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SASInstitute A00-255 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Model Development | - Regression modeling techniques - Decision trees and ensemble methods - Neural networks and advanced modeling in SAS Enterprise Miner |
| Business Understanding and Analytical Framework | - Translate business problems into data mining tasks - Define business objectives and analytics goals |
| Data Understanding and Preparation | - Handling missing values and outliers - Feature selection and transformation - Data collection and data source identification - Data cleaning and preprocessing |
| Model Evaluation and Validation | - Model performance metrics - Model comparison and selection - Validation and cross-validation techniques |
| Model Implementation and Deployment | - Model scoring and deployment in SAS Enterprise Miner - Monitoring model performance in production |
| Exploratory Data Analysis | - Descriptive statistics and data profiling - Visualization techniques for pattern discovery |
SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions:
What is the number of missing values for the TLSum variable in the sample generated by SAS Enterprise Miner?
Response:
- A. 1-19
- B. 20-39
- C. 40 or more
- D. 0
Correct Answer: C 🗳️
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. 25-49.99
- B. 0-24.99
- C. 50-74.99
- D. 75 or more
Correct Answer: D 🗳️
Perform this task using SAS Enterprise Miner:
Continue to use the same diagram. Use an Ensemble node (configure using default options) in SAS Enterprise Miner to combine all four models.
Compare the performance of the ensemble and the four models using average squared error in the validation data. Which is the best model in this comparison?
Response:
- A. Decision Tree
- B. Neural Network
- C. Ensemble
- D. Regression
Correct Answer: C 🗳️
What is the kurtosis value for the variable TLDel60Cnt24?
Response:
- A. between 10 and 13.99
- B. less than 10
- C. 17 or higher
- D. between 14 and 16.99
Correct Answer: D 🗳️
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. not impute any missing values because trees can handle them.
- C. impute only interval variables using the Tree method but do not impute the class variables
- D. impute all missing values using the Tree method
Correct Answer: B 🗳️
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