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SASInstitute A00-405 exam is a valuable certification for professionals who work with SAS Viya and want to demonstrate their expertise in natural language processing and computer vision. SAS Viya 3.5 Natural Language Processing and Computer Vision certification can help individuals stand out in a competitive job market and demonstrate their commitment to continuing education and professional development. A00-405 exam is challenging but rewarding, and passing it can open up new opportunities for individuals in the field of data science and analytics.
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To prepare for the SASInstitute A00-405 exam, candidates should have a solid understanding of natural language processing and computer vision concepts, as well as experience using SAS Viya for data analysis and modeling. They should also be familiar with SAS programming language, SAS Studio, and SAS Visual Data Mining and Machine Learning. A00-405 exam consists of multiple-choice questions and candidates have 2 hours and 30 minutes to complete it. Passing the exam demonstrates proficiency in natural language processing and computer vision using SAS Viya 3.5, and can lead to career advancement opportunities in data science and analytics.
SASInstitute A00-405 certification exam is designed for professionals who want to validate their skills in natural language processing and computer vision using SAS Viya 3.5. SAS Viya is a powerful analytics platform that allows users to build, deploy, and manage machine learning models. With the A00-405 certification, candidates can demonstrate their proficiency in using SAS Viya to implement natural language processing and computer vision techniques.
SASInstitute A00-405 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Loading and Exploring Data | 20% | - Create and explore a project in SAS Visual Text Analytics - Load and prepare image data - Import documents for analysis |
| Identifying Image Patterns Using Computer Vision Techniques | 38% | - Score new image data - Use fully connected layers in a Convolutional Neural Network (CNN) - Explain the impact of various architectural designs - Use regularization techniques - Use pooling in a Convolutional Neural Network (CNN) - Use padding in a Convolutional Neural Network (CNN) - Use output layers in a Convolutional Neural Network (CNN) - Use convolutional layers in a Convolutional Neural Network (CNN) - Tune the hyperparameters of a Convolutional Neural Network (CNN) |
| Identifying Text Patterns Using Natural Language Processing Techniques | 42% | - Use a Recurrent Neural Network (RNN) to recognize patterns - Write concept rules - Use the Topics Node to extract machine-generated topics - Use rules to identify documents belonging to specific categories - Write category rules - Use the Concepts and Text Parsing Nodes to extract terms and concepts |
