IBM Artificial Intelligence Fundamentals v1 - Associate : C1000-206

  • Exam Code: C1000-206
  • Exam Name: IBM Artificial Intelligence Fundamentals v1 - Associate
  • Updated: Aug 09, 2026
  • Q & A: 0 Questions and Answers

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IBM C1000-206 Exam Syllabus Topics:

SectionObjectives
Topic 1: AI Ethics and Responsible AI- Ethical considerations
  • 1. Transparency and explainability
    • 2. Bias in AI systems
      - Responsible AI practices
      • 1. Data privacy considerations
        • 2. Fairness and accountability
          Topic 2: Machine Learning Fundamentals- Model evaluation basics
          • 1. Training vs testing
            • 2. Basic evaluation metrics
              - Core ML concepts
              • 1. Unsupervised learning
                • 2. Supervised learning
                  Topic 3: Introduction to Artificial Intelligence- AI concepts and definitions
                  • 1. Types of AI systems
                    • 2. What is Artificial Intelligence
                      - AI applications
                      • 1. Industry adoption scenarios
                        • 2. Real-world use cases
                          Topic 4: Natural Language Processing and Computer Vision- NLP basics
                          • 1. Text processing concepts
                            • 2. Language understanding tasks
                              - Computer vision basics
                              • 1. Object detection overview
                                • 2. Image recognition concepts
                                  Topic 5: IBM AI Tools and Ecosystem- IBM Watson overview
                                  • 1. Enterprise AI applications
                                    • 2. AI services and capabilities
                                      Topic 6: Deep Learning and Neural Networks- Neural network fundamentals
                                      • 1. Perceptrons and layers
                                        • 2. Activation functions overview
                                          - Deep learning concepts
                                          • 1. Common architectures overview
                                            • 2. Training deep neural networks

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