DA0-002 Data Analysis Practice Question
A data analyst is evaluating a classification model that predicts whether customers will churn. The dataset is highly imbalanced, with only 5% of customers churning. The analyst wants to choose a metric that focuses on the model's ability to correctly identify actual churners. Which metric should the analyst prioritize?
⚠ Common exam trap
The trap here is defaulting to accuracy as the primary metric; in imbalanced datasets, it can be deceptively high even when the model fails to predict the minority class.
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 metric that quantifies the model's ability to find all relevant instances of the positive class. Since the analyst wants to correctly identify actual churners, recall is the most appropriate metric. Accuracy and specificity focus on the negative class or overall correctness, and precision does not capture missed positives.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Specificity
Why it's wrong here
Specificity measures the proportion of actual negatives that are correctly identified. It focuses on non-churners, not churners. While important for understanding false positive rate, it does not help the analyst prioritize the identification of actual churners, which is the stated objective.
- ✓
Recall
Why this is correct
Recall, also known as sensitivity or true positive rate, measures the proportion of actual positives that are correctly identified. In this scenario, the analyst wants to correctly identify actual churners, so recall directly addresses that goal. High recall means the model captures most churners, which is crucial when the cost of missing a churner is high.
- ✗
Precision
Why it's wrong here
Precision measures the proportion of positive predictions that are correct. While important, it does not directly assess the model's ability to capture actual churners. A model can have high precision but low recall, missing many churners. The analyst specifically wants to focus on identifying actual churners, so recall is more relevant.
- ✗
Accuracy
Why it's wrong here
Accuracy measures the overall proportion of correct predictions, but in imbalanced datasets, a model that predicts all customers as non-churners can achieve 95% accuracy while failing to identify any churners. Thus, accuracy is misleading and does not reflect the model's performance on the minority class.
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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
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