Exam AWS Certified Machine Learning - Specialty topic 1 question 292 discussion

Exam question from Amazon's AWS Certified Machine Learning - Specialty

Question #: 292
Topic #: 1

[All AWS Certified Machine Learning - Specialty Questions]

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.

The 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 PyTorch model.
• For customers who are from Europe, send all traffic to the TensorFlow model.

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

A. Create 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.
B. Create 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.
C. Create 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.
D. Create 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: D 🗳️

by Untamables at Feb. 11, 2024, 12:32 p.m.

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Comments

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Togy

1 month, 2 weeks ago

Selected Answer: A

A is the only option capable of region based routing to direct traffic from Europe to TF model.

upvoted 1 times

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pn12345

4 months, 3 weeks ago

A is correct

upvoted 1 times

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AIWave

7 months, 2 weeks ago

Selected Answer: D

Typical multivariant deployment workflow. - No additional endpoints required which eliminates A & C. - B: question isn't about autoscaling but traffic routing - D: textbook production variant deployment method

upvoted 1 times

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vkbajoria

7 months, 3 weeks ago

Selected Answer: D

needs to use both product variant and target variant in this requirement

upvoted 1 times

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Untamables

8 months, 2 weeks ago

Selected Answer: D

https://docs.aws.amazon.com/sagemaker/latest/dg/model-ab-testing.html

upvoted 2 times

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Exam AWS Certified Machine Learning - Specialty topic 1 question 292 discussion

Comments

Togy

pn12345

AIWave

vkbajoria

Untamables

SY0-701