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articles/ai-services/speech-service/how-to-pronunciation-assessment.md

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## Set configuration parameters
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::: zone pivot="programming-language-go"
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> [!NOTE]
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> Pronunciation assessment is not available with the Speech SDK for Go. You can read about the concepts in this guide. Select another programming language for your solution.
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::: zone-end
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In the `SpeechRecognizer`, you can specify the language to learn or practice improving pronunciation. The default locale is `en-US`. To learn how to specify the learning language for pronunciation assessment in your own application, you can use the following sample code.
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::: zone pivot="programming-language-csharp"
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```csharp
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var recognizer = new SpeechRecognizer(config, "en-US", audioInput);
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```
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::: zone-end
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::: zone pivot="programming-language-cpp"
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```cpp
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auto recognizer = SpeechRecognizer::FromConfig(config, "en-US", audioConfig);
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```
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::: zone-end
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::: zone pivot="programming-language-java"
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```Java
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SpeechRecognizer recognizer = new SpeechRecognizer(config, "en-US", audioInput);
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```
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::: zone-end
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In the `SpeechRecognizer`, you can specify the language to learn or practice improving pronunciation. The default locale is `en-US`. To learn how to specify the learning language for pronunciation assessment in your own application, see [sample code](https://github.com/Azure-Samples/cognitive-services-speech-sdk/blob/master/samples/csharp/sharedcontent/console/speech_recognition_samples.cs#LL1086C13-L1086C98).
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::: zone pivot="programming-language-python"
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```Python
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speech_recognizer = speechsdk.SpeechRecognizer(speech_config=speech_config, language="en-US", audio_config=audio_config)
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```
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::: zone-end
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::: zone pivot="programming-language-javascript"
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```JavaScript
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speechConfig.speechRecognitionLanguage = "en-US";
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```
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::: zone-end
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::: zone pivot="programming-language-objectivec"
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```ObjectiveC
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SPXSpeechRecognizer* speechRecognizer = [[SPXSpeechRecognizer alloc] initWithSpeechConfiguration:speechConfig language:@"en-US" audioConfiguration:pronAudioSource];
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```
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::: zone-end
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::: zone pivot="programming-language-swift"
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```swift
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let reco = try! SPXSpeechRecognizer(speechConfiguration: speechConfig, language: "en-US", audioConfiguration: audioInput)
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```
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::: zone-end
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::: zone pivot="programming-language-go"
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::: zone-end
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> [!TIP]
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> If you aren't sure which locale to set for a language that has multiple locales, try each locale separately. For instance, for Spanish, try `es-ES` and `es-MX`. Determine which locale scores higher for your scenario.

articles/ai-services/speech-service/includes/language-support/pronunciation-assessment.md

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| Language | Locale (BCP-47) |
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|--|--|
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|Arabic (Egypt)|`ar-EG`<sup>1</sup> |
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|Arabic (Egypt)|`ar-EG` |
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|Arabic (Saudi Arabia)|`ar-SA` |
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|Catalan|`ca-ES`<sup>1</sup>|
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|Chinese (Cantonese, Traditional)|`zh-HK`<sup>1</sup>|
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|Chinese (Mandarin, Simplified)|`zh-CN`|
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|Chinese (Taiwanese Mandarin, Traditional)|`zh-TW`<sup>1</sup>|
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|Chinese (Taiwanese Mandarin, Traditional)|`zh-TW`|
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|Danish (Denmark)|`da-DK`<sup>1</sup>|
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|Dutch (Netherlands)|`nl-NL`|
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|English (Australia)|`en-AU`|
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|English (Canada)|`en-CA` |
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|English (India)|`en-IN` |
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|English (United Kingdom)|`en-GB`|
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|English (United States)|`en-US`|
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|Finnish (Finland)|`fi-FI`<sup>1</sup>|
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|Finnish (Finland)|`fi-FI`|
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|French (Canada)|`fr-CA`|
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|French (France)|`fr-FR`|
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|German (Germany)|`de-DE`|
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|Korean (Korea)|`ko-KR`|
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|Malay (Malaysia)|`ms-MY`|
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|Norwegian Bokmål (Norway)|`nb-NO`|
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|Polish (Poland)|`pl-PL`<sup>1</sup>|
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|Polish (Poland)|`pl-PL`|
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|Portuguese (Brazil)|`pt-BR`|
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|Portuguese (Portugal)|`pt-PT`|
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|Russian (Russia)|`ru-RU`|
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|Spanish (Mexico)|`es-MX` |
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|Spanish (Spain)|`es-ES` |
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|Swedish (Sweden)|`sv-SE`|
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|Tamil (India)|`ta-IN` |
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|Thai (Thailand)|`th-TH`<sup>1</sup> |
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|Vietnamese (Vietnam)|`vi-VN` |
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<sup>1</sup> The language is in public preview for pronunciation assessment.

articles/ai-services/speech-service/language-support.md

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# [Pronunciation assessment](#tab/pronunciation-assessment)
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The table in this section summarizes the 32 locales supported for pronunciation assessment, and each language is available on all [Speech to text regions](regions.md#speech-service). Latest update extends support from English to 31 more languages and quality enhancements to existing features, including accuracy, fluency and miscue assessment. You should specify the language that you're learning or practicing improving pronunciation. The default language is set as `en-US`. If you know your target learning language, [set the locale](how-to-pronunciation-assessment.md#get-pronunciation-assessment-results) accordingly. For example, if you're learning British English, you should specify the language as `en-GB`. If you're teaching a broader language, such as Spanish, and are uncertain about which locale to select, you can run various accent models (`es-ES`, `es-MX`) to determine the one that achieves the highest score to suit your specific scenario. If you're interested in languages not listed in the following table, fill out this [intake form](https://aka.ms/speechpa/intake) for further assistance.
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The table in this section summarizes the 33 locales supported for pronunciation assessment, and each language is available on all [Speech to text regions](regions.md#speech-service). Latest update extends support from English to 32 more languages and quality enhancements to existing features, including accuracy, fluency and miscue assessment. You should specify the language that you're learning or practicing improving pronunciation. The default language is set as `en-US`. If you know your target learning language, [set the locale](how-to-pronunciation-assessment.md#get-pronunciation-assessment-results) accordingly. For example, if you're learning British English, you should specify the language as `en-GB`. If you're teaching a broader language, such as Spanish, and are uncertain about which locale to select, you can run various accent models (`es-ES`, `es-MX`) to determine the one that achieves the highest score to suit your specific scenario. If you're interested in languages not listed in the following table, fill out this [intake form](https://aka.ms/speechpa/intake) for further assistance.
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[!INCLUDE [Language support include](includes/language-support/pronunciation-assessment.md)]
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articles/aks/advanced-network-observability-cli.md

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1. Set up port forwarding for Hubble UI using the `kubectl port-forward` command.
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```azurecli-interactive
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kubectl port-forward svc/hubble-ui 12000:80
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kubectl -n kube-system port-forward svc/hubble-ui 12000:80
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```
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1. Access Hubble UI by entering `http://localhost:12000/` into your web browser.

articles/aks/azure-blob-csi.md

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## Before you begin
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- You need the Azure CLI version 2.42 or later installed and configured. Run `az --version` to find the version. If you need to install or upgrade, see [Install Azure CLI][install-azure-cli].
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- Make sure that you have the Azure CLI version 2.42 or later installed and configured. Run `az --version` to find the version. If you need to install or upgrade, see [Install Azure CLI][install-azure-cli]. If you have installed the Azure CLI `aks-preview` extension, make sure that you update the extension to the latest version by calling `az extension update --name aks-preview`.
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- Perform the steps in this [link][csi-blob-storage-open-source-driver-uninstall-steps] if you previously installed the [CSI Blob Storage open-source driver][csi-blob-storage-open-source-driver] to access Azure Blob storage from your cluster.
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> [!NOTE]

articles/aks/cluster-autoscaler-overview.md

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# Cluster autoscaling in Azure Kubernetes Service (AKS) overview
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To keep up with application demands in Azure Kubernetes Service (AKS), you might need to adjust the number of nodes that run your workloads. The cluster autoscaler component watches for pods in your cluster that can't be scheduled because of resource constraints. When the cluster autoscaler detects issues, it scales up the number of nodes in the node pool to meet the application demand. It also regularly checks nodes for a lack of running pods and scales down the number of nodes as needed.
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To keep up with application demands in Azure Kubernetes Service (AKS), you might need to adjust the number of nodes that run your workloads. The cluster autoscaler component watches for pods in your cluster that can't be scheduled because of resource constraints. When the cluster autoscaler detects unscheduled pods, it scales up the number of nodes in the node pool to meet the application demand. It also regularly checks nodes that don't have any scheduled pods and scales down the number of nodes as needed.
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This article helps you understand how the cluster autoscaler works in AKS. It also provides guidance, best practices, and considerations when configuring the cluster autoscaler for your AKS workloads. If you want to enable, disable, or update the cluster autoscaler for your AKS workloads, see [Use the cluster autoscaler in AKS](./cluster-autoscaler.md).
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:::image type="content" source="media/cluster-autoscaler/cluster-autoscaler.png" alt-text="Screenshot of how the cluster autoscaler and horizontal pod autoscaler often work together to support the required application demands.":::
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It's a common practice to enable cluster autoscaler for nodes and either the Vertical Pod Autoscaler or Horizontal Pod Autoscaler for pods. When you enable the cluster autoscaler, it applies the specified scaling rules when the node pool size is lower than the minimum or greater than the maximum. The cluster autoscaler waits to take effect until a new node is needed in the node pool or until a node might be safely deleted from the current node pool. For more information, see [How does scale down work?](https://github.com/kubernetes/autoscaler/blob/master/cluster-autoscaler/FAQ.md#how-does-scale-down-work)
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It's a common practice to enable cluster autoscaler for nodes and either the Vertical Pod Autoscaler or Horizontal Pod Autoscaler for pods. When you enable the cluster autoscaler, it applies the specified scaling rules when the node pool size is lower than the minimum node count, up to the maximum node count. The cluster autoscaler waits to take effect until a new node is needed in the node pool or until a node might be safely deleted from the current node pool. For more information, see [How does scale down work?](https://github.com/kubernetes/autoscaler/blob/master/cluster-autoscaler/FAQ.md#how-does-scale-down-work)
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* To **effectively run workloads concurrently on both Spot and Fixed node pools**, consider using [*priority expanders*](https://github.com/kubernetes/autoscaler/blob/master/cluster-autoscaler/FAQ.md#what-are-expanders). This approach allows you to schedule pods based on the priority of the node pool.
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* Exercise caution when **assigning CPU/Memory requests on pods**. The cluster autoscaler scales up based on pending pods rather than CPU/Memory pressure on nodes.
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* For **clusters concurrently hosting both long-running workloads, like web apps, and short/bursty job workloads**, we recommend separating them into distinct node pools with [Affinity Rules](./operator-best-practices-advanced-scheduler.md#node-affinity)/[expanders](https://github.com/kubernetes/autoscaler/blob/master/cluster-autoscaler/FAQ.md#what-are-expanders) or using [PriorityClass](https://kubernetes.io/docs/concepts/scheduling-eviction/pod-priority-preemption/#priorityclass) to help prevent unnecessary node drain or scale down operations.
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* In an autoscaler-enabled node pool, scale down nodes by removing workloads, instead of manually reducing the node count. This can be problematic if the node pool is already at maximum capacity or if there are active workloads running on the nodes, potentially causing unexpected behavior by the cluster autoscaler
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* In an autoscaler-enabled node pool, scale down nodes by removing workloads, instead of manually reducing the node count. This can be problematic if the node pool is already at maximum capacity or if there are active workloads running on the nodes, potentially causing unexpected behavior by the cluster autoscaler.
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* Nodes don't scale up if pods have a PriorityClass value below -10. Priority -10 is reserved for [overprovisioning pods](https://github.com/kubernetes/autoscaler/blob/master/cluster-autoscaler/FAQ.md#how-can-i-configure-overprovisioning-with-cluster-autoscaler). For more information, see [Using the cluster autoscaler with Pod Priority and Preemption](https://github.com/kubernetes/autoscaler/blob/master/cluster-autoscaler/FAQ.md#how-does-cluster-autoscaler-work-with-pod-priority-and-preemption).
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> **Do not make changes to individual nodes within the autoscaled node pools**. All nodes in the same node group should have uniform capacity, labels, taints and system pods running on them.
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> **Don't make changes to individual nodes within the autoscaled node pools**. All nodes in the same node group should have uniform capacity, labels, taints and system pods running on them.
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* The cluster autoscaler isn't responsible for enforcing a "maximum node count" in a cluster node pool irrespective of pod scheduling considerations. If any non-cluster autoscaler actor sets the node pool count to a number beyond the cluster autoscaler's configured maximum, the cluster autoscaler doesn't automatically remove nodes. The cluster autoscaler scale down behaviors remain scoped to removing only nodes that have no scheduled pods. The sole purpose of the cluster autoscaler's max node count configuration is to enforce an upper limit for scale up operations. It doesn't have any effect on scale down considerations.
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For clusters that handle substantial and bursty workloads with a primary focus on performance, we recommend increasing the `scan-interval` and decreasing the `scale-down-utilization-threshold`. These settings help batch multiple scaling operations into a single call, optimizing scaling time and the utilization of compute read/write quotas. It also helps mitigate the risk of swift scale down operations on underutilized nodes, enhancing the pod scheduling efficiency. Also increase `ok-total-unready-count`and `max-total-unready-percentage`.
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For clusters that handle substantial and bursty workloads with a primary focus on performance, we recommend increasing the `scan-interval` and decreasing the `scale-down-utilization-threshold`. These settings help batch multiple scaling operations into a single call, optimizing scaling time and the utilization of compute read/write quotas. It also helps mitigate the risk of swift scale down operations on underutilized nodes, enhancing the pod scheduling efficiency. Also increase `ok-total-unready-count`and `max-total-unready-percentage`.
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For clusters with daemonset pods, we recommend setting `ignore-daemonset-utilization` to `true`, which effectively ignores node utilization by daemonset pods and minimizes unnecessary scale down operations. See [profile for bursty workloads](./cluster-autoscaler.md#configure-cluster-autoscaler-profile-for-bursty-workloads)
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* Increase `ok-total-unready-count`and `max-total-unready-percentage`.
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## Common issues and mitigation recommendations
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<!-- LINKS --->
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[vertical-pod-autoscaler]: vertical-pod-autoscaler.md
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[horizontal-pod-autoscaler]:concepts-scale.md#horizontal-pod-autoscaler
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articles/aks/private-clusters.md

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title: Create a private Azure Kubernetes Service (AKS) cluster
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description: Learn how to create a private Azure Kubernetes Service (AKS) cluster
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ms.topic: article
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ms.author: schaffererin
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author: schaffererin
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---
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* Use a [Cloud Shell][cloud-shell-vnet] instance deployed into a subnet that's connected to the API server for the cluster.
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[az-network-vnet-peering-create]: /cli/azure/network/vnet/peering#az_network_vnet_peering_create
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[intro-azure-linux]: ../azure-linux/intro-azure-linux.md
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[cloud-shell-vnet]: ../cloud-shell/vnet/overview.md

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