Merge branch 'main' of https://github.com/MicrosoftDocs/azure-docs-pr into nw-nsgflow

Author: halkazwini

.openpublishing.redirection.json

@@ -4744,6 +4744,16 @@
       "source_path_from_root": "/articles/virtual-network/tutorial-filter-network-traffic-cli.md",
       "redirect_url": "/azure/virtual-network/tutorial-filter-network-traffic",
       "redirect_document_id": false
+    },
+    {
+      "source_path_from_root": "/articles/virtual-network/virtual-network-service-endpoint-policies-portal.md",
+      "redirect_url": "/azure/virtual-network/virtual-network-service-endpoint-policies",
+      "redirect_document_id": false
+    },
+    {
+      "source_path_from_root": "/articles/virtual-network/virtual-network-service-endpoint-policies-powershell.md",
+      "redirect_url": "/azure/virtual-network/virtual-network-service-endpoint-policies",
+      "redirect_document_id": false
     }
   ]
 }

articles/api-management/workspaces-overview.md

@@ -91,7 +91,7 @@ Manage gateway capacity by manually adding or removing scale units, similar to t
 Workspace gateways need to be in the same Azure region and subscription as the API Management service. 
 
 > [!NOTE]
-> Starting in August 2024, workspace gateway support will be rolled out in the following regions. These regions are a subset of those where API Management is available.
+> These regions are a subset of those where API Management is available.
 
 * West US
 * North Central US

articles/azure-app-configuration/concept-experimentation.md

@@ -63,25 +63,72 @@ The results help you to conclude the learnings and outcomes into actionable item
 
 ## Scenarios for using experimentation
 
-- **Intelligent applications (e.g., AI-based features)**
-Accelerate General AI (Gen AI) adoption and optimize AI models and use cases through rapid experimentation. Use Experimentation to iterate quickly on AI models, test different scenarios, and determine effective approaches.
-It helps enhance agility in adapting AI solutions to evolving user needs and market trends, and facilitate understanding of the most effective approaches for scaling AI initiatives.
+### Release defense
 
-- **CI, CD and continuous experimentation (Gradual feature rollouts and version updates)**
-Ensure seamless transitions and maintain or improve key metrics with each version update while managing feature releases. Utilize experimentation to gradually roll out new features to subsets of users using feature flags, monitor performance metrics, and collect feedback for iterative improvements.
-It's beneficial to reduce the risk of introducing bugs or performance issues to the entire user base. It enables data-driven decision-making during version rollouts and feature flag management, leading to improved product quality and user satisfaction.
+Objective: Ensure smooth transitions and maintain or improve key metrics with each release.
 
-- **User experience optimization (UI A/B testing)**
-Optimize business metrics by comparing different UI variations and determining the most effective design. Conduct A/B tests using experimentation to test UI elements, measure user interactions, and analyze performance metrics.
-The best return here's improved user experience by implementing UI changes based on empirical evidence.
+Approach: Employ experimentation to gradually roll out new features, monitor performance metrics, and collect feedback for iterative improvements.
 
-- **Personalization and targeting experiments**
-Deliver personalized content and experiences tailored to user preferences and behaviors. Use experimentation to test personalized content, measure engagement, and iterate on personalization strategies.
-Results are increased user engagement, conversion rates, and customer loyalty through relevant and personalized experiences. These results, in turn drive revenue growth and customer retention by targeting audiences with tailored messages and offers.
+Benefits:
 
-- **Performance optimization experiments**
-Improve application performance and provide an efficient user experience through performance optimization experiments. Conduct experiments to test performance enhancements, measure key metrics, and implement successful optimizations.
-Here, experimentation enhances application scalability, reliability, and responsiveness through proactive performance improvements. It optimizes resource utilization and infrastructure costs by implementing efficient optimizations.
+* Minimizes the risk of widespread issues by using guardrail metrics to detect and address problems early in the rollout.
+* Helps maintain or improve key performance and user satisfaction metrics by making informed decisions based on real-time data.
+ 
+### Test hypotheses
+
+Objective: Validate assumptions and hypotheses to make informed decisions about product features, user behaviors, or business strategies.
+
+Approach: Use experimentation to test specific hypotheses by creating different feature versions or scenarios, then analyze user interactions and performance metrics to determine outcomes.
+
+Benefits:
+
+* Provides evidence-based insights that reduce uncertainty and guide strategic decision-making.
+* Enables faster iteration and innovation by confirming or refuting hypotheses with real user data.
+* Enhances product development by focusing efforts on ideas that are proven to work, ultimately leading to more successful and user-aligned features.
+  
+### A/B testing
+
+Objective: Optimize business metrics by comparing different UI variations and determining the most effective design.
+
+Approach: Conduct A/B tests using experimentation to test UI elements, measure user interactions, and analyze performance metrics.
+
+Benefits:
+* Improves user experience by implementing UI changes based on empirical evidence.
+* Increases conversion rates, engagement levels, and overall effectiveness of digital products or services.
+ 
+### For intelligent applications (for example, AI-based features)
+
+Objective: Accelerate General AI (Gen AI) adoption and optimize AI models and use cases through rapid experimentation.
+
+Approach: Use experimentation to iterate quickly on AI models, test different scenarios, and determine effective approaches.
+
+Benefits:
+
+* Enhances agility in adapting AI solutions to evolving user needs and market trends.
+* Facilitates understanding of the most effective approaches for scaling AI initiatives.
+* Improves accuracy and performance of AI models based on real-world data and feedback.
+ 
+### Personalization and targeting experiments
+
+Objective: Deliver personalized content and experiences tailored to user preferences and behaviors.
+
+Approach: Leverage experimentation to test personalized content, measure engagement, and iterate on personalization strategies.
+
+Benefits:
+
+* Increases user engagement, conversion rates, and customer loyalty through relevant and personalized experiences.
+* Drives revenue growth and customer retention by targeting audiences with tailored messages and offers.
+ 
+### Performance optimization experiments
+
+Objective: Improve application performance and user experience through performance optimization experiments.
+
+Approach: Conduct experiments to test performance enhancements, measure key metrics, and implement successful optimizations.
+
+Benefits:
+
+* Enhances application scalability, reliability, and responsiveness through proactive performance improvements.
+* Optimizes resource utilization and infrastructure costs by implementing efficient optimizations.
 
 ## Experiment operations
 

articles/azure-monitor/agents/agent-linux.md

@@ -106,10 +106,10 @@ The following table highlights the packages required for [supported Linux distro
 
 |Required package |Description |Minimum version |
 |-----------------|------------|----------------|
-|Glibc |    GNU C library | 2.5-12
+|Glibc |    GNU C library | 2.5-12|
 |Openssl    | OpenSSL libraries | 1.0.x or 1.1.x |
 |Curl | cURL web client | 7.15.5 |
-|Python | | 2.7 or 3.6+
+|Python | | 2.7 or 3.6-3.11|
 |Python-ctypes | |
 |PAM | Pluggable authentication modules | |
 

articles/azure-monitor/agents/azure-monitor-agent-migration.md

@@ -25,7 +25,6 @@ Migration is a complex task. Start planning your migration to Azure Monitor Agen
 > - **Installation:** The ability to install the legacy agents will be removed from the Azure Portal and installation policies for legacy agents will be removed. You can still install the MMA agents extension as well as perform offline installations.
 > - **Customer Support:** You will not be able to get support for legacy agent issues.
 > - **OS Support:** Support for new Linux or Windows distros, including service packs, won't be added after the deprecation of the legacy agents.
-> - Log Analytics Agent will continue to function but not be able to connect Log Analytics workspaces.
 > - Log Analytics Agent can coexist with Azure Monitor Agent. Expect to see duplicate data if both agents are collecting the same data.
 
 

articles/azure-monitor/agents/data-collection-log-json.md

@@ -2,7 +2,7 @@
 title: Collect logs from a JSON file with Azure Monitor Agent 
 description: Configure a data collection rule to collect log data from a JSON file on a virtual machine using Azure Monitor Agent.
 ms.topic: conceptual
-ms.date: 08/23/2024
+ms.date: 08/28/2024
 author: guywi-ms
 ms.author: guywild
 ms.reviewer: jeffwo
@@ -16,7 +16,7 @@ Many applications and services will log information to a JSON files instead of s
 ## Prerequisites
 
 - Log Analytics workspace where you have at least [contributor rights](../logs/manage-access.md#azure-rbac).
-- A data collection endpoint (DCE) if you plan to use Azure Monitor Private Links. The data collection endpoint must be in the same region as the Log Analytics workspace. See [How to set up data collection endpoints based on your deployment](../essentials/data-collection-endpoint-overview.md#how-to-set-up-data-collection-endpoints-based-on-your-deployment) for details.
+- A data collection endpoint (DCE) in the same region as the Log Analytics workspace. See [How to set up data collection endpoints based on your deployment](../essentials/data-collection-endpoint-overview.md#how-to-set-up-data-collection-endpoints-based-on-your-deployment) for details.
 - Either a new or existing DCR described in [Collect data with Azure Monitor Agent](./azure-monitor-agent-data-collection.md).
 
 ## Basic operation
@@ -133,6 +133,7 @@ Use the following ARM template to create a DCR for collecting text log files, ma
 | Setting | Description |
 |:---|:---|
 | Data collection rule name | Unique name for the DCR. |
+| Data collection endpoint resource ID | Resource ID of the data collection endpoint (DCE). |
 | Location | Region for the DCR. Must be the same location as the Log Analytics workspace. |
 | File patterns | Identifies the location and name of log files on the local disk. Use a wildcard for filenames that vary, for example when a new file is created each day with a new name. You can enter multiple file patterns separated by commas (AMA version 1.26 or higher required for multiple file patterns on Linux).<br><br>Examples:<br>- C:\Logs\MyLog.json<br>- C:\Logs\MyLog*.json<br>- C:\App01\AppLog.json, C:\App02\AppLog.json<br>- /var/mylog.json<br>- /var/mylog*.json |
 | Table name | Name of the destination table in your Log Analytics Workspace. |     
@@ -152,6 +153,12 @@ Use the following ARM template to create a DCR for collecting text log files, ma
                 "description": "Unique name for the DCR. "
             }
         },
+        "dataCollectionEndpointResourceId": {
+            "type": "string",
+            "metadata": {
+              "description": "Resource ID of the data collection endpoint (DCE)."
+            }
+        },
         "location": {
             "type": "string",
             "metadata": {
@@ -175,12 +182,6 @@ Use the following ARM template to create a DCR for collecting text log files, ma
             "metadata": {
                 "description": "Resource ID of the Log Analytics workspace with the target table."
             }
-        },
-        "dataCollectionEndpointResourceId": {
-            "type": "string",
-            "metadata": {
-                "description": "Resource ID of the Data Collection Endpoint to be used with this rule."
-            }
         }
     },
     "variables": {
@@ -193,6 +194,7 @@ Use the following ARM template to create a DCR for collecting text log files, ma
             "name": "[parameters('dataCollectionRuleName')]",
             "location": "[parameters('location')]",
             "properties": {
+                "dataCollectionEndpointId": "[parameters('dataCollectionEndpointResourceId')]",
                 "streamDeclarations": {
                     "Custom-Json-stream": {
                         "columns": [
@@ -256,8 +258,7 @@ Use the following ARM template to create a DCR for collecting text log files, ma
                         "transformKql": "source",
                         "outputStream": "[variables('tableOutputStream')]"
                     }
-                ],
-                "dataCollectionEndpointId": "[parameters('dataCollectionEndpointResourceId')]"
+                ]
             }
         }
     ]

articles/azure-monitor/agents/data-collection-log-text.md

@@ -2,7 +2,7 @@
 title: Collect logs from a text file with Azure Monitor Agent 
 description: Configure a data collection rule to collect log data from a text file on a virtual machine using Azure Monitor Agent.
 ms.topic: conceptual
-ms.date: 08/23/2024
+ms.date: 08/28/2024
 author: guywi-ms
 ms.author: guywild
 ms.reviewer: jeffwo
@@ -16,7 +16,7 @@ Many applications and services will log information to text files instead of sta
 ## Prerequisites
 
 - Log Analytics workspace where you have at least [contributor rights](../logs/manage-access.md#azure-rbac).
-- A data collection endpoint (DCE) if you plan to use Azure Monitor Private Links. The data collection endpoint must be in the same region as the Log Analytics workspace. See [How to set up data collection endpoints based on your deployment](../essentials/data-collection-endpoint-overview.md#how-to-set-up-data-collection-endpoints-based-on-your-deployment) for details.
+- A data collection endpoint (DCE) in the same region as the Log Analytics workspace. See [How to set up data collection endpoints based on your deployment](../essentials/data-collection-endpoint-overview.md#how-to-set-up-data-collection-endpoints-based-on-your-deployment) for details.
 - Either a new or existing DCR described in [Collect data with Azure Monitor Agent](./azure-monitor-agent-data-collection.md).
 
 ## Basic operation
@@ -143,6 +143,12 @@ Use the following ARM template to create or modify a DCR for collecting text log
               "description": "Unique name for the DCR. "
             }
         },
+        "dataCollectionEndpointResourceId": {
+            "type": "string",
+            "metadata": {
+              "description": "Resource ID of the data collection endpoint (DCE)."
+            }
+        },
         "location": {
             "type": "string",
             "metadata": {
@@ -178,6 +184,7 @@ Use the following ARM template to create or modify a DCR for collecting text log
             "location": "[parameters('location')]",
             "apiVersion": "2022-06-01",
             "properties": {
+                "dataCollectionEndpointId": "[parameters('dataCollectionEndpointResourceId')]",
                 "streamDeclarations": {
                     "Custom-Text-stream": {
                         "columns": [