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

A00-255 Exam Simulator
  • Exam Code: A00-255
  • Exam Name: SAS Predictive Modeling Using SAS Enterprise Miner 14
  • Updated: Aug 28, 2026
  • Q & A: 87 Questions and Answers

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SASInstitute A00-255 exam simulator

SASInstitute A00-255 Exam Syllabus Topics:

SectionObjectives
Model Evaluation and Validation- Validation and cross-validation techniques
- Model performance metrics
- Model comparison and selection
Exploratory Data Analysis- Descriptive statistics and data profiling
- Visualization techniques for pattern discovery
Model Development- Decision trees and ensemble methods
- Neural networks and advanced modeling in SAS Enterprise Miner
- Regression modeling techniques
Data Understanding and Preparation- Feature selection and transformation
- Handling missing values and outliers
- Data collection and data source identification
- Data cleaning and preprocessing
Model Implementation and Deployment- Monitoring model performance in production
- Model scoring and deployment in SAS Enterprise Miner
Business Understanding and Analytical Framework- Define business objectives and analytics goals
- Translate business problems into data mining tasks

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

Question 1

The importance of an input variable in predicting a target in an MLP-based neural network can be figured out by which of the following?
Response:

A. the highest absolute value of the parameter estimate between the input and any of the hidden neurons
B. the average of the absolute values of parameter estimates between the input and all of the hidden neurons
C. the highest absolute value of the parameter estimate between the input and any of the hidden neurons multiplied by the absolute value of the parameter estimate of the hidden neuron
D. none of the above


Question 2

Assume the Target has an event proportion of 2% in the original data. Which of the following property values should be used in the Sample node of SAS Enterprise Miner to create a sample from that data with a balanced 50/50 split for Target?
Select one:
Response:

A. Sample Method: Random and Criterion: Proportional
B. Sample Method: Random and Criterion: Equal
C. Sample Method: Stratify and Criterion: Equal
D. Sample Method: Stratify and Criterion: Proportional


Question 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. $11,142.45
B. $9,586.55
C. $8,711.65
D. $0.00


Question 4

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

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


Question 5

Which model was picked as the best model by SAS Enterprise Miner?
Response:

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


Solutions:

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

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