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SASInstitute A00-255 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Model Development | - Neural networks and advanced modeling in SAS Enterprise Miner - Regression modeling techniques - Decision trees and ensemble methods |
| Data Understanding and Preparation | - Data cleaning and preprocessing - Handling missing values and outliers - Feature selection and transformation - Data collection and data source identification |
| Exploratory Data Analysis | - Visualization techniques for pattern discovery - Descriptive statistics and data profiling |
| Model Implementation and Deployment | - Monitoring model performance in production - Model scoring and deployment in SAS Enterprise Miner |
| Model Evaluation and Validation | - Model comparison and selection - Validation and cross-validation techniques - Model performance metrics |
| 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. Which of the following solves problems for you when you impute missing values?
Response:
A) When you impute a synthetic value, each missing value becomes an input to the model.
B) When you impute a synthetic value, it replaces missing values with 1 or 0.
C) When you impute a synthetic value, it eliminates the incomplete case problem.
D) When you impute a synthetic value, predictive information is retained.
2. Perform these tasks in SAS Enterprise Miner:
*Continue to use the same diagram. Define and create the data set CREDIT_SCORE for scoring. The variables (their roles and measurement levels) in the CREDIT_SCORE data should be set as identical to those in the CREDIT dat a. The only exception is that the scoring data does not have a TARGET variable.
* Find the best model out of Decision Tree, Decision Tree (3-way), Regression, and Neural Network as defined by each of the four model's overall performance in the validation data measured by average squared error. Now, use this best model to score the CREDIT_SCORE data.
CREDIT SCORE:
The percentage of TARGET=1 as predicted by the best model on the scoring data is in which of the following ranges?
Response:
A) 5%-5.99%
B) under 4.99%
C) 7% or higher
D) 6%-6.99%
3. Transformation of input variables to make their distributions more symmetric will likely have what impact in a logistic regression?
Select one:
Response:
A) neither increase nor decrease the performance of logistic regression
B) create convergence problems in maximum likelihood estimation
C) increase the performance of logistic regression
D) decrease the performance of logistic regression
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.
In the decision tree model, what is the importance of the variable InqCnt06?
Response:
A) 0.15-0.299999
B) less than 0.149999
C) 0.45 or higher
D) 0.30-0.449999
5. Assume a variable is coded as follows: 1=unmarried, 2=married, 3=divorced, and 4=widowed. Then which of the following measurement levels should be selected in SAS Enterprise Miner for this variable?
Response:
A) Nominal
B) Interval
C) Ordinal
D) Unary
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: B | Question # 3 Answer: C | Question # 4 Answer: D | Question # 5 Answer: A |
