A00-406 exam dumps

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  • Exam Code: A00-406
  • Exam Name: SAS® Viya® Supervised Machine Learning Pipelines
  • No. of Questions: 98 Questions and Answers
  • Updated: Sep 26, 2026

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SASInstitute A00-406 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Building Models40-46%- Understand supervised machine learning concepts
- Use model interpretability tools
- Integrate custom code
- Build decision trees and ensemble models
- Build regression models
- Build neural networks and SVM models
Topic 2: Model Assessment and Deployment24-30%- Apply model assessment principles
- Select champion models
- Evaluate and compare model performance
- Deploy models into production
Topic 3: Data Sources30-36%- Create a project in Model Studio
- Modify and prepare data
- Explore and understand data
- Reduce dimensionality
- Perform variable selection

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

Which feature extraction method can take both interval variables and class variables as inputs?

  • A. Principal component analysis
  • B. Autoencoder
  • C. Singular value decomposition
  • D. Robust PCA
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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

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

Correct Answer: B  🗳️

What is the significance of the "bias-variance trade-off" in machine learning?

  • A. It is not relevant in machine learning.
  • B. It indicates the trade-off between accuracy and precision.
  • C. It refers to the trade-off between the number of features and the model's complexity.
  • D. It represents the trade-off between underfitting and overfitting.
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

What is the purpose of data profiling in data source management?

  • A. To create data visualizations
  • B. To execute data queries
  • C. To optimize data storage
  • D. To assess the quality and characteristics of data
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Which of the following is a common technique for handling missing data in a machine learning pipeline?

  • A. Replacing missing values with zeros
  • B. Imputing missing values
  • C. Deleting rows with missing data
  • D. Ignoring missing data
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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