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

1. Which of the following best describes unstructured data?

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

2. What is metadata in the context of data sources?

A) Data that is encrypted for security
B) Data that is stored in a physical format
C) Data about data, providing information such as data source, structure, and context
D) Data that is in a non-standard, proprietary format

3. What does the term "bagging" refer to in ensemble learning?

A) The process of combining multiple identical models to reduce variance
B) A form of dimensionality reduction
C) A type of feature extraction
D) A technique that reduces model complexity

4. A project has been created and a pipeline has been run in Model Studio.
Which project setting can you edit?

A) Rules for model comparison statistic
B) Partition Data percentages
C) Event-based Sampling proportions
D) Advisor Options for missing values

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

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

Solutions: