SASInstitute A00-406 Questions & Answers - in .pdf
- Total Q&A: 98
- Update: Aug 25, 2026
- Price: $59.99
- Vendor: SASInstitute
- Exam Code: A00-406
- Exam Name: SAS® Viya® Supervised Machine Learning Pipelines
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- Total Q&A: 98
- Update: Aug 25, 2026
- Price: $59.99
- Vendor: SASInstitute
- Exam Code: A00-406
- Exam Name: SAS® Viya® Supervised Machine Learning Pipelines
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SASInstitute A00-406 Exam Overview:
| Certification Vendor: | SAS Institute |
|---|---|
| Exam Name: | SAS® Viya® Supervised Machine Learning Pipelines (SAS Certified Specialist: Machine Learning Using SAS Viya) |
| Exam Number: | A00-406 |
| Real Exam Qty: | 50-55 |
| Exam Duration: | 90 minutes |
| Exam Price: | USD 180 |
| Available Languages: | English |
| Passing Score: | 62% |
| Related Certifications: | SAS Certified Associate: Applied Statistics for Machine Learning SAS Certified Specialist: Natural Language Processing and Computer Vision Using SAS Viya SAS Certified Specialist: Forecasting and Optimization Using SAS Viya |
| Certificate Validity Period: | 5 years |
| Exam Format: | Multiple choice, Short answer |
| Sample Questions: | SASInstitute A00-406 Sample Questions |
| Exam Way: | Administered by SAS and Pearson VUE (online or test center) |
| Pre Condition: | Familiarity with SAS Visual Data Mining and Machine Learning software; basic understanding of statistics recommended |
| Official Syllabus URL: | https://www.sas.com/en_gb/certification/credentials/advanced-analytics/machine-learning-using-sas-viya.html |
SASInstitute A00-406 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Sources | 30-36% | - Importing and preparing data - Dimensionality reduction and feature engineering - Exploring and modifying data |
| Building Models | 40-46% | - Model comparison and selection - Supervised model creation (decision trees, ensembles, SVM, neural networks) |
| Model Assessment and Deployment | 24-30% | - Deploying models into production - Assessing model performance (metrics, ROC curves, confusion matrices) |
SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:
Question 1
What does the term "bagging" refer to in ensemble learning?
A. A type of feature extraction
B. The process of combining multiple identical models to reduce variance
C. A technique that reduces model complexity
D. A form of dimensionality reduction
Question 2
What is the primary objective of model validation during the model assessment phase?
A. To build a model from scratch
B. To ensure the model generalizes well to new, unseen data
C. To create synthetic data
D. To assess the accuracy of the model
Question 3
What does "feature selection" refer to in the context of model building?
A. The creation of synthetic features from existing data
B. The evaluation of model accuracy
C. The visualization of data distribution
D. The process of choosing the most relevant variables (features) for the model
Question 4
What is "model versioning" in the context of model deployment?
A. The process of creating synthetic data
B. The process of evaluating model performance
C. The process of training a model from scratch
D. The practice of keeping track of different versions of a model to maintain reproducibility
Question 5
In model evaluation, what is the purpose of a ROC curve (Receiver Operating Characteristic)?
A. To evaluate the mean squared error of a model
B. To visualize data distribution
C. To measure feature importance
D. To compare models' performance in terms of sensitivity and specificity
Solutions:
| Question 1 Answer: B | Question 2 Answer: B | Question 3 Answer: D | Question 4 Answer: D | Question 5 Answer: D |
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