SAS Certified Specialist: Machine Learning Using SAS Viya 4.0 Practice Exam
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SAS Certified Specialist: Machine Learning Using SAS Viya 4.0 Practice Exam
SAS Certified Specialist: Machine Learning Using SAS Viya 4.0 certification validates your ability to build and deploy supervised machine learning models using SAS Viya, a cloud-based analytics platform. It assesses your knowledge and skills in areas like data preparation, model selection, training, evaluation, and deployment.
Who Should Take This Exam?
This exam is ideal for individuals who:
- 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
The SAS Certified Specialist: Machine Learning Using SAS Viya 4.0 exam covers the given topics -
Domain 1: Understand 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
- Set up Node Configuration
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
Domain 2: Learn about 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
- 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)
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
Domain 3: Understand 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