Databricks Generative AI Engineer Associate exam dumps

Databricks Generative AI Engineer Associate practice question 175 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 175

Select 3

You are tasked with deploying a generative AI model that was fine-tuned on Databricks. The model will serve real-time predictions via an API. Which of the following steps must be included to successfully assemble and deploy this application?

  1. A

    Package the fine-tuned model and its dependencies into a containerized environment.

  2. B

    Configure a Databricks MLflow Model Serving endpoint for the fine-tuned model.

  3. C

    Train the model from scratch to optimize it for container deployment.

  4. D

    Set up an API layer to communicate with the deployed model for real-time predictions.

  5. E

    Deploy the model directly without adding any dependency management.

Show answer and explanation

Correct answers: A, B, D

Explanation

Deploying a generative AI model involves preparing the model and its dependencies for a production environment, hosting it for inference, and providing an API layer for real-time communication. Packaging the model into a container, configuring MLflow Model Serving, and setting up an API layer are critical steps in this process. Training the model again or skipping dependency management are not required or advisable.

  • A. Correct.

    Correct. Packaging the model and its dependencies into a containerized environment, such as a Docker image, ensures that the application is portable and can run in various deployment environments.

  • B. Correct.

    Correct. Configuring an MLflow Model Serving endpoint enables the model to be hosted and accessed for real-time inference on Databricks.

  • C. Incorrect.

    Incorrect. Training the model from scratch is unnecessary for deployment, as the model has already been fine-tuned. Deployment focuses on operationalizing the existing model.

  • D. Correct.

    Correct. Setting up an API layer is essential for enabling external applications to send requests and receive predictions from the deployed model.

  • E. Incorrect.

    Incorrect. Deploying the model without dependency management would likely lead to runtime failures due to missing libraries or mismatched versions.

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