Exam Name: SAS Predictive Modeling Using SAS Enterprise Miner 14
Certification Provider: SASInstitute
Corresponding Certification: SAS Institute SAS
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SASInstitute A00-255 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Sources | 20-25% | - Prepare Source Data
|
| Pattern Analysis | 10-15% | - Segmentation and Association Analysis
|
| Predictive Model Assessment and Implementation | 25-30% | - Model Evaluation
|
| Building Predictive Models | 35-40% | - Model Development
|
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.
The number of parameters (weights) estimated by the Neural Network model is in which of the following ranges?
Response:
- A. 16 or more
- B. 11-15
- C. less than or equal to 5
- D. 6-10
Correct Answer: A 🗳️
-> 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).
- Allow for 1 substitute rule in case the variable for the primary splitting rule is missing.
- Disable pruning for the decision tree.
-> 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.
-> Run the process flow.
What is the number of input variables being used by the Neural Network Model?
Enter your numeric answer in the space below:
Response:
- A. 10
- B. 13
- C. 11
- D. 16
Correct Answer: D 🗳️
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.
How many leaves are there in the decision tree?
Response:
- A. 21 or more
- B. 11-15
- C. 16-20
- D. 1-10
Correct Answer: B 🗳️
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.
Which of the following variables was used in the decision tree model?
Response:
- A. IMP_TLSatCnt
- B. TLDel3060Cnt24
- C. TLDel90Cnt24
- D. InqFinanceCnt24.
Correct Answer: B 🗳️
Multicollinearity in regression refers to which of the following?
Response:
- A. high correlations among input variables
- B. high skewness in distributions of input variables
- C. non-normality of the target variable
- D. non-constant variance of the target variable
Correct Answer: A 🗳️
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