Amazon AIP-C01 Online Practice Questions and Exam Preparation

Amazon AIP-C01 Online Questions & Answers

• Question 1:

  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.
  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.

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  Explanation

  The selected response matches the requirement by using this approach: 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. This fits the governance requirement because controls are applied consistently around the model workflow and the data owner remains in control instead of relying on informal prompt wording or scattered application code. The key AWS cue is Amazon Bedrock, AWS Step Functions, Amazon Comprehend, which aligns with the scenario instead of forcing unrelated services into the design. The alternatives focus on Amazon Bedrock, AWS Lambda, Amazon Comprehend, Amazon SageMaker, Amazon OpenSearch Service or on a different operating model, but they leave at least one required constraint unresolved. The exam takeaway is to identify the dominant constraint in the stem, then choose the managed AWS service or design pattern that satisfies that constraint with secure, reliable, and maintainable implementation effort.

• Question 2:

  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 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.
  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 that has a deployment stage for each environment that uses AWS CodeBuild deploy actions.

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  Explanation

  The selected response matches the requirement by using this approach: 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 that has a deployment stage for each environment that uses AWS CodeBuild deploy actions. This fits the validation requirement because it uses repeatable evidence and task-appropriate metrics rather than relying only on informal review. The key AWS cue is Amazon Bedrock, which aligns with the scenario instead of forcing unrelated services into the design. The alternatives focus on Amazon Bedrock or on a different operating model, but they leave at least one required constraint unresolved. The exam takeaway is to identify the dominant constraint in the stem, then choose the managed AWS service or design pattern that satisfies that constraint with secure, reliable, and maintainable implementation effort.

• Question 3:

  A financial services company is developing a generative AI (GenAI) application that serves both premium customers and standard customers. The application uses AWS Lambda functions behind an Amazon API Gateway REST API to process requests. The company needs to dynamically switch between AI models based on which customer tier each user belongs to. The company also wants to perform A/B testing for new features without redeploying code. The company needs to validate model parameters like temperature and maximum token limits before applying changes.

  Which solution will meet these requirements with the LEAST operational overhead?

  A. Create AWS Systems Manager Parameter Store parameters for each configuration. Use Lambda functions to poll for parameter updates. Use Amazon EventBridge events to trigger redeployments when configurations change.
  B. Store model configurations in Amazon DynamoDB tables. Optimize access patterns to retrieve configurations according to customer tier. Configure Lambda functions to query DynamoDB at the beginning of each request to determine which model to use.
  C. Use AWS AppConfig to manage model configurations. Use feature flags to perform A/B testing. Define JSON schema validation rules for model parameters. Configure Lambda functions to retrieve configurations by using the AWS AppConfig Agent.
  D. Create an Amazon ElastiCache (Redis OSS) cluster to store model configurations. Set short TTL values. Run custom validation logic in Lambda functions. Use Amazon CloudWatch metrics to monitor configuration usage.
  C. Use AWS AppConfig to manage model configurations. Use feature flags to perform A/B testing. Define JSON schema validation rules for model parameters. Configure Lambda functions to retrieve configurations by using the AWS AppConfig Agent.

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  Explanation

  The selected response matches the requirement by using this approach: Use AWS AppConfig to manage model configurations. Use feature flags to perform A/B testing. Define JSON schema validation rules for model parameters. Configure Lambda functions to retrieve configurations by using the AWS AppConfig Agent. This fits the retrieval requirement because the model receives targeted context instead of broad, stale, or irrelevant content. The alternatives focus on Amazon EventBridge, Amazon DynamoDB, Amazon CloudWatch or on a different operating model, but they leave at least one required constraint unresolved.

  The exam takeaway is to identify the dominant constraint in the stem, then choose the managed AWS service or design pattern that satisfies that constraint with secure, reliable, and maintainable implementation effort.

• Question 4:

  An ecommerce company is using Amazon Bedrock to build a generative AI (GenAI) application. The application uses AWS Step Functions to orchestrate a multi-agent workflow to produce detailed product descriptions. The workflow consists of three sequential states: a description generator, a technical specifications validator, and a brand voice consistency checker. Each state produces intermediate reasoning traces and outputs that are passed to the next state. The application uses an Amazon S3 bucket for process storage and to store outputs.

  During testing, the company discovers that outputs between Step Functions states frequently exceed the 256 KB quota and cause workflow failures. A GenAI Developer needs to revise the application architecture to efficiently handle the Step Functions 256 KB quota and maintain workflow observability. The revised architecture must preserve the existing multi-agent reasoning and acting (ReAct) pattern.

  Which solution will meet these requirements with the LEAST operational overhead?

  A. Store intermediate outputs in Amazon DynamoDB. Pass only references between states. Create a Map state that retrieves the complete data from DynamoDB when required for each agent's processing step.
  B. Configure an Amazon Bedrock integration to use the S3 bucket URI in the input parameters for large outputs. Use the ResultPath and ResultSelector fields to route S3 references between the agent steps while maintaining the sequential validation workflow.
  C. Use AWS Lambda functions to compress outputs to less than 256 KB before each agent state. Configure each agent task to decompress outputs before processing and to compress results before passing them to the next state.
  D. Configure a separate Step Functions state machine to handle each agent's processing. Use Amazon EventBridge to coordinate the execution flow between state machines. Use S3 references for the outputs as event data.
  B. Configure an Amazon Bedrock integration to use the S3 bucket URI in the input parameters for large outputs. Use the ResultPath and ResultSelector fields to route S3 references between the agent steps while maintaining the sequential validation workflow.

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  Explanation

  The selected response matches the requirement by using this approach: Configure an Amazon Bedrock integration to use the S3 bucket URI in the input parameters for large outputs. Use the ResultPath and ResultSelector fields to route S3 references between the agent steps while maintaining the sequential validation workflow. This fits the retrieval requirement because the model receives targeted context instead of broad, stale, or irrelevant content. The key AWS cue is Amazon Bedrock, which aligns with the scenario instead of forcing unrelated services into the design. The alternatives focus on Amazon DynamoDB, AWS Lambda, Amazon EventBridge or on a different operating model, but they leave at least one required constraint unresolved. The exam takeaway is to identify the dominant constraint in the stem, then choose the managed AWS service or design pattern that satisfies that constraint with secure, reliable, and maintainable implementation effort.

• Question 5:

  A medical company is creating a generative AI (GenAI) system by using Amazon Bedrock. The system processes data from various sources and must maintain end-to-end data lineage. The system must also use real-time personally identifiable information (PII) filtering and audit trails to automatically report compliance.

  Which solution will meet these requirements?

  A. Use AWS Glue Data Catalog to register all data sources and track lineage. Use Amazon Bedrock Guardrails PII filters. Enable AWS CloudTrail logging for all Amazon Bedrock API calls with Amazon S3 integration. Use Amazon Macie to scan stored data for sensitive information and publish findings to Amazon CloudWatch Logs. Create CloudWatch dashboards to visualize the findings and generate automated compliance reports.
  B. Use AWS Config to track data source configurations and changes. Use AWS WAF with custom rules to filter PII at the application layer before Amazon Bedrock processes the data. Configure Amazon EventBridge to capture and route audit events to Amazon S3. Use Amazon Comprehend Medical with scheduled AWS Lambda functions to analyze stored outputs for compliance violations.
  C. Use AWS DataSync to replicate data sources to track lineage. Configure Amazon Macie to scan Amazon Bedrock outputs for sensitive information. Use AWS Systems Manager Session Manager to log user interactions. Deploy Amazon Textract with AWS Step Functions workflows to identify and redact PII from generated reports.
  D. Configure Amazon Athena to query data sources to analyze and report on data lineage. Use Amazon CloudWatch custom metrics to monitor PII exposure in Amazon Bedrock responses and establish AWS X-Ray tracing to generate an audit trail. Use an Amazon Rekognition Custom Labels model to detect sensitive information in the data that Amazon Bedrock processes.
  A. Use AWS Glue Data Catalog to register all data sources and track lineage. Use Amazon Bedrock Guardrails PII filters. Enable AWS CloudTrail logging for all Amazon Bedrock API calls with Amazon S3 integration. Use Amazon Macie to scan stored data for sensitive information and publish findings to Amazon CloudWatch Logs. Create CloudWatch dashboards to visualize the findings and generate automated compliance reports.

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  Explanation

  The selected response matches the requirement by using this approach: Use AWS Glue Data Catalog to register all data sources and track lineage. Use Amazon Bedrock Guardrails PII filters. Enable AWS CloudTrail logging for all Amazon Bedrock API calls with Amazon S3 integration. Use Amazon Macie to scan stored data for sensitive information and publish findings to Amazon CloudWatch Logs. This fits the governance requirement because controls are applied consistently around the model workflow and the data owner remains in control instead of relying on informal prompt wording or scattered application code.

  The key AWS cue is Amazon Bedrock, Amazon S3, Amazon CloudWatch, AWS CloudTrail, which aligns with the scenario instead of forcing unrelated services into the design. The alternatives focus on Amazon Bedrock, Amazon S3, AWS Lambda, Amazon Comprehend, AWS Step Functions or on a different operating model, but they leave at least one required constraint unresolved. The exam takeaway is to identify the dominant constraint in the stem, then choose the managed AWS service or design pattern that satisfies that constraint with secure, reliable, and maintainable implementation effort.

• Question 6:

  A company runs a Retrieval Augmented Generation (RAG) application that uses Amazon Bedrock Knowledge Bases to perform regulatory compliance queries. The application uses the RetrieveAndGenerateStream API. The application retrieves relevant documents from a knowledge base that contains more than 50,000 regulatory documents, legal precedents, and policy updates. The RAG application is producing suboptimal responses because the initial retrieval often returns semantically similar but contextually irrelevant documents. The poor responses are causing model hallucinations and incorrect regulatory guidance. The company needs to improve the performance of the RAG application so it returns more relevant documents.

  Which solution will meet this requirement with the LEAST operational overhead?

  A. Deploy an Amazon SageMaker endpoint to run a fine-tuned ranking model. Use an Amazon API Gateway REST API to route requests. Configure the application to make requests through the REST API to rerank the results.
  B. Use Amazon Comprehend to classify documents and apply relevance scores. Integrate the RAG application's reranking process with Amazon Textract to run document analysis. Use Amazon Neptune to perform graph-based relevance calculations.
  C. Implement a retrieval pipeline that uses the Amazon Bedrock Knowledge Bases Retrieve API to perform initial document retrieval. Call the Amazon Bedrock Rerank API to rerank the results. Invoke the InvokeModelWithResponseStream operation to generate responses.
  D. Use the latest Amazon reranker model through the reranking configuration within Amazon Bedrock Knowledge Bases. Use the model to improve document relevance scoring and to reorder results based on contextual assessments.
  D. Use the latest Amazon reranker model through the reranking configuration within Amazon Bedrock Knowledge Bases. Use the model to improve document relevance scoring and to reorder results based on contextual assessments.

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  Explanation

  The selected response matches the requirement by using this approach: Use the latest Amazon reranker model through the reranking configuration within Amazon Bedrock Knowledge Bases. Use the model to improve document relevance scoring and to reorder results based on contextual assessments. This fits the governance requirement because controls are applied consistently around the model workflow and the data owner remains in control instead of relying on informal prompt wording or scattered application code.

  The key AWS cue is Amazon Bedrock, which aligns with the scenario instead of forcing unrelated services into the design. The alternatives focus on Amazon SageMaker, Amazon API Gateway, Amazon Textract, Amazon Comprehend, Amazon Bedrock or on a different operating model, but they leave at least one required constraint unresolved. The exam takeaway is to identify the dominant constraint in the stem, then choose the managed AWS service or design pattern that satisfies that constraint with secure, reliable, and maintainable implementation effort.

• Question 7:

  A Step Functions workflow coordinates several GenAI agents. Each agent produces detailed reasoning traces and intermediate artifacts. Some state outputs exceed the Step Functions payload quota. The team must preserve observability and keep the sequential workflow.

  Which change should the team make?

  A. Disable intermediate outputs so each agent passes only a final answer to the next state.
  B. Compress every state output into a string and pass the compressed data directly between all states.
  C. Replace Step Functions with a single long-running Lambda function that stores all state in memory.
  D. Store large intermediate artifacts in Amazon S3 and pass S3 object references and compact metadata between states.
  D. Store large intermediate artifacts in Amazon S3 and pass S3 object references and compact metadata between states.

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  Explanation

  The selected response matches the requirement by using this approach: Store large intermediate artifacts in Amazon S3 and pass S3 object references and compact metadata between states. This fits the agent workflow requirement because tools, permissions, and orchestration are explicit rather than hidden inside an unconstrained prompt. The key AWS cue is Amazon S3, which aligns with the scenario instead of forcing unrelated services into the design. The alternatives move toward Disable intermediate outputs so each agent passes only a final answer to the next state; Compress every state output into a string and pass the compressed data directly between; Replace Step Functions with a single long-running Lambda function that stores all state, but they do not satisfy the full requirement set as directly. The exam takeaway is to identify the dominant constraint in the stem, then choose the managed AWS service or design pattern that satisfies that constraint with secure, reliable, and maintainable implementation effort.

• Question 8:

  A company purchases Amazon Q Developer Pro subscriptions for 500 developers to improve code quality and productivity. The company needs to create an observability system that tracks adoption metrics across the company. The observability system must be able to identify active subscription users compared to underused subscriptions. The system must give the company the ability to recognize power users every quarter and to identify teams that require additional training. The system must provide visibility into usage patterns such as the number of lines of Amazon Q generated code that each user has accepted.

  Which solution will meet these requirements?

  A. Create a usage dashboard for Amazon Q Developer. Use the usage dashboard to track aggregated usage adoption metrics.
  B. Use the Amazon Q Developer built-in administrator dashboard to track user adoption metrics across the company's organization in AWS Organizations.
  C. Collect user-level metrics in Amazon Q Developer. Store the metrics in an Amazon S3 bucket. Use Amazon QuickSight to visualize the usage data. Create dashboards to show adoption metrics for users and teams.
  D. Configure AWS CloudTrail to track all Amazon Q Developer API calls in the company's organization in AWS Organizations. Use an AWS Lambda function to process the logs. Store the processed logs in Amazon DynamoDB. Create custom dashboards in Amazon Managed Grafana to visualize the data.
  B. Use the Amazon Q Developer built-in administrator dashboard to track user adoption metrics across the company's organization in AWS Organizations.

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  Explanation

  The selected response matches the requirement by using this approach: Use the Amazon Q Developer built-in administrator dashboard to track user adoption metrics across the company's organization in AWS Organizations. This fits the customization requirement because it uses a supported adaptation path and keeps training data, evaluation, and deployment aligned with the target behavior. The key AWS cue is Amazon Q Developer, which aligns with the scenario instead of forcing unrelated services into the design.

  The alternatives focus on Amazon Q Developer, Amazon S3, AWS Lambda, AWS CloudTrail, Amazon DynamoDB or on a different operating model, but they leave at least one required constraint unresolved.

  The exam takeaway is to identify the dominant constraint in the stem, then choose the managed AWS service or design pattern that satisfies that constraint with secure, reliable, and maintainable implementation effort.

• Question 9:

  A university is building an AI-powered application that includes several sub-applications. The sub-applications include AI assistants, assignment graders, and internal analytics applications. The university is defining and testing multiple prompts by using various foundation models (FMs). The university wants to compare variants of each prompt and choose the variant that yield outputs that are best-suited for specified use cases. The university requires a version control solution for the prompts. The university must be able to test prompt variations and collect audit trails for prompt changes and usage. The solution must also maintain consistency while allowing the prompts to integrate into the main application.

  Which combination of solutions will meet these requirements with the LEAST operational overhead? (Select TWO.)

  A. Use Amazon Bedrock Prompt Management to create versioned prompts. Include parameterized variables for each use case.
  B. Store prompts in Amazon S3. Use AWS Step Functions to orchestrate the model interactions and service integrations.
  C. Use Amazon Bedrock Flows to create workflows that combine FMs and AWS services.
  D. Configure AWS Config to record prompt changes. Use AWS CloudTrail to track prompt usage.
  E. Configure Amazon Bedrock intelligent prompt routing.
  A. Use Amazon Bedrock Prompt Management to create versioned prompts. Include parameterized variables for each use case.
  C. Use Amazon Bedrock Flows to create workflows that combine FMs and AWS services.

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  Explanation

  The selected responses are complementary: Use Amazon Bedrock Prompt Management to create versioned prompts. Include parameterized variables for each use case; Use Amazon Bedrock Flows to create workflows that combine FMs and AWS services. This fits the agent workflow requirement because tools, permissions, and orchestration are explicit rather than hidden inside an unconstrained prompt. The key AWS cue is Amazon Bedrock, which aligns with the scenario instead of forcing unrelated services into the design. The alternatives focus on Amazon S3, AWS Step Functions, AWS CloudTrail, Amazon Bedrock or on a different operating model, but they leave at least one required constraint unresolved. The exam takeaway is to identify the dominant constraint in the stem, then choose the managed AWS service or design pattern that satisfies that constraint with secure, reliable, and maintainable implementation effort.

• Question 10:

  A company has deployed an AI assistant as a React application that uses AWS Amplify, an AWS AppSync GraphQL API, and Amazon Bedrock Knowledge Bases. The application uses the GraphQL API to call the Amazon Bedrock RetrieveAndGenerate API for knowledge base interactions. The company configures an AWS Lambda resolver to use the RequestResponse invocation type. Application users report frequent timeouts and slow response times. Users report these problems more frequently for complex questions that require longer processing. The company needs a solution to fix these performance issues and enhance the user experience.

  Which solution will meet these requirements?

  A. Use AWS Amplify AI Kit to implement streaming responses from the GraphQL API and to optimize client-side rendering.
  B. Increase the timeout value of the Lambda resolver. Implement retry logic with exponential backoff.
  C. Update the application to send an API request to an Amazon SQS queue. Update the AWS AppSync resolver to poll and process the queue.
  D. Change the RetrieveAndGenerate API to the InvokeModelWithResponseStream API. Update the application to use an Amazon API Gateway WebSocket API to support the streaming response.
  A. Use AWS Amplify AI Kit to implement streaming responses from the GraphQL API and to optimize client-side rendering.

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  Explanation

  The selected response matches the requirement by using this approach: Use AWS Amplify AI Kit to implement streaming responses from the GraphQL API and to optimize client-side rendering. This fits the performance requirement because it keeps serving capacity warm, reduces startup delay, and can improve time to first token for interactive users. The alternatives focus on Amazon API Gateway or on a different operating model, but they leave at least one required constraint unresolved. The exam takeaway is to identify the dominant constraint in the stem, then choose the managed AWS service or design pattern that satisfies that constraint with secure, reliable, and maintainable implementation effort.