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SASInstitute A00-485 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Building Predictive Models | 40% | - Gradient boosting - Neural networks - Regression models - Decision trees |
| Model Assessment and Selection | 20% | - Model fit statistics - ROC and Lift curves - Model comparison |
| Data Preparation and Exploration | 20% | - Data exploration using SAS Visual Statistics - Importing data - Data quality |
| Scoring and Deployment | 20% | - Model scoring - Model deployment |
SASInstitute Modeling Using SAS Visual Statistics Sample Questions:
1. Which statement is true regarding the number of polylines created in a Parallel Coordinates plot during Cluster Analysis?
A) It is calculated using the number of variables used in the cluster analysis.
B) It is calculated using the number of variables visible in the plot.
C) It cannot exceed 3,000.
D) It is calculated using the variable with the greatest number of bins.
2. Refer to the exhibits:

An analyst has created a cluster model based on the settings in Exhibit 1 and chose to derive a cluster ID variable.
Why are there 6 cluster IDs in Exhibit 2 based on these settings?
A) Cluster Diagram Visible Roles setting is 6.
B) There are observations with missing values.
C) Parallel Coordinates Visible Roles setting is 6.
D) Variable Standardization has been chosen.
3. How can you apply filters to both visualization and data source in SAS Visual Analytics?
A) Apply filters using the interactive filter dialog in the visualization
B) Use the toolbar options to filter the visualization
C) Use the filter pane to apply filters to the visualization
D) Define filters directly in the data source properties
4. How can you export score code generated by SAS Visual Statistics for a trained model?
A) By copying and pasting it into a text editor
B) By printing it directly from the SAS Visual Statistics interface
C) By exporting it as a PDF document
D) By exporting it as a standalone script file
5. After a logistic regression has been created in SAS Visual Statistics, you discover that not all observations were used to create the model.
How would you run the model on all of the data?
A) Include the correct offset term.
B) Include the correct interaction term.
C) Select Use Variable Selection on the properties tab.
D) Select Informative Missingness on the properties tab.
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: B | Question # 3 Answer: A,B,C,D | Question # 4 Answer: A,D | Question # 5 Answer: D |


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