AWS Certified Generative AI Developer - Professional
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A GenAI developer is evaluating Amazon Bedrock foundation models (FMs) to enhance a Europe-based company's internal business application. The company has a multi-account landing zone in AWS Control Tower. The company uses Service Control Policies (SCPs) to allow its accounts to use only the eu-north-1 and eu-west-1 Regions. All customer data must remain in private networks within the approved AWS Regions.
The GenAI developer selects an FM based on analysis and testing and hosts the model in the eu-central-1 Region and the eu-west-3 Region. The GenAI developer must enable access to the FM for the company's employees. The GenAI developer must ensure that requests to the FM are private and remain within the same Regions as the FM.
Which solution will meet these requirements?
A
Deploy an AWS Lambda function that is exposed by a private Amazon API Gateway REST API to a VPC in eu-north-1. Create a VPC endpoint for the selected FM in eu-central-1 and eu-west-3. Extend existing SCPs to allow employees to use the FM. Integrate the REST API with the business application.
B
Deploy the FM on Amazon EC2 instances in eu-north-1. Deploy a private Amazon API Gateway REST API in front of the EC2 instances. Configure an Amazon Bedrock VPC endpoint. Integrate the REST API with the business application.
C
Configure the FM to use cross-Region inference through a Europe-scoped endpoint. Configure an Amazon Bedrock VPC endpoint. Extend existing SCPs to allow employees to use the FM through inference profiles in Europe-based Regions where the FM is available. Use an inference profile to integrate Amazon Bedrock with the business application.
D
Deploy the FM in Amazon SageMaker in eu-north-1. Configure a SageMaker VPC endpoint. Extend existing SCPs to allow employees to use the SageMaker endpoint. Integrate the FM in SageMaker with the business application.