AWS Certified Machine Learning Engineer Associate · Free Practice Question Medium
Question 17
A machine learning team is using SageMaker Model Registry to manage the lifecycle of their models. After training a new version of a model, they want to automate the registration of the model in the registry and deploy it to a real-time endpoint. What features of SageMaker Model Registry and Pipelines will help achieve this?
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A
SageMaker Model Registry automatically deploys the model to a real-time endpoint without any further configuration.
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B
The team must manually register the model in the Model Registry and deploy it using the AWS Management Console.
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C
SageMaker Pipelines can automate the registration of the model in the registry and then trigger the deployment to a real-time endpoint based on predefined steps.
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D
Model versions in the registry cannot be automatically deployed; manual steps are always required.
Reveal correct answer
Correct answer: C
A.
SageMaker Model Registry does not automatically deploy models; Pipelines must be used to automate this.
B.
The purpose of SageMaker Pipelines is to automate the workflow, so manual steps are not required for registration or deployment.
C.
SageMaker Pipelines integrates with the Model Registry, allowing automation of model registration and deployment steps. The pipeline can be configured to automatically deploy the model to a real-time endpoint after registration.
D.
Model versions can be automatically deployed using Pipelines.
Discussion
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