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SASInstitute A00-401 exam covers various topics related to supervised machine learning pipelines, including data preparation, feature engineering, model training, model assessment, and deployment. Candidates are expected to have a good understanding of data manipulation techniques, data visualization, and statistical modeling concepts. They should also have hands-on experience with SAS programming language and SAS Viya 3.4.
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SASInstitute A00-401 exam is designed to test the skills and knowledge of candidates in SAS Viya 3.4 Supervised Machine Learning Pipelines. SAS Viya 3.4 Supervised Machine Learning Pipelines certification exam is intended for data scientists, machine learning engineers, and programmers who are interested in developing predictive models using SAS Viya. A00-401 exam tests the candidates' ability to build, manage, and deploy machine learning pipelines using SAS Viya tools and techniques.
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To prepare for the SASInstitute A00-401 certification exam, candidates can take advantage of the various resources available, including online courses, study guides, practice exams, and hands-on exercises. These resources can help candidates develop the knowledge and skills needed to pass the exam and become certified SAS Viya 3.4 Supervised Machine Learning Pipelines professionals. With this certification, professionals can demonstrate their expertise in machine learning and open up new career opportunities in this rapidly growing field.
SASInstitute A00-401, also known as SAS Viya 3.4 Supervised Machine Learning Pipelines Exam, is a certification exam that measures an individual's knowledge and skills in using SAS Viya for creating, managing, and deploying supervised machine learning pipelines. A00-401 exam is designed for professionals who work in the field of data science, analytics, and machine learning, and want to demonstrate their expertise in using SAS Viya for building predictive models.
| Section | Weight | Objectives |
|---|---|---|
| Data Preparation | 20% | - Prepare data for modeling
|
| Model Assessment and Deployment | 20% | - Evaluate and deploy models
|
| Building Models | 35% | - Develop supervised machine learning models
|
| Data Sources and Data Exploration | 25% | - Access and manage data sources
|
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