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SASInstitute A00-255 Exam Syllabus Topics:

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

1. Open the diagram labeled Practice A within the project labeled Practice A. Perform the following in SAS Enterprise Miner:

1. Set the Clustering method to Average.
2. Run the Cluster node.
What is the Cubic Clustering Criterion statistic for this clustering?
Response:

A) 5862.76
B) 67409.93
C) 5.00
D) 14.69

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) sensitivity
C) confidence intervals
D) lift

3. 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 average target value is calculated for each level, and then passed on for testing if it is the optimal split point.
C) The levels that have target rate of 0 or 100% are re-binned first, then weighted and the weights are used for testing.
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.

4. Perform these tasks in SAS Enterprise Miner:
* Add a Decision Tree node, as shown below. (Make sure you use only default options in the Decision Tree node.)

* Run the Decision Tree node.
In the training data set, consider only those observations for which the actual value of the target variable equals 1, TARGET=1. What percentage of these observations is being correctly predicted by the decision tree?
Response:

A) 80.3571
B) 15
C) 66.6667
D) 14.6216

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

A) It uses a chi-squared Decision Tree with no Bonferoni adjustment to select the relevant inputs.
B) It looks for a set of colinear inputs that correlate with the target.
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 squared correlation and then a stepwise regression to eliminate irrelevant inputs.

Solutions: