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Exam Code: C1000-154
Exam Name: IBM Watson Data Scientist v1
Updated: Aug 18, 2026
Q & A: 82 Questions and Answers
C1000-154 Free Demo download
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Governance and Compliance | 5% | - Model governance and lineage tracking - Data security and privacy regulations |
| Topic 2: Evaluate the Model | 15% | - Assess classification/regression metrics - Validate model generalizability - Identify bias and overfitting |
| Topic 3: Understand the Business Problem | 12% | - Define success metrics and constraints - Apply data science methodologies (CRISP-DM) - Translate business requirements into data science objectives |
| Topic 4: Deploy the Solution | 10% | - Monitor model performance post-deployment - Ensure scalability and reliability - Deploy models as APIs in Watson |
| Topic 5: Collect and Explore the Data | 15% | - Identify and access data sources in Watson Studio - Perform descriptive statistics and exploratory analysis - Detect patterns, outliers, and correlations |
| Topic 6: Prepare the Data | 18% | - Feature engineering and selection - Clean, transform, and normalize datasets - Handle missing values and outliers - Use Watson tools for data preparation |
| Topic 7: Build the Model | 20% | - Train models using Watson AutoAI and SPSS - Perform hyperparameter tuning - Select appropriate ML algorithms - Compare and select best performing models |
| Topic 8: Visualization and Storytelling | 5% | - Create effective visualizations - Communicate results to stakeholders |
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