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

1. What is the main advantage of ensemble methods in model building?

A) They work well with high-dimensional data
B) They combine multiple models to improve predictive performance
C) They require minimal data preprocessing
D) They produce simple and interpretable models

2. What is the primary purpose of model deployment in the context of data science and machine learning?

A) Making the model available for use in real-world applications
B) Model building
C) Model evaluation
D) Data preprocessing

3. Which technique is used for feature selection in a machine learning pipeline when dealing with a large number of features?

A) Naive Bayes
B) Regularization
C) One-Hot Encoding
D) Principal Component Analysis (PCA)

4. Which hyperparameter in a decision tree model controls the depth of the tree and helps prevent overfitting?

A) Max depth
B) Min samples split
C) Learning rate
D) Max features

5. What is the significance of the "bias-variance trade-off" in machine learning?

A) It is not relevant in machine learning.
B) It refers to the trade-off between the number of features and the model's complexity.
C) It represents the trade-off between underfitting and overfitting.
D) It indicates the trade-off between accuracy and precision.

Solutions: