A company is designing a solution that uses foundation models (FMs) to support multiple AI workloads. Some FMs must be invoked on demand and in real time. Other FMs require consistent high-throughput

Question

Options

• AUse AWS Lambda to orchestrate low-latency FM inference by invoking FMs hosted on Amazon
• BConfigure provisioned throughput in Amazon Bedrock to ensure consistent performance for high-
• CDeploy FMs to Amazon SageMaker AI endpoints with support for edge deployment by using
• DUse Amazon Bedrock with auto-scaling to handle unpredictable traffic surges.
• EUse Amazon SageMaker JumpStart to host and invoke the FMs.

• A
  14% (6)
• B
  76% (32)
• D
  5% (2)
• E
  5% (2)

Explanation

The correct combination is B and C because together they address both workload diversity and hybrid deployment requirements with minimal custom engineering. Option B provides consistent, high-throughput access by configuring provisioned throughput in Amazon Bedrock. Provisioned throughput guarantees predictable capacity and performance, which is essential for batch processing workloads that require sustained inference rates. This eliminates cold starts and throttling concerns that can occur with purely on-demand usage, making it well suited for high-volume enterprise workloads. Option C enables hybrid deployment across cloud and on-premises environments by deploying foundation models to Amazon SageMaker AI endpoints and using Amazon SageMaker Neo for edge and on-premises optimization. SageMaker Neo compiles models for target hardware, allowing inference to run efficiently outside the AWS cloud while still using AWS-managed tooling. Orchestrating these deployments with AWS Lambda allows consistent invocation patterns across

No community discussion yet for this question.