@@ -8,7 +8,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 01/18/2024
+ms.date: 12/10/2024
 ---
 
 # Reference a path to enriched nodes using context and source properties an Azure AI Search skillset

@@ -8,7 +8,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 02/16/2024
+ms.date: 12/10/2024
 ---
 
 # Tips for AI enrichment in Azure AI Search 

@@ -9,12 +9,12 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 05/06/2024
+ms.date: 12/10/2024
 ---
 
 # Relevance in keyword search (BM25 scoring)
 
-This article explains the BM25 relevance scoring algorithm used to compute search scores for [full text search](search-lucene-query-architecture.md). BM25 relevance is exclusive to full text search. Filter queries, autocomplete and suggested queries, wildcard search or fuzzy search queries aren't scored or ranked for relevance.
+This article explains the BM25 relevance scoring algorithm used to compute search scores for [full text search](search-lucene-query-architecture.md). BM25 relevance is exclusive to full text search. Filter queries, autocomplete and suggested queries, wildcard search, and fuzzy search queries aren't scored or ranked for relevance.
 
 ## Scoring algorithms used in full text search
 
@@ -23,39 +23,36 @@ Azure AI Search provides the following scoring algorithms for full text search:
 | Algorithm | Usage | Range |
 |-----------|-------------|-------|
 | `BM25Similarity` | Fixed algorithm on all search services created after July 2020. You can configure this algorithm, but you can't switch to an older one (classic). | Unbounded. |
-|`ClassicSimilarity` | Present on older search services. You can [opt-in for BM25](index-ranking-similarity.md) and choose an algorithm on a per-index basis. | 0 < 1.00 |
+|`ClassicSimilarity` | Default on older search services that predate July 2020. On older services, you can [opt-in for BM25](index-ranking-similarity.md#enable-bm25-scoring-on-older-services) and choose a the BM25 algorithm on a per-index basis. | 0 < 1.00 |
 
-Both BM25 and Classic are TF-IDF-like retrieval functions that use the term frequency (TF) and the inverse document frequency (IDF) as variables to calculate relevance scores for each document-query pair, which is then used for ranking results. While conceptually similar to classic, BM25 is rooted in probabilistic information retrieval that produces more intuitive matches, as measured by user research. 
+Both BM25 and Classic are TF-IDF-like retrieval functions that use the term frequency (TF) and the inverse document frequency (IDF) as variables to calculate relevance scores for each document-query pair, which is then used for ranking results. While conceptually similar to classic, BM25 is rooted in probabilistic information retrieval that produces more intuitive matches, as measured by user research.
 
-BM25 offers advanced customization options, such as allowing the user to decide how the relevance score scales with the term frequency of matched terms. For more information, see [Configure the scoring  algorithm](index-ranking-similarity.md).
-
-> [!NOTE]
-> If you're using a search service that was created before July 2020, the scoring algorithm is most likely the previous default, `ClassicSimilarity`, which you can upgrade on a per-index basis. See [Enable BM25 scoring on older services](index-ranking-similarity.md#enable-bm25-scoring-on-older-services) for details.
-
-The following video segment fast-forwards to an explanation of the generally available ranking algorithms used in Azure AI Search. You can watch the full video for more background.
-
-> [!VIDEO https://www.youtube.com/embed/Y_X6USgvB1g?version=3&start=322&end=643]
+BM25 offers [advanced customization options](index-ranking-similarity.md), such as allowing the user to decide how the relevance score scales with the term frequency of matched terms.
 
 ## How BM25 ranking works
 
-Relevance scoring refers to the computation of a search score (**@search.score**) that serves as an indicator of an item's relevance in the context of the current query. The range is unbounded. However, the higher the score, the more relevant the item. 
+Relevance scoring refers to the computation of a search score (**@search.score**) that serves as an indicator of an item's relevance in the context of the current query. The range is unbounded. However, the higher the score, the more relevant the item.
 
 The search score is computed based on statistical properties of the string input and the query itself. Azure AI Search finds documents that match on search terms (some or all, depending on [searchMode](/rest/api/searchservice/documents/search-post#searchrequest)), favoring documents that contain many instances of the search term. The search score goes up even higher if the term is rare across the data index, but common within the document. The basis for this approach to computing relevance is known as *TF-IDF or* term frequency-inverse document frequency.
 
 Search scores can be repeated throughout a result set. When multiple hits have the same search score, the ordering of the same scored items is undefined and not stable. Run the query again, and you might see items shift position, especially if you're using the free service or a billable service with multiple replicas. Given two items with an identical score, there's no guarantee that one appears first.
 
-To break the tie among repeating scores, you can add an **$orderby** clause to first order by score, then order by another sortable field (for example, `$orderby=search.score() desc,Rating desc`). For more information, see [$orderby](search-query-odata-orderby.md).
+To break the tie among repeating scores, you can add an [**$orderby** clause](search-query-odata-orderby.md) to first order by score, then order by another sortable field (for example, `$orderby=search.score() desc,Rating desc`).
 
 Only fields marked as `searchable` in the index, or `searchFields` in the query, are used for scoring. Only fields marked as `retrievable`, or fields specified in `select` in the query, are returned in search results, along with their search score.
 
 > [!NOTE]
 > A `@search.score = 1` indicates an un-scored or un-ranked result set. The score is uniform across all results. Un-scored results occur when the query form is fuzzy search, wildcard or regex queries, or an empty search (`search=*`, sometimes paired with filters, where the filter is the primary means for returning a match).
 
+The following video segment fast-forwards to an explanation of the generally available ranking algorithms used in Azure AI Search. You can watch the full video for more background.
+
+> [!VIDEO https://www.youtube.com/embed/Y_X6USgvB1g?version=3&start=322&end=643]
+
 ## Scores in a text results
 
-Whenever results are ranked, **`@search.score`** property contains the value used to order the results. 
+Whenever results are ranked, **`@search.score`** property contains the value used to order the results.
 
-The following table identifies the scoring property returned on each match, algorithm, and range. 
+The following table identifies the scoring property, algorithm, and range.
 
 | Search method | Parameter | Scoring algorithm | Range |
 |---------------|-----------|-------------------|-------|
@@ -91,7 +88,7 @@ The diagram above is only one example. Many combinations of partitions and repli
 
 <a name="scoring-statistics"></a>
 
-### Scoring statistics and sticky sessions
+## Scoring statistics and sticky sessions
 
 For scalability, Azure AI Search distributes each index horizontally through a sharding process, which means that [portions of an index are physically separate](#sharding-effects-on-query-results).
 
@@ -107,7 +104,7 @@ POST https://[service name].search.windows.net/indexes/hotels/docs/search?api-ve
 }
 ```
 
-Using `scoringStatistics` will ensure that all shards in the same replica provide the same results. That said, different replicas may be slightly different from one another as they're always getting updated with the latest changes to your index. In some scenarios, you may want your users to get more consistent results during a "query session". In such scenarios, you can provide a `sessionId` as part of your queries. The `sessionId` is a unique string that you create to refer to a unique user session.
+Using `scoringStatistics` will ensure that all shards in the same replica provide the same results. That said, different replicas can be slightly different from one another as they're always getting updated with the latest changes to your index. In some scenarios, you might want your users to get more consistent results during a "query session". In such scenarios, you can provide a `sessionId` as part of your queries. The `sessionId` is a unique string that you create to refer to a unique user session.
 
 ```http
 POST https://[service name].search.windows.net/indexes/hotels/docs/search?api-version=2024-07-01
@@ -124,12 +121,12 @@ As long as the same `sessionId` is used, a best-effort attempt is made to target
 
 ## Relevance tuning
 
-In Azure AI Search, you can configure BM25 algorithm parameters, and tune search relevance and boost search scores through these mechanisms:
+In Azure AI Search, for keyword search and the text portion of a hybrid query, you can configure BM25 algorithm parameters plus tune search relevance and boost search scores through the following mechanisms.
 
 | Approach | Implementation | Description |
 |----------|----------------|-------------|
-| [Scoring algorithm configuration](index-ranking-similarity.md) | Search index |  |
-| [Scoring profiles](index-add-scoring-profiles.md) | Search index | Provide criteria for boosting the search score of a match based on content characteristics. For example, you might want to boost matches based on their revenue potential, promote newer items, or perhaps boost items that have been in inventory too long. A scoring profile is part of the index definition, composed of weighted fields, functions, and parameters. You can update an existing index with scoring profile changes, without incurring an index rebuild.|
+| [BM25 algorithm configuration](index-ranking-similarity.md) | Search index | Configure how document length and term frequency affect the relevance score. |
+| [Scoring profiles](index-add-scoring-profiles.md) | Search index | Provide criteria for boosting the search score of a match based on content characteristics. For example, you can boost matches based on their revenue potential, promote newer items, or perhaps boost items that have been in inventory too long. A scoring profile is part of the index definition, composed of weighted fields, functions, and parameters. You can update an existing index with scoring profile changes, without incurring an index rebuild.|
 | [Semantic ranking](semantic-search-overview.md) | Query request | Applies machine reading comprehension to search results, promoting more semantically relevant results to the top. |
 | [featuresMode parameter](#featuresmode-parameter-preview) | Query request | This parameter is mostly used for unpacking a BM25-ranked score, but it can be used for in code that provides a [custom scoring solution](https://github.com/Azure-Samples/search-ranking-tutorial). |
 
@@ -173,9 +170,9 @@ The featuresMode parameter isn't documented in the REST APIs, but you can use it
 
 ## Number of ranked results in a full text query response
 
-By default, if you aren't using pagination, the search engine returns the top 50 highest ranking matches for full text search. You can use the `top` parameter to return a smaller or larger number of items (up to 1000 in a single response). Full text search is subject to a maximum limit of 1,000 matches (see [API response limits](search-limits-quotas-capacity.md#api-response-limits)). Once 1,000 matches are found, the search engine no longer looks for more.
+By default, if you aren't using pagination, the search engine returns the top 50 highest ranking matches for full text search. You can use the `top` parameter to return a smaller or larger number of items (up to 1,000 in a single response). You can use `skip` and `next` to page results. Paging determines the number of results on each logical page and supports content navigation. For more information, see [Shape search results](search-pagination-page-layout.md).
 
-To return more or less results, use the paging parameters `top`, `skip`, and `next`. Paging is how you determine the number of results on each logical page and navigate through the full payload. For more information, see [How to work with search results](search-pagination-page-layout.md).
+Full text search is subject to a maximum limit of 1,000 matches (see [API response limits](search-limits-quotas-capacity.md#api-response-limits)). Once 1,000 matches are found, the search engine no longer looks for more.
 
 ## See also
 

@@ -9,7 +9,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 06/25/2024
+ms.date: 12/10/2024
 ---
 
 # Knowledge store in Azure AI Search

@@ -9,7 +9,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 03/18/2024
+ms.date: 12/10/2024
 ---
 
 # Connect a knowledge store with Power BI

@@ -7,7 +7,7 @@ ms.author: heidist
 manager: nitinme
 ms.service: azure-ai-search
 ms.topic: quickstart
-ms.date: 03/18/2024
+ms.date: 12/10/2024
 ms.custom:
   - mode-ui
   - ignite-2023

@@ -9,7 +9,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 03/08/2024
+ms.date: 12/10/2024
 ---
 
 # Create a knowledge store using REST

@@ -10,7 +10,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 03/08/2024
+ms.date: 12/10/2024
 ---
 
 # Detailed example of shapes and projections in a knowledge store

@@ -10,7 +10,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 01/18/2024
+ms.date: 12/10/2024
 ---
 
 # Knowledge store "projections" in Azure AI Search

@@ -8,7 +8,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 03/18/2024
+ms.date: 12/10/2024
 ---
 
 # Shaping data for projection into a knowledge store

@@ -10,7 +10,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 03/18/2024
+ms.date: 12/10/2024
 ---
 
 # Define projections in a knowledge store

@@ -1,7 +1,7 @@
 ---
 title: Built-in policy definitions for Azure Cognitive Search
 description: Lists Azure Policy built-in policy definitions for Azure Cognitive Search. These built-in policy definitions provide common approaches to managing your Azure resources.
-ms.date: 02/06/2024
+ms.date: 12/10/2024
 ms.topic: reference
 author: HeidiSteen
 ms.author: heidist

@@ -7,7 +7,7 @@ author: bevloh
 ms.author: beloh
 ms.service: azure-ai-search
 ms.topic: conceptual
-ms.date: 08/19/2024
+ms.date: 12/10/2024
 ---
 
 # OData language overview for `$filter`, `$orderby`, and `$select` in Azure AI Search

@@ -10,7 +10,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 01/17/2024
+ms.date: 12/10/2024
 ---
 
 # Simple query syntax in Azure AI Search

@@ -9,7 +9,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 02/26/2024
+ms.date: 12/10/2024
 ---
 
 # Partner spotlight

@@ -8,7 +8,7 @@ ms.author: heidist
 ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
-ms.topic: conceptual
+ms.topic: reference
 ms.date: 01/10/2024
 ---
 

@@ -9,7 +9,7 @@ ms.author: heidist
 ms.service: azure-ai-search
 ms.devlang: csharp
 ms.topic: conceptual
-ms.date: 10/19/2023
+ms.date: 12/10/2024
 ms.custom:
   - devx-track-csharp
   - devx-track-dotnet

@@ -11,7 +11,7 @@ ms.custom:
   - mode-arm
   - devx-track-bicep
   - ignite-2023
-ms.date: 02/26/2024
+ms.date: 12/10/2024
 ---
 
 # Quickstart: Deploy Azure AI Search using Bicep

@@ -2,7 +2,7 @@
 title: 'Quickstart: Deploy using Terraform'
 description: 'In this article, you create an Azure AI Search service using Terraform.'
 ms.topic: quickstart
-ms.date: 02/16/2024
+ms.date: 12/10/2024
 ms.custom:
   - devx-track-terraform
   - ignite-2023

@@ -8,7 +8,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 06/25/2024
+ms.date: 12/10/2024
 ---
 
 # Create an index alias in Azure AI Search

@@ -292,8 +292,7 @@ For data sources that provide change tracking and deletion detection, an indexer
 
 If you add new content to your data source, new chunks or child documents are added to the index on the next indexer run.
 
-If you modify existing content in the data source, chunks are updated incrementally in the search index if the data source you're using supports change tracking and deletion detection. For exammple, if a word or sentence changes in a document, the chunk in the target index that contains that word or sentence is updated on the next indexer run. Other types of updates, such as changing a field type and some attributions, aren't supported for existing fields. For more information about allowed updates, see [
-Change an index schema](search-howto-reindex.md#change-an-index-schema).
+If you modify existing content in the data source, chunks are updated incrementally in the search index if the data source you're using supports change tracking and deletion detection. For exammple, if a word or sentence changes in a document, the chunk in the target index that contains that word or sentence is updated on the next indexer run. Other types of updates, such as changing a field type and some attributions, aren't supported for existing fields. For more information about allowed updates, see [Update an index schema](search-howto-reindex.md#update-an-index-schema).
 
 Some data sources like [Azure Storage](search-howto-index-changed-deleted-blobs.md) support change and deletion tracking by default, based on the timestamp. Other data sources such as [OneLake](search-how-to-index-onelake-files.md), [Azure SQL](search-how-to-index-sql-database.md), or [Azure Cosmos DB](search-howto-index-cosmosdb.md) must be configured for change tracking.
 

@@ -10,7 +10,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 05/23/2024
+ms.date: 12/10/2024
 ---
 
 # Indexer connections to Azure SQL Managed Instance through a public endpoint

@@ -10,7 +10,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 05/01/2024
+ms.date: 12/10/2024
 ---
 
 # Indexer connections to a SQL Server instance on an Azure virtual machine

@@ -8,7 +8,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 06/25/2024
+ms.date: 12/10/2024
 ---
 
 # Enable caching for incremental enrichment in Azure AI Search

@@ -9,7 +9,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 08/23/2024
+ms.date: 12/10/2024
 ---
 
 # Index data from Azure Data Lake Storage Gen2

@@ -11,7 +11,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 02/28/2024
+ms.date: 12/10/2024
 ---
 
 # Index data from Azure Cosmos DB for Apache Gremlin for queries in Azure AI Search

@@ -8,7 +8,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 06/25/2024
+ms.date: 12/10/2024
 ---
 
 # Index data from Azure Cosmos DB for MongoDB for queries in Azure AI Search

@@ -12,7 +12,7 @@ ms.topic: how-to
 ms.custom:
   - kr2b-contr-experiment
   - ignite-2023
-ms.date: 06/25/2024
+ms.date: 12/10/2024
 ---
 
 # Index data from Azure Database for MySQL flexible server

@@ -10,7 +10,7 @@ ms.custom:
   - ignite-2023
 ms.service: azure-ai-search
 ms.topic: how-to
-ms.date: 02/22/2024
+ms.date: 12/10/2024
 ---
 
 # Set up an indexer connection to Azure SQL using a managed identity

@@ -11,7 +11,7 @@ ms.topic: how-to
 ms.custom:
   - subject-moving-resources
   - ignite-2023
-ms.date: 01/11/2024
+ms.date: 12/10/2024
 ---
 
 # Move your Azure AI Search service to another Azure region

@@ -11,7 +11,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2024
 ms.topic: how-to
-ms.date: 08/14/2024
+ms.date: 12/09/2024
 ---
 
 # Update or rebuild an index in Azure AI Search
@@ -20,28 +20,126 @@ This article explains how to update an existing index in Azure AI Search with sc
 
 During active development, it's common to drop and rebuild indexes when you're iterating over index design. Most developers work with a small representative sample of their data so that reindexing goes faster.
 
-For schema changes on applications already in production, we recommend creating and testing a new index that runs side by side an existing index. Use an [index alias](search-how-to-alias.md) to swap in the new index while avoiding changes your application code.
+For schema changes on applications already in production, we recommend creating and testing a new index that runs side by side an existing index. Use an [index alias](search-how-to-alias.md) to swap in the new index so that you can avoid changes your application code.
 
 ## Update content
 
-Incremental indexing and synchronizing an index against changes in source data is fundamental to most search applications. This section explains the workflow for updating field contents in a search index.
+Incremental indexing and synchronizing an index against changes in source data is fundamental to most search applications. This section explains the workflow for updating field contents in a search index through the REST API, but the Azure SDKs provide equivalent functionality.
 
-1. Use the same techniques for loading documents: [Documents - Index (REST)](/rest/api/searchservice/documents) or an equivalent API in the Azure SDKs. For more information about indexing techniques, see [Load documents](search-how-to-load-search-index.md).
+The body of the request contains one or more documents to be indexed. Documents are identified by a unique case-sensitive key. Each document is associated with an action: "upload", "delete", "merge", or "mergeOrUpload". Upload requests must include the document data as a set of key/value pairs.
 
-1. Set the `@search.action` parameter to determine the effect on existing documents:
+```json
+{  
+  "value": [  
+    {  
+      "@search.action": "upload (default) | merge | mergeOrUpload | delete",  
+      "key_field_name": "unique_key_of_document", (key/value pair for key field from index schema)  
+      "field_name": field_value (key/value pairs matching index schema)  
+        ...  
+    },  
+    ...  
+  ]  
+}
+```
+
++ First, use the APIs for loading documents, such as [Documents - Index (REST)](/rest/api/searchservice/documents) or an equivalent API in the Azure SDKs. For more information about indexing techniques, see [Load documents](search-how-to-load-search-index.md).
+
++ For a large update, batching (up to 1,000 documents per batch, or about 16 MB per batch, whichever limit comes first) is recommended and significantly improves indexing performance.
+
++ Set the `@search.action` parameter on the API to determine the effect on existing documents.
 
    | Action | Effect |
    |--------|--------|
    | delete | Removes the entire document from the index. If you want to remove an individual field, use merge instead, setting the field in question to null. Deleted documents and fields don't immediately free up space in the index. Every few minutes, a background process performs the physical deletion. Whether you use the Azure portal or an API to return index statistics, you can expect a small delay before the deletion is reflected in the Azure portal and through APIs. |
-   | merge | Updates a document that already exists, and fails a document that can't be found. Merge replaces existing values. For this reason, be sure to check for collection fields that contain multiple values, such as fields of type `Collection(Edm.String)`. For example, if a `tags` field starts with a value of `["budget"]` and you execute a merge with `["economy", "pool"]`, the final value of the `tags` field is `["economy", "pool"]`. It won't be `["budget", "economy", "pool"]`. |
+   | merge | Updates a document that already exists, and fails a document that can't be found. Merge replaces existing values. For this reason, be sure to check for collection fields that contain multiple values, such as fields of type `Collection(Edm.String)`. For example, if a `tags` field starts with a value of `["budget"]` and you execute a merge with `["economy", "pool"]`, the final value of the `tags` field is `["economy", "pool"]`. It won't be `["budget", "economy", "pool"]`. <br><br>The same behavior applies to complex collections. If the document contains a complex collection field named Rooms with a value of `[{ "Type": "Budget Room", "BaseRate": 75.0 }]`, and you execute a merge with a value of `[{ "Type": "Standard Room" }, { "Type": "Budget Room", "BaseRate": 60.5 }]`, the final value of the Rooms field will be `[{ "Type": "Standard Room" }, { "Type": "Budget Room", "BaseRate": 60.5 }]`. It won't append or merge new and existing values. |
    | mergeOrUpload | Behaves like merge if the document exists, and upload if the document is new. This is the most common action for incremental updates. |
    | upload | Similar to an "upsert" where the document is inserted if it's new, and updated or replaced if it exists. If the document is missing values that the index requires, the document field's value is set to null. |
 
-1. Post the update.
+Queries continue to run during indexing, but if you're updating or removing existing fields, you can expect mixed results and a higher incidence of throttling.
+
+> [!NOTE]
+> There are no ordering guarantees for which action in the request body is executed first. It's not recommended to have multiple "merge" actions associated with the same document in a single request body. If there are multiple "merge" actions required for the same document, perform the merging client-side before updating the document in the search index.
+
+### Responses
+
+Status code 200 is returned for a successful response, meaning that all items have been stored durably and will start to be indexed. Indexing runs in the background and makes new documents available (that is, queryable and searchable) a few seconds after the indexing operation completed. The specific delay depends on the load on the service.
+
+Successful indexing is indicated by the status property being set to true for all items, as well as the `statusCode` property being set to either 201 (for newly uploaded documents) or 200 (for merged or deleted documents):
+
+```json
+{
+  "value": [
+    {
+      "key": "unique_key_of_new_document",
+      "status": true,
+      "errorMessage": null,
+      "statusCode": 201
+    },
+    {
+      "key": "unique_key_of_merged_document",
+      "status": true,
+      "errorMessage": null,
+      "statusCode": 200
+    },
+    {
+      "key": "unique_key_of_deleted_document",
+      "status": true,
+      "errorMessage": null,
+      "statusCode": 200
+    }
+  ]
+}
+```
+
+Status code 207 is returned when at least one item wasn't successfully indexed. Items that haven't been indexed have the status field set to false. The `errorMessage` and `statusCode` properties indicate the reason for the indexing error:
 
-Queries continue to run, but if you're updating or removing existing fields, you can expect mixed results and a higher incidence of throttling.
+```json
+{
+  "value": [
+    {
+      "key": "unique_key_of_document_1",
+      "status": false,
+      "errorMessage": "The search service is too busy to process this document. Please try again later.",
+      "statusCode": 503
+    },
+    {
+      "key": "unique_key_of_document_2",
+      "status": false,
+      "errorMessage": "Document not found.",
+      "statusCode": 404
+    },
+    {
+      "key": "unique_key_of_document_3",
+      "status": false,
+      "errorMessage": "Index is temporarily unavailable because it was updated with the 'allowIndexDowntime' flag set to 'true'. Please try again later.",
+      "statusCode": 422
+    }
+  ]
+}  
+```
+
+The `errorMessage` property indicates the reason for the indexing error if possible.
+
+The following table explains the various per-document status codes that can be returned in the response. Some status codes indicate problems with the request itself, while others indicate temporary error conditions. The latter you should retry after a delay.
+
+| Status code | Meaning | Retryable | Notes |
+|-------------|---------|-----------|-------|
+| 200 | Document was successfully modified or deleted. | n/a | Delete operations are idempotent. That is, even if a document key doesn't exist in the index, attempting a delete operation with that key results in a 200 status code. |
+| 201 | Document was successfully created. | n/a |  |
+| 400 | There was an error in the document that prevented it from being indexed. | No | The error message in the response indicates what is wrong with the document.|
+| 404 | The document couldn't be merged because the given key doesn't exist in the index. | No | This error doesn't occur for uploads since they create new documents, and it doesn't occur for deletes because they're idempotent. |
+| 409 | A version conflict was detected when attempting to index a document.| Yes | This can happen when you're trying to index the same document more than once concurrently. |
+| 422 | The index is temporarily unavailable because it was updated with the 'allowIndexDowntime' flag set to 'true'. | Yes | |
+| 503 | Your search service is temporarily unavailable, possibly due to heavy load. | Yes | Your code should wait before retrying in this case or you risk prolonging the service unavailability.|
 
-## Tips for incremental indexing
+If your client code frequently encounters a 207 response, one possible reason is that the system is under load. You can confirm this by checking the statusCode property for 503. If the statusCode is 503, we recommend throttling indexing requests. Otherwise, if indexing traffic doesn't subside, the system could start rejecting all requests with 503 errors.
+
+Status code 429 indicates that you have exceeded your quota on the number of documents per index. You must either create a new index or upgrade for higher capacity limits.
+
+> [!NOTE]
+> When you upload `DateTimeOffset` values with time zone information to your index, Azure AI Search normalizes these values to UTC. For example, 2024-01-13T14:03:00-08:00 is stored as 2024-01-13T22:03:00Z. If you need to store time zone information, add an extra column to your index for this data point.
+
+### Tips for incremental indexing
 
 + [Indexers automate incremental indexing](search-indexer-overview.md). If you can use an indexer, and if the data source supports change tracking, you can run the indexer on a recurring schedule to add, update, or overwrite searchable content so that it's synchronized to your external data.
 
@@ -88,18 +186,22 @@ GET  {{baseUrl}}/indexes/hotels-vector-quickstart/docs('1')?api-version=2024-07-
     api-key: {{apiKey}}
 ```
 
-## Change an index schema
+## Update an index schema
+
+The index schema defines the physical data structures created on the search service, so there aren't many schema changes that you can make without incurring a full rebuild.
 
-The index schema defines the physical data structures created on the search service, so there aren't many schema changes that you can make without incurring a full rebuild. The following list enumerates the schema changes that can be introduced seamlessly into an existing index. Generally, the list includes new fields and functionality used during query execution.
+### Updates with no rebuild
+
+The following list enumerates the schema changes that can be introduced seamlessly into an existing index. Generally, the list includes new fields and functionality used during query execution.
 
 + Add a new field
 + Set the `retrievable` attribute on an existing field
 + Update `searchAnalyzer` on a field having an existing `indexAnalyzer`
-+ Add a new analyzer definition in an index (which can be applied to new fields)
-+ Add, update, or delete scoring profiles
++ Add a new [analyzer definition](index-add-custom-analyzers.md) in an index (which can be applied to new fields)
++ Add, update, or delete [scoring profiles](index-add-scoring-profiles.md)
++ Add, update, or delete [synonymMaps](search-synonyms.md)
++ Add, update, or delete [semantic configurations](semantic-how-to-configure.md)
 + Add, update, or delete CORS settings
-+ Add, update, or delete synonymMaps
-+ Add, update, or delete semantic configurations
 
 The order of operations is:
 
@@ -115,7 +217,7 @@ When you update an index schema to include a new field, existing documents in th
 
 There should be no query disruptions during the updates, but query results will vary as the updates take effect.
 
-## Drop and rebuild an index
+### Updates requiring a rebuild
 
 Some modifications require an index drop and rebuild, replacing a current index with a new one.
 
@@ -144,6 +246,8 @@ The order of operations is:
 
 When you create the index, physical storage is allocated for each field in the index schema, with an inverted index created for each searchable field and a vector index created for each vector field. Fields that aren't searchable can be used in filters or expressions, but don't have inverted indexes and aren't full-text or fuzzy searchable. On an index rebuild, these inverted indexes and vector indexes are deleted and recreated based on the index schema you provide.
 
+To minimize disruption to application code, consider [creating an index alias](search-how-to-alias.md). Application code references the alias, but you can update the name of the index that the alias points to.
+
 ## Balancing workloads
 
 Indexing doesn't run in the background, but the search service will balance any indexing jobs against ongoing queries. During indexing, you can [monitor query requests](search-monitor-queries.md) in the Azure portal to ensure queries are completing in a timely manner.
@@ -156,7 +260,13 @@ You can begin querying an index as soon as the first document is loaded. If you
 
 You can use [Search Explorer](search-explorer.md) or a [REST client](search-get-started-rest.md) to check for updated content.
 
-If you added or renamed a field, use [$select](search-query-odata-select.md) to return that field: `search=*&$select=document-id,my-new-field,some-old-field&$count=true`.
+If you added or renamed a field, use [select](search-query-odata-select.md) to return that field: 
+
+```json
+"search": "*",
+"select": "document-id, my-new-field, some-old-field",
+"count": true
+```
 
 The Azure portal provides index size and vector index size. You can check these values after updating an index, but remember to expect a small delay as the service processes the change and to account for portal refresh rates, which can be a few minutes.
 

@@ -9,7 +9,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 10/18/2023
+ms.date: 12/10/2024
 ---
 
 # Set up an indexer connection to Azure SQL Managed Instance using a managed identity

@@ -7,7 +7,7 @@ author: mattgotteiner
 ms.author: magottei
 ms.service: azure-ai-search
 ms.topic: how-to
-ms.date: 05/23/2024
+ms.date: 12/10/2024
 ---
 
 # Create a shared private link for a SQL managed instance from Azure AI Search

@@ -10,7 +10,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 05/01/2024
+ms.date: 12/10/2024
 ---
 
 # Configure IP firewall rules to allow indexer connections from Azure AI Search

@@ -10,7 +10,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 01/18/2024
+ms.date: 12/10/2024
 ---
 
 # Make indexer connections to Azure Storage as a trusted service

@@ -9,7 +9,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 06/25/2024
+ms.date: 12/10/2024
 ---
 
 # Indexer troubleshooting guidance for Azure AI Search

@@ -10,7 +10,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 01/11/2024
+ms.date: 12/10/2024
 ---
 
 # Create an index for multiple languages in Azure AI Search

@@ -198,17 +198,18 @@ Static rate request limits for operations related to a service:
 
 + Service Statistics (GET /servicestats): 4 per second per search unit
 
-### Semantic Ranker Throttling limits
+### Semantic ranker throttling limits
 
 [Semantic ranker](search-get-started-semantic.md) uses a queuing system to manage concurrent requests. This system allows search services get the highest number of queries per second possible. When the limit of concurrent requests is reached, additional requests are placed in a queue. If the queue is full, further requests are rejected and must be retried.
 
 Total semantic ranker queries per second varies based on the following factors:
-+ The SKU of the search service. Both queue capacity and concurrent request limits vary by SKU.
+
++ The tier of the search service. Both queue capacity and concurrent request limits vary by tier.
 + The number of search units in the search service. The simplest way to increase the maximum number of concurrent semantic ranker queries is to [add more search units to your search service](search-capacity-planning.md#how-to-change-capacity).
 + The total available semantic ranker capacity in the region.
 + The amount of time it takes to serve a query using semantic ranker. This varies based on how busy the search service is.
 
-The following table describes the semantic ranker throttling limits by SKU. Subject to available capacity in the region, contact support to request a limit increase.
+The following table describes the semantic ranker throttling limits by tier, subject to available capacity in the region. You can contact Microsoft support to request a limit increase.
 
 | Resource | Basic | S1 | S2 | S3 | S3-HD | L1 | L2 |
 |----------|-------|----|----|----|-------|----|----|

@@ -11,7 +11,7 @@ ms.custom:
   - devx-track-azurecli
   - ignite-2023
 ms.topic: how-to
-ms.date: 04/05/2024
+ms.date: 12/10/2024
 ---
 
 # Manage your Azure AI Search service with the Azure CLI

@@ -9,7 +9,7 @@ ms.author: heidist
 ms.service: azure-ai-search
 ms.devlang: powershell
 ms.topic: how-to
-ms.date: 04/05/2024
+ms.date: 12/10/2024
 ms.custom:
   - devx-track-azurepowershell
   - ignite-2023

@@ -8,7 +8,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 03/13/2024
+ms.date: 12/10/2024
 ---
 
 # Manage your Azure AI Search service with REST APIs

@@ -8,7 +8,7 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
 ms.topic: conceptual
-ms.date: 06/18/2024
+ms.date: 12/10/2024
 ---
 
 # Service administration for Azure AI Search in the Azure portal

@@ -8,7 +8,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 04/23/2024
+ms.date: 12/10/2024
 ---
 
 # Design patterns for multitenant SaaS applications and Azure AI Search

@@ -9,7 +9,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 04/24/2024
+ms.date: 12/10/2024
 ---
 
 # Visualize Azure AI Search Logs and Metrics with Power BI

@@ -9,7 +9,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: reference
-ms.date: 02/16/2024
+ms.date: 12/10/2024
 ---
 
 # moreLikeThis (preview) in Azure AI Search

@@ -9,7 +9,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: how-to
-ms.date: 01/11/2024
+ms.date: 12/10/2024
 ---
 
 # Text normalization for case-insensitive filtering, faceting and sorting

@@ -13,13 +13,9 @@ ms.topic: how-to
 ms.date: 12/09/2024
 ---
 
-# Shape search results or modify search results composition
+# Shape search results or modify search results composition in Azure AI Search
 
-This article explains search results composition in Azure AI Search and how to work with results in your apps. Search results are returned in a query response. The structure of a response is determined by parameters in the query itself.
-
-Search results include top-level fields such as count and optional semantic ranking-related elements such as `answers`, but most of the response consists of matching documents in a values array.
-
-For search results composition, parameters on the query determine:
+This article explains search results composition and how to shape search results to fit your scenarios. Search results are returned in a query response. The shape of a response is determined by parameters in the query itself. These parameters include:
 
 + Number of matches found in the index (`count`)
 + Number of matches returned in the response (50 by default, configurable through `top`) or per page (`skip` and `top`)
@@ -29,6 +25,8 @@ For search results composition, parameters on the query determine:
 + Highlighting of terms within a result, matching on either the whole or partial term in the body
 + Optional elements from the semantic ranker (`answers` at the top, `captions` for each match)
 
+Search results can include top-level fields, but most of the response consists of matching documents in an array.
+
 ## Clients and APIs for defining the query response
 
 You can use the following clients to configure a query response:
@@ -67,7 +65,7 @@ Occasionally, query output isn't what you're expecting to see. For example, you
 
 ## Counting matches
 
-The count parameter returns the number of documents in the index that are considered a match for the query. To return the count, add `count=true` to the query request. There's no maximum value imposed by the search service. Depending on your query and the content of your documents, the count could be as high as every document in the index.
+The `count` parameter returns the number of documents in the index that are considered a match for the query. To return the count, add `count=true` to the query request. There's no maximum value imposed by the search service. Depending on your query and the content of your documents, the count could be as high as every document in the index.
 
 Count is accurate when the index is stable. If the system is actively adding, updating, or deleting documents, the count is approximate, excluding any documents that aren't fully indexed.
 
@@ -82,17 +80,15 @@ Count won't be affected by routine maintenance or other workloads on the search
 
 ## Number of results in the response
 
-Azure AI Search uses server-side paging to prevent queries from retrieving too many documents at once. Query parameters that determine the number of results in a response are `top` and `skip`. `top` refers to the number of search results in a page. 
-
-The default page size is 50, while the maximum page size is 1,000. If you specify a value greater than 1,000 and there are more than 1,000 results found in your index, only the first 1,000 results are returned.
+Azure AI Search uses server-side paging to prevent queries from retrieving too many documents at once. Query parameters that determine the number of results in a response are `top` and `skip`. `top` refers to the number of search results in a page. `skip` is an interval of `top`, and it tells the search engine how many results to skip before getting the next set.
 
-If the number of matches exceed the page size, the response includes information to retrieve the next page of results. For example:
+The default page size is 50, while the maximum page size is 1,000. If you specify a value greater than 1,000 and there are more than 1,000 results found in your index, only the first 1,000 results are returned. If the number of matches exceed the page size, the response includes information to retrieve the next page of results. For example:
 
