Free AIP-C01 Practice Test Questions 2026

A company needs a system to automatically generate study materials from multiple content sources. The content sources include document files (PDF files, PowerPoint presentations, and Word documents) and multimedia files (recorded videos). The system must process more than 10,000 content sources daily with peak loads of 500 concurrent uploads. The system must also extract key concepts from document files and multimedia files and create contextually accurate summaries. The generated study materials must support real-time collaboration with version control.

Which solution will meet these requirements?

A. Use Amazon Bedrock Data Automation (BDA) with AWS Lambda functions to orchestrate document file processing. Use Amazon Bedrock Knowledge Bases to process all multimedia. Store the content in Amazon DocumentDB with replication. Collaborate by using Amazon SNS topic subscriptions. Track changes by using Amazon Bedrock Agents.

B. Use Amazon Bedrock Data Automation (BDA) with foundation models (FMs) to process document files. Integrate BDA with Amazon Textract for PDF extraction and with Amazon Transcribe for multimedia files. Store the processed content in Amazon S3 with versioning enabled. Store the metadata in Amazon DynamoDB. Collaborate in real time by using AWS AppSync GraphQL subscriptions and DynamoDB.

C. Use Amazon Bedrock Data Automation (BDA) with Amazon SageMaker AI endpoints to host content extraction and summarization models. Use Amazon Bedrock Guardrails to extract content from all file types. Store document files in Amazon Neptune for time series analysis. Collaborate by using Amazon Bedrock Chat for real-time messaging.

D. Use Amazon Bedrock Data Automation (BDA) with AWS Lambda functions to process batches of content files. Fine-tune foundation models (FMs) in Amazon Bedrock to classify documents across all content types. Store the processed data in Amazon ElastiCache (Redis OSS) by using Cluster Mode with sharding. Use Prompt management in Amazon Bedrock for version control.

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.

A healthcare company is developing a document management system that stores medical research papers in an Amazon S3 bucket. The company needs a comprehensive metadata framework to improve search precision for a GenAI application. The metadata must include document timestamps, author information, and research domain classifications.

The solution must maintain a consistent metadata structure across all uploaded documents and allow foundation models (FMs) to understand document context without accessing full content.

Which solution will meet these requirements?

A. Store document timestamps in Amazon S3 system metadata. Use S3 object tags for domain classification. Implement custom user-defined metadata to store author information.

B. Set up S3 Object Lock with legal holds to track document timestamps. Use S3 object tags for author information. Implement S3 access points for domain classification.

C. Use S3 Inventory reports to track timestamps. Create S3 access points for domain classification. Store author information in S3 Storage Lens dashboards.

D. Use custom user-defined metadata to store author information. Use S3 Object Lock retention periods for timestamps. Use S3 Event Notifications for domain classification.

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 publishing company is developing a chat assistant that uses a containerized large language model (LLM) that runs on Amazon SageMaker AI. The architecture consists of an Amazon API Gateway REST API that routes user requests to an AWS Lambda function. The Lambda function invokes a SageMaker AI real-time endpoint that hosts the LLM.

Users report uneven response times. Analytics show that a high number of chats are abandoned after 2 seconds of waiting for the first token. The company wants a solution to ensure that p95 latency is under 800 ms for interactive requests to the chat assistant.

Which combination of solutions will meet this requirement? (Select TWO.)

A. Enable model preload upon container startup. Implement dynamic batching to process multiple user requests together in a single inference pass.

B. Select a larger GPU instance type for the SageMaker AI endpoint. Set the minimum number of instances to 0. Continue to perform per-request processing. Lazily load model weights on the first request.

C. Switch to a multi-model endpoint. Use lazy loading without request batching.

D. Set the minimum number of instances to greater than 0. Enable response streaming.

E. Switch to Amazon SageMaker Asynchronous Inference for all requests. Store requests in an Amazon S3 bucket. Set the minimum number of instances to 0.

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? (Select 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.

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 company is building a generative AI (GenAI) application that produces content based on a variety of internal and external data sources. The company wants to ensure that the generated output is fully traceable. The application must support data source registration and enable metadata tagging to attribute content to its original source. The application must also maintain audit logs of data access and usage throughout the pipeline.

Which solution will meet these requirements?

A. Use AWS Lake Formation to catalog data sources and control access. Apply metadata tags directly in Amazon S3. Use AWS CloudTrail to monitor API activity.

B. Use AWS Glue Data Catalog to register and tag data sources. Use Amazon CloudWatch Logs to monitor access patterns and application behavior.

C. Store data in Amazon S3 and use object tagging for attribution. Use AWS Glue Data Catalog to manage schema information. Use AWS CloudTrail to log access to S3 buckets.

