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Passing the SAS Viya 3.5 Supervised Machine Learning Pipelines certification exam demonstrates that the candidate has the skills and knowledge required to work with SAS Viya 3.5 to build and deploy predictive models. It is a valuable credential for data scientists, statisticians, and other professionals who work with data and want to advance their careers.
The A00-402 exam is a performance-based test that evaluates the candidate’s ability to use SAS Viya to build and deploy supervised machine learning models. A00-402 exam consists of 60-65 multiple-choice and short-answer questions that must be completed in 2 hours and 30 minutes. A00-402 exam covers topics such as data preparation, feature engineering, model selection, model tuning, model deployment, and performance evaluation.
The A00-402 certification exam is primarily aimed at data scientists, statisticians, and other analytics professionals who want to leverage the power of the SAS Viya platform for machine learning and predictive modeling. A00-402 exam covers a wide range of topics related to supervised machine learning pipelines, including data preparation, feature engineering, model selection, hyperparameter tuning, model deployment, and monitoring. It also tests the candidate's ability to use various SAS tools and techniques, such as CAS, SAS Studio, and SAS Visual Data Mining and Machine Learning.
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SASInstitute A00-402 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Building Predictive Models | 25-30% | - Neural networks and deep learning basics - Decision trees and tree ensembles - Regression models (linear, logistic) - Regularization and optimization methods - Using appropriate modeling nodes |
| Topic 2: Model Deployment | 5-10% | - Model scoring and operationalization - Exporting score code - Registering and publishing models |
| Topic 3: Creating and Managing Pipelines | 15-20% | - Managing pipeline flow and execution - Configuring and connecting nodes - Building pipelines in Model Studio - Using pipeline templates and automation |
| Topic 4: 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 |
| Topic 5: Overview of Supervised Machine Learning | 10-15% | - Basic concepts and terminology - Model overfitting, underfitting, and generalization - Prediction types and modeling goals |
| Topic 6: Data Preparation and Exploration | 20-25% | - Data profiling and exploration - Partitioning data into training, validation, and test sets - Variable selection and reduction - Feature engineering and transformation - Handling missing values and outliers - Loading and accessing data sources |
