SASInstitute SAS® Viya® Supervised Machine Learning Pipelines : A00-406

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

2. What is the purpose of cross-entropy loss in machine learning, especially in the context of classification?

A) To measure the dissimilarity between predicted and actual class probabilities
B) To quantify the variance of a model
C) To evaluate feature importance
D) To calculate the mean squared error of a regression model

3. In natural language processing, what does "stemming" involve?

A) Reducing words to their base or root form
B) Grouping similar words together based on their meanings
C) Converting text to numbers for model input
D) Creating new words to improve model performance

4. Which of the following best describes unstructured data?

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

5. In model assessment, what does "cross-validation" aim to address?

A) Overfitting and generalization
B) Training a model
C) Model deployment
D) Data preprocessing

Solutions: