How can cross-dataset faceting be configured with connected data sources?

The correct approach to configuring cross-dataset faceting with connected data sources is by linking them through common fields between those datasets. This method allows for a seamless integration of data views, ensuring that the datasets can interactively relate to one another. When common fields exist—such as IDs or shared attributes—facets can be created that allow users to filter and analyze data across multiple sources simultaneously. This interlinking is essential for gaining holistic insights from different datasets.

The other options, while they may be relevant in different contexts, do not specifically address the requirement for cross-dataset faceting. Connecting datasets using shared user permissions pertains to access controls rather than data interaction. Ensuring that datasets have the same structure can facilitate data analysis but is not strictly necessary for faceting, as datasets can have different structures yet still link through shared fields. Using filters that apply to all connected datasets allows for refined data visualization but does not establish the foundational links necessary for faceting to occur.