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SASInstitute A00-255 Exam Syllabus Topics:

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

1. 1. Create a project named Insurance, with a diagram named Explore.
2. Create the data source, DEVELOP, in SAS Enterprise Miner. DEVELOP is in the directory c:\workshop\Practice.
3. Set the role of all variables to Input, with the exception of the Target variable, Ins (1= has insurance, 0= does not have insurance).
4. Set the measurement level for the Target variable, Ins, to Binary.
5. Ensure that Branch and Res are the only variables with the measurement level of Nominal.
6. All other variables should be set to Interval or Binary.
7. Make sure that the default sampling method is random and that the seed is 12345.
What is the mean credit card balance (CCBal) of the customers with a variable annuity?
Response:

A) $9,586.55
B) $8,711.65
C) $0.00
D) $11,142.45

2. Perform these tasks in SAS Enterprise Miner:
Add a Decision Tree node, as shown below. (Make sure you use only default options in the Decision Tree node.)

Run the Decision Tree node.
Suppose that the data has been oversampled and the probability that TARGET=1 is 0.10 in the population. Incorporate the above scenario and run the Decision Tree node again.
What is the misclassification rate in the validation data set?
Response:

A) 0.10
B) 0.157016
C) 0.154788
D) 0.162252

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

4. What is the purpose of the Kass (Bonferroni) adjustment in the decision tree split-search algorithm?
Select one:
Response:

A) To give categorical inputs a greater chance to be used the split.
B) To ensure a non-negative logworth value.
C) To reduce the number of surrogate splitting rules.
D) To ensure that the choice of split is not influenced by input measurement scale.

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

A) 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.
B) The variable goes through a non-linear transformation, and the transformed variable is used for testing.
C) Each unique value has the potential of being the optimal split point, except for the extreme observation.
D) Each unique value has the potential of being the optimal split point.

Solutions: