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SASInstitute A00-485 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Building Predictive Models | 40% | - Regression models - Neural networks - Decision trees - Gradient boosting |
| Topic 2: Model Assessment and Selection | 20% | - Model fit statistics - Model comparison - ROC and Lift curves |
| Topic 3: Data Preparation and Exploration | 20% | - Data quality - Data exploration using SAS Visual Statistics - Importing data |
| Topic 4: Scoring and Deployment | 20% | - Model deployment - Model scoring |
SASInstitute Modeling Using SAS Visual Statistics Sample Questions:
Question #1
When interpreting an Influence plot, what does it typically show?
A. The relationship between the response variable and the intercept term
B. The interaction between two predictor variables
C. The effect of each predictor variable on the response
D. The distribution of residuals in the model
Question #2
What is variable selection in SAS Visual Statistics used for in logistic regression modeling?
A. To exclude all predictor variables
B. To assign roles to variables
C. To identify and choose relevant predictor variables
D. To specify the distribution of the response variable
Question #3
Refer to the fit summary from SAS Visual Statisitcs in the exhibit below.
What can be concluded from the fit summary?
A. Customer Value Level C has no important variables associated with it.
B. Customer Value Level is not a significant predictor in this model.
C. Average Sales is a significant predictor when Customer Value Level = E.
D. Average Sales is an important predictor when Customer Value Level = C.
Question #4
Refer to the exhibits:
An analyst is evaluating which model to use.
What changed from Comparison 1 to Comparison 2 to select a different champion model?
A. Percentile changed from 5th percentile to 40th percentile
B. Misclassification was used instead of Cumulative Lift
C. Prediction Cutoff reduced from .5 to .19
D. Analyst clicked on Model 2 to highlight it
Question #5
When interpreting a Residual Plot, what does it reveal about the model's performance?
A. The distribution of predictor variables in the dataset
B. The presence of any outliers or patterns in prediction errors
C. The linear relationship between predictors and the response
D. The p-values associated with the predictor variables
Solutions:
| Question #1 Correct Answer: C | Question #2 Correct Answer: C | Question #3 Correct Answer: C | Question #4 Correct Answer: A | Question #5 Correct Answer: B |
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