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SASInstitute A00-485 Exam Syllabus Topics:

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

SASInstitute Modeling Using SAS Visual Statistics Sample Questions:

When examining the summary table for group-by processing, what information can you find?

  • A. The distribution of the group-by variable
  • B. The standard deviation of the target variable for each group
  • C. The mean value of the target variable for each group
  • D. The number of observations in each group
Reveal Solution  Discussion  0

Correct Answer: B,C,D  🗳️

When assigning roles in a GLM, what does the "offset" variable typically represent?

  • A. A variable used to adjust the intercept
  • B. The target variable
  • C. A variable with a known constant coefficient
  • D. A predictor variable
Reveal Solution  Discussion  0

Correct Answer: A,C  🗳️

Refer to the fit summary from in the exhibit below.

What can be concluded from the fit summary?

  • A. Job Type is not a significant predictor in this model.
  • B. Debt to income ratio is a significant predictor when Job Type = Mgr.
  • C. Debt to income ratio is a significant predictor when Job Type = Sales.
  • D. Job Type = Office has no important variables associated with it.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

What is one potential disadvantage of excessively deep decision trees?

  • A. Faster prediction times
  • B. Greater stability in the model
  • C. Overfitting to the training data
  • D. Increased interpretability
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

Refer to the fit summary from SAS Visual Statisitcs in the exhibit below.

What can be concluded from the fit summary?

  • A. Customer Value Level is not a significant predictor in this model.
  • B. Average Sales is an important predictor when Customer Value Level = C.
  • C. Average Sales is a significant predictor when Customer Value Level = E.
  • D. Customer Value Level C has no important variables associated with it.
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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