A data scientist registers a SAS model in the Model Manager repository and must specify the model's input and output variable definitions. Which input variable property must be correctly specified to ensure downstream scoring pipelines work correctly?
Select an answer to reveal the explanation.
Short Explanation
Here's the deal — you need to specify the variable's name, data type, and role because SAS Model Manager uses that metadata to map scoring columns to the model's expected inputs. If the role or data type is wrong, scoring pipelines fail.
Full Explanation
The correct property is the variable's name, data type (numeric/character), and role (input/target/prediction) as defined in the model metadata. SAS Model Manager uses this variable metadata to map columns between the scoring data source and the model's expected inputs. Incorrect role assignments or data type mismatches cause scoring failures. The variable's position (column number) in the source dataset is not used by Model Manager for variable mapping. The variable's SAS format (e.g., DATE9., DOLLAR12.2) as applied in the training DATA step affects display but is not required for scoring pipeline execution. The variable's storage type (SASHDAT vs. CSV) in the scoring data source is a data source concern, not model variable metadata.