GENERATIVE-AI-ENGINEER-ASSOCIATE · Question #52
GENERATIVE-AI-ENGINEER-ASSOCIATE Question #52: Real Exam Question with Answer & Explanation
The correct answer is B: Use MLflow to log the model directly into Unity Catalog, and enable READ access in the dev. The most cost-effective and secure approach is to use the MLflow Model Registry in conjunction with Unity Catalog. By logging the model directly into Unity Catalog from MLflow, the model becomes accessible across workspaces with proper access controls. Enabling READ access in the
Question
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?
Options
- AUse an external model registry which can be accessed from all workspaces.
- BUse MLflow to log the model directly into Unity Catalog, and enable READ access in the dev
- CSetup a duplicate training pipeline in dev, so that an identical model is available in dev.
- DSetup a script to export the model from prod and import it to dev.
Explanation
The most cost-effective and secure approach is to use the MLflow Model Registry in conjunction with Unity Catalog. By logging the model directly into Unity Catalog from MLflow, the model becomes accessible across workspaces with proper access controls. Enabling READ access in the dev workspace ensures that the dev team can test the model without requiring unnecessary duplication or manual export/import steps. This method leverages existing tools and access control mechanisms, ensuring both cost-effectiveness and security.
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