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

1. Perform these tasks in SAS Enterprise Miner:
*Continue to use the same diagram. Define and create the data set CREDIT_SCORE for scoring. The variables (their roles and measurement levels) in the CREDIT_SCORE data should be set as identical to those in the CREDIT dat a. The only exception is that the scoring data does not have a TARGET variable.
* Find the best model out of Decision Tree, Decision Tree (3-way), Regression, and Neural Network as defined by each of the four model's overall performance in the validation data measured by average squared error. Now, use this best model to score the CREDIT_SCORE data.
CREDIT SCORE:

The percentage of TARGET=1 as predicted by the best model on the scoring data is in which of the following ranges?
Response:

A) under 4.99%
B) 6%-6.99%
C) 7% or higher
D) 5%-5.99%

2. What is the average squared error in the training data?
Response:

A) 0.133665
B) 0.131709
C) 0.131583
D) 0.131208

3. What is the kurtosis value for the variable TLDel60Cnt24?
Response:

A) 17 or higher
B) between 14 and 16.99
C) between 10 and 13.99
D) less than 10

4. Refer to the exhibit:

What would be the decision threshold (probability cutoff) generated for this decision matrix. You may use a calculator for this question. On the certification exam, an on-screen calculator is provided for you.
Select one:
Response:

A) 0.21
B) 0.18
C) 0.82
D) 0.27

5. 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) 0-24.99
B) 50-74.99
C) 25-49.99
D) 75 or more

Solutions: