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IBM Watson Data Scientist v1 Sample Questions:

1. Which of the following best describes when to use deep learning over traditional machine learning algorithms?

A) For simple tasks that require straightforward predictive modeling.
B) When the dataset is small and easily interpretable.
C) When working with high-dimensional data, such as images or natural language, where feature extraction is complex.
D) When computational resources are limited and model interpretability is not a concern.

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

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

3. In the context of avoiding underfitting and overfitting, what role does splitting the data into training, testing, and validation sets play?

A) It increases the computational complexity without improving model performance
B) It guarantees that the model will perform with 100% accuracy on unseen data
C) It ensures that the model is trained on the maximum amount of data possible
D) It allows for the model to be validated and tested on different subsets of data to check its generalization ability

4. An E-retailer uses several important data sources, including web logs which contain all of the information on how customers navigate the web site. There are non-informative entries in the web logs that need to be removed.
During which phase should these non-informative entries be removed in the CRISP-DM model?

A) Modeling
B) Data Understanding
C) Data Preparation
D) Business Understanding

5. What is data leakage in the context of model training?

A) When data from outside the training dataset is accidentally included in the training process
B) Loss of data during the splitting process
C) A situation where the test data is not available
D) Leakage of sensitive information due to poor data handling practices

Solutions: