What must be done to allow the Generic Reader to handle datasets with different feature types?

What must be done to allow the Generic Reader to handle datasets with different feature types?

• A. Enable Generalized Reading Mode
• B. Nothing. Variations in feature types will be handled automatically.
• C. Turn on the Merge Feature Type parameter (with a wildcard) in the feature type properties.
• D. Add a FeatureMerger transformer to the Canvas.

Answer: (C)

The idea being tested is how to read datasets that include multiple feature types and still produce a consistent stream for processing. To make the Generic Reader handle different feature types, you merge them into a single feature type as you read. Turning on the Merge Feature Type option in the feature type properties and using a wildcard there tells the reader to merge all incoming feature types into one unified type. This creates one output schema that includes all fields from the various feature types, with any missing fields filled as null for features that don’t have them. It lets downstream transformers operate on a single, consistent data structure rather than needing separate paths for each feature type. If you don’t enable merging, the reader will expose multiple feature types, which complicates processing downstream. The other options don’t provide the built-in, read-time unification that the Merge Feature Type with a wildcard offers, and adding a FeatureMerger transformer would merge features after reading rather than unifying the input schema itself.

The idea being tested is how to read datasets that include multiple feature types and still produce a consistent stream for processing. To make the Generic Reader handle different feature types, you merge them into a single feature type as you read. Turning on the Merge Feature Type option in the feature type properties and using a wildcard there tells the reader to merge all incoming feature types into one unified type. This creates one output schema that includes all fields from the various feature types, with any missing fields filled as null for features that don’t have them. It lets downstream transformers operate on a single, consistent data structure rather than needing separate paths for each feature type.

If you don’t enable merging, the reader will expose multiple feature types, which complicates processing downstream. The other options don’t provide the built-in, read-time unification that the Merge Feature Type with a wildcard offers, and adding a FeatureMerger transformer would merge features after reading rather than unifying the input schema itself.