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SASInstitute A00-485 Exam Syllabus Topics:

SectionWeightObjectives
Building and Assessing Segmentation Models28%- Perform unsupervised segmentation using cluster analysis
- Perform supervised segmentation using decision trees
- Assess and interpret decision tree performance
- Analyze and interpret cluster results
Building and Assessing Regression-type Models41%- Perform linear regression modeling
- Assess model fit and diagnostics
- Perform generalized additive modeling
- Explain concepts of linear models
- Perform nonparametric logistic regression modeling
- Perform generalized linear regression modeling
SAS Visual Statistics Cross-functional Tasks22%- Perform model validation
- Filter data used for a model
- Prepare data using SAS Visual Analytics
- Use interactive group-by functionality
Model Comparison and Scoring9%- Compare multiple models using fit statistics
- Apply models to score new data

SASInstitute Modeling Using SAS Visual Statistics Sample Questions:

1. In cluster analysis, what does the term "centroid" refer to?

A) The center point of a cluster in the feature space
B) The number of variables used in the analysis
C) The total number of data points in a cluster
D) The minimum distance between two clusters


2. What does a Group-by Variable enable you to do in SAS Visual Statistics?

A) Fit a model for every value of a measure's distribution.
B) Fit a model for each data segment defined by one or more category variables.
C) Leverage informative missingness for measure and category variables.
D) Use variable selection.


3. In SAS Visual Analytics, what can you use to choose the best fitting group-by model?

A) Visualization of model residuals
B) Fit statistics such as AIC and BIC
C) Group-by variable with the highest p-value
D) Variable Importance


4. Given a scenario, what fit statistic can be used to select a champion model in model comparison?

A) The R-squared value
B) The AUC (Area Under the Curve)
C) The p-value of the intercept term
D) The learning rate of the model


5. How can you derive a leaf ID for use in other models or analyses based on a decision tree?

A) By applying a clustering algorithm to leaf node values
B) By calculating the distance between data points and leaf nodes
C) By extracting the node IDs of all leaf nodes
D) By concatenating the decision rules leading to a leaf node


Solutions:

Question # 1
Answer: A
Question # 2
Answer: B
Question # 3
Answer: B,D
Question # 4
Answer: B
Question # 5
Answer: C

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