AI0-001 Machine Learning and Deep Learning Practice Question
This AI0-001 practice question tests your understanding of machine learning and deep learning. 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 data scientist is building a classification model to detect fraudulent transactions. The dataset is highly imbalanced with only 1% fraudulent cases. Which approach should the scientist use to evaluate model performance most effectively?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
F1 score
In highly imbalanced datasets like fraud detection (1% positive class), accuracy is misleading because a model that predicts all transactions as legitimate would achieve 99% accuracy yet fail to detect any fraud. The F1 score (harmonic mean of precision and recall) is the most effective metric because it balances both false positives and false negatives, providing a single score that reflects the model's ability to correctly identify the minority class without being skewed by class imbalance.
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.
✓
F1 score
Why this is correct
F1 score is the harmonic mean of precision and recall, providing a balanced measure for imbalanced datasets.
Related concept
Read the scenario before looking for a memorised answer.
✗
Accuracy
Why it's wrong here
Accuracy is not suitable for imbalanced datasets as it can be high even if the model fails to detect fraud.
✗
Recall
Why it's wrong here
Recall only considers false negatives, not false positives, so it is insufficient alone.
✗
Precision
Why it's wrong here
Precision only considers false positives, not false negatives, so it does not fully capture model performance on fraud detection.
Common exam traps
Common exam trap: answer the scenario, not the keyword
CompTIA often tests the misconception that accuracy is always the best metric for classification, but in imbalanced datasets, accuracy is a trap because it does not reflect performance on the minority class, leading candidates to overlook metrics like F1 score that directly address class imbalance.
Detailed technical explanation
How to think about this question
The F1 score is defined as 2 * (precision * recall) / (precision + recall), and it is particularly sensitive to the minority class in imbalanced datasets because it requires both precision and recall to be high to achieve a good score. In practice, fraud detection models often use threshold tuning or cost-sensitive learning alongside F1 to optimize the trade-off between catching fraud and avoiding false alarms, as the cost of a false negative (missed fraud) is typically much higher than a false positive (legitimate transaction flagged).
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.
Machine Learning and Deep Learning — This question tests Machine Learning and Deep Learning — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: F1 score — In highly imbalanced datasets like fraud detection (1% positive class), accuracy is misleading because a model that predicts all transactions as legitimate would achieve 99% accuracy yet fail to detect any fraud. The F1 score (harmonic mean of precision and recall) is the most effective metric because it balances both false positives and false negatives, providing a single score that reflects the model's ability to correctly identify the minority class without being skewed by class imbalance.
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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