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Updated: Sep 03, 2026
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SASInstitute A00-255 Exam Overview:
| Certification Vendor: | SAS Institute |
|---|---|
| Exam Name: | SAS Predictive Modeling Using SAS Enterprise Miner 14 |
| Exam Number: | A00-255 |
| Exam Duration: | 165 minutes |
| Available Languages: | English |
| Exam Price: | USD 250 |
| Passing Score: | 725/1000 |
| Exam Format: | Performance-based, Multiple-choice, Short-answer |
| Real Exam Qty: | 55-60 |
| Certificate Validity Period: | No expiration |
| Related Certifications: | SAS Certified Data Scientist SAS Certified Predictive Modeler Using SAS Enterprise Miner 14 SAS Certified Advanced Analytics Professional |
| Sample Questions: | SASInstitute A00-255 Sample Questions |
| Exam Way: | Computer-based exam delivered through Pearson VUE testing centers and online proctored testing. |
| Pre Condition: | No mandatory prerequisite. Candidates should have hands-on experience with SAS Enterprise Miner and predictive modeling concepts. |
| Official Syllabus URL: | https://www.sas.com/en_us/certification/credentials/advanced-analytics/predictive-modeler.html |
SASInstitute A00-255 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Predictive Model Assessment and Implementation | 25-30% | - Model Evaluation
|
| Building Predictive Models | 35-40% | - Model Development
|
| Data Sources | 20-25% | - Create and Explore Data Sources
|
| Pattern Analysis | 10-15% | - Segmentation and Association Analysis
|
SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions:
Question 1
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. 0.42541
D. 0.32959
Question 2
Perform this task using SAS Enterprise Miner:
Continue to use the same diagram. Use an Ensemble node (configure using default options) in SAS Enterprise Miner to combine all four models.
Compare the performance of the ensemble and the four models using average squared error in the validation data. Which is the best model in this comparison?
Response:
A. Decision Tree
B. Ensemble
C. Neural Network
D. Regression
Question 3
For an ordinal variable with L levels, the number of possible splits in a decision tree is which of the following?
Response:
A. 2L-1-1
B. 2L-1
C. (L-1)/1
D. 2L
Question 4
A useful concept in logistic regression is the doubling amount. How would you calculate doubling amount for an input variable that has a parameter estimate of b1?
Response:
A. 2/log(b1)
B. 2*log(b1)
C. 2*b1
D. 0.69/b1
Question 5
What is the purpose of the Kass (Bonferroni) adjustment in the decision tree split-search algorithm?
Select one:
Response:
A. To give categorical inputs a greater chance to be used the split.
B. To ensure a non-negative logworth value.
C. To reduce the number of surrogate splitting rules.
D. To ensure that the choice of split is not influenced by input measurement scale.
Solutions:
| Question 1 Answer: A | Question 2 Answer: B | Question 3 Answer: A | Question 4 Answer: D | Question 5 Answer: D |
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