Merge pull request #285150 from MicrosoftDocs/main

Publish to Live Wednesday 4AM PST 8/21

Author: PMEds28

articles/ai-studio/concepts/evaluation-metrics-built-in.md

@@ -238,7 +238,7 @@ For groundedness, we provide two versions:
 | When to use it?  | Use the groundedness metric when you need to verify that AI-generated responses align with and are validated by the provided context. It's essential for applications where factual correctness and contextual accuracy are key, like information retrieval, question-answering, and content summarization. This metric ensures that the AI-generated answers are well-supported by the context. |
 | What does it need as input?  | Question, Context, Generated Answer |
 
-Built-in prompt used by Large Language Model judge to score this metric:
+Built-in prompt used by the Large Language Model judge to score this metric:
 
 ```
 You will be presented with a CONTEXT and an ANSWER about that CONTEXT. You need to decide whether the ANSWER is entailed by the CONTEXT by choosing one of the following rating: 
@@ -269,7 +269,7 @@ Note the ANSWER is generated by a computer system, it can contain certain symbol
 | What does it need as input?  | Question, Context, Generated Answer | 
 
 
-Built-in prompt used by Large Language Model judge to score this metric (For question answering data format): 
+Built-in prompt used by the Large Language Model judge to score this metric (For question answering data format): 
 
 ```
 Relevance measures how well the answer addresses the main aspects of the question, based on the context. Consider whether all and only the important aspects are contained in the answer when evaluating relevance. Given the context and question, score the relevance of the answer between one to five stars using the following rating scale: 
@@ -287,7 +287,7 @@ Five stars: the answer has perfect relevance
 This rating value should always be an integer between 1 and 5. So the rating produced should be 1 or 2 or 3 or 4 or 5. 
 ```
 
-Built-in prompt used by Large Language Model judge to score this metric (For conversation data format) (without Ground Truth available):
+Built-in prompt used by the Large Language Model judge to score this metric (For conversation data format) (without Ground Truth available):
 
 ```
 You will be provided a question, a conversation history, fetched documents related to the question and a response to the question in the {DOMAIN} domain. Your task is to evaluate the quality of the provided response by following the steps below:  
@@ -317,7 +317,7 @@ You will be provided a question, a conversation history, fetched documents relat
 - Your final response must include both the reference answer and the evaluation result. The evaluation result should be written in English.  
 ```
 
-Built-in prompt used by Large Language Model judge to score this metric (For conversation data format) (with Ground Truth available): 
+Built-in prompt used by the Large Language Model judge to score this metric (For conversation data format) (with Ground Truth available): 
 
 ```
 
@@ -361,7 +361,7 @@ Labeling standards are as following:
 | When to use it?   | Use it when assessing the readability and user-friendliness of your model's generated responses in real-world applications.   |
 | What does it need as input?  | Question, Generated Answer |
 
-Built-in prompt used by Large Language Model judge to score this metric:
+Built-in prompt used by the Large Language Model judge to score this metric:
 
 ```
 Coherence of an answer is measured by how well all the sentences fit together and sound naturally as a whole. Consider the overall quality of the answer when evaluating coherence. Given the question and answer, score the coherence of answer between one to five stars using the following rating scale: 
@@ -389,7 +389,7 @@ This rating value should always be an integer between 1 and 5. So the rating pro
 | When to use it?   | Use it when evaluating the linguistic correctness of the AI-generated text, ensuring that it adheres to proper grammatical rules, syntactic structures, and vocabulary usage in the generated responses.   |
 | What does it need as input?  | Question, Generated Answer | 
 
-Built-in prompt used by Large Language Model judge to score this metric: 
+Built-in prompt used by the Large Language Model judge to score this metric: 
 
 ```
 Fluency measures the quality of individual sentences in the answer, and whether they are well-written and grammatically correct. Consider the quality of individual sentences when evaluating fluency. Given the question and answer, score the fluency of the answer between one to five stars using the following rating scale: 
@@ -417,7 +417,7 @@ This rating value should always be an integer between 1 and 5. So the rating pro
 | When to use it?   | Use the retrieval score when you want to guarantee that the documents retrieved are highly relevant for answering your users' questions. This score helps ensure the quality and appropriateness of the retrieved content.    |
 | What does it need as input?  | Question, Context, Generated Answer  | 
 
-Built-in prompt used by Large Language Model judge to score this metric: 
+Built-in prompt used by the Large Language Model judge to score this metric: 
 
 ```
 A chat history between user and bot is shown below 
@@ -473,7 +473,7 @@ Think through step by step:
 
 
 
-Built-in prompt used by Large Language Model judge to score this metric: 
+Built-in prompt used by the Large Language Model judge to score this metric: 
 
 ```
 GPT-Similarity, as a metric, measures the similarity between the predicted answer and the correct answer. If the information and content in the predicted answer is similar or equivalent to the correct answer, then the value of the Equivalence metric should be high, else it should be low. Given the question, correct answer, and predicted answer, determine the value of Equivalence metric using the following rating scale: 

articles/app-service/app-service-ip-restrictions.md

@@ -123,7 +123,10 @@ With service endpoints, you can configure your app with application gateways or
 
    :::image type="content" source="media/app-service-ip-restrictions/access-restrictions-service-tag-add.png?v2" alt-text="Screenshot of the 'Add Restriction' pane with the Service Tag type selected.":::
 
-All available service tags are supported in access restriction rules. Each service tag represents a list of IP ranges from Azure services. A list of these services and links to the specific ranges can be found in the [service tag documentation][servicetags]. Use Azure Resource Manager templates or scripting to configure more advanced rules like regional scoped rules.
+All publicly available service tags are supported in access restriction rules. Each service tag represents a list of IP ranges from Azure services. A list of these services and links to the specific ranges can be found in the [service tag documentation][servicetags]. Use Azure Resource Manager templates or scripting to configure more advanced rules like regional scoped rules.
+
+> [!NOTE]
+> When creating service tag-based rules through Azure portal or Azure CLI you will need read access at the subscription level to get the full list of service tags for selection/validation. In addition, the `Microsoft.Network` resource provider needs to be registered on the subscription.
 
 ### Edit a rule
 

articles/backup/azure-file-share-support-matrix.md

@@ -2,7 +2,7 @@
 title: Support Matrix for Azure file share backup by using Azure Backup
 description: Provides a summary of support settings and limitations when backing up Azure file shares.
 ms.topic: conceptual
-ms.date: 03/29/2024
+ms.date: 08/16/2024
 ms.custom: references_regions, engagement-fy24
 ms.service: azure-backup
 author: AbhishekMallick-MS
@@ -188,6 +188,22 @@ Vaulted backup for Azure Files (preview) is available in West Central US, Southe
 
 ---
 
+## Daylight savings
+
+Azure Backup doesn't support automatic clock adjustment for daylight saving time for Azure VM backups. It doesn't shift the hour of the backup forward or backwards. To ensure the backup runs at the desired time, modify the backup policies manually as required.
+
+## Support for customer-managed failover
+
+This section describes how your backups and restores are affected after customer-managed failovers. 
+
+The following table lists the behavior of backups due to customer-initiated failovers:
+
+| Failover type | Backups | Restore | Enabling protection (re-protection) of failed over account in secondary region |
+| --- | --- | --- | --- |
+| Customer-managed planned failover | Supported | Supported | Not supported |
+| Customer-managed unplanned failover | Not supported | Only cross-region restore from the vault is supported. | Not supported |
+
+
 ## Next steps
 
 * Learn how to [Back up Azure file shares](backup-afs.md)

articles/iot-operations/discover-manage-assets/howto-manage-assets-remotely.md

@@ -204,8 +204,8 @@ Now you can define the tags associated with the asset. To add OPC UA tags:
 
     | Node ID | Tag name | Observability mode |
     | ------- | -------- | ------------------ |
-    | ns=3;s=FastUInt10 | temperature | none |
-    | ns=3;s=FastUInt100 | Tag 10 | none |
+    | ns=3;s=FastUInt10 | temperature | None |
+    | ns=3;s=FastUInt100 | Tag 10 | None |
 
 1. Select **Manage default settings** to configure default telemetry settings for the asset. These settings apply to all the OPC UA tags that belong to the asset. You can override these settings for each tag that you add. Default telemetry settings include:
 
@@ -219,11 +219,11 @@ You can import up to 1000 OPC UA tags at a time from a CSV file:
 
 1. Create a CSV file that looks like the following example:
 
-    | NodeID              | TagName  | Sampling Interval Milliseconds | QueueSize | ObservabilityMode |
-    |---------------------|----------|--------------------------------|-----------|-------------------|
-    | ns=3;s=FastUInt1000 | Tag 1000 | 1000                           | 5         | none              |
-    | ns=3;s=FastUInt1001 | Tag 1001 | 1000                           | 5         | none              |
-    | ns=3;s=FastUInt1002 | Tag 1002 | 5000                           | 10        | none              |
+    | NodeID              | TagName  | QueueSize | ObservabilityMode | Sampling Interval Milliseconds |
+    |---------------------|----------|-----------|-------------------|--------------------------------|
+    | ns=3;s=FastUInt1000 | Tag 1000 | 5         | None              | 1000                           |
+    | ns=3;s=FastUInt1001 | Tag 1001 | 5         | None              | 1000                           |
+    | ns=3;s=FastUInt1002 | Tag 1002 | 10        | None              | 5000                           |
 
 1. Select **Add tag or CSV > Import CSV (.csv) file**. Select the CSV file you created and select **Open**. The tags defined in the CSV file are imported:
 

articles/iot-operations/get-started-end-to-end-sample/quickstart-add-assets.md

@@ -156,10 +156,10 @@ Add two OPC UA tags on the **Add tags** page. To add each tag, select **Add tag
 
 | Node ID            | Tag name    | Observability mode |
 | ------------------ | ----------- | ------------------ |
-| ns=3;s=FastUInt10  | temperature | none               |
-| ns=3;s=FastUInt100 | Tag 10      | none               |
+| ns=3;s=FastUInt10  | temperature | None               |
+| ns=3;s=FastUInt100 | Tag 10      | None               |
 
-The **Observability mode** is one of the following values: `none`, `gauge`, `counter`, `histogram`, or `log`.
+The **Observability mode** is one of the following values: `None`, `Gauge`, `Counter`, `Histogram`, or `Log`.
 
 You can select **Manage default settings** to change the default sampling interval and queue size for each tag.