Databricks Certified Generative AI Engineer Associate — Question 70

A Generative AI Engineer wants their finetuned LLMs in their prod Databricks workspace available for testing in their dev workspace as well. All of their workspaces are Unity Catalog enabled and they are currently logging their models into the Model Registry in MLflow.

What is the most cost-effective and secure option for the Generative AI Engineer to accomplish their goal?

Answer options

• A. Use an external model registry which can be accessed from all workspaces.
• B. Use MLflow to log the model directly into Unity Catalog, and enable READ access in the dev workspace to the model.
• C. Setup a duplicate training pipeline in dev, so that an identical model is available in dev.
• D. Setup a script to export the model from prod and import it to dev.

Correct answer: B

Explanation

The correct answer, B, is the most efficient because it allows for secure access to the model in the development workspace without unnecessary duplication or external dependencies. Options A and D introduce additional complexity and potential security risks, while C requires extra resources to maintain a duplicate training pipeline.