This DA0-001 practice question tests your understanding of analyzing and modeling data. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 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?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Accuracy
Why wrong: Accuracy is high (95%) but misleading due to imbalance.
F1 score
Why wrong: F1 combines precision and recall, but recall alone directly shows the issue.
Recall
Recall is low (10%), showing the model fails to detect churners.
Precision
Why wrong: Precision may be high if false positives are low, but it does not capture missed churners.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Recall
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Accuracy
Why it's wrong here
Accuracy is high (95%) but misleading due to imbalance.
F1 score
Why it's wrong here
F1 combines precision and recall, but recall alone directly shows the issue.
Recall
Why this is correct
Recall is low (10%), showing the model fails to detect churners.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Precision
Why it's wrong here
Precision may be high if false positives are low, but it does not capture missed churners.
Common exam traps
The trap here is that candidates may choose accuracy because it is a familiar and seemingly high value (95%), failing to recognize that in imbalanced datasets, accuracy can be deceptive and does not reflect poor performance on the minority class.
Trap categories for this question
Command / output trap
F1 combines precision and recall, but recall alone directly shows the issue.
Detailed technical explanation
Recall is calculated as TP / (TP + FN). In this scenario, with 500 actual churners and only 10% identified, TP = 50 and FN = 450, yielding recall = 0.1. In imbalanced classification, accuracy can be inflated by predicting the majority class for all instances, a phenomenon known as the accuracy paradox. Real-world churn models often prioritize recall to minimize revenue loss from undetected churners, even at the cost of lower precision.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Analyzing and Modeling Data — This question tests Analyzing and Modeling Data — Read the scenario before looking for a memorised answer..
The correct answer is: Recall — 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
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.
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