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

  • Exam Code: A00-406
  • Exam Name: SAS® Viya® Supervised Machine Learning Pipelines
  • Updated: Oct 08, 2026
  • Q & A: 98 Questions and Answers

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SASInstitute A00-406 Exam Syllabus Topics:

SectionWeightObjectives
Building Models40-46%- Supervised model creation (decision trees, ensembles, SVM, neural networks)
- Model comparison and selection
Model Assessment and Deployment24-30%- Assessing model performance (metrics, ROC curves, confusion matrices)
- Deploying models into production
Data Sources30-36%- Importing and preparing data
- Exploring and modifying data
- Dimensionality reduction and feature engineering

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

Question #1

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

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

Correct Answer: A  🗳️

Question #2

What is a data lake?

  • A. A data storage solution designed for high-speed data retrieval
  • B. A centralized repository for storing all structured and unstructured data at any scale
  • C. A specialized database for time-series data
  • D. A backup system for relational databases
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Question #3

Which of the following metrics is commonly used to evaluate the performance of a binary classification model in a machine learning pipeline?

  • A. R-squared
  • B. Accuracy
  • C. Root Mean Squared Error (RMSE)
  • D. Mean Absolute Error (MAE)
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Question #4

In the context of data integration, what does "data transformation" refer to?

  • A. Backing up data for disaster recovery
  • B. Extracting data from source systems
  • C. Storing data in a centralized repository
  • D. Converting and reshaping data to match the target schema
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Question #5

What is the primary purpose of model deployment in the context of data science and machine learning?

  • A. Model building
  • B. Making the model available for use in real-world applications
  • C. Data preprocessing
  • D. Model evaluation
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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