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SASInstitute A00-255 Exam Syllabus Topics:

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

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

2. For the variable TLCnt24, apply a Max Normal transformation. What transformation was selected by SAS Enterprise Miner?
Response:

A) Log
B) Exponential
C) Square Root
D) Square

3. What is the variable worth of the PromCntCardAll variable in Segment 1?
Select one:
Response:

A) 0.24914
B) 0.24169
C) 0.27649
D) 0.10844

4. 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) 75 or more
B) 25-49.99
C) 50-74.99
D) 0-24.99

5. 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.
In the training data set, consider only those observations for which the actual value of the target variable equals 1, TARGET=1. What percentage of these observations is being correctly predicted by the decision tree?
Response:

A) 66.6667
B) 15
C) 80.3571
D) 14.6216

Solutions: