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SASInstitute A00-255 Exam Syllabus Topics:

Section	Objectives
Business Understanding and Analytical Framework	- Define business objectives and analytics goals - Translate business problems into data mining tasks
Exploratory Data Analysis	- Visualization techniques for pattern discovery - Descriptive statistics and data profiling
Model Evaluation and Validation	- Validation and cross-validation techniques - Model performance metrics - Model comparison and selection
Model Implementation and Deployment	- Monitoring model performance in production - Model scoring and deployment in SAS Enterprise Miner
Data Understanding and Preparation	- Handling missing values and outliers - Feature selection and transformation - Data cleaning and preprocessing - Data collection and data source identification
Model Development	- Neural networks and advanced modeling in SAS Enterprise Miner - Regression modeling techniques - Decision trees and ensemble methods

SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions:

Question #1

The selected model, based on the misclassification rate for the validation data, has how many input variables?
Response:

A. 4 or more
B. 1
C. 3
D. 2

Question #2

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. 0-24.99
C. 50-74.99
D. 25-49.99

Question #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.
Which of the following variable(s) is (are) statistically significant at the 5% level in the selected model?
Response:

A. IMP_TLOpen24Pct
B. TLTimeFirst
C. TLDel3060Cnt24
D. all of the above

Question #4

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.
Consider the variable TLCnt03 in the selected model. Based on the model results, changing this variable by 1 unit will result in which of the following?
Response:

A. reduction of odds for TARGET=1 by 0.3457
B. reduction of odds for TARGET=1 by 0.708
C. change of odds for TARGET=1 by a factor 0.708
D. change of odds for TARGET=1 by a factor 0.3457

Question #5

Refer to the exhibit:

What would be the decision threshold (probability cutoff) generated for this decision matrix. You may use a calculator for this question. On the certification exam, an on-screen calculator is provided for you.
Select one:
Response: