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SASInstitute A00-255 Exam Syllabus Topics:

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

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

Question 1

The number of hidden layers in this Neural Network model is which of the following?
Response:

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

Question 2

Perform these tasks in SAS Enterprise Miner:
*Continue to use the same diagram. Define and create the data set CREDIT_SCORE for scoring. The variables (their roles and measurement levels) in the CREDIT_SCORE data should be set as identical to those in the CREDIT dat a. The only exception is that the scoring data does not have a TARGET variable.
* Find the best model out of Decision Tree, Decision Tree (3-way), Regression, and Neural Network as defined by each of the four model's overall performance in the validation data measured by average squared error. Now, use this best model to score the CREDIT_SCORE data.
CREDIT SCORE:

The percentage of TARGET=1 as predicted by the best model on the scoring data is in which of the following ranges?
Response:

A. 5%-5.99%
B. under 4.99%
C. 7% or higher
D. 6%-6.99%

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.
For the validation data, in what range does cumulative percent captured response at the 60th percentile lie?
Response:

A. 25-49.99
B. 75 or more
C. 0-24.99
D. 50-74.99

Question 4

What is the average squared error in the training data?
Response:

A. 0.131583
B. 0.133665
C. 0.131709
D. 0.131208

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: