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SAS Institute A00-255: SAS Predictive Modeling Using SAS Enterprise Miner 14 is a popular certification program for professionals interested in learning predictive modelling using SAS enterprise miner. A00-255 exam is designed for individuals who want to gain in-depth knowledge of data mining techniques and predictive modelling principles. It is a challenging certification program that requires extensive knowledge of SAS Enterprise Miner and statistical data analysis. A00-255 Exam assesses an individual's ability to use SAS Enterprise Miner and other SAS tools to solve real-world business problems.

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SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions (Q53-Q58):

NEW QUESTION # 53
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.)
A00-255-3f531c7a91c60fa88d016023d5477787.jpg
* 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. 0
  • C. 14.6216
  • D. 66.6667

Answer: B

 

NEW QUESTION # 54
The number of hidden layers in this Neural Network model is which of the following?
Response:

  • A. 4 or more
  • B. 0
  • C. 1
  • D. 2

Answer: B

 

NEW QUESTION # 55
For the variable TLCnt24, apply a Max Normal transformation. What transformation was selected by SAS Enterprise Miner?
Response:

  • A. Square Root
  • B. Exponential
  • C. Square
  • D. Log

Answer: A

 

NEW QUESTION # 56
Perform these tasks in SAS Enterprise Miner:
- Use the Regression node to build another regression model with TARGET as the dependent variable and all other input variables as independent variables (main effects only).
- Configure the regression model to use Stepwise for Selection Model and Validation Error for Selection Criteri a. Do not change any other property for the regression model.
Which of the following variable(s) is (are) statistically significant at the 5% level in the selected model?
Response:

  • A. all of the above
  • B. IMP_TLOpen24Pct
  • C. TLTimeFirst
  • D. TLDel3060Cnt24

Answer: A

 

NEW QUESTION # 57
Assume that a company has an excellent customer segmentation in place and the segment scheme is a variable in the input data set. What is the best partition method that one should use?
Select one:
Response:

  • A. Random
  • B. Systemic
  • C. Cluster
  • D. Stratify

Answer: D

 

NEW QUESTION # 58
......

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