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 new 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.

B. Run an AWS Step Functions step and a predefined SageMaker pipeline to perform the transformations on each new dataset 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.

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: C

Community Answer: D




This question is in MLS-C01 AWS Certified Machine Learning – Specialty Exam
For getting AWS Certified Machine Learning – Specialty Certificate


Disclaimers:
The website is not related to, affiliated with, endorsed or authorized by Amazon.
Trademarks, certification & product names are used for reference only and belong to Amazon.
The website does not contain actual questions and answers from Amazon's Certification Exam.