- Exam Code: C1000-154
- Exam Name: IBM Watson Data Scientist v1
- Updated: Oct 22, 2026
- Q & A: 82 Questions and Answers
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| Section | Weight | Objectives |
|---|---|---|
| Collect and Explore the Data | 15% | - Detect patterns, outliers, and correlations - Perform descriptive statistics and exploratory analysis - Identify and access data sources in Watson Studio |
| Build the Model | 20% | - Perform hyperparameter tuning - Compare and select best performing models - Select appropriate ML algorithms - Train models using Watson AutoAI and SPSS |
| Prepare the Data | 18% | - Handle missing values and outliers - Use Watson tools for data preparation - Feature engineering and selection - Clean, transform, and normalize datasets |
| Deploy the Solution | 10% | - Ensure scalability and reliability - Monitor model performance post-deployment - Deploy models as APIs in Watson |
| Understand the Business Problem | 12% | - Define success metrics and constraints - Apply data science methodologies (CRISP-DM) - Translate business requirements into data science objectives |
| Visualization and Storytelling | 5% | - Create effective visualizations - Communicate results to stakeholders |
| Governance and Compliance | 5% | - Data security and privacy regulations - Model governance and lineage tracking |
| Evaluate the Model | 15% | - Identify bias and overfitting - Assess classification/regression metrics - Validate model generalizability |
Which search algorithm is known for its exhaustive search over a specified parameter space for hyperparameter tuning?
Correct Answer: C 🗳️
What is the primary purpose of hyperparameter tuning in machine learning models?
Correct Answer: D 🗳️
In the context of IBM Garage Methodology, which of the following best describes the "Enterprise Design Thinking" stage?
Correct Answer: B 🗳️
In Cognos Analytics, which two features distinguish stories from dashboards?
Correct Answer: B,D 🗳️
Understanding how to use libraries in Python within a deployment environment is essential for:
Correct Answer: A 🗳️
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