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A company wants to build an anomaly detection ML model.

A company wants to build an anomaly detection ML model. The model will use large-scale tabular data that is stored in an Amazon S3 bucket. The company does not have expertise in Python, Spark, or other languages for ML.

An ML engineer needs to transform and prepare the data for ML model training.

Which solution will meet these requirements?

A.

Prepare the data by using Amazon EMR Serverless applications that host Amazon SageMaker Studio notebooks.

B.

Prepare the data by using the Amazon SageMaker Data Wrangler visual interface in Amazon SageMaker Canvas.

C.

Run SQL queries from a JupyterLab space in Amazon SageMaker Studio. Process the data further by using pandas DataFrames.

D.

Prepare the data by using a JupyterLab notebook in Amazon SageMaker Studio.

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