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SASInstitute A00-255 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Sources | 20–25% | - Create data sources from SAS tables - Explore and assess data sources - Modify and prepare source data for modeling |
| Building Predictive Models | 35–40% | - Build models using neural networks - Understand predictive modeling concepts - Build models using regression techniques - Build models using decision trees |
| Predictive Model Assessment and Implementation | 25–30% | - Score and deploy models - Adjust for oversampling and sampling methods - Apply appropriate fit statistics - Evaluate performance via profit/loss and comparison |
| Pattern Analysis | 10–15% | - Interpret pattern discovery results - Identify clusters and segments |
SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions:
In SAS Enterprise Miner's Decision Tree node, which of the following types of target variable can be used?
Response:
- A. binary
- B. nominal with any number of categories
- C. interval
- D. all of the above
Correct Answer: D 🗳️
What percentage of observations in the test data has TARGET=1?
Response:
- A. 16.5924
- B. 16.8874
- C. 83.3333
- D. 16.6627
Correct Answer: B 🗳️
Multicollinearity in regression refers to which of the following?
Response:
- A. high correlations among input variables
- B. non-normality of the target variable
- C. non-constant variance of the target variable
- D. high skewness in distributions of input variables
Correct Answer: A 🗳️
If you only consider observations for which TARGET=0, what percentage of such observations has BanruptcyInd=1?
Response:
- A. between 50%-79.99%
- B. less than 15%
- C. between 15%-49.99%
- D. 80% or higher
Correct Answer: B 🗳️
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. Forward
- B. Stepwise
- C. None
- D. Backward
Correct Answer: C 🗳️
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