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

1. A project has been created and a pipeline has been run in Model Studio.
Which project setting can you edit?

A) Event-based Sampling proportions
B) Advisor Options for missing values
C) Partition Data percentages
D) Rules for model comparison statistic

2. What is the purpose of hyperparameter tuning in a machine learning pipeline?

A) To train the model
B) To select the most important features
C) To evaluate the model's predictions
D) To optimize the model's hyperparameters for better performance

3. Which hyperparameter in a decision tree model controls the depth of the tree and helps prevent overfitting?

A) Max depth
B) Learning rate
C) Min samples split
D) Max features

4. 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 evaluate model accuracy
C) To assess data quality
D) To create synthetic data

5. When building a deep learning neural network, what is the purpose of the activation function in each neuron?

A) To define the learning rate
B) To initialize the model
C) To control the number of hidden layers
D) To introduce non-linearity

Solutions: