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articles/active-directory/authentication/concept-authentication-passwordless.md

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### FIDO2 security key providers
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The following providers offer FIDO2 security keys of different form factors that are known to be compatible with the passwordless experience. We encourage you to evaluate the security properties of these keys by contacting the vendor as well as FIDO Alliance.
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The following providers offer FIDO2 security keys of different form factors that are known to be compatible with the passwordless experience. We encourage you to evaluate the security properties of these keys by contacting the vendor as well as the [FIDO Alliance](https://fidoalliance.org/).
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| Provider | Biometric | USB | NFC | BLE | FIPS Certified | Contact |
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|---------------------------|:-----------------:|:---:|:---:|:---:|:--------------:|-----------------------------------------------------------------------------------------------------|

articles/active-directory/authentication/howto-authentication-passwordless-security-key.md

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![Screenshot of FIDO2 security key options](media/howto-authentication-passwordless-security-key/optional-settings.png)
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- **Allow self-service set up** should remain set to **Yes**. If set to no, your users won't be able to register a FIDO key through the MySecurityInfo portal, even if enabled by Authentication Methods policy.
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- **Enforce attestation** setting to **Yes** requires the FIDO security key metadata to be published and verified with the FIDO Alliance Metadata Service, and also pass Microsoft’s additional set of validation testing. For more information, see [What is a Microsoft-compatible security key?](/windows/security/identity-protection/hello-for-business/microsoft-compatible-security-key)
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- **Enforce attestation** setting to **Yes** requires the FIDO security key metadata to be published and verified with the FIDO Alliance Metadata Service, and also pass Microsoft’s additional set of validation testing. For more information, see [What is a Microsoft-compatible security key?](concept-authentication-passwordless.md#fido2-security-key-providers)
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**Key Restriction Policy**
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articles/active-directory/cloud-sync/how-to-install.md

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</configuration>
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```
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For more information about security and FIPS, see [Azure AD password hash sync, encryption, and FIPS compliance](https://blogs.technet.microsoft.com/enterprisemobility/2014/06/28/aad-password-sync-encryption-and-fips-compliance/).
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For information about security and FIPS, see [Azure AD password hash sync, encryption, and FIPS compliance](https://techcommunity.microsoft.com/t5/microsoft-entra-azure-ad-blog/aad-password-sync-encryption-and-fips-compliance/ba-p/243709).
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## Next steps
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articles/active-directory/develop/v2-protocols-oidc.md

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Receiving an ID token in your app might not always be sufficient to fully authenticate the user. You might also need to validate the ID token's signature and verify its claims per your app's requirements. Like all OpenID providers, the Microsoft identity platform's ID tokens are [JSON Web Tokens (JWTs)](https://tools.ietf.org/html/rfc7519) signed by using public key cryptography.
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Web apps and web APIs that use ID tokens for authorization must validate them because such applications gate access to data. Other types of application might not benefit from ID token validation, however. Native and single-page apps (SPAs), for example, rarely benefit from ID token validation because any entity with physical access to the device or browser can potentially bypass the validation.
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Web apps and web APIs that use ID tokens for authorization must validate them because such applications get access to data. Other types of application might not benefit from ID token validation, however. Native and single-page apps (SPAs), for example, rarely benefit from ID token validation because any entity with physical access to the device or browser can potentially bypass the validation.
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Two examples of token validation bypass are:
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articles/active-directory/fundamentals/4-secure-access-groups.md

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articles/active-directory/hybrid/how-to-connect-install-prerequisites.md

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| Number of objects in Active Directory | CPU | Memory | Hard drive size |
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| --- | --- | --- | --- |
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| Fewer than 10,000 |1.6 GHz |4 GB |70 GB |
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| 10,000–50,000 |1.6 GHz |4 GB |70 GB |
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| Fewer than 10,000 |1.6 GHz |6 GB |70 GB |
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| 10,000–50,000 |1.6 GHz |6 GB |70 GB |
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| 50,000–100,000 |1.6 GHz |16 GB |100 GB |
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| For 100,000 or more objects, the full version of SQL Server is required. For performance reasons, installing locally is preferred. | | | |
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| For 100,000 or more objects, the full version of SQL Server is required. For performance reasons, installing locally is preferred. The following values are valid only for Azure AD Connect installation. If SQL Server will be installed on the same server, further memory, drive, and CPU is required. | | | |
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| 100,000–300,000 |1.6 GHz |32 GB |300 GB |
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| 300,000–600,000 |1.6 GHz |32 GB |450 GB |
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| More than 600,000 |1.6 GHz |32 GB |500 GB |

articles/aks/includes/servicemesh/osm/open-service-mesh-binary-install-windows.md

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```powershell
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# Specify the OSM version that will be leveraged throughout these instructions
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$OSM_VERSION="v1.2=0"
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$OSM_VERSION="v1.2.0"
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[Net.ServicePointManager]::SecurityProtocol = "tls12"
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$ProgressPreference = 'SilentlyContinue'; Invoke-WebRequest -URI "https://github.com/openservicemesh/osm/releases/download/$OSM_VERSION/osm-$OSM_VERSION-windows-amd64.zip" -OutFile "osm-$OSM_VERSION.zip"

articles/aks/limit-egress-traffic.md

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|--------------------------------------------------------------------------------|---------------|----------|
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| **`security.ubuntu.com`, `azure.archive.ubuntu.com`, `changelogs.ubuntu.com`** | **`HTTP:80`** | This address lets the Linux cluster nodes download the required security patches and updates. |
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If you choose to block/not allow these FQDNs, the nodes will only receive OS updates when you do a [node image upgrade](node-image-upgrade.md) or [cluster upgrade](upgrade-cluster.md).
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If you choose to block/not allow these FQDNs, the nodes will only receive OS updates when you do a [node image upgrade](node-image-upgrade.md) or [cluster upgrade](upgrade-cluster.md). Keep in mind that Node Image Upgrades also come with updated packages including security fixes.
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## GPU enabled AKS clusters
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| **`onegetcdn.azureedge.net, go.microsoft.com`** | **`HTTPS:443`** | To install windows-related binaries |
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| **`*.mp.microsoft.com, www.msftconnecttest.com, ctldl.windowsupdate.com`** | **`HTTP:80`** | To install windows-related binaries |
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If you choose to block/not allow these FQDNs, the nodes will only receive OS updates when you do a [node image upgrade](node-image-upgrade.md) or [cluster upgrade](upgrade-cluster.md). Keep in mind that Node Image Upgrades also come with updated packages including security fixes.
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## AKS addons and integrations
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### Microsoft Defender for Containers

articles/applied-ai-services/form-recognizer/concept-custom-label-tips.md

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ms.service: applied-ai-services
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ms.date: 01/30/2023
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This article highlights the best methods for labeling custom model datasets in the Form Recognizer Studio. Labeling documents can be time consuming when you have a large number of labels, long documents, or documents with varying structure. These tips should help you label documents more efficiently.
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## Video: Custom labels best practices
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* The following video is the second of two presentations intended to help you build custom models with higher accuracy (the first presentation explores [How to create a balanced data set](concept-custom-label.md#video-custom-label-tips-and-pointers)).
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* Here, we'll examine best practices for labeling your selected documents. With semantically relevant and consistent labeling, you should see an improvement in model performance.</br></br>
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> [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RE5fZKB ]
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## Search
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The Studio now includes a search box for instances when you know you need to find specific words to label, but just don't know where they're located in the document. Simply search for the word or phrase and navigate to the specific section in the document to label the occurrence.

articles/applied-ai-services/form-recognizer/concept-custom-label.md

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* A `{file}.labels.json` file is created or updated when a field is labeled in a document. The label file contains the spans of text and associated polygons from the layout output for each span of text the user adds as a value for a specific field.
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## Video: Custom label tips and pointers
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* The following video is the first of two presentations intended to help you build custom models with higher accuracy (The second presentation examines [Best practices for labeling documents](concept-custom-label-tips.md#video-custom-labels-best-practices)).
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* Here, we'll explore how to create a balanced data set and select the right documents to label. This process will set you on the path to higher quality models.</br></br>
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> [!VIDEO https://www.microsoft.com/en-us/videoplayer/embed/RWWHru]
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Before you start labeling, it's a good idea to look at a few different samples of the document to identify which samples you want to use in your labeled dataset. A balanced dataset represents all the typical variations you would expect to see for the document. Creating a balanced dataset will result in a model with the highest possible accuracy. A few examples to consider are:
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* **Document formats**: If you expect to analyze both digital and scanned documents, add a few examples of each type to the training dataset
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* **Variations (template model)**: Consider splitting the dataset into folders and train a model for each of variation. Variations that include either structure or layout should be split into different models. You can then compose the individual models into a single [composed model](concept-composed-models.md).
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* **Variations (template model)**: Consider splitting the dataset into folders and train a model for each of variation. Any variations that include either structure or layout should be split into different models. You can then compose the individual models into a single [composed model](concept-composed-models.md).
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* **Variations (Neural models)**: When your dataset has a manageable set of variations, about 15 or fewer, create a single dataset with a few samples of each of the different variations to train a single model. If the number of template variations is larger than 15, you'll train multiple models and [compose](concept-composed-models.md) them together.
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