MLS-C01 · Question #354
An ecommerce company wants to update a production real-time machine learning (ML) recommendation engine API that uses Amazon SageMaker. The company wants to release a new model but does not want to ma
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Question
An ecommerce company wants to update a production real-time machine learning (ML) recommendation engine API that uses Amazon SageMaker. The company wants to release a new model but does not want to make changes to applications that rely on the API. The company also wants to evaluate the performance of the new model in production traffic before the company fully rolls out the new model to all users. Which solution will meet these requirements with the LEAST operational overhead?
Options
- ACreate a new SageMaker endpoint for the new model. Configure an Application Load Balancer
- BModify the existing endpoint to use SageMaker production variants to distribute traffic between
- CModify the existing endpoint to use SageMaker batch transform to distribute traffic between the
- DCreate a new SageMaker endpoint for the new model. Configure a Network Load Balancer (NLB)
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