Try and practice the latest SASInstitute : A00-406 real questions & answers
Updated: Jul 20, 2026
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SASInstitute A00-406 Exam Overview:
| Certification Vendor: | SAS Institute |
| Exam Name: | SAS® Viya® Supervised Machine Learning Pipelines |
| Exam Number: | A00-406 |
| Certificate Validity Period: | 5 years |
| Exam Duration: | 90 minutes |
| Real Exam Qty: | 50-55 |
| Passing Score: | 62% |
| Exam Price: | $180 USD |
| Available Languages: | English |
| 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 |
SASInstitute A00-406 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Model Assessment and Deployment | 24-30% | - Apply model assessment principles - Evaluate and compare model performance - Select champion models - Deploy models into production |
| Building Models | 40-46% | - Build regression models - Understand supervised machine learning concepts - Integrate custom code - Build neural networks and SVM models - Build decision trees and ensemble models - Use model interpretability tools |
| Data Sources | 30-36% | - Modify and prepare data - Perform variable selection - Explore and understand data - Create a project in Model Studio - Reduce dimensionality |
SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:
1. What is the significance of the "bias-variance trade-off" in machine learning?
A) It is not relevant in machine learning.
B) It represents the trade-off between underfitting and overfitting.
C) It indicates the trade-off between accuracy and precision.
D) It refers to the trade-off between the number of features and the model's complexity.
2. Which type of model is commonly used for anomaly detection in datasets?
A) Linear Regression
B) Decision Trees
C) Clustering Models
D) Principal Component Analysis (PCA)
3. Which metric is commonly used to evaluate the performance of a regression model?
A) Mean Absolute Error (MAE)
B) Precision
C) Confusion Matrix
D) F1 Score
4. What is overfitting in machine learning, and how can it be addressed in a pipeline?
A) Overfitting is not a concern in machine learning pipelines.
B) Overfitting occurs when the model is too simple and underperforms.
C) Overfitting occurs when the model is too complex and overperforms.
D) Overfitting occurs when the model fits the training data too closely and may not generalize well. It can be addressed by regularization techniques.
5. Which of the following is NOT a common data format for exchanging data between systems?
A) HTML (Hypertext Markup Language)
B) SQL (Structured Query Language)
C) JSON (JavaScript Object Notation)
D) CSV (Comma-Separated Values)
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: C | Question # 3 Answer: A | Question # 4 Answer: D | Question # 5 Answer: B |
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