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SASInstitute A00-255 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Sources | 20-25% | - Prepare Source Data
|
| Topic 2: Building Predictive Models | 35-40% | - Model Development
|
| Topic 3: Pattern Analysis | 10-15% | - Segmentation and Association Analysis
|
| Topic 4: Predictive Model Assessment and Implementation | 25-30% | - Model Evaluation
|
SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions:
The selected model, based on the misclassification rate for the validation data, has how many input variables?
Response:
- A. 3
- B. 2
- C. 1
- D. 4 or more
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. 12
- B. 19
- C. 8
- D. 47
Refer to the exhibit:
The SAS data set credit_customers contains a numeric variable units_sold that holds only the values: 1, 2, 3, 4. Based on the settings provided in the Advanced Advisor Options, what will be the Role and Level of the units_sold variable when the credit_customers data set is created using Advanced Metadata Advisor in the Data Source Wizard?
Select one:
Response:
- A. Role: IntervalLevel: Input
- B. Role: RejectedLevel: Nominal
- C. Role: InputLevel: Interval
- D. Role: InputLevel: Nominal
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.
Now suppose that the bank expects to make a profit of $200 USD when TARGET=1, but it expects to lose $25 USD when TARGET=0. Incorporate the above scenario, change the assessment measure of the decision tree to average square error, and then run the Decision Tree node. What is the total profit for the test data set?
Response:
- A. 1,600 or higher
- B. 1,000-1,599
- C. less than or equal to 299
- D. 300-999
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. Ensemble
- B. Regression
- C. Neural Network
- D. Decision Tree
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