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| Section | Weight | Objectives |
|---|---|---|
| Monitor Models in Production | 17% | - Maintenance and retraining strategy - Model performance assessment - Production monitoring and drift detection - Bias and fairness evaluation |
| Exploratory Data Analysis and Preparation | 18% | - Data visualization and statistical analysis - Data balancing, partitioning, and transformation - Data cleaning, labeling, and anonymization |
| Refine and Deploy the Model | 18% | - Feature engineering and selection - Hyperparameter optimization - Model explainability and interpretability - Use IBM Watson Studio and related tools - Environment configuration and training setup |
| Implement the Proper Machine Learning Model | 26% | - Model selection and validation - Unsupervised learning: Dimensionality reduction - Supervised learning: Regression - Supervised learning: Classification - Unsupervised learning: Clustering |
| Evaluate Business Problem Including Ethical Implications | 21% | - Understand business requirements - Apply AI design thinking - Assess available data and feasibility - Follow the AI Ladder methodology - Identify ethical and compliance challenges |
No mandatory prerequisites; recommended 1+ year experience in data science, Python/R, and IBM Watson Studio Eligibility details like these are worth double-checking before you register — vendors revise them periodically, and the official exam page (official C1000-144 exam page) always has the current version.
The official IBM Machine Learning Data Scientist v1 outline has 5 domains — the biggest include Implement the Proper Machine Learning Model (26%), Refine and Deploy the Model (18%), Evaluate Business Problem Including Ethical Implications (21%). Those weightings are your study compass: the largest domains hide the most points. See the full outline above for every subtopic.
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The pass mark is 65% / 74% (varies by version; 45/61 questions), and the official fee is $200 USD. That fee resets to full price on every retake, so the real money-saver is preparation — run the 0 practice questions from Prep4away until passing becomes routine, then book your seat.
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Classroom hours alone won't tell you if you're ready — the C1000-144 practice questions from Prep4away will. Use them after the coursework to convert knowledge into exam performance.
The IBM Machine Learning Data Scientist v1 is IBM's official exam for the IBM Certified Data Scientist - Machine Learning Specialist v1 certification, a Specialist-level credential. Candidates take it to validate real skill — and employers read it exactly that way. It also connects to related credentials such as IBM Certified Data Scientist, IBM AI and Data Science Professional.
The exam gives you 90 minutes for 60-61 questions. The candidates who struggle aren't usually short on knowledge — they're short on pacing. Fix that before test day: set a time budget per question, practice moving past hard items without stalling, and rehearse full timed sessions in the Prep4away engine until the clock stops being a factor.
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