Google Professional Machine Learning Engineer
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Ultimate access to all questions.
You have successfully deployed a machine learning pipeline in Vertex AI Pipelines which trains and pushes a model to a Vertex AI endpoint for serving real-time traffic. As part of continuous improvement, you need to keep experimenting and iterating on your pipeline to enhance model performance. Additionally, you plan to utilize Cloud Build to set up a CI/CD (Continuous Integration/Continuous Deployment) process. Your main objectives are to efficiently deploy new pipeline versions into production while minimizing the risk of breaking existing functionalities in the production environment. What strategy should you adopt?
A
Set up a CI/CD pipeline that builds and tests your source code. If the tests are successful, use the Google Cloud Console to upload the built container to Artifact Registry and upload the compiled pipeline to Vertex AI Pipelines.
B
Set up a CI/CD pipeline that builds your source code and then deploys built artifacts into a pre-production environment. Run unit tests in the pre-production environment. If the tests are successful, deploy the pipeline to production.
C
Set up a CI/CD pipeline that builds and tests your source code and then deploys built artifacts into a pre-production environment. After a successful pipeline run in the pre-production environment, deploy the pipeline to production.
D
Set up a CI/CD pipeline that builds and tests your source code and then deploys built artifacts into a pre-production environment. After a successful pipeline run in the pre-production environment, rebuild the source code and deploy the artifacts to production.