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

Author: PatAltimore

articles/ai-services/computer-vision/includes/how-to-guides/analyze-image-40-rest.md

@@ -25,7 +25,7 @@ To authenticate against the Image Analysis service, you need a Computer Vision k
 The SDK example assumes that you defined the environment variables `VISION_KEY` and `VISION_ENDPOINT` with your key and endpoint.
 
 
-Authentication is done by adding the HTTP request header **Ocp-Apim-Subscription-Key** and setting it to your vision key. The call is made to the URL `https://<endpoint>/computervision/imageanalysis:analyze&api-version=2023-10-01`, where `<endpoint>` is your unique computer vision endpoint URL. You add query strings based on your analysis options.
+Authentication is done by adding the HTTP request header **Ocp-Apim-Subscription-Key** and setting it to your vision key. The call is made to the URL `https://<endpoint>/computervision/imageanalysis:analyze?api-version=2023-10-01`, where `<endpoint>` is your unique computer vision endpoint URL. You add query strings based on your analysis options.
 
 
 ## Select the image to analyze

articles/ai-services/computer-vision/reference-video-search.md

@@ -334,6 +334,7 @@ Represents the create ingestion request model for the JSON document.
 | moderation | boolean | Gets or sets the moderation flag, indicating if the content should be moderated. | No |
 | generateInsightIntervals | boolean | Gets or sets the interval generation flag, indicating if insight intervals should be generated. | No |
 | filterDefectedFrames | boolean | Frame filter flag indicating frames will be evaluated and all defected (e.g. blurry, lowlight, overexposure) frames will be filtered out. | No |
+| includeSpeechTranscript | boolean | Gets or sets the transcript generation flag, indicating if transcript should be generated. | No |
 
 ### DatetimeFilterModel
 

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

@@ -347,18 +347,18 @@ Every response includes a `"finish_details"` field. The subfield `"type"` has th
 
 If `finish_details.type` is `stop`, then there is another `"stop"` property that specifies the token that caused the output to end.
 
-## Low or high fidelity image understanding
+## Detail parameter settings in image processing: Low, High, Auto  
 
-By controlling the _detail_ parameter, which has two options, `low` or `high`, you can control how the model processes the image and generates its textual understanding.
-- `low` disables the "high res" mode. The model receives a low-res 512x512 version of the image and represents the image with a budget of 65 tokens. This allows the API to return faster responses and consume fewer input tokens for use cases that don't require high detail.
-- `high` enables "high res" mode, which first allows the model to see the low res image and then creates detailed crops of input images as 512x512 squares based on the input image size. Each of the detailed crops uses twice the token budget (65 tokens) for a total of 129 tokens.
+The detail parameter in the model offers three choices: `low`, `high`, or `auto`, to adjust the way the model interprets and processes images. The default setting is auto, where the model decides between low or high based on the size of the image input.  
+- `low` setting: the model does not activate the "high res" mode, instead processing a lower resolution 512x512 version of the image using 65 tokens, resulting in quicker responses and reduced token consumption for scenarios where fine detail isn't crucial.  
+- `high` setting activates "high res" mode. Here, the model initially views the low-resolution image and then generates detailed 512x512 segments from the input image. Each segment uses double the token budget, amounting to 129 tokens per segment, allowing for a more detailed interpretation of the image.
 
 ## Limitations
 
 ### Image support
 
 - **Limitation on image enhancements per chat session**: Enhancements cannot be applied to multiple images within a single chat call.
-- **Maximum input image size**: The maximum size for input images is restricted to 4 MB.
+- **Maximum input image size**: The maximum size for input images is restricted to 20 MB.
 - **Object grounding in enhancement API**: When the enhancement API is used for object grounding, and the model detects duplicates of an object, it will generate one bounding box and label for all the duplicates instead of separate ones for each.
 - **Low resolution accuracy**: When images are analyzed using the "low resolution" setting, it allows for faster responses and uses fewer input tokens for certain use cases. However, this could impact the accuracy of object and text recognition within the image.
 - **Image chat restriction**: When uploading images in the chat playground or the API, there is a limit of 10 images per chat call.

