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Last Updated: Aug 15, 2026
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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Build the Model | 20% | - Perform hyperparameter tuning - Select appropriate ML algorithms - Compare and select best performing models - Train models using Watson AutoAI and SPSS |
| Topic 2: Governance and Compliance | 5% | - Data security and privacy regulations - Model governance and lineage tracking |
| Topic 3: Visualization and Storytelling | 5% | - Create effective visualizations - Communicate results to stakeholders |
| Topic 4: Understand the Business Problem | 12% | - Define success metrics and constraints - Translate business requirements into data science objectives - Apply data science methodologies (CRISP-DM) |
| Topic 5: Deploy the Solution | 10% | - Deploy models as APIs in Watson - Ensure scalability and reliability - Monitor model performance post-deployment |
| Topic 6: 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 |
| Topic 7: Evaluate the Model | 15% | - Validate model generalizability - Assess classification/regression metrics - Identify bias and overfitting |
| Topic 8: Prepare the Data | 18% | - Clean, transform, and normalize datasets - Feature engineering and selection - Use Watson tools for data preparation - Handle missing values and outliers |
IBM Watson Data Scientist v1 Sample Questions:
1. In the context of IBM Garage Methodology, which of the following best describes the "Enterprise Design Thinking" stage?
A) It focuses on maintaining and operating solutions at scale.
B) It is primarily concerned with the technical deployment of solutions.
C) It emphasizes understanding user outcomes and business needs.
D) It involves the rapid building of prototypes to validate ideas.
2. In the context of deployment environments, understanding resources is crucial.
What does this typically involve?
A) Determining the computational power and memory requirements for the deployed solution
B) Focusing exclusively on the cost of storage
C) Selecting the programming language with the least number of keywords
D) Choosing the most aesthetically pleasing user interface
3. In IBM Garage Methodology, the 'Minimum Viable Product' (MVP) concept is crucial for:
A) Testing hypotheses with the smallest investment of time and resources
B) Extending the timeline of the project indefinitely
C) Waiting for all possible features to be developed before release
D) Maximizing the budget before the product launch
4. How does the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology begin its process model?
A) Evaluation
B) Data Preparation
C) Business Understanding
D) Model Building
5. Which metric is commonly used to evaluate the performance of a regression model?
A) Mean Squared Error (MSE)
B) Recall
C) Accuracy
D) Precision
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: A | Question # 3 Answer: A | Question # 4 Answer: C | Question # 5 Answer: A |
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