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

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

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

2. Which of the following metrics is commonly used to evaluate the performance of a binary classification model in a machine learning pipeline?

A) Accuracy
B) Root Mean Squared Error (RMSE)
C) R-squared
D) Mean Absolute Error (MAE)

3. In the context of model building, what is the purpose of hyperparameter tuning?

A) Optimizing the model's hyperparameters for better performance
B) Selecting the most important features
C) Visualizing data
D) Training the model

4. Which SAS Viya component is typically used for deploying and monitoring machine learning models in production?

A) SAS Visual Analytics
B) SAS Enterprise Miner
C) SAS Data Loader
D) SAS Model Manager

5. Which evaluation metric is commonly used for assessing the performance of a regression model?

A) Confusion Matrix
B) F1 Score
C) Precision
D) Mean Absolute Error (MAE)

Solutions: