Welcome to your one-stop solution for all the information you need to excel in the SAS Viya Supervised Machine Learning Pipelines (A00-406) Certification exam. This page provides an in-depth overview of the SAS A00-406 Exam Summary, Syllabus Topics, and Sample Questions, designed to lay the foundation for your exam preparation. We aim to help you achieve your SAS Certified Specialist - Machine Learning Using SAS Viya certification goals seamlessly. Our detailed syllabus outlines each topic covered in the exam, ensuring you focus on the areas that matter most. With our sample questions and practice exams, you can gauge your readiness and boost your confidence to take on the SAS Supervised Machine Learning exam.
Why SAS Supervised Machine Learning Certification Matters
The SAS A00-406 exam is globally recognized for validating your knowledge and skills. With the SAS Certified Specialist - Machine Learning Using SAS Viya credential, you stand out in a competitive job market and demonstrate your expertise to make significant contributions within your organization. The SAS Viya Supervised Machine Learning Pipelines Certification exam will test your proficiency in the various syllabus topics.
SAS A00-406 Exam Summary:
| Exam Name | SAS Viya Supervised Machine Learning Pipelines |
| Exam Code | A00-406 |
| Exam Duration | 90 minutes |
| Exam Questions | 50-55 |
| Passing Score | 62% |
| Exam Price | $180 (USD) |
| Books / Training | Machine Learning Using SAS Viya |
| Exam Registration | Pearson VUE |
| Sample Questions | SAS Supervised Machine Learning Certification Sample Question |
| Practice Exam | SAS Supervised Machine Learning Certification Practice Exam |
SAS A00-406 Exam Syllabus Topics:
| Objective | Details |
|---|---|
Data Sources (30-36%) |
|
| Create a project in Model Studio |
- Bring data into Model Studio for analysis
- Create Model Studio Pipelines with the New Pipeline window
- Advanced Advisor options
- Partition data into training, validation, and test
- Use Event Based Sampling for rare events. |
| Explore the data |
- Use the DATA EXPLORATION node - Profile data during data definition - Preliminary data exploration using the data tab - Save data with the SAVE DATA node |
| Modify data |
- Explain concepts of replacement, transformation, imputation, filtering, outlier detection - Modify metadata within the DATA tab - Modify metadata with the MANAGE VARIABLES node - Use the REPLACEMENT node to update variable values - Use the TRANSFORMATION node to correct problems with input data sources, such as variables distribution or outliers - Use the IMPUTE node to impute missing values and create missing value indicators - Prepare text data for modeling with the TEXT MINING node - Explain common data challenges and remedies for supervised learning |
| Use the VARIABLE SELECTION node to identify important variables to be included in a predictive model |
- Unsupervised Selection - Fast Supervised Selection - Linear Regression Selection - Decision Tree Selection - Forest Selection - Gradient Boosting Selection - Create Validation from Training - Use multiple methods within the same VARIABLE SELECTION node |
Building Models (40-46%) |
|
| Describe key machine learning terms and concepts |
- Data partitioning: training, validation, test data sets - Observations (cases), independent (input) variables/features, dependent (target) variables - Measurement scales: Interval, ordinal, nominal (categorical), binary variables - Supervised vs unsupervised learning - Prediction types: decisions, rankings, estimates - Curse of dimensionality, redundancy, irrelevancy - Decision trees, neural networks, regression models, support vector machines (SVM) - Model optimization, overfitting, underfitting, model selection - Describe ensemble models - Explain autotuning |
| Build models with decision trees and ensemble of trees |
- Explain how decision trees identify split points
- Explain the effect of missing values on decision trees
- Build models with the GRADIENT BOOSTING node
- Build models with the FOREST node
- Interpret decision tree, gradient boosting, and forest results (fit statistics, output, tree diagrams, tree maps, variable importance, error plots, autotuned results) |
| Build models with neural networks |
- Describe the characteristics of neural network models
- Build models with the NEURAL NETWORK node
- Interpret NEURAL NETWORK node results (network diagram, iteration plots, and output) |
| Build models with support vector machines |
- Describe the characteristics of support vector machines. - Build model with the SVM node
- Interpret SVM node results (Output) |
| Use Model Interpretability tools to explain black box models |
- Partial Dependence plots - Individual Conditional Expectation plots - Local Interpretable Model-Agnostic Explanations plots - Kernel-SHAP plots |
| Incorporate externally written code |
- Open Source Code node - SAS Code node - Score Code Import node |
Model Assessment and Deployment Models (24-30%) |
|
| Explain the principles of Model Assessment |
- Explain different dimensions for model comparison
- Explain honest assessment
- Use the appropriate fit statistic for different prediction types
- Explain results from the INSIGHTS tab |
| Assess and compare models in Model Studio |
- Compare models with the MODEL COMPARISON node - Compare models with the PIPELINE COMPARISON tab - Interpret Fit Statistics, Lift Reports, ROC reports, Event Classification chart - Interpret Fairness and Bias plots |
| Deploy a model |
- Exporting score code - Registering a model - Publish a model - SCORE DATA node |
The SAS has created this credential to assess your knowledge and understanding in the specified areas through the A00-406 certification exam. The SAS Certified Specialist - Machine Learning Using SAS Viya exam holds significant value in the market due to the brand reputation of SAS. We highly recommend thorough study and extensive practice to ensure you pass the SAS Viya Supervised Machine Learning Pipelines exam with confidence.