You are building a Fabric notebook named MasterNotebook1 in a workspace. MasterNotebook1 contains the following code. DAG = { "activities": [ { "name": "execute_notebook_1", "path": "notebook_01"…

Moving `notebook_02` to the bottom would change its execution order but doesn't necessarily enforce it runs after `notebook_03` without explicit dependencies or a clear understanding of the full DAG's implicit ordering logic.

Adding dependencies to `notebook_03` would control when `notebook_03` runs, not directly control when `notebook_02` runs relative to `notebook_03`, unless `notebook_02` was already dependent on `notebook_03`.

Splitting the DAG into three definitions would create multiple independent execution flows, not resolve a dependency or ordering issue between two specific notebooks within the same workflow.

Adding dependencies to `notebook_02` allows you to explicitly define which other activities, such as `notebook_03`, must complete successfully before `notebook_02` begins execution, ensuring a desired sequential flow.

Changing concurrency to 3 only affects how many activities can run in parallel, not their specific execution order or dependencies.

Moving the declaration of `notebook_03` to the top of the DAG definition can, in some DAG implementations, imply an earlier execution priority or simply make the intended order clearer in the code, ensuring it runs before subsequently defined activities like `notebook_02`.