Exam Name: SAS Predictive Modeling Using SAS Enterprise Miner 14
Certification Provider: SASInstitute
Corresponding Certification: SAS Institute SAS
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SASInstitute A00-255 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Exploratory Data Analysis | - Visualization techniques for pattern discovery - Descriptive statistics and data profiling |
| Topic 2: Data Understanding and Preparation | - Data cleaning and preprocessing - Data collection and data source identification - Feature selection and transformation - Handling missing values and outliers |
| Topic 3: Model Development | - Neural networks and advanced modeling in SAS Enterprise Miner - Regression modeling techniques - Decision trees and ensemble methods |
| Topic 4: Model Evaluation and Validation | - Model comparison and selection - Model performance metrics - Validation and cross-validation techniques |
| Topic 5: Business Understanding and Analytical Framework | - Translate business problems into data mining tasks - Define business objectives and analytics goals |
| Topic 6: 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:
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. None
- B. Forward
- C. Backward
- D. Stepwise
Correct Answer: A 🗳️
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.
What is the mean credit card balance (CCBal) of the customers with a variable annuity?
Response:
- A. $9,586.55
- B. $11,142.45
- C. $8,711.65
- D. $0.00
Correct Answer: A 🗳️
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. 7% or higher
- B. 5%-5.99%
- C. 6%-6.99%
- D. under 4.99%
Correct Answer: D 🗳️
Perform these tasks in SAS Enterprise Miner:
- Add a Decision Tree node after the Impute node with TARGET as the dependent variable and all other input variables as independent variables (main effects only). Configure the decision tree to use 1 for Number of Surrogate Rules and Largest for Method in Subtree. Do not change any other property of the Decision Tree node.
- Add another Neural Network node after the decision tree with TARGET as the dependent variable and all other input variables as independent variables (main effects only). Configure the Neural Network model to use Average Error for Model Selection Criterion. Do not change any other property for the Neural Network node. Run the process flow.
How many leaves are there in the decision tree?
Response:
- A. 16-20
- B. 11-15
- C. 1-10
- D. 21 or more
Correct Answer: B 🗳️
Assume you have two equally appealing logistic regression models. Then, if you have to select only one out of these two models, you should select the one that has which of the following?
Response:
- A. smaller value of gamma
- B. higher value of AIC (Akaike,s information criterion)
- C. smaller value of SBC (Schwarz,s Bayesian criterion)
- D. all of the above
Correct Answer: C 🗳️
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