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

1. In model assessment, what is the purpose of feature importance analysis?

A) To assess data quality
B) To evaluate the significance of input features in making predictions
C) To create synthetic features
D) To visualize data distribution

2. What is the difference between a classification problem and a regression problem in machine learning?

A) Regression predicts categorical outcomes, while classification predicts numeric outcomes.
B) Classification is a type of regression problem.
C) There is no difference; the terms are used interchangeably.
D) Classification predicts categorical outcomes, while regression predicts numeric outcomes.

3. 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 fits the training data too closely and may not generalize well. It can be addressed by regularization techniques.
D) Overfitting occurs when the model is too complex and overperforms.

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

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

5. Refer to the exhibit below:

Based on the output from the Data Exploration node shown in the exhibit, which variable has the most thin tails (most platykurtic distribution)?

A) Logi_rfm4
B) Logi_rfm12
C) Logi_rfm6
D) Logi_rfm8

Solutions: