A00-272: SAS Certified Visual Modeler Using SAS Visual Statistics 7.4

Exam ID: A00-272

Exam Name: SAS Certified Visual Modeler Using SAS Visual Statistics 7.4

Successful candidates should be skilled in topics such as

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  • Building and exploring descriptive models
  • Building and exploring predictive models with continuous and categorical targets
  • Assessing model goodness of fit
  • Modifying and comparing models
  • Scoring models.

SAS A00-272 Exam Summary:

Exam Name SAS Certified Visual Modeler Using SAS Visual Statistics 7.4
Exam Code   A00-272
Exam Duration   90 minutes
Exam Questions   50 Multiple choice questions
Passing Score   68%
Exam Price   $180 (USD)
Training & Books  SAS Visual Statistics: Interactive Model Building  & 
An Introduction to SAS Visual Analytics
Sample Questions   SAS Visual Modeler Certification Sample Question
Practice Exam   SAS Visual Modeler Certification Practice Exam

SAS A00-272 Exam Topics:

Objective   Details
SAS® Visual Statistics Cross-functional Tasks – 18%
Prepare data using SAS® Visual Analytics. – Manage explorations and visualizations.
– Impute a variable.
– Transform a variable.
– Create an aggregated measure.
– Replace dirty data with missing values.
– Combine multiple categories into fewer levels.
– Create dummy variables in SAS® Visual Analytics and SAS® Visual Data Builder.
Filter data used for a model. – Exclude selections to filter data.
– Apply filters to visualization and data source.
– Review Measure Details. 
Use interactive group-by. – Explain group-by modeling.
– Assign a group-by variable to a predictive model (logistic regression, linear regression model and generalized linear model).
– Interactively examine the Fit Summary for group-by models.
– Choose the best fitting group-by model using fit statistics and Variable Importance.
– Interpret model results using the advanced group-by feature.
– Examine the summary table for group-by processing.
Building and Assessing Segmentation Models – 32%
Perform unsupervised segmentation using cluster analysis. – Explain the unsupervised classification.
– Given a scenario, set proper inputs for k-means algorithm.
– Build a cluster analysis in SAS® Visual Statistics.
– Assign roles for cluster analysis.
– View and edit cluster properties.
– Set Parallel Coordinate properties for a cluster.
– Given a scenario, appropriately change the number of clusters.
– Derive a cluster ID variable and use it in another visualization.
Analyze cluster results. – Interpret a Cluster Matrix.
– Interpret Parallel Coordinates plot.
– Interpret Cluster Summary tab.
Perform supervised segmentation using decision trees. – Explain how split points are determined.
– Assign variable roles for a decision tree.
– Define decision tree properties.
– Describe how predictions are formulated for a decision tree.
– Explain variable selection methods for decision trees.
– Derive a leaf ID for use in other models.
– Prune a decision tree.
Asses decision tree results. – Interpret tree with Tree Map.
– Interpret Leaf statistics.
– Interpret Assessment panel.
– Investigate leaf nodes.
– Explain icicle plot.
Building and Assessing Regression-type Models – 40%
Explain linear models. – Explain linear regression.
– Model effects usage.
– Given a scenario, determine when to use a linear regression model vs. a generalized linear model.
Perform linear regression modeling. – Assign linear regression roles.
– Add Interaction Effect.
– Define linear regression properties.
– Explain informative missingness.
– Review outlier details and exclude outliers.
Perform generalized linear regression modeling. – Assign generalized linear model roles.
– Assign offset variable.
– Define linear regression properties.
– Link functions and distributions in generalized linear models.
– Given a scenario, choose appropriate distribution and link function. 
Perform logistic regression modeling. – Explain logistic regression essentials.
– Explain prediction in logistic regression.
– Explain variable selection in SAS® Visual Statistics.
– Specify which variable is the event (binary).
– Specify how a multinomial response variable is used in SAS® Visual Statistics.
– Assign logistic regression roles.
– Define logistic regression properties.
– Specify when to use appropriate link function when building a predictive model. 
Assess model results. – Interpret Fit Summary window.
– Interpret Residual Plot.
– Interpret ROC chart (KS Statistic).
– Evaluate the Misclassification plot.
– Evaluate the Lift chart.
– Interpret Influence plot.
– Interpret Summary bar.
– Assess residuals and other model diagnostics to choose an appropriate distribution and link function.
– Derive predicted values and describe in terms of predicted probabilities in SAS® Visual Statistics.
– Apply prediction cut-off.
Model Comparison and Scoring – 10%
Compare models – Explain model comparison features.
– Assign model comparison properties.
– Interpret comparison results using Assessment panel, Fit Statistics, ROC charts, concordance statistics, misclassification, etc.
– Interpret Summary Table for model comparison (statistics, variable importance).
– Given a scenario, use a particular fit statistic to select a champion model.
– Define the conditions that make models comparable in SAS® Visual Statistics.
Score models – Explain scoring functionality.
– Export score code.
– Implement score code.
– Identify which SAS® tools can score new data using score code generated by SAS® Visual Statistics.