Answer: 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.

## 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

Author: LeetQuiz .