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SASInstitute A00-255 Exam Syllabus Topics:

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

1. Refer to the exhibit:

The SAS data set credit_customers contains a numeric variable units_sold that holds only the values: 1, 2, 3, 4. Based on the settings provided in the Advanced Advisor Options, what will be the Role and Level of the units_sold variable when the credit_customers data set is created using Advanced Metadata Advisor in the Data Source Wizard?
Select one:
Response:

A) Role: InputLevel: Interval
B) Role: IntervalLevel: Input
C) Role: RejectedLevel: Nominal
D) Role: InputLevel: Nominal

2. A separate sample has been taken such that the target distribution in the separate sample is different from the target distribution in the original sample.
What should be adjusted?
Select one:
Response:

A) specificity
B) lift
C) sensitivity
D) confidence intervals

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

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

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.
How many leaves are there in the decision tree?
Response:

A) 16-20
B) 21 or more
C) 1-10
D) 11-15

5. Perform this task using SAS Enterprise Miner:
Continue to use the same diagram. Use an Ensemble node (configure using default options) in SAS Enterprise Miner to combine all four models.
Compare the performance of the ensemble and the four models using average squared error in the validation data. Which is the best model in this comparison?
Response:

A) Neural Network
B) Decision Tree
C) Ensemble
D) Regression

Solutions: