Explanation:
Correct Answers: B and C
Why B is correct:
Why C is correct:
Why other options are incorrect:
A: Incorrect because AWS Lambda orchestrating SageMaker asynchronous endpoints doesn't address hybrid deployment or on-premises requirements. Asynchronous endpoints are for offline/batch processing, not real-time low-latency needs.
D: Incorrect because auto-scaling handles unpredictable traffic surges, but doesn't address the hybrid deployment requirement or provide consistent throughput for batch processing.
E: Incorrect because Amazon SageMaker JumpStart is for quickly deploying pre-trained models, but doesn't specifically address hybrid deployment patterns or edge deployment requirements.
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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? (Select TWO.)
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.