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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Watson Studio and Watson Knowledge Catalog | 20-25% | - Project management and collaboration - Data asset management - Data governance and cataloging - AutoAI and automatic model building |
| Machine Learning and Model Development | 20-25% | - Model training, evaluation, and optimization - Model deployment and monitoring - Supervised and unsupervised learning concepts - Feature engineering and selection |
| Data Visualization and Storytelling | 15-20% | - Visualization best practices - Communicating findings to stakeholders - Interactive dashboards and reports |
| Watson AI Services and Deployment | 10-15% | - Watson Assistant integration - Deploying models as REST APIs - Monitoring deployed models - Watson Discovery overview |
| Data Science and Watson Fundamentals | 20-25% | - IBM Watson ecosystem and components - Data collection, preparation, and exploration - Data science methodology and CRISP-DM framework |
IBM Watson Data Scientist v1 Sample Questions:
1. Which Python library is commonly used for data manipulation and analysis, and is available in Cloud Pak for Data?
A) PyTorch
B) TensorFlow
C) Keras
D) Pandas
2. When comparing models to choose the best one, which factor is least likely to be considered?
A) The performance of the model on validation data
B) The complexity of the model
C) The color scheme of the model's output visualizations
D) The explainability of the model's predictions
3. When determining upskill requirements for a team working on a solution, it is important to consider:
A) The geographical location of the team members
B) The preferred programming languages of the team
C) The existing skill level of the team and the skills required for the project
D) The budget allocation for the project
4. What is a critical consideration when selecting the right model class for a given problem?
A) The model's ability to produce results quickly, regardless of accuracy.
B) The nature of the problem (e.g., classification, regression) and the characteristics of the data.
C) The availability of high-performance computing resources.
D) The theoretical complexity of the model, with more complex models always being preferred.
5. Which of the following is true about the AUC measure in the context of classification models?
A) It measures the model's accuracy using a single threshold.
B) It represents the degree of separability between classes.
C) It is less useful when the classes are highly imbalanced.
D) It indicates the number of false positives.
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: C | Question # 3 Answer: C | Question # 4 Answer: B | Question # 5 Answer: B |






