Add mirror traffic section in SDKv2 doc

Add mirror traffic section in SDKv2 doc, reflect the contents in azureml-examples
Also add some images exists in CLIv2 version of this page.

Author: shohei1029

articles/machine-learning/how-to-safely-rollout-managed-endpoints-sdk-v2.md

@@ -256,7 +256,7 @@ green_deployment = ManagedOnlineDeployment(
 ml_client.begin_create_or_update(green_deployment)
 ```
 
-## Test the 'green' deployment
+### Test the new deployment
 
 Though green has 0% of traffic allocated, you can still invoke the endpoint and deployment with [json](https://github.com/Azure/azureml-examples/blob/main/sdk/endpoints/online/model-2/sample-request.json) file.
 
@@ -268,38 +268,79 @@ ml_client.online_endpoints.invoke(
 )
 ```
 
-1. Test the new deployment with a small percentage of live traffic:
+## Test the deployment with mirrored traffic (preview)
 
-    Once you've tested your green deployment, allocate a small percentage of traffic to it:
+[!INCLUDE [preview disclaimer](../../includes/machine-learning-preview-generic-disclaimer.md)]
 
-    ```python
-    endpoint.traffic = {"blue": 90, "green": 10}
-    ml_client.begin_create_or_update(endpoint)
-    ```
+Once you've tested your `green` deployment, you can copy (or 'mirror') a percentage of the live traffic to it. Mirroring traffic doesn't change results returned to clients. Requests still flow 100% to the blue deployment. The mirrored percentage of the traffic is copied and submitted to the `green` deployment so you can gather metrics and logging without impacting your clients. Mirroring is useful when you want to validate a new deployment without impacting clients. For example, to check if latency is within acceptable bounds and that there are no HTTP errors.
 
-    Now, your green deployment will receive 10% of requests.
+> [!WARNING]
+> Mirroring traffic uses your [endpoint bandwidth quota](how-to-manage-quotas.md#azure-machine-learning-managed-online-endpoints) (default 5 MBPS). Your endpoint bandwidth will be throttled if you exceed the allocated quota. For information on monitoring bandwidth throttling, see [Monitor managed online endpoints](how-to-monitor-online-endpoints.md#metrics-at-endpoint-scope).
 
-1. Send all traffic to your new deployment:
+The following command mirrors 10% of the traffic to the `green` deployment:
 
-    Once you're satisfied that your green deployment is fully satisfactory, switch all traffic to it.
+```python
+endpoint.mirror_traffic = {"green": 10}
+ml_client.begin_create_or_update(endpoint)
+```
 
-    ```python
-    endpoint.traffic = {"blue": 0, "green": 100}
-    ml_client.begin_create_or_update(endpoint)
-    ```
+> [!IMPORTANT]
+> Mirroring has the following limitations:
+> * You can only mirror traffic to one deployment.
+> * A deployment can only be set to live or mirror traffic, not both.
+> * Mirrored traffic is not currently supported with K8s.
+> * The maximum mirrored traffic you can configure is 50%. This limit is to reduce the impact on your endpoint bandwidth quota.
 
-1. Remove the old deployment:
+:::image type="content" source="./media/how-to-safely-rollout-managed-endpoints/endpoint-concept-mirror.png" alt-text="Diagram showing 10% traffic mirrored to one deployment.":::
 
-    ```python
-    ml_client.online_deployments.delete(name="blue", endpoint_name=online_endpoint_name)
-    ```
+After testing, you can set the mirror traffic to zero to disable mirroring:
+
+```python
+endpoint.mirror_traffic = {"green": 0}
+ml_client.begin_create_or_update(endpoint)
+```
+
+## Test the new deployment with a small percentage of live traffic:
+
+Once you've tested your green deployment, allocate a small percentage of traffic to it:
+
+```python
+endpoint.traffic = {"blue": 90, "green": 10}
+ml_client.begin_create_or_update(endpoint)
+```
+
+Now, your green deployment will receive 10% of requests.
+    
+:::image type="content" source="./media/how-to-safely-rollout-managed-endpoints/endpoint-concept.png" alt-text="Diagram showing traffic split between deployments.":::
+
+## Send all traffic to your new deployment:
+
+Once you're satisfied that your green deployment is fully satisfactory, switch all traffic to it.
+
+```python
+endpoint.traffic = {"blue": 0, "green": 100}
+ml_client.begin_create_or_update(endpoint)
+```
+
+## Remove the old deployment:
+
+```python
+ml_client.online_deployments.delete(name="blue", endpoint_name=online_endpoint_name)
+```
 
 ## Delete endpoint
 
+If you aren't going use the deployment, you should delete it with:
+
 ```python
 ml_client.online_endpoints.begin_delete(name=online_endpoint_name)
 ```
 
 ## Next steps
-
-* Explore online endpoint samples - [https://github.com/Azure/azureml-examples/tree/main/sdk/endpoints](https://github.com/Azure/azureml-examples/tree/main/sdk/endpoints)
+- [Explore online endpoint samples](https://github.com/Azure/azureml-examples/tree/main/sdk/endpoints)
+- [Access Azure resources with a online endpoint and managed identity](how-to-access-resources-from-endpoints-managed-identities.md)
+- [Monitor managed online endpoints](how-to-monitor-online-endpoints.md)
+- [Manage and increase quotas for resources with Azure Machine Learning](how-to-manage-quotas.md#azure-machine-learning-managed-online-endpoints)
+- [View costs for an Azure Machine Learning managed online endpoint](how-to-view-online-endpoints-costs.md)
+- [Managed online endpoints SKU list](reference-managed-online-endpoints-vm-sku-list.md)
+- [Troubleshooting  online endpoints de