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SASInstitute A00-255 Exam Syllabus Topics:

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

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

Question 1

What is the variable worth of the PromCntCardAll variable in Segment 1?
Select one:
Response:

A. 0.10844
B. 0.24169
C. 0.24914
D. 0.27649

Question 2

For the variable InqTimeLast, which term best describes the shape of its distribution?
Response:

A. left skewed
B. bimodal
C. right skewed
D. symmetric

Question 3

Perform these tasks in SAS Enterprise Miner:
- Add a Decision Tree node after the Impute node with TARGET as the dependent variable and all other input variables as independent variables (main effects only). Configure the decision tree to use 1 for Number of Surrogate Rules and Largest for Method in Subtree. Do not change any other property of the Decision Tree node.
- Add another Neural Network node after the decision tree with TARGET as the dependent variable and all other input variables as independent variables (main effects only). Configure the Neural Network model to use Average Error for Model Selection Criterion. Do not change any other property for the Neural Network node. Run the process flow.
How many leaves are there in the decision tree?
Response:

A. 21 or more
B. 1-10
C. 11-15
D. 16-20

Question 4

Which of the following sequential selection methods do you use so that SAS Enterprise Miner will look at all variables already included in the model and delete any variable that is not significant at the specified level?
Response:

A. Backward
B. Forward
C. None
D. Stepwise

Question 5

Perform these tasks in SAS Enterprise Miner:
* Add a Decision Tree node, as shown below. (Make sure you use only default options in the Decision Tree node.)

* Run the Decision Tree node.
What is the probability that TARGET=0 for ID=000355 in the training data?
Response: