Which operation allows you to efficiently combine rows from multiple sources into one table?

Appending queries is the operation that efficiently combines rows from multiple data sources into a single table. This method is particularly useful when you have datasets with similar structures and you want to stack them on top of each other. For instance, if you're working with monthly sales data from different regions where each region has a table with the same columns, appending those queries allows you to create a unified table that aggregates all the sales data into one comprehensive view.

In the context of Power BI, appending queries streamlines the process and maintains the structure of the original datasets, ensuring that any subsequent analysis can easily handle the consolidated data without losing the context of its origin. This operation is straightforward and effective for scenarios where the goal is to increase the number of rows in a dataset rather than join data based on common fields.

The other options, while useful in their own capacities, serve different purposes. Merging queries is designed for combining data based on matching columns, which is distinct from simply stacking rows. Transforming data refers to modifying the format or structure of the existing data rather than combining it. Combining files tends to be used in a context where multiple files of a similar type (like Excel sheets) are brought together, but it generally focuses more on file-level operations