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IBM C1000-144 Exam Syllabus Topics:

SectionWeightObjectives
Model Monitoring in Production17%- Operational monitoring
  • 1. Bias detection and fairness assessment
    • 2. Model performance evaluation
      • 3. Production monitoring
        Evaluate business problem including ethical implications21%- Business requirements understanding
        • 1. Understand available data
          • 2. Ethical challenges in AI/ML problem framing
            • 3. AI design thinking process
              • 4. Understand business requirements
                • 5. AI Ladder assessment
                  Exploratory Data Analysis and Data Preparation18%- Data preparation techniques
                  • 1. Data balancing and partitioning
                    • 2. Data cleaning, labeling, anonymization
                      • 3. Data visualization
                        Model Refinement and Deployment18%- Model lifecycle operations
                        • 1. Feature engineering and selection
                          • 2. Hyperparameter tuning
                            • 3. Model explainability
                              • 4. Training environment configuration
                                • 5. Tool selection
                                  Model Implementation26%- Machine learning methods
                                  • 1. Unsupervised learning: clustering
                                    • 2. Supervised learning: classification
                                      • 3. Supervised learning: regression
                                        • 4. Dimensionality reduction techniques

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