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IBM Watson Data Scientist v1 Sample Questions:

1. Profiling and visualizing data using Watson tools primarily helps in:

A) Creating aesthetically pleasing presentations without regard to data relevance
B) Simplifying the data collection process without analyzing quality
C) Identifying patterns, outliers, and insights in the data
D) Increasing the quantity of data for analysis

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

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

3. In unsupervised learning, which algorithm is best suited for grouping customers based on their purchase history to target marketing efforts more effectively?

A) K-Means Clustering
B) Support Vector Machines
C) Linear Regression
D) Decision Trees

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

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

5. In the case of imbalanced data, what technique is recommended to ensure that the train and test sets have similar distributions of the target variable?

A) Random split without considering the target variable
B) Using only the majority class for splitting
C) Stratified split
D) Splitting based on the order of data collection

Solutions: