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

A00-406 real exams

Exam Code: A00-406

Exam Name: SAS® Viya® Supervised Machine Learning Pipelines

Updated: Nov 05, 2026

Q & A: 98 Questions and Answers

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SASInstitute A00-406 Exam Syllabus Topics:

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

Your SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Questions, Answered

The A00-406 exam is the official exam behind the SAS Certified Specialist: Machine Learning Using SAS Viya credential from SASInstitute. It sits at the Specialist level of the SASInstitute certification track. The PassTorrent practice questions on this page map to the same objectives, so you can measure your readiness before you book a seat.

The A00-406 exam contains 50-55 questions and gives you 90 minutes to complete them. That pace leaves little room for getting stuck, so train yourself to flag a hard question, move on, and circle back later. Run at least one full timed session in the PassTorrent test engine a week before your exam date to check whether your pacing holds under pressure.

You need 62% to pass the A00-406 exam, and the official registration fee is $180 USD. A retake means paying that fee again in full, which makes thorough preparation the cheaper option by far. Before scheduling, take a timed PassTorrent practice test; if you are not scoring comfortably above the passing line, give yourself more study time instead of booking on hope.

Basic knowledge of SAS Viya, statistics, and supervised machine learning concepts; experience with Model Studio recommended

Requirements can change, so confirm the latest details on the official SASInstitute exam page before you register.

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The A00-406 syllabus is divided into 3 domains, including Model Assessment and Deployment (24-30%), Building Models (40-46%), and Data Sources (30-36%). The complete breakdown, with every domain and its subtopics, is listed in the Exam Topics section above — review it line by line and flag the areas where you feel weakest.

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

Question #1

What is the purpose of regularization techniques in model building, such as L1 and L2 regularization?

  • A. To prevent overfitting and reduce model complexity
  • B. To speed up model training
  • C. To increase model complexity
  • D. To add more features to the model
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

Question #2

In natural language processing, what does "stemming" involve?

  • A. Converting text to numbers for model input
  • B. Creating new words to improve model performance
  • C. Grouping similar words together based on their meanings
  • D. Reducing words to their base or root form
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Question #3

What is the purpose of a confusion matrix in the context of classification models?

  • A. To compute the mean squared error
  • B. To visualize the data
  • C. To evaluate model performance, especially for binary classification
  • D. To summarize the distribution of target variables
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

Question #4

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_rfm6
  • B. Logi_rfm8
  • C. Logi_rfm4
  • D. Logi_rfm12
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Question #5

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

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

Correct Answer: B  🗳️

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