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SASInstitute A00-255 Exam Syllabus Topics:

Section	Objectives
Topic 1: Model Implementation and Deployment	- Monitoring model performance in production - Model scoring and deployment in SAS Enterprise Miner
Topic 2: Model Evaluation and Validation	- Model comparison and selection - Model performance metrics - Validation and cross-validation techniques
Topic 3: Data Understanding and Preparation	- Data collection and data source identification - Data cleaning and preprocessing - Handling missing values and outliers - Feature selection and transformation
Topic 4: Exploratory Data Analysis	- Descriptive statistics and data profiling - Visualization techniques for pattern discovery
Topic 5: Business Understanding and Analytical Framework	- Translate business problems into data mining tasks - Define business objectives and analytics goals
Topic 6: Model Development	- Decision trees and ensemble methods - Regression modeling techniques - Neural networks and advanced modeling in SAS Enterprise Miner

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

Question 1

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. 12
B. 8
C. 19
D. 47

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

Question 3

Open the diagram labeled Practice A within the project labeled Practice A. Perform the following in SAS Enterprise Miner:

1. Set the Clustering method to Average.
2. Run the Cluster node.
What is the Cubic Clustering Criterion statistic for this clustering?
Response:

A. 5862.76
B. 14.69
C. 67409.93
D. 5.00

Question 4

Which method of input selection for regression analysis evaluates the statistical significance of all included inputs after each input is added?
Select one:
Response:

A. Forward
B. Simple
C. Backward
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: