Databricks Generative AI Engineer Associate Question 234
Select 3You are building a generative AI application using a Foundation Model API. The application needs to handle high traffic, ensure low latency, and manage API costs effectively. Which of the following strategies should you implement to serve the application efficiently?
- A
Implement request batching to send multiple prompts in a single API call.
- B
Deploy the Foundation Model locally to avoid API usage altogether.
- C
Cache responses for commonly used prompts to reduce repetitive API calls.
- D
Use a load balancer to distribute requests across multiple API endpoints.
- E
Prioritize GPU-accelerated inference for faster response times.
Show answer and explanation
Correct answers: A, C, D
Explanation
When serving an LLM application with Foundation Model APIs, optimizing performance and cost is crucial. Request batching minimizes API calls and reduces latency, caching avoids redundant API usage for repetitive prompts, and load balancing ensures efficient handling of high traffic. These strategies align with the requirements of leveraging Foundation Model APIs effectively.
- A. Correct.
Implementing request batching can help reduce the number of API calls, which improves efficiency and reduces costs. It is a best practice for serving applications leveraging Foundation Model APIs.
- B. Incorrect.
Deploying the Foundation Model locally is not a viable strategy if you are relying on Foundation Model APIs, as they are hosted and maintained by the provider. This option contradicts the scenario's context.
- C. Correct.
Caching responses for frequently used prompts can significantly reduce API usage and latency, making it an effective way to manage cost and performance.
- D. Correct.
A load balancer can improve performance and reliability by evenly distributing traffic across multiple API endpoints, especially under high traffic conditions.
- E. Incorrect.
GPU-accelerated inference is relevant for local model deployments but is not applicable when using Foundation Model APIs, as the underlying infrastructure is managed by the API provider.