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

A00-401 real exams

Exam Code: A00-401

Exam Name: SAS Viya 3.4 Supervised Machine Learning Pipelines

Updated: Jul 23, 2026

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About SASInstitute A00-401 Exam

Earning the SAS Viya 3.4 Supervised Machine Learning Pipelines certification demonstrates that an individual has the skills and knowledge needed to build and deploy machine learning models using SAS Viya. It is a valuable credential for data scientists, machine learning engineers, and other professionals who work with data and analytics. By passing the A00-401 exam, individuals can demonstrate their proficiency in using SAS Viya to solve complex business problems and gain a competitive edge in the job market.

SASInstitute A00-401 certification exam is recognized globally and is highly valued in the industry. It is an excellent way for individuals to demonstrate their expertise in machine learning and SAS Viya 3.4. SAS Viya 3.4 Supervised Machine Learning Pipelines certification can help individuals advance their careers in data science and machine learning and open up new opportunities for them. Employers also benefit from hiring SASInstitute A00-401 certified professionals as they can be assured of their skills and knowledge in building and managing machine learning pipelines using SAS Viya 3.4.

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The A00-401 exam covers a range of topics related to supervised machine learning, including data preparation, feature engineering, model selection, and evaluation. Candidates are expected to have a strong understanding of basic statistical concepts, such as regression and classification, as well as experience with programming in SAS Viya. A00-401 exam consists of multiple-choice questions, and passing it requires a score of at least 68%.

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SASInstitute A00-401 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Building Models35%- Develop supervised machine learning models
  • 1. Support vector machines
  • 2. Model tuning and comparison
  • 3. Gradient boosting
  • 4. Decision trees
  • 5. Neural networks
  • 6. Forest models
Topic 2: Model Assessment and Deployment20%- Evaluate and deploy models
  • 1. Assess model performance metrics
  • 2. Select champion models
  • 3. Publish and deploy models
  • 4. ROC and lift analysis
  • 5. Compare competing models
Topic 3: Data Sources and Data Exploration25%- Access and manage data sources
  • 1. Identify data quality issues
  • 2. Understand metadata and project structure
  • 3. Import and load data into SAS Viya
  • 4. Explore data distributions and relationships
Topic 4: Data Preparation20%- Prepare data for modeling
  • 1. Perform variable selection and feature engineering
  • 2. Handle missing values
  • 3. Transform and standardize variables
  • 4. Create derived variables and features

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