AI0-001 AI Models and Data Engineering Practice Question
A financial institution is building a fraud detection system using a supervised learning model. The dataset is highly imbalanced with 99.9% legitimate transactions and 0.1% fraudulent ones. Which approach would be MOST effective to train the model to detect fraud?
⚠ Common exam trap
CompTIA often tests the misconception that simply changing the performance metric (like using F1-score or precision-recall) alone is sufficient to handle imbalance, but the trap here is that without addressing the data distribution itself, the model still lacks sufficient fraudulent examples to learn meaningful patterns.
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 SMOTE to generate synthetic fraudulent transactions
SMOTE (Synthetic Minority Oversampling Technique) is the most effective approach because it generates synthetic fraudulent transactions by interpolating between existing minority class samples, thereby balancing the dataset without losing information. This allows the model to learn decision boundaries for fraud detection more effectively than simple undersampling or metric adjustments, especially given the extreme 99.9% vs 0.1% imbalance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Train the model using accuracy as the performance metric
Why it's wrong here
Accuracy as a metric treats all correct predictions equally, so a model that classifies every transaction as legitimate achieves 99.9% accuracy without detecting any fraud. This fails because the 0.1% fraudulent class is the target of detection, and accuracy is blind to class distribution. It is tempting because accuracy is the default metric for balanced datasets, where misclassification costs are symmetric and each class is equally represented.
- ✗
Undersample the legitimate transactions to match the number of fraudulent ones
Why it's wrong here
Undersampling loses valuable data and may degrade model performance.
- ✓
Use SMOTE to generate synthetic fraudulent transactions
Why this is correct
SMOTE creates synthetic samples of the minority class, effectively balancing the dataset without losing data.
- ✗
Increase the regularization strength in the model
Why it's wrong here
Regularization does not address class imbalance.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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