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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: Sep 15, 2026
- Q & A: 87 Questions and Answers
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SASInstitute A00-255 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Pattern Analysis | 10–15% | - Interpret pattern discovery results - Identify clusters and segments |
| Data Sources | 20–25% | - Explore and assess data sources - Modify and prepare source data for modeling - Create data sources from SAS tables |
| Predictive Model Assessment and Implementation | 25–30% | - Score and deploy models - Evaluate performance via profit/loss and comparison - Apply appropriate fit statistics - Adjust for oversampling and sampling methods |
| Building Predictive Models | 35–40% | - Build models using regression techniques - Build models using neural networks - Build models using decision trees - Understand predictive modeling concepts |
SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions:
Question #1
For the Variable Selection node, which statement describes the R-squared variable selection criterion?
Select one:
Response:
- A. It uses a chi-squared Decision Tree with no Bonferoni adjustment to select the relevant inputs.
- B. It looks for a set of colinear inputs that correlate with the target.
- C. It is similar to a decision tree algorithm in being able to detect nonlinear and non-additive relationships between inputs and the target.
- D. It uses a squared correlation and then a stepwise regression to eliminate irrelevant inputs.
Correct Answer: D 🗳️
Question #2
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. Backward
- B. Simple
- C. Forward
- D. Stepwise
Correct Answer: C 🗳️
Question #3
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.
The number of parameters (weights) estimated by the Neural Network model is in which of the following ranges?
Response:
- A. less than or equal to 5
- B. 6-10
- C. 11-15
- D. 16 or more
Correct Answer: D 🗳️
Question #4
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. Neural Network
- B. Regression
- C. Decision Tree (3-way)
- D. Decision Tree
Correct Answer: B 🗳️
Question #5
Open the diagram labeled Practice A within the project labeled Practice A. Perform the following in SAS Enterprise Miner:
1. Set the Clustering method to Average.
2. Run the Cluster node.
What is the Importance statistic for MTGBal (Mortgage Balance)?
Response:
- A. 0.60485
- B. 0.42541
- C. 0.42667
- D. 0.32959
Correct Answer: A 🗳️
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