Question
HOTSPOT -
A company stores historical data in .csv files in Amazon S3. Only some of the rows and columns in the .csv files are populated. The columns are not labeled. An ML engineer needs to prepare and store the data so that the company can use the data to train ML models.
Select and order the correct steps from the following list to perform this task. Each step should be selected one time or not at all. (Select and order three.)
• Create an Amazon SageMaker batch transform job for data cleaning and feature engineering.
• Store the resulting data back in Amazon S3.
• Use Amazon Athena to infer the schemas and available columns.
• Use AWS Glue crawlers to infer the schemas and available columns.
• Use AWS Glue DataBrew for data cleaning and feature engineering.
Explanation
AWS Glue crawlers are used to infer schemas from the .csv files stored in Amazon S3, which is essential given that the columns are not labeled and sparsely populated. AWS Glue DataBrew then enables the ML engineer to perform necessary data cleaning and feature engineering tasks on this inferred schema. Finally, storing the processed data back in Amazon S3 ensures it is ready for use in training ML models.
