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SASInstitute A00-402 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 |
| Topic 2: Building Predictive Models | 25-30% | - Regularization and optimization methods - Decision trees and tree ensembles - Using appropriate modeling nodes - Regression models (linear, logistic) - Neural networks and deep learning basics |
| Topic 3: Overview of Supervised Machine Learning | 10-15% | - Basic concepts and terminology - Model overfitting, underfitting, and generalization - Prediction types and modeling goals |
| Topic 4: Data Preparation and Exploration | 20-25% | - Handling missing values and outliers - Loading and accessing data sources - Data profiling and exploration - Variable selection and reduction - Feature engineering and transformation - Partitioning data into training, validation, and test sets |
| Topic 5: Model Assessment and Comparison | 15-20% | - Regression metrics: RMSE, R-squared, MAE - Profit/loss analysis and cutoff adjustment - Interpreting model results and diagnostics - Comparing models and selecting best performer - Classification metrics: accuracy, precision, recall, F1, AUC |
| Topic 6: Model Deployment | 5-10% | - Registering and publishing models - Model scoring and operationalization - Exporting score code |
