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IBM C1000-154 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| IBM Watson Tools and Platform | - IBM Watson Studio usage - Model development and deployment |
| Machine Learning Methods | - Unsupervised learning - Supervised learning - Model evaluation and validation |
| Data Science Fundamentals | - Types of data and data sources - Data science lifecycle |
| Data Preparation and Analysis | - Exploratory data analysis - Data cleaning and preprocessing - Feature engineering basics |
| Data Visualization and Communication | - Communicating insights to stakeholders - Visualization techniques |
IBM Watson Data Scientist v1 Sample Questions:
Which type of machine learning algorithm would be most appropriate for predicting house prices based on various features like location, size, and number of bedrooms?
- A. Dimensionality Reduction
- B. Classification
- C. Regression
- D. Clustering
Correct Answer: C 🗳️
Which of the following is NOT a type of data source commonly integrated with Cloud Pak for Data?
- A. Cloud storage services
- B. Paper-based records
- C. Social media feeds
- D. Proprietary in-memory databases
Correct Answer: B 🗳️
Which hyperparameter is NOT commonly adjusted in a deep learning model?
- A. The color of the model's output
- B. Activation function
- C. Learning rate
- D. Number of layers
Correct Answer: A 🗳️
Which of the following is a key feature of Watson Knowledge Catalog (WKC) for identifying appropriate data sources?
- A. Cloud storage optimization
- B. Automated code compilation
- C. Real-time messaging
- D. Data discovery and categorization
Correct Answer: D 🗳️
In defining a business problem, what is essential to align with the stakeholders?
- A. Business objectives
- B. Data sources
- C. Technical requirements
- D. Project milestones
Correct Answer: A 🗳️
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