SASInstitute A00-401 dumps - in .pdf

A00-401 pdf
  • Exam Code: A00-401
  • Exam Name: SAS Viya 3.4 Supervised Machine Learning Pipelines
  • Updated: Aug 16, 2026
  • Q & A: 0 Questions and Answers
  • PDF Price: $888.00
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  • Exam Code: A00-401
  • Exam Name: SAS Viya 3.4 Supervised Machine Learning Pipelines
  • Updated: Aug 16, 2026
  • Q & A: 0 Questions and Answers
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  • Exam Code: A00-401
  • Exam Name: SAS Viya 3.4 Supervised Machine Learning Pipelines
  • Updated: Aug 16, 2026
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  • Software Price: $888.00
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SASInstitute A00-401: SAS Viya 3.4 Supervised Machine Learning Pipelines Exam is one of the most sought-after certifications in the world of data science. A00-401 exam is designed to test the candidate's knowledge and skills in implementing supervised machine learning algorithms in SAS Viya. If you are looking forward to demonstrating your proficiency in supervised machine learning, A00-401 exam is definitely for you.

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SASInstitute A00-401 is a certification exam that evaluates the knowledge and skills of individuals in building, implementing and managing machine learning pipelines using SAS Viya 3.4 supervised machine learning. The SAS Viya 3.4 platform is a powerful tool that enables data scientists and analysts to build, train, and deploy machine learning models at scale. Passing A00-401 exam will certify that an individual has the necessary skills and knowledge to work with SAS Viya 3.4 and build machine learning pipelines.

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SASInstitute A00-401 Exam Syllabus Topics:

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

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