articles/ai-services/openai/includes/gpt-v-rest.md

@@ -61,11 +61,11 @@ Create a new Python file named _quickstart.py_. Open the new file in your prefer
     endpoint = f"{base_url}/chat/completions?api-version=2023-12-01-preview" 
     data = { 
         "messages": [ 
-            { "role": "system", "content": "You are a helpful assistant." }, # Content can be a string, OR 
-            { "role": "user", "content": [       # It can be an array containing strings and images. 
-                "Describe this picture:", 
-                { "image": "<base_64_encoded_image>" }      # Images are represented like this. 
-            ] } 
+            { "role": "system", "content": "You are a helpful assistant." }, 
+            { "role": "user", "content": [ 
+               { "type": "text", "text": "Describe this picture:" }, 
+               { "type": "image_url", "url": "<URL or base-64-encoded image>" } 
+           ] } 
         ], 
         "max_tokens": 100 
     }   
@@ -136,11 +136,11 @@ The **object grounding** integration brings a new layer to data analysis and use
             }
         }],
         "messages": [ 
-            { "role": "system", "content": "You are a helpful assistant." }, # Content can be a string, OR 
-            { "role": "user", "content": [       # It can be an array containing strings and images. 
-                "Describe this picture:", 
-                { "image": "<base_64_encoded_image>" }      # Images are represented like this. 
-            ]} 
+            { "role": "system", "content": "You are a helpful assistant." },
+            { "role": "user", "content": [ 
+               { "type": "text", "text": "Describe this picture:" }, 
+               { "type": "image_url", "url": "<URL or base-64-encoded image>" } 
+           ]} 
         ], 
         "max_tokens": 100 
     }   

articles/ai-studio/concepts/ai-resources.md

@@ -18,9 +18,14 @@ ms.author: eur
 
 In Azure, resources enable access to Azure services for individuals and teams. Access to many Azure AI capabilities is available via a unified resource called Azure AI. 
 
-An Azure AI resource can be used to access multiple Azure AI services. The Azure AI resource provides a hosted environment for teams to organize their [Azure AI project](#project-assets) work in, and is configurable with enterprise-grade security controls, which are passed down to each project environment. The Azure AI resource doesn't directly contain the keys and endpoints needed to authenticate your requests to Azure AI services. Instead, the Azure AI resource contains an [Azure AI services](#azure-ai-services-resource-keys) resource with keys and endpoints that you use to access Azure AI services.
+The preview 'Azure AI' resource used in AI studio can be used to access multiple Azure AI services with a single setup. Previously, different Azure AI services including [Azure OpenAI](../../ai-services/openai/overview.md), [Azure Machine Learning](../../machine-learning/overview-what-is-azure-machine-learning.md), [Azure Speech](../../ai-services/speech-service/overview.md), required their individual setup.
 
-In this article, you learn more about its capabilities, and how to set up Azure AI for your organization. You can see the resources that have created in the [Azure portal](https://portal.azure.com/) and in [Azure AI Studio](https://ai.azure.com).
+The AI resource provides the working environment for a team to build and manage AI applications, catering to two persona:
+
+* To AI developers, the Azure AI resource provides the working environment for building AI applications granting access to various tools for AI model building. Tools can be used together, and lets you use and produce shareable components including datasets, indexes, models. AI resources allows you to configure connections to external resources, provide compute resources used by tools and [endpoints and access keys to pre-built AI models](#azure-ai-services-resource-keys).
+* To IT administrators and team leads, the Azure AI resource provides a single pane of glass on projects created by a team, audit connections that are in use to external resources, and additional governance controls to help meet cost and compliance requirements. Security settings are configured on the Azure AI resource, and once set up apply to all projects created under it, allowing administrators to enable developers to self-serve create projects to organize work.
+
+In this article, you learn more about Azure AI resource's capabilities, and how to set up Azure AI for your organization. You can see the resources that have created in the [Azure portal](https://portal.azure.com/) and in [Azure AI Studio](https://ai.azure.com).
 
 ## Unified assets across projects
 

articles/azure-arc/data/includes/azure-arc-data-preview-release.md

@@ -5,13 +5,13 @@ ms.service: azure-arc
 ms.custom:
   - ignite-2023
 ms.topic: include
-ms.date: 12/06/2023
+ms.date: 12/12/2023
 ---
 
-<!--
 At this time, a test or preview build is not available for the next release.
- -->
 
+<!--
+
 Dec 2023 preview release is now available.
 
 |Component|Value|
@@ -46,3 +46,5 @@ Dec 2023 preview release is now available.
 Arc SQL Server | Show the Data Processing Service (DPS) connectivity status in the Azure portal | GA
 
 Arc SQL Server | Monitoring | Add IOPS, Queue Latency Storage IO charts in Performance Dashboard in Azure portal
+
+-->

articles/azure-arc/data/version-log.md

@@ -19,6 +19,32 @@ ms.topic: conceptual
 
 This article identifies the component versions with each release of Azure Arc-enabled data services.
 
+## December 12, 2023
+
+|Component|Value|
+|-----------|-----------|
+|Container images tag |`v1.26.0_2023-12-12`|
+|**CRD names and version:**| |
+|`activedirectoryconnectors.arcdata.microsoft.com`| v1beta1, v1beta2, v1, v2|
+|`datacontrollers.arcdata.microsoft.com`| v1beta1, v1 through v5|
+|`exporttasks.tasks.arcdata.microsoft.com`| v1beta1, v1, v2|
+|`failovergroups.sql.arcdata.microsoft.com`| v1beta1, v1beta2, v1, v2|
+|`kafkas.arcdata.microsoft.com`| v1beta1 through v1beta4|
+|`monitors.arcdata.microsoft.com`| v1beta1, v1, v3|
+|`postgresqls.arcdata.microsoft.com`| v1beta1 through v1beta6|
+|`postgresqlrestoretasks.tasks.postgresql.arcdata.microsoft.com`| v1beta1|
+|`sqlmanagedinstances.sql.arcdata.microsoft.com`| v1beta1, v1 through v13|
+|`sqlmanagedinstancemonitoringprofiles.arcdata.microsoft.com`| v1beta1, v1beta2|
+|`sqlmanagedinstancereprovisionreplicatasks.tasks.sql.arcdata.microsoft.com`| v1beta1|
+|`sqlmanagedinstancerestoretasks.tasks.sql.arcdata.microsoft.com`| v1beta1, v1|
+|`telemetrycollectors.arcdata.microsoft.com`| v1beta1 through v1beta5|
+|`telemetryrouters.arcdata.microsoft.com`| v1beta1 through v1beta5|
+|Azure Resource Manager (ARM) API version|2023-11-01-preview|
+|`arcdata` Azure CLI extension version|1.5.8 ([Download](https://aka.ms/az-cli-arcdata-ext))|
+|Arc-enabled Kubernetes helm chart extension version|1.26.0|
+|Azure Arc Extension for Azure Data Studio<br/>`arc`<br/>`azcli`|<br/>1.8.0 ([Download](https://aka.ms/ads-arcdata-ext))</br>1.8.0 ([Download](https://aka.ms/ads-azcli-ext))|
+|SQL Database version | 957 |
+
 ## November 14, 2023
 
 |Component|Value|

articles/azure-monitor/agents/azure-monitor-agent-migration-tools.md

@@ -4,8 +4,8 @@ description: This article describes various migration tools and helpers availabl
 ms.topic: conceptual
 author: guywi-ms
 ms.author: guywild
-ms.reviewer: shseth
-ms.date: 1/18/2023 
+ms.reviewer: jeffwo
+ms.date: 12/12/2023 
 ms.custom:
 # Customer intent: As an Azure account administrator, I want to use the available Azure Monitor tools to migrate from Log Analytics Agent to Azure Monitor Agent and track the status of the migration in my account.
 ---
@@ -42,75 +42,67 @@ Use the DCR Config Generator tool to parse Log Analytics Agent configuration fro
 > [!NOTE]
 > DCR Config Generator does not currently support additional configuration for [Azure solutions or services](./azure-monitor-agent-migration.md#migrate-additional-services-and-features) dependent on Log Analytics Agent.
 
-### Prerequisites
-To install DCR Config Generator, you need:
+### Prerequisites\Setup
+1. `Powershell version 7.1.3` or higher is recommended (minimum version 5.1)
+2. Uses `Az Powershell module` to pull workspace agent configuration information [Az PowerShell module](https://learn.microsoft.com/powershell/azure/install-azps-windows?view=azps-11.0.0&tabs=powershell&pivots=windows-psgallery)
+3. User will need Read/Write access to the specified workspace resource
+4. Connect-AzAccount and Select-AzSubscription will be used to set the context for the script to run so proper Azure credentials will be needed
 
-1. PowerShell version 5.1 or higher. We recommend using PowerShell version 7.1.3 or higher.
-1. Read access for the specified workspace resources.
-1. The `Az Powershell` module to pull workspace agent configuration information. Make sure `Az.Accounts` and `Az.OperationalInsights` modules are installed.
-1. The Azure credentials for running `Connect-AzAccount` and `Select-AzContext`, which set the context for the script to run.
-
-To install DCR Config Generator:
+### To install DCR Config Generator:
 
 1. [Download the PowerShell script](https://github.com/microsoft/AzureMonitorCommunity/tree/master/Azure%20Services/Azure%20Monitor/Agents/Migration%20Tools/DCR%20Config%20Generator).
-
-1. Run the script:
-
-    Option 1: Outputs **ready-to-deploy ARM template files** only, which creates the generated DCR in the specified subscription and resource group, when deployed.
-
-    ```powershell
-    .\WorkspaceConfigToDCRMigrationTool.ps1 -SubscriptionId $subId -ResourceGroupName $rgName -WorkspaceName $workspaceName -DCRName $dcrName -Location $location -FolderPath $folderPath
-    ```
-    Option 2: Outputs **ready-to-deploy ARM template files** and **the DCR JSON files** separately for you to deploy via other means. You need to set the `GetDcrPayload` parameter.
-
-    ```powershell
-    .\WorkspaceConfigToDCRMigrationTool.ps1 -SubscriptionId $subId -ResourceGroupName $rgName -WorkspaceName $workspaceName -DCRName $dcrName -Location $location -FolderPath $folderPath -GetDcrPayload
-    ```
-
-    **Parameters**
-
-    | Parameter | Required? | Description |
-    |------|------|------|
-    | `SubscriptionId` | Yes | ID of the subscription that contains the target workspace. |
-    | `ResourceGroupName` | Yes | Resource group that contains the target workspace. |
-    | `WorkspaceName` | Yes | Name of the target workspace. |
-    | `DCRName` | Yes | Name of the new DCR. |
-    | `Location` | Yes | Region location for the new DCR. |
-    | `GetDcrPayload` | No | When set, it generates additional DCR JSON files 
-    | `FolderPath` | No | Path in which to save the ARM template files and JSON files (optional). By default, Azure Monitor uses the current directory. |
-
-1. Review the output ARM template files. The script can produce two types of ARM template files, depending on the agent configuration in the target workspace:
-
-    - Windows ARM template and parameter files - if the target workspace contains Windows performance counters or Windows events.
-    - Linux ARM template and parameter files - if the target workspace contains Linux performance counters or Linux Syslog events.
-
-    If the Log Analytics workspace wasn't [configured to collect data](./log-analytics-agent.md#data-collected) from connected agents, the generated files will be empty. This is a scenario in which the agent was connected to a Log Analytics workspace, but wasn't configured to send any data from the host machine.
-
-1. Deploy the generated ARM templates:
-    
-
-    ### [Portal](#tab/portal-1)
-    1. In the portal's search box, type in *template* and then select **Deploy a custom template**.
+1. Run the script using the sample parameters below:
+
+	```powershell
+	.\WorkspaceConfigToDCRMigrationTool.ps1 -SubscriptionId $subId -ResourceGroupName $rgName -WorkspaceName $workspaceName -DCRName $dcrName -OutputFolder $outputFolderPath
+	```
+	---
+
+	| Name                    | Required  | Description                                                                   |
+	|:----------------------- |:---------|:-----------------------------------------------------------------------------|
+	| `SubscriptionId`        | YES       | This is the subscription ID of the workspace                                  |
+	| `ResourceGroupName`     | YES       | This is the resource Group of the workspace                                   |
+	| `WorkspaceName`         | YES       | This is the name of the workspace (Azure resource IDs are case insensitive)   |
+	| `DCRName`               | YES       | The base name that will be used for each one the outputs DCRs                 |
+	| `OutputFolder`          | NO        | The output folder path. If not provided, the working directory path is used   |
+
+3. Outputs:
+
+	For each supported `DCR type`, the script produces a DCR ARM template (ready to be deployed) and a DCR payload (for users that don't need the ARM template). This is the list of currently supported DCR types:
+ 	 - **Windows** contains `WindowsPerfCounters` and `WindowsEventLogs` data sources only
+	  - **Linux** contains `LinuxPerfCounters` and `Syslog` data sources only
+	  - **Custom Logs** contains `logFiles` data sources only:
+  		- Each custom log gets its own DCR ARM template
+	  - **IIS Logs** contains `iisLogs` data sources only
+	  - **Extensions** contains `extensions` data sources only along with any associated perfCounters data sources
+	    - `VMInsights` 
+ 	   - If you would like to add support for a new extension type, please reach out to us.
+
+
+4. **Deploy the generated ARM templates:**
+
+	Portal
+   
+	- In the portal's search box, type in *template* and then select **Deploy a custom template**.
 
