Merge pull request #266006 from MicrosoftDocs/repo_sync_working_branch

Confirm merge from repo_sync_working_branch to main to sync with https://github.com/MicrosoftDocs/azure-docs (branch main)

Author: v-alje

articles/ai-studio/how-to/prompt-flow-tools/prompt-flow-tools-overview.md

@@ -19,7 +19,7 @@ The following table provides an index of tools in prompt flow.
 
 | Tool name | Description | Environment | Package name |
 |------|-----------|-------------|--------------|
-| [LLM](./llm-tool.md) | Use Azure Open AI large language models (LLM) for tasks such as text completion or chat. | Default | [promptflow-tools](https://pypi.org/project/promptflow-tools/) |
+| [LLM](./llm-tool.md) | Use Azure OpenAI large language models (LLM) for tasks such as text completion or chat. | Default | [promptflow-tools](https://pypi.org/project/promptflow-tools/) |
 | [Prompt](./prompt-tool.md) | Craft a prompt by using Jinja as the templating language. | Default | [promptflow-tools](https://pypi.org/project/promptflow-tools/) |
 | [Python](./python-tool.md) | Run Python code. | Default | [promptflow-tools](https://pypi.org/project/promptflow-tools/) |
 | [Azure OpenAI GPT-4 Turbo with Vision](./azure-open-ai-gpt-4v-tool.md) | Use AzureOpenAI GPT-4 Turbo with Vision model deployment to analyze images and provide textual responses to questions about them. | Default | [promptflow-tools](https://pypi.org/project/promptflow-tools/) |
@@ -28,7 +28,7 @@ The following table provides an index of tools in prompt flow.
 | [Vector Index Lookup](./vector-index-lookup-tool.md) | Search text or a vector-based query from a vector index. | Default | [promptflow-vectordb](https://pypi.org/project/promptflow-vectordb/) |
 | [Faiss Index Lookup](./faiss-index-lookup-tool.md) | Search a vector-based query from the Faiss index file. | Default | [promptflow-vectordb](https://pypi.org/project/promptflow-vectordb/) |
 | [Vector DB Lookup](./vector-db-lookup-tool.md) | Search a vector-based query from an existing vector database. | Default | [promptflow-vectordb](https://pypi.org/project/promptflow-vectordb/) |
-| [Embedding](./embedding-tool.md) | Use Azure Open AI embedding models to create an embedding vector that represents the input text. | Default | [promptflow-tools](https://pypi.org/project/promptflow-tools/) |
+| [Embedding](./embedding-tool.md) | Use Azure OpenAI embedding models to create an embedding vector that represents the input text. | Default | [promptflow-tools](https://pypi.org/project/promptflow-tools/) |
 | [Serp API](./serp-api-tool.md) | Use Serp API to obtain search results from a specific search engine. | Default | [promptflow-tools](https://pypi.org/project/promptflow-tools/) |
 
 The following table shows an index of custom tools created by the community to extend prompt flow's capabilities for specific use cases. They aren't officially maintained or endorsed by prompt flow team. For questions or issues when using a tool, please see the support contact in the description.

articles/azure-app-configuration/monitor-app-configuration-reference.md

@@ -26,6 +26,7 @@ Resource Provider and Type: [App Configuration Platform Metrics](../azure-monito
 | Daily Storage Usage | Percent |	Represents the amount of storage in use as a percentage of the maximum allowance. This metric is updated at least once daily. |
 | Request Quota Usage | Percent |	Represents the current total request usage in percentage. |
 | Replication Latency | Milliseconds |	Represents the average time it takes for a replica to be consistent with current state. |
+| Snapshot Storage Size | Count | Represents the total storage usage of configuration snapshot(s) in bytes. |
 
 For more information, see a list of [all platform metrics supported in Azure Monitor](../azure-monitor/essentials/metrics-supported.md).
 
@@ -41,6 +42,7 @@ App Configuration has the following dimensions associated with its metr
 | Daily Storage Usage | This metric does not have any dimensions.  |
 | Request Quota Usage | The supported dimensions are the **OperationType** ("Read"or "Write") and **Endpoint** of each request.  |
 | Replication Latency | The **Endpoint** of the replica that data was replicated to is included as a dimension.  |
+| Snapshot Storage Size | This metric does not have any dimensions.  |
 
  For more information on what metric dimensions are, see [Multi-dimensional metrics](../azure-monitor/essentials/data-platform-metrics.md#multi-dimensional-metrics).
 

articles/azure-app-configuration/monitor-app-configuration.md

@@ -103,6 +103,7 @@ You can analyze metrics for App Configuration with metrics from other Azure serv
 * Daily Storage Usage
 * Request Quota Usage
 * Replication Latency
+* Snapshot Storage Size
 
 In the portal, navigate to the **Metrics** section and select the **Metric Namespaces** and **Metrics** you want to analyze. This screenshot shows you the metrics view when selecting **Http Incoming Request Count** for your configuration store.
 

articles/azure-monitor/essentials/azure-monitor-workspace-overview.md

@@ -80,6 +80,8 @@ This section provides answers to common questions.
 
 An Azure Monitor workspace is a unique environment for data collected by Azure Monitor. Each workspace has its own data repository, configuration, and permissions. Azure Monitor workspaces will eventually contain all metrics collected by Azure Monitor, including native metrics. Currently, the only data hosted by an Azure Monitor workspace is Prometheus metrics.
 
+You cannot use quota limits, similar to the Log Anlytics Workspace (Daily Cap or Data retention) in the Azure Monitor workspace.
+
 ### Can I delete Prometheus metrics from an Azure Monitor workspace?
         
 Data is removed from the Azure Monitor workspace according to its data retention period, which is 18 months. 

articles/azure-resource-manager/managed-applications/tutorial-create-managed-app-with-custom-provider.md

@@ -222,7 +222,7 @@ az account set --subscription <subscriptionID>
 az group create --name $resourceGroup --location eastus
 
 # Get object ID of your identity
-userid=$(az ad user show --upn-or-object-id [email protected] --query objectId --output tsv)
+userid=$(az ad user list --upn [email protected] --query [0].id --output tsv)
 # Get role definition ID for the Owner role
 roleid=$(az role definition list --name Owner --query [].name --output tsv)
 

articles/container-apps/service-discovery-resiliency.md

@@ -358,7 +358,7 @@ matches: {
     '5xx'
     'reset'
     'connect-failure'
-    'retriabe-4xx'
+    'retriable-4xx'
   ]
 }
 ```

articles/cosmos-db/choose-api.md

@@ -73,7 +73,7 @@ You can use your existing MongoDB apps with API for MongoDB by just changing the
 
 Azure Cosmos DB for PostgreSQL is a managed service for running PostgreSQL at any scale, with the [Citus open source](https://github.com/citusdata/citus) superpower of distributed tables. It stores data either on a single node, or distributed in a multi-node configuration.
 
-Azure Cosmos DB for PostgreSQL is built on native PostgreSQL--rather than a PostgreSQL fork--and lets you choose any major database versions supported by the PostgreSQL community. It's ideal for starting on a single-node database with rich indexing, geospatial capabilities, and JSONB support. Later, if need more performance, you can add nodes to the cluster with zero downtime.
+Azure Cosmos DB for PostgreSQL is built on native PostgreSQL--rather than a PostgreSQL fork--and lets you choose any major database versions supported by the PostgreSQL community. It's ideal for starting on a single-node database with rich indexing, geospatial capabilities, and JSONB support. Later, if you need more performance, you can add nodes to the cluster with zero downtime.
 
 If you’re looking for a managed open source relational database with high performance and geo-replication, Azure Cosmos DB for PostgreSQL is the recommended choice. To learn more, see the [Azure Cosmos DB for PostgreSQL introduction](postgresql/introduction.md).
 

articles/defender-for-iot/organizations/traffic-mirroring/configure-mirror-span.md

@@ -33,7 +33,7 @@ Cisco2960# configure terminal
 Cisco2960(config)# monitor session 1 source interface fastehernet 0/2 - 23 rx
 Cisco2960(config)# monitor session 1 destination interface fastethernet 0/24
 Cisco2960(config)# end
-Cisco2960# show monitor 1
+Cisco2960# show monitor session 1
 Cisco2960# running-copy startup-config
 ```