Exam Professional Machine Learning Engineer topic 1 question 310 discussion

Actual exam question from Google's Professional Machine Learning Engineer

Question #: 310
Topic #: 1

[All Professional Machine Learning Engineer Questions]

You recently deployed an image classification model on Google Cloud. You used Cloud Build to build a CI/CD pipeline for the model. You need to ensure that the model stays up-to-date with data and code changes by using an efficient retraining process. What should you do?

A. Use Cloud Run functions to monitor data drift in real time and trigger a Vertex AI Training job to retrain the model when data drift exceeds a predetermined threshold.
B. Configure a Git repository trigger in Cloud Build to initiate retraining when there are new code commits to the model's repository and a Pub/Sub trigger when there is new data in Cloud Storage.
C. Use Cloud Scheduler to initiate a daily retraining job in Vertex AI Pipelines.
D. Configure Cloud Composer to orchestrate a weekly retraining job that includes data extraction from BigQuery, model retraining with Vertex AI Training, and model deployment to a Vertex AI endpoint.

Show Suggested Answer

Suggested Answer: B 🗳️

by hit_cloudie at May 21, 2025, 8:57 a.m.

Comments

Submit Cancel

qaz09

1 month ago

Selected Answer: B

B -> both code changes and data changes are addressed in this option C, D --> Efficient retraining process suggests that retraining should be triggered when there is a need for one (when data changes or new code is added) -> this excludes option C (daily retraining) and D (weekly retraining) A -> in Option A there does not trigger retraining when the code changes

upvoted 1 times

...

hit_cloudie

2 months, 3 weeks ago

Selected Answer: B

This is a CI/CD best practice: Cloud Build handles retraining on code changes (via Git trigger). Pub/Sub can trigger retraining on new data (e.g., new files in Cloud Storage). Efficient and automated.

upvoted 1 times

...