A specialty coffee company has a mobile app that generates personalized coffee roast profiles by using Amazon Bedrock with a three-stage prompt chain. The prompt chain converts user inputs into structured metadata, retrieves relevant logs for coffee roasts, and generates a personalized roast recommendation for each customer.
Users in multiple AWS Regions report inconsistent roast recommendations for identical inputs, slow inference during the retrieval step, and unsafe recommendations such as brewing at excessively high temperatures. The company must improve the stability of outputs for repeated inputs. The company must also improve app performance and the safety of the app's outputs. The updated solution must ensure 99.5% output consistency for identical inputs and achieve inference latency of less than 1 second. The solution must also block unsafe or hallucinated recommendations by using validated safety controls.
Which solution will meet these requirements?
A. Deploy Amazon Bedrock with provisioned throughput to stabilize inference latency. Apply Amazon Bedrock guardrails that have semantic denial rules to block unsafe outputs. Use Amazon Bedrock Prompt Management to manage prompts by using approval workflows.
B. Use Amazon Bedrock Agents to manage chaining. Log model inputs and outputs to Amazon CloudWatch Logs. Use logs from Amazon CloudWatch to perform A/B testing for prompt versions.
C. Cache prompt results in Amazon ElastiCache. Use AWS Lambda functions to pre-process metadata and to trace end-to-end latency. Use AWS X-Ray to identify and remediate performance bottlenecks.
D. Use Amazon Kendra to improve roast log retrieval accuracy. Store normalized prompt metadata within Amazon DynamoDB. Use AWS Step Functions to orchestrate multistep prompts.
Show Answer
Correct Answer: A
Explanation: Option A is the only choice that directly addresses all stated requirements: Amazon Bedrock Provisioned Throughput improves and stabilizes inference latency, Bedrock Guardrails with semantic denial rules provide validated safety controls to block unsafe or hallucinated outputs, and Bedrock Prompt Management with approval workflows helps ensure prompt version consistency and governed prompt execution, improving output stability for repeated inputs.
Question 22
A company uses AWS Lambda functions to build an AI agent solution. A GenAI developer must set up a Model Context Protocol (MCP) server that accesses user information. The GenAI developer must also configure the AI agent to use the new MCP server. The GenAI developer must ensure that only authorized users can access the MCP server.
Which solution will meet these requirements?
A. Use a Lambda function to host the MCP server. Grant the AI agent Lambda functions permission to invoke the Lambda function that hosts the MCP server. Configure the AI agent's MCP client to invoke the MCP server asynchronously.
B. Use a Lambda function to host the MCP server. Grant the AI agent Lambda functions permission to invoke the Lambda function that hosts the MCP server. Configure the AI agent to use the STDIO transport with the MCP server.
C. Use a Lambda function to host the MCP server. Create an Amazon API Gateway HTTP API that proxies requests to the Lambda function. Configure the AI agent solution to use the Streamable HTTP transport to make requests through the HTTP API. Use Amazon Cognito to enforce OAuth 2.1.
D. Use a Lambda layer to host the MCP server. Add the Lambda layer to the AI agent Lambda functions. Configure the agentic AI solution to use the STDIO transport to send requests to the MCP server. In the AI agent's MCP configuration, specify the Lambda layer ARN as the command. Specify the user credentials as environment variables.
Show Answer
Correct Answer: C
Explanation: A remote MCP server hosted on AWS Lambda should be exposed through an Amazon API Gateway HTTP API using the Streamable HTTP transport. API Gateway combined with Amazon Cognito can enforce OAuth 2.1 authentication and authorization so only authorized users can access the MCP server. STDIO is intended for local process communication, not a remote Lambda-hosted server, and directly invoking Lambda does not provide the required MCP transport and user authorization model.
Question 23
A GenAI developer is building a Retrieval Augmented Generation (RAG)-based customer support application that uses Amazon Bedrock foundation models (FMs). The application needs to process 50 GB of historical customer conversations that are stored in an Amazon S3 bucket as JSON files. The application must use the processed data as its retrieval corpus. The application's data processing workflow must extract relevant data from customer support documents, remove customer personally identifiable information (PII), and generate embeddings for vector storage. The processing workflow must be cost-effective and must finish within 4 hours.
Which solution will meet these requirements with the LEAST operational overhead?
A. Use AWS Lambda and Amazon Comprehend to process files in parallel, remove PII, and call Amazon Bedrock APIs to generate vectors. Configure Lambda concurrency limits and memory settings to optimize throughput.
