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Question # 1
A GenAI developer is building a RAG system that uses Amazon Bedrock Knowledge
Bases. The system needs to process 50 textbooks that are stored in an Amazon S3
bucket. The textbooks are all an average of 500 pages long. The system needs to generate
a knowledge base to answer domain-specific customer questions.
During initial testing on a subset of documents, the GenAI developer notices that query
responses contain irrelevant information and sometimes miss critical context from the
source materials. The GenAI developer must ensure that the solution provides accurate
responses with low latency and no hallucinations.
Which solution will meet these requirements?
A. Configure fixed-size chunking with a 256-token chunk size. Implement metadata filtering based on document sections. Use an Amazon Titan Embeddings model to create vector representations that are optimized for semantic search.
B. Use Amazon ElastiCache to implement semantic caching for common queries. Use Anthropic Claude Sonnet to reformulate user queries. Configure real-time model feedback loops to continuously improve response quality based on user interactions.
C. Apply hierarchical chunking with both 200-token chunks and 1,000-token chunks. Implement hybrid search that combines vector and keyword search. Adjust the relevance score threshold to filter out low-confidence retrieval results.
D. Use semantic chunking to automatically segment documents based on topic boundaries. Configure the knowledge base to use a single embedding model for all content types. Implement query expansion to reformulate user questions before retrieval.
Question # 2
A company uses an application to process customer support tickets. The company wants
to integrate AI-powered sentiment analysis and auto-response generation into the
application by using Amazon Bedrock. The company wants to prioritize urgent issues and
reduce initial response times by 40% compared to manual responses. The solution must
process 100 concurrent webhook requests with response times under 500 ms. The solution
must maintain 99.9% availability across multiple AWS Regions and authenticate all
incoming requests. The company must avoid any authentication failures. The company
does not want to modify the existing application infrastructure, which includes several
ticketing systems that use multiple webhook authentication methods. The solution must
support scaling to handle occasional spikes up to 250,000 daily tickets during peak periods.
Which solution will meet these requirements?
A. Use an Amazon API Gateway REST API with a Regional endpoint to receive webhook requests and invoke AWS Lambda functions. Configure Lambda authorizers to validate all the webhook authentication methods. Configure the Lambda functions to call Amazon Bedrock to perform sentiment analysis and generate responses. Store results in Amazon DynamoDB global tables to provide multi-Region availability.
B. Create AWS Lambda function URLs for each ticketing system. Configure the function URLs with the NONE authentication type. Configure separate Lambda functions to verify webhook signatures by using Hash-based Message Authentication Code (HMAC) validation in the function code. Deploy the functions to multiple Regions and use AWS Global Accelerator to route traffic. Use Amazon Bedrock to perform sentiment analysis and generate responses. Return responses through webhook callbacks.
C. Set up an Amazon SQS queue in each Region to receive webhook messages. Use the SQS queue to invoke AWS Lambda functions that call Amazon Comprehend to perform sentiment analysis and Amazon Lex to generate responses. Use Amazon EventBridge to retry message delivery to the application API.
D. Deploy an AWS AppSync GraphQL API to multiple Regions. Configure API tokens to authenticate incoming requests. Create GraphQL mutation resolvers that publish events to Amazon EventBridge. Configure EventBridge rules to invoke AWS Lambda functions that use Amazon Bedrock to perform sentiment analysis and generate responses. Use Amazon CloudFront to reduce latency.
Question # 3
A financial services company is building a fraud detection system by using Amazon
Bedrock. The system will monitor activity in multiple stock trading applications that run in
the United States and Europe. The system must process 1,000 transactions every second
with sub-500 ms response times. The system must also maintain high availability during connectivity disruptions.
The company must ensure that data for European customers is processed only in AWS
Regions that are based in Europe.
Which solution will meet these requirements?
A. Configure AWS Lambda functions and Amazon EKS applications to use the InvokeModel API with a global inference profile. Deploy an automated failover system that uses Amazon Route 53 health checks. Create a dedicated European inference profile and enable geographic cross-Region inference for European applications. Use Amazon CloudWatch alarms to monitor utilization metrics.
