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SASInstitute A00-406 Exam Syllabus Topics:

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

1. 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

2. In the context of model assessment, what does "bias" refer to?

A) The process of feature engineering
B) The simplicity of the model
C) A measure of the model's precision
D) A systematic error that causes a model to consistently overpredict

3. In model assessment, what is the purpose of feature importance analysis?

A) To assess data quality
B) To evaluate the significance of input features in making predictions
C) To create synthetic features
D) To visualize data distribution

4. When building a recommendation system, what does "collaborative filtering" rely on?

A) The popularity of items
B) Item-based clustering
C) The past behavior or preferences of users
D) The characteristics of the items being recommended

5. Which metric is commonly used to evaluate the performance of a regression model?

A) Confusion Matrix
B) Mean Absolute Error (MAE)
C) F1 Score
D) Precision

Solutions: