Merge pull request #262154 from msft-edguevar/patch-1

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

Author: prmerger-automator[bot]

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

@@ -755,7 +755,7 @@ The following table contains common error codes when consuming managed online en
 | 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. If 424 comes with liveness or readiness probe failing, consider adjusting [probe settings](reference-yaml-deployment-managed-online.md#probesettings) to allow longer time to probe liveness or readiness of the container. |
 | 429         | Too many pending requests | Your model is currently getting more requests than it can handle. Azure Machine Learning has implemented a system that permits a maximum of `2 * max_concurrent_requests_per_instance * instance_count requests` to be processed in parallel at any given moment to guarantee smooth operation. Other requests that exceed this maximum are rejected. You can review your model deployment configuration under the request_settings and scale_settings sections to verify and adjust these settings. Additionally, as outlined in the [YAML definition for RequestSettings](reference-yaml-deployment-managed-online.md#requestsettings), it's important to ensure that the environment variable `WORKER_COUNT` is correctly passed. <br><br> If you're using autoscaling and get this error, it means your model is getting requests quicker than the system can scale up. In this situation, consider resending requests with an [exponential backoff](https://en.wikipedia.org/wiki/Exponential_backoff) to give the system the time it needs to adjust. You could also increase the number of instances by using [code to calculate instance count](#how-to-calculate-instance-count). These steps, combined with setting autoscaling, help ensure that your model is ready to handle the influx of requests. |
-| 429         | Rate-limiting             | The number of requests per second reached the [limits](how-to-manage-quotas.md#azure-machine-learning-online-endpoints-and-batch-endpoints) of managed online endpoints.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
+| 429         | Rate-limited             | The number of requests per second reached the [limits](how-to-manage-quotas.md#azure-machine-learning-online-endpoints-and-batch-endpoints) of managed online endpoints.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
 | 500         | Internal server error     | Azure Machine Learning-provisioned infrastructure is failing.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
 
 #### Common error codes for kubernetes online endpoints