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IBM A1000-144 Exam Overview:
| Certification Vendor: | IBM |
|---|---|
| Exam Name: | Assessment: IBM Machine Learning Data Scientist v1 |
| Exam Number: | A1000-144 / C1000-144 |
| Passing Score: | 74% (45 out of 61) |
| Exam Price: | $200 USD |
| Exam Duration: | 90 minutes |
| Related Certifications: | IBM Certified Data Scientist - Watson Specialist v1 |
| Exam Format: | Multiple-choice, Scenario-based |
| Available Languages: | English |
| Real Exam Qty: | 61 |
| Certificate Validity Period: | 2 years |
| Recommended Training: | IBM Learning Path: Data Scientist IBM Machine Learning with Watson Studio |
| Exam Registration: | IBM Certification Portal Pearson VUE Registration |
| Exam Way: | Online proctored or at authorized Pearson VUE test centers |
| Pre Condition: | No mandatory prerequisites; recommended: basic Python/R, SQL, statistics, and Watson Studio experience |
| Official Syllabus URL: | https://www.ibm.com/certify/certifications/c1000-144 |
IBM A1000-144 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Monitor and Maintain Models in Production | 10% | - Update and retrain models as needed - Identify and mitigate bias and fairness issues - Assess model performance and accuracy - Detect model drift and data drift |
| Select and Implement Machine Learning Models | 25% | - Supervised learning: Regression techniques - Supervised learning: Classification techniques - Unsupervised learning: Dimensionality reduction - Model selection criteria and trade-offs - Unsupervised learning: Clustering algorithms |
| Evaluate Business Problem and Ethical Considerations | 20% | - Assess ethical, legal, and compliance implications - Apply AI design thinking and AI Ladder framework - Identify available data sources and constraints - Understand business requirements and objectives |
| Refine, Optimize and Deploy Models | 20% | - Feature engineering and feature selection - Model explainability and interpretability - Use IBM Watson Studio and related tools - Prepare environment for model deployment - Hyperparameter tuning and model optimization |
| Exploratory Data Analysis and Data Preparation | 25% | - Perform statistical analysis and data visualization - Handle missing values and outliers - Balance, partition, and split datasets - Clean, label, anonymize, and transform data |
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