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SASInstitute A00-255 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Model Development | - Neural networks and advanced modeling in SAS Enterprise Miner - Regression modeling techniques - Decision trees and ensemble methods |
| Topic 2: Model Evaluation and Validation | - Model performance metrics - Validation and cross-validation techniques - Model comparison and selection |
| Topic 3: Exploratory Data Analysis | - Visualization techniques for pattern discovery - Descriptive statistics and data profiling |
| Topic 4: Data Understanding and Preparation | - Data collection and data source identification - Data cleaning and preprocessing - Feature selection and transformation - Handling missing values and outliers |
| Topic 5: Model Implementation and Deployment | - Model scoring and deployment in SAS Enterprise Miner - Monitoring model performance in production |
| Topic 6: Business Understanding and Analytical Framework | - Define business objectives and analytics goals - Translate business problems into data mining tasks |
SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions:
1. Consider a binary target variable. Assume Accuracy is the desired assessment measure. Accuracy is not an option in the Decision Tree node. Which assessment measure can you use as a proxy for accuracy?
Select one:
Response:
A) Mean Square Error
B) Average Squared Error
C) Total Profit
D) 1 - Misclassification Rate
2. 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) 21 or more
B) 1-10
C) 11-15
D) 16-20
3. In segment 2, what percentage of GiftAvgCard36 values are between 6.6638 and 11.998?
Select one:
Response:
A) 14.00%
B) 13.39%
C) 48.59%
D) 47.82%
4. What is the purpose of the Kass (Bonferroni) adjustment in the decision tree split-search algorithm?
Select one:
Response:
A) To reduce the number of surrogate splitting rules.
B) To ensure that the choice of split is not influenced by input measurement scale.
C) To give categorical inputs a greater chance to be used the split.
D) To ensure a non-negative logworth value.
5. 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) $0.00
C) $8,711.65
D) $11,142.45
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: C | Question # 3 Answer: C | Question # 4 Answer: B | Question # 5 Answer: A |
