Download AWS Certified Generative AI Developer - Professional.AIP-C01.ExamTopics.2026-04-03.79q.tqb

Vendor: Amazon
Exam Code: AIP-C01
Exam Name: AWS Certified Generative AI Developer - Professional
Date: Apr 03, 2026
File Size: 489 KB
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Demo Questions

Question 1
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?
  1. Launch an Amazon MemoryDB cluster and configure the index by using the Flat algorithm. Configure a horizontal scaling policy based on performance metrics.
  2. 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.
  3. 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.
  4. 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.
Correct answer: A
Question 2
A company is creating a workflow to review customer-facing communications before the company sends the communications. The company uses a pre-defined message template to generate the communications and stores the communications in an Amazon S3 bucket. The workflow needs to capture a specific portion from the template and send it to an Amazon Bedrock model. The workflow must store model responses back to the original S3 bucket.
Which solution will meet these requirements?
  1. Create a flow in Amazon Bedrock Flows. Configure S3 action nodes at the beginning and end of the flow to retrieve and store the communications and the model responses. In the middle of the flow, configure an expression to parse each communication. Configure an agent step to send the parsed input to the model for review.
  2. Create an AWS Step Functions Express workflow state machine. Use an Amazon S3 integration GetObject step to retrieve the original communications. Use an intrinsic function Pass step to parse the communications and to pass the results to an Amazon Bedrock InvokeModel step. Configure an Amazon S3 integration PutObject step to store the model responses back to the S3 bucket.
  3. Create an Amazon Bedrock agent that has an action group. Configure instructions to define how the agent should parse the communications. Configure the action group to retrieve the communications from the S3 bucket, invoke the Amazon Bedrock model, and store the model responses back to the S3 bucket.
  4. Create an Amazon Bedrock agent that has a single action group. Configure three AWS Lambda functions in the action group. Configure the functions to retrieve the communications from the S3 bucket, parse the communications and invoke the Amazon Bedrock model, and store the model responses back to the S3 bucket.
Correct answer: B
Question 3
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?
  1. 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.
  2. 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.
  3. 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.
  4. 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.
Correct answer: C
Question 4
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?
  1. Create a custom Lambda invocation transport by using the Lambda Invoke API. Implement IAM authentication and grant InvokeFunction permissions to authorized users and roles.
  2. 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.
  3. 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.
  4. 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.
Correct answer: C
Question 5
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?
  1. 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.
  2. 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.
  3. 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.
  4. 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.
Correct answer: C
Question 6
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. 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.
  2. 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.
  3. 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.
  4. 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.
Correct answer: A
Question 7
A company is building a legal research AI assistant that uses Amazon Bedrock with an Anthropic Claude foundation model (FM). The AI assistant must retrieve highly relevant case law documents to augment the FM's responses. The AI assistant must identify semantic relationships between legal concepts, specific legal terminology, and citations. The AI assistant must perform quickly and return precise results.
Which solution will meet these requirements?
  1. Configure an Amazon Bedrock knowledge base to use a default vector search configuration. Use Amazon Bedrock to expand queries to improve retrieval for legal documents based on specific terminology and citations.
  2. Use Amazon OpenSearch service to deploy a hybrid search architecture that combines vector search with keyword search. Apply an Amazon Bedrock reranker model to optimize result relevance.
  3. Enable the Amazon Kendra query suggestion feature for end users. Use Amazon Bedrock to perform post-processing of search results to identify semantic similarity in the documents and to produce precise results.
  4. Use Amazon OpenSearch Service with vector search and Amazon Bedrock Titan embeddings to index and search legal documents. Use custom AWS Lambda functions to merge results with keyword-based filters that are stored in an Amazon RDS database.
Correct answer: B
Question 8
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?
  1. 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.
  2. 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.
  3. 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.
  4. 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.
Correct answer: B
Question 9
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?
  1. 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.
  2. 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.
  3. 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.
  4. 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.
Correct answer: D
Question 10
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?
  1. 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.
  2. 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.
  3. 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.
  4. 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.
Correct answer: C
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