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IBM Watson Data Scientist v1 Sample Questions:

1. In the case of imbalanced data, what technique is recommended to ensure that the train and test sets have similar distributions of the target variable?

A) Random split without considering the target variable
B) Using only the majority class for splitting
C) Splitting based on the order of data collection
D) Stratified split

2. In the context of model selection, explainability refers to:

A) The ease with which humans can understand how the model makes decisions.
B) How colorful and visually appealing the model's output is.
C) The model's ability to operate without any data.
D) The complexity of the algorithm used to build the model.

3. Which hyperparameter is NOT commonly adjusted in a deep learning model?

A) Number of layers
B) Activation function
C) Learning rate
D) The color of the model's output

4. 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) Receiver Operating Characteristic
D) Random Output Curve

5. Which metric would be most appropriate for evaluating a model in a highly imbalanced classification problem?

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

Solutions: