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SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 : A00-255 Exam

A00-255 actual test
  • Exam Code: A00-255
  • Exam Name: SAS Predictive Modeling Using SAS Enterprise Miner 14
  • Updated: Oct 14, 2026
  • Q & A: 87 Questions and Answers
  • PDF Demo
  • PC Test Engine
  • Online Test Engine
  • Total Price: $59.99  

About SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 : A00-255 Exam

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SASInstitute A00-255 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Predictive Model Assessment and Implementation25–30%- Score and deploy models
- Evaluate performance via profit/loss and comparison
- Adjust for oversampling and sampling methods
- Apply appropriate fit statistics
Topic 2: Pattern Analysis10–15%- Identify clusters and segments
- Interpret pattern discovery results
Topic 3: Data Sources20–25%- Explore and assess data sources
- Modify and prepare source data for modeling
- Create data sources from SAS tables
Topic 4: Building Predictive Models35–40%- Understand predictive modeling concepts
- Build models using neural networks
- Build models using decision trees
- Build models using regression techniques

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

Question #1

What is the kurtosis value for the variable TLDel60Cnt24?
Response:

  • A. between 10 and 13.99
  • B. 17 or higher
  • C. between 14 and 16.99
  • D. less than 10
Reveal Solution  Discussion  0

Correct Answer: C  πŸ—³οΈ

Question #2

What percentage of observations in the test data has TARGET=1?
Response:

  • A. 16.8874
  • B. 83.3333
  • C. 16.6627
  • D. 16.5924
Reveal Solution  Discussion  0

Correct Answer: A  πŸ—³οΈ

Question #3

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. TLDel3060Cnt24
  • B. all of the above
  • C. IMP_TLOpen24Pct
  • D. TLTimeFirst
Reveal Solution  Discussion  0

Correct Answer: B  πŸ—³οΈ

Question #4

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.
Suppose that the data has been oversampled and the probability that TARGET=1 is 0.10 in the population. Incorporate the above scenario and run the Decision Tree node again.
What is the misclassification rate in the validation data set?
Response:

  • A. 0.157016
  • B. 0.154788
  • C. 0.162252
  • D. 0.10
Reveal Solution  Discussion  0

Correct Answer: A  πŸ—³οΈ

Question #5

What is the variable worth of the PromCntCardAll variable in Segment 1?
Select one:
Response:

  • A. 0.10844
  • B. 0.24914
  • C. 0.24169
  • D. 0.27649
Reveal Solution  Discussion  0

Correct Answer: D  πŸ—³οΈ

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