Google Cloud Professional Machine Learning Engineer — Question 166

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 tram 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 adjust the training parameters in AutoML to improve model performance? (Choose two.)

Answer options

• A. Increase the score threshold
• B. Decrease the score threshold.
• C. Add more positive examples to the training set
• D. Add more negative examples to the training set
• E. Reduce the maximum number of node hours for training

Correct answer: B

Explanation

Lowering the score threshold (option B) will increase the likelihood that the model flags transactions as fraudulent, thereby enhancing detection rates. Increasing the score threshold (option A) would do the opposite, making it harder for the model to identify fraud. Adding more positive examples (option C) can help improve model accuracy, but it is not as directly effective as lowering the score threshold in this case. Adding more negative examples (option D) or reducing the training time (option E) is unlikely to address the immediate issue of missed fraudulent transactions.