Amazon
MLA-C01 · Question #168
An ML engineer wants to use, prepare, and load data from Amazon S3 for analytics. The ML engineer must run an extract, transform, and load (ETL) job to discover the schema of the data and to store the
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Data Preparation for Machine Learning
Question
An ML engineer wants to use, prepare, and load data from Amazon S3 for analytics. The ML engineer must run an extract, transform, and load (ETL) job to discover the schema of the data and to store the metadata. Which solution will meet these requirements with the LEAST manual effort?
Options
- AUse AWS Glue to run the ETL job. Use the job to discover the schema and to store the
- BCreate an Amazon SageMaker Data Wrangler flow to run the ETL job. Use the job to discover the
- CCreate an ETL pipeline by using Amazon Athena integrated with AWs Step Functions. Use the
- DLaunch an Amazon EC2 instance that includes the scikit-learn library to run the ETL job. Use the
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Topics
#ETL#AWS Glue#Schema Discovery#Data Catalog