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SASInstitute A00-255 Exam Overview:
| Certification Vendor: | SAS Institute |
| Exam Name: | Predictive Modeling Using SAS Enterprise Miner 14 |
| Exam Number: | A00-255 |
| Real Exam Qty: | 60-65 |
| Passing Score: | Approximately 68-70% |
| Available Languages: | English |
| Related Certifications: | SAS Certified Data Scientist SAS Certified Advanced Analytics Professional |
| Certificate Validity Period: | 3 years |
| Exam Price: | $180 USD |
| Exam Format: | Multiple Choice |
| Exam Duration: | 110 minutes |
| Recommended Training: | SAS Predictive Modeling Using Enterprise Miner Training SAS e-Learning Courses |
| Exam Registration: | Pearson VUE Exam Registration SAS Certification Registration |
| Sample Questions: | SASInstitute A00-255 Sample Questions |
| Exam Way: | Delivered via Pearson VUE (online proctored or test center) |
| Pre Condition: | No mandatory prerequisite exam; recommended experience with SAS Enterprise Miner and predictive modeling concepts |
| Official Syllabus URL: | https://www.sas.com/en_us/certification.html |
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: Model Development | - Decision trees and ensemble methods - Neural networks and advanced modeling in SAS Enterprise Miner - Regression modeling techniques |
| Topic 3: Exploratory Data Analysis | - Descriptive statistics and data profiling - Visualization techniques for pattern discovery |
| Topic 4: Business Understanding and Analytical Framework | - Translate business problems into data mining tasks - Define business objectives and analytics goals |
| Topic 5: Model Evaluation and Validation | - Model comparison and selection - Model performance metrics - Validation and cross-validation techniques |
| Topic 6: Data Understanding and Preparation | - Feature selection and transformation - Data cleaning and preprocessing - Handling missing values and outliers - Data collection and data source identification |
SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions:
1. The number of neurons in this Neural Network model is which of the following:
Response:
A) 1
B) 4 or more
C) 2
D) 3
2. Choose the correct statement that illustrates Decision Tree Split Search for continuous (interval) inputs:
Select one:
Response:
A) Each unique value has the potential of being the optimal split point.
B) The variable goes through a binning process, the bins are weighted based on the proportion of events in each bin, and then finally tested as an optimal split point.
C) The variable goes through a non-linear transformation, and the transformed variable is used for testing.
D) Each unique value has the potential of being the optimal split point, except for the extreme observation.
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: IntervalLevel: Input
B) Role: RejectedLevel: Nominal
C) Role: InputLevel: Interval
D) Role: InputLevel: Nominal
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) 16.8874%
B) 83.1126%
C) 84.5212%
D) 85.2222%
5. What is the kurtosis value for the variable TLDel60Cnt24?
Response:
A) between 14 and 16.99
B) less than 10
C) between 10 and 13.99
D) 17 or higher
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: A | Question # 3 Answer: D | Question # 4 Answer: B | Question # 5 Answer: A |
By Maurice
