Google Cloud Professional Machine Learning Engineer — Question 223

You are building a MLOps platform to automate your company’s ML experiments and model retraining. You need to organize the artifacts for dozens of pipelines. How should you store the pipelines’ artifacts?

Answer options

• A. Store parameters in Cloud SQL, and store the models’ source code and binaries in GitHub.
• B. Store parameters in Cloud SQL, store the models’ source code in GitHub, and store the models’ binaries in Cloud Storage.
• C. Store parameters in Vertex ML Metadata, store the models’ source code in GitHub, and store the models’ binaries in Cloud Storage.
• D. Store parameters in Vertex ML Metadata and store the models’ source code and binaries in GitHub.

Correct answer: C

Explanation

The correct answer is C because Vertex ML Metadata is designed to manage ML metadata and artifacts effectively, making it suitable for storing parameters. Storing the models' source code in GitHub and binaries in Cloud Storage aligns with best practices for version control and scalable storage. The other options either do not utilize Vertex ML Metadata or misplace the artifact storage, which can hinder organization and accessibility.