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: Model Implementation and Deployment | - Monitoring model performance in production - Model scoring and deployment in SAS Enterprise Miner |
| Topic 2: Exploratory Data Analysis | - Visualization techniques for pattern discovery - Descriptive statistics and data profiling |
| Topic 3: Business Understanding and Analytical Framework | - Define business objectives and analytics goals - Translate business problems into data mining tasks |
| Topic 4: Model Development | - Decision trees and ensemble methods - Regression modeling techniques - Neural networks and advanced modeling in SAS Enterprise Miner |
| Topic 5: Model Evaluation and Validation | - Validation and cross-validation techniques - Model performance metrics - Model comparison and selection |
| Topic 6: Data Understanding and Preparation | - Data collection and data source identification - Handling missing values and outliers - Data cleaning and preprocessing - Feature selection and transformation |
SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions:
Refer to the exhibit:
The SAS data set retail contains information on the count of retail store sales based on the following item types: bargain, essential, gourmet, and health. Based on the results from the Cluster Profile node, which statement is true?
Select one:
Response:
- A. The overall distribution of essential item sales is approximately normal and Segment 1 contains stores selling fewer than average essential items.
- B. The overall distribution of bargain item sales is approximately normal and Segment 1 contains stores selling fewer than average bargain items.
- C. The overall distribution of essential item sales is right skewed and Segment 4 contains stores selling higher than average essential items.
- D. The overall distribution of bargain item sales is left-skewed and Segment 4 contains stores selling fewer than average bargain items.
Correct Answer: B 🗳️
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.
In the training data set, consider only those observations for which the actual value of the target variable equals 1, TARGET=1. What percentage of these observations is being correctly predicted by the decision tree?
Response:
- A. 80.3571
- B. 66.6667
- C. 14.6216
- D. 15
Correct Answer: D 🗳️
Perform these tasks in SAS Enterprise Miner:
- Use the Regression node to build another regression model with TARGET as the dependent variable and all other input variables as independent variables (main effects only).
- Configure the regression model to use Stepwise for Selection Model and Validation Error for Selection Criteri a. Do not change any other property for the regression model.
For the validation data, in what range does cumulative percent captured response at the 60th percentile lie?
Response:
- A. 25-49.99
- B. 50-74.99
- C. 0-24.99
- D. 75 or more
Correct Answer: D 🗳️
What is the kurtosis value for the variable TLDel60Cnt24?
Response:
- A. between 10 and 13.99
- B. between 14 and 16.99
- C. less than 10
- D. 17 or higher
Correct Answer: B 🗳️
Which of the following solves problems for you when you impute missing values?
Response:
- A. When you impute a synthetic value, predictive information is retained.
- B. When you impute a synthetic value, it eliminates the incomplete case problem.
- C. When you impute a synthetic value, each missing value becomes an input to the model.
- D. When you impute a synthetic value, it replaces missing values with 1 or 0.
Correct Answer: B 🗳️
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