Company configures a landing zone in AWS Control Tower. The company handles sensitive data that must remain within the European Union. The company must use only the eu-central-1 Region. The company uses Service Control Policies (SCPs) to enforce data residency policies. GenAI developers at the company are assigned IAM roles that have full permissions for Amazon Bedrock.
The company must ensure that GenAI developers can use the Amazon Nova Pro model through Amazon Bedrock only by using cross-Region inference (CRI) and only in eu- central-1. The company enables model access for the GenAI developer IAM roles in Amazon Bedrock. However, when a GenAI developer attempts to invoke the model through the Amazon Bedrock Chat/Text playground, the GenAI developer receives the following error:
User arn:aws:sts:123456789012:assumed-role/AssumedDevRole/DevUserName Action: bedrock:InvokeModelWithResponseStream
On resource(s): arn:aws:bedrock:eu-west-3::foundation-model/amazon.nova-pro-v1:0 Context: a service control policy explicitly denies the action
The company needs a solution to resolve the error. The solution must retain the company's existing governance controls and must provide precise access control. The solution must comply with the company's existing data residency policies.
Which combination of solutions will meet these requirements? (Select TWO.)

1. A. Add an AdministratorAccess policy to the GenAI developer IAM role
2. B. Extend the existing SCPs to enable CRI for the eu.amazon.nova-pro-v1:0 inference profile
3. C. Enable Amazon Bedrock model access for Amazon Nova Pro in the eu-west-3 Region
4. D. Validate that the GenAI developer IAM roles have permissions to invoke Amazon Nova Pro through the eu.amazon.nova-pro-v1:0 inference profile on all European Union AWS Regions that can serve the model
5. E. Extend the existing SCP to enable CRI for the eu-* inference profile

Correct Answer: BE

A healthcare company is using Amazon Bedrock to develop a real-time patient care AI assistant to respond to queries for separate departments that handle clinical inquiries, insurance verification, appointment scheduling, and insurance claims. The company wants to use a multi-agent architecture.
The company must ensure that the AI assistant is scalable and can onboard new features for patients. The AI assistant must be able to handle thousands of parallel patient interactions. The company must ensure that patients receive appropriate domain-specific responses to queries.
Which solution will meet these requirements?

1. A. Isolate data for each agent by using separate knowledge base
2. B. Use IAM filtering to control access to each knowledge bas
3. C. Deploy a supervisor agent to perform natural language intent classification on patient inquirie
4. D. Configure the supervisor agent to route queries to specialized collaborator agents to respond to department-specific querie
5. E. Configure each specialized collaborator agent to use Retrieval Augmented Generation (RAG) with the agent??s department-specific knowledge base.
6. F. Create a separate supervisor agent for each departmen
7. G. Configure individual collaborator agents to perform natural language intent classification for each specialty domain within each departmen
8. H. Integrate each collaborator agent with department-specific knowledge bases onl
9. I. Implement manual handoff processes between the supervisor agents.
10. J. Isolate data for each department in separate knowledge base
11. K. Use IAM filtering to control access to each knowledge bas
12. L. Deploy a single general-purpose agen
13. M. Configure multiple action groups within the general-purpose agent to perform specific department function
14. N. Implement rule-based routing logic within the general-purpose agent instructions.
15. O. Implement multiple independent supervisor agents that run in parallel to respond to patient inquiries for each departmen
16. P. Configure multiple collaborator agents for each supervisor agen
17. Q. Integrate all agents with the same knowledge bas
18. R. Use external routing logic to merge responses from multiple supervisor agents.

Correct Answer: A

A book publishing company wants to build a book recommendation system that uses an AI assistant. The AI assistant will use ML to generate a list of recommended books from the company's book catalog. The system must suggest books based on conversations with customers.
The company stores the text of the books, customers' and editors' reviews of the books, and extracted book metadata in Amazon S3. The system must support low-latency responses and scale efficiently to handle more than 10,000 concurrent users.
Which solution will meet these requirements?

1. A. Use Amazon Bedrock Knowledge Bases to generate embedding
2. B. Store the embeddings as a vector store in Amazon OpenSearch Servic
3. C. Create an AWS Lambda function that queries the knowledge bas
4. D. Configure Amazon API Gateway to invoke the Lambda function when handling user requests.
5. E. Use Amazon Bedrock Knowledge Bases to generate embedding
6. F. Store the embeddings as a vector store in Amazon DynamoD
7. G. Create an AWS Lambda function that queries the knowledge bas
8. H. Configure Amazon API Gateway to invoke the Lambda function when handling user requests.
9. I. Use Amazon SageMaker AI to deploy a pre-trained model to build a personalized recommendation engine for book
10. J. Deploy the model as a SageMaker AI endpoin
11. K. Invoke the model endpoint by using Amazon API Gateway.
12. L. Create an Amazon Kendra GenAI Enterprise Edition index that uses the S3 connector to index the book catalog data stored in Amazon S3. Configure built-in FAQ in the Kendra inde
13. M. Develop an AWS Lambda function that queries the Kendra index based on user conversation
14. N. Deploy Amazon API Gateway to expose this functionality and invoke the Lambda function.

Correct Answer: A

A finance company is developing an AI assistant to help clients plan investments and manage their portfolios. The company identifies several high-risk conversation patterns such as requests for specific stock recommendations or guaranteed returns. High-risk conversation patterns could lead to regulatory violations if the company cannot implement appropriate controls.
The company must ensure that the AI assistant does not provide inappropriate financial advice, generate content about competitors, or make claims that are not factually grounded in the company's approved financial guidance. The company wants to use Amazon Bedrock Guardrails to implement a solution.
Which combination of steps will meet these requirements? (Select THREE)

1. A. Add the high-risk conversation patterns to a denied topics guardrail.
2. B. Configure a content filter guardrail to filter prompts that contain the high-risk conversation patterns.
3. C. Configure a content filter guardrail to filter prompts that contain competitor names.
4. D. Add the names of competitors as custom word filter
5. E. Set the input and output actions to block.
6. F. Set a low grounding score threshold.
7. G. Set a high grounding score threshold.

Correct Answer: ADF

A media company is launching a platform that allows thousands of users every hour to upload images and text content. The platform uses Amazon Bedrock to process the uploaded content to generate creative compositions.
The company needs a solution to ensure that the platform does not process or produce inappropriate content. The platform must not expose personally identifiable information (PII) in the compositions. The solution must integrate with the company's existing Amazon S3 storage workflow.
Which solution will meet these requirements with the LEAST infrastructure management overhead?

1. A. Enable the Enhanced Monitoring too
2. B. Use an Amazon CloudWatch alarm to filter traffic to the platfor
3. C. Use Amazon Comprehend PII detection to pre-process the dat
4. D. Create a CloudWatch alarm to monitor for Amazon Comprehend PII detection event
5. E. Create an AWS Step Functions workflow that includes an Amazon Rekognition image moderation step.
6. F. Use an Amazon API Gateway HTTP API with request validation templates to screen content before storing the uploaded content in Amazon S3. Use Amazon SageMaker AI to build custom content moderation models that process content before sending the processed content to Amazon Bedrock.
7. G. Create an Amazon Cognito user pool that uses pre-authentication AWS Lambda functions to run content moderation check
8. H. Use Amazon Textract to filter text content and Amazon Rekognition to filter image content before allowing users to upload content to the platform.
9. I. Create an AWS Step Functions workflow that uses built-in Amazon Bedrock guardrails to filter conten
10. J. Use Amazon Comprehend PII detection to pre-process the conten
11. K. Use Amazon Rekognition image moderation.

Correct Answer: D