SASInstitute SAS Viya 3.5 Supervised Machine Learning Pipelines A00-402 Certified Exam Dumps

A00-402 Exam Dumps

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Certification Provider: SASInstitute
Exam Code / Number: A00-402
Exam Name: SAS Viya 3.5 Supervised Machine Learning Pipelines
Exam Questions: 0
Corresponding Certification: SASInstitute Certification

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SASInstitute A00-402 exam is one of the most sought-after certifications in the field of data science and analytics. A00-402 exam focuses on SAS Viya 3.5 Supervised Machine Learning Pipelines, which is a powerful tool for building machine learning models. A00-402 exam is designed to test the knowledge and skills of individuals who work with SAS Viya 3.5 and are interested in building supervised machine learning models.

Reference: https://www.sas.com/en_us/certification/credentials/advanced-analytics/machine-learning-specialist.html

SASInstitute A00-402 exam is an excellent opportunity for data professionals who wish to enhance their expertise in supervised machine learning pipelines. Passing A00-402 exam will enable individuals to demonstrate their proficiency in managing, preparing, and analyzing data, constructing predictive models using machine learning algorithms, and deploying models into production environments. The SAS Viya 3.5 Supervised Machine Learning Pipelines certification can open up several career opportunities in this field and serves as a testament to an individual’s commitment to advancing their career.

SASInstitute A00-402 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Sources and Data Exploration30%- Access and manage data in Model Studio
  • 1. Explore data distributions and quality
  • 2. Create projects and pipelines
  • 3. Partition data into training, validation, and test sets
  • 4. Import and load data into SAS Viya
Topic 2: Model Assessment and Deployment30%- Evaluate and deploy models
  • 1. Deploy models to production
  • 2. Select champion models
  • 3. ROC and lift curve analyses
  • 4. Assess model performance metrics
Topic 3: Data Preparation- Prepare and modify data for modeling
  • 1. Feature extraction and selection
  • 2. Transform, standardize and derive variables
  • 3. Handle missing values
Topic 4: Building Supervised Models40%- Develop and tune machine learning models
  • 1. Gradient boosting models
  • 2. Model comparison and tuning
  • 3. Support vector machines
  • 4. Decision trees and forest models


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