The question requires a solution to resolve class imbalance with the least operational effort before model training. Amazon SageMaker Data Wrangler is specifically designed for preparing data for machine learning within the SageMaker ecosystem. It includes a built-in "Balance Data" transform that allows an engineer to oversample the minority class using techniques like SMOTE or random oversampling through a simple graphical interface. Since the engineer is already developing the model on AWS, using an integrated tool like Data Wrangler within SageMaker Studio provides the most streamlined workflow and thus represents the least operational effort.

A. Use Amazon Athena to identify patterns that contribute to the imbalance. Adjust the dataset accordingly.

Athena is a query service for analysis, not a data transformation tool. Manually adjusting the data after analysis would require significant operational effort.

B. Use Amazon SageMaker Studio Classic built-in algorithms to process the imbalanced dataset.

This addresses the imbalance during training (e.g., using hyperparameters), but the requirement is to resolve the issue before training the model.

C. Use AWS Glue DataBrew built-in features to oversample the minority class.

While DataBrew can perform this task, SageMaker Data Wrangler is more tightly integrated into the SageMaker ML workflow, making it the more direct and lower-effort path.

1. Amazon SageMaker Data Wrangler - Balance Data Transform: The official documentation details the "Balance Data" transform

which supports various oversampling and undersampling techniques. This is a point-and-click feature

minimizing operational effort.

AWS Documentation: Amazon SageMaker Developer Guide

"Balance Data". (URL: https://docs.aws.amazon.com/sagemaker/latest/dg/data-wrangler-transform-balance-data.html)

2. AWS Glue DataBrew - Balancing Transformations: The DataBrew documentation shows it also has balancing capabilities

confirming the technical validity of option C but highlighting it as a separate service from the core SageMaker workflow.

AWS Documentation: AWS Glue DataBrew Developer Guide

"Creating dataset-balancing transformations". (URL: https://docs.aws.amazon.com/databrew/latest/dg/creating-dataset-balancing-transformations.html)

3. Amazon SageMaker XGBoost Algorithm - Hyperparameters: The documentation for SageMaker's XGBoost algorithm lists scaleposweight as a hyperparameter to handle imbalanced data during training

confirming the mechanism described in option B is a training-time solution.

AWS Documentation: Amazon SageMaker Developer Guide

"XGBoost Hyperparameters". (URL: https://docs.aws.amazon.com/sagemaker/latest/dg/xgboosthyperparameters.html)