Prepare with SASInstitute A00-255 exam practice material, pass for sure
Updated: Aug 23, 2026
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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) |
| Available Languages: | English |
| Exam Duration: | 165 minutes |
| Real Exam Qty: | 55–60 |
| Related Certifications: | SAS Certified Advanced Analytics Professional |
| Certificate Validity Period: | Valid indefinitely once earned |
| Exam Format: | Multiple choice, Short answer, Performance-based |
| Exam Price: | $250 USD |
| 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 - Explore and assess data sources - Modify and prepare source data for modeling |
| Pattern Analysis | 10–15% | - Interpret pattern discovery results - Identify clusters and segments |
| Building Predictive Models | 35–40% | - Build models using decision trees - Build models using neural networks - Build models using regression techniques - Understand predictive modeling concepts |
| Predictive Model Assessment and Implementation | 25–30% | - Score and deploy models - Apply appropriate fit statistics - Evaluate performance via profit/loss and comparison - Adjust for oversampling and sampling methods |
SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions:
1. Perform these tasks in SAS Enterprise Miner:
- Use the Regression node to build another regression model with TARGET as the dependent variable and all other input variables as independent variables (main effects only).
- Configure the regression model to use Stepwise for Selection Model and Validation Error for Selection Criteri a. Do not change any other property for the regression model.
Which of the following variable(s) is (are) statistically significant at the 5% level in the selected model?
Response:
A) TLDel3060Cnt24
B) TLTimeFirst
C) all of the above
D) IMP_TLOpen24Pct
2. Which model was picked as the best model by SAS Enterprise Miner?
Response:
A) Decision Tree
B) None of the above
C) Regression
D) Decision Tree (3-way)
3. If the bank wanted to select the best model based on the models' overall performances on the validation data as measured by the average squared error, then the best model is which of the following?
Response:
A) Decision Tree
B) Neural Network
C) Regression
D) Decision Tree (3-way)
4. Assume you have two equally appealing logistic regression models. Then, if you have to select only one out of these two models, you should select the one that has which of the following?
Response:
A) higher value of AIC (Akaike,s information criterion)
B) all of the above
C) smaller value of gamma
D) smaller value of SBC (Schwarz,s Bayesian criterion)
5. What is the variable worth of the PromCntCardAll variable in Segment 1?
Select one:
Response:
A) 0.24169
B) 0.24914
C) 0.27649
D) 0.10844
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: A | Question # 3 Answer: C | Question # 4 Answer: D | Question # 5 Answer: C |
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