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IBM Watson Data Scientist v1 Sample Questions:

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

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

2. The process of aligning on user intents for a solution involves:

A) Identifying and understanding the needs and goals of end-users
B) Understanding the business model in depth
C) Determining the technical feasibility exclusively
D) Focusing on the data management strategies

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

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

4. In the deployment phase, why is it important to know the different data sources available in Cloud Pak for Data?

A) To ensure that all data sources are manually processed
B) To limit the deployment to only use local file storage
C) To effectively integrate and manage data from various sources for analysis and model training
D) Because only one type of data source can be used in any deployment

5. The first step in performing exploratory data analysis (EDA) typically involves:

A) Connecting to as many data sources as possible
B) Selecting a random sample of data to analyze
C) Determining the hypothesis for the analysis
D) Choosing a color palette for data visualization

Solutions: