- A
Apply cost-sensitive learning by assigning a higher misclassification cost to the minority class.
Cost-sensitive methods penalize minority class errors more heavily.
- B
Reduce the number of features using principal component analysis (PCA).
Why wrong: PCA reduces dimensionality but does not address class imbalance.
- C
Use accuracy as the primary evaluation metric.
Why wrong: Accuracy can be misleading in imbalanced datasets because a model that predicts all majority class can achieve high accuracy.
- D
Evaluate model performance using precision-recall curves and F1 score.
Precision-recall curves focus on the minority class and are more informative than ROC for imbalanced data.
- E
Use synthetic oversampling (SMOTE) to create additional minority class samples.
SMOTE generates synthetic examples to balance classes.
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.
A company is implementing an AI solution for fraud detection. The dataset is highly imbalanced (only 1% fraudulent transactions). Which THREE techniques are most appropriate to address class imbalance? (Select three.)
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
Apply cost-sensitive learning by assigning a higher misclassification cost to the minority class.
Option A is correct because cost-sensitive learning directly addresses class imbalance by assigning a higher misclassification cost to the minority class (fraudulent transactions). This forces the model to penalize false negatives more heavily, thereby improving recall for the minority class without altering the dataset distribution.
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.
- ✓
Apply cost-sensitive learning by assigning a higher misclassification cost to the minority class.
Why this is correct
Cost-sensitive methods penalize minority class errors more heavily.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Reduce the number of features using principal component analysis (PCA).
Why it's wrong here
PCA reduces dimensionality but does not address class imbalance.
- ✗
Use accuracy as the primary evaluation metric.
Why it's wrong here
Accuracy can be misleading in imbalanced datasets because a model that predicts all majority class can achieve high accuracy.
- ✓
Evaluate model performance using precision-recall curves and F1 score.
Why this is correct
Precision-recall curves focus on the minority class and are more informative than ROC for imbalanced data.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Use synthetic oversampling (SMOTE) to create additional minority class samples.
Why this is correct
SMOTE generates synthetic examples to balance classes.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
CompTIA often tests the misconception that accuracy is a valid metric for imbalanced datasets, but the trap here is that candidates overlook how a high accuracy can mask poor minority class performance, leading them to select option C instead of focusing on precision-recall curves and F1 score.
Detailed technical explanation
How to think about this question
SMOTE (Synthetic Minority Oversampling Technique) works by interpolating between existing minority class samples in feature space, creating synthetic examples that are not simple duplicates. In fraud detection, this helps the model learn decision boundaries that separate rare fraudulent patterns from legitimate ones, but care must be taken to avoid overfitting or introducing noise when the minority class is extremely sparse.
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.
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FAQ
Questions learners often ask
What does this AI0-001 question test?
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: Apply cost-sensitive learning by assigning a higher misclassification cost to the minority class. — Option A is correct because cost-sensitive learning directly addresses class imbalance by assigning a higher misclassification cost to the minority class (fraudulent transactions). This forces the model to penalize false negatives more heavily, thereby improving recall for the minority class without altering the dataset distribution.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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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