You are a machine learning specialist at a financial services company. Your team has recently been assigned a project to prepare financial risk data and use it in a risk management machine learning model. The project is on an expedited schedule. So you need to produce your engineered data as quickly as possible.
Which AWS service(s) will allow you to engineer your risk data as expeditiously as possible?

Answer options:

A.SageMaker Studio
B.SageMaker Augmented AI
C.Deep Learning Containers
D.SageMaker Processing

Answer: D
Option A is incorrect. You could use SageMaker Studio to perform your data engineering tasks. But more of the infrastructure and coding work would have to be done by you and your team when compared to using SageMaker Processing.
Option B is incorrect. SageMaker Augmented AI is used to leverage human review of low confidence predictions. It wouldn’t help your team expedite your data engineering work.
Option C is incorrect. Deep Learning Containers are a set of Docker images used for training and serving models in TensorFlow, PyTorch, and Apache MXNet. Deep Learning Containers wouldn’t help your team expedite your data engineering work.
Option D is CORRECT. SageMaker Processing is an AWS managed service that you can use to run data engineering workloads in SageMaker using simple SageMaker Processing APIs. SageMaker Processing manages your SageMaker environment for you in a processing container. This managed service removes much of the infrastructure and coding work need to perform data engineering tasks.
Reference:
Please see the Amazon SageMaker developer guide titled Process Data and Evaluate Models.
Please see the Amazon SageMaker developer guide titled Using Amazon Augmented AI for Human Review.
Please see the Amazon SageMaker developer guide titled Amazon SageMaker Studio.
Please see the GitHub repository titled Amazon SageMaker Processing jobs.
Please see the AWS Deep Learning Containers development guide titled What are AWS Deep Learning Containers?