SAS Certified Specialist: Machine Learning Using SAS Viya 4.0 Practice Exam

SAS Certified Specialist: Machine Learning Using SAS Viya 4.0 Practice Exam

Who Should Take This Exam?

• Basic understanding of the SAS Viya environment and its functionalities is essential.
• Is comfortable with concepts like classification, regression, and model selection techniques.
• Proficiency in building and deploying machine learning models using SAS Viya.
• Are data scientists, analysts, or aspiring professionals

Exam Details

• Exam Code: A00 - 406
• Exam Name: SAS Certified Specialist: Machine Learning Using SAS Viya 4.0
• Exam Languages: English
• Exam Questions: 50-55 Questions
• Time Duration: 90 minutes
• Passing Score: 62%

Exam Course Outline 

• 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
• Set up Node Configuration

• Use the DATA EXPLORATION node
• Profile data during data definition
• Preliminary data exploration using the data tab
• Save data with the SAVE DATA node

• 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

• 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

• 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

• Explain how decision trees identify split points
• Explain the effect of missing values on decision trees
• Explain surrogate rules
• Explain the purpose of pruning decision trees
• Explain bagging vs. boosting methods
• Build models with the DECISION TREE node
• 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)

• 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)

• Describe the characteristics of support vector machines.
• Build model with the SVM node
• Interpret SVM node results (Output)

• Partial Dependence plots
• Individual Conditional Expectation plots
• Local Interpretable Model-Agnostic Explanations plots
• Kernel-SHAP plots

• Open Source Code node
• SAS Code node
• Score Code Import node

• 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

• Exporting score code
• Registering a model
• Publish a model
• SCORE DATA node