The A00-401 exam is a comprehensive test that covers a range of topics, including data preparation, data exploration, and data visualization. It requires candidates to have hands-on experience in building and evaluating predictive models, identifying variable importance, and selecting optimal model parameters. In addition, candidates must be familiar with different types of model evaluation metrics such as accuracy, precision, recall, ROC, and AUC.
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SASInstitute A00-401: SAS Viya 3.4 Supervised Machine Learning Pipelines is a professional certification exam that assesses an individual's knowledge and skills in implementing supervised machine learning pipelines using the SAS Viya platform. A00-401 exam is designed for data scientists, statisticians, and analysts who want to demonstrate their expertise in the field of machine learning.
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SASInstitute A00-401 certification exam is a valuable credential for professionals who want to demonstrate their expertise in supervised machine learning pipelines using SAS Viya. It tests the candidate's knowledge and practical skills in developing and deploying machine learning models, which is essential for businesses that rely on data-driven insights to make informed decisions. With this certification, individuals can stand out in the job market and demonstrate their commitment to ongoing professional development.
To prepare for the SASInstitute A00-401 (SAS Viya 3.4 Supervised Machine Learning Pipelines) Certification Exam, it is recommended that you have hands-on experience with SAS Viya 3.4 and its machine learning capabilities. You can also take the SAS Viya 3.4 Supervised Machine Learning Pipelines course, which covers all the topics included in the exam. Additionally, you can use SAS Viya 3.4 documentation, videos, and other resources to enhance your knowledge and skills in the field of machine learning.
SASInstitute A00-401 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Sources and Data Exploration | 25% | - Access and manage data sources
|
| Data Preparation | 20% | - Prepare data for modeling
|
| Model Assessment and Deployment | 20% | - Evaluate and deploy models
|
| Building Models | 35% | - Develop supervised machine learning models
|
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