Topic 1 Question 46
A company has AWS Glue data processing jobs that are orchestrated by an AWS Glue workflow. The AWS Glue jobs can run on a schedule or can be launched manually. The company is developing pipelines in Amazon SageMaker Pipelines for ML model development. The pipelines will use the output of the AWS Glue jobs during the data processing phase of model development. An ML engineer needs to implement a solution that integrates the AWS Glue jobs with the pipelines. Which solution will meet these requirements with the LEAST operational overhead?
Use AWS Step Functions for orchestration of the pipelines and the AWS Glue jobs.
Use processing steps in SageMaker Pipelines. Configure inputs that point to the Amazon Resource Names (ARNs) of the AWS Glue jobs.
Use Callback steps in SageMaker Pipelines to start the AWS Glue workflow and to stop the pipelines until the AWS Glue jobs finish running.
Use Amazon EventBridge to invoke the pipelines and the AWS Glue jobs in the desired order.
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- 正解だと思う選択肢: C
LEAST effort: https://aws.amazon.com/blogs/machine-learning/extend-amazon-sagemaker-pipelines-to-include-custom-steps-using-callback-steps/ The example is exactly for the same use-case as the question.
👍 2GiorgioGss2024/11/27 - 正解だと思う選択肢: C👍 2Linux_master2024/11/28
- 正解だと思う選択肢: B
This approach allows you to directly integrate the outputs of AWS Glue jobs into SageMaker Pipelines, leveraging the processing steps to handle the data. It minimizes additional orchestration overhead and keeps the workflow streamlined within SageMaker Pipelines
👍 1a4002bd2024/11/26
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