Exam Name: SAS Viya 3.5 Supervised Machine Learning Pipelines
Certification Provider: SASInstitute
Corresponding Certification: SASInstitute Certification
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SASInstitute A00-402 exam is designed to test the skills and knowledge of candidates in SAS Viya 3.5 Supervised Machine Learning Pipelines. A00-402 exam is a professional-level certification that aims to validate the understanding of candidates in areas such as data preparation, model development, and deployment of supervised machine learning models using SAS Viya.
SASInstitute A00-402 exam is a certification exam that focuses on SAS Viya 3.5 Supervised Machine Learning Pipelines. A00-402 exam is designed to test the candidate's knowledge and skills in using SAS Viya 3.5 to build and deploy supervised machine learning pipelines. A00-402 exam covers topics such as data preparation, feature engineering, model training, and model deployment.
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SASInstitute A00-402 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Model Assessment and Comparison | 15-20% | - Comparing models and selecting best performer - Profit/loss analysis and cutoff adjustment - Interpreting model results and diagnostics - Regression metrics: RMSE, R-squared, MAE - Classification metrics: accuracy, precision, recall, F1, AUC |
| Creating and Managing Pipelines | 15-20% | - Building pipelines in Model Studio - Managing pipeline flow and execution - Configuring and connecting nodes - Using pipeline templates and automation |
| Model Deployment | 5-10% | - Exporting score code - Registering and publishing models - 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 |
| Building Predictive Models | 25-30% | - Decision trees and tree ensembles - Regression models (linear, logistic) - Neural networks and deep learning basics - Regularization and optimization methods - Using appropriate modeling nodes |
| Data Preparation and Exploration | 20-25% | - Loading and accessing data sources - Data profiling and exploration - Partitioning data into training, validation, and test sets - Feature engineering and transformation - Variable selection and reduction - Handling missing values and outliers |
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