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SASInstitute A00-406 Exam Syllabus Topics:

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

Question 1

Which statement is true regarding decision trees and models based on ensembles of trees?

A. In the gradient boosting algorithm, for all but the first iteration, the target is the residual from the previous decision tree model.
B. For a Forest model, the out-of-bag sample is simply the original validation data set from when the raw data partitioning took place.
C. A single decision tree will always be outperformed by a model based on an ensemble of trees.
D. In the Forest algorithm, each individual tree is pruned based on using minimum Average Squared Error.

Question 2

What is the primary goal of hyperparameter tuning during model building?

A. To increase model complexity
B. To optimize the settings that control the model's learning process
C. To improve data preprocessing techniques
D. To add more features to the model

Question 3

What is the main purpose of feature engineering in model building?

A. Data visualization
B. Creating new features or transforming existing ones to improve model performance
C. Data preprocessing
D. Model evaluation

Question 4

What is the main advantage of using a RESTful API (Representational State Transfer) as a data source?

A. Support for complex data structures
B. Real-time data processing
C. High security features
D. Simple and standardized communication

Question 5

What does the term "bias" in machine learning refer to?

A. A model's inability to generalize to new data
B. The overall accuracy of a model
C. The simplicity of a model
D. Systematic errors that cause a model to consistently underpredict or overpredict

Solutions: