In addition to filtering parts based on customer data, what else can filter variables enable you to do?

Choosing the option regarding applying additional filtering based on unrelated tables is correct because filter variables in data management and analytics tools typically allow users to refine, manipulate, and segment datasets beyond the primary dataset's direct context. By using filter variables, analysts can cross-reference and apply conditions or filtering criteria from different, unrelated data tables, enhancing the ability to produce more complex and insightful analysis.

This functionality is especially useful when dealing with multifaceted datasets where different data sources contain relevant information that can inform decisions or analysis. It allows for a richer understanding of how various components interact by applying filtering criteria across different contexts, thus providing a more comprehensive view.

The other choices do not specifically relate to the primary function of filter variables. For instance, merging different worksheets usually involves data compilation rather than filtration, sorting data alphabetically describes arranging items in a specific order rather than filtering, and highlighting specific records pertains to data visualization or formatting rather than the logical application of filters. Hence, the selection regarding additional filtering capabilities maximizes analytical depth and relevance.