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

Author: v-thepet

.openpublishing.redirection.azure-monitor.json

@@ -6649,9 +6649,14 @@
             "redirect_url": "/azure/azure-functions/functions-monitoring",
             "redirect_document_id": false
         },
+        {
+            "source_path_from_root": "/articles/azure-monitor/app/app-insights-azure-ad-api.md",
+            "redirect_url": "/azure/azure-monitor/app/azure-ad-authentication",
+            "redirect_document_id": false
+        },
         {
             "source_path_from_root": "/articles/azure-monitor/app/resources-roles-access-control.md",
-            "redirect_url": "/azure/azure-monitor//roles-permissions-security",
+            "redirect_url": "/azure/azure-monitor/roles-permissions-security",
             "redirect_document_id": false
         },
         {

.openpublishing.redirection.json

@@ -3633,6 +3633,11 @@
             "redirect_url": "https://www.twilio.com/docs/usage/tutorials/serverless-webhooks-azure-functions-and-csharp",
             "redirect_document_id": false
         },
+        {
+            "source_path_from_root": "/articles/azure-functions/create-first-function-vs-code-web.md",
+            "redirect_url": "/azure/azure-functions",
+            "redirect_document_id": false
+        },
         {
             "source_path_from_root": "/articles/twilio-dotnet-how-to-use-for-voice-sms.md",
             "redirect_url": "https://www.twilio.com/docs/usage/tutorials/serverless-webhooks-azure-functions-and-csharp",
@@ -4055,8 +4060,18 @@
         },
         {
             "source_path_from_root":"/articles/cosmos-db/high-availability.md",
-            "redirect_url":"/azure/reliability/reliability-cosmos-db-nosql.md",
+            "redirect_url":"/azure/reliability/reliability-cosmos-db-nosql",
             "redirect_document_id":false
+        },
+        {
+            "source_path_from_root":"/articles/migrate/how-to-assess.md",
+            "redirect_url":"/azure/migrate/whats-new#update-april-2024",
+            "redirect_document_id":false 
+        },
+        {
+            "source_path_from_root":"/articles/migrate/prepare-isv-movere.md",
+            "redirect_url":"/azure/migrate/whats-new#update-april-2024",
+            "redirect_document_id":false 
         }
     ]
 }

articles/active-directory-b2c/customize-ui-with-html.md

@@ -264,7 +264,7 @@ Configure Blob storage for Cross-Origin Resource Sharing by performing the follo
 Validate that you're ready by performing the following steps:
 
 1. Repeat the configure CORS step. For **Allowed origins**, enter `https://www.test-cors.org`
-1. Navigate to [www.test-cors.org](https://www.test-cors.org/) 
+1. Navigate to [www.test-cors.org](https://cors-test.codehappy.dev/) 
 1. For the **Remote URL** box, paste the URL of your HTML file. For example, `https://your-account.blob.core.windows.net/root/azure-ad-b2c/unified.html`
 1. Select **Send Request**.
     The result should be `XHR status: 200`. 

articles/ai-services/Anomaly-Detector/includes/quickstarts/anomaly-detector-client-library-python-multivariate.md

@@ -120,6 +120,7 @@ from datetime import datetime, timezone
 from azure.ai.anomalydetector import AnomalyDetectorClient
 from azure.core.credentials import AzureKeyCredential
 from azure.ai.anomalydetector.models import *
+import os
 
 SUBSCRIPTION_KEY =  os.environ['ANOMALY_DETECTOR_API_KEY']
 ANOMALY_DETECTOR_ENDPOINT = os.environ['ANOMALY_DETECTOR_ENDPOINT']
@@ -175,12 +176,12 @@ result = ad_client.detect_multivariate_batch_anomaly(model_id, batch_inference_b
 result_id = result.result_id
 
 # Get results (may need a few seconds)
-r = ad_client.get_multivariate_batch_detection_result(result_id)
+anomaly_results = ad_client.get_multivariate_batch_detection_result(result_id)
 print("Get detection result...(it may take a few seconds)")
 
-while r.summary.status != MultivariateBatchDetectionStatus.READY and r.summary.status != MultivariateBatchDetectionStatus.FAILED and r.summary.status !=MultivariateBatchDetectionStatus.CREATED:
+while anomaly_results.summary.status != MultivariateBatchDetectionStatus.READY and anomaly_results.summary.status != MultivariateBatchDetectionStatus.FAILED:
     anomaly_results = ad_client.get_multivariate_batch_detection_result(result_id)
-    print("Detection is {}".format(r.summary.status))
+    print("Detection is {}".format(anomaly_results.summary.status))
     time.sleep(5)
 
 

articles/ai-services/computer-vision/concept-brand-detection.md

@@ -18,7 +18,7 @@ Brand detection is a specialized mode of [object detection](concept-object-detec
 
 The Azure AI Vision service detects whether there are brand logos in a given image; if there are, it returns the brand name, a confidence score, and the coordinates of a bounding box around the logo.
 
-The built-in logo database covers popular brands in consumer electronics, clothing, and more. If you find that the brand you're looking for is not detected by the Azure AI Vision service, you could also try creating and training your own logo detector using the [Custom Vision](../custom-vision-service/index.yml) service.
+The built-in logo database covers popular brands in consumer electronics, clothing, and more. If you find that the brand you're looking for isn't detected by the Azure AI Vision service, you could also try creating and training your own logo detector using the [Custom Vision](../custom-vision-service/index.yml) service.
 
 ## Brand detection example
 
@@ -40,7 +40,7 @@ The following JSON responses illustrate what Azure AI Vision returns when detect
 ]
 ```
 
-In some cases, the brand detector will pick up both the logo image and the stylized brand name as two separate logos.
+In some cases, the brand detector picks up both the logo image and the stylized brand name as two separate logos.
 
 ![A gray sweatshirt with a Microsoft label and logo on it](./Images/gray-shirt-logo.jpg)
 
@@ -69,6 +69,6 @@ In some cases, the brand detector will pick up both the logo image and the styli
 
 ## Use the API
 
-The brand detection feature is part of the [Analyze Image](https://westcentralus.dev.cognitive.microsoft.com/docs/services/computer-vision-v3-2/operations/56f91f2e778daf14a499f21b) API. You can call this API through a native SDK or through REST calls. Include `Brands` in the **visualFeatures** query parameter. Then, when you get the full JSON response, simply parse the string for the contents of the `"brands"` section.
+The brand detection feature is part of the [Analyze Image](/rest/api/computervision/analyze-image?view=rest-computervision-v3.1) API. You can call this API through a native SDK or through REST calls. Include `Brands` in the **visualFeatures** query parameter. Then, when you get the full JSON response, parse the string for the contents of the `"brands"` section.
 
 * [Quickstart: Vision REST API or client libraries](./quickstarts-sdk/image-analysis-client-library.md?pivots=programming-language-csharp)

articles/ai-services/computer-vision/concept-categorizing-images.md

@@ -56,7 +56,7 @@ The following table illustrates a typical image set and the category returned by
 
 ## Use the API
 
-The categorization feature is part of the [Analyze Image 3.2](https://westcentralus.dev.cognitive.microsoft.com/docs/services/computer-vision-v3-2/operations/56f91f2e778daf14a499f21b) API. You can call this API through a native SDK or through REST calls. Include `Categories` in the **visualFeatures** query parameter. Then, when you get the full JSON response, simply parse the string for the contents of the `"categories"` section.
+The categorization feature is part of the [Analyze Image 3.2](/rest/api/computervision/analyze-image?view=rest-computervision-v3.1) API. You can call this API through a native SDK or through REST calls. Include `Categories` in the **visualFeatures** query parameter. Then, when you get the full JSON response, simply parse the string for the contents of the `"categories"` section.
 
 * [Quickstart: Vision REST API or client libraries](./quickstarts-sdk/image-analysis-client-library.md?pivots=programming-language-csharp)
 

articles/ai-services/computer-vision/concept-describing-images.md

@@ -56,7 +56,7 @@ The following JSON response illustrates what the Analyze API returns when descri
 
 ## Use the API
 
-The image description feature is part of the [Analyze Image](https://westcentralus.dev.cognitive.microsoft.com/docs/services/computer-vision-v3-2/operations/56f91f2e778daf14a499f21b) API. You can call this API through a native SDK or through REST calls. Include `Description` in the **visualFeatures** query parameter. Then, when you get the full JSON response, parse the string for the contents of the `"description"` section.
+The image description feature is part of the [Analyze Image](/rest/api/computervision/analyze-image?view=rest-computervision-v3.1) API. You can call this API through a native SDK or through REST calls. Include `Description` in the **visualFeatures** query parameter. Then, when you get the full JSON response, parse the string for the contents of the `"description"` section.
 
 * [Quickstart: Image Analysis REST API or client libraries](./quickstarts-sdk/image-analysis-client-library.md?pivots=programming-language-csharp)
 

articles/ai-services/computer-vision/concept-detecting-adult-content.md

@@ -34,6 +34,6 @@ The "adult" classification contains several different categories:
 
 ## Use the API
 
-You can detect adult content with the [Analyze Image 3.2](https://westcentralus.dev.cognitive.microsoft.com/docs/services/computer-vision-v3-2/operations/56f91f2e778daf14a499f21b) API. When you add the value of `Adult` to the **visualFeatures** query parameter, the API returns three boolean properties—`isAdultContent`, `isRacyContent`, and `isGoryContent`—in its JSON response. The method also returns corresponding properties—`adultScore`, `racyScore`, and `goreScore`—which represent confidence scores between zero and one for each respective category.
+You can detect adult content with the [Analyze Image 3.2](/rest/api/computervision/analyze-image?view=rest-computervision-v3.1) API. When you add the value of `Adult` to the **visualFeatures** query parameter, the API returns three boolean properties—`isAdultContent`, `isRacyContent`, and `isGoryContent`—in its JSON response. The method also returns corresponding properties—`adultScore`, `racyScore`, and `goreScore`—which represent confidence scores between zero and one for each respective category.
 
 - [Quickstart: Vision REST API or client libraries](./quickstarts-sdk/image-analysis-client-library.md?pivots=programming-language-csharp)

articles/ai-services/computer-vision/concept-detecting-color-schemes.md

@@ -77,6 +77,6 @@ The following table shows Azure AI Vision's black and white evaluation in the sa
 
 ## Use the API
 
-The color scheme detection feature is part of the [Analyze Image 3.2](https://westcentralus.dev.cognitive.microsoft.com/docs/services/computer-vision-v3-2/operations/56f91f2e778daf14a499f21b) API. You can call this API through a native SDK or through REST calls. Include `Color` in the **visualFeatures** query parameter. Then, when you get the full JSON response, simply parse the string for the contents of the `"color"` section.
+The color scheme detection feature is part of the [Analyze Image 3.2](/rest/api/computervision/analyze-image?view=rest-computervision-v3.1) API. You can call this API through a native SDK or through REST calls. Include `Color` in the **visualFeatures** query parameter. Then, when you get the full JSON response, simply parse the string for the contents of the `"color"` section.
 
 * [Quickstart: Vision REST API or client libraries](./quickstarts-sdk/image-analysis-client-library.md?pivots=programming-language-csharp)

articles/ai-services/computer-vision/concept-detecting-domain-content.md

@@ -14,15 +14,15 @@ ms.author: pafarley
 
 # Domain-specific content detection
 
-In addition to tagging and high-level categorization, Azure AI Vision also supports further domain-specific analysis using models that have been trained on specialized data.
+In addition to tagging and high-level categorization, Azure AI Vision also supports further domain-specific analysis using models that are trained on specialized data.
 
 There are two ways to use the domain-specific models: by themselves (scoped analysis) or as an enhancement to the image [categorization](./concept-categorizing-images.md) feature.
 
 ### Scoped analysis
 
-You can analyze an image using only the chosen domain-specific model by calling the [Models/\<model\>/Analyze](https://westcentralus.dev.cognitive.microsoft.com/docs/services/computer-vision-v3-2/operations/56f91f2e778daf14a499f21b) API.
+You can analyze an image using only the chosen domain-specific model by calling the [Models/\<model\>/Analyze](/rest/api/computervision/analyze-image?view=rest-computervision-v3.1) API.
 
-The following is a sample JSON response returned by the **models/celebrities/analyze** API for the given image:
+The following is a sample JSON response returned by the `models/celebrities/analyze` API for the given image:
 
 ![Satya Nadella standing, smiling](./images/satya.jpeg)
 
@@ -51,7 +51,7 @@ The following is a sample JSON response returned by the **models/celebrities/ana
 
 ### Enhanced categorization analysis
 
-You can also use domain-specific models to supplement general image analysis. You do this as part of [high-level categorization](concept-categorizing-images.md) by specifying domain-specific models in the *details* parameter of the [Analyze](https://westcentralus.dev.cognitive.microsoft.com/docs/services/computer-vision-v3-2/operations/56f91f2e778daf14a499f21b) API call.
+You can also use domain-specific models to supplement general image analysis. You do this as part of [high-level categorization](concept-categorizing-images.md) by specifying domain-specific models in the *details* parameter of the [Analyze Image](/rest/api/computervision/analyze-image?view=rest-computervision-v3.1) API call.
 
 In this case, the 86-category taxonomy classifier is called first. If any of the detected categories have a matching domain-specific model, the image is passed through that model as well and the results are added.
 
@@ -99,7 +99,7 @@ Currently, Azure AI Vision supports the following domain-specific models:
 | celebrities | Celebrity recognition, supported for images classified in the `people_` category |
 | landmarks | Landmark recognition, supported for images classified in the `outdoor_` or `building_` categories |
 
-Calling the [Models](https://westcentralus.dev.cognitive.microsoft.com/docs/services/computer-vision-v3-2/operations/56f91f2e778daf14a499f20e) API will return this information along with the categories to which each model can apply:
+Calling the [Models](/rest/api/computervision/list-models/list-models?view=rest-computervision-v3.1&tabs=HTTP) API returns this information along with the categories to which each model can apply:
 
 ```json
 {
@@ -133,6 +133,6 @@ Calling the [Models](https://westcentralus.dev.cognitive.microsoft.com/docs/serv
 
 ## Use the API
 
-This feature is available through the [Analyze Image 3.2 API](https://westcentralus.dev.cognitive.microsoft.com/docs/services/computer-vision-v3-2/operations/56f91f2e778daf14a499f21b). You can call this API through a native SDK or through REST calls. Include `Celebrities` or `Landmarks` in the **details** query parameter. Then, when you get the full JSON response, simply parse the string for the contents of the `"details"` section.
+This feature is available through the [Analyze Image 3.2 API](/rest/api/computervision/analyze-image/analyze-image?view=rest-computervision-v3.1&tabs=HTTP). You can call this API through a native SDK or through REST calls. Include `Celebrities` or `Landmarks` in the **details** query parameter. Then, when you get the full JSON response, parse the string for the contents of the `"details"` section.
 
 * [Quickstart: Vision REST API or client libraries](./quickstarts-sdk/image-analysis-client-library.md?pivots=programming-language-csharp)