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Updated: Jul 30, 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 |
| Exam Price: | USD 180 |
| Available Languages: | English |
| Related Certifications: | SAS Certified Specialist: Machine Learning Using SAS Viya |
| Exam Format: | Multiple Choice, Scenario-based Questions |
| Certificate Validity Period: | 3 years |
| Exam Duration: | 110 minutes |
| Recommended Training: | SAS Machine Learning Using SAS Viya Training |
| Exam Registration: | SAS Certification Portal Registration |
| Sample Questions: | SASInstitute A00-406 Sample Questions |
| Exam Way: | Online proctored exam via Pearson VUE or authorized testing centers |
| Pre Condition: | No strict prerequisites; recommended familiarity with basic statistics, machine learning concepts, and SAS Viya platform |
| Official Syllabus URL: | https://www.sas.com/en_us/certification.html |
SASInstitute A00-406 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Machine Learning Pipelines in SAS Viya | - Model tuning and optimization
|
| Supervised Machine Learning Models | - Model selection techniques
|
| Model Evaluation and Deployment | - Model assessment metrics
|
| Data Preparation for Machine Learning | - Data cleaning and preprocessing
|
SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:
1. Which type of model is well-suited for solving classification problems when dealing with high- dimensional data, such as text?
A) Linear Regression
B) Support Vector Machine (SVM)
C) Random Forest
D) K-Means Clustering
2. Which type of model is commonly used for anomaly detection in datasets?
A) Linear Regression
B) Clustering Models
C) Decision Trees
D) Principal Component Analysis (PCA)
3. Which metric is commonly used to evaluate the performance of a regression model?
A) Confusion Matrix
B) Precision
C) Mean Absolute Error (MAE)
D) F1 Score
4. What is the purpose of regularization techniques in model building, such as L1 and L2 regularization?
A) To add more features to the model
B) To increase model complexity
C) To prevent overfitting and reduce model complexity
D) To speed up model training
5. Refer to the exhibit below:
Based on the output from the Data Exploration node shown in the exhibit, which variable has the most thin tails (most platykurtic distribution)?
A) Logi_rfm4
B) Logi_rfm6
C) Logi_rfm12
D) Logi_rfm8
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: B | Question # 3 Answer: C | Question # 4 Answer: C | Question # 5 Answer: C |
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