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Updated: Aug 22, 2026
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| Certification Vendor: | IBM |
| Exam Name: | IBM Watson Data Scientist v1 |
| Exam Number: | C1000-154 |
| Certificate Validity Period: | 2 years |
| Related Certifications: | IBM Cloud Pak for Data IBM Watson Studio |
| Exam Format: | Multiple Choice, Multiple Response |
| Available Languages: | English, Japanese |
| Exam Duration: | 90 minutes |
| Exam Price: | $200 USD |
| Passing Score: | 68% - 70% |
| Real Exam Qty: | 60 |
| Recommended Training: | IBM C1000-154 Study Guide IBM Watson Studio Learning Path |
| Exam Registration: | Pearson VUE Registration IBM Certification Portal |
| 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 |
| Section | Weight | Objectives |
|---|---|---|
| Evaluate the Model | 15% | - Assess classification/regression metrics - Validate model generalizability - Identify bias and overfitting |
| Build the Model | 20% | - Compare and select best performing models - Select appropriate ML algorithms - Train models using Watson AutoAI and SPSS - Perform hyperparameter tuning |
| Collect and Explore the Data | 15% | - Perform descriptive statistics and exploratory analysis - Identify and access data sources in Watson Studio - Detect patterns, outliers, and correlations |
| Governance and Compliance | 5% | - Model governance and lineage tracking - Data security and privacy regulations |
| Deploy the Solution | 10% | - Ensure scalability and reliability - Deploy models as APIs in Watson - Monitor model performance post-deployment |
| Prepare the Data | 18% | - Feature engineering and selection - Use Watson tools for data preparation - Clean, transform, and normalize datasets - Handle missing values and outliers |
| Understand the Business Problem | 12% | - Apply data science methodologies (CRISP-DM) - Translate business requirements into data science objectives - Define success metrics and constraints |
| Visualization and Storytelling | 5% | - Communicate results to stakeholders - Create effective visualizations |
1. In the context of deployment environments, understanding resources is crucial.
What does this typically involve?
A) Choosing the most aesthetically pleasing user interface
B) Selecting the programming language with the least number of keywords
C) Determining the computational power and memory requirements for the deployed solution
D) Focusing exclusively on the cost of storage
2. When selecting a small number of algorithms based on model requirements, what factor should you primarily consider?
A) Choosing algorithms that are only based on supervised learning.
B) The algorithm that requires the least amount of data preprocessing.
C) The popularity of the algorithm in recent academic papers.
D) Compatibility of the algorithm with the data characteristics and the predictive task.
3. Which of the following best exemplifies the use of the CRISP-DM methodology in a business context?
A) A company following a strict top-down approach for all decisions
B) A team iterating between different stages as needed based on project feedback
C) A project manager focusing exclusively on deployment
D) A business starting with data collection before understanding the problem
4. What does the term "complexity" in model comparison refer to?
A) The size of the dataset the model can handle
B) The amount of computational resources required for training and inference
C) The aesthetic appeal of the model's graphical representations
D) The number of hyperparameters that need to be tuned
5. How do you determine which tool to use based on algorithm requirements and expertise?
A) Select tools that the team is already familiar with, even if they are not the best fit for the algorithm.
B) Always use the most complex tool to ensure the model's accuracy.
C) Choose the newest tools on the market for the most up-to-date features.
D) Consider the tool's compatibility with the algorithm requirements and the team's expertise.
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: D | Question # 3 Answer: B | Question # 4 Answer: B | Question # 5 Answer: D |
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