AWS Certified Generative AI Developer - Professional AIP-C01 Practice Test

AWS Certified Generative AI Developer - Professional AIP-C01 Practice Test practice questions, page 5 of 6, with answer review and explanations.

Question 21

A company is using Amazon Bedrock to build a customer-facing AI assistant to handle sensitive customer inquiries. The company must use defense-in-depth safety controls to block sophisticated prompt injection attacks. The company must keep audit logs of all safety interventions. The AI assistant must have cross-Region failover capabilities. Which solution will meet these requirements?

  1. Configure Amazon Bedrock guardrails to use content filters to protect against prompt injection attacks. Set the content filters to high. Use a guardrail profile to implement cross-Region guardrail inference. Use Amazon CloudWatch Logs with custom metrics to capture detailed guardrail intervention events.
  2. Configure Amazon Bedrock guardrails to use content filters to protect against prompt injection attacks. Set the content filters to high. Use AWS WAF to block suspicious inputs. Use AWS CloudTrail to log API calls for audits.
  3. Deploy Amazon Comprehend custom classification to detect prompt injection attacks. Use Amazon API Gateway to validate requests. Use Amazon CloudWatch Logs with custom metrics to capture detailed intervention events.
  4. Configure Amazon Bedrock guardrails to use custom content filters to protect against harmful content. Set the content filters to high. Use word filters to protect against known attack patterns. Configure cross-Region guardrail replication to provide failover capabilities. Store logs in AWS CloudTrail for compliance auditing.

Question 22

A company is designing a canary deployment strategy for a payment processing API. The system must support automated gradual traffic shifting between multiple Amazon Bedrock models based on real-time inference metrics, historical traffic patterns, and service health. The solution must be able to gradually increase traffic to new model versions. The system must increase traffic if metrics remain healthy and decrease traffic if the performance degrades below acceptable thresholds. The company needs to comprehensively monitor inference latency and error rates during the deployment phase. The company must also be able to halt deployments and revert to a previous model version without any manual intervention. Which solution will meet these requirements?

  1. Use Amazon Bedrock with provisioned throughput to host the versions of the model. Configure an Amazon EventBridge rule to invoke an AWS Step Functions workflow when a new model version is released. Configure the workflow to shift traffic in stages, wait for a specified time period, and invoke an AWS Lambda function to check Amazon CloudWatch performance metrics. Configure the workflow to increase traffic if the metrics meet thresholds and to trigger a traffic rollback if performance metrics fall below thresholds.
  2. Use AWS Lambda functions to invoke various Amazon Bedrock model versions. Use an Amazon API Gateway HTTP API with stage variables and weighted routing to shift traffic gradually to new model versions. Use Amazon CloudWatch to monitor performance metrics. Use external logic to adjust traffic between model versions and to roll back if performance falls below thresholds.
  3. Use Amazon SageMaker AI endpoint variants to represent multiple Amazon Bedrock model versions. Use variant weights to shift traffic. Use Amazon CloudWatch to monitor performance metrics. Use SageMaker Model Monitor to trigger AWS Lambda functions to roll back a model deployment if performance drops below a specified threshold. Configure an Amazon EventBridge rule to roll back model deployments if an anomaly is detected.
  4. Use Amazon OpenSearch Service to track inference logs. Configure OpenSearch Service to invoke an AWS Systems Manager Automation runbook to update Amazon Bedrock model endpoints to shift traffic based on the inference logs.

Question 23

A financial services company uses an AI application to process financial documents by using Amazon Bedrock. During business hours, the application handles approximately 10,000 requests each hour, which requires consistent throughput. The company uses the CreateProvisionedModelThroughput API to purchase provisioned throughput. Amazon CloudWatch metrics show that the provisioned capacity is unused while on-demand requests are being throttled. The company finds the following code in the application: python response = bedrock_runtime.invoke_model(modelId="anthropic.claude-v2", body=json.dumps(payload)) The company needs the application to use the provisioned throughput and to resolve the throttling issues. Which solution will meet these requirements?

  1. Increase the number of model units (MUs) in the provisioned throughput configuration.
  2. Replace the model ID parameter with the ARN of the provisioned model that the CreateProvisionedModelThroughput API returns.
  3. Add exponential backoff retry logic to handle throttling exceptions during peak hours.
  4. Modify the application to use the InvokeModelWithResponseStream API instead of the InvokeModel API.

Question 24

A company is building an AI advisory application by using Amazon Bedrock. The application will provide recommendations to customers. The company needs the application to explain its reasoning process and cite specific sources for data. The application must retrieve information from company data sources and show step-by-step reasoning for recommendations. The application must also link data claims to source documents and maintain response latency under 3 seconds. Which solution will meet these requirements with the LEAST operational overhead?

  1. Use Amazon Bedrock Knowledge Bases with source attribution enabled. Use the Anthropic Claude Messages API with RAG to set high-relevance thresholds for source documents. Store reasoning and citations in Amazon S3 for auditing purposes.
  2. Use Amazon Bedrock with Anthropic Claude models and extended thinking. Configure a 4,000-token thinking budget. Store reasoning traces and citations in Amazon DynamoDB for auditing purposes.
  3. Configure Amazon SageMaker AI with a custom Anthropic Claude model. Use the model's reasoning parameter and AWS Lambda to process responses. Add source citations from a separate Amazon RDS database.
  4. Use Amazon Bedrock with Anthropic Claude models and chain-of-thought reasoning. Configure custom retrieval tracking with the Amazon Bedrock Knowledge Bases API. Use Amazon CloudWatch to monitor response latency metrics.

Question 25

A financial services company uses multiple foundation models (FMs) through Amazon Bedrock for its generative AI (GenAI) applications. To comply with a new regulation for GenAI use with sensitive financial data, the company needs a token management solution. The token management solution must proactively alert when applications approach model-specific token limits. The solution must also process more than 5,000 requests each minute and maintain token usage metrics to allocate costs across business units. Which solution will meet these requirements?

  1. Develop model-specific tokenizers in an AWS Lambda function. Configure the Lambda function to estimate token usage before sending requests to Amazon Bedrock. Configure the Lambda function to publish metrics to Amazon CloudWatch and trigger alarms when requests approach thresholds. Store detailed token usage in Amazon DynamoDB to report costs.
  2. Implement Amazon Bedrock Guardrails with token quota policies. Capture metrics on rejected requests. Configure Amazon EventBridge rules to trigger notifications based on Amazon Bedrock Guardrails metrics. Use Amazon CloudWatch dashboards to visualize token usage trends across models.
  3. Deploy an Amazon SQS dead-letter queue for failed requests. Configure an AWS Lambda function to analyze token-related failures. Use Amazon CloudWatch Logs Insights to generate reports on token usage patterns based on error logs from Amazon Bedrock API responses.
  4. Use Amazon API Gateway to create a proxy for all Amazon Bedrock API calls. Configure request throttling based on custom usage plans with predefined token quotas. Configure API Gateway to reject requests that will exceed token limits.