What is the purpose of the SchemaScanner transformer?

• A. To scan for missing data in datasets
• B. To retrieve the schema of datasets and create flexible workflows
• C. To modify data types in datasets
• D. To exclude attributes from datasets

Answer: (B)

Schema discovery is the key idea here. The SchemaScanner looks at the input data to determine what fields exist, what their data types are, and how the features are structured, then produces a representation of that schema that downstream transformers can use. This lets you build workflows that adapt to different sources or datasets without hard-coding every field, making the process more flexible and robust. It’s not about finding missing values, changing data types, or filtering attributes by itself; those tasks can be handled later, but the primary purpose is to expose the dataset’s schema so the rest of the workflow can be designed around the actual structure of the data.