Merge branch 'main' of https://github.com/MicrosoftDocs/azure-docs-pr into workbooks-arm-actions

Author: AbbyMSFT

.openpublishing.redirection.json

@@ -3849,6 +3849,11 @@
             "redirect_url": "/azure/reliability/reliability-guidance-overview",
             "redirect_document_id": false
         },
+        {
+            "source_path_from_root": "/articles/aks/cluster-configuration.md",
+            "redirect_url": "/azure/aks/concepts-clusters-workloads.md",
+            "redirect_document_id": false
+        },
         {
             "source_path_from_root": "/articles/orbital/overview-analytics.md",
             "redirect_url": "/azure/orbital/overview",
@@ -3984,6 +3989,11 @@
             "source_path_from_root":"/articles/container-instances/availability-zones.md",
             "redirect_url":"/azure/reliability/reliability-containers",
             "redirect_document_id":false 
-        }
+        },
+		{
+			"source_path_from_root":"/articles/service-connector/quickstart-cli-aks-connection.md",
+			"redirect_url":"/azure/service-connector/quickstart-portal-aks-connection",
+			"redirect_document_id":false
+		}
     ]
 }

.openpublishing.redirection.sentinel.json

@@ -1089,6 +1089,11 @@
             "source_path_from_root": "/articles/sentinel/notebooks-with-synapse-hunt.md",
             "redirect_url": "/azure/sentinel/notebooks-hunt",
             "redirect_document_id": false
+        },
+        {
+            "source_path_from_root": "/articles/sentinel/data-connectors/dns.md",
+            "redirect_url": "/azure/sentinel/data-connectors/windows-dns-events-via-ama",
+            "redirect_document_id": false
         }
     ]
 }

articles/ai-services/document-intelligence/concept-accuracy-confidence.md

@@ -8,7 +8,7 @@ ms.service: azure-ai-document-intelligence
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 02/29/2024
+ms.date: 04/16/2023
 ms.author: lajanuar
 ---
 
@@ -53,10 +53,11 @@ Field confidence indicates an estimated probability between 0 and 1 that the pre
 ## Interpret accuracy and confidence scores for custom models
 
 When interpreting the confidence score from a custom model, you should consider all the confidence scores returned from the model. Let's start with a list of all the confidence scores.
-1. **Document type confidence score**: The document type confidence is an indicator of closely the analyzed document resembleds documents in the training dataset. When the document type confidence is low, this is indicative of template or structural variations in the analyzed document. To improve the document type confidence, label a document with that specific variation and add it to your training dataset. Once the model is re-trained, it should be better equipped to handl that class of variations.
-2. **Field level confidence**: Each labled field extracted has an associated confidence score. This score reflects the model's confidence on the position of the value extracted. While evaluating the confidence you should also look at the underlying extraction confidence to generate a comprehensive confidence for the extracted result. Evaluate the OCR results for text extraction or selection marks depending on the field type to generate a composite confidence score for the field.
-3. **Word confidence score** Each word extracted within the document has an associated confidence score. The score represents the confidence of the transcription. The pages array contains an array of words, each word has an associated span and confidence. Spans from the custom field extracted values will match the spans of the extracted words.
-4. **Selection mark confidence score**: The pages array also contains an array of selection marks, each selection mark has a confidence score representing the confidence of the seletion mark and selection state detection. When a labeled field is a selection mark, the custom field selection confidence combined with the selection mark confidence is an accurate representation of the overall confidence that the field was extracted correctly.
+
+1. **Document type confidence score**: The document type confidence is an indicator of closely the analyzed document resembles documents in the training dataset. When the document type confidence is low, it's indicative of template or structural variations in the analyzed document. To improve the document type confidence, label a document with that specific variation and add it to your training dataset. Once the model is retrained, it should be better equipped to handle that class of variations.
+2. **Field level confidence**: Each labeled field extracted has an associated confidence score. This score reflects the model's confidence on the position of the value extracted. While evaluating confidence scores, you should also look at the underlying extraction confidence to generate a comprehensive confidence for the extracted result. Evaluate the `OCR` results for text extraction or selection marks depending on the field type to generate a composite confidence score for the field.
+3. **Word confidence score** Each word extracted within the document has an associated confidence score. The score represents the confidence of the transcription. The pages array contains an array of words and each word has an associated span and confidence score. Spans from the custom field extracted values match the spans of the extracted words.
+4. **Selection mark confidence score**: The pages array also contains an array of selection marks. Each selection mark has a confidence score representing the confidence of the selection mark and selection state detection. When a labeled field has a selection mark, the custom field selection combined with the selection mark confidence is an accurate representation of overall confidence accuracy.
 
 The following table demonstrates how to interpret both the accuracy and confidence scores to measure your custom model's performance.
 
@@ -69,7 +70,7 @@ The following table demonstrates how to interpret both the accuracy and confiden
 
 ## Table, row, and cell confidence
 
-With the addition of table, row and cell confidence with the ```2024-02-29-preview``` API, here are some common questions that should help with interpreting the table, row and cell scores:
+With the addition of table, row and cell confidence with the ```2024-02-29-preview``` API, here are some common questions that should help with interpreting the table, row, and cell scores:
 
 **Q:** Is it possible to see a high confidence score for cells, but a low confidence score for the row?<br>
 

articles/ai-services/document-intelligence/how-to-guides/includes/v4-0/javascript-sdk.md

@@ -5,7 +5,7 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-document-intelligence
 ms.topic: include
-ms.date: 03/28/2024
+ms.date: 04/16/2024
 ms.author: lajanuar
 ms.custom:
   - devx-track-csharp
@@ -106,7 +106,8 @@ Open the `index.js` file in Visual Studio Code or your favorite IDE and select o
 ## Use the Read model
 
 ```javascript
-const { AzureKeyCredential, DocumentIntelligence } = require("@azure-rest/ai-document-intelligence");
+const { DocumentIntelligenceClient } = require("@azure-rest/ai-document-intelligence");
+const  { AzureKeyCredential } = require("@azure/core-auth");
 
 //use your `key` and `endpoint` environment variables
 const key = process.env['DI_KEY'];
@@ -202,7 +203,8 @@ Visit the Azure samples repository on GitHub and view the [`read` model output](
 ## Use the Layout model
 
 ```javascript
-const { AzureKeyCredential, DocumentIntelligence } = require("@azure-rest/ai-document-intelligence");
+const { DocumentIntelligenceClient } = require("@azure-rest/ai-document-intelligence");
+const  { AzureKeyCredential } = require("@azure/core-auth");
 
 //use your `key` and `endpoint` environment variables
 const key = process.env['DI_KEY'];
@@ -272,7 +274,8 @@ Visit the Azure samples repository on GitHub and view the [layout model output](
 ## Use the General document model
 
 ```javascript
-const { AzureKeyCredential, DocumentIntelligence } = require("@azure-rest/ai-document-intelligence");
+const { DocumentIntelligenceClient } = require("@azure-rest/ai-document-intelligence");
+const  { AzureKeyCredential } = require("@azure/core-auth");
 
 //use your `key` and `endpoint` environment variables
 const key = process.env['DI_KEY'];
@@ -318,7 +321,8 @@ Visit the Azure samples repository on GitHub and view the [general document mode
 ## Use the W-2 tax model
 
 ```javascript
-const { AzureKeyCredential, DocumentIntelligence } = require("@azure-rest/ai-document-intelligence");
+const { DocumentIntelligenceClient } = require("@azure-rest/ai-document-intelligence");
+const  { AzureKeyCredential } = require("@azure/core-auth");
 
 //use your `key` and `endpoint` environment variables
 const key = process.env['DI_KEY'];
@@ -397,7 +401,8 @@ Visit the Azure samples repository on GitHub and view the [W-2 tax model output]
 ## Use the Invoice model
 
 ```javascript
-const { AzureKeyCredential, DocumentIntelligence } = require("@azure-rest/ai-document-intelligence");
+const { DocumentIntelligenceClient } = require("@azure-rest/ai-document-intelligence");
+const  { AzureKeyCredential } = require("@azure/core-auth");
 
 //use your `key` and `endpoint` environment variables
 const key = process.env['DI_KEY'];
@@ -459,7 +464,8 @@ Visit the Azure samples repository on GitHub and view the [invoice model output]
 ## Use the Receipt model
 
 ```javascript
-const { AzureKeyCredential, DocumentIntelligence } = require("@azure-rest/ai-document-intelligence");
+const { DocumentIntelligenceClient } = require("@azure-rest/ai-document-intelligence");
+const  { AzureKeyCredential } = require("@azure/core-auth");
 
 //use your `key` and `endpoint` environment variables
 const key = process.env['DI_KEY'];
@@ -518,7 +524,8 @@ Visit the Azure samples repository on GitHub and view the [receipt model output]
 ## Use the ID document model
 
 ```javascript
-const { AzureKeyCredential, DocumentIntelligence } = require("@azure-rest/ai-document-intelligence");
+const { DocumentIntelligenceClient } = require("@azure-rest/ai-document-intelligence");
+const  { AzureKeyCredential } = require("@azure/core-auth");
 
 //use your `key` and `endpoint` environment variables
 const key = process.env['DI_KEY'];

articles/ai-services/document-intelligence/quickstarts/includes/javascript-sdk.md

@@ -73,7 +73,7 @@ In this quickstart, use the following features to analyze and extract data and v
  4. Install the `ai-document-intelligence` client library and `azure/identity` npm packages:
 
     ```console
-    npm i @azure-rest/[email protected]
+    npm i @azure-rest/[email protected] @azure/identity
 
     ```
 
@@ -146,10 +146,11 @@ Extract text, selection marks, text styles, table structures, and bounding regio
 :::moniker range="doc-intel-4.0.0"
 
 ```javascript
-    const { AzureKeyCredential, DocumentIntelligenceClient } = require("@azure-rest/ai-document-intelligence");
+    const { DocumentIntelligenceClient } = require("@azure-rest/ai-document-intelligence");
+    const  { AzureKeyCredential } = require("@azure/core-auth");
 
     // set `<your-key>` and `<your-endpoint>` variables with the values from the Azure portal.
-    const key = "<your-key>";
+    const key = "<your-key";
     const endpoint = "<your-endpoint>";
 
     // sample document
@@ -311,7 +312,8 @@ In this example, we analyze an invoice using the **prebuilt-invoice** model.
 
 ```javascript
 
-const { AzureKeyCredential, DocumentIntelligenceClient } = require("@azure-rest/ai-document-intelligence");
+const { DocumentIntelligenceClient } = require("@azure-rest/ai-document-intelligence");
+const  { AzureKeyCredential } = require("@azure/core-auth");
 
     // set `<your-key>` and `<your-endpoint>` variables with the values from the Azure portal.
     const key = "<your-key>";

articles/ai-services/immersive-reader/overview.md

@@ -69,6 +69,10 @@ With Immersive Reader, you can break words into syllables to improve readability
 
 Immersive Reader is a standalone web application. When it's invoked, the Immersive Reader client library displays on top of your existing web application in an `iframe`. When your web application calls the Immersive Reader service, you specify the content to show the reader. The Immersive Reader client library handles the creation and styling of the `iframe` and communication with the Immersive Reader backend service. The Immersive Reader service processes the content for parts of speech, text to speech, translation, and more.
 
+## Data privacy for Immersive reader
+
+Immersive reader doesn't store any customer data.
+
 ## Next step
 
 The Immersive Reader client library is available in C#, JavaScript, Java (Android), Kotlin (Android), and Swift (iOS). Get started with:

articles/ai-services/openai/how-to/content-filters.md

@@ -6,7 +6,7 @@ description: Learn how to use content filters (preview) with Azure OpenAI Servic
 manager: nitinme
 ms.service: azure-ai-openai
 ms.topic: how-to
-ms.date: 03/29/2024
+ms.date: 04/16/2024
 author: mrbullwinkle
 ms.author: mbullwin
 recommendations: false
@@ -15,7 +15,7 @@ recommendations: false
 # How to configure content filters with Azure OpenAI Service
 
 > [!NOTE]
-> All customers have the ability to modify the content filters to be stricter (for example, to filter content at lower severity levels than the default). Approval is required for turning the content filters partially or fully off. Managed customers only may apply for full content filtering control via this form: [Azure OpenAI Limited Access Review: Modified Content Filters](https://ncv.microsoft.com/uEfCgnITdR).
+> All customers have the ability to modify the content filters and configure the severity thresholds (low, medium, high). Approval is required for turning the content filters partially or fully off. Managed customers only may apply for full content filtering control via this form: [Azure OpenAI Limited Access Review: Modified Content Filters](https://ncv.microsoft.com/uEfCgnITdR).
 
 The content filtering system integrated into Azure OpenAI Service runs alongside the core models and uses an ensemble of multi-class classification models to detect four categories of harmful content (violence, hate, sexual, and self-harm) at four severity levels respectively (safe, low, medium, and high), and optional binary classifiers for detecting jailbreak risk, existing text, and code in public repositories. The default content filtering configuration is set to filter at the medium severity threshold for all four content harms categories for both prompts and completions. That means that content that is detected at severity level medium or high is filtered, while content detected at severity level low or safe is not filtered by the content filters. Learn more about content categories, severity levels, and the behavior of the content filtering system [here](../concepts/content-filter.md). Jailbreak risk detection and protected text and code models are optional and off by default. For jailbreak and protected material text and code models, the configurability feature allows all customers to turn the models on and off. The models are by default off and can be turned on per your scenario. Some models are required to be on for certain scenarios to retain coverage under the [Customer Copyright Commitment](/legal/cognitive-services/openai/customer-copyright-commitment?context=%2Fazure%2Fai-services%2Fopenai%2Fcontext%2Fcontext).
 

articles/ai-services/openai/how-to/latency.md

@@ -59,7 +59,7 @@ Latency varies based on what model you're using. For an identical request, expec
 When you send a completion request to the Azure OpenAI endpoint, your input text is converted to tokens that are then sent to your deployed model. The model receives the input tokens and then begins generating a response. It's an iterative sequential process, one token at a time. Another way to think of it is like a for loop with `n tokens = n iterations`. For most models, generating the response is the slowest step in the process.  
 
 At the time of the request, the requested generation size (max_tokens parameter) is used as an initial estimate of the generation size. The compute-time for generating the full size is reserved by the model as the request is processed. Once the generation is completed, the remaining quota is released. Ways to reduce the number of tokens:
-- Set the `max_token` parameter on each call as small as possible.
+- Set the `max_tokens` parameter on each call as small as possible.
 - Include stop sequences to prevent generating extra content.
 - Generate fewer responses: The best_of & n parameters can greatly increase latency because they generate multiple outputs. For the fastest response, either don't specify these values or set them to 1.
 
@@ -136,4 +136,4 @@ Time from the first token to the last token, divided by the number of generated
 
 * **Streaming**: Enabling streaming can be useful in managing user expectations in certain situations by allowing the user to see the model response as it is being generated rather than having to wait until the last token is ready.
 
-* **Content Filtering** improves safety, but it also impacts latency. Evaluate if any of your workloads would benefit from [modified content filtering policies](./content-filters.md).
+* **Content Filtering** improves safety, but it also impacts latency. Evaluate if any of your workloads would benefit from [modified content filtering policies](./content-filters.md).

articles/ai-services/openai/how-to/monitoring.md

@@ -6,7 +6,7 @@ ms.author: mbullwin
 ms.service: azure-ai-openai
 ms.topic: how-to
 ms.custom: subject-monitoring
-ms.date: 03/29/2024
+ms.date: 04/16/2024
 ---
 
 # Monitoring Azure OpenAI Service
@@ -60,7 +60,9 @@ The following table summarizes the current subset of metrics available in Azure
 | `Processed FineTuned Training Hours` | Usage |Sum| Number of training hours processed on an Azure OpenAI fine-tuned model. |  `ApiName`, `ModelDeploymentName`,`ModelName`, `Region`|
 | `Processed Inference Tokens` | Usage | Sum|  Number of inference tokens processed by an Azure OpenAI model. Calculated as prompt tokens (input) + generated tokens. Applies to PayGo, PTU, and PTU-manged SKUs.|`ApiName`, `ModelDeploymentName`,`ModelName`, `Region`|
 | `Processed Prompt Tokens` | Usage | Sum | Total number of prompt tokens (input) processed on an Azure OpenAI model. Applies to PayGo, PTU, and PTU-managed SKUs.|`ApiName`, `ModelDeploymentName`,`ModelName`, `Region`|
-| `Provision-managed Utilization V2` | Usage | Average | Provision-managed utilization is the utilization percentage for a given provisioned-managed deployment. Calculated as (PTUs consumed/PTUs deployed)*100. When utilization is at or above 100%, calls are throttled and return a 429 error code. | `ModelDeploymentName`,`ModelName`,`ModelVersion`, `Region`, `StreamType`|
+| `Provision-managed Utilization V2` | HTTP | Average | Provision-managed utilization is the utilization percentage for a given provisioned-managed deployment. Calculated as (PTUs consumed/PTUs deployed)*100. When utilization is at or above 100%, calls are throttled and return a 429 error code. | `ModelDeploymentName`,`ModelName`,`ModelVersion`, `Region`, `StreamType`|
+|`Prompt Token Cache Match Rate` | HTTP | Average | **Provisioned-managed only**. The prompt token cache hit ration expressed as a percentage. | `ModelDeploymentName`, `ModelVersion`, `ModelName`, `Region`|
+|`Time to Response` | HTTP | Average | Recommended latency (responsiveness) measure for streaming requests. **Applies to PTU, and PTU-managed deployments**. This metric does not apply to standard pay-go deployments. Calculated as time taken for the first response to appear after a user sends a prompt, as measured by the API gateway. This number increases as the prompt size increases and/or cache hit size reduces. Note: this metric is an approximation as measured latency is heavily dependent on multiple factors, including concurrent calls and overall workload pattern. In addition, it does not account for any client- side latency that may exist between your client and the API endpoint. Please refer to your own logging for optimal latency tracking.| `ModelDepIoymentName`, `ModelName`, and `ModelVersion` |
 
 ## Configure diagnostic settings