A company is using Amazon Bedrock to design an application to help researchers apply for grants. The application is based on an Amazon Nova Pro foundation model (FM). The application contains four required inputs and must provide responses in a consistent text format. The company wants to receive a notification in Amazon Bedrock if a response contains bullying language. However, the company does not want to block all flagged responses.
The company creates an Amazon Bedrock flow that takes an input prompt and sends it to the Amazon Nova Pro FM. The Amazon Nova Pro FM provides a response.
Which additional steps must the company take to meet these requirements? (Choose two.)
A. Use Amazon Bedrock Prompt Management to specify the required inputs as variables. Select an Amazon Nova Pro FM. Specify the output format for the response. Add the prompt to the prompts node of the flow.
B. Create an Amazon Bedrock guardrail that applies the hate content filter. Set the filter response to block. Add the guardrail to the prompts node of the flow.
C. Create an Amazon Bedrock prompt router. Specify an Amazon Nova Pro FM. Add the required inputs as variables to the input node of the flow. Add the prompt router to the prompts node. Add the output format to the output node.
D. Create an Amazon Bedrock guardrail that applies the insults content filter. Set the filter response to detect. Add the guardrail to the prompts node of the flow.
E. Create an Amazon Bedrock application inference profile that specifies an Amazon Nova Pro FM. Specify the output format for the response in the description. Include a tag for each of the input variables. Add the profile to the prompts node of the flow.
Show Answer
Correct Answer: A, D
Explanation: Prompt Management lets the company define the four required inputs as variables and specify a consistent output format for the Nova Pro prompt used in the flow. Bullying language is covered by the guardrail insults filter. Setting the filter to detect flags the response without blocking it; a block action would prevent flagged responses from being returned.
Question 14
A financial services company is creating a Retrieval Augmented Generation (RAG) application that uses Amazon Bedrock to generate summaries of market activities. The application relies on a vector database that stores a small proprietary dataset that has a low index count. The application must perform similarity searches. The Amazon Bedrock model's responses must maximize accuracy and maintain high performance.
The company needs to configure the vector database and integrate it with the application.
Which solution will meet these requirements?
A. Launch an Amazon MemoryDB cluster and configure the index by using the Flat algorithm. Configure a horizontal scaling policy based on performance metrics.
B. Launch an Amazon MemoryDB cluster and configure the index by using the Hierarchical Navigable Small World (HNSW) algorithm. Configure a vertical policy based on performance metrics.
C. Launch an Amazon Aurora PostgresSQL cluster and configure the index by using the Inverted File with Flat Compression (IVFFlat) algorithm. Configure the instance class to scale to a larger size when the load increases.
D. Launch an Amazon DocumentDB cluster that has an Inverted File with Flat Compression (IVFFlat) index and a high probe value. Configure connections to the cluster as a replica set Distribute reads to replica instances.
Show Answer
Correct Answer: B
Explanation: Amazon MemoryDB is an appropriate vector database for low-latency similarity search. For vector search in MemoryDB, HNSW provides the best balance of high recall and performance, and MemoryDB vector indexes support vertical and replica scaling rather than horizontal sharding for vector search. Option A is incorrect because it proposes horizontal scaling for vector search, which is not supported. IVFFlat is approximate and less suitable when maximizing accuracy is the priority for a small dataset.
Sources:
https://aws.amazon.com/blogs/database/the-role-of-vector-datastores-in-generative-ai-applications
https://dev.to/aws-builders/aws-certified-generative-ai-developer-professional-aip-c01-study-reference-4h84
Question 15
A company has set up Amazon Q Developer Pro licenses for all developers at the company. The company maintains a list of approved resources that developers must use when developing applications. The approved resources include internal libraries, proprietary algorithmic techniques, and sample code with approved styling.
A new team of developers is using Amazon Q Developer to develop a new Java-based application. The company must ensure that the new developer team uses the company's approved resources. The company does not want to make project-level modifications.
Which solution will meet these requirements?
A. Create a Git repository that contains all of the approved internal libraries, algorithms, and code samples. Include this Git repository in the application project locally as part of the workspace. Ensure that the developers use the @workspace context to retrieve suggestions from the Git repository.
B. In the project root folder, create a folder named .amazonq/rules. Add the approved internal libraries, algorithms, and code samples to the folder.
C. Create a folder in the application project named rules. Store the guidelines and code in the folder for Amazon Q Developer to reference product code suggestions.
D. Create an Amazon Q Developer customization that includes the approved data sources. Ensure that the developers use the customization to develop the application.
Show Answer
Correct Answer: D
Explanation: The requirement is to enforce use of approved internal resources across developers without making project-level modifications. Amazon Q Developer Customizations are designed for this use case: administrators create a customization from approved internal data sources (such as internal code, libraries, documentation, and examples), and developers select that customization in their IDE to receive suggestions grounded in those approved resources. Options B and C require project changes, and A relies on local workspace setup rather than centralized, managed customization.
Question 16
A company is developing a new AI-powered application that needs to integrate with various specialized tools. These tools currently run as Model Context Protocol (MCP) servers on the local machines of developers and do not maintain states between invocations. The company plans to deploy each MCP server as an AWS Lambda function to support the company's production application.
The solution must be accessible to both internal applications and authorized third-party partners. The solution must use strict authentication and authorization controls.
Which additional steps will meet these requirements with the LEAST operational overhead?
A. Create a custom Lambda invocation transport by using the Lambda Invoke API. Implement IAM authentication and grant InvokeFunction permissions to authorized users and roles.
B. Expose the Lambda functions through Amazon API Gateway REST API endpoints. Implement API keys for authentication. Configure the applications that need to access the MCP servers to use standard HTTP requests instead of the MCP protocol.
C. Create Lambda function URLs and enable a custom Streamable HTTP transport and SigV4. Implement AWS IAM authentication. Grant InvokeFunctionUrl permissions to authorized users and roles.
D. Expose the Lambda function through Amazon API Gateway HTTP API endpoints with the Streamable HTTP transport. Use Amazon Cognito to implement OAuth authentication. Configure API Gateway to validate OAuth tokens.
Show Answer
Correct Answer: C
Explanation: The MCP servers are stateless, making AWS Lambda a good fit. To preserve MCP compatibility, use the Streamable HTTP transport rather than replacing the protocol. Lambda Function URLs provide a native HTTPS endpoint with lower operational overhead than API Gateway. Enabling SigV4 with IAM authentication provides strong authentication and authorization, and permissions can be controlled with InvokeFunctionUrl IAM policies for internal users and authorized partners. Option A is not HTTP/MCP transport, B replaces MCP and API keys are weaker, and D adds API Gateway and Cognito, increasing operational overhead.
Question 17
A company uses an AI assistant application to summarize the company's website content and provide information to customers. The company plans to use Amazon Bedrock to give the application access to a foundation model (FM).
The company needs to deploy the AI assistant application to a development environment and a production environment. The solution must integrate the environments with the FM. The company wants to test the effectiveness of various FMs in each environment. The solution must provide product owners with the ability to easily switch between FMs for testing purposes in each environment.
Which solution will meet these requirements?
A. Create one AWS CDK application. Create multiple pipelines in AWS CodePipeline. Configure each pipeline to have its own settings for each FM. Configure the application to invoke the Amazon Bedrock FMs by using the aws_bedrock.ProvisionedModel.fromProvisionedModelArn() method.
B. Create a separate AWS CDK application for each environment. Configure the applications to invoke the Amazon Bedrock FMs by using the aws_bedrock.FoundationModel.fromFoundationModelId() method. Create a separate pipeline in AWS CodePipeline for each environment.
C. Create one AWS CDK application. Configure the application to invoke the Amazon Bedrock FMs by using the aws_bedrock.FoundationModel.fromFoundationModelId() method. Create a pipeline in AWS CodePipeline pipeline that has a deployment stage for each environment that uses AWS CodeBuild deploy actions.
D. Create one AWS CDK application for the production environment. Configure the application to invoke the Amazon Bedrock FMs by using the aws_bedrock.ProvisionedModel.fromProvisionedModelArn() method. Create a pipeline in AWS CodePipeline. Configure the pipeline to deploy to the production environment by using an AWS CodeBuild deploy action. For the development environment, manually recreate the resources by referring to the production application code.
Show Answer
Correct Answer: C
Explanation: A single AWS CDK application can be parameterized for multiple environments, avoiding duplicated infrastructure code. Using FoundationModel.fromFoundationModelId() references on-demand Amazon Bedrock foundation models by model ID, making it easy to switch models per environment through configuration rather than code changes. A single CodePipeline with separate deployment stages for development and production is the standard multi-environment deployment pattern. ProvisionedModel is unnecessary for simply evaluating different foundation models and would reduce flexibility.
Question 19
A company is planning to deploy multiple generative AI (GenAI) applications to five independent business units that operate in multiple countries in Europe and the Americas. Each application uses Amazon Bedrock Retrieval Augmented Generation (RAG) patterns with business unit-specific knowledge bases that store terabytes of unstructured data.
The company must establish well-architected, standardized components for security controls, observability practices, and deployment patterns across all the GenAI applications. The components must be reusable, versioned, and governed consistently.
Which solution will meet these requirements?
A. Configure Amazon API Gateway REST API endpoints for the GenAI applications. Deploy common security, observability, and RAG patterns based on the AWS Well-Architected Generative AI Lens in standardized AWS CloudFormation templates. Use CloudFormation Guard after the deployment to validate policy compliance in each business unit.
B. Create standardized AWS CloudFormation templates to implement security, observability, and RAG patterns based on the AWS Well-Architected Generative AI Lens. Establish a centralized repository that performs version control. Integrate a CI/CD pipeline with CloudFormation Guard to enforce consistent and repeatable deployments across business units.
C. Use AWS Service Catalog to define standardized portfolios and versioned products for each business unit. Use the portfolios to enforce security, observability, and RAG patterns based on the AWS Well-Architected Generative AI Lens. Require the business units to use the Service Catalog console to deploy resources.
D. Document security controls, observability requirements, and RAG patterns based on the AWS Well-Architected Generative AI Lens in a shared design document. Use Amazon Macie to enforce deployment. Delegate implementation responsibility to each business unit.
Show Answer
Correct Answer: B
Explanation: Option B best satisfies the requirements for reusable, versioned, and consistently governed infrastructure. Standardized CloudFormation templates capture security, observability, and Amazon Bedrock RAG patterns, a centralized repository provides version control, and a CI/CD pipeline integrated with CloudFormation Guard enforces policy compliance before deployment, enabling consistent deployments across all business units.
Question 20
A bank is developing a generative AI (GenAI)-powered AI assistant that uses Amazon Bedrock to assist the bank's website users with account inquiries and financial guidance. The bank must ensure that the AI assistant does not reveal any personally identifiable information (PII) in customer interactions.
The AI assistant must not send PII in prompts to the GenAI model. The AI assistant must not respond to customer requests to provide investment advice. The bank must collect audit logs of all customer interactions, including any images or documents that are transmitted during customer interactions.
Which solution will meet these requirements with the LEAST operational effort?
A. Use Amazon Macie to detect and redact PII in user inputs and in the model responses. Apply prompt engineering techniques to force the model to avoid investment advice topics. Use AWS CloudTrail to capture conversation logs.
B. Use an AWS Lambda function and Amazon Comprehend to detect and redact PII. Use Amazon Comprehend topic modeling to prevent the AI assistant from discussing investment advice topics. Set up custom metrics in Amazon CloudWatch to capture customer conversations.
C. Configure Amazon Bedrock guardrails to apply a sensitive information policy to detect and filter PII. Set up a topic policy to ensure that the AI assistant avoids investment advice topics. Use the Converse API to log model invocations. Enable delivery and image logging to Amazon S3.
D. Use regex controls to match patterns for PII. Apply prompt engineering techniques to avoid returning PII or investment advice topics to customers. Enable model invocation logging, delivery logging, and image logging to Amazon S3.
Show Answer
Correct Answer: C
Explanation: Amazon Bedrock Guardrails can enforce sensitive information policies to detect and block/redact PII and topic policies to deny investment advice topics with minimal operational effort. Bedrock model invocation logging supports audit logging, and delivery/image logging to Amazon S3 captures prompts, responses, and transmitted images/documents for auditing.
Question 21
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.
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