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

2. 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

3. What is the purpose of a "canary release" in the context of model deployment?

A) To deploy a new model version to a small subset of users or systems for testing
B) To assess data quality
C) To create synthetic data
D) To evaluate model accuracy

4. When building a recommendation system, which type of filtering is based on the user's behavior and preferences?

A) Singular Value Decomposition (SVD)
B) Collaborative filtering
C) Matrix factorization
D) Content-based filtering

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

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

Solutions: