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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Science and Watson Fundamentals | 20-25% | - IBM Watson ecosystem and components - Data science methodology and CRISP-DM framework - Data collection, preparation, and exploration |
| Data Visualization and Storytelling | 15-20% | - Interactive dashboards and reports - Communicating findings to stakeholders - Visualization best practices |
| Watson AI Services and Deployment | 10-15% | - Deploying models as REST APIs - Monitoring deployed models - Watson Discovery overview - Watson Assistant integration |
| 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 |
| Watson Studio and Watson Knowledge Catalog | 20-25% | - Data governance and cataloging - AutoAI and automatic model building - Data asset management - Project management and collaboration |
IBM Watson Data Scientist v1 Sample Questions:
1. Assessing the feasibility of a solution(s) often requires evaluating:
A) Technical feasibility, cost, and time constraints
B) The color scheme of the user interface
C) Preferred communication channels of the project manager
D) Market competition only
2. Selecting the right model for a data science project depends on:
A) The preference of the data scientist
B) The size of the dataset only
C) The type of data and the problem to be solved
D) The project's budget only
3. 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 availability of high-performance computing resources.
C) The nature of the problem (e.g., classification, regression) and the characteristics of the data.
D) The theoretical complexity of the model, with more complex models always being preferred.
4. When implementing cross-validation, which of the following is NOT a common approach?
A) Leave-One-Out Cross-Validation (LOOCV)
B) K-Fold Cross-Validation
C) Stratified K-Fold Cross-Validation for imbalanced datasets
D) Using the entire dataset as both the training and the test set in each iteration
5. Which of the following is a common issue identified during the preprocessing of data?
A) Excessively large file names
B) Overly detailed documentation
C) Presence of missing values
D) Aesthetically unpleasing charts
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: C | Question # 3 Answer: C | Question # 4 Answer: D | Question # 5 Answer: C |
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