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IBM C1000-144 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Monitor Models in Production | 17% | - Maintenance and retraining strategy - Production monitoring and drift detection - Model performance assessment - Bias and fairness evaluation |
| Exploratory Data Analysis and Preparation | 18% | - Data balancing, partitioning, and transformation - Data cleaning, labeling, and anonymization - Data visualization and statistical analysis |
| Evaluate Business Problem Including Ethical Implications | 21% | - Identify ethical and compliance challenges - Understand business requirements - Assess available data and feasibility - Apply AI design thinking - Follow the AI Ladder methodology |
| Implement the Proper Machine Learning Model | 26% | - Supervised learning: Regression - Model selection and validation - Supervised learning: Classification - Unsupervised learning: Dimensionality reduction - Unsupervised learning: Clustering |
| Refine and Deploy the Model | 18% | - Environment configuration and training setup - Feature engineering and selection - Use IBM Watson Studio and related tools - Model explainability and interpretability - Hyperparameter optimization |
