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

1. You are building a model to identify fraud. Your model will produce predictions that can be interpreted as the probability of fraud. You will pass on the top 100 scoring cases to management for investigation. Assume that you have sufficient data to hold out a validation and test data set for model evaluation.
Which selection would represent a reasonable ordering of fit statistics (best to worst) for this situation?
Select one:
Response:

A) ASE, Lift for the top 10%, ROC index
B) Misclassification rate, ROC index, ASE
C) R-square, AIC, K-S statistic
D) ROC index, ASE, misclassification rate

2. Perform this task using SAS Enterprise Miner:
Continue to use the same diagram. Use an Ensemble node (configure using default options) in SAS Enterprise Miner to combine all four models.
The percentage of observations correctly predicted in the validation data by the Ensemble model is in which of the following ranges?
Response:

A) 94% or higher
B) less than 83.99%
C) 84-89.99%
D) 90-93.99%-

3. 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) 690
B) 69
C) 6.9
D) 86.25

4. 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) 3
B) 6
C) 8
D) 9

5. Which of the following sequential selection methods do you use so that SAS Enterprise Miner will look at all variables already included in the model and delete any variable that is not significant at the specified level?
Response:

A) Stepwise
B) Forward
C) None
D) Backward

Solutions: