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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 input variables being used by the Neural Network model is which of the following?
Response:

A) less than or equal to 10
B) 11-15
C) 16-20
D) 21 or more

2. Which of the following is not a good reason to"regularize" input distributions using a simple transformation?
Response:

A) Regression models are sensitive to extreme or outlying values in the input space.
B) One benefit is improved model performance.
C) When you perform regression, inputs with highly skewed or highly kurtotic distributions can be selected over inputs that would yield better overall predictions.
D) Another benefit is ease in model interpretation.

3. For the Variable Selection node, which statement describes the R-squared variable selection criterion?
Select one:
Response:

A) It looks for a set of colinear inputs that correlate with the target.
B) It uses a squared correlation and then a stepwise regression to eliminate irrelevant inputs.
C) It is similar to a decision tree algorithm in being able to detect nonlinear and non-additive relationships between inputs and the target.
D) It uses a chi-squared Decision Tree with no Bonferoni adjustment to select the relevant inputs.

4. For an ordinal variable with L levels, the number of possible splits in a decision tree is which of the following?
Response:

A) 2L-1-1
B) (L-1)/1
C) 2L
D) 2L-1

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

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

Solutions: