AWS Certified Generative AI – Professional (AIP-C01) — Question 62

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 access for batch processing.
The solution must support hybrid deployment patterns and run workloads across cloud infrastructure and on-premises infrastructure to comply with data residency and compliance requirements.
Which combination of steps will meet these requirements? (Choose two.)

Answer options

• A. Use AWS Lambda to orchestrate low-latency FM inference by invoking FMs hosted on Amazon SageMaker AI asynchronous endpoints.
• B. Configure provisioned throughput in Amazon Bedrock to ensure consistent performance for high-volume workloads.
• C. Deploy FMs to Amazon SageMaker AI endpoints with support for edge deployment by using Amazon SageMaker Neo. Orchestrate the FMs by using AWS Lambda to support hybrid deployment.
• D. Use Amazon Bedrock with auto-scaling to handle unpredictable traffic surges.
• E. Use Amazon SageMaker JumpStart to host and invoke the FMs.

Correct answer: B, C

Explanation

Option B is correct because configuring provisioned throughput in Amazon Bedrock ensures that high-volume workloads can consistently perform as needed. Option C is also correct as deploying FMs to Amazon SageMaker AI endpoints with edge support and orchestrating them with AWS Lambda facilitates hybrid deployment. The other options do not adequately address both the real-time and high-throughput requirements or do not support the necessary hybrid infrastructure.