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IBM C1000-144 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement the Proper Machine Learning Model | 26% | - Unsupervised learning: Clustering - Supervised learning: Regression - Model selection and validation - Supervised learning: Classification - Unsupervised learning: Dimensionality reduction |
| Topic 2: Evaluate Business Problem Including Ethical Implications | 21% | - Apply AI design thinking - Assess available data and feasibility - Follow the AI Ladder methodology - Identify ethical and compliance challenges - Understand business requirements |
| Topic 3: Refine and Deploy the Model | 18% | - Model explainability and interpretability - Use IBM Watson Studio and related tools - Feature engineering and selection - Hyperparameter optimization - Environment configuration and training setup |
| Topic 4: Exploratory Data Analysis and Preparation | 18% | - Data balancing, partitioning, and transformation - Data cleaning, labeling, and anonymization - Data visualization and statistical analysis |
| Topic 5: Monitor Models in Production | 17% | - Bias and fairness evaluation - Maintenance and retraining strategy - Model performance assessment - Production monitoring and drift detection |
