Databricks Generative AI Engineer Associate exam dumps

Databricks Generative AI Engineer Associate practice question 178 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 178

Select 3

You are deploying a generative AI application on Databricks that uses a pre-trained foundation model for text generation. To ensure efficient inference and scaling, which steps should you include during the deployment process?

  1. A

    Use Databricks Model Serving to host the model with REST API endpoints.

  2. B

    Optimize the model by applying quantization techniques to reduce memory and compute requirements.

  3. C

    Deploy the model directly on a single-node cluster without considering scaling requirements.

  4. D

    Integrate the model deployment with a feature store to retrieve real-time features for inference.

  5. E

    Test the deployed application using a small dataset to evaluate latency and throughput.

Show answer and explanation

Correct answers: A, B, E

Explanation

Deploying a generative AI application in Databricks involves using tools like Databricks Model Serving to provide scalable inference capabilities. Optimizing the model (e.g., via quantization) helps minimize resource requirements, making it suitable for production. Additionally, testing the application with a small dataset ensures the deployment meets expected performance metrics. While feature stores are useful in other contexts, they are not a core part of deploying pre-trained generative AI models.

  • A. Correct.

    Using Databricks Model Serving is a best practice for deploying machine learning and generative AI models as REST API endpoints, ensuring efficient and scalable deployment.

  • B. Correct.

    Optimizing the model through quantization reduces resource usage, making it more suitable for deployment in production environments.

  • C. Incorrect.

    Deploying on a single-node cluster without considering scaling requirements is not recommended for production applications as it could lead to performance bottlenecks.

  • D. Incorrect.

    While a feature store can be valuable for retrieving features, it is not directly relevant for deploying a generative AI model that operates on pre-trained capabilities rather than feature-based inputs.

  • E. Correct.

    Testing the deployed application is crucial to assess its performance, including latency and throughput, to ensure it meets production requirements.

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