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SASInstitute A00-255 certification exam is recognized globally and is highly valued by employers in various industries. SAS Predictive Modeling Using SAS Enterprise Miner 14 certification demonstrates the candidate's ability to work with complex data sets, build predictive models, and deploy them for business applications. The SASInstitute A00-255 Certification also indicates the candidate's proficiency in using SAS Enterprise Miner, which is a widely used tool for data mining and predictive modeling.

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SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions (Q34-Q39):

NEW QUESTION # 34
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 decision tree model, what is the importance of the variable InqCnt06?
Response:

A. 0.30-0.449999
B. less than 0.149999
C. 0.15-0.299999
D. 0.45 or higher

Answer: A

NEW QUESTION # 35
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. Stratify
C. Systemic
D. Cluster

Answer: B

NEW QUESTION # 36
Transformation of input variables to make their distributions more symmetric will likely have what impact in a logistic regression?
Select one:
Response:

A. create convergence problems in maximum likelihood estimation
B. decrease the performance of logistic regression
C. increase the performance of logistic regression
D. neither increase nor decrease the performance of logistic regression

Answer: C

NEW QUESTION # 37
Refer to the exhibit:

When the Explore button is selected in the graphic, what information will be displayed?
Select one:
Response:

A. Sample Properties, Sample Statistics, sample data for the selected variable, and a pie chart of the selected variable
B. Sample Properties, Sample Statistics, sample data for the selected variable, and a histogram of the data for the selected variable
C. Sample Properties, Sample Statistics for all variables, and sample data for all variables
D. Sample Properties, Sample Statistics, sample data with all variables and values, and a histogram of the data for the selected variable

Answer: B

NEW QUESTION # 38
Perform these tasks in SAS Enterprise Miner:
*Continue to use the same diagram. Define and create the data set CREDIT_SCORE for scoring. The variables (their roles and measurement levels) in the CREDIT_SCORE data should be set as identical to those in the CREDIT dat a. The only exception is that the scoring data does not have a TARGET variable.
* Find the best model out of Decision Tree, Decision Tree (3-way), Regression, and Neural Network as defined by each of the four model's overall performance in the validation data measured by average squared error. Now, use this best model to score the CREDIT_SCORE data.
CREDIT SCORE:

The percentage of TARGET=1 as predicted by the best model on the scoring data is in which of the following ranges?
Response:

A. 5%-5.99%
B. 7% or higher
C. 6%-6.99%
D. under 4.99%

Answer: D

NEW QUESTION # 39
......

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Go With SASInstitute A00-255 Exam Dumps [2025] For Instant Success

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SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions (Q34-Q39):

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