Which Azure tool is primarily used for data manipulation and preparation in machine learning?

The primary tool for data manipulation and preparation in machine learning within the Azure ecosystem is Azure ML Studio. This platform provides various features that facilitate tasks such as data preprocessing, feature selection, and model training. With its user-friendly interface, Azure ML Studio allows users to visually design machine learning workflows, making it easier to manipulate datasets directly and apply data transformation techniques.

In addition to these functionalities, Azure ML Studio integrates seamlessly with other Azure services, which enhances its data preparation capabilities. Users can easily connect to different data sources, clean and preprocess data, and transform it into a suitable format for training machine learning models.

Though Azure Notebooks offers a code-based environment for data analysis and experimentation, it does not focus specifically on data preparation for machine learning workflows. Azure Data Lake is essential for data storage and handling large volumes of data but does not provide the tools necessary for direct data manipulation and model preparation. Azure Synapse Analytics, while powerful for analytics and big data processing, is generally used for integrating and analyzing large datasets rather than specifically for machine learning data preparation tasks.