Updated: Aug 22, 2026
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| Certification Vendor: | SAS Institute |
| Exam Name: | SAS® Viya® Supervised Machine Learning Pipelines |
| Exam Number: | A00-406 |
| Exam Price: | $180 USD |
| Passing Score: | 62% |
| Available Languages: | English |
| Exam Duration: | 90 minutes |
| Certificate Validity Period: | 5 years |
| Real Exam Qty: | 50-55 |
| Exam Format: | Multiple choice, Short answer |
| Recommended Training: | SAS Viya Machine Learning Path SAS Visual Data Mining and Machine Learning |
| Exam Registration: | Pearson VUE Scheduling SAS Certification Registration |
| Sample Questions: | SASInstitute A00-406 Sample Questions |
| Exam Way: | Online proctored or onsite at Pearson VUE test centers |
| Pre Condition: | Basic knowledge of SAS Viya, statistics, and supervised machine learning concepts; experience with Model Studio recommended |
| Official Syllabus URL: | https://www.sas.com/en_us/certification/credentials/advanced-analytics/machine-learning-using-sas-viya.html |
| Section | Weight | Objectives |
|---|---|---|
| Data Sources | 30-36% | - Reduce dimensionality - Create a project in Model Studio - Modify and prepare data - Explore and understand data - Perform variable selection |
| Model Assessment and Deployment | 24-30% | - Select champion models - Evaluate and compare model performance - Apply model assessment principles - Deploy models into production |
| Building Models | 40-46% | - Build neural networks and SVM models - Build regression models - Understand supervised machine learning concepts - Use model interpretability tools - Integrate custom code - Build decision trees and ensemble models |
1. What does the term "bagging" refer to in ensemble learning?
A) The process of combining multiple identical models to reduce variance
B) A type of feature extraction
C) A form of dimensionality reduction
D) A technique that reduces model complexity
2. What is the main purpose of feature engineering in model building?
A) Model evaluation
B) Creating new features or transforming existing ones to improve model performance
C) Data visualization
D) Data preprocessing
3. What is the primary role of a data warehouse in an organization?
A) Data exploration and visualization
B) Long-term data storage and analysis
C) Real-time data analysis
D) Data transformation and cleaning
4. Which data source typically provides access to real-time financial market data?
A) Weather stations
B) Online news websites
C) Social media platforms
D) Stock market APIs
5. What is feature engineering in the context of machine learning pipelines?
A) Building a machine learning model from scratch
B) Creating new features from existing data
C) Applying the model to new data
D) Testing the model's performance
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: B | Question # 3 Answer: B | Question # 4 Answer: D | Question # 5 Answer: B |
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