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Exam AWS Certified Machine Learning Engineer - Associate MLA-C01 All Questions

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Exam AWS Certified Machine Learning Engineer - Associate MLA-C01 topic 1 question 3 discussion

Case Study -
A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.
The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.
The company must implement a manual approval-based workflow to ensure that only approved models can be deployed to production endpoints.
Which solution will meet this requirement?

  • A. Use SageMaker Experiments to facilitate the approval process during model registration.
  • B. Use SageMaker ML Lineage Tracking on the central model registry. Create tracking entities for the approval process.
  • C. Use SageMaker Model Monitor to evaluate the performance of the model and to manage the approval.
  • D. Use SageMaker Pipelines. When a model version is registered, use the AWS SDK to change the approval status to "Approved."
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Suggested Answer: D 🗳️

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S_201996
4 weeks, 1 day ago
Selected Answer: D
SageMaker Pipelines is designed to orchestrate machine learning workflows, including manual approval steps for model registration. You can define a step in the pipeline where a manual approval process is required before the model's status is changed to "Approved" for deployment.
upvoted 1 times
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ninomfr64
1 month ago
Selected Answer: D
This tricked my as option D is not clearly worded: A. No, SageMaker Experiments allows to track and organize your experiment but not for approving models B. No, SageMaker ML Lineage Tracking allows to track model lineage but do not allow to approve a model C. No, SageMaker Model Monitor allows to monitor data quality, model quality, bias and feature attribution D. Yes, After you create a model version, you typically evaluate its performance and then update the approval status of the model version. You can update the approval status of a model version by using the SDK, SageMaker Studio console or with a condition step in a SageMaker AI pipeline
upvoted 1 times
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tigrex73
1 month, 3 weeks ago
Selected Answer: D
The SageMaker Model Registry within the pipeline provides functionality to manually or programmatically approve models for production deployment.
upvoted 3 times
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GiorgioGss
1 month, 3 weeks ago
Selected Answer: D
https://docs.aws.amazon.com/en_us/sagemaker/latest/dg/model-registry-approve.html "You can update the approval status of a model version by using the AWS SDK "
upvoted 3 times
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