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SASInstitute A00-485 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Model Comparison and Scoring | 9% | - Compare multiple models using fit statistics - Apply models to score new data |
| Topic 2: Building and Assessing Segmentation Models | 28% | - Assess and interpret decision tree performance - Analyze and interpret cluster results - Perform supervised segmentation using decision trees - Perform unsupervised segmentation using cluster analysis |
| Topic 3: Building and Assessing Regression-type Models | 41% | - Perform generalized linear regression modeling - Perform generalized additive modeling - Assess model fit and diagnostics - Explain concepts of linear models - Perform linear regression modeling - Perform nonparametric logistic regression modeling |
| Topic 4: SAS Visual Statistics Cross-functional Tasks | 22% | - Use interactive group-by functionality - Prepare data using SAS Visual Analytics - Filter data used for a model - Perform model validation |
SASInstitute Modeling Using SAS Visual Statistics Sample Questions:
Question 1
What does a ROC chart help assess in model comparison?
A. The correlation between predictor variables
B. The trade-off between true positives and false positives at different thresholds
C. The stability of model predictions
D. The distribution of residuals in the models
Question 2
Refer to the following exhibit:
After creating a decision tree in SAS Visual Statistics, what is the value in deriving a Leaf ID Variable?
A. Use the leaf ID to identify clusters.
B. Use the leaf ID data item in filters in other types of visualizations.
C. Use the leaf ID to identify the most important variables.
D. Use the leaf ID to rank order by posterior probabilities.
Question 3
Explain what an icicle plot represents in decision tree analysis.
A. It summarizes the performance metrics of the decision tree.
B. It visualizes the distribution of data points in each leaf node.
C. It displays the hierarchical structure of the decision tree.
D. It shows the feature importance for each variable in the tree.
Question 4
What can you interpret from the Assessment panel in a decision tree analysis?
A. The tree's training and validation performance metrics
B. The decision boundaries between leaf nodes
C. The tree's structural properties, such as depth and number of nodes
D. The feature importance for each variable in the tree
Question 5
What properties can be assigned when comparing models in SAS Visual Statistics?
A. The variables to include in the model comparison
B. The color scheme for visualization
C. The number of observations in the dataset
D. The learning rate for gradient boosting models
Solutions:
| Question 1 Answer: B | Question 2 Answer: B | Question 3 Answer: C,D | Question 4 Answer: A | Question 5 Answer: A |
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