Databricks Generative AI Engineer Associate Question 230
Select 3You are tasked with deploying an LLM application that leverages a Foundation Model API to provide real-time responses to users. Which steps are essential to ensure the application is properly served and scalable?
- A
Integrate the Foundation Model API with a backend service to handle requests and responses.
- B
Deploy the application directly on a user's local machine to reduce latency.
- C
Implement caching mechanisms for frequently used prompts and responses.
- D
Set up autoscaling for the backend service to handle variable traffic.
- E
Train a custom model from scratch to replace the Foundation Model API.
Show answer and explanation
Correct answers: A, C, D
Explanation
To serve an LLM application using Foundation Model APIs, it is crucial to integrate the API with a scalable backend service, optimize performance using caching, and ensure the system can handle variable traffic through autoscaling. These steps ensure the application is reliable, responsive, and cost-effective. Deploying the application locally or training a custom model would not align with the goal of leveraging Foundation Model APIs efficiently.
- A. Correct.
Integrating the Foundation Model API with a backend service is necessary to manage API calls, enforce security, and process user requests effectively.
- B. Incorrect.
Deploying the application directly on a user's local machine is generally impractical for real-time LLM applications as it lacks scalability and requires significant local resources.
- C. Correct.
Implementing caching mechanisms reduces redundant API calls and improves performance, especially for commonly used inputs and outputs.
- D. Correct.
Setting up autoscaling ensures the backend service can handle varying traffic loads efficiently, maintaining performance and availability.
- E. Incorrect.
Training a custom model from scratch is resource-intensive and unnecessary when leveraging a Foundation Model API.