DA0-002 Data Analysis Practice Question
A data analyst is evaluating a classification model that predicts whether a customer will churn. The model's confusion matrix shows 80 true positives, 20 false negatives, 30 false positives, and 120 true negatives. Which TWO of the following metrics can be directly calculated from this confusion matrix? (Choose two.)
⚠ Common exam trap
The trap here is selecting regression metrics like R-squared or RMSE for a classification problem, confusing the evaluation metrics of different model types.
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
✓
Accuracy
Accuracy and precision are both classification metrics that can be computed directly from the counts in a confusion matrix. Accuracy gives the overall correct prediction rate, while precision focuses on the reliability of positive predictions. R-squared, RMSE, and adjusted R-squared are regression metrics and cannot be calculated from a confusion matrix, so they are not applicable.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Accuracy
Why this is correct
Accuracy is the proportion of correct predictions: (TP + TN) / total. Here, (80 + 120) / (80+20+30+120) = 200/250 = 0.8. It can be directly calculated from the confusion matrix, making it a valid metric for this scenario. It provides an overall measure of correct classifications.
- ✗
R-squared
Why it's wrong here
R-squared is a metric for regression models, not classification. It measures the proportion of variance in the dependent variable explained by the independent variables. It cannot be calculated from a confusion matrix of a classification model. Therefore, it is not applicable in this scenario.
- ✗
Root Mean Square Error (RMSE)
Why it's wrong here
RMSE is a regression metric that quantifies the difference between predicted and actual continuous values. It is not defined for classification problems like churn prediction. The confusion matrix does not provide the necessary continuous predictions to compute RMSE, so it is inappropriate here.
- ✓
Precision
Why this is correct
Precision is TP / (TP + FP) = 80 / (80 + 30) = 80/110 ≈ 0.727. It measures how many predicted positives are actually positive. This can be directly computed from the confusion matrix, so it is a valid metric here. It is particularly useful when the cost of false positives is high.
- ✗
Adjusted R-squared
Why it's wrong here
Adjusted R-squared is a modified version of R-squared that adjusts for the number of predictors in a regression model. Like R-squared, it is specific to regression and cannot be derived from a classification confusion matrix. It is irrelevant for evaluating a churn classification model.
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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.