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SASInstitute A00-255 Exam Syllabus Topics:

SectionWeightObjectives
Data Sources20-25%- Prepare Source Data
  • 1. Prepare data for predictive modeling
  • 2. Modify source data
- Create and Explore Data Sources
  • 1. Explore and assess data sources
  • 2. Create data sources from SAS tables in Enterprise Miner
Pattern Analysis10-15%- Segmentation and Association Analysis
  • 1. Perform sequence analysis and market basket analysis
  • 2. Perform association analysis
  • 3. Identify clusters using Cluster and Segment Profile nodes
Building Predictive Models35-40%- Model Development
  • 1. Describe predictive modeling concepts and terminology
  • 2. Build neural network models
  • 3. Build decision tree models
  • 4. Build regression models
Predictive Model Assessment and Implementation25-30%- Model Deployment
  • 1. Score data sets within Enterprise Miner
- Model Evaluation
  • 1. Assess model performance using profit and loss information
  • 2. Select appropriate fit statistics
  • 3. Use decision processing for oversampling adjustment
  • 4. Compare models using Model Comparison node

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

1. The number of neurons in this Neural Network model is which of the following:
Response:

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


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

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


3. You are building a model to identify fraud. Your model will produce predictions that can be interpreted as the probability of fraud. You will pass on the top 100 scoring cases to management for investigation. Assume that you have sufficient data to hold out a validation and test data set for model evaluation.
Which selection would represent a reasonable ordering of fit statistics (best to worst) for this situation?
Select one:
Response:

A) R-square, AIC, K-S statistic
B) Misclassification rate, ROC index, ASE
C) ASE, Lift for the top 10%, ROC index
D) ROC index, ASE, misclassification rate


4. If the bank wanted to select the best model based on which model achieves the highest lift for the third decile in the validation data, then which of the following is the best model?
Response:

A) Decision Tree (3-way)
B) Regression
C) Neural Network
D) Decision Tree


5. 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.
Compare the performance of the ensemble and the four models using average squared error in the validation data. Which is the best model in this comparison?
Response:

A) Regression
B) Neural Network
C) Decision Tree
D) Ensemble


Solutions:

Question # 1
Answer: D
Question # 2
Answer: D
Question # 3
Answer: D
Question # 4
Answer: D
Question # 5
Answer: D

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