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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.
Which of the following variables was used in the decision tree model?
Response:

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

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

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

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

4. Look over the output from the Neural Network model. Which of the following statement(s) is (are) true?
Response:

A) The model has too few input variables.
B) All of the above
C) The misclassification error for the test data is 0.154255.
D) The optimization for the model has not been completed.

5. The importance of an input variable in predicting a target in an MLP-based neural network can be figured out by which of the following?
Response:

A) the average of the absolute values of parameter estimates between the input and all of the hidden neurons
B) the highest absolute value of the parameter estimate between the input and any of the hidden neurons
C) none of the above
D) the highest absolute value of the parameter estimate between the input and any of the hidden neurons multiplied by the absolute value of the parameter estimate of the hidden neuron

Solutions: