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

1. What is "model deployment" in the context of data science and machine learning?

A) The process of selecting features
B) The process of building a model
C) Making the model available for use in real-world applications
D) The process of data cleaning

2. What does API stand for in the context of data sources?

A) Application Program Interface
B) Application Programming Interface
C) Advanced Programming Integration
D) Automated Program Integration

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

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

4. In model evaluation, what is the purpose of a ROC curve (Receiver Operating Characteristic)?

A) To visualize data distribution
B) To evaluate the mean squared error of a model
C) To measure feature importance
D) To compare models' performance in terms of sensitivity and specificity

5. Which technique is commonly used for feature scaling or normalization in machine learning pipelines?

A) One-Hot Encoding
B) Decision Trees
C) Standardization
D) Principal Component Analysis (PCA)

Solutions: