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SASInstitute A00-255 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Predictive Model Assessment and Implementation | 25–30% | - Score and deploy models - Evaluate performance via profit/loss and comparison - Apply appropriate fit statistics - Adjust for oversampling and sampling methods |
| Topic 2: Pattern Analysis | 10–15% | - Identify clusters and segments - Interpret pattern discovery results |
| Topic 3: Data Sources | 20–25% | - Modify and prepare source data for modeling - Create data sources from SAS tables - Explore and assess data sources |
| Topic 4: Building Predictive Models | 35–40% | - Understand predictive modeling concepts - Build models using decision trees - Build models using regression techniques - Build models using neural networks |
SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions:
Question 1
What is the average squared error in the training data?
Response:
A. 0.133665
B. 0.131709
C. 0.131583
D. 0.131208
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
Choose the correct statement that illustrates Decision Tree Split Search for continuous (interval) inputs:
Select one:
Response:
A. Each unique value has the potential of being the optimal split point.
B. The variable goes through a non-linear transformation, and the transformed variable is used for testing.
C. The variable goes through a binning process, the bins are weighted based on the proportion of events in each bin, and then finally tested as an optimal split point.
D. Each unique value has the potential of being the optimal split point, except for the extreme observation.
Question 4
Which method of input selection for regression analysis evaluates the statistical significance of the total model to see if it improves on the baseline as the variables are added and once no further improvement is made then variable selection ends?
Select one:
Response:
A. Stepwise
B. Simple
C. Backward
D. Forward
Question 5
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. Stepwise
B. None
C. Backward
D. Forward
Solutions:
| Question 1 Answer: C | Question 2 Answer: D | Question 3 Answer: A | Question 4 Answer: D | Question 5 Answer: B |
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