B. Configure all applications to use the InvokeModel API with provisioned throughput for an Anthropic Claude model in each Region separately. Set up a custom Application Load Balancer to distribute traffic based on Regional capacity and response times. Implement a Regional failover mechanism that uses Amazon EventBridge rules to handle connectivity disruptions.
C. Configure all applications to use the InvokeModelWithResponseStream API with ondemand throughput. Deploy an Amazon API Gateway REST API with Regional endpoints in each location where the company operates to route requests to the closest Amazon Bedrock endpoint. Create separate IAM roles for applications that run in the United States and Europe. Grant the IAM roles Region-specific permissions.
D. Configure applications that run in the United States to use provisioned throughput with the InvokeModel API. Configure European applications to use a Europe-specific geographic inference profile to ensure data sovereignty. Configure automatic scaling for provisioned capacity based on utilization metrics. Use Amazon EventBridge and AWS Lambda functions to implement cross-Region failover mechanisms.
Question # 4
A healthcare company is deploying an AI system that uses a foundation model (FM) to help
clinicians make diagnostic decisions. The company’s ethics board requires the AI system to
demonstrate fairness across patient demographic groups and comply with medical AI
governance policies. During initial testing, the AI system provides recommendations
without clear explanations or decision tracing. Clinicians are unable to review how the AI
system produces diagnostic conclusions.
The company needs to implement a solution that provides transparent reasoning for AI
outputs, enables systematic fairness testing, and ensures policy compliance for responsible
AI use in healthcare settings. The solution must balance comprehensive explainability with
real-time performance requirements. The solution must support rapid iteration for bias
testing across multiple demographic variables. The solution must integrate seamlessly with
existing clinical workflows while maintaining strict data privacy controls. The solution must
handle complex medical and regulatory terminology.
Which solution will meet these requirements?
A. Use Amazon SageMaker Clarify to generate model explanations. Use Amazon Augmented AI (Amazon A2I) to implement human review workflows. Use AWS Config to enforce compliance policies across the AI system.
B. Use Amazon Comprehend Medical to analyze medical terminology. Use Amazon Textract to process documents. Use AWS CloudFormation to standardize deployment configurations.
C. Use Amazon Bedrock agent tracing to provide reasoning traces. Use Amazon Bedrock Prompt Management with A/B testing to perform fairness evaluations. Use Amazon Bedrock Guardrails to ensure policy compliance.
D. Use Amazon CloudWatch to collect performance metrics. Use Amazon EventBridge to trigger compliance checks. Use AWS Lambda functions to generate custom explanation reports.
Question # 5
A global financial services company hosts a fraud-alert system that uses an Amazon
Bedrock foundation model (FM) to generate explanations for suspicious transactions. The
company processes regulated financial data across three geographic areas. The system
must maintain consistent responsiveness globally, support multi-Region failover, and
provide full observability for audit and compliance teams.
Load testing shows that the FM’s total inference time cannot be reduced. The company
cannot increase its inference costs, change the FM, modify token counts, or provision
additional compute capacity. Users report that the UI performs slowly because it waits for
the complete model response before it shows any content.
The company must improve perceived responsiveness during peak periods, when the
system can receive 10,000–15,000 concurrent requests. The solution must maintain multiRegion resiliency and full monitoring visibility.
Which solution will meet these requirements?
A. Enable response streaming by using the InvokeModelWithResponseStream API so the frontend can display generated tokens as the tokens arrive. Collect metrics in Amazon CloudWatch and enable distributed tracing to monitor streaming latency and Regional performance.
B. Deploy Regional Amazon Bedrock inference endpoints. Set up latency-based Amazon Route 53 routing. Cache partially processed explanations in a global Amazon DynamoDB table to serve responses more quickly during peak periods.
C. Use a Lambda@Edge preprocessing layer to condense inputs during peak periods. Asynchronously call Amazon Bedrock while the system returns interim placeholder responses to customers.
D. Deploy AWS Lambda functions to handle inference requests across multiple AWS Regions. Increase Lambda concurrency limits. Scale down Amazon CloudWatch Logs retention to reduce backend load during peak periods.
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