Practice Free AIP-C01 Exam Online Questions

Question #1

A company uses Amazon Bedrock to generate technical content for customers. The company has recently experienced a surge in hallucinated outputs when the company’s model generates summaries of long technical documents. The model outputs include inaccurate or fabricated details. The company’s current solution uses a large foundation model (FM) with a basic one-shot prompt that includes the full document in a single input.

The company needs a solution that will reduce hallucinations and meet factual accuracy goals. The solution must process more than 1,000 documents each hour and deliver summaries within 3 seconds for each document.

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

A . Implement zero-shot chain-of-thought (CoT) instructions that require step-by-step reasoning with explicit fact verification before the model generates each summary.
B . Use Retrieval Augmented Generation (RAG) with an Amazon Bedrock knowledge base. Apply semantic chunking and tuned embeddings to ground summaries in source content.
C . Configure Amazon Bedrock guardrails to block any generated output that matches patterns that are associated with hallucinated content.
D . Increase the temperature parameter in Amazon Bedrock.
E . Prompt the Amazon Bedrock model to summarize each full document in one pass.

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Question #2

A company is developing a generative AI (GenAI) application by using Amazon Bedrock. The application will analyze patterns and relationships in the company’s data. The application will process millions of new data points daily across AWS Regions in Europe, North America, and Asia before storing the data in Amazon S3.

The application must comply with local data protection and storage regulations. Data residency and processing must occur within the same continent. The application must also maintain audit trails of the application’s decision-making processes and provide data classification capabilities.

Which solution will meet these requirements?

A . Deploy the application in each Region with local IAM policies. Use Amazon Bedrock cross-Region inference to distribute the workload. Use Amazon CloudWatch to log AI decision-making processes. Manually track compliance certifications across Regions.
B . Use SCPs with AWS Organizations to manage location-specific permissions. Use AWS CloudTrail immutable logs to audit decision-making processes. Import a custom model into Amazon Bedrock and deploy the model to each Region.
C . Use Amazon S3 Object Lock with Region-specific S3 bucket policies. Pre-process the data points within the Region based on geographic origin before sending the data points to Amazon Bedrock. Use Amazon Macie to classify the data. Use AWS CloudTrail immutable logs to audit the decision-making processes.
D . Create separate AWS accounts for each Region with individual compliance frameworks. Use Amazon SageMaker AI with custom monitoring. Create manual compliance reports for each regulatory jurisdiction.

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Question #3

A financial services company processes more than 10,000 customer inquiries every day through a multi-agent GenAI application that uses Amazon Bedrock AgentCore. The application agents invoke several custom tools. During peak usage periods, users report that the custom tools experience up to

40% failure rates. The tools perform inconsistently for different teams at the company.

A GenAI developer must implement an observability solution that provides end-to-end visibility into agent interactions and tool behavior. The solution must use built-in Amazon Bedrock capabilities and must not require custom instrumentation. The GenAI developer needs a solution that requires minimal performance overhead.

Which solution will meet these requirements?

A . Enable AgentCore Observability and trace collection. Use AWS X-Ray to capture distributed traces for the custom tools. Build Amazon CloudWatch dashboards to visualize metrics for errors, throttling, and latency during peak usage periods.
B . Use Amazon CloudWatch Container Insights to monitor the agents. Configure an AWS Lambda function to poll the Amazon Bedrock API for tool usage metrics. Configure the function to store results in CloudWatch to generate alerts.
C . Build a custom ETL pipeline that uses AWS Lambda functions to process Amazon CloudWatch logs from Amazon Bedrock. Store the processed data in Amazon DynamoDB. Use Amazon QuickSight to visualize cross-team performance patterns.
D . Enable AgentCore Observability and send trace data to Amazon CloudWatch Logs. Use a custom AWS Lambda function to extract tool performance metrics from the logs. Use Amazon Managed Grafana to visualize trends.

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Correct Answer: A
A

Explanation:

Amazon Bedrock AgentCore Observability is the best fit because the requirement is specifically for built-in Amazon Bedrock visibility into agent interactions, tool behavior, and failures without custom instrumentation. AWS documentation states that AgentCore provides built-in metrics, logs, and traces for AgentCore modular services, and that this data can be viewed in Amazon CloudWatch. AgentCore observability also supports sessions, traces, and spans, where traces represent full request-response cycles and spans represent operations inside a trace. This directly matches the need to analyze multi-agent interactions, tool invocations, latency, throttling, and error behavior during peak traffic.

Option A is also correct because Amazon Bedrock AgentCore integrates with AWS X-Ray for distributed tracing of AI agents and tools. AWS X-Ray support helps follow requests across service boundaries and identify performance bottlenecks in agent and tool execution. CloudWatch provides curated GenAI observability views for AgentCore and can show operational health, metrics, traces, sessions, and error breakdowns. This gives the developer end-to-end visibility with minimal additional overhead because the core telemetry is service-provided rather than extracted through a custom pipeline.

Option B is incorrect because Container Insights focuses on container infrastructure and would not provide native visibility into Bedrock AgentCore agent reasoning paths or tool-level traces. Polling

APIs with Lambda adds custom logic and operational overhead.

Option C is incorrect because a custom ETL pipeline, DynamoDB storage, and QuickSight reporting do not satisfy the “built-in Amazon Bedrock capabilities” and “minimal overhead” requirements.

Option D starts correctly with AgentCore Observability, but then adds custom Lambda processing and Grafana, which introduces unnecessary custom instrumentation and processing.

Therefore, option A is the most AWS-aligned solution.

Question #4

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 Code Pipeline. 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.

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Correct Answer: C
C

Explanation:

Option C best satisfies the requirement for flexible FM testing across environments while minimizing operational complexity and aligning with AWS-recommended deployment practices. Amazon Bedrock supports invoking on-demand foundation models through the FoundationModel abstraction, which allows applications to dynamically reference different models without requiring dedicated provisioned capacity. This is ideal for experimentation and A/B testing in both development and production environments.

Using a single AWS CDK application ensures infrastructure consistency and reduces duplication.

Environment-specific configuration, such as selecting different foundation model IDs, can be externalized through parameters, context variables, or environment-specific configuration files. This allows product owners to easily switch between FMs in each environment without modifying application logic.

A single AWS CodePipeline with distinct deployment stages for development and production is an AWS best practice for multi-environment deployments. It enforces consistent build and deployment steps while still allowing environment-level customization. AWS CodeBuild deploy actions enable automated, repeatable deployments, reducing manual errors and improving governance.

Option A increases complexity by introducing multiple pipelines and relies on provisioned models, which are not necessary for FM evaluation and experimentation. Provisioned throughput is better suited for predictable, high-volume production workloads rather than frequent model switching.

Option B creates unnecessary operational overhead by duplicating CDK applications and pipelines, making long-term maintenance more difficult.

Option D directly conflicts with infrastructure-as-code best practices by manually recreating development resources, which increases configuration drift and reduces reliability.

Therefore, Option C provides the most flexible, scalable, and AWS-aligned solution for testing and switching foundation models across development and production environments.

Question #5

A company is using Amazon Bedrock to develop a customer support AI assistant. The AI assistant must respond to customer questions about their accounts. The AI assistant must not expose personal information in responses. The company must comply with data residency policies by ensuring that all processing occurs within the same AWS Region where each customer is located.

The company wants to evaluate how effective the AI assistant is at preventing the exposure of personal information before the company makes the AI assistant available to customers.

Which solution will meet these requirements?

A . Configure a cross-Region Amazon Bedrock guardrail to apply sensitive information filters. Set the guardrail to detect mode during development and testing. Switch to block mode for production deployment.
B . Configure an Amazon Bedrock guardrail to apply sensitive information filters. Set the guardrail to mask mode during development and testing. Switch to block mode for production deployment.
Deploy a copy of the guardrail to each Region where the company operates.
C . Configure an Amazon Bedrock guardrail to apply content and topic filters. Set the guardrail to detect mode during development, testing, and production. Disable invocation logging for the Amazon Bedrock model.
D . Configure a cross-Region Amazon Bedrock guardrail to apply a set of content and word filters. Set the guardrail to detect mode during development and testing. Switch to mask mode for production deployment.

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Correct Answer: B
B

Explanation:

Option B best meets all stated requirements by correctly combining PII protection, evaluation before launch, and data residency compliance using Amazon Bedrock Guardrails. Amazon Bedrock guardrails provide native sensitive information filtering that operates inline during model invocation, making them well suited for preventing personal data exposure in customer-facing AI assistants.

The requirement to evaluate how effective the AI assistant is at preventing exposure before release is best addressed by using mask mode during development and testing. Mask mode allows responses to be generated while automatically redacting detected personal information, making it easy for developers and reviewers to see where and how PII would have appeared. This provides concrete validation that the guardrail rules are correctly configured without fully blocking responses, which is ideal for quality assurance and pre-production evaluation.

For production, switching the guardrail to block mode ensures that responses containing personal information are fully prevented from being returned to users. This offers the strongest protection and aligns with compliance expectations for customer account data. Block mode is appropriate once confidence in the guardrail configuration has been established during testing.

The data residency requirement is addressed by deploying a copy of the guardrail in each AWS Region where the application operates. Amazon Bedrock guardrails are Region-specific resources, and using Region-local guardrails ensures that inference, filtering, and enforcement all occur within the same Region as the customer data. This avoids cross-Region processing and helps the company comply with regulatory and contractual data residency policies.

Option A and D incorrectly rely on cross-Region guardrails, which can violate data residency constraints.

Option C focuses on topic filtering rather than sensitive information filtering and keeps detect mode enabled in production, which does not actively prevent PII exposure.

Therefore, B is the only option that fully satisfies safety, compliance, and evaluation requirements.

Question #6

A media company is launching a platform that allows thousands of users every hour to upload images and text content. The platform uses Amazon Bedrock to process the uploaded content to generate creative compositions.

The company needs a solution to ensure that the platform does not process or produce inappropriate content. The platform must not expose personally identifiable information (PII) in the compositions. The solution must integrate with the company’s existing Amazon S3 storage workflow.

Which solution will meet these requirements with the LEAST infrastructure management overhead?

A . Enable the Enhanced Monitoring tool. Use an Amazon CloudWatch alarm to filter traffic to the platform. Use Amazon Comprehend PII detection to pre-process the data. Create a CloudWatch alarm to monitor for Amazon Comprehend PII detection events. Create an AWS Step Functions workflow that includes an Amazon Rekognition image moderation step.
B . Use an Amazon API Gateway HTTP API with request validation templates to screen content before storing the uploaded content in Amazon S3. Use Amazon SageMaker AI to build custom content moderation models that process content before sending the processed content to Amazon Bedrock.
C . Create an Amazon Cognito user pool that uses pre-authentication AWS Lambda functions to run content moderation checks. Use Amazon Textract to filter text content and Amazon Rekognition to filter image content before allowing users to upload content to the platform.
D . Create an AWS Step Functions workflow that uses built-in Amazon Bedrock guardrails to filter content. Use Amazon Comprehend PII detection to pre-process the content. Use Amazon Rekognition image moderation.

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Question #7

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.

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Question #8

A healthcare company is deploying an AI system that uses a foundation model (FM) to help clinicians make diagnostic decisions. The company’s ethics board requires the AI system to demonstrate fairness across patient demographic groups and comply with medical AI governance policies. During initial testing, the AI system provides recommendations without clear explanations or decision tracing. Clinicians are unable to review how the AI system produces diagnostic conclusions.

The company needs to implement a solution that provides transparent reasoning for AI outputs, enables systematic fairness testing, and ensures policy compliance for responsible AI use in healthcare settings. The solution must balance comprehensive explainability with real-time performance requirements. The solution must support rapid iteration for bias testing across multiple demographic variables. The solution must integrate seamlessly with existing clinical workflows while maintaining strict data privacy controls. The solution must handle complex medical and regulatory terminology.

Which solution will meet these requirements?

A . Use Amazon SageMaker Clarify to generate model explanations. Use Amazon Augmented AI (Amazon A2I) to implement human review workflows. Use AWS Config to enforce compliance policies across the AI system.
B . Use Amazon Comprehend Medical to analyze medical terminology. Use Amazon Textract to process documents. Use AWS CloudFormation to standardize deployment configurations.
C . Use Amazon Bedrock agent tracing to provide reasoning traces. Use Amazon Bedrock Prompt Management with A/B testing to perform fairness evaluations. Use Amazon Bedrock Guardrails to ensure policy compliance.
D . Use Amazon CloudWatch to collect performance metrics. Use Amazon EventBridge to trigger compliance checks. Use AWS Lambda functions to generate custom explanation reports.

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Correct Answer: C
C

Explanation:

Option C is the best answer because the scenario is centered on a foundation-model GenAI application that needs explainability, fairness iteration, and responsible AI controls inside an Amazon

Bedrock workflow. Amazon Bedrock Agents support tracing, and AWS documentation states that traces can show the agent’s path from user input to response, including action group inputs and outputs, knowledge base queries, and the reasoning the agent uses to decide which action or query to take. AWS also describes trace data as a way to understand how an agent arrived at a response. This directly addresses clinicians’ need to review diagnostic reasoning and decision flow.

Amazon Bedrock Guardrails are also the right service for policy compliance in GenAI applications. AWS documentation states that Guardrails can implement safeguards aligned with responsible AI policies, configure denied topics, filter harmful content, and remove sensitive information. For healthcare and life sciences GenAI use cases, AWS Prescriptive Guidance recommends evaluating bias, fairness, and hallucinations and implementing guardrails to prevent harmful responses. This supports strict governance and privacy-sensitive clinical workflows.

The prompt testing and A/B testing component supports rapid iteration. AWS guidance for generative AI operations recommends prompt template management, creating and testing prompt variants, using A/B testing workflows for prompt variants, and analyzing performance against metrics. This is relevant for testing fairness behavior across demographic prompt sets and clinical scenarios.

Option A includes SageMaker Clarify, which is useful for bias detection and model explainability, but it is less directly aligned to Bedrock-native real-time tracing and policy enforcement for an FM application.

Option B handles medical text extraction and terminology but not reasoning transparency or governance.

Option D provides operational metrics and custom reports, but not native Bedrock reasoning traces or guardrails.

Therefore, option C best meets the GenAI governance requirements.

Question #9

A media company must use Amazon Bedrock to implement a robust governance process for AI-

generated content. The company needs to manage hundreds of prompt templates. Multiple teams use the templates across multiple AWS Regions to generate content. The solution must provide version control with approval workflows that include notifications for pending reviews. The solution must also provide detailed audit trails that document prompt activities and consistent prompt parameterization to enforce quality standards.

Which solution will meet these requirements?

A . Configure Amazon Bedrock Studio prompt templates. Use Amazon CloudWatch dashboards to display prompt usage metrics. Store approval status in Amazon DynamoDB. Use AWS Lambda functions to enforce approvals.
B . Use Amazon Bedrock Prompt Management to implement version control. Configure AWS CloudTrail for audit logging. Use AWS Identity and Access Management policies to control approval permissions. Create parameterized prompt templates by specifying variables.
C . Use AWS Step Functions to create an approval workflow. Store prompts in Amazon S3. Use tags to implement version control. Use Amazon EventBridge to send notifications.
D . Deploy Amazon SageMaker Canvas with prompt templates stored in Amazon S3. Use AWS CloudFormation for version control. Use AWS Config to enforce approval policies.

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Question #10

A company wants to select a new FM for its AI assistant. A GenAI developer needs to generate evaluation reports to help a data scientist assess the quality and safety of various foundation models FMs. The data scientist provides the GenAI developer with sample prompts for evaluation. The GenAI developer wants to use Amazon Bedrock to automate report generation and evaluation.

Which solution will meet this requirement?

A . Combine the sample prompts into a single JSON document. Create an Amazon Bedrock knowledge base with the document. Write a prompt that asks the FM to generate a response to each sample prompt. Use the RetrieveAndGenerate API to generate a report for each model.
B . Combine the sample prompts into a single JSONL document. Store the document in an Amazon S3 bucket. Create an Amazon Bedrock evaluation job that uses a judge model. Specify the S3 location as input and a different S3 location as output. Run an evaluation job for each FM and select the FM as the generator.
C . Combine the sample prompts into a single JSONL document. Store the document in an Amazon S3 bucket. Create an Amazon Bedrock evaluation job that uses a judge model. Specify the S3 location as input and Amazon QuickSight as output. Run an evaluation job for each FM and select the FM as the evaluator.
D . Combine the sample prompts into a single JSON document. Create an Amazon Bedrock knowledge base from the document. Create an Amazon Bedrock evaluation job that uses the retrieval and response generation evaluation type. Specify an Amazon S3 bucket as the output. Run an evaluation job for each FM.

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