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SASInstitute A00-255 Exam Overview:
| Certification Vendor: | SASInstitute |
| Exam Name: | SAS Predictive Modeling Using SAS Enterprise Miner 14 |
| Exam Number: | A00-255 |
| Passing Score: | 725 (scale 200–1000) |
| Certificate Validity Period: | Valid indefinitely once earned |
| Real Exam Qty: | 55–60 |
| Exam Duration: | 165 minutes |
| Related Certifications: | SAS Certified Advanced Analytics Professional |
| Exam Price: | $250 USD |
| Available Languages: | English |
| Exam Format: | Multiple choice, Short answer, Performance-based |
| Recommended Training: | SAS Enterprise Miner Training Courses |
| Exam Registration: | SAS Certification Registration Pearson VUE Registration |
| Sample Questions: | SASInstitute A00-255 Sample Questions |
| Exam Way: | Online proctored or onsite at Pearson VUE test centers |
| Pre Condition: | Recommended: experience with SAS Enterprise Miner, data mining and predictive modeling concepts; no mandatory prerequisites |
| Official Syllabus URL: | https://www.sas.com/en_us/certification/credentials/advanced-analytics/predictive-modeler-14.html |
SASInstitute A00-255 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Sources | 20–25% | - Create data sources from SAS tables - Modify and prepare source data for modeling - Explore and assess data sources |
| Pattern Analysis | 10–15% | - Identify clusters and segments - Interpret pattern discovery results |
| Predictive Model Assessment and Implementation | 25–30% | - Evaluate performance via profit/loss and comparison - Apply appropriate fit statistics - Score and deploy models - Adjust for oversampling and sampling methods |
| Building Predictive Models | 35–40% | - Build models using decision trees - Build models using neural networks - Understand predictive modeling concepts - Build models using regression techniques |
SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions:
1. For the variable InqCnt06, replace all values over 10.1 with the value 10. How many values are replaced?
Response:
A) 150-199
B) 0-99
C) 100-149
D) 200 or higher
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) less than or equal to 299
B) 1,000-1,599
C) 300-999
D) 1,600 or higher
3. Open the diagram labeled Practice A within the project labeled Practice A. Perform the following in SAS Enterprise Miner:
1. Set the Clustering method to Average.
2. Run the Cluster node.
What is the Importance statistic for MTGBal (Mortgage Balance)?
Response:
A) 0.60485
B) 0.42667
C) 0.32959
D) 0.42541
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) 6-10
B) less than or equal to 5
C) 11-15
D) 16 or more
5. What is the purpose of the Kass (Bonferroni) adjustment in the decision tree split-search algorithm?
Select one:
Response:
A) To ensure a non-negative logworth value.
B) To ensure that the choice of split is not influenced by input measurement scale.
C) To give categorical inputs a greater chance to be used the split.
D) To reduce the number of surrogate splitting rules.
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: D | Question # 3 Answer: A | Question # 4 Answer: D | Question # 5 Answer: B |
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