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SASInstitute A00-255 Exam Syllabus Topics:

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

1. 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.
The number of parameters (weights) estimated by the Neural Network model is in which of the following ranges?
Response:

A) less than or equal to 5
B) 6-10
C) 16 or more
D) 11-15

2. 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.
Which of the following variable(s) is (are) statistically significant at the 5% level in the selected model?
Response:

A) all of the above
B) TLTimeFirst
C) IMP_TLOpen24Pct
D) TLDel3060Cnt24

3. 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 distribution of the predicted probabilities of TARGET=0 in the scoring data is approximately which of the following?
Response:

A) bimodal
B) left skewed
C) normal
D) right skewed

4. Assume in a data mining project that the task is to predict rankings of a target variable as accurately as possible. Which of the following should be used to judge prediction models?
Response:

A) Gini coefficient
B) KS statistic
C) average squared error
D) misclassification

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

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

Solutions: