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Updated: Jul 27, 2026
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IBM C1000-154 Exam Overview:
| Certification Vendor: | IBM |
| Exam Name: | IBM Watson Data Scientist v1 |
| Exam Number: | C1000-154 |
| Available Languages: | English, Japanese |
| Exam Price: | $200 USD |
| Real Exam Qty: | 60 |
| Exam Duration: | 90 minutes |
| Certificate Validity Period: | 2 years |
| Related Certifications: | IBM Cloud Pak for Data IBM Watson Studio |
| Passing Score: | 68% - 70% |
| Exam Format: | Multiple Choice, Multiple Response |
| Recommended Training: | IBM Watson Studio Learning Path IBM C1000-154 Study Guide |
| Exam Registration: | IBM Certification Portal Pearson VUE Registration |
| Sample Questions: | IBM C1000-154 Sample Questions |
| Exam Way: | Online proctored or onsite at Pearson VUE authorized test centers |
| Pre Condition: | No formal prerequisites; recommended 6-12 months hands-on experience with IBM Watson Studio and data science workflows |
| Official Syllabus URL: | https://www.ibm.com/certify/exams/C1000-154 |
IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Governance and Compliance | 5% | - Model governance and lineage tracking - Data security and privacy regulations |
| Build the Model | 20% | - Train models using Watson AutoAI and SPSS - Compare and select best performing models - Perform hyperparameter tuning - Select appropriate ML algorithms |
| Deploy the Solution | 10% | - Ensure scalability and reliability - Monitor model performance post-deployment - Deploy models as APIs in Watson |
| Visualization and Storytelling | 5% | - Communicate results to stakeholders - Create effective visualizations |
| Prepare the Data | 18% | - Feature engineering and selection - Handle missing values and outliers - Use Watson tools for data preparation - Clean, transform, and normalize datasets |
| Understand the Business Problem | 12% | - Define success metrics and constraints - Translate business requirements into data science objectives - Apply data science methodologies (CRISP-DM) |
| 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 |
| Evaluate the Model | 15% | - Validate model generalizability - Identify bias and overfitting - Assess classification/regression metrics |
IBM Watson Data Scientist v1 Sample Questions:
1. Why is it important to create data splits that are reproducible?
A) To guarantee that the model will perform with 100% accuracy on unseen data
B) To use more data for testing than for training
C) To ensure that each model run can be exactly replicated for verification and comparison
D) To allow for larger test sets for more comprehensive testing
2. Which analytic technique is NOT typically used to address business requirements?
A) Proofreading
B) Regression analysis
C) Clustering
D) Decision trees
3. Which two graph types are used in EDA to show the relationship between two or more quantitative variables?
A) Histogram
B) Heat map
C) Stem-and-leaf plot
D) Scatter plot
E) Box plot
4. Understanding how to use libraries in Python within a deployment environment is essential for:
A) Leveraging specific functionalities for data analysis, manipulation, and model building
B) Deploying models that are incompatible with the deployment environment
C) Increasing the complexity and maintenance cost of the deployed solution
D) Ensuring that all models are developed without any external libraries
5. Which Python library is commonly used for data manipulation and analysis, and is available in Cloud Pak for Data?
A) PyTorch
B) TensorFlow
C) Pandas
D) Keras
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: A | Question # 3 Answer: B,D | Question # 4 Answer: A | Question # 5 Answer: C |
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