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

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

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

2. When deploying a model, what is "model explainability"?

A) The process of data preprocessing
B) The time it takes to make predictions
C) The simplicity of the model
D) The capability to interpret and understand the model's decisions and predictions

3. In the context of data integration, what does "data transformation" refer to?

A) Storing data in a centralized repository
B) Extracting data from source systems
C) Converting and reshaping data to match the target schema
D) Backing up data for disaster recovery

4. In model assessment, what does "cross-validation" aim to address?

A) Model deployment
B) Data preprocessing
C) Overfitting and generalization
D) Training a model

5. Which metric is commonly used to evaluate the performance of a regression model?

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

Solutions: