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Question 258 - MLS-C01 discussion

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A data scientist uses Amazon SageMaker Data Wrangler to define and perform transformations and feature engineering on historical data. The data scientist saves the transformations to SageMaker Feature Store.

The historical data is periodically uploaded to an Amazon S3 bucket. The data scientist needs to transform the new historic data and add it to the online feature store The data scientist needs to prepare the .....historic data for training and inference by using native integrations.

Which solution will meet these requirements with the LEAST development effort?

A.
Use AWS Lambda to run a predefined SageMaker pipeline to perform the transformations on each new dataset that arrives in the S3 bucket.
Answers
A.
Use AWS Lambda to run a predefined SageMaker pipeline to perform the transformations on each new dataset that arrives in the S3 bucket.
B.
Run an AWS Step Functions step and a predefined SageMaker pipeline to perform the transformations on each new dalaset that arrives in the S3 bucket
Answers
B.
Run an AWS Step Functions step and a predefined SageMaker pipeline to perform the transformations on each new dalaset that arrives in the S3 bucket
C.
Use Apache Airflow to orchestrate a set of predefined transformations on each new dataset that arrives in the S3 bucket.
Answers
C.
Use Apache Airflow to orchestrate a set of predefined transformations on each new dataset that arrives in the S3 bucket.
D.
Configure Amazon EventBridge to run a predefined SageMaker pipeline to perform the transformations when a new data is detected in the S3 bucket.
Answers
D.
Configure Amazon EventBridge to run a predefined SageMaker pipeline to perform the transformations when a new data is detected in the S3 bucket.
Suggested answer: D

Explanation:

The best solution is to configure Amazon EventBridge to run a predefined SageMaker pipeline to perform the transformations when a new data is detected in the S3 bucket. This solution requires the least development effort because it leverages the native integration between EventBridge and SageMaker Pipelines, which allows you to trigger a pipeline execution based on an event rule. EventBridge can monitor the S3 bucket for new data uploads and invoke the pipeline that contains the same transformations and feature engineering steps that were defined in SageMaker Data Wrangler. The pipeline can then ingest the transformed data into the online feature store for training and inference.

The other solutions are less optimal because they require more development effort and additional services. Using AWS Lambda or AWS Step Functions would require writing custom code to invoke the SageMaker pipeline and handle any errors or retries. Using Apache Airflow would require setting up and maintaining an Airflow server and DAGs, as well as integrating with the SageMaker API.

References:

Amazon EventBridge and Amazon SageMaker Pipelines integration

Create a pipeline using a JSON specification

Ingest data into a feature group

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asked 16/09/2024
Sorin Craia
39 questions
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