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

1. In model evaluation, what is the purpose of a ROC curve (Receiver Operating Characteristic)?

A) To measure feature importance
B) To evaluate the mean squared error of a model
C) To compare models' performance in terms of sensitivity and specificity
D) To visualize data distribution

2. When building a recommendation system, what does "collaborative filtering" rely on?

A) The characteristics of the items being recommended
B) The popularity of items
C) The past behavior or preferences of users
D) Item-based clustering

3. Which of the following best describes unstructured data?

A) Data with a clear schema
B) Data stored in a relational database
C) Data that is difficult to process and lacks a predefined structure
D) Data that is organized in rows and columns

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

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

5. What is feature engineering in the context of machine learning pipelines?

A) Testing the model's performance
B) Building a machine learning model from scratch
C) Creating new features from existing data
D) Applying the model to new data

Solutions: