- A
Accuracy
Why wrong: Accuracy can be high even if the model fails on the minority class, making it inappropriate for imbalanced data.
- B
Recall
Recall measures the proportion of actual positives correctly identified, critical for minority class performance.
- C
Precision
Precision measures the proportion of positive identifications that were actually correct, important for imbalanced data.
- D
Mean squared error (MSE)
Why wrong: MSE is a regression metric and not suitable for classification.
- E
F1 score
Why wrong: Although F1 is useful, it is a single metric that combines precision and recall; the question explicitly asks for TWO metrics, and precision/recall are the foundational pair.
Question 28 of 1,000
AI0-001 AI Models and Data Engineering Practice Question
This AI0-001 practice question tests your understanding of ai models and data engineering. 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.
A data scientist is evaluating a logistic regression model for binary classification on highly imbalanced data. Which TWO metrics are most appropriate to assess model performance? (Choose TWO.)
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
Recall (B) is correct because in highly imbalanced binary classification, the minority class (e.g., fraud or disease) is the focus. Recall measures the proportion of actual positives correctly identified, which is critical when missing a positive has high cost. Precision (C) is correct because it measures the proportion of predicted positives that are truly positive, which is essential when false positives are costly or when the model's positive predictions must be trustworthy.
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.
- ✗
Accuracy
Why it's wrong here
Accuracy can be high even if the model fails on the minority class, making it inappropriate for imbalanced data.
- ✓
Recall
Why this is correct
Recall measures the proportion of actual positives correctly identified, critical for minority class performance.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Precision
Why this is correct
Precision measures the proportion of positive identifications that were actually correct, important for imbalanced data.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Mean squared error (MSE)
Why it's wrong here
MSE is a regression metric and not suitable for classification.
- ✗
F1 score
Why it's wrong here
Although F1 is useful, it is a single metric that combines precision and recall; the question explicitly asks for TWO metrics, and precision/recall are the foundational pair.
Common exam traps
Common exam trap: answer the scenario, not the keyword
CompTIA often tests the misconception that accuracy is always a valid metric, or that F1 score is a primary metric rather than a derived one, leading candidates to select accuracy or F1 instead of the pair of precision and recall.
Detailed technical explanation
How to think about this question
In logistic regression, the decision threshold (default 0.5) can be tuned to trade off precision and recall. For imbalanced data, techniques like class weighting or SMOTE are often used to adjust the model's learning, and metrics like precision-recall curves or area under the precision-recall curve (AUPRC) provide more insight than ROC-AUC. In real-world scenarios like credit card fraud detection, a model with high recall catches most frauds but may generate many false alarms (low precision), requiring business cost analysis to set the optimal threshold.
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.
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FAQ
Questions learners often ask
What does this AI0-001 question test?
AI Models and Data Engineering — This question tests AI Models and Data Engineering — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Recall — Recall (B) is correct because in highly imbalanced binary classification, the minority class (e.g., fraud or disease) is the focus. Recall measures the proportion of actual positives correctly identified, which is critical when missing a positive has high cost. Precision (C) is correct because it measures the proportion of predicted positives that are truly positive, which is essential when false positives are costly or when the model's positive predictions must be trustworthy.
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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Last reviewed: Jul 4, 2026
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