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SASInstitute A00-255 Exam Syllabus Topics:

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

1. Perform this task using SAS Enterprise Miner:
Continue to use the same diagram. Use an Ensemble node (configure using default options) in SAS Enterprise Miner to combine all four models.
The percentage of observations correctly predicted in the validation data by the Ensemble model is in which of the following ranges?
Response:

A) 84-89.99%
B) less than 83.99%
C) 94% or higher
D) 90-93.99%-

2. The number of neurons in this Neural Network model is which of the following:
Response:

A) 1
B) 3
C) 2
D) 4 or more

3. Perform this task using SAS Enterprise Miner:
Continue to use the same diagram. Use an Ensemble node (configure using default options) in SAS Enterprise Miner to combine all four models.
Compare the performance of the ensemble and the four models using average squared error in the validation data. Which is the best model in this comparison?
Response:

A) Regression
B) Ensemble
C) Decision Tree
D) Neural Network

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

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

5. The importance of an input variable in predicting a target in an MLP-based neural network can be figured out by which of the following?
Response:

A) the average of the absolute values of parameter estimates between the input and all of the hidden neurons
B) none of the above
C) the highest absolute value of the parameter estimate between the input and any of the hidden neurons
D) the highest absolute value of the parameter estimate between the input and any of the hidden neurons multiplied by the absolute value of the parameter estimate of the hidden neuron

Solutions: