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SASInstitute A00-255 Exam Syllabus Topics:

SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions:

1. The selected model, based on the misclassification rate for the validation data, has how many input variables?
Response:

A) 4 or more
B) 2
C) 3
D) 1

2. Transformation of input variables to make their distributions more symmetric will likely have what impact in a logistic regression?
Select one:
Response:

A) decrease the performance of logistic regression
B) neither increase nor decrease the performance of logistic regression
C) create convergence problems in maximum likelihood estimation
D) increase the performance of logistic regression

3. Perform these tasks in SAS Enterprise Miner:
- Use the Regression node to build another regression model with TARGET as the dependent variable and all other input variables as independent variables (main effects only).
- Configure the regression model to use Stepwise for Selection Model and Validation Error for Selection Criteri a. Do not change any other property for the regression model.
For the validation data, in what range does cumulative percent captured response at the 60th percentile lie?
Response:

A) 75 or more
B) 50-74.99
C) 25-49.99
D) 0-24.99

4. Which of the following solves problems for you when you impute missing values?
Response:

A) When you impute a synthetic value, each missing value becomes an input to the model.
B) When you impute a synthetic value, predictive information is retained.
C) When you impute a synthetic value, it eliminates the incomplete case problem.
D) When you impute a synthetic value, it replaces missing values with 1 or 0.

5. Choose the correct statement that illustrates Decision Tree Split Search for continuous (interval) inputs:
Select one:
Response:

A) The variable goes through a non-linear transformation, and the transformed variable is used for testing.
B) Each unique value has the potential of being the optimal split point, except for the extreme observation.
C) The variable goes through a binning process, the bins are weighted based on the proportion of events in each bin, and then finally tested as an optimal split point.
D) Each unique value has the potential of being the optimal split point.

Solutions: