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SASInstitute A00-255 Exam Syllabus Topics:

Section	Weight	Objectives
Pattern Analysis	10-15%	- Segmentation and Association Analysis 1. Identify clusters using Cluster and Segment Profile nodes 2. Perform sequence analysis and market basket analysis 3. Perform association analysis
Building Predictive Models	35-40%	- Model Development 1. Build regression models 2. Describe predictive modeling concepts and terminology 3. Build decision tree models 4. Build neural network models
Data Sources	20-25%	- Create and Explore Data Sources 1. Create data sources from SAS tables in Enterprise Miner 2. Explore and assess data sources - Prepare Source Data 1. Prepare data for predictive modeling 2. Modify source data
Predictive Model Assessment and Implementation	25-30%	- Model Deployment 1. Score data sets within Enterprise Miner - Model Evaluation 1. Compare models using Model Comparison node 2. Select appropriate fit statistics 3. Use decision processing for oversampling adjustment 4. Assess model performance using profit and loss information

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

Question 1

Assume you have two equally appealing logistic regression models. Then, if you have to select only one out of these two models, you should select the one that has which of the following?
Response:

A. higher value of AIC (Akaike,s information criterion)
B. all of the above
C. smaller value of SBC (Schwarz,s Bayesian criterion)
D. smaller value of gamma

Question 2

For the variable TLCnt24, apply a Max Normal transformation. What transformation was selected by SAS Enterprise Miner?
Response:

A. Square
B. Exponential
C. Square Root
D. Log

Question 3

What is the number of missing values for the TLSum variable in the sample generated by SAS Enterprise Miner?
Response:

A. 40 or more
B. 0
C. 20-39
D. 1-19

Question 4

Perform this task using SAS Enterprise Miner:
Continue to use the same diagram. Use an Ensemble node (configure using default options) in SAS Enterprise Miner to combine all four models.
The percentage of observations correctly predicted in the validation data by the Ensemble model is in which of the following ranges?
Response:

A. 90-93.99%-
B. 84-89.99%
C. 94% or higher
D. less than 83.99%

Question 5

Transformation of input variables to make their distributions more symmetric will likely have what impact in a logistic regression?
Select one:
Response:

A. increase the performance of logistic regression
B. neither increase nor decrease the performance of logistic regression
C. decrease the performance of logistic regression
D. create convergence problems in maximum likelihood estimation

Solutions:

Question 1
Answer: C

Question 2
Answer: C

Question 3
Answer: A

Question 4
Answer: D

Question 5
Answer: A