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Updated: Oct 20, 2026
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SASInstitute A00-255 Exam Overview:
| Certification Vendor: | SAS Institute |
|---|---|
| Exam Name: | Predictive Modeling Using SAS Enterprise Miner 14 |
| Exam Number: | A00-255 |
| Certificate Validity Period: | 3 years |
| Real Exam Qty: | 60-65 |
| Passing Score: | Approximately 68-70% |
| Exam Format: | Multiple Choice |
| Exam Price: | $180 USD |
| Exam Duration: | 110 minutes |
| Available Languages: | English |
| Related Certifications: | SAS Certified Advanced Analytics Professional SAS Certified Data Scientist |
| Recommended Training: | SAS Predictive Modeling Using Enterprise Miner Training SAS e-Learning Courses |
| Exam Registration: | SAS Certification Registration Pearson VUE Exam Registration |
| Sample Questions: | SASInstitute A00-255 Sample Questions |
| Exam Way: | Delivered via Pearson VUE (online proctored or test center) |
| Pre Condition: | No mandatory prerequisite exam; recommended experience with SAS Enterprise Miner and predictive modeling concepts |
| Official Syllabus URL: | https://www.sas.com/en_us/certification.html |
SASInstitute A00-255 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Model Development | - Regression modeling techniques - Neural networks and advanced modeling in SAS Enterprise Miner - Decision trees and ensemble methods |
| Topic 2: Model Evaluation and Validation | - Model performance metrics - Model comparison and selection - Validation and cross-validation techniques |
| Topic 3: Model Implementation and Deployment | - Model scoring and deployment in SAS Enterprise Miner - Monitoring model performance in production |
| Topic 4: Business Understanding and Analytical Framework | - Define business objectives and analytics goals - Translate business problems into data mining tasks |
| Topic 5: Data Understanding and Preparation | - Data collection and data source identification - Feature selection and transformation - Data cleaning and preprocessing - Handling missing values and outliers |
| Topic 6: Exploratory Data Analysis | - Descriptive statistics and data profiling - Visualization techniques for pattern discovery |
SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions:
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. Forward
- B. Stepwise
- C. Simple
- D. Backward
Correct Answer: A 🗳️
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 training data set, consider only those observations for which the actual value of the target variable equals 1, TARGET=1. What percentage of these observations is being correctly predicted by the decision tree?
Response:
- A. 66.6667
- B. 14.6216
- C. 15
- D. 80.3571
Correct Answer: C 🗳️
Assume a variable is coded as follows: 1=unmarried, 2=married, 3=divorced, and 4=widowed. Then which of the following measurement levels should be selected in SAS Enterprise Miner for this variable?
Response:
- A. Nominal
- B. Ordinal
- C. Unary
- D. Interval
Correct Answer: A 🗳️
Which of the following is not a good reason to"regularize" input distributions using a simple transformation?
Response:
- A. Another benefit is ease in model interpretation.
- B. One benefit is improved model performance.
- C. Regression models are sensitive to extreme or outlying values in the input space.
- D. When you perform regression, inputs with highly skewed or highly kurtotic distributions can be selected over inputs that would yield better overall predictions.
Correct Answer: A 🗳️
The Chi Square statistic for measuring association between the variables BanruptcyInd and TARGET is which of the following?
Response:
- A. between 2.00 and 2.99
- B. between 1.00 and 1.99
- C. less than 1.00
- D. 3.00 or higher
Correct Answer: B 🗳️
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