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

1. What is the primary objective of model validation during the model assessment phase?

A) To assess the accuracy of the model
B) To create synthetic data
C) To build a model from scratch
D) To ensure the model generalizes well to new, unseen data

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

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

3. What is the purpose of regularization techniques in model building, such as L1 and L2 regularization?

A) To speed up model training
B) To prevent overfitting and reduce model complexity
C) To increase model complexity
D) To add more features to the model

4. What is "model retraining" in the context of model deployment?

A) The process of feature selection
B) Periodically updating and improving a deployed model with new data
C) The process of data preprocessing
D) The process of building the initial model

5. Which type of data source typically stores structured data in a tabular format?

A) APIs
B) Text documents
C) Relational databases
D) NoSQL databases

Solutions: