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A machine learning (ML) company continuously develops new versions of its models.

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A machine learning (ML) company continuously develops new versions of its models. The models are integrated into a web application that uses a RESTful API to call custom Python code to invoke the models. The application runs in an Amazon EC2 Auto Scaling group.

Customer feedback indicates that new model versions sometimes perform worse than previous versions. The company wants to implement a gradual deployment approach in which 10% of traffic is initially routed to new models to verify performance. The solution must also reduce operational overhead for infrastructure management.

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

A.

Deploy model versions to an Amazon SageMaker AI multi-model endpoint. Configure the weight for the new version to 10% and the previous version to 90%. Migrate the Python code to an AWS Lambda function. Use Amazon API Gateway to invoke the Lambda function.

B.

Deploy each model version to a separate Amazon SageMaker Serverless Inference endpoint. Configure an AWS Lambda function to route 10% of invocation calls to the new endpoint and 90% to the previous version based on weighted random selection. Use Amazon API Gateway to invoke the Lambda function.

C.

Deploy each model version to a separate Amazon SageMaker AI single-model endpoint. Configure an AWS Lambda function to route 10% of invocation calls to the new endpoint ' s URL and 90% to the previous version. Use Amazon API Gateway to invoke the Lambda function.

D.

Deploy model versions to an Amazon SageMaker Serverless Inference endpoint. Use Amazon API Gateway with a canary deployment to gradually shift 10% of traffic to the new model version while maintaining 90% of traffic on the previous version.

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  • Vendor: Amazon Web Services
  • Product: SAP-C02
  • Update on: Jul 5, 2026
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