D. Use AWS Glue Data Catalog to register all data sources. Apply metadata tags to attribute data sources. Use AWS CloudTrail to log access and activity across services.

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 for 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.

A healthcare company uses Amazon Bedrock to deploy an application that generates summaries of clinical documents. The application experiences inconsistent response quality with occasional factual hallucinations. Monthly costs exceed the company’s projections by 40%. A GenAI developer must implement a near real-time monitoring solution to detect hallucinations, identify abnormal token consumption, and provide early warnings of cost anomalies. The solution must require minimal custom development work and maintenance overhead.

Which solution will meet these requirements?

A. Configure Amazon CloudWatch alarms to monitor InputTokenCount and OutputTokenCount metrics to detect anomalies. Store model invocation logs in an Amazon S3 bucket. Use AWS Glue and Amazon Athena to identify potential hallucinations.

B. Run Amazon Bedrock evaluation jobs that use LLM-based judgments to detect hallucinations. Configure Amazon CloudWatch to track token usage. Create an AWS Lambda function to process CloudWatch metrics. Configure the Lambda function to send usage pattern notifications.

C. Configure Amazon Bedrock to store model invocation logs in an Amazon S3 bucket. Enable text output logging. Configure Amazon Bedrock guardrails to run contextual grounding checks to detect hallucinations. Create Amazon CloudWatch anomaly detection alarms for token usage metrics.

D. Use AWS CloudTrail to log all Amazon Bedrock API calls. Create a custom dashboard in Amazon QuickSight to visualize token usage patterns. Use Amazon SageMaker Model Monitor to detect quality drift in generated summaries.

Company configures a landing zone in AWS Control Tower. The company handles sensitive data that must remain within the European Union. The company must use only the eu-central-1 Region. The company uses Service Control Policies (SCPs) to enforce data residency policies. GenAI developers at the company are assigned IAM roles that have full permissions for Amazon Bedrock.

The company must ensure that GenAI developers can use the Amazon Nova Pro model through Amazon Bedrock only by using cross-Region inference (CRI) and only in eucentral- 1. The company enables model access for the GenAI developer IAM roles in Amazon Bedrock. However, when a GenAI developer attempts to invoke the model through the Amazon Bedrock Chat/Text playground, the GenAI developer receives the following error:

User arn:aws:sts:123456789012:assumed-role/AssumedDevRole/DevUserName
Action: bedrock:InvokeModelWithResponseStream
On resource(s): arn:aws:bedrock:eu-west-3::foundation-model/amazon.nova-pro-v1:0
Context: a service control policy explicitly denies the action

The company needs a solution to resolve the error. The solution must retain the company's existing governance controls and must provide precise access control. The solution must comply with the company's existing data residency policies.

Which combination of solutions will meet these requirements? (Select TWO.)

A. Add an AdministratorAccess policy to the GenAI developer IAM role

B. Extend the existing SCPs to enable CRI for the eu.amazon.nova-pro-v1:0 inference profile

C. Enable Amazon Bedrock model access for Amazon Nova Pro in the eu-west-3 Region

D. Validate that the GenAI developer IAM roles have permissions to invoke Amazon Nova Pro through the eu.amazon.nova-pro-v1:0 inference profile on all European Union AWS Regions that can serve the model

E. Extend the existing SCP to enable CRI for the eu-* inference profile

A book publishing company wants to build a book recommendation system that uses an AI assistant. The AI assistant will use ML to generate a list of recommended books from the company's book catalog. The system must suggest books based on conversations with customers.

The company stores the text of the books, customers' and editors' reviews of the books, and extracted book metadata in Amazon S3. The system must support low-latency responses and scale efficiently to handle more than 10,000 concurrent users.

Which solution will meet these requirements?

A. Use Amazon Bedrock Knowledge Bases to generate embeddings. Store the embeddings as a vector store in Amazon OpenSearch Service. Create an AWS Lambda function that queries the knowledge base. Configure Amazon API Gateway to invoke the Lambda function when handling user requests.

B. Use Amazon Bedrock Knowledge Bases to generate embeddings. Store the embeddings as a vector store in Amazon DynamoDB. Create an AWS Lambda function that queries the knowledge base. Configure Amazon API Gateway to invoke the Lambda function when handling user requests.

C. Use Amazon SageMaker AI to deploy a pre-trained model to build a personalized recommendation engine for books. Deploy the model as a SageMaker AI endpoint. Invoke the model endpoint by using Amazon API Gateway.

D. Create an Amazon Kendra GenAI Enterprise Edition index that uses the S3 connector to index the book catalog data stored in Amazon S3. Configure built-in FAQ in the Kendra index. Develop an AWS Lambda function that queries the Kendra index based on user conversations. Deploy Amazon API Gateway to expose this functionality and invoke the Lambda function.