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IBM Watson Data Scientist v1 Sample Questions:

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

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

2. What is the primary purpose of partitioning data into training and test sets?

A) To evaluate the model's performance on unseen data
B) To ensure that the model gets exposed to all possible data scenarios during training
C) To maximize the accuracy of the model by using all data for training
D) To increase the computational efficiency of model training

3. How do you determine which tool to use based on algorithm requirements and expertise?

A) Choose the newest tools on the market for the most up-to-date features.
B) Consider the tool's compatibility with the algorithm requirements and the team's expertise.
C) Select tools that the team is already familiar with, even if they are not the best fit for the algorithm.
D) Always use the most complex tool to ensure the model's accuracy.

4. Assessing the feasibility of a solution(s) often requires evaluating:

A) Preferred communication channels of the project manager
B) Technical feasibility, cost, and time constraints
C) Market competition only
D) The color scheme of the user interface

5. What is a key advantage of using supervised learning techniques over unsupervised learning techniques?

A) Supervised learning can work without any labeled data.
B) Supervised learning is more effective for discovering hidden patterns in data without prior labeling.
C) Supervised learning is typically used for prediction with known outcomes, providing clear metrics for model performance.
D) Supervised learning algorithms can automatically label data.

Solutions: