# SAS Predictive Modeler (A00-255) Certification Exam Sample Questions

Here are the sample questions which will help you be familiar with SAS Predictive Modeling Using SAS Enterprise Miner 14 (A00-255) exam style and structure. We encourage you to try our Demo SAS Predictive Modeler Certification Practice Exam to measure your understanding of exam structure in an environment which simulates the SAS Certified Predictive Modeler Using SAS Enterprise Miner 14 Certification test environment.

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## SAS A00-255 Sample Questions:

Q 1:
1. Create a project named Insurance, with a diagram named Explore.
2. Create the data source, DEVELOP, in SAS Enterprise Miner. DEVELOP is in the directory c:\workshop\Practice.
3. Set the role of all variables to Input, with the exception of the Target variable, Ins (1= has insurance, 0= does not have insurance).
4. Set the measurement level for the Target variable, Ins, to Binary.
5. Ensure that Branch and Res are the only variables with the measurement level of Nominal.
6. All other variables should be set to Interval or Binary.
7. Make sure that the default sampling method is random and that the seed is 12345.

The variable Branch has how many levels?
Options:
A: 19
B: 47
C: 12
D: 8

Q 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?
Options:
A: 5.00
B: 5862.76
C: 67409.93
D: 14.69

Q 3:
1. Set the Clustering method to Average.
2. Run the Cluster node.

What is the Importance statistic for MTGBal (Mortgage Balance)?
Options:
A: 0.32959
B: 0.42541
C: 1.000000
D: 0.42667

Q 4:
1. Create a project named Insurance, with a diagram named Explore.
2. Create the data source, DEVELOP, in SAS Enterprise Miner. DEVELOP is in the directory c:\workshop\Practice.
3. Set the role of all variables to Input, with the exception of the Target variable, Ins (1= has insurance, 0= does not have insurance).
4. Set the measurement level for the Target variable, Ins, to Binary.
5. Ensure that Branch and Res are the only variables with the measurement level of Nominal.
6. All other variables should be set to Interval or Binary.
7. Make sure that the default sampling method is random and that the seed is 12345.

What is the mean credit card balance (CCBal) of the customers with a variable annuity?
Options:
A: \$0.00
B: \$8,711.65
C: \$11,142.45
D: \$9,586.55

Q 5: Which of the following is not a good reason to”regularize” input distributions using a simple transformation?
Options:
A: Regression models are sensitive to extreme or outlying values in the input space.
B: Another benefit is ease in model interpretation.
C: One benefit is improved model performance.
D: When you perform regression, inputs with highly skewed or highly kurtotic distributions can be selected over inputs that would yield better overall predictions.

Q 6: Which of the following is not true about results produced by the Regression node?
Options:
A: Fit Statistics can provide information that affects decision predictions, but does not affect estimate predictions.
B: Type 3 Analysis of Effects provides you with information about the number of parameters that each input contributes to the model.
C: Model Information provides you with information that includes the number of target categories and the number of model parameters.
D: Variable Summary information identifies the roles of variables used by the Regression node.

Q 7: 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?
Options:
A: Backward
B: Forward
C: Stepwise
D: None

Q 8: Which of the following solves problems for you when you impute missing values?
Options:
A: When you impute a synthetic value, it replaces missing values with 1 or 0.
B: When you impute a synthetic value, it eliminates the incomplete case problem.
C: When you impute a synthetic value, predictive information is retained.
D: When you impute a synthetic value, each missing value becomes an input to the model.