AI0-001 Machine Learning and Deep Learning Practice Question
A financial institution is developing a fraud detection model using historical transaction data. The dataset contains over 10 million records, but only 0.01% of transactions are fraudulent. The current model uses a neural network trained with standard cross-entropy loss, and the team applies random undersampling of the majority class to create a balanced training set. However, the model still produces a high number of false positives (legitimate transactions flagged as fraud) and misses approximately 30% of actual fraud cases. The business requires that at least 95% of frauds be caught, and the false positive rate must be below 1% to avoid overwhelming fraud analysts. The team has limited resources to collect additional data and cannot change the model architecture significantly. Which approach should the team take to best meet the business requirements?
⚠ Common exam trap
The trap is assuming that balancing the dataset (undersampling) or switching to anomaly detection solves imbalance, when the real issue is the asymmetric misclassification cost—candidates overlook cost-sensitive learning as the direct lever for meeting recall/FPR targets.
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
✓
Use cost-sensitive learning by assigning a higher misclassification cost to the fraud class.
Cost-sensitive learning directly addresses the business requirement by penalizing fraud misclassifications more heavily, which shifts the decision threshold to favor recall on the fraud class while still allowing the team to tune the trade-off between false positives and false negatives. Because the team cannot collect more data or change the architecture significantly, adjusting the loss function's class weights is the most practical lever to hit the 95% recall and <1% FPR targets. It also avoids the information loss caused by random undersampling, which discards 99.99% of legitimate transactions and distorts the true class distribution.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use cost-sensitive learning by assigning a higher misclassification cost to the fraud class.
Why this is correct
This directly penalizes false negatives more, encouraging the model to catch more frauds while maintaining a low false positive rate through tuning.
- ✗
Apply feature selection to remove noisy predictors and then retrain the current model.
Why it's wrong here
Feature selection may help but does not directly address the class imbalance or the specific performance targets for recall and false positive rate.
- ✗
Switch to an anomaly detection algorithm such as Isolation Forest or One-Class SVM.
Why it's wrong here
Anomaly detection typically assumes outliers are rare and distinct, but transaction fraud can be very similar to legitimate behavior, leading to high false positive rates.
- ✗
Collect more transaction data, especially fraudulent examples, to naturally balance the classes.
Why it's wrong here
Collecting more data is resource-intensive and may not be feasible; oversampling existing fraud data could cause overfitting.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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.