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

1. Which machine learning technique is typically used for building a model to predict a numeric target variable?

A) Classification
B) Regression
C) Dimensionality reduction
D) Clustering

2. Which statement is true regarding decision trees and models based on ensembles of trees?

A) A single decision tree will always be outperformed by a model based on an ensemble of trees.
B) In the Forest algorithm, each individual tree is pruned based on using minimum Average Squared Error.
C) For a Forest model, the out-of-bag sample is simply the original validation data set from when the raw data partitioning took place.
D) In the gradient boosting algorithm, for all but the first iteration, the target is the residual from the previous decision tree model.

3. In reinforcement learning, what is the "reward signal"?

A) The accuracy of the model's predictions
B) A numerical value that indicates the performance of an action taken by the agent
C) A regularization parameter
D) The final prediction made by the model

4. What does "feature selection" refer to in the context of model building?

A) The visualization of data distribution
B) The process of choosing the most relevant variables (features) for the model
C) The creation of synthetic features from existing data
D) The evaluation of model accuracy

5. 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

Solutions: