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IBM C1000-154 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Data Preparation and Analysis | - Data cleaning and preprocessing - Feature engineering basics - Exploratory data analysis |
| IBM Watson Tools and Platform | - Model development and deployment - IBM Watson Studio usage |
| Data Science Fundamentals | - Types of data and data sources - Data science lifecycle |
| Data Visualization and Communication | - Communicating insights to stakeholders - Visualization techniques |
| Machine Learning Methods | - Model evaluation and validation - Unsupervised learning - Supervised learning |
IBM Watson Data Scientist v1 Sample Questions:
1. What is a key disadvantage of using Grid Search for hyperparameter tuning?
A) It requires no prior knowledge of the hyperparameters
B) It is unable to handle discrete parameters
C) It is too quick and may miss out on evaluating some hyperparameters
D) It can be computationally expensive and time-consuming due to its exhaustive nature
2. 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) Clustering
B) Dimensionality Reduction
C) Regression
D) Classification
3. 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
4. How does the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology begin its process model?
A) Data Preparation
B) Evaluation
C) Business Understanding
D) Model Building
5. When would you use AutoAI to select algorithms for your model?
A) When you have a deep understanding of all available algorithms and want to manually tune hyperparameters.
B) When the model requirements are extremely specific and no standard algorithm fits.
C) When you want to automatically explore multiple algorithms and hyperparameters to find the best model.
D) Only when working with small datasets due to processing limitations.
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: C | Question # 3 Answer: D | Question # 4 Answer: C | Question # 5 Answer: C |
