Câu 18: AWS Certified Generative AI Developer - Professional AIP-C01

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. A. 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. — đáp án hiện tại
2. B. 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. C. 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. D. 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.