incorporating feedback

Author: Larry Franks

articles/machine-learning/how-to-deploy-managed-online-endpoints.md

@@ -733,6 +733,9 @@ for endpoint in ml_client.online_endpoints.list():
 
 # [ARM template](#tab/arm)
 
+> [!TIP]
+> While templates are useful for deploying resources, they can't be used to list, show, or invoke resources.
+
 The `show` command contains information in `provisioning_status` for endpoint and deployment:
 
 ::: code language="azurecli" source="~/azureml-examples-main/cli/deploy-managed-online-endpoint.sh" ID="get_status" :::
@@ -776,6 +779,9 @@ ml_client.online_deployments.get_logs(
 
 # [ARM template](#tab/arm)
 
+> [!TIP]
+> While templates are useful for deploying resources, they can't be used to list, show, or invoke resources.
+
 :::code language="azurecli" source="~/azureml-examples-main/cli/deploy-managed-online-endpoint.sh" ID="get_logs" :::
 
 By default, logs are pulled from inference-server. To see the logs from storage-initializer (it mounts assets like model and code to the container), add the `--container storage-initializer` flag.
@@ -831,6 +837,9 @@ ml_client.online_endpoints.invoke(
 
 # [ARM template](#tab/arm)
 
+> [!TIP]
+> While templates are useful for deploying resources, they can't be used to list, show, or invoke resources.
+
 You can use either the `invoke` command or a REST client of your choice to invoke the endpoint and score some data: 
 
 ```azurecli