What capability does Amazon SageMaker provide regarding data sources?

Amazon SageMaker offers access to data sources through an integrated Jupyter authoring notebook, which is a key feature for data scientists and machine learning practitioners. This capability enables users to easily explore and preprocess their data, create machine learning models, and visualize results all within a seamless environment. The integrated Jupyter notebook provides a rich set of tools for coding in Python, allowing for the execution of data operations directly from the notebook interface, making it easier for users to iterate on their models and analyze data.

While other options may contain relevant components that are commonly associated with data and machine learning tasks, they do not specifically highlight the core strength of SageMaker in facilitating a straightforward, interactive environment for data manipulation and model development. The Jupyter notebook integration is particularly crucial as it allows for interactive coding and experimentation, which is essential in the data science workflow.