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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 ensures that the model is trained on the maximum amount of data possible
B) It guarantees that the model will perform with 100% accuracy on unseen data
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. Which metric would be most appropriate for evaluating a model in a highly imbalanced classification problem?

A) Accuracy
B) Precision
C) F1-score
D) Recall

3. Which feature is NOT available when managing models with Watson Machine Learning?

A) Rollback capabilities for model versions
B) Version control of deployed models
C) Automatic conversion of all models to deep learning models
D) Real-time performance monitoring

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

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

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

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

Solutions: