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

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

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

2. When deploying a model, what is "model explainability"?

A) The time it takes to make predictions
B) The simplicity of the model
C) The process of data preprocessing
D) The capability to interpret and understand the model's decisions and predictions

3. 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 visualize the data distribution
D) To evaluate the model's performance on new data

4. Which data source allows for real-time data streaming and processing?

A) Data warehouses
B) Cloud storage
C) IoT devices
D) Static data files

5. When deploying a machine learning model, what is meant by "model latency"?

A) The time it takes to train a model
B) The time it takes to build a model
C) The time it takes for the model to make predictions once deployed
D) The time it takes to create synthetic data

Solutions: