The verified answer is C. Use batch inference with Amazon Bedrock. The workload has three decisive characteristics: it is repetitive, it processes a large volume of requests, and it does not require immediate responses. AWS documentation states that with Amazon Bedrock batch inference, users can submit multiple prompts and generate responses asynchronously. It also states that batch inference helps process a large number of requests efficiently by sending a single request and generating responses in an Amazon S3 bucket. That directly matches the weekly generation of exercise routines for all users.
AWS also explains that after the batch job is complete, the output files can be retrieved from Amazon S3, and batch inference can improve model inference performance on large datasets. This is the right architecture for offline or scheduled workloads where immediate response latency is not required.
Amazon Bedrock AgentCore is incorrect because AgentCore is for building, running, and managing agents, tools, memory, gateways, and agent workflows. The problem is not agent orchestration; it is cost-efficient high-volume offline inference.
Real-time inference with Amazon Bedrock on-demand endpoints is incorrect because real-time inference is better for interactive applications where users need immediate responses. The question explicitly says immediate responses are not required.
Real-time inference with SageMaker AI hosted endpoints is also incorrect for the same reason. Hosted endpoints are appropriate for low-latency inference requests, but this workload is scheduled and large-volume.
Therefore, Amazon Bedrock batch inference is the best solution.
