Google Cloud Professional Machine Learning Engineer — Question 155

You are an ML engineer at a manufacturing company. You are creating a classification model for a predictive maintenance use case. You need to predict whether a crucial machine will fail in the next three days so that the repair crew has enough time to fix the machine before it breaks. Regular maintenance of the machine is relatively inexpensive, but a failure would be very costly. You have trained several binary classifiers to predict whether the machine will fail, where a prediction of 1 means that the ML model predicts a failure.

You are now evaluating each model on an evaluation dataset. You want to choose a model that prioritizes detection while ensuring that more than 50% of the maintenance jobs triggered by your model address an imminent machine failure. Which model should you choose?

Answer options

• A. The model with the highest area under the receiver operating characteristic curve (AUC ROC) and precision greater than 0.5
• B. The model with the lowest root mean squared error (RMSE) and recall greater than 0.5.
• C. The model with the highest recall where precision is greater than 0.5.
• D. The model with the highest precision where recall is greater than 0.5.

Correct answer: C

Explanation

The correct answer is C because selecting the model with the highest recall ensures that it effectively identifies most of the imminent failures, which is critical for predictive maintenance. Although precision is also important, the requirement that it be greater than 0.5 allows for a balance between identifying true failures and limiting false positives. The other options either prioritize metrics that do not directly support the detection of imminent failures (A and D) or focus on error metrics that are less relevant in this specific context (B).