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 9095% 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.