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IBM C1000-154 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Machine Learning Methods | - Model evaluation and validation - Unsupervised learning - Supervised learning |
| Data Science Fundamentals | - Data science lifecycle - Types of data and data sources |
| Data Preparation and Analysis | - Feature engineering basics - Data cleaning and preprocessing - Exploratory data analysis |
| IBM Watson Tools and Platform | - IBM Watson Studio usage - Model development and deployment |
| Data Visualization and Communication | - Visualization techniques - Communicating insights to stakeholders |
IBM Watson Data Scientist v1 Sample Questions:
What does the term "complexity" in model comparison refer to?
- A. The number of hyperparameters that need to be tuned
- B. The size of the dataset the model can handle
- C. The amount of computational resources required for training and inference
- D. The aesthetic appeal of the model's graphical representations
Correct Answer: C 🗳️
Which method is NOT traditionally used for collecting data?
- A. Using Python APIs for external data
- B. Scraping data from a webpage
- C. Using SQL to fetch data from a data warehouse
- D. Predicting future trends with machine learning models
Correct Answer: D 🗳️
In the context of model selection, explainability refers to:
- A. How colorful and visually appealing the model's output is.
- B. The complexity of the algorithm used to build the model.
- C. The model's ability to operate without any data.
- D. The ease with which humans can understand how the model makes decisions.
Correct Answer: D 🗳️
Profiling and visualizing data using Watson tools primarily helps in:
- A. Creating aesthetically pleasing presentations without regard to data relevance
- B. Increasing the quantity of data for analysis
- C. Identifying patterns, outliers, and insights in the data
- D. Simplifying the data collection process without analyzing quality
Correct Answer: C 🗳️
Selecting the right model for a data science project depends on:
- A. The project's budget only
- B. The preference of the data scientist
- C. The type of data and the problem to be solved
- D. The size of the dataset only
Correct Answer: C 🗳️
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