SAS® Viya® Natural Language Processing and Computer Vision: A00-408 Exam

"SAS® Viya® Natural Language Processing and Computer Vision", also known as A00-408 exam, is a SASInstitute Certification. With the complete collection of questions and answers, UpdateDumps has assembled to take you through 0 Q&As to your A00-408 Exam preparation. In the A00-408 exam resources, you will cover every field and category in SASInstitute Certification Certification helping to ready you for your successful SASInstitute Certification.

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SASInstitute A00-408 Exam Syllabus Topics:

SectionWeightObjectives
Loading and Exploring Data18-22%- Import Documents for Analysis
  • 1. Convert documents for analysis
  • 2. Troubleshoot language encoding issues
  • 3. Explore and prepare documents
  • 4. Minimize information loss when converting proprietary formats
- Load and Prepare Image Data
  • 1. Augment image data
  • 2. Load labeled image data
  • 3. Prepare image data for modeling
- Create and Explore a Project in SAS Visual Text Analytics
  • 1. Identify the default pipeline
  • 2. Assign text and category roles
  • 3. Identify key features of the term table
  • 4. Export score code for new data
  • 5. Explore the Documents Table
Identifying Text Patterns Using Natural Language Processing Techniques40-45%- Analyze and Model Text Data
  • 1. Apply text analytics techniques
  • 2. Identify text patterns
  • 3. Interpret natural language processing results
  • 4. Perform text categorization
- Extract Terms and Concepts
  • 1. View document matches and similarity scores
  • 2. Create custom concepts
  • 3. Use predefined concepts
  • 4. Modify the term table and evaluate impact
  • 5. Use include, exclude, and synonym lists
  • 6. Explain the value of concepts
  • 7. Evaluate concept impact on the pipeline
  • 8. Explore and interpret the term map
Identifying Text Patterns Using Computer Vision Techniques35-40%- Prepare and Process Images
  • 1. Perform image augmentation
  • 2. Apply image preprocessing techniques
  • 3. Load and manage image data
- Build and Evaluate Deep Learning Models
  • 1. Apply regularization and optimization methods
  • 2. Evaluate model performance
  • 3. Score and classify images
  • 4. Train convolutional neural networks
  • 5. Apply transfer learning techniques

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