PDF Version DemoSASInstitute A00-255 Exam Overview:
| Certification Vendor: | SASInstitute |
|---|---|
| Exam Name: | SAS Predictive Modeling Using SAS Enterprise Miner 14 |
| Exam Number: | A00-255 |
| Certificate Validity Period: | Valid indefinitely once earned |
| Real Exam Qty: | 55–60 |
| Passing Score: | 725 (scale 200–1000) |
| Exam Price: | $250 USD |
| Exam Duration: | 165 minutes |
| Exam Format: | Performance-based, Short answer, Multiple choice |
| Available Languages: | English |
| Related Certifications: | SAS Certified Advanced Analytics Professional |
| 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 |
|---|---|---|
| Pattern Analysis | 10–15% | - Interpret pattern discovery results - Identify clusters and segments |
| Data Sources | 20–25% | - Create data sources from SAS tables - Explore and assess data sources - Modify and prepare source data for modeling |
| Predictive Model Assessment and Implementation | 25–30% | - Adjust for oversampling and sampling methods - Evaluate performance via profit/loss and comparison - Apply appropriate fit statistics - Score and deploy models |
| Building Predictive Models | 35–40% | - Build models using regression techniques - Build models using decision trees - Build models using neural networks - Understand predictive modeling concepts |
SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions:
Question 1
Assume in a data mining project that the task is to predict rankings of a target variable as accurately as possible. Which of the following should be used to judge prediction models?
Response:
A. average squared error
B. Gini coefficient
C. KS statistic
D. misclassification
Question 2
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. 0.69/b1
B. 2*b1
C. 2/log(b1)
D. 2*log(b1)
Question 3
What is the average squared error in the training data?
Response:
A. 0.131709
B. 0.131583
C. 0.133665
D. 0.131208
Question 4
Transformation of input variables to make their distributions more symmetric will likely have what impact in a logistic regression?
Select one:
Response:
A. decrease the performance of logistic regression
B. create convergence problems in maximum likelihood estimation
C. increase the performance of logistic regression
D. neither increase nor decrease the performance of logistic regression
Question 5
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. 84.5212%
C. 83.1126%
D. 85.2222%
Solutions:
| Question 1 Answer: B | Question 2 Answer: A | Question 3 Answer: B | Question 4 Answer: C | Question 5 Answer: C |
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