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

1. What is the difference between a classification problem and a regression problem in machine learning?

A) Classification is a type of regression problem.
B) Classification predicts categorical outcomes, while regression predicts numeric outcomes.
C) Regression predicts categorical outcomes, while classification predicts numeric outcomes.
D) There is no difference; the terms are used interchangeably.

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

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

3. Which feature extraction method can take both interval variables and class variables as inputs?

A) Robust PCA
B) Singular value decomposition
C) Principal component analysis
D) Autoencoder

4. Which of the following best describes unstructured data?

A) Data that is organized in rows and columns
B) Data that is difficult to process and lacks a predefined structure
C) Data stored in a relational database
D) Data with a clear schema

5. In machine learning, what does "overfitting" refer to?

A) A model that is undertrained and has high bias
B) A model that is unable to make predictions
C) A model that performs well on new, unseen data
D) A model that has too much complexity and fits the training data too closely

Solutions: