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SASInstitute A00-255 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Data Understanding and Preparation | - Handling missing values and outliers - Data collection and data source identification - Feature selection and transformation - Data cleaning and preprocessing |
| Topic 2: Model Development | - Neural networks and advanced modeling in SAS Enterprise Miner - Regression modeling techniques - Decision trees and ensemble methods |
| Topic 3: Model Implementation and Deployment | - Monitoring model performance in production - Model scoring and deployment in SAS Enterprise Miner |
| Topic 4: Business Understanding and Analytical Framework | - Define business objectives and analytics goals - Translate business problems into data mining tasks |
| Topic 5: Exploratory Data Analysis | - Visualization techniques for pattern discovery - Descriptive statistics and data profiling |
| Topic 6: Model Evaluation and Validation | - Model comparison and selection - Validation and cross-validation techniques - Model performance metrics |
SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions:
1. What is the average squared error in the training data?
Response:
A) 0.131583
B) 0.131208
C) 0.131709
D) 0.133665
2. Which model was picked as the best model by SAS Enterprise Miner?
Response:
A) None of the above
B) Decision Tree (3-way)
C) Regression
D) Decision Tree
3. Refer to the exhibit:
The SAS data set credit_customers contains a numeric variable units_sold that holds only the values: 1, 2, 3, 4. Based on the settings provided in the Advanced Advisor Options, what will be the Role and Level of the units_sold variable when the credit_customers data set is created using Advanced Metadata Advisor in the Data Source Wizard?
Select one:
Response:
A) Role: InputLevel: Interval
B) Role: InputLevel: Nominal
C) Role: RejectedLevel: Nominal
D) Role: IntervalLevel: Input
4. Perform these tasks in SAS Enterprise Miner:
- 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). Configure the decision tree to use 1 for Number of Surrogate Rules and Largest for Method in Subtree. Do not change any other property of the Decision Tree node.
- 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. Do not change any other property for the Neural Network node. Run the process flow.
The number of parameters (weights) estimated by the Neural Network model is in which of the following ranges?
Response:
A) less than or equal to 5
B) 11-15
C) 6-10
D) 16 or more
5. If we were to add a Transformation node, what would be the default transformation for interval inputs for the present scenario?
Response:
A) Optimal
B) none of the above
C) Maximum Correlation
D) Maximum Normal
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: D | Question # 3 Answer: B | Question # 4 Answer: D | Question # 5 Answer: B |
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