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‎.openpublishing.redirection.json

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},
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{
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"source_path_from_root": "/articles/cosmos-db/how-to-move-regions.md",
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"redirect_url": "/azure/operational-excellence/relocation-cosmos-db",
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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/site-recovery/move-vaults-across-regions.md",
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"redirect_url": "/azure/operational-excellence/relocation-site-recovery",
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"redirect_document_id": false

‎articles/active-directory-b2c/faq.yml

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* API connectors
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* Conditional Access
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- question: |
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I am using rolling refresh tokens for my application and I am getting an invalid_grant error on redeeming newly acquired refresh tokens well within their set validity period. Why does this happen?
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answer: |
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While determining validity for rolling refresh tokens, B2C will consider the initial login time of the user in the application also to calculate the token validity skew. If the user haven't logged out of the application for a very long time, this skew value will exceed the validity period of the token and hence for security reasons the tokens will be considered as invalid. Hence the error. Inform the user to perform a proper logout and login back into the application and this should reset the skew. This scenario is not applicable if refresh token rolling is set as infinite rolling.
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I've revoked the refresh token using Microsoft Graph invalidateAllRefreshTokens, or Microsoft Graph PowerShell, Revoke-MgUserSignInSession. Why is Azure AD B2C still accepting the old refresh token?
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1. Wait for 10 minutes.
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1. Retrieve the `RefreshToken` again.
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> [!TIP]
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> With Azure AD B2C [custom policy](custom-policy-overview.md), you can reduce the above mentioned skew time of 5 minutes (300000 milliseconds) by adjusting the value for InputParameter "TreatAsEqualIfWithinMillseconds" under claim transformation Id "AssertRefreshTokenIssuedLaterThanValidFromDate". This claim transformation can be found in the TrustFrameworkBase.xml file under latest custom policy [stater-pack](https://learn.microsoft.com/en-us/azure/active-directory-b2c/tutorial-create-user-flows?pivots=b2c-custom-policy#get-the-starter-pack).
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I use multiple tabs in a web browser to sign in to multiple applications that I registered in the same Azure AD B2C tenant. When I try to perform a single sign-out, not all of the applications are signed out. Why does this happen?

‎articles/active-directory-b2c/javascript-and-page-layout.md

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[!INCLUDE [active-directory-b2c-choose-user-flow-or-custom-policy](../../includes/active-directory-b2c-choose-user-flow-or-custom-policy.md)]
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With Azure Active Directory B2C (Azure AD B2C) [HTML templates](customize-ui-with-html.md), you can craft your users' identity experiences. Your HTML templates can contain only certain HTML tags and attributes. Basic HTML tags, such as &lt;b&gt;, &lt;i&gt;, &lt;u&gt;, &lt;h1&gt;, and &lt;hr&gt; are allowed. More advanced tags such as &lt;script&gt;, and &lt;iframe&gt; are removed for security reasons but the `<script>` tag should be added in the `<head>` tag.
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With Azure Active Directory B2C (Azure AD B2C) [HTML templates](customize-ui-with-html.md), you can craft your users' identity experiences. Your HTML templates can contain only certain HTML tags and attributes. Basic HTML tags, such as &lt;b&gt;, &lt;i&gt;, &lt;u&gt;, &lt;h1&gt;, and &lt;hr&gt; are allowed. More advanced tags such as &lt;script&gt;, and &lt;iframe&gt; are removed for security reasons but the `<script>` tag should be added in the `<head>` tag. From selfasserted page layout version 2.1.21 / unifiedssp version 2.1.10 / multifactor version 1.2.10 onwards B2C doesn't support adding scripts in `<body>` tag (as this can pose a risk for cross site scripting attack). Migrating existing scripts from `<body>` to `<head>` may at-times require rewriting existing scripts with mutation observers for proper working.
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The `<script>` tag should be added in the `<head>` tag in two ways:
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‎articles/ai-services/computer-vision/concept-model-customization.md

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| Max # training images | 1,000,000 | 200,000 |
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| Max # evaluation images | 100,000 | 100,000 |
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| Min # training images per category | 2 | 2 |
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| Max # tags per image | multiclass: 1 | NA |
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| Max # regions per image | NA | 1,000 |
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| Max # tags per image | 1 | N/A |
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| Max # regions per image | N/A | 1,000 |
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| Max # categories | 2,500 | 1,000 |
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| Min # categories | 2 | 1 |
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| Max image size (Training) | 20 MB | 20 MB |

‎articles/ai-services/content-safety/overview.md

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The following are a few scenarios in which a software developer or team would require a content moderation service:
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- User prompts submitted to a generative AI service.
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- Content produced by generative AI models.
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- Online marketplaces that moderate product catalogs and other user-generated content.
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- Gaming companies that moderate user-generated game artifacts and chat rooms.
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- Social messaging platforms that moderate images and text added by their users.

‎articles/ai-services/openai/how-to/gpt-with-vision.md

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#### [REST](#tab/rest)
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Send a POST request to `https://{RESOURCE_NAME}.openai.azure.com/openai/deployments/{DEPLOYMENT_NAME}/chat/completions?api-version=2023-12-01-preview` where
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Send a POST request to `https://{RESOURCE_NAME}.openai.azure.com/openai/deployments/{DEPLOYMENT_NAME}/chat/completions?api-version=2024-02-15-preview` where
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- RESOURCE_NAME is the name of your Azure OpenAI resource
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- DEPLOYMENT_NAME is the name of your GPT-4 Turbo with Vision model deployment
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api_base = '<your_azure_openai_endpoint>' # your endpoint should look like the following https://YOUR_RESOURCE_NAME.openai.azure.com/
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api_key="<your_azure_openai_key>"
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deployment_name = '<your_deployment_name>'
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api_version = '2023-12-01-preview' # this might change in the future
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api_version = '2024-02-15-preview' # this might change in the future
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client = AzureOpenAI(
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api_key=api_key,
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api_version=api_version,
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base_url=f"{api_base}openai/deployments/{deployment_name}/extensions",
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base_url=f"{api_base}openai/deployments/{deployment_name}",
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)
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```
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#### [REST](#tab/rest)
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Send a POST request to `https://{RESOURCE_NAME}.openai.azure.com/openai/deployments/{DEPLOYMENT_NAME}/extensions/chat/completions?api-version=2023-12-01-preview` where
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Send a POST request to `https://{RESOURCE_NAME}.openai.azure.com/openai/deployments/{DEPLOYMENT_NAME}/chat/completions?api-version=2024-02-15-preview` where
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- RESOURCE_NAME is the name of your Azure OpenAI resource
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1. Prepare a POST request to `https://{RESOURCE_NAME}.openai.azure.com/openai/deployments/{DEPLOYMENT_NAME}/extensions/chat/completions?api-version=2023-12-01-preview` where
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1. Prepare a POST request to `https://{RESOURCE_NAME}.openai.azure.com/openai/deployments/{DEPLOYMENT_NAME}/chat/completions?api-version=2024-02-15-preview` where
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- RESOURCE_NAME is the name of your Azure OpenAI resource
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‎articles/ai-studio/how-to/deploy-models-timegen-1.md

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- An [Azure AI Studio project](../how-to/create-projects.md).
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- Azure role-based access controls (Azure RBAC) are used to grant access to operations in Azure AI Studio. To perform the steps in this article, your user account must be assigned the __Azure AI Developer role__ on the resource group. For more information on permissions, visit [Role-based access control in Azure AI Studio](../concepts/rbac-ai-studio.md).
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### Pricing information
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#### Estimate the number of tokens needed
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Before you create a deployment, it's useful to estimate the number of tokens that you plan to use and be billed for.
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One token corresponds to one data point in your input dataset or output dataset.
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Suppose you have the following input time series dataset:
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| Unique_id | Timestamp | Target Variable | Exogenous Variable 1 | Exogenous Variable 2 |
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|:---------:|:-------------------:|:---------------:|:--------------------:|:--------------------:|
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| BE | 2016-10-22 00:00:00 | 70.00 | 49593.0 | 57253.0 |
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| BE | 2016-10-22 01:00:00 | 37.10 | 46073.0 | 51887.0 |
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To determine the number of tokens, multiply the number of rows (in this example, two) and the number of columns used for forecasting—not counting the unique_id and timestamp columns (in this example, three) to get a total of six tokens.
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Given the following output dataset:
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| Unique_id | Timestamp | Forecasted Target Variable |
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|:---------:|:-------------------:|:--------------------------:|
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| BE | 2016-10-22 02:00:00 | 46.57 |
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| BE | 2016-10-22 03:00:00 | 48.57 |
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You can also determine the number of tokens by counting the number of data points returned after data forecasting. In this example, the number of tokens is two.
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#### Estimate the pricing
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There are four pricing meters, as described in the following table:
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| Pricing Meter | Description |
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|-----------------------------------------------|-----------------------------------------------------------------------------------------|
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| paygo-inference-input-tokens | Costs associated with the tokens used as input for inference when *finetune_steps* = 0 |
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| paygo-inference-output-tokens | Costs associated with the tokens used as output for inference when *finetune_steps* = 0 |
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| paygo-finetuned-model-inference-input-tokens | Costs associated with the tokens used as input for inference when *finetune_steps* > 0 |
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| paygo-finetuned-model-inference-output-tokens | Costs associated with the tokens used as output for inference when *finetune_steps* > 0 |
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These steps demonstrate the deployment of TimeGEN-1. To create a deployment:

‎articles/aks/create-node-pools.md

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The following limitations apply when you create AKS clusters that support multiple node pools:
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* See [Quotas, virtual machine size restrictions, and region availability in Azure Kubernetes Service (AKS)](quotas-skus-regions.md).
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* You can delete the system node pool if you have another system node pool to take its place in the AKS cluster. Otherwise, you cannot delete the system node pool.
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* System pools must contain at least one node, and user node pools may contain zero or more nodes.
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* The AKS cluster must use the Standard SKU load balancer to use multiple node pools. This feature isn't supported with Basic SKU load balancers.
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--location $LOCATION \
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‎articles/aks/istio-deploy-ingress.md

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> [!NOTE]
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```bash

‎articles/aks/istio-plugin-ca.md

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az keyvault secret set --vault-name $AKV_NAME --name ca-key --file <path-to-folder/ca-key.pem>
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az keyvault secret set --vault-name $AKV_NAME --name cert-chain --file <path-to-folder/cert-chain.pem>
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1. Enable [Azure Key Vault provider for Secret Store CSI Driver for your cluster][akv-addon]:

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