B. Create an AWS Glue ETL job to run PII detection scripts on the data. Use Amazon SageMaker Processing to run the HuggingFaceProcessor to generate embeddings by using a pre-trained model. Store the embeddings in Amazon OpenSearch Service.
C. Deploy an Amazon EMR cluster that runs Apache Spark with user-defined functions (UDFs) that call Amazon Comprehend to detect PII. Use Amazon Bedrock APIs to generate vectors. Store outputs in Amazon Aurora PostgreSQL with the pgvector extension.
D. Implement a data processing pipeline that uses AWS Step Functions to orchestrate a workload that uses Amazon Comprehend to detect PII and Amazon Bedrock to generate embeddings. Directly integrate the workflow with Amazon OpenSearch Serverless to store vectors and provide similarity search capabilities.
Show Answer
Correct Answer: D
Explanation: AWS Step Functions provides managed orchestration with low operational overhead and can coordinate parallel processing at scale (for example, using Distributed Map) across objects in Amazon S3. It can invoke Amazon Comprehend to detect and redact PII, call Amazon Bedrock to generate embeddings, and store vectors in Amazon OpenSearch Serverless for retrieval. Compared with managing Lambda concurrency, Glue plus SageMaker, or EMR clusters, this is the most managed and operationally simple approach while meeting the scale and time requirements.
Question 24
A company is using Amazon Bedrock to develop an AI-powered application that uses a foundation model (FM) that supports cross-Region inference and provisioned throughput. The application must serve users in Europe and North America with consistently low latency. The application must comply with data residency regulations that require European user data to remain within Europe-based AWS Regions.
During testing, the application experiences service degradation when Regional traffic spikes reach service quotas. The company needs a solution that maintains application resilience and minimizes operational complexity.
Which solution will meet these requirements?
A. Deploy separate Amazon Bedrock instances in North American and European Regions. Use a custom routing layer that directs traffic based on user location. Configure Amazon CloudWatch alarms to monitor Regional service usage. Use Amazon SNS to send email alerts to the company when usage approaches specified thresholds.
B. Use Amazon Bedrock cross-Region inference profiles by specifying geographical codes in profile IDs when the application calls the InvokeModel API. Configure separate Amazon API Gateway HTTP APIs to direct European and North American users to the appropriate Regional endpoints.
C. Deploy a multi-Region Amazon API Gateway HTTP API and AWS Lambda functions that implement retry logic to handle throttling. Configure the Lambda functions to call the FM in the nearest secondary Region when the application reaches service quotas in the primary Region. Use intelligent routing to ensure compliance with data residency requirements.
D. Configure provisioned throughput for Amazon Bedrock in multiple Regions. Implement failover logic in the application code to switch between Regions when throttling occurs. Use AWS Global Accelerator to route traffic to the appropriate endpoints based on user location.
Show Answer
Correct Answer: B
Explanation: Cross-Region inference profiles are the native Amazon Bedrock capability for distributing inference requests across multiple Regions while improving resilience during Regional capacity constraints. Using geographical profile IDs keeps European requests within European Regions to satisfy data residency requirements and similarly serves North American users locally for low latency. This minimizes operational complexity compared with building custom routing, retry, or failover logic.
Question 25
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?
A. Isolate data for each agent by using separate knowledge bases. Use IAM filtering to control access to each knowledge base. Deploy a supervisor agent to perform natural language intent classification on patient inquiries. Configure the supervisor agent to route queries to specialized collaborator agents to respond to department-specific queries. Configure each specialized collaborator agent to use Retrieval Augmented Generation (RAG) with the agent's department-specific knowledge base.
B. Create a separate supervisor agent for each department. Configure individual collaborator agents to perform natural language intent classification for each specialty domain within each department. Integrate each collaborator agent with department-specific knowledge bases only. Implement manual handoff processes between the supervisor agents.
C. Isolate data for each department in separate knowledge bases. Use IAM filtering to control access to each knowledge base. Deploy a single general-purpose agent. Configure multiple action groups within the general-purpose agent to perform specific department functions. Implement rule-based routing logic within the general-purpose agent instructions.
D. Implement multiple independent supervisor agents that run in parallel to respond to patient inquiries for each department. Configure multiple collaborator agents for each supervisor agent. Integrate all agents with the same knowledge base. Use external routing logic to merge responses from multiple supervisor agents.
