Which statements best describe the functionality of tags in dbt?

Tags in dbt serve as a method to categorize and segment your dbt models, which aids in organizing your data transformations. Essentially, using tags allows you to easily filter and select specific models when running commands or generating documentation. This is especially helpful when you want to execute a subset of models based on certain criteria or characteristics, enhancing the overall manageability and clarity of your data projects.

While other statements may touch on aspects of dbt's functionality, they do not accurately convey the core purpose and benefits of tags. For instance, labeling tables or documenting data sources doesn't capture the specific role that tags play in model organization and execution. Enhancing performance does not directly relate to the concept of tagging; rather, performance optimization is addressed through other functions and configurations within dbt.