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