SASInstitute A00-408 Q&A - in .pdf

  • Exam Code: A00-408
  • Exam Name: SAS® Viya® Natural Language Processing and Computer Vision
  • Updated: Aug 31, 2026
  • Q & A: 0 Questions and Answers
  • PDF Price: $888.00
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SASInstitute A00-408 Q&A - Testing Engine

  • Exam Code: A00-408
  • Exam Name: SAS® Viya® Natural Language Processing and Computer Vision
  • Updated: Aug 31, 2026
  • Q & A: 0 Questions and Answers
  • Install on multiple computers for self-paced, at-your-convenience training.
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SASInstitute A00-408 Exam Syllabus Topics:
SectionWeightObjectives
Topic 1: Loading and Exploring Data18-22%- Load and Prepare Image Data
  • 1. Prepare image data for modeling
  • 2. Load labeled image data
  • 3. Augment image data
- Import Documents for Analysis
  • 1. Convert documents for analysis
  • 2. Minimize information loss when converting proprietary formats
  • 3. Explore and prepare documents
  • 4. Troubleshoot language encoding issues
- Create and Explore a Project in SAS Visual Text Analytics
  • 1. Export score code for new data
  • 2. Identify key features of the term table
  • 3. Identify the default pipeline
  • 4. Assign text and category roles
  • 5. Explore the Documents Table
Topic 2: Identifying Text Patterns Using Natural Language Processing Techniques40-45%- Analyze and Model Text Data
  • 1. Interpret natural language processing results
  • 2. Identify text patterns
  • 3. Apply text analytics techniques
  • 4. Perform text categorization
- Extract Terms and Concepts
  • 1. Use predefined concepts
  • 2. View document matches and similarity scores
  • 3. Evaluate concept impact on the pipeline
  • 4. Modify the term table and evaluate impact
  • 5. Explore and interpret the term map
  • 6. Create custom concepts
  • 7. Use include, exclude, and synonym lists
  • 8. Explain the value of concepts
Topic 3: Identifying Text Patterns Using Computer Vision Techniques35-40%- Build and Evaluate Deep Learning Models
  • 1. Train convolutional neural networks
  • 2. Evaluate model performance
  • 3. Apply regularization and optimization methods
  • 4. Score and classify images
  • 5. Apply transfer learning techniques
- Prepare and Process Images
  • 1. Perform image augmentation
  • 2. Load and manage image data
  • 3. Apply image preprocessing techniques

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