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IBM Watson Data Scientist v1 Sample Questions:

1. What does the term "complexity" in model comparison refer to?

A) The size of the dataset the model can handle
B) The amount of computational resources required for training and inference
C) The number of hyperparameters that need to be tuned
D) The aesthetic appeal of the model's graphical representations

2. A virtual assistant has been developed and deployed based on the Watson Assistant service. The assistant will support customers by answering FAQs (Frequent Answered Questions).
Which metric is a good indicator of the performance of the virtual assistant?

A) The Root Mean Squared Error (RMSE) of words
B) The Area Under the Curve (AUC)
C) Measure escalated calls using A/B testing
D) The F1 score of predicted intents in the Analytics tab

3. Why is it important to create data splits that are reproducible?

A) To ensure that each model run can be exactly replicated for verification and comparison
B) To guarantee that the model will perform with 100% accuracy on unseen data
C) To allow for larger test sets for more comprehensive testing
D) To use more data for testing than for training

4. A model's performance is not solely dependent on its accuracy but also on:

A) The number of features selected
B) The color of the visualization charts
C) Metrics like precision, recall, and F1 score
D) The choice of programming language

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

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

Solutions: