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SASInstitute A00-401 certification exam is recognized globally and is a testament to a candidate's expertise in supervised machine learning pipelines. It can help professionals differentiate themselves in a competitive job market and open up new career opportunities. Additionally, SASInstitute offers a range of resources to help candidates prepare for the exam, including training courses, practice exams, and study materials.
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SASInstitute A00-401 certification exam is a rigorous and comprehensive test of a candidate's ability to use SAS Viya 3.4 to develop and deploy supervised machine learning pipelines. It is an excellent opportunity for data science professionals to demonstrate their skills and expertise in this critical area of data analytics. By earning the certification, candidates can position themselves for success in a rapidly growing and evolving field.
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The SASInstitute A00-401 exam comprises 70 multiple-choice and short-answer questions that assess theoretical and practical knowledge in SAS Viya 3.4 supervised machine learning pipelines. The questions are designed to test different knowledge areas such as data preparation, feature engineering, modeling techniques, model assessment, and deployment. Passing the SASInstitute A00-401 (SAS Viya 3.4 Supervised Machine Learning Pipelines) Exam will help showcase your expertise in creating efficient machine learning pipelines, testing model efficiency, and deploying models to a production environment.
| Section | Weight | Objectives |
|---|---|---|
| Building Models | 50% | - Neural networks - Supervised learning concepts - Incorporate external and custom code - Autotuning and regularization - Regression models - Decision trees and ensemble models - Support vector machines |
| Model Assessment and Deployment | 20% | - Fit statistics, ROC, Lift and comparison reports - Model comparison in Model Studio - Register and publish models - Model evaluation principles - Export score code |
| Data Sources | 30% | - Understand variable types and measurement scales - Create a project in Model Studio - Reduce dimensionality and handle redundancy - Explore and modify data - Use Variable Selection node |
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