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A company is building an AI-powered customer support application that uses Amazon Bedrock FMs.

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A company is building an AI-powered customer support application that uses Amazon Bedrock FMs. The application must provide users with recommendations based on an internal Amazon Bedrock knowledge base.

Users will query documents that the company stores in Amazon S3 and structured data that is in Amazon RDS. The application must provide citations for the sources it uses to generate recommendations. The application must also provide secure access to sensitive data.

Which solution will meet these requirements?

A.

Associate one Amazon Bedrock AgentCore agent with two knowledge bases. Configure one knowledge base to access documents from Amazon S3 and the other to access structured data from Amazon RDS. Configure the agent to preserve attribution metadata in responses. Use IAM roles to control access to sensitive data.

B.

Create two Amazon Bedrock AgentCore agents. Associate one agent with a knowledge base that is connected to the documents in Amazon S3 and the other agent with a knowledge base that is connected to Amazon RDS. Develop a custom AWS Lambda function to query both agents simultaneously and combine the results.

C.

Create one Amazon Bedrock AgentCore agent that uses AWS Lambda functions to directly query Amazon S3 for documents and Amazon RDS for structured data. Process the responses and merge the results into a single response.

D.

Configure one Amazon Bedrock AgentCore agent to use a knowledge base that contains the documents from Amazon S3. Configure AWS Lambda functions to query Amazon RDS directly for structured data and to combine the results before returning responses.

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