- A
Class imbalance
With only 500 churners out of 10,000, the model predicts most as non-churners, achieving high accuracy but low precision for the minority class.
- B
Non‑linear decision boundary
Why wrong: Logistic regression assumes a linear decision boundary; if the boundary is non‑linear, overall accuracy would drop, but precision for the minority class could still be higher if the model captures it.
- C
Multicollinearity among predictor variables
Why wrong: Multicollinearity inflates standard errors but does not typically cause low precision for a minority class.
- D
Overfitting due to too many features
Why wrong: Overfitting would cause high training accuracy but lower test accuracy; it does not directly cause low precision for a specific class unless combined with imbalance.
Quick Answer
The answer is class imbalance. This is the most likely cause because with only 500 churners out of 10,000 records, the dataset has a severe class imbalance of just 5% for the positive class. A logistic regression model can achieve 98% accuracy by simply predicting the majority class (non-churn) for every record, which yields 95% accuracy without learning any patterns about churn. The very low precision of 15% for the churn class reveals that most positive predictions are false positives, a direct consequence of the model being biased toward the majority class due to the imbalance. On the CompTIA Data+ DA0-001 exam, this scenario tests your understanding that accuracy is misleading when classes are skewed, and it often appears as a trap where high accuracy hides poor minority-class performance. A common memory tip: when the minority class is under 10%, always question accuracy and check precision or recall instead—think "5% churn, 95% guess."
DA0-001 Analyzing and Modeling Data Practice Question
This DA0-001 practice question tests your understanding of analyzing and modeling data. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 scientist is building a classification model to predict customer churn. The dataset has 10,000 records with 500 churners. The scientist uses logistic regression and achieves 98% accuracy, but the precision for churn class is only 15%. Which of the following is the most likely cause?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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
Class imbalance
The dataset has only 500 churners out of 10,000 records (5% churn rate), which is a classic class imbalance. Logistic regression can achieve high accuracy by simply predicting the majority class (non-churn) for all records, yielding 95% accuracy even without learning anything about churn. The very low precision (15%) for the churn class indicates that most of the positive predictions are false positives, a direct consequence of the model being biased toward the majority class due to imbalance.
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.
- ✓
Class imbalance
Why this is correct
With only 500 churners out of 10,000, the model predicts most as non-churners, achieving high accuracy but low precision for the minority class.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Non‑linear decision boundary
Why it's wrong here
Logistic regression assumes a linear decision boundary; if the boundary is non‑linear, overall accuracy would drop, but precision for the minority class could still be higher if the model captures it.
- ✗
Multicollinearity among predictor variables
Why it's wrong here
Multicollinearity inflates standard errors but does not typically cause low precision for a minority class.
- ✗
Overfitting due to too many features
Why it's wrong here
Overfitting would cause high training accuracy but lower test accuracy; it does not directly cause low precision for a specific class unless combined with imbalance.
Common exam traps
Common exam trap: answer the scenario, not the keyword
CompTIA often tests the misconception that high accuracy always means a good model, hiding the fact that with imbalanced data, accuracy is misleading and metrics like precision, recall, or F1-score for the minority class are critical.
Detailed technical explanation
How to think about this question
In logistic regression, the loss function (log-loss) is optimized globally, so with severe class imbalance the model minimizes overall error by predicting the majority class. Techniques like class weighting (assigning higher penalties to minority misclassifications), SMOTE (synthetic minority oversampling), or threshold tuning can mitigate this. In production churn models, a 15% precision means 85% of customers flagged as churners are actually false positives, wasting retention resources.
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.
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FAQ
Questions learners often ask
What does this DA0-001 question test?
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: Class imbalance — The dataset has only 500 churners out of 10,000 records (5% churn rate), which is a classic class imbalance. Logistic regression can achieve high accuracy by simply predicting the majority class (non-churn) for all records, yielding 95% accuracy even without learning anything about churn. The very low precision (15%) for the churn class indicates that most of the positive predictions are false positives, a direct consequence of the model being biased toward the majority class due to imbalance.
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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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Last reviewed: Jun 30, 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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