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Exam AWS Certified Machine Learning - Specialty topic 1 question 327 discussion

Exam question from Amazon's AWS Certified Machine Learning - Specialty
Question #: 327
Topic #: 1
[All AWS Certified Machine Learning - Specialty Questions]

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?

  • A. Create a new SageMaker endpoint for the new model. Configure an Application Load Balancer (ALB) to distribute traffic between the old model and the new model.
  • B. Modify the existing endpoint to use SageMaker production variants to distribute traffic between the old model and the new model.
  • C. Modify the existing endpoint to use SageMaker batch transform to distribute traffic between the old model and the new model.
  • D. Create a new SageMaker endpoint for the new model. Configure a Network Load Balancer (NLB) to distribute traffic between the old model and the new model.
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Suggested Answer: B 🗳️
by Peter_Hsieh at March 14, 2024, 1:39 p.m.
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vkbajoria
6 months, 3 weeks ago
Selected Answer: B
deploy in same endpoint and control the percent of traffic via Production Variants
upvoted 1 times
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AIWave
7 months, 1 week ago
Selected Answer: B
Production variants -> deploy multiple models behind a single endpoint to distribute traffic between different variants of the model without making changes to the applications that rely on the API.
upvoted 1 times
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Peter_Hsieh
7 months, 1 week ago
Selected Answer: B
https://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works-model-validation.html
upvoted 1 times
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