FREE PDF QUIZ 2025 SASINSTITUTE A00-255: SAS PREDICTIVE MODELING USING SAS ENTERPRISE MINER 14 UPDATED RELIABLE EXAM BLUEPRINT

Free PDF Quiz 2025 SASInstitute A00-255: SAS Predictive Modeling Using SAS Enterprise Miner 14 Updated Reliable Exam Blueprint

Free PDF Quiz 2025 SASInstitute A00-255: SAS Predictive Modeling Using SAS Enterprise Miner 14 Updated Reliable Exam Blueprint

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SASInstitute A00-255 Certification Exam consists of 70 multiple-choice and short-answer questions that must be completed within 2 hours and 30 minutes. A00-255 exam evaluates the candidate's knowledge of SAS Enterprise Miner and predictive modeling concepts such as data preparation, variable selection, model building, and model assessment.

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SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions (Q80-Q85):

NEW QUESTION # 80
Which model was picked as the best model by SAS Enterprise Miner?
Response:

  • A. Regression
  • B. Decision Tree
  • C. None of the above
  • D. Decision Tree (3-way)

Answer: B


NEW QUESTION # 81
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.
Consider the variable TLCnt03 in the selected model. Based on the model results, changing this variable by 1 unit will result in which of the following?
Response:

  • A. change of odds for TARGET=1 by a factor 0.708
  • B. reduction of odds for TARGET=1 by 0.708
  • C. reduction of odds for TARGET=1 by 0.3457
  • D. change of odds for TARGET=1 by a factor 0.3457

Answer: A


NEW QUESTION # 82
What percentage of observations in the test data has TARGET=1?
Response:

  • A. 16.8874
  • B. 83.3333
  • C. 16.6627
  • D. 16.5924

Answer: A


NEW QUESTION # 83
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. 7% or higher
  • B. 5%-5.99%
  • C. under 4.99%
  • D. 6%-6.99%

Answer: C


NEW QUESTION # 84
Which method of input selection for regression analysis evaluates the statistical significance of the total model to see if it improves on the baseline as the variables are added and once no further improvement is made then variable selection ends?
Select one:
Response:

  • A. Simple
  • B. Stepwise
  • C. Forward
  • D. Backward

Answer: C


NEW QUESTION # 85
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