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SASInstitute A00-255 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Model Evaluation and Validation | - Model comparison and selection - Model performance metrics - Validation and cross-validation techniques |
| Business Understanding and Analytical Framework | - Define business objectives and analytics goals - Translate business problems into data mining tasks |
| Model Development | - Regression modeling techniques - Decision trees and ensemble methods - Neural networks and advanced modeling in SAS Enterprise Miner |
| Data Understanding and Preparation | - Handling missing values and outliers - Data collection and data source identification - Data cleaning and preprocessing - Feature selection and transformation |
| Exploratory Data Analysis | - Descriptive statistics and data profiling - Visualization techniques for pattern discovery |
| Model Implementation and Deployment | - Monitoring model performance in production - Model scoring and deployment in SAS Enterprise Miner |
SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions:
Question 1
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. 8
B. 12
C. 19
D. 47
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 distribution of the predicted probabilities of TARGET=0 in the scoring data is approximately which of the following?
Response:
A. normal
B. left skewed
C. right skewed
D. bimodal
Question 3
What is the kurtosis value for the variable TLDel60Cnt24?
Response:
A. between 10 and 13.99
B. 17 or higher
C. less than 10
D. between 14 and 16.99
Question 4
Refer to the graphs shown below. The graphs are from a study of response rate to a marketing campaign.
How much more likely are the top 20% of targeted respondents to purchase the product than a randomly selected sample?
Select one:
Response:
A. 140%
B. 30%
C. 60%
D. 25%
Question 5
If the bank wanted to select the best model based on the models' overall performances on the validation data as measured by the average squared error, then the best model is which of the following?
Response:
A. Decision Tree (3-way)
B. Regression
C. Decision Tree
D. Neural Network
Solutions:
| Question 1 Answer: C | Question 2 Answer: B | Question 3 Answer: D | Question 4 Answer: A | Question 5 Answer: B |



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