SASInstitute A00-406 : SAS® Viya® Supervised Machine Learning Pipelines

  • Exam Code: A00-406
  • Exam Name: SAS® Viya® Supervised Machine Learning Pipelines
  • Updated: Sep 26, 2026
  • Q & A: 98 Questions and Answers

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SASInstitute A00-406 Exam Syllabus Topics:

SectionWeightObjectives
Building Models40-46%- Build regression models
- Understand supervised machine learning concepts
- Build decision trees and ensemble models
- Integrate custom code
- Use model interpretability tools
- Build neural networks and SVM models
Data Sources30-36%- Modify and prepare data
- Create a project in Model Studio
- Reduce dimensionality
- Perform variable selection
- Explore and understand data
Model Assessment and Deployment24-30%- Select champion models
- Apply model assessment principles
- Evaluate and compare model performance
- Deploy models into production

SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:

Question #1

What is the main advantage of ensemble methods in model building?

  • A. They produce simple and interpretable models
  • B. They require minimal data preprocessing
  • C. They work well with high-dimensional data
  • D. They combine multiple models to improve predictive performance
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Question #2

In the context of data sources, what is meant by data versioning?

  • A. Storing multiple copies of the same data to increase redundancy
  • B. Keeping track of different versions or changes to data over time
  • C. Compressing data to reduce storage space
  • D. Encrypting data to protect against unauthorized access
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Question #3

When assessing a classification model, what is the confusion matrix used to measure?

  • A. Precision and recall
  • B. Data distribution
  • C. Model complexity
  • D. Model accuracy
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

Question #4

Which statements are true for the F1 score?
(Choose 2.)

  • A. F1 score is calculated based on a depth value.
  • B. F1 score is applicable to a model with an interval target.
  • C. F1 score is applicable to a model with a binary target.
  • D. F1 score is calculated based on a cut off value.
Reveal Solution  Discussion  0

Correct Answer: C,D  🗳️

Question #5

What is the primary objective of model validation during the model assessment phase?

  • A. To assess the accuracy of the model
  • B. To ensure the model generalizes well to new, unseen data
  • C. To build a model from scratch
  • D. To create synthetic data
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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