What does superclassing in data transforms achieve?

Superclassing in data transforms allows for the setting of values at various levels in the class hierarchy. This means that when you create a data transform in a superclass, any subclasses can inherit these settings, allowing for streamlined configurations and consistent data handling across related classes. By defining logic or defaults at the superclass level, you can reduce the need to replicate the same data transformations in every subclass, promoting reusability and maintainability of your data transformations within the Pega application. This hierarchical approach effectively allows for a more organized way to manage shared logic and data, making it easier to implement changes at a higher level that propagate down to subclasses without needing to alter each one individually.

The other options do not accurately describe the benefits of superclassing in data transforms. While superclassing can help prevent redundancy, it does not eliminate it entirely, and it does not restrict the functionality of subclass data transforms. Additionally, there is no inherent requirement for each class to have a unique data transform name; rather, names might be reused as long as they are in different contexts or classes.