Update how-to-troubleshoot-online-endpoints.md

Author: siyigao1212

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

@@ -448,7 +448,7 @@ When you access online endpoints with REST requests, the returned status codes a
 | 404 | Not found | The endpoint doesn't have any valid deployment with positive weight. |
 | 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 | Too many pending requests | Your model is getting more requests than it can handle. We allow maximum 2 * `max_concurrent_requests_per_instance` * `instance_count` / `request_process_time (in seconds)` requests per second. Additional requests are rejected. You can confirm these settings in your model deployment config under `request_settings` and `scale_settings`, respectively. 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. Apart from enable auto-scaling, you could also increase the number of instances by using the below [code](#how-to-calculate-instance-count). |
+| 429 | Too many pending requests | Your model is getting more requests than it can handle. We allow maximum 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`, respectively. 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. Apart from enable auto-scaling, you could also increase the number of instances by using the below [code](#how-to-calculate-instance-count). |
 | 429 | Rate-limiting | The number of requests per second reached the [limit](./how-to-manage-quotas.md#azure-machine-learning-managed-online-endpoints) of managed online endpoints.|
 | 500 | Internal server error | Azure ML-provisioned infrastructure is failing. |
 
@@ -458,7 +458,7 @@ To increase the number of instances, you could calculate the required replicas f
 from math import ceil
 # target requests per second
 target_rps = 20
-# time to process the request (in seconds)
+# time to process the request (in seconds, choose appropriate percentile)
 request_process_time = 10
 # Maximum concurrent requests per instance
 max_concurrent_requests_per_instance = 1