Exam Name: Modeling Using SAS Visual Statistics
Certification Provider: SASInstitute
Corresponding Certification: SASInstitute Certification
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Last Updated: Sep 28, 2026
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SASInstitute A00-485 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Machine Learning Methods in Visual Statistics | - Decision trees and random forests concepts - Clustering techniques |
| Model Evaluation and Interpretation | - Assessing model performance - Interpreting model outputs and reports |
| Data Preparation and Exploration in SAS Viya | - Exploratory data analysis and visualization - Loading and preparing data for Visual Statistics |
| Predictive Modeling Fundamentals | - Model selection and comparison techniques - Regression modeling concepts
|
SASInstitute Modeling Using SAS Visual Statistics Sample Questions:
Refer to the exhibits:
Exhibit 1:
Exhibit 2:
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 5 cluster IDs in Exhibit 2 based on these settings?
- A. Variable Standardization has been chosen.
- B. There are observations with missing values.
- C. Parallel Coordinates Visible Roles setting is 5.
- D. Cluster Matrix Visible Roles setting is 5.
Correct Answer: B 🗳️
How can you apply filters to both visualization and data source in SAS Visual Analytics?
- A. Use the toolbar options to filter the visualization
- B. Define filters directly in the data source properties
- C. Use the filter pane to apply filters to the visualization
- D. Apply filters using the interactive filter dialog in the visualization
Correct Answer: A,B,C,D 🗳️
You would like to compare multiple models that you've built in SAS Visual Statistics.
Which parameters must be the same for all models being compared? (choose 3)
- A. Event Level
- B. Response Variable
- C. Model Type
- D. Link Function
- E. Data Source
- F. Assessment Bins
Correct Answer: A,B,E 🗳️
What is the primary purpose of assigning variable roles in linear regression?
- A. To specify which variables are categorical and which are continuous
- B. To indicate which variables are predictors and which are responses
- C. To determine the order in which variables are entered into the model
- D. To determine the level of significance for each variable
Correct Answer: A 🗳️
What does a Lift chart evaluate in terms of model performance?
- A. The interaction effects between predictor variables
- B. The correlation between predictor variables
- C. The distribution of the response variable
- D. The relative performance of the model compared to a random model
Correct Answer: D 🗳️
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