Certified Generative AI Engineer AssociateDemo

By Databricks · Question Mode

QuestionQ82

Assembling and Deploying Applications

A Generative AI Engineer wants the fine-tuned LLMs in their production Databricks workspace to also be available for testing in their development workspace. All workspaces are Unity Catalog-enabled, and the models are currently being logged to the MLflow Model Registry.

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

  • 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.
Explanation

Models registered in Unity Catalog are centrally governed and available across workspaces connected to the metastore when appropriate permissions are granted. Registering the MLflow model in Unity Catalog and granting the development environment access provides secure cross-workspace use without duplicating training, copying model artifacts, or introducing an external registry.

Learn more

Community Discussion

No comments yet. Be the first to start the discussion!