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SASInstitute A00-255 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Exploratory Data Analysis | - Descriptive statistics and data profiling - Visualization techniques for pattern discovery |
| Model Evaluation and Validation | - Validation and cross-validation techniques - Model comparison and selection - Model performance metrics |
| Data Understanding and Preparation | - Data cleaning and preprocessing - Data collection and data source identification - Handling missing values and outliers - Feature selection and transformation |
| Business Understanding and Analytical Framework | - Translate business problems into data mining tasks - Define business objectives and analytics goals |
| Model Implementation and Deployment | - Monitoring model performance in production - Model scoring and deployment in SAS Enterprise Miner |
| Model Development | - Neural networks and advanced modeling in SAS Enterprise Miner - Regression modeling techniques - Decision trees and ensemble methods |
SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions:
Assume that a company has an excellent customer segmentation in place and the segment scheme is a variable in the input data set. What is the best partition method that one should use?
Select one:
Response:
- A. Systemic
- B. Stratify
- C. Random
- D. Cluster
Correct Answer: B π³οΈ
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.
Suppose that the data has been oversampled and the probability that TARGET=1 is 0.10 in the population. Incorporate the above scenario and run the Decision Tree node again.
What is the misclassification rate in the validation data set?
Response:
- A. 0.154788
- B. 0.162252
- C. 0.10
- D. 0.157016
Correct Answer: D π³οΈ
Sometimes in predictive modeling we build models using a sample that has a primary outcome proportion different from true population proportion. This is usually done when the ratio of primary to secondary outcomes in a binary target variable in the population is close to which of the following?
Response:
- A. 0.05
- B. 1.2
- C. 1
- D. 0.8
Correct Answer: A π³οΈ
-> 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).
- Allow for 1 substitute rule in case the variable for the primary splitting rule is missing.
- Disable pruning for the decision tree.
-> 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.
-> Run the process flow.
What is the number of input variables being used by the Neural Network Model?
Enter your numeric answer in the space below:
Response:
- A. 11
- B. 10
- C. 13
- D. 16
Correct Answer: D π³οΈ
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. 20,000-29,999.99
- B. 40,000 or higher
- C. less than 19,999.99
- D. 30,000-39,999.99
Correct Answer: C π³οΈ
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