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SASInstitute A00-406 Exam Syllabus Topics:

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

1. What does "feature selection" refer to in the context of model building?

A) The process of choosing the most relevant variables (features) for the model
B) The visualization of data distribution
C) The creation of synthetic features from existing data
D) The evaluation of model accuracy

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

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

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

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

4. Which evaluation metric is commonly used for assessing the performance of a binary classification model?

A) Mean Absolute Error (MAE)
B) R-squared
C) Root Mean Squared Error (RMSE)
D) Accuracy

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

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

Solutions: