Google Professional Machine Learning Engineer
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You have deployed an image classification model on Google Cloud using a Cloud Build CI/CD pipeline. How can you implement an efficient retraining process to keep the model current with data and code changes?
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.