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IBM Watson Data Scientist v1 Sample Questions:

1. When anticipating additional data sources that might be relevant, what is a crucial factor to consider?

A) The relevance of the data source to the business problem
B) The data source's popularity on social media
C) The color scheme of the data visualization
D) The graphical interface of the data source

2. When implementing cross-validation, which of the following is NOT a common approach?

A) Stratified K-Fold Cross-Validation for imbalanced datasets
B) K-Fold Cross-Validation
C) Using the entire dataset as both the training and the test set in each iteration
D) Leave-One-Out Cross-Validation (LOOCV)

3. In the context of model selection, explainability refers to:

A) The ease with which humans can understand how the model makes decisions.
B) How colorful and visually appealing the model's output is.
C) The complexity of the algorithm used to build the model.
D) The model's ability to operate without any data.

4. Which of the following is a critical first step in understanding a business problem for data science projects?

A) Defining the project scope
B) Deploying the model
C) Selecting the machine learning algorithm
D) Choosing the visualization tools

5. What is a benefit of creating data pipelines to automate the model lifecycle?

A) It necessitates frequent manual updates and checks
B) Encourages a one-size-fits-all approach to model development
C) It provides a structured approach to processing, validating, and deploying models
D) Reduces the need for understanding the underlying data

Solutions: