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SASInstitute A00-255 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Model Implementation and Deployment | - Model scoring and deployment in SAS Enterprise Miner - Monitoring model performance in production |
| Model Evaluation and Validation | - Validation and cross-validation techniques - Model performance metrics - Model comparison and selection |
| Exploratory Data Analysis | - Descriptive statistics and data profiling - Visualization techniques for pattern discovery |
| Model Development | - Decision trees and ensemble methods - Neural networks and advanced modeling in SAS Enterprise Miner - Regression modeling techniques |
| Data Understanding and Preparation | - Data cleaning and preprocessing - Handling missing values and outliers - Feature selection and transformation - Data collection and data source identification |
| Business Understanding and Analytical Framework | - Translate business problems into data mining tasks - Define business objectives and analytics goals |
SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions:
1. How many unique indicator variables were created during imputation?
Response:
A) none
B) 1-10
C) 11-20
D) 21 or higher
2. Refer to the following profit matrix and confusion matrix for a campaign soliciting product purchases. The predicted variable is a binary outcome.
Based on the above tables, what is the average profit? You may use a calculator for this question. On the certification exam, an on-screen calculator is provided for you.
Select one:
Response:
A) 690
B) 69
C) 86.25
D) 6.9
3. 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) 0-24.99
B) 25-49.99
C) 50-74.99
D) 75 or more
4. A useful concept in logistic regression is the doubling amount. How would you calculate doubling amount for an input variable that has a parameter estimate of b1?
Response:
A) 2*log(b1)
B) 0.69/b1
C) 2/log(b1)
D) 2*b1
5. 1. Create a project named Insurance, with a diagram named Explore.
2. Create the data source, DEVELOP, in SAS Enterprise Miner. DEVELOP is in the directory c:\workshop\Practice.
3. Set the role of all variables to Input, with the exception of the Target variable, Ins (1= has insurance, 0= does not have insurance).
4. Set the measurement level for the Target variable, Ins, to Binary.
5. Ensure that Branch and Res are the only variables with the measurement level of Nominal.
6. All other variables should be set to Interval or Binary.
7. Make sure that the default sampling method is random and that the seed is 12345.
The variable Branch has how many levels?
Response:
A) 8
B) 12
C) 47
D) 19
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: B | Question # 3 Answer: D | Question # 4 Answer: B | Question # 5 Answer: D |
