Merge pull request #98221 from zema-MSFT/patch-1

Update how-to-troubleshoot-online-endpoints.md for 429 response

Author: ShannonLeavitt

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

@@ -437,7 +437,7 @@ When you access online endpoints with REST requests, the returned status codes a
 | 408 | Request timeout | The model execution took longer than the timeout supplied in `request_timeout_ms` under `request_settings` of your model deployment config.|
 | 424 | Model Error | If your model container returns a non-200 response, Azure returns a 424. Check the `Model Status Code` dimension under the `Requests Per Minute` metric on your endpoint's [Azure Monitor Metric Explorer](../azure-monitor/essentials/metrics-getting-started.md). Or check response headers `ms-azureml-model-error-statuscode` and `ms-azureml-model-error-reason` for more information. |
 | 429 | Rate-limiting | You attempted to send more than 100 requests per second to your endpoint. |
-| 429 | Too many pending requests | Your model is getting more requests than it can handle. We allow 2 * `max_concurrent_requests_per_instance` * `instance_count` requests at any time. Additional requests are rejected. You can confirm these settings in your model deployment config under `request_settings` and `scale_settings`. If you're using auto-scaling, your model is getting requests faster than the system can scale up. With auto-scaling, you can try to resend requests with [exponential backoff](https://aka.ms/exponential-backoff). Doing so can give the system time to adjust. |
+| 429 | Too many pending requests | Your model is getting more requests than it can handle. We allow 2 * `max_concurrent_requests_per_instance` * `instance_count` requests in parallel at any time. Additional requests are rejected. You can confirm these settings in your model deployment config under `request_settings` and `scale_settings`. If you're using auto-scaling, your model is getting requests faster than the system can scale up. With auto-scaling, you can try to resend requests with [exponential backoff](https://aka.ms/exponential-backoff). Doing so can give the system time to adjust. |
 | 500 | Internal server error | Azure ML-provisioned infrastructure is failing. |
 
 ## Common network isolation issues