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

1. Which algorithm is commonly used for binary classification in machine learning pipelines, especially when dealing with imbalanced datasets?

A) Support Vector Machine (SVM)
B) Linear Regression
C) Principal Component Analysis (PCA)
D) K-Means Clustering

2. Which of the following is a common technique for handling missing data in a machine learning pipeline?

A) Deleting rows with missing data
B) Imputing missing values
C) Replacing missing values with zeros
D) Ignoring missing data

3. What is the primary function of a data catalog in managing data sources?

A) Data storage
B) Data analysis
C) Data visualization
D) Data documentation and discovery

4. What is overfitting in machine learning, and how can it be addressed in a pipeline?

A) Overfitting occurs when the model is too complex and overperforms.
B) Overfitting occurs when the model fits the training data too closely and may not generalize well. It can be addressed by regularization techniques.
C) Overfitting occurs when the model is too simple and underperforms.
D) Overfitting is not a concern in machine learning pipelines.

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

A) In the gradient boosting algorithm, for all but the first iteration, the target is the residual from the previous decision tree model.
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) A single decision tree will always be outperformed by a model based on an ensemble of trees.

Solutions: