The "Remove Useless Attributes" operator does what it says and removes attributes that are useless. The default for numbers is to remove those that have zero deviation. This is fair enough since it means these attributes are the same for all examples; there's nothing they are bringing to the party. For nominal values, the default is to remove an attribute where all its values are the same. Again, fair enough.
What happens if you remove some attributes and you want to know which ones? You might ask why and that's a good question. All I can say is that it turns out that there are situations where no one will believe you. The conversation goes like this.
"Where are those attributes that I lovingly made?"
"They don't add any value"
Anyway, you get the picture.
Here's a process that finds the useless attributes and outputs an example set so that you can confirm that they really should be allowed to leave.
It uses the "Data to Weights" operator on the example set after the useless attributes have been sent home. The "Select by Weights" operator is then applied to the original example set containing all the attributes but with the "Weight Relation" set to be less than 1.0 and crucially "deselect unknown" is unchecked. This has the nice effect that the returned example set contains the attributes that were marked as useless.