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SASInstitute A00-255 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Model Implementation and Deployment | - Model scoring and deployment in SAS Enterprise Miner - Monitoring model performance in production |
| Model Evaluation and Validation | - Model comparison and selection - Validation and cross-validation techniques - Model performance metrics |
| Exploratory Data Analysis | - Descriptive statistics and data profiling - Visualization techniques for pattern discovery |
| Data Understanding and Preparation | - Handling missing values and outliers - Data cleaning and preprocessing - Feature selection and transformation - Data collection and data source identification |
| Model Development | - Regression modeling techniques - Neural networks and advanced modeling in SAS Enterprise Miner - Decision trees and ensemble methods |
| Business Understanding and Analytical Framework | - Translate business problems into data mining tasks - Define business objectives and analytics goals |
SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions:
1. For the variable TLCnt24, apply a Max Normal transformation. What transformation was selected by SAS Enterprise Miner?
Response:
A) Square
B) Log
C) Square Root
D) Exponential
2. 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.
Now suppose that the bank expects to make a profit of $200 USD when TARGET=1, but it expects to lose $25 USD when TARGET=0. Incorporate the above scenario, change the assessment measure of the decision tree to average square error, and then run the Decision Tree node. What is the total profit for the test data set?
Response:
A) 1,600 or higher
B) 300-999
C) 1,000-1,599
D) less than or equal to 299
3. Assume in a data mining project that the task is to predict rankings of a target variable as accurately as possible. Which of the following should be used to judge prediction models?
Response:
A) KS statistic
B) misclassification
C) average squared error
D) Gini coefficient
4. 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.
What percentage of all observations is being correctly predicted in the test data set by the decision tree?
Response:
A) 84.5212%
B) 16.8874%
C) 85.2222%
D) 83.1126%
5. What is the variable worth of the PromCntCardAll variable in Segment 1?
Select one:
Response:
A) 0.10844
B) 0.27649
C) 0.24169
D) 0.24914
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: A | Question # 3 Answer: D | Question # 4 Answer: D | Question # 5 Answer: B |
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