Exam Name: SAS Viya 3.5 Natural Language Processing and Computer Vision
Certification Provider: SASInstitute
Corresponding Certification: Natural Language and Computer Vision Specialist
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Updated: Jul 28, 2026
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SASInstitute A00-405 exam is a certification exam that tests candidates' knowledge and skills in using SAS Viya for natural language processing and computer vision. A00-405 exam is suitable for professionals who work with data analysis, data science, and artificial intelligence. To prepare for the exam, candidates can take advantage of various resources provided by SASInstitute, including training courses, practice exams, and study materials.
SASInstitute A00-405 exam is an industry-recognized certification that focuses on SAS Viya 3.5 Natural Language Processing and Computer Vision. A00-405 exam is designed for professionals who are responsible for developing, implementing, or managing natural language processing and computer vision solutions using SAS Viya 3.5. It is an advanced-level exam that tests the candidate’s ability to design, develop, and deploy machine learning models that can process unstructured data such as text, image, and audio.
SASInstitute A00-405 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Identifying Text Patterns Using Natural Language Processing Techniques | 42% | - Write concept rules - Use the Topics Node to extract machine-generated topics - Use the Concepts and Text Parsing Nodes to extract terms and concepts - Use a Recurrent Neural Network (RNN) to recognize patterns - Write category rules - Use rules to identify documents belonging to specific categories |
| Loading and Exploring Data | 20% | - Load and prepare image data - Create and explore a project in SAS Visual Text Analytics - Import documents for analysis |
| Identifying Image Patterns Using Computer Vision Techniques | 38% | - Use padding in a Convolutional Neural Network (CNN) - Use regularization techniques - Score new image data - Tune the hyperparameters of a Convolutional Neural Network (CNN) - Use pooling in a Convolutional Neural Network (CNN) - Use output layers in a Convolutional Neural Network (CNN) - Explain the impact of various architectural designs - Use fully connected layers in a Convolutional Neural Network (CNN) - Use convolutional layers in a Convolutional Neural Network (CNN) |
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