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IBM Watson Data Scientist v1 Sample Questions:

1. In the context of IBM Garage Methodology, which of the following best describes the "Enterprise Design Thinking" stage?

A) It focuses on maintaining and operating solutions at scale.
B) It is primarily concerned with the technical deployment of solutions.
C) It emphasizes understanding user outcomes and business needs.
D) It involves the rapid building of prototypes to validate ideas.

2. In the context of deployment environments, understanding resources is crucial.
What does this typically involve?

A) Determining the computational power and memory requirements for the deployed solution
B) Focusing exclusively on the cost of storage
C) Selecting the programming language with the least number of keywords
D) Choosing the most aesthetically pleasing user interface

3. In IBM Garage Methodology, the 'Minimum Viable Product' (MVP) concept is crucial for:

A) Testing hypotheses with the smallest investment of time and resources
B) Extending the timeline of the project indefinitely
C) Waiting for all possible features to be developed before release
D) Maximizing the budget before the product launch

4. How does the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology begin its process model?

A) Evaluation
B) Data Preparation
C) Business Understanding
D) Model Building

5. Which metric is commonly used to evaluate the performance of a regression model?

A) Mean Squared Error (MSE)
B) Recall
C) Accuracy
D) Precision

Solutions: