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

1. In natural language processing, what does "stemming" involve?

A) Creating new words to improve model performance
B) Converting text to numbers for model input
C) Reducing words to their base or root form
D) Grouping similar words together based on their meanings

2. What is feature engineering in the context of machine learning pipelines?

A) Applying the model to new data
B) Creating new features from existing data
C) Building a machine learning model from scratch
D) Testing the model's performance

3. What is the purpose of cross-entropy loss in machine learning, especially in the context of classification?

A) To evaluate feature importance
B) To measure the dissimilarity between predicted and actual class probabilities
C) To calculate the mean squared error of a regression model
D) To quantify the variance of a model

4. What is the primary difference between supervised and unsupervised learning in model building?

A) The presence or absence of a target variable
B) The amount of labeled data required
C) The type of data used
D) The use of feature engineering

5. In a machine learning pipeline, what is the purpose of cross-validation?

A) To evaluate the model's performance on new data
B) To visualize the data distribution
C) To train multiple models on different subsets of the data to assess generalization
D) To split the dataset into training and testing sets

Solutions: