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% | - Modify and prepare source data for modeling - Create data sources from SAS tables - Explore and assess data sources |
| Pattern Analysis | 10–15% | - Interpret pattern discovery results - Identify clusters and segments |
| Predictive Model Assessment and Implementation | 25–30% | - Adjust for oversampling and sampling methods - Apply appropriate fit statistics - Score and deploy models - Evaluate performance via profit/loss and comparison |
| Building Predictive Models | 35–40% | - Build models using decision trees - Build models using neural networks - Build models using regression techniques - Understand predictive modeling concepts |
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SASInstitute specifies these prerequisites for the SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14: Recommended: experience with SAS Enterprise Miner, data mining and predictive modeling concepts; no mandatory prerequisites.
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The SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 blueprint is structured around these principal domains:
- Data Sources (20–25%)
- Predictive Model Assessment and Implementation (25–30%)
- Pattern Analysis (10–15%)
Additional domains complete the official outline — the bank covers the full set.
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SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions:
Look over the output from the Neural Network model. Which of the following statement(s) is (are) true?
Response:
- A. The optimization for the model has not been completed.
- B. The model has too few input variables.
- C. The misclassification error for the test data is 0.154255.
- D. All of the above
Correct Answer: A 🗳️
Refer to the exhibit:
The SAS data set credit_customers contains a numeric variable units_sold that holds only the values: 1, 2, 3, 4. Based on the settings provided in the Advanced Advisor Options, what will be the Role and Level of the units_sold variable when the credit_customers data set is created using Advanced Metadata Advisor in the Data Source Wizard?
Select one:
Response:
- A. Role: InputLevel: Interval
- B. Role: InputLevel: Nominal
- C. Role: IntervalLevel: Input
- D. Role: RejectedLevel: Nominal
Correct Answer: B 🗳️
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. Interval
- B. Nominal
- C. Ordinal
- D. Unary
Correct Answer: B 🗳️
Suppose your input variables have missing values. Before running a decision tree with these input variables, you should do which of the following?
Response:
- A. impute only class variables using the Tree method but do not impute the interval variables
- B. impute all missing values using the Tree method
- C. impute only interval variables using the Tree method but do not impute the class variables
- D. not impute any missing values because trees can handle them.
Correct Answer: D 🗳️
Perform these tasks in SAS Enterprise Miner:
- Use the Regression node to build another regression model with TARGET as the dependent variable and all other input variables as independent variables (main effects only).
- Configure the regression model to use Stepwise for Selection Model and Validation Error for Selection Criteri a. Do not change any other property for the regression model.
For the validation data, in what range does cumulative percent captured response at the 60th percentile lie?
Response:
- A. 75 or more
- B. 50-74.99
- C. 25-49.99
- D. 0-24.99
Correct Answer: A 🗳️
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