AI0-001 Machine Learning and Deep Learning Practice Question
A financial institution uses a deep learning model for fraud detection. The model is a feedforward neural network with three hidden layers. It was trained on a balanced dataset of 100,000 transactions. During deployment, the model achieves high accuracy on the test set but the fraud detection rate (true positive rate) is only 40% while the false positive rate is 0.1%. The business requires a true positive rate of at least 80%. Which of the following actions is most likely to achieve the required true positive rate while minimizing the increase in false positives?
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
✓
Change the threshold for classifying a transaction as fraud from the default 0.5 to a lower value
(increase hidden layers) may capture more complexity but does not directly increase TPR and could overfit. Option B (SMOTE) rebalances the training set, but the dataset is already balanced, so this is unlikely to improve TPR. Option D (L2 regularization) reduces overfitting but increases bias, which could lower TPR. Option C (change threshold) is the most direct approach: lowering the classification threshold increases the true positive rate, and by tuning, it can achieve 80% TPR with a minimal increase in false positives.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the number of hidden layers to five to capture more complex patterns
Why it's wrong here
More layers increase complexity but may not improve recall; could cause overfitting.
- ✗
Use synthetic minority oversampling (SMOTE) to rebalance the training set
Why it's wrong here
The training set is already balanced, so SMOTE addresses a class imbalance that does not exist here; the low true positive rate stems from the model’s decision threshold being too conservative, not from insufficient minority-class examples. SMOTE is tempting because it is designed to improve recall on an underrepresented class, and would be correct if the original dataset had far fewer fraudulent than legitimate transactions.
- ✓
Change the threshold for classifying a transaction as fraud from the default 0.5 to a lower value
Why this is correct
Lowering threshold increases TPR; the optimal threshold can be chosen based on the precision-recall curve.
- ✗
Add L2 regularization to reduce overfitting
Why it's wrong here
Regularization increases bias, likely decreasing TPR further.
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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.