You work for a large manufacturer of consumer electronic devices. Your company wishes to build a machine learning model to predict which product has the most dedicated following among its consumer base. This product will receive funding for future investment in new models and/or enhancements to existing models. You and your machine learning team have a vast amount of observations of using the current product base. You know you and your team need to perform feature engineering on the large dataset before using it to train your XGBoost algorithm-based model for predictions.
What SageMaker feature can you use to perform the required feature engineering of your dataset in the most efficient way?

Answer options:

A.Automatic Model Tuning
B.Built-In Transforms
C.Batch Transform
D.Hosting Services

Answer: C
Option A is incorrect. The SageMaker Automatic Model Tuning feature is used to automatically adjusting thousands of different combinations of hyperparameters to give you the most accurate predictions for your model. But you are trying to perform feature engineering transformation prior to training. So this option is not correct.
Option B is incorrect. The Built-In Transforms feature is part of the AWS Glue service, not SageMaker.
Option C is correct. The SageMaker Batch Transform feature can be used to preprocess your data before using the data in your training runs.
Option D is incorrect. The SageMaker Hosting Services feature is used to allow your model to provide inferences once you’ve trained your model. But you are trying to perform feature engineering transformation prior to training. So this option is not correct.
Reference:
Please see the Amazon SageMaker developer guide titled Run Batch Transforms with Inference Pipelines, the Amazon SageMaker developer guide titled Get Inferences for an Entire Dataset with Batch Transform, the Amazon SageMaker Features overview page, the Amazon SageMaker developer guide titled Deploy a Model on Amazon SageMaker Hosting Services, and the AWS Glue developer guide titled Built-In Transforms.