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AWS Certified Generative AI Developer - Professional AIP-C01

Here you have the best Amazon AWS Certified Generative AI Developer - Professional AIP-C01 practice exam questions

Preview the first 5 of 97 questions for free
These questions were last updated on June 12, 2026
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Question 1 of 97

A retail company has a generative AI (GenAI) product recommendation application that uses Amazon Bedrock. The application suggests products to customers based on browsing history and demographics. The company needs to implement fairness evaluation across multiple demographic groups to detect and measure bias in recommendations between two prompt approaches. The company wants to collect and monitor fairness metrics in real time. The company must receive an alert if the fairness metrics show a discrepancy of more than 15% between demographic groups. The company must receive weekly reports that compare the performance of the two prompt approaches.

Which solution will meet these requirements with the LEAST custom development effort?

Configure an Amazon CloudWatch dashboard to display default metrics from Amazon Bedrock API calls. Create custom metrics based on model outputs. Set up Amazon EventBridge rules to invoke AWS lambda functions that perform post-processing analysis on model responses and publish custom fairness metrics.

Create the two prompt variants in Amazon Bedrock Prompt Management. Use Amazon Bedrock Flows to deploy the prompt variants with defined traffic allocation. Configure Amazon Bedrock guardrails that have content filters to monitor demographic fairness. Set up Amazon CloudWatch alarms on the GuardrailContentSource dimension that use InvocationsIntervened metrics to detect recommendation discrepancy threshold violations.

Set up Amazon SageMaker Clarify to analyze model outputs. Publish fairness metrics to Amazon CloudWatch. Create CloudWatch composite alarms that combine SageMaker Clarify bias metrics with Amazon Bedrock latency metrics to provide a comprehensive fairness evaluation dashboard.

Create an Amazon Bedrock model evaluation job to compare fairness between the two prompt variants. Enable model invocation logging in Amazon CloudWatch. Set up CloudWatch alarms for InvocationsIntervened metrics with a dimension for each demographic group.

Question 2 of 97

A finance company is developing an AI assistant to help clients plan investments and manage their portfolios. The company identifies several high-risk conversation patterns such as requests for specific stock recommendations or guaranteed returns. High-risk conversation patterns could lead to regulatory violations if the company cannot implement appropriate controls.

The company must ensure that the AI assistant does not provide inappropriate financial advice, generate content about competitors, or make claims that are not factually grounded in the company's approved financial guidance. The company wants to use Amazon Bedrock Guardrails to implement a solution.

Which combination of steps will meet these requirements? (Choose three.)

Add the high-risk conversation patterns to a denied topics guardrail.

Configure a content filter guardrail to filter prompts that contain the high-risk conversation patterns.

Configure a content filter guardrail to filter prompts that contain competitor names.

Add the names of competitors as custom word filters. Set the input and output actions to block.

Set a low grounding score threshold.

Set a high grounding score threshold.

Question 3 of 97

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?

Use AWS Amplify AI Kit to implement streaming responses from the GraphQL API and to optimize client-side rendering.

Increase the timeout value of the Lambda resolver. Implement retry logic with exponential backoff.

Update the application to send an API request to an Amazon SQS queue. Update the AWS AppSync resolver to poll and process the queue.

Change the RetrieveAndGenerate API to the InvokeModelWithResponseStream API. Update the application to use an Amazon API Gateway WebSocket API to support the streaming response.

Question 4 of 97

An ecommerce company operates a global product recommendation system that needs to switch between multiple foundation models (FM) in Amazon Bedrock based on regulations, cost optimization, and performance requirements. The company must apply custom controls based on proprietary business logic, including dynamic cost thresholds, AWS Region-specific compliance rules, and real-time A/B testing across multiple FMs. The system must be able to switch between FMs without deploying new code. The system must route user requests based on complex rules including user tier, transaction value, regulatory zone, and real-time cost metrics that change hourly and require immediate propagation across thousands of concurrent requests.

Which solution will meet these requirements?

Deploy an AWS Lambda function that uses environment variables to store routing rules and Amazon Bedrock FM IDs. Use the Lambda console to update the environment variables when business requirements change. Configure an Amazon API Gateway REST API to read request parameters to make routing decisions.

Deploy Amazon API Gateway REST API request transformation templates to implement routing logic based on request attributes. Store Amazon Bedrock FM endpoints as REST API stage variables. Update the variables when the system switches between models.

Configure an AWS Lambda function to fetch routing configurations from the AWS AppConfig Agent for each user request. Run business logic in the Lambda function to select the appropriate FM for each request. Expose the FM through a single Amazon API Gateway REST API endpoint.

Use AWS Lambda authorizers for an Amazon API Gateway REST API to evaluate routing rules that are stored in AWS AppConfig. Return authorization contexts based on business logic. Route requests to model-specific Lambda functions for each Amazon Bedrock FM.

Question 5 of 97

A company is developing an internal generative AI (GenAI) assistant that uses Amazon Bedrock to summarize corporate documents for multiple business units. The GenAI assistant must generate responses in a consistent format that includes a document summary, classification of business risks, and terms that are flagged for review. The GenAI assistant must adapt the tone of responses for each user's business unit, such as legal, human resources, or finance. The GenAI assistant must block hate speech, inappropriate topics, and sensitive information such as personal health information.

The company needs a solution to centrally manage prompt variants across business units and teams. The company wants to minimize ongoing orchestration efforts and maintenance for post-processing logic. The company also wants to have the ability to adjust content moderation criteria for the GenAI assistant over time.

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

Use Amazon Bedrock Prompt Management to configure reusable templates and business unit-specific prompt variants. Apply Amazon Bedrock guardrails that have category filters and sensitive term lists to block prohibited content.

Use Amazon Bedrock Prompt Management to define base templates. Enforce business unit-specific tone by using system prompt variables. Configure Amazon Bedrock guardrails to apply audience-based threshold tuning. Manage the guardrails by using an internal administration API.

Use Amazon Bedrock with business unit-based instruction injection in API calls. Store response formatting rules in Amazon DynamoDB. Use AWS Step functions to validate responses. Use Amazon Comprehend to apply content filters after the GenAI assistant generates responses.

Use Amazon Bedrock with custom prompt templates that are stored in Amazon DynamoDB. Create one AWS Lambda function to select business unit-specific prompts. Create a second Lambda function to call Amazon Comprehend to filter prohibited content from responses.

Question 6 of 97

Which of the following best describes the approach used when configuring high availability across multiple regions to ensure minimal downtime during failover events?

Deploy active-active clusters with cross-region replication and automated DNS failover to maintain service continuity

Use a single-region deployment with periodic manual snapshots for disaster recovery purposes

Configure load balancers in each region with independent database instances and no synchronization

Implement cold standby servers that require manual intervention to activate during outages

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