The answer is Recall (also called sensitivity). This metric is the most appropriate for imbalanced binary classification with a focus on positives because it measures the proportion of actual churners that the model correctly identifies, directly addressing the cost of missing positive cases (false negatives). In a dataset where only 10% of customers churn, accuracy would be misleadingly high by simply predicting the majority class, while recall ensures the model prioritizes catching those rare but critical churners. On the CompTIA Data+ DA0-001 exam, this scenario tests your understanding of how class imbalance distorts common metrics; a common trap is choosing precision, which focuses on the reliability of positive predictions rather than catching all positives. To remember: Recall = “Did we catch all the real positives?”—think of it as the “catch rate” for the minority class.
DA0-001 Analyzing and Modeling Data Practice Question
This DA0-001 practice question tests your understanding of analyzing and modeling data. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 is building a binary classification model to predict customer churn. The dataset is imbalanced, with only 10% churners. The analyst wants to evaluate model performance with a focus on correctly identifying churners. Which metric is most appropriate?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Recall (sensitivity)
Recall (sensitivity) is the most appropriate metric because it measures the proportion of actual churners correctly identified by the model. Since the dataset is imbalanced (only 10% churners) and the analyst's focus is on correctly identifying churners, recall directly addresses the cost of missing positive cases (false negatives). Accuracy would be misleading due to class imbalance, while precision and F1-score prioritize different trade-offs.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 (sensitivity)
Why this is correct
Recall measures how many actual churners were correctly found, directly addressing the focus.
Related concept
Read the scenario before looking for a memorised answer.
✗
F1-score
Why it's wrong here
F1 is harmonic mean of precision and recall; recall is more directly aligned with the goal.
✗
Precision
Why it's wrong here
Precision measures how many predicted churners are actually churners, not directly about finding churners.
✗
Accuracy
Why it's wrong here
Accuracy is high even if model predicts all non-churners, not useful for imbalanced data.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often default to accuracy as the default metric, failing to recognize that class imbalance renders accuracy misleading, and that the question's explicit focus on 'correctly identifying churners' points directly to recall, not precision or F1-score.
Detailed technical explanation
How to think about this question
Recall is calculated as TP / (TP + FN), where FN represents churners incorrectly labeled as non-churners. In imbalanced datasets, a model can achieve high recall by lowering the decision threshold, which increases true positives but may also increase false positives; this trade-off is often visualized with precision-recall curves. Real-world churn prediction models often use recall as the primary metric when the cost of losing a customer (e.g., high lifetime value) outweighs the cost of retention offers to non-churners.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
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
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Analyzing and Modeling Data — This question tests Analyzing and Modeling Data — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Recall (sensitivity) — Recall (sensitivity) is the most appropriate metric because it measures the proportion of actual churners correctly identified by the model. Since the dataset is imbalanced (only 10% churners) and the analyst's focus is on correctly identifying churners, recall directly addresses the cost of missing positive cases (false negatives). Accuracy would be misleading due to class imbalance, while precision and F1-score prioritize different trade-offs.
What should I do if I get this DA0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data analyst is building a model to predict customer churn. The dataset has 10,000 records with 500 churned customers. The model predicts churn with 95% accuracy, but only identifies 10% of actual churners. Which metric best highlights this issue?
hard
A.Accuracy
B.F1 score
✓ C.Recall
D.Precision
Why C: Recall (also known as sensitivity or true positive rate) measures the proportion of actual positives correctly identified. With only 10% of actual churners detected, the model has a recall of 0.1, which directly highlights the failure to capture churners despite high overall accuracy.
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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.
Question Discussion
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