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SASInstitute A00-406 Exam Syllabus Topics:

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

1. What is the purpose of cross-validation in model building and evaluation?

A) Reducing the dataset size
B) Generating synthetic data
C) Splitting the dataset into training and testing sets
D) Assessing the model's generalization performance

2. What is the primary difference between supervised and unsupervised learning in model building?

A) The presence or absence of a target variable
B) The use of feature engineering
C) The type of data used
D) The amount of labeled data required

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

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

4. What is the purpose of hyperparameter tuning in a machine learning pipeline?

A) To evaluate the model's predictions
B) To optimize the model's hyperparameters for better performance
C) To train the model
D) To select the most important features

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

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

Solutions: