Which transformer automatically creates dynamic schema attributes specifically so you can create a new schema?

• A. PythonCaller
• B. SchemaMapper
• C. Clipper
• D. AttributePivoter

Answer: (B)

SchemaMapper is designed to translate an input schema into a target schema by defining how each input attribute maps to an output attribute. This means you can establish a new set of attributes, rename or reorder them, drop unused fields, and even introduce new derived attributes as part of the mapping. Because the target schema is built from your mapping rules, it effectively creates a new dynamic schema that downstream transformers can use without needing separate steps to define the schema first.

The other transformers don’t primarily serve this purpose. PythonCaller runs custom code and can generate values, but it isn’t built to automatically produce a new, complete output schema. Clipper focuses on clipping features, not on schema creation. AttributePivoter changes the order of existing attributes (and can group them) but doesn’t inherently create a new, entirely different schema.

So the best fit for automatically creating dynamic schema attributes to define a new schema is SchemaMapper.