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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
| Data Science and Watson Fundamentals | 20-25% | - Data collection, preparation, and exploration
- Data science methodology and CRISP-DM framework
- IBM Watson ecosystem and components
|
| Machine Learning and Model Development | 20-25% | - Feature engineering and selection
- Model deployment and monitoring
- Supervised and unsupervised learning concepts
- Model training, evaluation, and optimization
|
| Watson Studio and Watson Knowledge Catalog | 20-25% | - Data governance and cataloging
- Project management and collaboration
- Data asset management
- AutoAI and automatic model building
|
| Watson AI Services and Deployment | 10-15% | - Watson Assistant integration
- Monitoring deployed models
- Watson Discovery overview
- Deploying models as REST APIs
|
| Data Visualization and Storytelling | 15-20% | - Communicating findings to stakeholders
- Visualization best practices
- Interactive dashboards and reports
|
IBM Watson Data Scientist v1 Sample Questions:
1. Which method is used for merging records in SPSS Modeler Merge node that allows specifying a requirement to be satisfied in order for the merge to take place?
A) Key
B) Filter
C) Order
D) Condition
2. When implementing cross-validation, which of the following is NOT a common approach?
A) Stratified K-Fold Cross-Validation for imbalanced datasets
B) Leave-One-Out Cross-Validation (LOOCV)
C) K-Fold Cross-Validation
D) Using the entire dataset as both the training and the test set in each iteration
3. 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
4. Choosing the best model often involves trade-offs.
Which scenario represents such a trade-off?
A) Preferring a model with higher accuracy over one that is slightly less accurate but much more interpretable
B) Selecting a model based solely on its execution speed, without regard to accuracy
C) Choosing the model with the largest number of features, regardless of performance
D) Opting for the most complex model to ensure ease of use
5. In the context of avoiding underfitting and overfitting, what role does splitting the data into training, testing, and validation sets play?
A) It ensures that the model is trained on the maximum amount of data possible
B) It guarantees that the model will perform with 100% accuracy on unseen data
C) It increases the computational complexity without improving model performance
D) It allows for the model to be validated and tested on different subsets of data to check its generalization ability
Solutions:
Question # 1 Answer: D | Question # 2 Answer: D | Question # 3 Answer: C | Question # 4 Answer: A | Question # 5 Answer: D |