DA0-002 Data Analysis Practice Question
A data analyst is evaluating a classification model for predicting customer churn. The model's confusion matrix shows 100 true positives, 20 false positives, 30 false negatives, and 850 true negatives. The analyst wants to assess the model's ability to correctly identify actual churners. Which metric should the analyst use?
⚠ Common exam trap
Many candidates confuse recall with precision; recall measures how many actual positives were found, while precision measures how many predicted positives were correct.
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
The analyst wants to assess the model's ability to correctly identify actual churners, which is the definition of recall (sensitivity). Recall is calculated as true positives divided by the sum of true positives and false negatives. In this scenario, recall is 100/(100+30) ≈ 0.769. Precision and specificity focus on different aspects, and accuracy can be misleading in imbalanced datasets.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Precision
Why it's wrong here
Precision is the proportion of positive predictions that are correct, calculated as TP/(TP+FP) = 100/120 ≈ 0.833. It measures how many of the predicted churners are actual churners, which is important when the cost of false positives is high. However, the analyst wants to assess the ability to correctly identify actual churners, which is recall, not precision.
- ✗
Accuracy
Why it's wrong here
Accuracy is the proportion of all correct predictions, calculated as (TP+TN)/(TP+TN+FP+FN) = (100+850)/1000 = 0.95. While high, accuracy can be misleading in imbalanced datasets because it does not distinguish between types of errors. Here, the analyst is specifically interested in identifying actual churners, so a metric that focuses on the positive class is more appropriate than overall accuracy.
- ✗
Specificity
Why it's wrong here
Specificity is the proportion of actual negatives that are correctly identified, calculated as TN/(TN+FP) = 850/870 ≈ 0.977. It measures the model's ability to correctly identify non-churners. While important, it does not address the analyst's goal of identifying actual churners. Specificity focuses on the negative class, whereas recall focuses on the positive class.
- ✓
Recall
Why this is correct
Recall, also known as sensitivity, is the proportion of actual positives that are correctly identified, calculated as TP/(TP+FN) = 100/130 ≈ 0.769. It measures the model's ability to find all actual churners, which aligns with the analyst's goal. A high recall means few churners are missed, which is often critical in churn prediction to avoid losing customers.
About these practice questions
This DA0-002 question is part of Courseiva's 1,004-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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.