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SASInstitute A00-255 Exam Syllabus Topics:

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

Question 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 Importance statistic for MTGBal (Mortgage Balance)?
Response:

A. 0.42541
B. 0.60485
C. 0.32959
D. 0.42667

Question 2

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

Question 3

1. Define a new data source, PatternData, in SAS Enterprise Miner (SAS data set Patterndata.sas7bdat in the zip file distributed with this practice exam).
2. Set the role of all variables to Input, with the exception set the ID variable role to ID.
3. Set the measurement level for all variables to Interval, except:
- Set DemHomeOwner and StatusCatStarAll to Binary.
- Set DemCluster, DemGender, ID, and StatusCat96NK to Nominal.
4. Create a new diagram (name it Section6) within the project labeled Test.
5. Add the data source, PatternData, to this diagram. Make sure the variable roles and measurements are the same as in the table below. (Check the highlighted rows carefully and reset roles/levels as needed.)
6. Connect a Cluster node to the data source.
7. Modify the Cluster node to exclude nominal and binary input variables.
8. Run the Cluster node.

How many clusters are created by the Cluster node?
Response:

A. 3
B. 8
C. 6
D. 9

Question 4

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 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 looks for a set of colinear inputs that correlate with the target.

Question 5

How many unique indicator variables were created during imputation?
Response:

A. 21 or higher
B. none
C. 1-10
D. 11-20

Solutions: