Google Cloud Professional Machine Learning Engineer — Question 308

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?

Answer options

• 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.

Correct answer: B

Explanation

The correct answer is B because it effectively combines triggers for both code changes and new data, ensuring that the model is retrained in response to both types of updates. Option A focuses solely on data drift monitoring, which might not address code changes. Option C schedules retraining daily, which may not be necessary, and option D introduces complexity with orchestration that might not be required for simple updates.