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SASInstitute A00-255 Exam Syllabus Topics:

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

Question 1

Consider a binary target variable. Assume Accuracy is the desired assessment measure. Accuracy is not an option in the Decision Tree node. Which assessment measure can you use as a proxy for accuracy?
Select one:
Response:

A. Mean Square Error
B. 1 - Misclassification Rate
C. Average Squared Error
D. Total Profit

Question 2

Which statement describes the Decision Tree Split Search mechanism for categorical inputs?
Select one:
Response:

A. All levels are weighted and the weights are used for testing.
B. The levels that have target rate of 0 or 100% are re-binned first, then weighted and the weights are used for testing.
C. The average target value is calculated for each level, and then passed on for testing if it is the optimal split point.
D. A clustering mechanism eliminates observations in outlier clusters as potential split points as a first step. Then, for the remaining observations, the average target value is calculated for each level, and then passed on for testing if it is the optimal split point.

Question 3

Which of the following sequential selection methods do you use so that SAS Enterprise Miner will look at all variables already included in the model and delete any variable that is not significant at the specified level?
Response:

A. Backward
B. Forward
C. Stepwise
D. None

Question 4

Which of the following solves problems for you when you impute missing values?
Response:

A. When you impute a synthetic value, it replaces missing values with 1 or 0.
B. When you impute a synthetic value, predictive information is retained.
C. When you impute a synthetic value, it eliminates the incomplete case problem.
D. When you impute a synthetic value, each missing value becomes an input to the model.

Question 5

Transformation of input variables to make their distributions more symmetric will likely have what impact in a logistic regression?
Select one:
Response:

A. neither increase nor decrease the performance of logistic regression
B. increase the performance of logistic regression
C. create convergence problems in maximum likelihood estimation
D. decrease the performance of logistic regression

Solutions: