Databricks Certified Generative AI Engineer - Associate
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Question: 10
When serving an LLM application using Foundation Model APIs on Databricks, which of the following is a key consideration for ensuring efficient deployment and scalability?
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A
Fine-tune the LLM on Databricks and register the model with MLflow for version control, then use a Databricks REST API endpoint to serve the model.
B
Ensure the LLM is fully retrained on your specific dataset before deploying it to Databricks, as pre-trained models are not suitable for Foundation Model APIs.
C
Store the LLM as a Delta table in Unity Catalog and query it in real-time using SQL endpoints.
D
The LLM should be downloaded locally and deployed on a custom virtual machine for scalability.
Explanation:
Explanation
Option A is the correct answer because it follows Databricks' best practices for LLM deployment:
- Fine-tuning on Databricks: Databricks provides optimized environments for fine-tuning LLMs
- MLflow model registration: Enables version control, model tracking, and reproducibility
- Databricks REST API endpoint: Provides scalable, managed serving infrastructure with automatic scaling, monitoring, and security
Why other options are incorrect:
- Option B: Pre-trained models ARE suitable for Foundation Model APIs - the key is proper fine-tuning and deployment, not complete retraining
- Option C: LLMs cannot be stored as Delta tables; they are large model artifacts that require specialized serving infrastructure
- Option D: Downloading locally defeats the purpose of Databricks' managed infrastructure and doesn't provide the scalability benefits of Databricks serving endpoints
Key benefits of Option A approach:
- Automatic scaling based on traffic
- Built-in monitoring and logging
- Security and access controls
- Cost optimization through managed infrastructure
- Seamless integration with Databricks ecosystem
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