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SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:

1. What is a common example of an external data source for an organization?

A) Intranet portals
B) Employee databases
C) Customer surveys
D) Internal emails

2. When deploying a model, what is "model explainability"?

A) The simplicity of the model
B) The process of data preprocessing
C) The time it takes to make predictions
D) The capability to interpret and understand the model's decisions and predictions

3. What is metadata in the context of data sources?

A) Data that is encrypted for security
B) Data about data, providing information such as data source, structure, and context
C) Data that is in a non-standard, proprietary format
D) Data that is stored in a physical format

4. Which statements are true for the F1 score?
(Choose 2.)

A) F1 score is calculated based on a cut off value.
B) F1 score is calculated based on a depth value.
C) F1 score is applicable to a model with an interval target.
D) F1 score is applicable to a model with a binary target.

5. What is overfitting in machine learning, and how can it be addressed in a pipeline?

A) Overfitting occurs when the model fits the training data too closely and may not generalize well. It can be addressed by regularization techniques.
B) Overfitting is not a concern in machine learning pipelines.
C) Overfitting occurs when the model is too complex and overperforms.
D) Overfitting occurs when the model is too simple and underperforms.

Solutions: