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Merge pull request #51041 from ivorb/bugfix
assorted AI content bug fixes
2 parents 8355ce4 + 3dde124 commit 6411570

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learn-pr/wwl-data-ai/analyze-video/6-knowledge-check.yml

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@@ -14,7 +14,7 @@ azureSandbox: false
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labModal: false
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durationInMinutes: 3
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quiz:
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title: "Checkyourknowledge"
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title: "Check your knowledge"
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questions:
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- content: "You want Azure Video Indexer to analyze a video. What must you do first?"
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choices:
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choices:
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- content: "Edit the Brands model to show brands suggested by Bing, and add any new brands you want to detect."
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isCorrect: true
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explanation: "That's correct. You can both detect known brands, and well as include new brands you want to detect by providing information about it."
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- content: "Edit the conference call videos to include a caption of each brand seen on their first appearance."
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explanation: "That's correct. You can both detect known brands, and include new brands you want to detect by providing information about it."
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- content: "Edit all recorded conference call videos to include a caption of each brand seen on their first appearance."
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isCorrect: false
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explanation: "That's incorrect. You can both detect known brands, and well as include new brands you want to detect by providing information about it."
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- content: "Embed the Azure Video Indexer widgets in a custom web site that has all the brand images stored for reference."
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explanation: "That's incorrect. You can both detect known brands, and include new brands you want to detect by providing information about it. Editing the videos isn't necessary."
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- content: "Embed the Azure Video Indexer widgets in a custom web site."
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isCorrect: false
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explanation: "That's incorrect. You can both detect known brands, and well as include new brands you want to detect by providing information about it."
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explanation: "That's incorrect. You can both detect known brands, and include new brands you want to detect by providing information about it. Embedding the widgets isn't solving the problem."
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learn-pr/wwl-data-ai/create-azure-cognitive-search-solution/10-knowledge-check.yml

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- content: "Enable anonymous access for the blob container"
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isCorrect: false
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explanation: "Incorrect. Anonymous access is not required for Azure AI Search to access documents in a blob container."
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- content: "In an Azure AI Services resource, add a data source that references the container where the files are stored"
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- content: "In an Azure AI Search resource, add a data source that references the container where the files are stored"
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isCorrect: true
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explanation: "Correct. To search files in a blob container, you should create a data source"
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- content: "You're creating an index that includes a field named modified_date. You want to ensure that the modified_date field can be included in search results. Which attribute must you apply to the modified_date field in the index definition?"

learn-pr/wwl-data-ai/detect-objects-images/includes/4-use-trained-detector.md

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@@ -13,7 +13,7 @@ prediction_client = CustomVisionPredictionClient(endpoint="<YOUR_PREDICTION_RESO
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credentials=credentials)
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# Get classification predictions for an image
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image_data = open("<PATH_TO_IMAGE_FILE>"), "rb").read()
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image_data = open("<PATH_TO_IMAGE_FILE>", "rb").read()
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results = prediction_client.detect_image("<YOUR_PROJECT_ID>",
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"<YOUR_PUBLISHED_MODEL_NAME>",
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image_data)

learn-pr/wwl-data-ai/develop-ai-agent-azure/includes/3-how-use-agent-service.md

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The Foundry Agent Service allows developers to create AI agents tailored to their needs through custom instructions and advanced tools like code interpreters and custom functions. These agents can answer questions, perform actions, or automate workflows by combining generative AI models with tools that interact with real-world data sources. The service simplifies the development process by reducing the amount of code required and managing the underlying infrastructure.
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Previously, developers could create an agent-like experience by using standard APIs in Azure AI Foundry and connect to custom functions or other tools, but doing so would take a significant coding effort. Foundry Agent Service handles all of that for you through AI Foundry to build agents via the portal or in your own app in fewer than 50 lines of code. The exercise in the module explores both methods of building an agent.e
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Previously, developers could create an agent-like experience by using standard APIs in Azure AI Foundry and connect to custom functions or other tools, but doing so would take a significant coding effort. Foundry Agent Service handles all of that for you through AI Foundry to build agents via the portal or in your own app in fewer than 50 lines of code. The exercise in the module explores both methods of building an agent.
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Foundry Agent Service is ideal for scenarios requiring advanced language models for workflow automation. It can be used to:
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