If a reader feature type is named <All>, this means the reader feature type is merged, and it will read all features into one stream rather than individual feature types per feature class.

If a reader feature type is named <All>, this means the reader feature type is merged, and it will read all features into one stream rather than individual feature types per feature class.

• A. False
• B. It depends on the data source
• C. The reader will read features as separate streams per feature type
• D. True

Answer: (D)

When a reader’s feature type is named <All>, the reader is told to merge all feature types into a single feature stream. Instead of keeping features from each feature class separate, every feature from the various types is delivered together in one continuous flow. This makes downstream processing see a unified set of features regardless of their original type, which is handy when you want to treat everything uniformly or feed into transformers that don’t care about the feature class. That’s why the statement is true: the <All> designation explicitly instructs the reader to merge into one stream. It isn’t a matter of the data source; it’s a defined behavior of using <All>. The alternative—reading features as separate streams per feature type—would apply if you used explicit, distinct feature types rather than the merged <All> approach.

When a reader’s feature type is named , the reader is told to merge all feature types into a single feature stream. Instead of keeping features from each feature class separate, every feature from the various types is delivered together in one continuous flow. This makes downstream processing see a unified set of features regardless of their original type, which is handy when you want to treat everything uniformly or feed into transformers that don’t care about the feature class.

That’s why the statement is true: the designation explicitly instructs the reader to merge into one stream. It isn’t a matter of the data source; it’s a defined behavior of using . The alternative—reading features as separate streams per feature type—would apply if you used explicit, distinct feature types rather than the merged approach.