Show Answer
Correct Answer: A
Explanation: A follows the recommended Amazon Bedrock multi-agent pattern: a single supervisor agent performs intent classification and routes requests to specialized collaborator agents. Each collaborator uses its own department-specific knowledge base with RAG, providing accurate domain-specific responses while keeping data isolated. This architecture is scalable, supports onboarding new departments/features, and can handle large numbers of parallel interactions more cleanly than multiple supervisors, a single general-purpose agent, or shared knowledge bases.
Question 26
A company is designing an API for a generative AI (GenAI) application that uses a foundation model (FM) that is hosted on a managed model service. The API must stream responses to reduce latency, enforce token limits to manage compute resource usage, and implement retry logic to handle model timeouts and partial responses.
Which solution will meet these requirements with the LEAST operational overhead?
A. Integrate an Amazon API Gateway HTTP API with an AWS Lambda function to invoke Amazon Bedrock. Use Lambda response streaming to stream responses. Enforce token limits within the Lambda function. Implement retry logic for model timeouts by using Lambda and API Gateway timeout configurations.
B. Connect an Amazon API Gateway HTTP API directly to Amazon Bedrock. Simulate streaming by using client-side polling. Enforce token limits on the frontend. Configure retry behavior by using API Gateway integration settings.
C. Connect an Amazon API Gateway WebSocket API to an Amazon ECS service that hosts a containerized inference server. Stream responses by using the WebSocket protocol. Enforce token limits within Amazon ECS. Handle model timeouts by using ECS task lifecycle hooks and restart policies.
D. Integrate an Amazon API Gateway REST API with an AWS Lambda function that invokes Amazon Bedrock. Use Lambda response streaming to stream responses. Enforce token limits within the Lambda function. Implement retry logic by using Lambda and API Gateway timeout configurations.
Show Answer
Correct Answer: D
Explanation: The key differentiator is response streaming support through API Gateway. Lambda response streaming is supported with API Gateway REST APIs, making the REST API + Lambda + Amazon Bedrock pattern the managed solution that satisfies streaming, token enforcement in Lambda, and retry handling with minimal operational overhead. HTTP API direct integration does not provide the required streaming behavior here, client-side polling is not true streaming, and the ECS option introduces unnecessary infrastructure management.
Question 27
An insurance company uses existing Amazon SageMaker AI infrastructure to support a web-based application that allows customers to predict what their insurance premiums will be. The company stores customer data that is used to train the SageMaker AI model in an Amazon S3 bucket. The dataset is growing rapidly. The company wants a solution to continuously re-train the model. The solution must automatically re-train and re-deploy the model to the application when an employee uploads a new customer data file to the S3 bucket.
Which solution will meet these requirements?
A. Use AWS Glue to run an ETL job on each uploaded file. Configure the ETL job to use the AWS SDK to invoke the Sage Maker AI model endpoint. Use real-time inference with the endpoint to re-deploy the model after it is re-trained on the updated customer dataset.
B. Create an AWS Lambda function and webhook handlers to generate an event when an employee uploads a new file. Configure SageMaker Pipelines to re-deploy the model after it is re-trained on the updated customer dataset. Use Amazon EventBridge to create an event bus. Set the Lambda function event as the source and SageMaker Pipelines as the target.
C. Create an AWS Step Functions Express workflow with AWS SDK integrations to retrieve the customer data from the S3 bucket when an employee uploads a new file to the S3 bucket. Use a SageMaker Data Wrangler flow to export the data from the S3 bucket to SageMaker Autopilot. Use SageMaker Autopilot to re-deploy the model after it has been re-trained on the updated customer dataset.
D. Create an AWS Step Functions Standard workflow. Configure the first state to call an AWS Lambda function to respond when an employee uploads a new file to the S3 bucket. Use a pipeline in SageMaker Pipelines to re-deploy the model after it has been re-trained on the updated customer dataset. Use the next state in the workflow to run the pipeline when the first state receives a response.
Show Answer
Correct Answer: D
Explanation: The required pattern is an event-driven ML workflow: an S3 upload triggers automation, an orchestrator coordinates the long-running retraining process, and SageMaker Pipelines performs retraining and model deployment. Option D uses Step Functions Standard to orchestrate the flow, invokes Lambda in response to the upload, and then starts a SageMaker Pipeline to retrain and redeploy the model. A incorrectly uses inference endpoints for retraining. B introduces unnecessary webhook/EventBridge flow and an incorrect event pattern. C relies on Data Wrangler and Autopilot, which are not required for an existing SageMaker training pipeline.
Question 28
A legal research company has a Retrieval Augmented Generation (RAG) application that uses Amazon Bedrock and Amazon OpenSearch Service. The application stores 768-dimensional vector embeddings for 15 million legal documents, including statutes, court rulings, and case summaries.
The company's current chunking strategy segments text into fixed-length blocks of 500 tokens. The current chunking strategy often splits contextually linked information such as legal arguments, court opinions, or statute references across separate chunks. Researchers report that generated outputs frequently omit key context or cite outdated legal information.
Recent application logs show a 40% increase in response times. The p95 latency metric exceeds 2 seconds. The company expects storage needs for the application to grow from 90 GB to 360 GB within a year.
The company needs a solution to improve retrieval relevance and system performance at scale.
Which solution will meet these requirements?
A. Increase the embedding vector dimensionality from 768 to 4,096 without changing the existing chunking or pre-processing strategy.
B. Replace dynamic retrieval with static, pre-written summaries that are stored in Amazon S3. Use Amazon CloudFront to serve the summaries to reduce compute demand and improve predictability.
C. Update the chunking strategy to use semantic boundaries such as complete legal arguments, clauses, or sections rather than fixed token limits. Regenerate vector embeddings to align with the new chunk structure.
D. Migrate from OpenSearch Service to Amazon DynamoDB. Implement keyword-based indexes to enable faster lookups for legal concepts.
Show Answer
Correct Answer: C
Explanation: The primary issue is that fixed-size chunking splits semantically related legal content, reducing retrieval relevance. Using semantic chunking based on legal arguments, clauses, or sections preserves context, and regenerating embeddings ensures the vector index matches the improved chunk boundaries. This directly improves retrieval quality while avoiding the increased storage and latency costs of larger embeddings. The other options either increase cost without addressing the root cause (A), remove dynamic RAG capabilities (B), or replace semantic vector search with keyword lookup that is unsuitable for the stated requirements (D).
Question 29
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? (Choose 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.
Show Answer
Correct Answer: B, C
Explanation: Provisioned Throughput in Amazon Bedrock provides dedicated capacity for consistent high-throughput foundation model inference, which fits batch processing requirements. For hybrid deployment spanning cloud and on-premises, SageMaker with Neo enables compiling and deploying supported models to edge/on-premises environments, and SageMaker endpoints plus Lambda orchestration can support inference workflows across environments. SageMaker asynchronous endpoints are not intended for low-latency real-time inference, and Bedrock does not offer customer-managed auto-scaling in the way described.
Question 30
Example Corp provides a personalized video generation service that millions of enterprise customers use. Customers generate marketing videos by submitting prompts to the company's proprietary generative AI (GenAI) model. To improve output relevance and personalization, Example Corp wants to enhance the prompts by using customer-specific context such as product preferences, customer attributes, and business history.
The customers have strict data governance requirements. The customers must retain full ownership and control over their own data. The customers do not require real-time access. However, semantic accuracy must be high and retrieval latency must remain low to support customer experience use cases.
Example Corp wants to minimize architectural complexity in its integration pattern. Example Corp does not want to deploy and manage services in each customer's environment unless necessary.
Which solution will meet these requirements?
A. Ensure that each customer sets up an Amazon Q Business index that includes the customer's internal data. Ensure that each customer designates Example Corp as a data accessor to allow Example Corp to retrieve relevant content by using a secure API to enrich prompts at runtime.
B. Use federated search with Model Context Protocol (MCP) by deploying real-time MCP servers for each customer. Retrieve data in real time during prompt generation.
C. Ensure that each customer configures an Amazon Bedrock knowledge base. Allow cross-account querying so Example Corp can retrieve structured data for prompt augmentation.
D. Configure Amazon Kendra to crawl customer data sources. Share the resulting indexes across accounts so Example Corp can query each customer's Amazon Kendra index to retrieve augmentation data.
Show Answer
Correct Answer: A
Explanation: Amazon Q Business supports the data accessor pattern, allowing customers to retain ownership of their indexed data while granting a provider secure retrieval access for prompt enrichment. This fits the requirements for customer-controlled data, asynchronous indexing, high-quality semantic retrieval, low query latency, and minimal architectural complexity without deploying custom services in each customer environment. Amazon Bedrock Knowledge Bases do not provide the described cross-account querying pattern as the intended solution in this scenario, MCP requires per-customer deployment, and Kendra cross-account index sharing is not the recommended integration.
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