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

Question #1
Look over the output from the Neural Network model. Which of the following statement(s) is (are) true?
Response:

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

Question #2
Which of the following solves problems for you when you impute missing values?
Response:

A. When you impute a synthetic value, it eliminates the incomplete case problem.
B. When you impute a synthetic value, it replaces missing values with 1 or 0.
C. When you impute a synthetic value, predictive information is retained.
D. When you impute a synthetic value, each missing value becomes an input to the model.

Question #3
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. 0.157016
B. 0.162252
C. 0.154788
D. 0.10

Question #4
How many unique indicator variables were created during imputation?
Response:

A. 21 or higher
B. 11-20
C. 1-10
D. none

Question #5
A multilayer perceptron neural network is using three interval inputs to model one interval target (outcome). The neural network has ten hidden units and one hidden layer. How many weights, including biases are being estimated?
You may use a calculator for this question. On the certification exam, an on-screen calculator is provided for you.
Select one:
Response:

A. 51
B. 50
C. 40
D. 41

Solutions: