SASInstitute SAS Viya 3.5 Natural Language Processing and Computer Vision : A00-405

A00-405 real exams

Exam Code: A00-405

Exam Name: SAS Viya 3.5 Natural Language Processing and Computer Vision

Updated: Jul 21, 2026

Q & A: 62 Questions and Answers

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About SASInstitute A00-405 Exam

SASInstitute A00-405 exam is designed to test the knowledge and skills of professionals in using SAS Viya 3.5 for natural language processing and computer vision. A00-405 exam covers various topics such as text analytics, sentiment analysis, image analysis, and machine learning algorithms. A00-405 exam also includes questions on SAS Viya 3.5 architecture and deployment, data preparation and management, and model deployment and monitoring.

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SASInstitute A00-405 certification exam is designed for professionals who want to validate their skills and knowledge in Natural Language Processing (NLP) and Computer Vision using SAS Viya 3.5. A00-405 exam covers a wide range of topics, including data preparation, feature engineering, model building, and evaluation. To pass the exam, candidates must demonstrate their ability to use SAS Viya to solve real-world problems related to NLP and Computer Vision.

SASInstitute A00-405 exam consists of 65 multiple-choice questions that must be completed within 2 hours and 15 minutes. A00-405 exam is available in English and is delivered through Pearson VUE. Candidates who pass the exam will receive a SAS Certified Specialist: Viya 3.5 Natural Language Processing and Computer Vision certificate.

Reference: https://www.sas.com/en_ph/certification/exam-content-guides/natural-language-computer-vision-specialist.html

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SASInstitute A00-405 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Loading and Exploring Data20%- Load and prepare image data
- Create and explore a project in SAS Visual Text Analytics
- Import documents for analysis
Topic 2: Identifying Text Patterns Using Natural Language Processing Techniques42%- Use the Concepts and Text Parsing Nodes to extract terms and concepts
- 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
Topic 3: Identifying Image Patterns Using Computer Vision Techniques38%- Explain the impact of various architectural designs
- Tune the hyperparameters of a Convolutional Neural Network (CNN)
- Use convolutional layers in a Convolutional Neural Network (CNN)
- Use fully connected layers in a Convolutional Neural Network (CNN)
- Score new image data
- Use pooling in a Convolutional Neural Network (CNN)
- Use output layers in a Convolutional Neural Network (CNN)
- Use regularization techniques
- Use padding in a Convolutional Neural Network (CNN)

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