SASInstitute A00-401 - SAS Viya 3.4 Supervised Machine Learning Pipelines
Updated: Sep 02, 2026
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Updated: Sep 02, 2026
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Sources | 30% | - Create a project in Model Studio - Understand variable types and measurement scales - Reduce dimensionality and handle redundancy - Use Variable Selection node - Explore and modify data |
| Topic 2: Building Models | 50% | - Neural networks - Supervised learning concepts - Regression models - Support vector machines - Decision trees and ensemble models - Incorporate external and custom code - Autotuning and regularization |
| Topic 3: Model Assessment and Deployment | 20% | - Model comparison in Model Studio - Export score code - Fit statistics, ROC, Lift and comparison reports - Register and publish models - Model evaluation principles |
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