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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) 86.25
B) 690
C) 69
D) 6.9

2. 1. Define a new data source, PatternData, in SAS Enterprise Miner (SAS data set Patterndata.sas7bdat in the zip file distributed with this practice exam).
2. Set the role of all variables to Input, with the exception set the ID variable role to ID.
3. Set the measurement level for all variables to Interval, except:
- Set DemHomeOwner and StatusCatStarAll to Binary.
- Set DemCluster, DemGender, ID, and StatusCat96NK to Nominal.
4. Create a new diagram (name it Section6) within the project labeled Test.
5. Add the data source, PatternData, to this diagram. Make sure the variable roles and measurements are the same as in the table below. (Check the highlighted rows carefully and reset roles/levels as needed.)
6. Connect a Cluster node to the data source.
7. Modify the Cluster node to exclude nominal and binary input variables.
8. Run the Cluster node.

How many clusters are created by the Cluster node?
Response:

A) 9
B) 3
C) 6
D) 8

3. 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 reduce the number of surrogate splitting rules.
D) To give categorical inputs a greater chance to be used the split.

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

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

5. For the Variable Selection node, which statement describes the R-squared variable selection criterion?
Select one:
Response:

A) It uses a squared correlation and then a stepwise regression to eliminate irrelevant inputs.
B) It looks for a set of colinear inputs that correlate with the target.
C) It uses a chi-squared Decision Tree with no Bonferoni adjustment to select the relevant inputs.
D) It is similar to a decision tree algorithm in being able to detect nonlinear and non-additive relationships between inputs and the target.

Solutions: