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IBM Watson Data Scientist v1 Sample Questions:

1. The ROC curve is a graphical representation that shows the performance of a classification model at all classification thresholds.
What does ROC stand for?

A) Regression Operation Characteristic
B) Recall Operation Curve
C) Random Output Curve
D) Receiver Operating Characteristic

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

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

3. When comparing models to choose the best one, which factor is least likely to be considered?

A) The complexity of the model
B) The performance of the model on validation data
C) The color scheme of the model's output visualizations
D) The explainability of the model's predictions

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

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

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

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

Solutions: