Exam AWS Certified Machine Learning Engineer - Associate MLA-C01 topic 1 question 11 discussion

Why Transform Categorical Data into Numerical Data? Machine learning algorithms generally require categorical data to be converted into numerical representations (e.g., one-hot encoding or embeddings) for training. Transforming numerical data into categorical data is unnecessary unless the problem explicitly requires it (e.g., binning for some specific applications). Why Use SageMaker Data Wrangler? Minimal Operational Overhead: Amazon SageMaker Data Wrangler provides a user-friendly interface to clean, preprocess, and transform data without needing to write custom code. Comprehensive Data Handling: Supports data sources like S3 and on-premises databases, and can handle both categorical and numerical data transformations efficiently. Why Not AWS Glue? AWS Glue is more suitable for large-scale ETL (Extract, Transform, Load) operations, such as schema inference or combining large datasets. It has higher operational overhead for specific ML data preprocessing tasks compared to SageMaker Data Wrangler.

https://docs.aws.amazon.com/sagemaker/latest/dg/data-wrangler-analyses.html "Amazon SageMaker Data Wrangler includes built-in analyses that help you generate visualizations and data analyses in a few clicks. " This question is tricky because it makes you think you need Quicksight for the "visualization' part.