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Update how-to-troubleshoot-online-endpoints.md
Add separate section for all known Bad Argument errors
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articles/machine-learning/how-to-troubleshoot-online-endpoints.md

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@@ -198,13 +198,16 @@ The specified VM Size failed to provision due to a lack of Azure Machine Learnin
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Below is a list of reasons you might run into this error:
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* [Resource request was greater than limits](#resource-requests-greater-than-limits)
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* [Unable to download resources](#unable-to-download-resources)
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* [Startup task failed due to authorization error](#authorization-error)
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* [Startup task failed due to incorrect role assignments on resource](#authorization-error)
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* [Unable to download user container image](#unable-to-download-user-container-image)
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* [Unable to download user model or code artifacts](#unable-to-download-user-model-or-code-artifacts)
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#### Resource requests greater than limits
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Requests for resources must be less than or equal to limits. If you don't set limits, we set default values when you attach your compute to an Azure Machine Learning workspace. You can check limits in the Azure portal or by using the `az ml compute show` command.
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#### Unable to download resources
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#### Authorization error
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After provisioning the compute resource, during deployment creation, Azure tries to pull the user container image from the workspace private Azure Container Registry (ACR) and mount the user model and code artifacts into the user container from the workspace storage account.
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- If you created the associated endpoint with UserAssigned, the user's managed identity must have Storage blob data reader permission on the workspace storage account.
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During this process, you can run into a few different issues depending on which stage the operation failed at:
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* [Unable to download user container image](#unable-to-download-user-container-image)
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* [Unable to download user model or code artifacts](#unable-to-download-user-model-or-code-artifacts)
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To get more details about these errors, run:
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```azurecli
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az ml online-deployment get-logs -n <endpoint-name> --deployment <deployment-name> --l 100
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```
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#### Unable to download user container image
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It is possible that the user container could not be found.

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