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SASInstitute A00-485 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Building and Assessing Regression-type Models | 41% | - Perform generalized linear regression modeling - Assess model fit and diagnostics - Perform nonparametric logistic regression modeling - Perform generalized additive modeling - Perform linear regression modeling - Explain concepts of linear models |
| Topic 2: Building and Assessing Segmentation Models | 28% | - Assess and interpret decision tree performance - Analyze and interpret cluster results - Perform unsupervised segmentation using cluster analysis - Perform supervised segmentation using decision trees |
| Topic 3: Model Comparison and Scoring | 9% | - Compare multiple models using fit statistics - Apply models to score new data |
| Topic 4: SAS Visual Statistics Cross-functional Tasks | 22% | - Filter data used for a model - Perform model validation - Use interactive group-by functionality - Prepare data using SAS Visual Analytics |
SASInstitute Modeling Using SAS Visual Statistics Sample Questions:
1. What is the purpose of implementing score code generated by SAS Visual Statistics?
A) To calculate the p-values of predictor variables
B) To apply the trained model to new data for predictions
C) To evaluate the performance of the scoring code
D) To assess the distribution of residuals in the model
2. Cluster Analysis in SAS Visual Statistics is categorized as which type of machine learning?
A) Reinforcement learning
B) Semi-supervised learning
C) Supervised learning
D) Unsupervised learning
3. Refer to the exhibit:
Which is the modeling approach that should be used when fitting the Target Gift Amount variable?
A) Generalized linear model with a Normal distribution and Log Link.
B) Logistic regression model.
C) Generalized linear model with a Poisson distribution and Identity link.
D) Linear regression model with Interaction effects.
4. When setting proper inputs for the k-means algorithm, what parameter represents the number of clusters to create?
A) Learning rate
B) Cluster centroid initialization
C) Random seed
D) K-value
5. What can you interpret from the Assessment panel in a decision tree analysis?
A) The tree's training and validation performance metrics
B) The tree's structural properties, such as depth and number of nodes
C) The decision boundaries between leaf nodes
D) The feature importance for each variable in the tree
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: D | Question # 3 Answer: A | Question # 4 Answer: D | Question # 5 Answer: A |
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