What does the term "notebook-scoped" refer to in Databricks?

The term "notebook-scoped" in Databricks refers to variables that are accessible only within a specific notebook. This means that any variable defined as notebook-scoped is limited to the context of that particular notebook and cannot be accessed from other notebooks. This scoping mechanism helps in managing variable names and reduces the risk of conflicts, ensuring that variables are isolated to their specific notebook environment.

When you declare a variable as notebook-scoped, it allows you to maintain clean and organized code, especially in collaborative environments where multiple users may be working on different notebooks. This feature is essential for avoiding accidental overwrites or data contamination that could occur if variables were accessible globally across all notebooks.

In contrast, the other options imply broader scopes or functionalities that do not align with the concept of notebook-scoped variables. For instance, global variables apply across all notebooks, configurations that apply to the entire workspace affect the full Databricks environment rather than an individual notebook, and permanent data storage options would pertain to how data is stored rather than the scope of variable accessibility.