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SASInstitute A00-406 Exam Syllabus Topics:

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

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

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

2. Which type of data source typically stores structured data in a tabular format?

A) APIs
B) Text documents
C) Relational databases
D) NoSQL databases

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

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

4. What is the main advantage of ensemble learning methods, such as Random Forest, in a machine learning pipeline?

A) They combine multiple models to improve predictive performance.
B) They are simple and easy to interpret.
C) They require minimal data preprocessing.
D) They are not suitable for large datasets.

5. 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 undertrained and has high bias
C) A model that performs well on new, unseen data
D) A model that is unable to make predictions

Solutions: