This page is a one-stop solution for any information you may require for SAS Certified Specialist - Machine Learning Using SAS Viya 3.5 (A00-402) Certification exam. The SAS A00-402 Exam Summary, Syllabus Topics and Sample Questions provide the base for the actual SAS Machine Learning Specialist exam preparation, we have designed these resources to help you get ready to take your dream exam.
The SAS Certified Specialist - Machine Learning Using SAS Viya 3.5 credential is globally recognized for validating SAS Machine Learning knowledge. With the SAS Machine Learning Specialist Certification credential, you stand out in a crowd and prove that you have the SAS Machine Learning knowledge to make a difference within your organization. The SAS Certified Specialist - Machine Learning Using SAS Viya 3.5 Certification (A00-402) exam will test the candidate's knowledge on following areas.
SAS A00-402 Exam Summary:
| Exam Name | SAS Certified Specialist - Machine Learning Using SAS Viya 3.5 |
| Exam Code | A00-402 |
| Exam Duration | 100 minutes |
| Exam Questions | 50-55 |
| Passing Score | 65% |
| Exam Price | $180 (USD) |
| Training | Machine Learning Using SAS Viya |
| Books | Machine Learning with SAS® Viya |
| Exam Registration | Pearson VUE |
| Sample Questions | SAS Machine Learning Certification Sample Question |
| Practice Exam | SAS Machine Learning Certification Practice Exam |
SAS A00-402 Exam Topics:
| Objective | Details |
|---|---|
Data Sources (30%) |
|
| 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 to oversample 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 |
- 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 - Modify data within the DATA tab |
| Reduce the dimensionality of the data |
- Use the FEATURE EXTRACTION node - Prepare text data for modeling with the TEXT MINING node |
| 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 (50%) |
|
| Describe key supervised 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 - Prediction types: decisions, rankings, estimates - Dimensionality, redundancy, irrelevancy - Decision trees, neural networks, regression models - Model optimization, overfitting, underfitting, model selection - Describe ensemble models |
| 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 (20%) |
|
| Explain the principles of Model Assessment |
- Explain different dimensions for model comparison
- Explain honest assessment
- Use the appropriate fit statistic for different prediction types
|
| 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. |
| Deploy a model |
- Exporting score code - Registering a model - Publish a model |
The SAS has created this credential to assess the knowledge and understanding of a candidate in the area as above via the certification exam. The SAS Machine Learning (A00-402) Certification exam contains a high value in the market being the brand value of the SAS attached with it. It is highly recommended to a candidate to do a thorough study and also get a hand full of the practice to clear SAS Certified Specialist - Machine Learning Using SAS Viya 3.5 exam without any hiccups.