This AI0-001 practice question tests your understanding of ai models and data engineering. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 dataset for a binary classification problem has 95% of samples in class "0" and 5% in class "1". The data scientist trains a logistic regression model and achieves 95% accuracy. Which metric should the scientist primarily use to evaluate model performance?
Precision, recall, and F1-score.
These metrics evaluate performance on the minority class, crucial for imbalanced data.
R-squared.
Why wrong: R-squared is for regression.
Accuracy.
Why wrong: Accuracy is high due to majority class, masking poor performance on minority class.
Mean squared error.
Why wrong: MSE is for regression, not classification.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Precision, recall, and F1-score.
In a highly imbalanced dataset (95% class 0, 5% class 1), accuracy is misleading because a model can achieve 95% accuracy by simply predicting the majority class for all samples. Precision, recall, and F1-score provide a more nuanced view of performance on the minority class, which is typically the class of interest in binary classification problems. The F1-score, in particular, balances precision and recall, making it the primary metric for evaluating model effectiveness on imbalanced data.
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.
Precision, recall, and F1-score.
Why this is correct
These metrics evaluate performance on the minority class, crucial for imbalanced data.
Related concept
Read the scenario before looking for a memorised answer.
R-squared.
Why it's wrong here
R-squared is for regression.
Accuracy.
Why it's wrong here
Accuracy is high due to majority class, masking poor performance on minority class.
Mean squared error.
Why it's wrong here
MSE is for regression, not classification.
Common exam traps
CompTIA often tests the concept that accuracy is a poor metric for imbalanced datasets, trapping candidates who assume high accuracy always indicates good model performance without considering class distribution.
Detailed technical explanation
Precision measures the proportion of true positive predictions among all positive predictions (TP/(TP+FP)), while recall measures the proportion of true positives identified among all actual positives (TP/(TP+FN)). The F1-score is the harmonic mean of precision and recall, providing a single metric that penalizes extreme imbalances between the two. In practice, for a dataset with 5% positive class, a model with high accuracy but low recall on the minority class would fail to detect critical positive cases, such as fraud detection or disease diagnosis, where false negatives are costly.
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
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FAQ
AI Models and Data Engineering — This question tests AI Models and Data Engineering — Read the scenario before looking for a memorised answer..
The correct answer is: Precision, recall, and F1-score. — In a highly imbalanced dataset (95% class 0, 5% class 1), accuracy is misleading because a model can achieve 95% accuracy by simply predicting the majority class for all samples. Precision, recall, and F1-score provide a more nuanced view of performance on the minority class, which is typically the class of interest in binary classification problems. The F1-score, in particular, balances precision and recall, making it the primary metric for evaluating model effectiveness on imbalanced data.
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
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.
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