Databricks Generative AI Engineer Associate exam dumps

Databricks Generative AI Engineer Associate practice question 214 of 306

Databricks Certified Generative AI Engineer Associate. Free level, Databricks. Free question with the correct answer and a full explanation.

Databricks Generative AI Engineer Associate Question 214

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You are a Generative AI Engineer working on a Databricks project that involves deploying a trained language model for production use. To enable proper model governance and centralization, you need to register your model to the Unity Catalog using MLflow. Which of the following steps is required to successfully register the model to Unity Catalog?

  1. A

    Set up a Unity Catalog metastore in your Databricks workspace.

  2. B

    Use the mlflow.register_model() function with the Unity Catalog model registry URI.

  3. C

    Ensure that the model is stored in a Unity Catalog-enabled storage location, such as a managed Delta table.

  4. D

    Assign appropriate permissions to users through Unity Catalog for accessing the model.

  5. E

    Run the mlflow.log_model() method with the registered_model_name parameter set to a Unity Catalog model registry path.

Show answer and explanation

Correct answers: A, D, E

Explanation

To successfully register a model to Unity Catalog using MLflow, you need to first set up a Unity Catalog metastore in your Databricks workspace to enable model governance. You also need to use the mlflow.log_model() method with the registered_model_name parameter set to the correct Unity Catalog model registry path. Additionally, to ensure secure access, permissions must be properly assigned to users via Unity Catalog. Steps such as using mlflow.register_model() or storing the model in a specific storage location are not relevant in this context.

  • A. Correct.

    Setting up a Unity Catalog metastore is a prerequisite for using Unity Catalog for model registration and governance. Without a metastore, Unity Catalog cannot function.

  • B. Incorrect.

    The mlflow.register_model() function is not used for Unity Catalog model registration. Instead, the mlflow.log_model() method with the proper registered_model_name parameter is required.

  • C. Incorrect.

    Unity Catalog does not require the model to be stored in a Unity Catalog-enabled storage location. This is not a mandatory step for registering a model to Unity Catalog.

  • D. Correct.

    Assigning permissions to users through Unity Catalog is important to ensure that only authorized users can access or modify the registered model.

  • E. Correct.

    The mlflow.log_model() method with the registered_model_name parameter pointing to a Unity Catalog model registry path is the correct approach to register a model to Unity Catalog.

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