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IBM A1000-144 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Evaluate business problem including ethical implications21%- Business requirements understanding
  • 1. AI Ladder assessment
    • 2. Define business requirements
      • 3. Identify available data
        • 4. Ethical considerations in AI
          • 5. AI design thinking
            Topic 2: Model Monitoring in Production17%- Operational ML monitoring
            • 1. Bias detection
              • 2. Model evaluation
                • 3. Production monitoring
                  Topic 3: Exploratory Data Analysis and Data Preparation18%- Data understanding and preparation
                  • 1. Data balancing and partitioning
                    • 2. Data cleaning and anonymization
                      • 3. Data visualization
                        Topic 4: Model Refinement and Deployment18%- ML pipeline and deployment
                        • 1. Training environment setup
                          • 2. Tool selection
                            • 3. Hyperparameter tuning
                              • 4. Model explainability
                                • 5. Feature engineering
                                  Topic 5: Model Implementation26%- Machine learning modeling
                                  • 1. Supervised learning (classification)
                                    • 2. Dimensionality reduction
                                      • 3. Supervised learning (regression)
                                        • 4. Unsupervised learning (clustering)

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