Exam AWS Certified Machine Learning - Specialty topic 1 question 305 discussion

Databrew lacks explicit time-series resampling features; focuses on general ETL, not forecasting workflows.

Databrew lacks explicit time-series resampling features; focuses on general ETL, not forecasting workflows.

There is a need for scheduling daily resampling. This can be automated in Databrew more easily than in Data Wrangler.

While SageMaker Data Wrangler (option C) is also a strong contender, DataBrew is slightly easier to use and requires even less implementation effort, especially for users who may not be as familiar with the SageMaker ecosystem.

Data Wrangler is better for ML work. Brew can be used as well

Data wrangler supports tight integration with Sagemaker and is better suited for this scenario since resampled data is used in further modelling. AWS Glue DataBrew is a data preparation service more for general purpose use.

Best for Data Wrangler

This is exactly what Data Wrangler is for

Answer: C Explanation: Amazon SageMaker Studio Data Wrangler is a visual data preparation tool that enables users to clean and normalize data without writing any code. Using Data Wrangler, the data scientist can easily import the time-series data from various sources, such as Amazon S3, Amazon Athena, or Amazon Redshift. Data Wrangler can automatically generate data insights and quality reports, which can help identify and fix missing values, outliers, and anomalies in the data. Data Wrangler also provides over 250 built-in transformations, such as resampling, interpolation, aggregation, and filtering, which can be applied to the data with a point-and-click interface. Data Wrangler can also export the prepared data to different destinations, such as Amazon S3, Amazon SageMaker Feature Store, or Amazon SageMaker Pipelines, for further modeling and analysis. D