Regarding aggregate functions, what is true about the inclusion of attributes in the output?

The statement that only the aggregated attribute is included in the output is accurate because aggregate functions in data processing typically focus on summarizing or performing calculations on a dataset. When an aggregate function is applied, such as SUM, AVG, COUNT, etc., it operates on a specific attribute of the dataset. The result of this operation is what gets returned in the output.

In scenarios involving aggregate functions, the intention is often to derive insights or summaries from the data. Therefore, the output represents the result of the aggregation rather than individual attributes that might have been part of the original dataset. For example, if you have a list of sales transactions and apply an aggregate function to calculate total sales, the output will present just that total, rather than each individual sale alongside it.

Attributes must be manually set is misleading in the context of aggregates since aggregate outputs focus on the calculated results rather than requiring individual attributes to be explicitly included. The inclusion of all attributes or none is also inaccurate, as aggregate functions are designed specifically to condense information into a summary rather than retain all original data. Overall, understanding how aggregate functions work helps in determining the relevant output that aligns with summarization goals.