Exam Professional Machine Learning Engineer topic 1 question 321 discussion

Actual exam question from Google's Professional Machine Learning Engineer

Question #: 321
Topic #: 1

[All Professional Machine Learning Engineer Questions]

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?

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.

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Suggested Answer: D 🗳️

by hit_cloudie at May 21, 2025, 8:45 a.m.

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hit_cloudie

4 weeks, 1 day ago

Selected Answer: D

This will increase the number of transactions flagged as fraudulent, improving recall (even at the cost of more false positives, which is acceptable in fraud detection).

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