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SASInstitute A00-255 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Pattern Analysis | 10-15% | - Segmentation and Association Analysis
|
| Topic 2: Predictive Model Assessment and Implementation | 25-30% | - Model Evaluation
|
| Topic 3: Data Sources | 20-25% | - Create and Explore Data Sources
|
| Topic 4: Building Predictive Models | 35-40% | - Model Development
|
SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions:
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.
Suppose that the data has been oversampled and the probability that TARGET=1 is 0.10 in the population. Incorporate the above scenario and run the Decision Tree node again.
What is the misclassification rate in the validation data set?
Response:
- A. 0.10
- B. 0.154788
- C. 0.157016
- D. 0.162252
Correct Answer: C 🗳️
The number of hidden layers in this Neural Network model is which of the following?
Response:
- A. 2
- B. 3
- C. 1
- D. 4 or more
Correct Answer: C 🗳️
Impute the missing values for the variable TLSum using the Tree method. What is the mean of the new variable (with the imputed values)?
Response:
- A. 40,000 or higher
- B. less than 19,999.99
- C. 30,000-39,999.99
- D. 20,000-29,999.99
Correct Answer: B 🗳️
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. 47
- B. 12
- C. 8
- D. 19
Correct Answer: D 🗳️
A useful concept in logistic regression is the doubling amount. How would you calculate doubling amount for an input variable that has a parameter estimate of b1?
Response:
- A. 2*b1
- B. 0.69/b1
- C. 2/log(b1)
- D. 2*log(b1)
Correct Answer: B 🗳️
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