Exam Professional Machine Learning Engineer topic 1 question 318 discussion

Actual exam question from Google's Professional Machine Learning Engineer

Question #: 318
Topic #: 1

[All Professional Machine Learning Engineer Questions]

You developed an ML model using Vertex AI and deployed it to a Vertex AI endpoint. You anticipate that the model will need to be retrained as new data becomes available. You have configured a Vertex AI Model Monitoring Job. You need to monitor the model for feature attribution drift and establish continuous evaluation metrics. What should you do?

A. Set up alerts using Cloud Logging, and use the Vertex AI console to review feature attributions.
B. Set up alerts using Cloud Logging, and use Looker Studio to create a dashboard that visualizes feature attribution drift. Review the dashboard periodically.
C. Enable request-response logging for the Vertex AI endpoint, and set up alerts using Pub/Sub. Create a Cloud Run function to run TensorFlow Data Validation on your dataset.
D. Enable request-response logging for the Vertex AI endpoint, and set up alerts using Cloud Logging. Review the feature attributions in the Google Cloud console when an alert is received.

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Suggested Answer: D 🗳️

by hit_cloudie at May 21, 2025, 8:48 a.m.

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qaz09

1 month ago

Selected Answer: A

not C, D -> If an endpoint has Model Monitoring enabled, you can't enable request-response logging for the same endpoint. https://cloud.google.com/vertex-ai/docs/predictions/online-prediction-logging#model-monitoring A is simpler solution than B, I don't think there is a need to create custom Looker Studio dashboards, since you can use Vertex AI console.

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hit_cloudie

2 months, 3 weeks ago

Selected Answer: D

This leverages Vertex AI Model Monitoring with request-response logging, Cloud Logging alerts, and built-in feature attribution drift review.

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