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Free AWS-CERTIFIED-GENERATIVE-AI-DEVELOPER-PROFESSIONAL-AIP-C01 – Amazon AWS-CERTIFIED-GENERATIVE-AI-DEVELOPER-PROFESSIONAL-AIP-C01 Practice Test Question

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Showing 1–3 of 10 questions

Question 1 (Topic 1)

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?

Select an option, then click Submit answer.

  • ○ 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 (Topic 1)

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?

Select an option, then click Submit answer.

  • ○ 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 3 (Topic 1)

A medical company is building a generative AI (GenAI) application that uses RAG to provide evidence-based medical information. The application uses Amazon OpenSearch Service to retrieve vector embeddings. Users report that searches frequently miss results that contain exact medical terms and acronyms and return too many semantically similar but irrelevant documents. The company needs to improve retrieval quality and maintain low end user latency, even as the document collection grows to millions of documents. Which solution will meet these requirements with the LEAST operational overhead?

Select an option, then click Submit answer.

  • ○ Configure hybrid search by combining vector similarity with keyword matching to improve semantic understanding and exact term and acronym matching.
  • ○ Increase the dimensions of the vector embeddings from 384 to 1536. Use a post-processing AWS Lambda function to filter out irrelevant results after retrieval.
  • ○ Replace OpenSearch Service with Amazon Kendra. Use query expansion to handle medical acronyms and terminology variants during pre-processing.
  • ○ Implement a two-stage retrieval architecture in which initial vector search results are re-ranked by an ML model that is hosted on Amazon SageMaker AI.