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.openpublishing.redirection.json

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{
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"source_path_from_root": "/articles/orbital/overview-analytics.md",
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"redirect_url": "/azure/orbital/overview",
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"redirect_document_id": false
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},
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{
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"source_path_from_root": "/articles/aks/intro-kubernetes.md",
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"redirect_url": "/azure/aks/what-is-aks",
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"redirect_document_id": false
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},
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{

articles/advisor/advisor-how-to-calculate-total-cost-savings.md

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---
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title: Export cost savings in Azure Advisor
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title: Calculate cost savings in Azure Advisor
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ms.topic: article
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ms.date: 02/06/2024
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description: Export cost savings in Azure Advisor and calculate the aggregated potential yearly savings by using the cost savings amount for each recommendation.
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---
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# Export cost savings
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# Calculate cost savings
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This article provides guidance on how to calculate total cost savings in Azure Advisor.
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## Export cost savings for recommendations
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To calculate aggregated potential yearly savings, follow these steps:
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[![Screenshot of the Azure Advisor cost recommendations page that shows download option.](./media/advisor-how-to-calculate-total-cost-savings.png)](./media/advisor-how-to-calculate-total-cost-savings.png#lightbox)
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> [!NOTE]
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> Recommendations show savings individually, and may overlap with the savings shown in other recommendations, for example – you can only benefit from savings plans for compute or reservations for virtual machines, but not from both.
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> Different types of cost savings recommendations are generated using overlapping datasets (for example, VM rightsizing/shutdown, VM reservations and savings plan recommendations all consider on-demand VM usage). As a result, resource changes (e.g., VM shutdowns) or reservation/savings plan purchases will impact on-demand usage, and the resulting recommendations and associated savings forecast.
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## Understand cost savings
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Azure Advisor provides recommendations for resizing/shutting down underutilized resources, purchasing compute reserved instances, and savings plans for compute.
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These recommendations contain one or more calls-to-action and forecasted savings from following the recommendations. Recommendations should be followed in a specific order: rightsizing/shutdown, followed by reservation purchases, and finally, the savings plan purchase. This sequence allows each step to impact the subsequent ones positively.
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For example, rightsizing or shutting down resources reduces on-demand costs immediately. This change in your usage pattern essentially invalidates your existing reservation and savings plan recommendations, as they were based on your pre-rightsizing usage and costs. Updated reservation and savings plan recommendations (and their forecasted savings) should appear within three days.
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The forecasted savings from reservations and savings plans are based on actual rates and usage, while the forecasted savings from rightsizing/shutdown are based on retail rates. The actual savings may vary depending on the usage patterns and rates. Assuming there are no material changes to your usage patterns, your actual savings from reservations and savings plan should be in line with the forecasts. Savings from rightsizing/shutdown vary based on your actual rates. This is important if you intend to track cost savings forecasts from Azure Advisor.

articles/advisor/toc.yml

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href: advisor-azure-resource-graph.md
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- name: Consume Advisor score
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href: azure-advisor-score.md
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- name: Export cost savings
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- name: Calculate total cost savings
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href: advisor-how-to-calculate-total-cost-savings.md
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- name: Reference
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items:

articles/ai-services/document-intelligence/disaster-recovery.md

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ms.custom:
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- ignite-2023
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ms.topic: how-to
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ms.date: 03/06/2024
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ms.date: 04/17/2024
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ms.author: lajanuar
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---
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The following HTTP request gets copy authorization from your target resource. You need to enter the endpoint and key of your target resource as headers.
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```http
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POST https://<your-resource-name>/documentintelligence/documentModels/{modelId}:copyTo?api-version=2024-02-29-preview
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POST https://<your-resource-endpoint>/documentintelligence/documentModels/{modelId}:copyTo?api-version=2024-02-29-preview
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Ocp-Apim-Subscription-Key: {<your-key>}
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```
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The following HTTP request starts the copy operation on the source resource. You need to enter the endpoint and key of your source resource as the url and header. Notice that the request URL contains the model ID of the source model you want to copy.
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```http
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POST https://<your-resource-name>/documentintelligence/documentModels/{modelId}:copyTo?api-version=2024-02-29-preview
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POST https://<your-resource-endpoint>/documentintelligence/documentModels/{modelId}:copyTo?api-version=2024-02-29-preview
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Ocp-Apim-Subscription-Key: {<your-key>}
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```
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```http
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HTTP/1.1 202 Accepted
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Operation-Location: https://<your-resource-name>.cognitiveservices.azure.com/documentintelligence/operations/{operation-id}?api-version=2024-02-29-preview
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Operation-Location: https://<your-resource-endpoint>.cognitiveservices.azure.com/documentintelligence/operations/{operation-id}?api-version=2024-02-29-preview
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```
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> [!NOTE]
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## Track Copy progress
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```console
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GET https://<your-resource-name>.cognitiveservices.azure.com/documentintelligence/operations/{<operation-id>}?api-version=2024-02-29-preview
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GET https://<your-resource-endpoint>.cognitiveservices.azure.com/documentintelligence/operations/{<operation-id>}?api-version=2024-02-29-preview
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You can also use the **[Get model](/rest/api/aiservices/document-models/get-model?view=rest-aiservices-2023-07-31&preserve-view=true&tabs=HTTP)** API to track the status of the operation by querying the target model. Call the API using the target model ID that you copied down from the [Generate Copy authorization request](#generate-copy-authorization-request) response.
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```http
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GET https://<your-resource-name>/documentintelligence/documentModels/{modelId}?api-version=2024-02-29-preview" -H "Ocp-Apim-Subscription-Key: <your-key>
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GET https://<your-resource-endpoint>/documentintelligence/documentModels/{modelId}?api-version=2024-02-29-preview" -H "Ocp-Apim-Subscription-Key: <your-key>
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In the response body, you see information about the model. Check the `"status"` field for the status of the model.
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**Request**
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```bash
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curl -i -X POST "<your-resource-name>/documentintelligence/documentModels:authorizeCopy?api-version=2024-02-29-preview"
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curl -i -X POST "<your-resource-endpoint>/documentintelligence/documentModels:authorizeCopy?api-version=2024-02-29-preview"
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--data-ascii "{
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**Request**
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curl -i -X POST "<your-resource-endpoint>/documentintelligence/documentModels/{modelId}:copyTo?api-version=2024-02-29-preview"
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Operation-Location: https://<your-resource-endpoint>.cognitiveservices.azure.com/documentintelligence/operations/{operation-id}?api-version=2024-02-29-preview
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### Track copy operation progress

articles/ai-services/openai/how-to/content-filters.md

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# How to configure content filters with Azure OpenAI Service
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> [!NOTE]
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> All customers have the ability to modify the content filters to be stricter (for example, to filter content at lower severity levels than the default). Approval is required for turning the content filters partially or fully off. Managed customers only may apply for full content filtering control via this form: [Azure OpenAI Limited Access Review: Modified Content Filters](https://ncv.microsoft.com/uEfCgnITdR).
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> All customers have the ability to modify the content filters and configure the severity thresholds (low, medium, high). Approval is required for turning the content filters partially or fully off. Managed customers only may apply for full content filtering control via this form: [Azure OpenAI Limited Access Review: Modified Content Filters](https://ncv.microsoft.com/uEfCgnITdR).
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The content filtering system integrated into Azure OpenAI Service runs alongside the core models and uses an ensemble of multi-class classification models to detect four categories of harmful content (violence, hate, sexual, and self-harm) at four severity levels respectively (safe, low, medium, and high), and optional binary classifiers for detecting jailbreak risk, existing text, and code in public repositories. The default content filtering configuration is set to filter at the medium severity threshold for all four content harms categories for both prompts and completions. That means that content that is detected at severity level medium or high is filtered, while content detected at severity level low or safe is not filtered by the content filters. Learn more about content categories, severity levels, and the behavior of the content filtering system [here](../concepts/content-filter.md). Jailbreak risk detection and protected text and code models are optional and off by default. For jailbreak and protected material text and code models, the configurability feature allows all customers to turn the models on and off. The models are by default off and can be turned on per your scenario. Some models are required to be on for certain scenarios to retain coverage under the [Customer Copyright Commitment](/legal/cognitive-services/openai/customer-copyright-commitment?context=%2Fazure%2Fai-services%2Fopenai%2Fcontext%2Fcontext).
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articles/ai-services/speech-service/includes/language-support/pronunciation-assessment.md

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|Arabic (Saudi Arabia)|`ar-SA` |
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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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|Dutch (Netherlands)|`nl-NL`<sup>1</sup>|
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|Chinese (Taiwanese Mandarin, Traditional)|`zh-TW`<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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|French (France)|`fr-FR`|
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|Korean (Korea)|`ko-KR`|
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|Polish (Poland)|`pl-PL`<sup>1</sup>|
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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 27 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 26 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.
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The table in this section summarizes the 30 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 29 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.
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[!INCLUDE [Language support include](includes/language-support/pronunciation-assessment.md)]
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