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SASInstitute A00-485 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Building and Assessing Regression-type Models | 41% | - Explain concepts of linear models - Perform nonparametric logistic regression modeling - Assess model fit and diagnostics - Perform generalized additive modeling - Perform linear regression modeling - Perform generalized linear regression modeling |
| Building and Assessing Segmentation Models | 28% | - Perform unsupervised segmentation using cluster analysis - Assess and interpret decision tree performance - Perform supervised segmentation using decision trees - Analyze and interpret cluster results |
| SAS Visual Statistics Cross-functional Tasks | 22% | - Prepare data using SAS Visual Analytics - Filter data used for a model - Use interactive group-by functionality - Perform model validation |
| 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. What is the maximum number of response variables that SAS Visual Statistics allows for a decision tree?
A) 7
B) 5
C) 1
D) 3
2. In the context of linear regression, what does the term "linearity" refer to?
A) The use of polynomial functions for modeling
B) The absence of any relationship between variables
C) The requirement for variables to be categorical
D) The use of a straight line to model relationships
3. Refer to the exhibit:
Based on the Validation Data in the Model Comparison output which conclusions are valid? (Choose two.)
A) Decision Tree model would be selected at lift=20
B) Decision Tree model would be selected at lift=5
C) Logistic Regression model would be selected at lift=5
D) Logistic Regression model would be selected at lift=20
4. What does it mean to create dummy variables in SAS Visual Data Builder?
A) Aggregating multiple measures into one variable
B) Converting categorical variables into binary variables
C) Replacing missing values with zeros
D) Generating artificial data points
5. What is the purpose of deriving a cluster ID variable in cluster analysis?
A) To create a scatter plot of data points
B) To set parallel coordinate properties
C) To assign cluster labels to data points
D) To calculate the distance between clusters
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: D | Question # 3 Answer: A,C | Question # 4 Answer: B | Question # 5 Answer: C |


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