Google Cloud Professional Machine Learning Engineer — Question 300

You are developing a model to detect fraudulent credit card transactions. You need to prioritize detection, because missing even one fraudulent transaction could severely impact the credit card holder. You used AutoML to train a model on users' profile information and credit card transaction data. After training the initial model, you notice that the model is failing to detect many fraudulent transactions. How should you increase the number of fraudulent transactions that are detected?

Answer options

• A. Add more non-fraudulent examples to the training set.
• B. Reduce the maximum number of node hours for training.
• C. Increase the probability threshold to classify a fraudulent transaction.
• D. Decrease the probability threshold to classify a fraudulent transaction.

Correct answer: D

Explanation

The correct approach is to decrease the probability threshold to classify a fraudulent transaction, which will allow the model to identify more transactions as fraudulent. Increasing the threshold (Option C) would result in fewer transactions being flagged, while adding more non-fraudulent examples (Option A) and reducing node hours (Option B) would not directly improve the detection of fraudulent cases.