Google Cloud Professional Machine Learning Engineer — Question 52

You work for a credit card company and have been asked to create a custom fraud detection model based on historical data using AutoML Tables. You need to prioritize detection of fraudulent transactions while minimizing false positives. Which optimization objective should you use when training the model?

Answer options

• A. An optimization objective that minimizes Log loss
• B. An optimization objective that maximizes the Precision at a Recall value of 0.50
• C. An optimization objective that maximizes the area under the precision-recall curve (AUC PR) value
• D. An optimization objective that maximizes the area under the receiver operating characteristic curve (AUC ROC) value

Correct answer: C

Explanation

The correct answer is C because maximizing the area under the precision-recall curve (AUC PR) focuses on improving the model's ability to correctly identify positive instances (fraudulent transactions) while keeping false positives low. Options A, B, and D do not specifically prioritize the balance between detecting fraud and minimizing false positives as effectively as option C does.