SASInstitute A00-406 dumps - in .pdf

A00-406 pdf
  • Exam Code: A00-406
  • Exam Name: SAS® Viya® Supervised Machine Learning Pipelines
  • Updated: Sep 12, 2026
  • Q & A: 98 Questions and Answers
  • PDF Price: $59.99
  • Free Demo

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  • Exam Code: A00-406
  • Exam Name: SAS® Viya® Supervised Machine Learning Pipelines
  • Updated: Sep 12, 2026
  • Q & A: 98 Questions and Answers
  • PDF Version + PC Test Engine + Online Test Engine
  • Value Pack Total: $119.98  $79.99
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SASInstitute A00-406 dumps - Testing Engine

A00-406 Testing Engine
  • Exam Code: A00-406
  • Exam Name: SAS® Viya® Supervised Machine Learning Pipelines
  • Updated: Sep 12, 2026
  • Q & A: 98 Questions and Answers
  • Software Price: $59.99
  • Testing Engine

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SASInstitute A00-406 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Building Models40-46%- Build decision trees and ensemble models
- Understand supervised machine learning concepts
- Use model interpretability tools
- Build neural networks and SVM models
- Integrate custom code
- Build regression 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%- Explore and understand data
- Reduce dimensionality
- Create a project in Model Studio
- Perform variable selection
- Modify and prepare data

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

Question #1

Given the following properties for a neural network model, which statement is true regrading hidden units in the model? The following SAS program is submitted:

  • A. The number of hidden units is 1.
  • B. The number of hidden units is 50.
  • C. There are no hidden units in the model.
  • D. The number of hidden units is 26.
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Question #2

Which technique is used for feature selection in a machine learning pipeline when dealing with a large number of features?

  • A. One-Hot Encoding
  • B. Regularization
  • C. Naive Bayes
  • D. Principal Component Analysis (PCA)
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Question #3

Which evaluation metric is commonly used for assessing the performance of a binary classification model?

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

Correct Answer: A  🗳️

Question #4

Which hyperparameter in a decision tree model controls the depth of the tree and helps prevent overfitting?

  • A. Max features
  • B. Min samples split
  • C. Max depth
  • D. Learning rate
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

Question #5

What is the primary difference between supervised and unsupervised learning in model building?

  • A. The amount of labeled data required
  • B. The presence or absence of a target variable
  • C. The use of feature engineering
  • D. The type of data used
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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