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SASInstitute A00-406 Exam Syllabus Topics:

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

1. In machine learning, what does "overfitting" refer to?

A) A model that has too much complexity and fits the training data too closely
B) A model that is unable to make predictions
C) A model that is undertrained and has high bias
D) A model that performs well on new, unseen data

2. What is the primary objective of model validation during the model assessment phase?

A) To build a model from scratch
B) To assess the accuracy of the model
C) To ensure the model generalizes well to new, unseen data
D) To create synthetic data

3. When deploying a model, what is "model explainability"?

A) The capability to interpret and understand the model's decisions and predictions
B) The time it takes to make predictions
C) The process of data preprocessing
D) The simplicity of the model

4. Which statements are true for the F1 score?
(Choose 2.)

A) F1 score is applicable to a model with an interval target.
B) F1 score is calculated based on a depth value.
C) F1 score is applicable to a model with a binary target.
D) F1 score is calculated based on a cut off value.

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

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

Solutions: