AIP-C01 Question #35: Real Exam Question with Answer & Explanation

Question

A publishing company is developing a chat assistant that uses a containerized large language model (LLM) that runs on Amazon SageMaker AI. The architecture consists of an Amazon API Gateway REST API that routes user requests to an AWS Lambda function. The Lambda function invokes a SageMaker AI real-time endpoint that hosts the LLM. Users report uneven response times. Analytics show that a high number of chats are abandoned after 2 seconds of waiting for the first token. The company wants a solution to ensure that p95 latency is under 800 ms for interactive requests to the chat assistant. Which combination of solutions will meet this requirement? (Select TWO.)

Options

• AEnable model preload upon container startup. Implement dynamic batching to process multiple
• BSelect a larger GPU instance type for the SageMaker AI endpoint. Set the minimum number of
• CSwitch to a multi-model endpoint. Use lazy loading without request batching.
• DSet the minimum number of instances to greater than 0. Enable response streaming.
• ESwitch to Amazon SageMaker Asynchronous Inference for all requests. Store requests in an