SASInstitute A00-255 : SAS Predictive Modeling Using SAS Enterprise Miner 14
- Exam Code: A00-255
- Exam Name: SAS Predictive Modeling Using SAS Enterprise Miner 14
- Updated: Oct 09, 2026
- Q & A: 87 Questions and Answers
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SASInstitute A00-255 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Building Predictive Models | 35-40% | - Model Development
|
| Pattern Analysis | 10-15% | - Segmentation and Association Analysis
|
| Data Sources | 20-25% | - Prepare Source Data
|
| Predictive Model Assessment and Implementation | 25-30% | - Model Deployment
|
SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions:
Perform these tasks in SAS Enterprise Miner:
- Add a Decision Tree node after the Impute node with TARGET as the dependent variable and all other input variables as independent variables (main effects only). Configure the decision tree to use 1 for Number of Surrogate Rules and Largest for Method in Subtree. Do not change any other property of the Decision Tree node.
- Add another Neural Network node after the decision tree with TARGET as the dependent variable and all other input variables as independent variables (main effects only). Configure the Neural Network model to use Average Error for Model Selection Criterion. Do not change any other property for the Neural Network node. Run the process flow.
In the validation data, the lift corresponding to the fourth decile is in which of the following ranges?
Response:
- A. 0-1.24999
- B. 1.75 or more
- C. 1.25-.49999
- D. 1.5-1.74999
Correct Answer: D 🗳️
If the bank wanted to select the best model based on the models' overall performances on the validation data as measured by the average squared error, then the best model is which of the following?
Response:
- A. Decision Tree (3-way)
- B. Neural Network
- C. Decision Tree
- D. Regression
Correct Answer: D 🗳️
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. 15
- B. 14.6216
- C. 80.3571
- D. 66.6667
Correct Answer: A 🗳️
What is the kurtosis value for the variable TLDel60Cnt24?
Response:
- A. 17 or higher
- B. less than 10
- C. between 10 and 13.99
- D. between 14 and 16.99
Correct Answer: D 🗳️
How many unique indicator variables were created during imputation?
Response:
- A. 21 or higher
- B. 1-10
- C. none
- D. 11-20
Correct Answer: D 🗳️
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