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

A00-255
  • Exam Code: A00-255
  • Exam Name: SAS Predictive Modeling Using SAS Enterprise Miner 14
  • Updated: Aug 23, 2026
  • Q & A: 87 Questions and Answers

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SASInstitute A00-255 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Predictive Model Assessment and Implementation25-30%- Model Deployment
  • 1. Score data sets within Enterprise Miner
- Model Evaluation
  • 1. Select appropriate fit statistics
  • 2. Assess model performance using profit and loss information
  • 3. Compare models using Model Comparison node
  • 4. Use decision processing for oversampling adjustment
Topic 2: Data Sources20-25%- Prepare Source Data
  • 1. Prepare data for predictive modeling
  • 2. Modify source data
- Create and Explore Data Sources
  • 1. Explore and assess data sources
  • 2. Create data sources from SAS tables in Enterprise Miner
Topic 3: Pattern Analysis10-15%- Segmentation and Association Analysis
  • 1. Identify clusters using Cluster and Segment Profile nodes
  • 2. Perform sequence analysis and market basket analysis
  • 3. Perform association analysis
Topic 4: Building Predictive Models35-40%- Model Development
  • 1. Describe predictive modeling concepts and terminology
  • 2. Build regression models
  • 3. Build neural network models
  • 4. Build decision tree models

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

1. 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) IMP_TLOpen24Pct
B) TLDel3060Cnt24
C) TLTimeFirst
D) all of the above


2. Impute the missing values for the variable TLSum using the Tree method. What is the mean of the new variable (with the imputed values)?
Response:

A) 30,000-39,999.99
B) 20,000-29,999.99
C) 40,000 or higher
D) less than 19,999.99


3. 1. Create a project named Insurance, with a diagram named Explore.
2. Create the data source, DEVELOP, in SAS Enterprise Miner. DEVELOP is in the directory c:\workshop\Practice.
3. Set the role of all variables to Input, with the exception of the Target variable, Ins (1= has insurance, 0= does not have insurance).
4. Set the measurement level for the Target variable, Ins, to Binary.
5. Ensure that Branch and Res are the only variables with the measurement level of Nominal.
6. All other variables should be set to Interval or Binary.
7. Make sure that the default sampling method is random and that the seed is 12345.
What is the mean credit card balance (CCBal) of the customers with a variable annuity?
Response:

A) $8,711.65
B) $0.00
C) $9,586.55
D) $11,142.45


4. A separate sample has been taken such that the target distribution in the separate sample is different from the target distribution in the original sample.
What should be adjusted?
Select one:
Response:

A) specificity
B) confidence intervals
C) lift
D) sensitivity


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

A) 0.24914
B) 0.10844
C) 0.27649
D) 0.24169


Solutions:

Question # 1
Answer: D
Question # 2
Answer: D
Question # 3
Answer: C
Question # 4
Answer: C
Question # 5
Answer: C

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