An operations team notices that a few AWS Glue jobs for a given ETL application are failing. The AWS Glue jobs read a large number of small JSON files from an Amazon S3 bucket and write the data to a different S3 bucket in Apache Parquet format with no major transformations. Upon initial investigation, a data engineer notices the following error message in the History tab on the AWS Glue console: "Command Failed with Exit Code 1." Upon further investigation, the data engineer notices that the driver memory profile of the failed jobs crosses the safe threshold of 50% usage quickly and reaches 90-95% soon after. The average memory usage across all executors continues to be less than 4%. The data engineer also notices the following error while examining the related Amazon CloudWatch Logs. What should the data engineer do to solve the failure in the MOST cost-effective way?
A) Change the worker type from Standard to G.2X.
B) Modify the AWS Glue ETL code to use the 'groupFiles': 'inPartition' feature.
C) Increase the fetch size setting by using AWS Glue dynamics frame.
D) Modify maximum capacity to increase the total maximum data processing units (DPUs) used.

Question

An operations team notices that a few AWS Glue jobs for a given ETL application are failing. The AWS Glue jobs read a large number of small JSON files from an Amazon S3 bucket and write the data to a different S3 bucket in Apache Parquet format with no major transformations. Upon initial investigation, a data engineer notices the following error message in the History tab on the AWS Glue console: "Command Failed with Exit Code 1." Upon further investigation, the data engineer notices that the driver memory profile of the failed jobs crosses the safe threshold of 50% usage quickly and reaches 90-95% soon after. The average memory usage across all executors continues to be less than 4%. The data engineer also notices the following error while examining the related Amazon CloudWatch Logs.  What should the data engineer do to solve the failure in the MOST cost-effective way?
A) Change the worker type from Standard to G.2X. 
B) Modify the AWS Glue ETL code to use the 'groupFiles': 'inPartition' feature. 
C) Increase the fetch size setting by using AWS Glue dynamics frame. 
D) Modify maximum capacity to increase the total maximum data processing units (DPUs) used.

Quizplus · Accepted Answer

The Answer of An operations team notices that a few AWS Glue jobs for a given ETL application are failing. The AWS Glue jobs read a large number of small JSON files from an Amazon S3 bucket and write the data to a different S3 bucket in Apache Parquet format with no major transformations. Upon initial investigation, a data engineer notices the following error message in the History tab on the AWS Glue console: "Command Failed with Exit Code 1." Upon further investigation, the data engineer notices that the driver memory profile of the failed jobs crosses the safe threshold of 50% usage quickly and reaches 90-95% soon after. The average memory usage across all executors continues to be less than 4%. The data engineer also notices the following error while examining the related Amazon CloudWatch Logs.  What should the data engineer do to solve the failure in the MOST cost-effective way?
A) Change the worker type from Standard to G.2X. 
B) Modify the AWS Glue ETL code to use the 'groupFiles': 'inPartition' feature. 
C) Increase the fetch size setting by using AWS Glue dynamics frame. 
D) Modify maximum capacity to increase the total maximum data processing units (DPUs) used.

An Operations Team Notices That a Few AWS Glue Jobs