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AI Concepts and FoundationsmediumMultiple ChoiceObjective-mapped

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

    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.

  • 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.

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.