SASInstitute A00-402 - SAS Viya 3.5 Supervised Machine Learning Pipelines
Updated: Sep 06, 2026
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Updated: Sep 06, 2026
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Exam Duration: 100 minutes to complete the exam.
Passing Score: Must achieve a score of 65 percent correct to pass
Exam Length: 50-55 Questions
Language: English
Exam Format: Multiple choice and Short-answer questions
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| Section | Weight | Objectives |
|---|---|---|
| Creating and Managing Pipelines | 15-20% | - Building pipelines in Model Studio - Managing pipeline flow and execution - Using pipeline templates and automation - Configuring and connecting nodes |
| Building Predictive Models | 25-30% | - Decision trees and tree ensembles - Using appropriate modeling nodes - Neural networks and deep learning basics - Regularization and optimization methods - Regression models (linear, logistic) |
| Model Assessment and Comparison | 15-20% | - Profit/loss analysis and cutoff adjustment - Classification metrics: accuracy, precision, recall, F1, AUC - Regression metrics: RMSE, R-squared, MAE - Comparing models and selecting best performer - Interpreting model results and diagnostics |
| Data Preparation and Exploration | 20-25% | - Variable selection and reduction - Feature engineering and transformation - Handling missing values and outliers - Loading and accessing data sources - Partitioning data into training, validation, and test sets - Data profiling and exploration |
| Model Deployment | 5-10% | - Registering and publishing models - Exporting score code - Model scoring and operationalization |
| Overview of Supervised Machine Learning | 10-15% | - Model overfitting, underfitting, and generalization - Prediction types and modeling goals - Basic concepts and terminology |
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