SASInstitute A00-401 Valid Test : SAS Viya 3.4 Supervised Machine Learning Pipelines

  • Exam Code: A00-401
  • Exam Name: SAS Viya 3.4 Supervised Machine Learning Pipelines
  • Updated: Aug 19, 2026
  • Q&As: 0 Questions and Answers

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About SASInstitute A00-401 Real Exam

Passing the SASInstitute A00-401: SAS Viya 3.4 Supervised Machine Learning Pipelines Exam validates the candidates' skills in using SAS Viya 3.4 for building and deploying machine learning pipelines. SAS Viya 3.4 Supervised Machine Learning Pipelines certification demonstrates proficiency in building robust and scalable machine learning solutions, which is a valuable asset for any data scientist or machine learning engineer. SAS Viya 3.4 Supervised Machine Learning Pipelines certification also enhances the candidates' career prospects, as it is recognized by industry leaders worldwide.

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The A00-401 exam covers a range of topics, including data preparation, feature engineering, model selection, hyperparameter tuning, model deployment, and monitoring. Candidates will be required to demonstrate their proficiency in using SAS Viya programming languages such as SAS, Python, and R. Passing the A00-401 exam validates the candidate's ability to apply supervised machine learning techniques to solve complex business problems using the latest tools and technologies available in SAS Viya.

SASInstitute A00-401 (SAS Viya 3.4 Supervised Machine Learning Pipelines) Exam is designed to test the knowledge and skills of individuals who are interested in pursuing a career in machine learning. A00-401 exam provides a comprehensive evaluation of the candidate's ability to build, test, and deploy machine learning models using SAS Viya. The SAS Viya platform is a powerful tool for data scientists and analysts who want to develop advanced analytical models and gain insights from complex data.

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SASInstitute A00-401 Exam Syllabus Topics:

SectionWeightObjectives
Model Assessment and Deployment20%- Export score code
- Model comparison in Model Studio
- Model evaluation principles
- Register and publish models
- Fit statistics, ROC, Lift and comparison reports
Data Sources30%- Reduce dimensionality and handle redundancy
- Create a project in Model Studio
- Understand variable types and measurement scales
- Use Variable Selection node
- Explore and modify data
Building Models50%- Autotuning and regularization
- Neural networks
- Decision trees and ensemble models
- Support vector machines
- Supervised learning concepts
- Regression models
- Incorporate external and custom code

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