Amazon Exam MLS-C01 Topic 5 Question 71 Discussion

Actual exam question for Amazon's MLS-C01 exam

Question #: 71
Topic #: 5

An ecommerce company has used Amazon SageMaker to deploy a factorization machines (FM) model to suggest products for customers. The company's data science team has developed two new models by using the TensorFlow and PyTorch deep learning frameworks. The company needs to use A/B testing to evaluate the new models against the deployed model.

...required A/B testing setup is as follows:

* Send 70% of traffic to the FM model, 15% of traffic to the TensorFlow model, and 15% of traffic to the Py Torch model.

* For customers who are from Europe, send all traffic to the TensorFlow model

..sh architecture can the company use to implement the required A/B testing setup?

ACreate two new SageMaker endpoints for the TensorFlow and PyTorch models in addition to the existing SageMaker endpoint. Create an Application Load Balancer Create a target group for each endpoint. Configure listener rules and add weight to the target groups. To send traffic to the TensorFlow model for customers who are from Europe, create an additional listener rule to forward traffic to the TensorFlow target group.

BCreate two production variants for the TensorFlow and PyTorch models. Create an auto scaling policy and configure the desired A/B weights to direct traffic to each production variant Update the existing SageMaker endpoint with the auto scaling policy. To send traffic to the TensorFlow model for customers who are from Europe, set the TargetVariant header in the request to point to the variant name of the TensorFlow model.

CCreate two new SageMaker endpoints for the TensorFlow and PyTorch models in addition to the existing SageMaker endpoint. Create a Network Load Balancer. Create a target group for each endpoint. Configure listener rules and add weight to the target groups. To send traffic to the TensorFlow model for customers who are from Europe, create an additional listener rule to forward traffic to the TensorFlow target group.

DCreate two production variants for the TensorFlow and PyTorch models. Specify the weight for each production variant in the SageMaker endpoint configuration. Update the existing SageMaker endpoint with the new configuration. To send traffic to the TensorFlow model for customers who are from Europe, set the TargetVariant header in the request to point to the variant name of the TensorFlow model.

Show Suggested Answer

Suggested Answer: A

by Donette at Mar 01, 2023, 08:18 PM

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Nan

12 days ago

Option C is similar to A, but using a Network Load Balancer instead. I'm not sure if that's necessary for this setup, but it could be a valid choice.

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Ciara

13 days ago

Option B is an interesting approach, using production variants and auto-scaling. This could provide more flexibility in the future, but might be overkill for this use case.

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Ria

16 days ago

Option B allows for auto scaling and setting A/B weights easily, which can be more efficient in the long run.

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Kimi

17 days ago

Option A seems like the most straightforward approach. Using an Application Load Balancer and creating separate target groups for each model makes it easy to configure the traffic distribution.

upvoted 0 times