You receive a 50 MB Apache Parquet file for a fraud detection model that contains several correlated, unnecessary columns. What is the least-effort way to remove those columns from the file?

The least-effort way to remove columns from a Parquet file, especially when dealing with correlated and unnecessary features for a fraud detection model, is to use SageMaker Data Wrangler. Data Wrangler provides a visual interface to build data flows, allowing you to easily import data, add transform steps (like dropping columns), and export the processed data. This eliminates the need for writing custom code or managing infrastructure. Downloading the file locally and using a custom Python script is inefficient for larger files and requires manual execution. Creating an Apache Spark job on Amazon EMR is powerful but involves setting up and managing an EMR cluster, which is more effort than Data Wrangler for a simple column drop. Launching a SageMaker processing job with the SageMaker Python SDK is a programmatic approach, requiring you to write and manage the processing script, which is more effort than Data Wrangler's visual interface for this specific task.