Question
An ML engineer needs to create data ingestion pipelines and ML model deployment pipelines on AWS. All the raw data is stored in Amazon S3 buckets.
Which solution will meet these requirements?
- A. Use Amazon Data Firehose to create the data ingestion pipelines. Use Amazon SageMaker Studio Classic to create the model deployment pipelines.
- B. Use AWS Glue to create the data ingestion pipelines. Use Amazon SageMaker Studio Classic to create the model deployment pipelines. Correct
- C. Use Amazon Redshift ML to create the data ingestion pipelines. Use Amazon SageMaker Studio Classic to create the model deployment pipelines.
- D. Use Amazon Athena to create the data ingestion pipelines. Use an Amazon SageMaker notebook to create the model deployment pipelines.
Explanation
AWS Glue is designed for data ingestion and ETL processes, making it suitable for creating data ingestion pipelines from S3 buckets. Amazon SageMaker Studio Classic provides a comprehensive environment for building, training, and deploying ML models, thus meeting the requirement for model deployment pipelines.
