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| Section | Objectives |
|---|---|
| Model Implementation and Deployment | - Monitoring model performance in production - Model scoring and deployment in SAS Enterprise Miner |
| Data Understanding and Preparation | - Feature selection and transformation - Data cleaning and preprocessing - Handling missing values and outliers - Data collection and data source identification |
| Exploratory Data Analysis | - Descriptive statistics and data profiling - Visualization techniques for pattern discovery |
| Business Understanding and Analytical Framework | - Translate business problems into data mining tasks - Define business objectives and analytics goals |
| Model Development | - Neural networks and advanced modeling in SAS Enterprise Miner - Decision trees and ensemble methods - Regression modeling techniques |
| Model Evaluation and Validation | - Model comparison and selection - Validation and cross-validation techniques - Model performance metrics |
1. The Chi Square statistic for measuring association between the variables BanruptcyInd and TARGET is which of the following?
Response:
A) less than 1.00
B) between 2.00 and 2.99
C) 3.00 or higher
D) between 1.00 and 1.99
2. 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) less than 19,999.99
B) 30,000-39,999.99
C) 20,000-29,999.99
D) 40,000 or higher
3. Which of the following solves problems for you when you impute missing values?
Response:
A) When you impute a synthetic value, it replaces missing values with 1 or 0.
B) When you impute a synthetic value, each missing value becomes an input to the model.
C) When you impute a synthetic value, it eliminates the incomplete case problem.
D) When you impute a synthetic value, predictive information is retained.
4. What is the average squared error in the training data?
Response:
A) 0.131709
B) 0.133665
C) 0.131208
D) 0.131583
5. 1. Define a new data source, PatternData, in SAS Enterprise Miner (SAS data set Patterndata.sas7bdat in the zip file distributed with this practice exam).
2. Set the role of all variables to Input, with the exception set the ID variable role to ID.
3. Set the measurement level for all variables to Interval, except:
- Set DemHomeOwner and StatusCatStarAll to Binary.
- Set DemCluster, DemGender, ID, and StatusCat96NK to Nominal.
4. Create a new diagram (name it Section6) within the project labeled Test.
5. Add the data source, PatternData, to this diagram. Make sure the variable roles and measurements are the same as in the table below. (Check the highlighted rows carefully and reset roles/levels as needed.)
6. Connect a Cluster node to the data source.
7. Modify the Cluster node to exclude nominal and binary input variables.
8. Run the Cluster node.
How many clusters are created by the Cluster node?
Response:
A) 8
B) 3
C) 6
D) 9
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: A | Question # 3 Answer: C | Question # 4 Answer: D | Question # 5 Answer: B |
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