AI0-001 AI Concepts and Foundations Practice Question
An AI model is trained to predict loan default. The training data contains 95% non-default and 5% default. Which metric is most appropriate to evaluate model performance given the imbalanced dataset?
⚠ Common exam trap
CompTIA often tests the misconception that accuracy is always the best metric, leading candidates to overlook its failure in imbalanced scenarios where a trivial classifier can achieve high accuracy.
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
✓
F1-score
The F1-score is the harmonic mean of precision and recall, making it robust to class imbalance. In this dataset with 95% non-default and 5% default, accuracy would be misleadingly high (95%) even if the model never predicts default, while F1-score penalizes poor recall of the minority class.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Mean squared error
Why it's wrong here
Mean squared error averages squared deviations of continuous predictions, so a model predicting all non-default still scores low error while missing every default. It is the standard loss for regression on continuous targets, such as forecasting house prices.
- ✓
F1-score
Why this is correct
With only 5% defaults, accuracy misleads by favouring the majority class. F1-score is the harmonic mean of precision and recall, so it penalises both false positives and false negatives, giving a balanced view of performance on the minority default class.
- ✗
Accuracy
Why it's wrong here
Accuracy counts both classes equally, so a model predicting "no default" for every applicant scores 95% while detecting zero defaults — the minority class this scenario cares about. Accuracy suits balanced datasets where class frequencies are comparable; here the 95/5 split makes it misleading.
- ✗
R-squared
Why it's wrong here
R-squared measures the proportion of variance in a continuous target explained by a regression, so it is undefined for binary class labels. It is the right fit for regression tasks such as predicting continuous credit exposure amounts.
About these practice questions
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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.