         :::image type="content" source="../logs/media/tutorial-workspace-transformations-api/deploy-custom-template.png" lightbox="../logs/media/tutorial-workspace-transformations-api/deploy-custom-template.png" alt-text="Screenshot of the Deploy custom template screen.":::
 
-    1. Select **Build your own template in the editor**.
+	- Select **Build your own template in the editor**.
 
         :::image type="content" source="../logs/media/tutorial-workspace-transformations-api/build-custom-template.png" lightbox="../logs/media/tutorial-workspace-transformations-api/build-custom-template.png" alt-text="Screenshot of the template editor.":::
 
-    1. Paste the generated template into the editor and select **Save**. 
-    1. On the **Custom deployment** screen, specify a **Subscription**, **Resource group**, and **Region**.    
-    1. Select **Review + create** > **Create**.
-
-    ### [PowerShell](#tab/azure-powershell)
-
-    ```powershell-interactive
-    New-AzResourceGroupDeployment -ResourceGroupName <resource-group-name> -TemplateFile <path-to-template>
-    ```
-    ---
-
-    > [!NOTE]
-    > You can include up to 100 'counterSpecifiers' in a data collection rule. 'samplingFrequencyInSeconds' must be between 1 and 300, inclusive.
+	- Paste the generated template into the editor and select **Save**. 
+	- On the **Custom deployment** screen, specify a **Subscription**, **Resource group**, and **Region**.    
+	- Select **Review + create** > **Create**.
+
+   	PowerShell
+   
+	```powershell-interactive
+   	New-AzResourceGroupDeployment -ResourceGroupName <resource-group-name> -TemplateFile <path-to-template>
+   	```
+
+	> [!NOTE]
+	> You can include up to 100 'counterSpecifiers' in a data collection rule. 'samplingFrequencyInSeconds' must be between 1 and 300, inclusive.
 
 1. Associate machines to your data collection rules: