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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:
1. 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
2. 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
3. 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
4. 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
5. 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
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: B | Question # 3 Answer: A | Question # 4 Answer: D | Question # 5 Answer: A |
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