AI0-001 Implementing AI Solutions Practice Question
A data scientist is building a binary classification model to predict customer churn. The dataset has 90% non-churn and 10% churn. After training, the model achieves 90% accuracy, but the recall for the churn class is only 20%. Which metric should the team primarily focus on to evaluate the model's effectiveness?
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 for the churn class
When classes are imbalanced, accuracy is misleading. Recall (or F1) for the minority class is more informative.
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 for the churn class
Why this is correct
Recall measures how many actual churners are correctly identified, which is the key concern.
- ✗
Accuracy
Why it's wrong here
Accuracy is misleading because the model can achieve 90% by simply predicting the majority class.
- ✗
Area Under the ROC Curve (AUC-ROC)
Why it's wrong here
AUC-ROC is a good overall metric but does not directly indicate recall for the minority class.
- ✗
Precision for the non-churn class
Why it's wrong here
Precision for the majority class is not addressing the problem of missing churners.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI0-001 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 AI0-001 exam.