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SASInstitute A00-485 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Building and Assessing Regression-type Models | 41% | - Perform nonparametric logistic regression modeling - Perform linear regression modeling - Perform generalized linear regression modeling - Perform generalized additive modeling - Assess model fit and diagnostics - Explain concepts of linear models |
| SAS Visual Statistics Cross-functional Tasks | 22% | - Use interactive group-by functionality - Filter data used for a model - Prepare data using SAS Visual Analytics - Perform model validation |
| Building and Assessing Segmentation Models | 28% | - Perform unsupervised segmentation using cluster analysis - Perform supervised segmentation using decision trees - Analyze and interpret cluster results - Assess and interpret decision tree performance |
| Model Comparison and Scoring | 9% | - Compare multiple models using fit statistics - Apply models to score new data |
SASInstitute Modeling Using SAS Visual Statistics Sample Questions:
1. When examining the Fit Summary for group-by models, what can you analyze to evaluate the model fit?
A) AIC (Akaike Information Criterion)
B) R-squared value
C) p-value for the group-by variable
D) BIC (Bayesian Information Criterion)
2. Which SAS tool is most suitable for managing explorations and visualizations in a business intelligence context?
A) SAS Enterprise Guide
B) SAS Studio
C) SAS Enterprise Miner
D) SAS Visual Data Builder
3. During cluster creation, if the Variable Standardization option is selected, where in SAS Visual Statistics can you view the standardization formula?
A) parallel coordinates plot
B) cluster summary table
C) score code
D) cluster matrix plot
4. Your company has a dataset that represents global sales. You are a part of a team of analysts that each have responsibility for a certain region of the world. You decide to create a data source filter to suppress every region but yours.
What effect will this have on any new explorations that your teammates create?
A) It will delete all observations that do not match your region.
B) It will suppress all observations that do not match your region.
C) It will suppress all observations that do not match their corresponding region.
D) It will have no effect on any observations in the dataset.
5. When exporting score code, what information is typically included in the code?
A) Instructions for preprocessing new data
B) The entire training dataset
C) The model parameters and coefficients
D) The distribution of the response variable
Solutions:
| Question # 1 Answer: A,B,D | Question # 2 Answer: D | Question # 3 Answer: C | Question # 4 Answer: D | Question # 5 Answer: A,C |






