The correct option is Select the existing Synapse workspace then choose the Spark pool within that workspace then enable the managed identity for the compute resource then save the compute and use Synapse Studio to grant the managed identity the Synapse Administrator role.

Registering an attached Synapse Spark pool requires you to pick the Synapse workspace first and then choose the specific Spark pool inside that workspace. You must enable the managed identity for the compute resource so Azure ML can use that identity. After you save the compute, you grant the managed identity the Synapse Administrator role from Synapse Studio so the compute has the permissions it needs to run jobs and access workspace artifacts.

Select a Spark pool then attach it to a newly created Synapse workspace then enable the compute managed identity and after saving go to Synapse Studio to give the managed identity the Synapse Administrator role is incorrect because you do not attach a Spark pool by first creating a new workspace in the Azure ML compute wizard. The correct flow selects the existing workspace and then the pool inside it rather than attaching a pool to a newly created workspace.

Choose an existing Synapse workspace then enable a workspace managed identity and assign that identity the Azure ML Administrator role is incorrect because enabling the workspace managed identity and assigning the Azure ML Administrator role is not the required step. The compute resource needs its managed identity enabled and you must grant that identity the Synapse Administrator role so the compute can operate within Synapse.

Pick an existing Spark pool then enable a managed identity for the Synapse workspace and assign that identity the Azure ML Administrator role is incorrect because the managed identity must be enabled for the compute resource and the role required is Synapse Administrator. Assigning the Azure ML Administrator role to the workspace managed identity does not give the compute the necessary Synapse permissions.

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