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

1. Perform these tasks in SAS Enterprise Miner:
- Use the Regression node to build another regression model with TARGET as the dependent variable and all other input variables as independent variables (main effects only).
- Configure the regression model to use Stepwise for Selection Model and Validation Error for Selection Criteri a. Do not change any other property for the regression model.
For the validation data, in what range does cumulative percent captured response at the 60th percentile lie?
Response:

A) 0-24.99
B) 75 or more
C) 25-49.99
D) 50-74.99

2. An analyst is performing a market basket analysis (affinity analysis) on the purchase of Shaving Cream and Seltzer Water. The purchase data from a set of 250 customers is shown below:

What is the confidence of the rule "Shaving Cream implies Seltzer Water"? You may use a calculator for this question. On the certification exam, an on-screen calculator is provided for you.
Select one:
Response:

A) 40%
B) 57%
C) 67%
D) 60%

3. Which statement describes the Decision Tree Split Search mechanism for categorical inputs?
Select one:
Response:

A) A clustering mechanism eliminates observations in outlier clusters as potential split points as a first step. Then, for the remaining observations, the average target value is calculated for each level, and then passed on for testing if it is the optimal split point.
B) The levels that have target rate of 0 or 100% are re-binned first, then weighted and the weights are used for testing.
C) All levels are weighted and the weights are used for testing.
D) The average target value is calculated for each level, and then passed on for testing if it is the optimal split point.

4. 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%

5. 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 data. 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 median of the predicted probabilities of TARGET=1 in the scoring data is in which of the following ranges?
Response:

A) 0.85 or more
B) less than 0.149999
C) 0.15-0.499999
D) 0.50-0.849999

Solutions: