# SAS A00-274 Certification Exam Syllabus

This page is a one-stop solution for any information you may require for SAS Certified Visual Modeling Using SAS Visual Statistics 8.4 (A00-274) Certification exam. The SAS A00-274 Exam Summary, Syllabus Topics and Sample Questions provide the base for the actual SAS Interactive Model Building and Exploration Using SAS Visual Statistics 8.4 exam preparation, we have designed these resources to help you get ready to take your dream exam.

The SAS Certified Visual Modeling Using SAS Visual Statistics 8.4 credential is globally recognized for validating SAS Visual Modeling knowledge. With the SAS Interactive Model Building and Exploration Using SAS Visual Statistics 8.4 Certification credential, you stand out in a crowd and prove that you have the SAS Visual Modeling knowledge to make a difference within your organization. The SAS Certified Visual Modeling Using SAS Visual Statistics 8.4 Certification (A00-274) exam will test the candidate's knowledge on following areas.

## SAS A00-274 Exam Summary:

 Exam Name SAS Certified Visual Modeling Using SAS Visual Statistics 8.4 Exam Code A00-274 Exam Duration 105 minutes Exam Questions 58 Passing Score 68% Exam Price \$180 (USD) Training SAS® Visual Statistics: Interactive Model Building Exam Registration Pearson VUE Sample Questions SAS Visual Modeling Certification Sample Question Practice Exam SAS Visual Modeling Certification Practice Exam

## SAS A00-274 Exam Topics:

Objective Details

## SAS® Visual Statistics Cross-functional Tasks - 22%

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.
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 advanced group-by feature.
- Examine the summary table for group-by processing.
Perform model validation - Explain model complexity.
- Create and assign a partition.
- Explain model selection based off partitioned data.
- Choose the best fitting model with partitioned data.
- Interpret model results with partitioned data.

## Building and Assessing Segmentation Models - 28%

Perform unsupervised segmentation using cluster analysis. - Explain 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.
Assess 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 - 41%

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.
- Define linear regression properties.
- Explain informative missingness.
- Review outlier details and exclude outliers.
Assess model results. - Interpret Fit Summary window.
- Interpret Residual Plot.
- Interpret ROC chart (KS Statistic).
- Evaluate Misclassification plot.
- Evaluate the Lift chart.
- Explain spline term essentials.
- Explain differences and similarities between generalized additive models and generalized linear regression.
- Assess model fit statistics that are common to generalized additive models (GCV, UBRE).
Perform nonparametric logistic regression modeling - Explain nonparametric logistic regression essentials.
- Explain differences between nonparametric logistic regression and logistic regression.

## Model Comparison and Scoring - 9%

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.

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 Visual Modeling (A00-274) 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 Visual Modeling Using SAS Visual Statistics 8.4 exam without any hiccups.