A company uses Amazon SageMaker for its ML workloads. The company's ML engineer receives a 50 MB Apache Parquet data file to build a fraud detection model. The file includes several correlated columns that are not required. What should the ML engineer do to drop the unnecessary columns in the file with the LEAST effort?

Amazon SageMaker Data Wrangler is specifically designed to simplify and accelerate data preparation for machine learning using a visual, low-code interface. For the task of dropping unnecessary columns from a 50 MB file, Data Wrangler offers the most direct and lowest-effort solution. An ML engineer can import the dataset into a Data Wrangler flow and apply the built-in "Drop column" transform through a point-and-click user interface without writing any code. This visual approach is significantly faster and requires less technical overhead than scripting a solution or configuring a separate processing cluster.

A: Downloading the file for local processing is inefficient, involves manual data movement, and does not leverage the integrated, scalable AWS environment. B: Using Amazon EMR is intended for large-scale big data processing. Configuring an EMR cluster for a single 50 MB file is excessive and represents the highest effort. C: A SageMaker processing job is a valid method but requires writing, testing, and managing a custom Python script, which constitutes more effort than using a pre-built visual transform in Data Wrangler.

1. SageMaker Data Wrangler: "Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare data for machine learning (ML) from weeks to minutes... You can simplify your data preparation and feature engineering process and complete each step of the data preparation workflow

including data selection

cleansing

exploration

and visualization from a single visual interface."

Source: AWS Documentation

"Prepare ML Data with Amazon SageMaker Data Wrangler

" Introduction. https://docs.aws.amazon.com/sagemaker/latest/dg/data-wrangler.html

2. SageMaker Processing: "With Amazon SageMaker Processing

you can use a simplified

managed experience to run your data processing workloads... You provide a script that SageMaker runs as a processing job."

Source: AWS Documentation

"Process Data with Amazon SageMaker Processing

" Introduction. https://docs.aws.amazon.com/sagemaker/latest/dg/processing-job.html

3. Amazon EMR: "Amazon EMR is the industry-leading cloud big data solution for petabyte-scale data processing

interactive analytics

and machine learning using open-source frameworks such as Apache Spark

Apache Hive

and Presto."

Source: AWS Documentation

"What is Amazon EMR?

" Introduction. https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-what-is-emr.html