority class, which is critical when missing a churner is costly.
- ✗
Specificity
Why it's wrong here
Specificity measures the proportion of actual non-churners correctly identified. It focuses on the majority class and does not reflect the model's ability to detect churners. High specificity can be achieved by predicting 'no churn' for everyone, which is not useful for identifying at-risk customers.
- ✗
Accuracy
Why it's wrong here
Accuracy is the overall proportion of correct predictions. With 95% non-churners, a model that always predicts 'no churn' would achieve 95% accuracy but have zero recall for churners. Thus, accuracy is not informative for assessing performance on the minority class in imbalanced datasets.
- ✗
Precision
Why it's wrong here
Precision measures the proportion of predicted churners that are actual churners. While important, it does not capture how many churners were missed. A model could have high precision by only predicting churn for a few very likely cases, but still miss many churners. In this scenario, recall is more directly aligned with identifying churners.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
This DA0-002 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 DA0-002 exam.