The F1-score is the most appropriate metric for evaluating an imbalanced dataset like this one, where 95% of loans are non-default and only 5% are default. This is because the F1-score calculates the harmonic mean of precision and recall, which penalizes a model that achieves high accuracy by simply predicting the majority class. In this scenario, accuracy would be misleadingly high at 95% even if the model never identifies a single default, whereas the F1-score drops sharply if the model fails to recall the minority default class. On the CompTIA AI+ AI0-001 exam, this question tests your understanding of why standard accuracy fails with skewed class distributions—a common trap is choosing accuracy because it seems intuitive. A helpful memory tip: think of F1 as the “balance score” that forces the model to care about both catching the rare cases (recall) and being correct when it does (precision).
AI0-001 AI Concepts and Foundations Practice Question
This AI0-001 practice question tests your understanding of ai concepts and foundations. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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.
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?
Why wrong: MSE is used for regression and is not appropriate for evaluating classification predictions.
B
F1-score
F1-score considers both false positives and false negatives, providing a balanced measure for minority class performance.
C
Accuracy
Why wrong: Accuracy is misleading in imbalanced datasets because a model predicting all non-default would achieve 95% accuracy without identifying any defaults.
D
R-squared
Why wrong: R-squared is a metric for regression models and not applicable to classification tasks.
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.
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.
✗
Mean squared error
Why it's wrong here
MSE is used for regression and is not appropriate for evaluating classification predictions.
✓
F1-score
Why this is correct
F1-score considers both false positives and false negatives, providing a balanced measure for minority class performance.
Related concept
Read the scenario before looking for a memorised answer.
✗
Accuracy
Why it's wrong here
Accuracy is misleading in imbalanced datasets because a model predicting all non-default would achieve 95% accuracy without identifying any defaults.
✗
R-squared
Why it's wrong here
R-squared is a metric for regression models and not applicable to classification tasks.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
F1-score balances precision (positive predictive value) and recall (sensitivity), which is critical when the cost of false negatives (missing a default) is high. In practice, lenders often use F1-score alongside the ROC-AUC curve to tune decision thresholds, as a high F1-score ensures the model identifies a meaningful proportion of defaults without overwhelming false positives.
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 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
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.
AI Concepts and Foundations — This question tests AI Concepts and Foundations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this AI0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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