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IBM A1000-144 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Monitor Models in Production | 17% | - Determine whether unfair bias exists in the model - Monitor the model in production - Assess the model |
| Topic 2: Implement the Proper Model | 26% | - Implement unsupervised learning using clustering - Implement supervised learning using regression - Implement unsupervised learning using dimensional reduction - Implement supervised learning using classification |
| Topic 3: Refine and Deploy the Model | 18% | - Implement model explainability - Configure environment specifications for model training - Select appropriate tools - Identify operations and transformations used for feature selection and engineering - Train the model and optimize hyperparameters |
| Topic 4: Evaluate Business Problem Including Ethical Implications | 21% | - Perform AI design thinking - Understand ethical challenges in the business problem - Understand business requirements - Assess progress on the AI Ladder - Understand what data is available |
| Topic 5: Exploratory Data Analysis Including Data Preparation | 18% | - Identify methods used to clean, label, and anonymize data - Balance and partition data - Visualize data |
