You work as a machine learning specialist for a marketing consulting firm with a new client that requires marketing data to help them determine which marketing campaign will be the most productive for their new product. You and your machine learning team are using SageMaker Studio to create a Data Flow in SageMaker Studio. You plan to use this Data Flow to prepare and visualize your data using SageMaker Data Wrangler. In SageMaker Data Wrangler, you are building your data preparation pipeline. The data you are using is gathered from marketing data providers. You have imported your source data from your S3 bucket, and you are now configuring your transform for your dataset. However, you have discovered that the built-in transforms provided by Data Wrangler do not meet your transformation needs. Which options are a viable approach to building your transform in Data Wrangler? (Select TWO)

Answer options:

A.Select a custom transform step and write your custom transform in the Python (Scikit) programming language.
B.Choose the built-in transform that most closely resembles the transform you need and customize that built-in transform.
C.Select a custom transform step and write your custom transform in the Python (Pandas) programming language.
D.Select a custom transform step and write your custom transform in the Scala programming language.
E.Select a custom transform step and write your custom transform in the Python (PySpark) programming language.

Correct Answers: C and E
Option A is incorrect. There are only three options for the language when creating a custom transform in SageMaker Data Wrangler: Python (PySpark), Python (Pandas), and SQL (PySpark SQL).
Option B is incorrect. You cannot customize the built-in SageMaker Data Wrangler transforms.
Option C is correct. Python (Pandas) is one of the available languages for creating a custom SageMaker Data Wrangler transform.
Option D is incorrect. There are only three options for the language when creating a custom transform in SageMaker Data Wrangler: Python (PySpark), Python (Pandas), and SQL (PySpark SQL).
Option E is correct. Python (PySpark) is one of the available languages for creating a custom SageMaker Data Wrangler transform.
References:
Please see the Amazon SageMaker developer guide titled Use the Amazon SageMaker Studio Launcher (https://docs.aws.amazon.com/sagemaker/latest/dg/studio-launcher.html),
The Amazon SageMaker developer guide titled Create and Use a Data Wrangler Flow (https://docs.aws.amazon.com/sagemaker/latest/dg/data-wrangler-data-flow.html)