AI0-001 Machine Learning and Deep Learning Practice Question
A company uses a neural network for fraud detection. The dataset has 99% legitimate, 1% fraudulent. The model achieves 99% accuracy but fails to detect most frauds. Which metric should they focus on?
⚠ Common exam trap
The AI0-001 exam often tests the misconception that high accuracy implies good model performance, especially in imbalanced datasets, leading candidates to overlook recall as the critical metric for detecting rare events like fraud.
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 (sensitivity) measures the proportion of actual positives correctly identified. In this fraud detection scenario with 99% legitimate and 1% fraudulent transactions, a 99% accuracy can be achieved by simply predicting all transactions as legitimate, which yields 0% recall for the fraud class. Focusing on recall ensures the model captures the majority of fraudulent cases, addressing the critical failure to detect fraud despite high accuracy.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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Precision
Why it's wrong here
Precision measures how many predicted frauds are truly fraudulent, so a model flagging almost nothing can still score highly while missing most fraud. Precision is the right focus when false positives are the costly error and the positive class is common.
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F1-score
Why it's wrong here
F1-score averages precision and recall, so strong precision on a rare positive class can mask the poor recall causing the missed frauds. F1 is the correct choice when both false positives and false negatives matter roughly equally and classes are balanced.
- ✓
Recall
Why this is correct
Recall measures the proportion of actual frauds correctly identified, directly addressing the 99:1 class imbalance where accuracy is misleading. Optimising recall reduces false negatives, ensuring the minority fraudulent cases are detected rather than ignored by a model that predicts "legitimate" almost always.
- ✗
AUC-ROC
Why it's wrong here
AUC-ROC summarises ranking across all thresholds and stays high when negatives dominate, hiding the low recall on the 1% fraud class. AUC-ROC is the right metric when comparing classifiers on balanced data or when threshold-independent ranking quality is the goal.
About these practice questions
Courseiva writes every AI0-001 question from scratch — 962 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
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.