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

1. What is the number of missing values for the TLSum variable in the sample generated by SAS Enterprise Miner?
Response:

A) 20-39
B) 0
C) 1-19
D) 40 or more

2. 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%

3. The number of hidden layers in this Neural Network model is which of the following?
Response:

A) 1
B) 2
C) 4 or more
D) 3

4. Perform these tasks in SAS Enterprise Miner:
- Add a Decision Tree node after the Impute node with TARGET as the dependent variable and all other input variables as independent variables (main effects only). Configure the decision tree to use 1 for Number of Surrogate Rules and Largest for Method in Subtree. Do not change any other property of the Decision Tree node.
- Add another Neural Network node after the decision tree with TARGET as the dependent variable and all other input variables as independent variables (main effects only). Configure the Neural Network model to use Average Error for Model Selection Criterion. Do not change any other property for the Neural Network node. Run the process flow.
Which of the following variables was used in the decision tree model?
Response:

A) TLDel3060Cnt24
B) IMP_TLSatCnt
C) InqFinanceCnt24.
D) TLDel90Cnt24

5. Which of the following solves problems for you when you impute missing values?
Response:

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

Solutions: