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learn-pr/wwl-data-ai/analyze-content-ai/06-knowledge-check.yml

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@@ -17,7 +17,7 @@ quiz:
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choices:
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- content: "Chatbots that automatically translate between multiple spoken and written languages."
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isCorrect: false
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explanation: "Incorrect. Azure AI Content Understanding is not designed to build translation chatbots."
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explanation: "Incorrect. Azure AI Content Understanding isn't designed to build translation chatbots."
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- content: "Analyzers that extract information from documents, images, videos, and audio files."
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isCorrect: true
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explanation: "Correct. Azure AI Content Understanding is designed to build content analyzers."
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choices:
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- content: "Microsoft Visual Studio."
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isCorrect: false
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explanation: "Incorrect. While you could use the REST API from Visual Studio to create a Content Understanding analyzer, it is not the best tool for creating a project."
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explanation: "Incorrect. While you could use the REST API from Visual Studio to create a Content Understanding analyzer, it isn't the best tool for creating a project."
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- content: "Azure Machine Learning studio."
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isCorrect: false
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explanation: "Incorrect. Azure Machine learning studio is not a tool for working with Content Udnerstanding projects."
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explanation: "Incorrect. Azure Machine Learning studio isn't a tool for working with Content Understanding projects."
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- content: "Azure AI Foundry portal."
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isCorrect: true
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explanation: "Correct. Azure AI Foundry portal provides a visual interface for creating Content Understanding projects."
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explanation: "Correct. A schema is used to define the information your analyzer will extract."
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- content: "An index."
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isCorrect: false
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explanation: "Incorrect. You do not need to create an index for a Content Understanding analyzer."
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explanation: "Incorrect. You don't need to create an index for a Content Understanding analyzer."
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- content: "A cluster."
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isCorrect: false
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explanation: "Incorrect. You do not need to create a cluster to use Content Understanding."
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explanation: "Incorrect. You don't need to create a cluster to use Content Understanding."
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learn-pr/wwl-data-ai/analyze-content-ai/includes/04-use-api.md

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## Using the REST API to analyze content
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One of the most common uses of the REST API is to submit content to an existing analyzer that you have previously built, and retrieve the results of analysis. The analysis request returns an operation ID value that represents an asynchronous task. Your client application must then use the GET request to pass the operation ID back to the endpoint and retrieve the operation status - potentially polling multiple times until the operation is complete and the results are returned in JSON format.
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One of the most common uses of the REST API is to submit content to an existing analyzer that you have previously built, and retrieve the results of analysis. The analysis request returns an operation ID value that represents an asynchronous task. Your client application must then use another request to pass the operation ID back to the endpoint and retrieve the operation status - potentially polling multiple times until the operation is complete and the results are returned in JSON format.
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For example, to analyze a document, a client application might submit a POST request to the **Analyze** operation containing the following JSON body:
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For example, to analyze a document, a client application might submit a POST request to the `analyze` function containing the following JSON body:
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```json
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POST {endpoint}/contentunderstanding/analyzers/{analyzer}:analyze?api-version={api version}
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}
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```
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Your client application must then use the operation ID that has been returned to check the status of the operation until it has succeeded (or failed) by submitting a GET request to the **Get Analysis Results** operation.
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Your client application must then use the operation ID that has been returned to check the status of the operation until it has succeeded (or failed) by submitting a GET request to the `results` function.
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```http
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GET {endpoint}/contentunderstanding/analyzers/{analyzer}/results/1234abcd-1234-abcd-1234-abcd1234abcd?api-version={api version}
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```
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When the operation has completed successfully, the response to the GET request contains a JSON payload representing the results of the analysis. The specific results depend on the content and schema.
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When the operation has completed successfully, the response contains a JSON payload representing the results of the analysis. The specific results depend on the content and schema.
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> [!NOTE]
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> For more information about the Content Understanding REST API, see the **[reference documentation](/rest/api/contentunderstanding/operation-groups)**.

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