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SASInstitute A00-485 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Building and Assessing Segmentation Models | 28% | - Perform unsupervised segmentation using cluster analysis - Assess and interpret decision tree performance - Analyze and interpret cluster results - Perform supervised segmentation using decision trees |
| Model Comparison and Scoring | 9% | - Apply models to score new data - Compare multiple models using fit statistics |
| Building and Assessing Regression-type Models | 41% | - Explain concepts of linear models - Perform generalized additive modeling - Perform nonparametric logistic regression modeling - Assess model fit and diagnostics - Perform linear regression modeling - Perform generalized linear regression modeling |
| 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:
In the context of linear regression, what does the term "linearity" refer to?
- A. The use of a straight line to model relationships
- B. The absence of any relationship between variables
- C. The requirement for variables to be categorical
- D. The use of polynomial functions for modeling
Correct Answer: A 🗳️
What properties can be defined in a logistic regression model?
- A. Coefficients and p-values
- B. The number of iterations
- C. The distribution of the response variable
- D. The learning rate
Correct Answer: A,C 🗳️
When setting proper inputs for the k-means algorithm, what parameter represents the number of clusters to create?
- A. K-value
- B. Learning rate
- C. Random seed
- D. Cluster centroid initialization
Correct Answer: A 🗳️
Cluster Analysis in SAS Visual Statistics is categorized as which type of machine learning?
- A. Unsupervised learning
- B. Reinforcement learning
- C. Semi-supervised learning
- D. Supervised learning
Correct Answer: A 🗳️
When evaluating a Misclassification plot, what does it typically show?
- A. The p-values associated with the predictor variables
- B. The distribution of predictor variables in the dataset
- C. The relationship between two predictor variables
- D. The trade-off between false positives and false negatives at different thresholds
Correct Answer: D 🗳️
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