Fix duplicate and reorder sections

Author: dem108

articles/machine-learning/how-to-troubleshoot-online-endpoints.md

@@ -265,10 +265,6 @@ This issue happens when the size of the model is larger than the available disk
 #### Memory quota
 This issue happens when the memory footprint of the model is larger than the available memory. Try a [SKU](reference-managed-online-endpoints-vm-sku-list.md) with more memory.
 
-#### Endpoint quota
-
-Try to delete some unused endpoints in this subscription. If all of your endpoints are actively in use, you can try [requesting an endpoint quota increase](how-to-manage-quotas.md#endpoint-quota-increases).
-
 #### Role assignment quota
 
 When you are creating a managed online endpoint, role assignment is required for the [managed identity](../active-directory/managed-identities-azure-resources/overview.md) to access workspace resources. If you've reached the [role assignment limit](../azure-resource-manager/management/azure-subscription-service-limits.md#azure-rbac-limits), try to delete some unused role assignments in this subscription. You can check all role assignments in the Azure portal by navigating to the Access Control menu.
@@ -281,22 +277,6 @@ Try to delete some unused endpoints in this subscription. If all of your endpoin
 
 Due to a lack of Azure Machine Learning capacity in the region, the service has failed to provision the specified VM size. Retry later or try deploying to a different region.
 
-#### Endpoint quota
-
-Try to delete some unused endpoints in this subscription. If all of your endpoints are actively in use, you can try [requesting an endpoint quota increase](how-to-manage-quotas.md#endpoint-quota-increases).
-
-#### Region-wide VM capacity
-
-Due to a lack of Azure Machine Learning capacity in the region, the service has failed to provision the specified VM size. Retry later or try deploying to a different region.
-
-#### Kubernetes quota
-
-This issue happens when the requested CPU or memory couldn't be satisfied due to all nodes are unschedulable for this deployment, such as nodes are cordoned or nodes are unavailable.
-
-The error message will typically indicate which resource you need more of. For instance, if you see an error message detailing `0/3 nodes are available: 3 Insufficient nvidia.com/gpu`, that means that the service requires GPUs and there are three nodes in the cluster that don't have sufficient GPUs. This can be addressed by adding more nodes if you're using a GPU SKU, switching to a GPU-enabled SKU if you aren't, or changing your environment to not require GPUs.
-
-You can also try adjusting your request in the cluster, you can directly [adjust the resource request of the instance type](how-to-manage-kubernetes-instance-types.md).
-
 #### Other quota
 
 To run the `score.py` provided as part of the deployment, Azure creates a container that includes all the resources that the `score.py` needs, and runs the scoring script on that container.
@@ -329,6 +309,14 @@ Use the **Endpoints** in the studio:
 1. Select the **Deployment logs** tab in the endpoint's details page.
 1. Use the dropdown to select the deployment whose log you want to see.
 
+#### Kubernetes quota
+
+This issue happens when the requested CPU or memory couldn't be satisfied due to all nodes are unschedulable for this deployment, such as nodes are cordoned or nodes are unavailable.
+
+The error message will typically indicate which resource you need more of. For instance, if you see an error message detailing `0/3 nodes are available: 3 Insufficient nvidia.com/gpu`, that means that the service requires GPUs and there are three nodes in the cluster that don't have sufficient GPUs. This can be addressed by adding more nodes if you're using a GPU SKU, switching to a GPU-enabled SKU if you aren't, or changing your environment to not require GPUs.
+
+You can also try adjusting your request in the cluster, you can directly [adjust the resource request of the instance type](how-to-manage-kubernetes-instance-types.md).
+
 ---
 
 ### ERROR: BadArgument