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

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

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

2. In a machine learning pipeline, what is the purpose of cross-validation?

A) To split the dataset into training and testing sets
B) To train multiple models on different subsets of the data to assess generalization
C) To evaluate the model's performance on new data
D) To visualize the data distribution

3. Which type of model is typically used for time-series forecasting?

A) Logistic Regression
B) Decision Trees
C) K-Means Clustering
D) AutoRegressive Integrated Moving Average (ARIMA)

4. What is the purpose of data profiling in data source management?

A) To execute data queries
B) To create data visualizations
C) To assess the quality and characteristics of data
D) To optimize data storage

5. When deploying a machine learning model, what is "model drift"?

A) A sudden increase in the model's accuracy
B) A change in the distribution of the input data or target variable over time
C) A measure of feature importance
D) The process of feature extraction

Solutions: