Quick Answer
The answer is the F1-score. In a highly imbalanced dataset like this one, where only 5% of customers actually churn, accuracy becomes a deceptive metric because a model can achieve 96% accuracy simply by predicting the majority class every time, completely missing the critical churn cases. The F1-score, as the harmonic mean of precision and recall, directly evaluates performance on the minority class, and a score of only 0.2 reveals that the model is failing to identify churners—the very outcome the business needs to act on. On the CompTIA AI+ AI0-001 exam, this scenario tests your understanding of why accuracy is a trap in imbalanced classification, and it often appears in questions about selecting the right evaluation metric for business-critical minority classes. A common memory tip is to think of the F1-score as the "minority class guardian"—when the dataset is skewed, always trust the F1 over raw accuracy.
This AI0-001 practice question tests your understanding of machine learning and deep learning. 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 company is deploying a machine learning model to predict customer churn. The dataset is highly imbalanced (95% non-churn, 5% churn). The model achieves 96% accuracy, but the F1-score for the churn class is only 0.2. Which metric should the team prioritize to evaluate model performance for this business problem?
F1-score
F1-score balances precision and recall, suitable for imbalanced data.
Accuracy
Why wrong: Accuracy is misleading when classes are imbalanced.
Log loss
Why wrong: Log loss does not directly reflect performance on the minority class.
AUC-ROC
Why wrong: AUC-ROC is useful but less direct for the minority class.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
F1-score
In a highly imbalanced dataset (95% non-churn, 5% churn), accuracy is misleading because a model can achieve 96% accuracy by simply predicting the majority class for all instances. The F1-score, which is the harmonic mean of precision and recall, specifically measures the model's performance on the minority (churn) class. A low F1-score of 0.2 indicates the model fails to correctly identify churners, which is the critical business outcome, making F1-score the correct metric to prioritize.
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.
F1-score
Why this is correct
F1-score balances precision and recall, suitable for imbalanced data.
Related concept
Read the scenario before looking for a memorised answer.
Accuracy
Why it's wrong here
Accuracy is misleading when classes are imbalanced.
Log loss
Why it's wrong here
Log loss does not directly reflect performance on the minority class.
AUC-ROC
Why it's wrong here
AUC-ROC is useful but less direct for the minority class.
Common exam traps
CompTIA often tests the misconception that high accuracy is always good, especially in imbalanced datasets, leading candidates to overlook the F1-score as the appropriate metric for minority class performance.
Detailed technical explanation
The F1-score is calculated as 2 * (precision * recall) / (precision + recall), where precision is TP/(TP+FP) and recall is TP/(TP+FN). In churn prediction, false negatives (failing to identify a churner) often have a higher business cost than false positives, so recall is critical. The F1-score balances both, and a score of 0.2 indicates the model has very low precision or recall for the churn class, often due to the model defaulting to the majority class. Techniques like SMOTE (Synthetic Minority Over-sampling Technique) or cost-sensitive learning are commonly used to improve F1-score in such scenarios.
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 AI0-001 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
Machine Learning and Deep Learning — This question tests Machine Learning and Deep Learning — Read the scenario before looking for a memorised answer..
The correct answer is: F1-score — In a highly imbalanced dataset (95% non-churn, 5% churn), accuracy is misleading because a model can achieve 96% accuracy by simply predicting the majority class for all instances. The F1-score, which is the harmonic mean of precision and recall, specifically measures the model's performance on the minority (churn) class. A low F1-score of 0.2 indicates the model fails to correctly identify churners, which is the critical business outcome, making F1-score the correct metric to prioritize.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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