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

1. Refer to the following profit matrix and confusion matrix for a campaign soliciting product purchases. The predicted variable is a binary outcome.

Based on the above tables, what is the average profit? You may use a calculator for this question. On the certification exam, an on-screen calculator is provided for you.
Select one:
Response:

A) 69
B) 6.9
C) 86.25
D) 690

2. Which method of input selection for regression analysis evaluates the statistical significance of the total model to see if it improves on the baseline as the variables are added and once no further improvement is made then variable selection ends?
Select one:
Response:

A) Stepwise
B) Backward
C) Forward
D) Simple

3. 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) 7% or higher
C) 6%-6.99%
D) 5%-5.99%

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

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

5. Look over the output from the Neural Network model. Which of the following statement(s) is (are) true?
Response:

A) The optimization for the model has not been completed.
B) The model has too few input variables.
C) All of the above
D) The misclassification error for the test data is 0.154255.

Solutions: