Welcome to your one-stop solution for all the information you need to excel in the Modeling using SAS Visual Statistics (A00-485) Certification exam. This page provides an in-depth overview of the SAS A00-485 Exam Summary, Syllabus Topics, and Sample Questions, designed to lay the foundation for your exam preparation. We aim to help you achieve your SAS Certified Associate - Modeling Using SAS Visual Statistics certification goals seamlessly. Our detailed syllabus outlines each topic covered in the exam, ensuring you focus on the areas that matter most. With our sample questions and practice exams, you can gauge your readiness and boost your confidence to take on the SAS Visual Statistics exam.
Why SAS Visual Statistics Certification Matters
The SAS A00-485 exam is globally recognized for validating your knowledge and skills. With the SAS Certified Associate - Modeling Using SAS Visual Statistics credential, you stand out in a competitive job market and demonstrate your expertise to make significant contributions within your organization. The Modeling using SAS Visual Statistics Certification exam will test your proficiency in the various syllabus topics.
SAS A00-485 Exam Summary:
| Exam Name | Modeling using SAS Visual Statistics |
| Exam Code | A00-485 |
| Exam Duration | 110 minutes |
| Exam Questions | 55-60 |
| Passing Score | 68% |
| Exam Price | $120 (USD) |
| Books / Training | SAS Visual Statistics: Interactive Model Building |
| Exam Registration | Pearson VUE |
| Sample Questions | SAS Visual Statistics Certification Sample Question |
| Practice Exam | SAS Visual Statistics Certification Practice Exam |
SAS A00-485 Exam Syllabus 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. - 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 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. |
| Perform generalized additive modeling |
- Explain generalized additive model essentials. - Explain spline term essentials. - Explain differences and similarities between generalized additive models and generalized linear regression. - Explain advantages and disadvantages of generalized additive models. - 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. - Explain advantages and disadvantages of nonparametric 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 your knowledge and understanding in the specified areas through the A00-485 certification exam. The SAS Certified Associate - Modeling Using SAS Visual Statistics exam holds significant value in the market due to the brand reputation of SAS. We highly recommend thorough study and extensive practice to ensure you pass the Modeling using SAS Visual Statistics exam with confidence.