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learn-pr/azure/run-docker-with-azure-container-instances/includes/2-run-aci.md

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az group create --name learn-deploy-aci-rg --location eastus
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```
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Create a container by providing a name, a Docker image, and an Azure resource group to the `az container create` command. You can optionally expose the container to the Internet by specifying a DNS name label. In this example, deploy a container that hosts a small web app. You can also select the location to place the image; we use the **East US** region, but you can change it to a location close to you.
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1. Provide a DNS name to expose your container to the Internet. Your DNS name must be unique. For learning purposes, run this command from Cloud Shell to create a Bash variable that holds a unique name:
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```azurecli

learn-pr/paths/develop-language-solutions-azure-ai/index.yml

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### YamlMime:LearningPath
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uid: learn.wwl.develop-language-solutions-azure-ai
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metadata:
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title: Develop natural language processing solutions with Azure AI Services AI-3003
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description: Develop natural language processing solutions with Azure AI Services (AI-3003)
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ms.date: 09/14/2023
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title: Develop natural language solutions in Azure
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description: Develop natural language solutions in Azure
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ms.date: 06/09/2025
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author: ivorb
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ms.author: berryivor
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ms.topic: learning-path
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title: Develop natural language processing solutions with Azure AI Services
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title: Develop natural language solutions in Azure
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prerequisites: |
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Before starting this learning path, you should already have:
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* Familiarity with Azure and the Azure portal.
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* Experience programming with C# or Python.
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If you have no previous programming experience, we recommend you complete the [Take your first steps with C#](/training/paths/csharp-first-steps/) or [Take your first steps with Python](/training/paths/python-first-steps/) learning path before starting this one.
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summary: |
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Natural language processing (NLP) solutions use language models to interpret the semantic meaning of written or spoken language. You can use the Language Understanding service to build language models for your applications.
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Natural language solutions use language models to interpret the semantic meaning of written or spoken language, and in some cases respond based on that meaning. You can use the Language service to build language models for your applications, and explore Azure AI Foundry to use generative models for speech.
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iconUrl: /training/achievements/create-language-solution-azure-cognitive-services.svg
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levels:
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- intermediate

learn-pr/wwl-sci/analyze-data-in-sentinel/2-exercise-setup.yml

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metadata:
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title: Exercise - Detect threats with Microsoft Sentinel analytics
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description: "Detect threats by using Microsoft Sentinel Analytics."
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ms.date: 05/23/2025
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ms.date: 06/09/2025
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author: KenMAG
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ms.author: kelawson
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ms.topic: unit

learn-pr/wwl-sci/analyze-data-in-sentinel/includes/2-exercise-setup.md

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| Workspace Name | Provide a unique name for the Microsoft Sentinel Workspace such as `<yourName>-sentinel`, where *\<yourName>* represents the workspace name that you chose in the previous task. |
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| Location | Accept the default value of **[resourceGroup().location]**. |
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| Simplevm Name | Accept the default value of **simple-vm**. |
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| Simplevm Windows OS Version | Accept the default value of **2016-Datacenter**. |
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| Simplevm Windows OS Version | Accept the default value of **2022-Datacenter**. |
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1. Select the **Review + create**. When validation passes, select **Create**.
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:::image type="content" source="../media/02-custom-deployment.png" alt-text="Screenshot of the Custom Deployment page." border="true":::
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> [!Note]
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> [!NOTE]
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> Wait for the deployment to complete. The deployment should take less than five minutes.
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## Task 2: Check the resources created

learn-pr/wwl-sci/analyze-data-in-sentinel/index.yml

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metadata:
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title: Threat detection with Microsoft Sentinel analytics
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description: "Threat detection with Microsoft Sentinel analytics"
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ms.date: 05/23/2025
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ms.date: 06/09/2025
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author: KenMAG
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ms.author: kelawson
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ms.topic: module
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learn-pr/wwl-sci/hunt-threats-sentinel/2-exercise-setup.yml

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metadata:
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title: Exercise setup
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description: Learn how to set up your Azure resources to hunt threats using Microsoft Sentinel.
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ms.date: 05/25/2023
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author: wwlpublish
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ms.date: 06/09/2025
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author: KenMAG
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ms.author: kelawson
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ms.topic: unit
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ms.custom:

learn-pr/wwl-sci/hunt-threats-sentinel/includes/2-exercise-setup.md

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| **Workspace name** | Provide a unique name for the Microsoft Sentinel workspace, such as `<yourName>-sentinel`, where *\<yourName>* represents the workspace name that you chose in the previous task. |
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| **Location** | Accept the default value **[resourceGroup().location]**. |
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| **Simplevm Name** | Accept the default value **simple-vm**. |
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| **Simplevm Windows OS Version** | Accept the default value **2016-Datacenter**.|
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| **Simplevm Windows OS Version** | Accept the default value **2022-Datacenter**.|
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1. Select **Review + create**, and then select **Create**.
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:::image type="content" source="../media/02-custom-deployment.png" alt-text="Screenshot of the Custom Deployment page." border="true":::
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> [!NOTE]
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> Wait for the deployment to finish. The deployment should take less than five minutes.
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learn-pr/wwl-sci/hunt-threats-sentinel/index.yml

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metadata:
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title: Threat hunting with Microsoft Sentinel
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description: Learn how to proactively identify threat behaviors by using Microsoft Sentinel queries.
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ms.date: 03/06/2025
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author: wwlpublish
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ms.date: 06/09/2025
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author: KenMAG
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ms.author: kelawson
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ms.topic: module
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ms.service: microsoft-sentinel
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