Merge branch 'main' of https://github.com/MicrosoftDocs/azure-docs-pr into fixParameter

Author: roygara

articles/active-directory/develop/msal-js-sso.md

@@ -50,7 +50,7 @@ When a user authenticates, a session cookie is set on the Azure AD domain in the
 To improve performance and ensure that the authorization server will look for the correct account session, you can pass one of the following options in the request object of the `ssoSilent` method to obtain the token silently.
 
 - Session ID `sid` (which can be retrieved from `idTokenClaims` of an `account` object)
-- `login_hint` (which can be retrieved from the `account` object username property or the `upn` claim in the ID token)
+- `login_hint` (which can be retrieved from the `account` object username property or the `upn` claim in the ID token) (if your app is authenticating users with B2C, see: [Configure B2C user-flows to emit username in ID tokens](https://github.com/AzureAD/microsoft-authentication-library-for-js/blob/dev/lib/msal-browser/FAQ.md#why-is-getaccountbyusername-returning-null-even-though-im-signed-in) )
 - `account` (which can be retrieved from using one the [account methods](https://github.com/AzureAD/microsoft-authentication-library-for-js/blob/dev/lib/msal-browser/docs/login-user.md#account-apis))
 
 #### Using a session ID
@@ -183,11 +183,13 @@ InteractionRequiredAuthError: login_required: AADSTS50058: A silent sign-in requ
 
 To resolve the error, the user must create an interactive authentication request using the `loginPopup()` or `loginRedirect()`. In some cases, the prompt value **none** can be used together with an interactive MSAL.js method to achieve SSO. See [Interactive requests with prompt=none](msal-js-prompt-behavior.md#interactive-requests-with-promptnone) for more. If you already have the user's sign-in information, you can pass either the `loginHint` or `sid` optional parameters to sign-in a specific account.
 
-## SSO in ADAL.js to MSAL.js update
+## Negating SSO with prompt=login
 
-MSAL.js brings feature parity with ADAL.js for Azure AD authentication scenarios. To make the migration from ADAL.js to MSAL.js easy and to avoid prompting your users to sign in again, the library reads the ID token representing user’s session in ADAL.js cache, and seamlessly signs in the user in MSAL.js.
+If you like Azure AD to prompt the user for entering their credentials despite there being an active session with the authorization server, you can use the **login** prompt parameter in requests with MSAL.js. See [MSAL.js prompt behavior](msal-js-prompt-behavior.md) for more.
 
-To take advantage of the SSO behavior when updating from ADAL.js, you'll need to ensure the libraries are using `localStorage` for caching tokens. Set the `cacheLocation` to `localStorage` in both the MSAL.js and ADAL.js configuration at initialization as follows:
+## Sharing authentication state between ADAL.js and MSAL.js
+
+MSAL.js brings feature parity with ADAL.js for Azure AD authentication scenarios. To make the migration from ADAL.js to MSAL.js easy and share authentication state between apps, the library reads the ID token representing user’s session in ADAL.js cache. To take advantage of this when migrating from ADAL.js, you'll need to ensure that the libraries are using `localStorage` for caching tokens. Set the `cacheLocation` to `localStorage` in both the MSAL.js and ADAL.js configuration at initialization as follows:
 
 ```javascript
 
@@ -212,8 +214,6 @@ const config = {
 const msalInstance = new msal.PublicClientApplication(config);
 ```
 
-Once the `cacheLocation` is configured, MSAL.js can read the cached state of the authenticated user in ADAL.js and use that to provide SSO in MSAL.js.
-
 ## Next steps
 
 For more information about SSO, see:

articles/cognitive-services/Anomaly-Detector/overview.md

@@ -8,7 +8,7 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: anomaly-detector
 ms.topic: overview
-ms.date: 07/06/2022
+ms.date: 10/17/2022
 ms.author: mbullwin
 keywords: anomaly detection, machine learning, algorithms
 ms.custom: cog-serv-seo-aug-2020
@@ -18,7 +18,6 @@ ms.custom: cog-serv-seo-aug-2020
 
 Anomaly Detector is an AI service with a set of APIs, which enables you to monitor and detect anomalies in your time series data with little ML knowledge, either batch validation or real-time inference.
 
-
 This documentation contains the following types of articles:
 * The [quickstarts](./Quickstarts/client-libraries.md) are step-by-step instructions that let you make calls to the service and get results in a short period of time. 
 * The [how-to guides](./how-to/identify-anomalies.md) contain instructions for using the service in more specific or customized ways.
@@ -31,15 +30,14 @@ With the Anomaly Detector, you can either detect anomalies in one variable using
 
 |Feature  |Description  |
 |---------|---------|
-|Univariate Anomaly Detector | Detect anomalies in one variable, like revenue, cost, etc. The model was selected automatically based on your data pattern. |
-|Multivariate Anomaly Detector| Detect anomalies in multiple variables with correlations, which are usually gathered from equipment or other complex system. The underlying model used is Graph attention network.|
+|Univariate Anomaly Detector | Detect anomalies in one variable, like revenue, cost, etc. The model is selected automatically based on your data pattern. |
+|Multivariate Anomaly Detector| Detect anomalies in multiple variables with correlations, which are usually gathered from equipment or other complex system. The underlying model used is a Graph Attention Network (GAT).|
 
 ### When to use **Univariate Anomaly Detector** v.s. **Multivariate Anomaly Detector**
 
 If your goal is to detect anomalies out of a normal pattern on each individual time series purely based on their own historical data, use univariate anomaly detection APIs. For example, you want to detect daily revenue anomalies based on revenue data itself, or you want to detect a CPU spike purely based on CPU data.
 
-If your goal is to detect system level anomalies from a group of time series data, use multivariate anomaly detection APIs. Particularly, when any individual time series won't tell you much, and you have to look at all signals (a group of time series) holistically to determine a system level issue. For example, you have an expensive physical asset like aircraft, equipment on an oil rig, or a satellite. Each of these assets has tens or hundreds of different types of sensors. You would have to look at all those time series signals from those sensors to decide whether there is system level issue.
-
+If your goal is to detect system level anomalies from a group of time series data, use multivariate anomaly detection APIs. Particularly, when any individual time series won't tell you much, and you have to look at all signals (a group of time series) holistically to determine a system level issue. For example, you have an expensive physical asset like aircraft, equipment on an oil rig, or a satellite. Each of these assets has tens or hundreds of different types of sensors. You would have to look at all those time series signals from those sensors to decide whether there is a system level issue.
 
 ## Demo
 

articles/storage/blobs/secure-file-transfer-protocol-known-issues.md

@@ -52,7 +52,7 @@ To transfer files to or from Azure storage via client applications, see the foll
 | Category | Unsupported operations |
 |---|---|
 | ACLs | <li>`chgrp` - change group<li>`chmod` - change permissions/mode<li>`chown` - change owner<li>`put/get -p` - preserving permissions |
-| Resume operations |<li>`reget`, `get -a`- resume download<li>`reput`. `put -a` - resume upload |
+| Resuming Uploads | `reput`. `put -a` |
 | Random writes and appends | <li>Operations that include both READ and WRITE flags. For example: [SSH.NET create API](https://github.com/sshnet/SSH.NET/blob/develop/src/Renci.SshNet/SftpClient.cs#:~:text=public%20SftpFileStream-,Create,-(string%20path))<li>Operations that include APPEND flag. For example: [SSH.NET append API](https://github.com/sshnet/SSH.NET/blob/develop/src/Renci.SshNet/SftpClient.cs#:~:text=public%20void-,AppendAllLines,-(string%20path%2C%20IEnumerable%3Cstring%3E%20contents)). |
 | Links |<li>`symlink` - creating symbolic links<li>`ln` - creating hard links<li>Reading links not supported |
 | Capacity Information | `df` - usage info for filesystem |

includes/digital-twins-limits.md

@@ -3,7 +3,7 @@ author: baanders
 description: include file for Azure Digital Twins limits
 ms.service: digital-twins
 ms.topic: include
-ms.date: 4/4/2022
+ms.date: 10/17/2022
 ms.author: baanders
 ---
 
@@ -17,7 +17,7 @@ The following table lists the functional limits of Azure Digital Twins.
 | Area | Capability | Default limit | Adjustable? |
 | --- | --- | --- | --- |
 | Azure resource | Number of Azure Digital Twins instances in a region, per subscription | 10 | Yes |
-| Digital twins | Number of twins in an Azure Digital Twins instance | 1,000,000 | Yes |
+| Digital twins | Number of twins in an Azure Digital Twins instance | 2,000,000 | Yes |
 | Digital twins | Number of incoming relationships to a single twin | 5,000 | No |
 | Digital twins | Number of outgoing relationships from a single twin | 5,000 | No |
 | Digital twins | Maximum size (of JSON body in a PUT or PATCH request) of a single twin | 32 KB | No |