Databricks Generative AI Engineer Associate Question 232
Select 3You are tasked with deploying a large language model (LLM) application that interacts with a Foundation Model API. The application needs to handle multiple user requests with minimal latency while ensuring scalability. Which of the following approaches should you consider for serving the application?
- A
Use a RESTful API service to expose the LLM functionality and scale it with a load balancer.
- B
Deploy the application directly on a single local machine with limited compute resources.
- C
Leverage serverless compute platforms to dynamically scale based on user demand.
- D
Use a caching mechanism to store frequently used responses from the LLM API.
- E
Configure a GPU-based cluster to directly run LLM inference without using the Foundation Model API.
Show answer and explanation
Correct answers: A, C, D
Explanation
To serve an LLM application leveraging Foundation Model APIs, it is essential to focus on scalability, performance optimization, and efficient resource utilization. Using a RESTful API with a load balancer ensures scalability, while serverless compute platforms provide dynamic scaling as traffic fluctuates. Implementing caching mechanisms reduces latency by avoiding redundant API calls. These approaches collectively ensure the application can handle multiple user requests efficiently. Deploying on a single local machine or running inference directly on a GPU cluster does not align with the requirement of leveraging the Foundation Model API in a scalable manner.
- A. Correct.
Exposing the LLM application through a RESTful API service and using a load balancer ensures scalability and handles multiple user requests efficiently. This is a standard approach for serving scalable applications.
- B. Incorrect.
Deploying the application on a single local machine with limited resources may bottleneck performance and fail to handle multiple user requests effectively. It is not suitable for scalable production environments.
- C. Correct.
Serverless compute platforms allow dynamic scaling based on user demand, making them a great choice for handling variable traffic loads without over-provisioning resources.
- D. Correct.
Caching frequently used responses can significantly reduce the round-trip time to the Foundation Model API and improve application performance, especially for repeated queries.
- E. Incorrect.
Directly running LLM inference on a GPU-based cluster bypasses the Foundation Model API and is not applicable in this scenario where the application depends on the Foundation Model API.