Exam Code: A00-485
Exam Name: Modeling Using SAS Visual Statistics
Certification Provider: SASInstitute
Corresponding Certification: SASInstitute Certification
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SASInstitute A00-485 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Building and Assessing Regression-type Models41%- Explain concepts of linear models
- Perform generalized linear regression modeling
- Perform nonparametric logistic regression modeling
- Perform linear regression modeling
- Perform generalized additive modeling
- Assess model fit and diagnostics
Topic 2: Building and Assessing Segmentation Models28%- Perform unsupervised segmentation using cluster analysis
- Analyze and interpret cluster results
- Perform supervised segmentation using decision trees
- Assess and interpret decision tree performance
Topic 3: Model Comparison and Scoring9%- Compare multiple models using fit statistics
- Apply models to score new data
Topic 4: SAS Visual Statistics Cross-functional Tasks22%- Filter data used for a model
- Use interactive group-by functionality
- Perform model validation
- Prepare data using SAS Visual Analytics

SASInstitute Modeling Using SAS Visual Statistics Sample Questions:

How can you export score code generated by SAS Visual Statistics for a trained model?

  • A. By exporting it as a PDF document
  • B. By copying and pasting it into a text editor
  • C. By exporting it as a standalone script file
  • D. By printing it directly from the SAS Visual Statistics interface
Answer: B,C

What is unsupervised classification in the context of cluster analysis?

  • A. Using a decision tree for data classification
  • B. Assigning predefined labels to data points
  • C. Grouping similar data points together without predefined labels
  • D. Creating a scatter plot of data points
Answer: C

How can you interpret the impurity measures in an icicle plot of a decision tree?

  • A. Lower impurity implies more homogeneity within a leaf node.
  • B. Impurity measures are irrelevant in decision tree analysis.
  • C. Impurity measures represent the number of data points in a leaf node.
  • D. Higher impurity indicates better separation of classes.
Answer: A

What can you interpret from the Assessment panel in a decision tree analysis?

  • A. The tree's structural properties, such as depth and number of nodes
  • B. The decision boundaries between leaf nodes
  • C. The tree's training and validation performance metrics
  • D. The feature importance for each variable in the tree
Answer: C

What are variable selection methods for decision trees used to determine the most important features?

  • A. Principal Component Analysis (PCA)
  • B. F-test for feature significance
  • C. Recursive feature elimination
  • D. Information gain or Gini impurity reduction
Answer: D

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