 ```json
 "@odata.nextLink": "https://contoso-search-eastus.search.windows.net/indexes/realestate-us-sample-index/docs/search?api-version=2024-07-01"
 ```
 
-The top 50 are determined by search score, assuming the query is full text search or semantic. Otherwise, the top 50 are an arbitrary order for exact match queries (where uniform `@search.score=1.0` indicates arbitrary ranking).
+The top matches are determined by search score, assuming the query is full text search or semantic. Otherwise, the top matches are an arbitrary order for exact match queries (where uniform `@search.score=1.0` indicates arbitrary ranking).
 
 Set `top` to override the default of 50. In newer preview APIs, if you're using a hybrid query, you can [specify maxTextRecallSize](hybrid-search-how-to-query.md#set-maxtextrecallsize-and-countandfacetmode-preview) to return up to 10,000 documents.
 
@@ -109,7 +105,7 @@ POST https://contoso-search-eastus.search.windows.net/indexes/realestate-us-samp
 }
 ```
 
-To return the second set, skip the first 15 to get the next 15:
+This query returns the second set, skipping the first 15 to get the next 15 (16 through 30):
 
 ```http
 POST https://contoso-search-eastus.search.windows.net/indexes/realestate-us-sample-index/docs/search?api-version=2024-07-01

@@ -6,7 +6,7 @@ author: mattgotteiner
 ms.author: magottei
 ms.service: azure-ai-search
 ms.topic: conceptual
-ms.date: 06/06/2024
+ms.date: 12/10/2024
 ---
 
 # Tips for better performance in Azure AI Search

@@ -8,7 +8,7 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
 ms.topic: conceptual
-ms.date: 05/23/2024
+ms.date: 12/10/2024
 ---
 
 # Querying in Azure AI Search

@@ -9,7 +9,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 03/18/2024
+ms.date: 12/10/2024
 ---
 # Troubleshooting OData collection filters in Azure AI Search
 

@@ -9,7 +9,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 01/11/2024
+ms.date: 12/10/2024
 ---
 
 # Understand how OData collection filters work in Azure AI Search

@@ -10,7 +10,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: tutorial
-ms.date: 03/13/2024
+ms.date: 12/10/2024
 
 ---
 

@@ -10,7 +10,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 06/19/2024
+ms.date: 12/10/2024
 ---
 
 # Plan and manage costs of an Azure AI Search service

@@ -10,25 +10,25 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2024
 ms.topic: overview
-ms.date: 10/27/2024
+ms.date: 12/10/2024
 ---
 
 # What's Azure AI Search?
 
-Azure AI Search ([formerly known as "Azure Cognitive Search"](whats-new.md#new-service-name)) is an enterprise-ready search and retrieval system, with a comprehensive set of advanced search technologies, built for high-performance applications at any scale.
+Azure AI Search ([formerly known as "Azure Cognitive Search"](whats-new.md#new-service-name)) is an enterprise-ready information retrieval system for your heterogeneous content that you ingest into a search index. It comes with a comprehensive set of advanced search technologies, built for high-performance applications at any scale.
 
 Azure AI Search is the recommended retrieval system for building RAG-based applications on Azure, with native LLM integrations between Azure OpenAI Service and Azure Machine Learning, and multiple strategies for relevance tuning.
 
-Azure AI Search can be used in both traditional and GenAI scenarios. Common use cases include knowledge base insights (catalog or document search), information discovery (data exploration), retrieval-augmented generation (RAG), and indexing automation.  
+Azure AI Search can be used in both traditional and GenAI scenarios. Common use cases include catalog or document search, information discovery (data exploration), and retrieval-augmented generation (RAG) for conversational search.  
 
 When you create a search service, you work with the following capabilities:
 
-+ A search engine for [vector search](vector-search-overview.md) and [full text](search-lucene-query-architecture.md) and [hybrid search](hybrid-search-overview.md) over a search index 
-+ Rich indexing with [integrated data chunking and vectorization](vector-search-integrated-vectorization.md), [lexical analysis](search-analyzers.md) for text, and [optional applied AI](cognitive-search-concept-intro.md) for content extraction and transformation
-+ Rich query syntax for [vector queries](vector-search-how-to-query.md), text search, [hybrid queries](hybrid-search-how-to-query.md), fuzzy search, autocomplete, geo-search and others
-+ Relevance and query performance tuning with [semantic ranking](semantic-search-overview.md), [scoring profiles](index-add-scoring-profiles.md), [quantization for vector queries](vector-search-how-to-quantization.md), and parameters for controlling query behaviors at runtime 
-+ Azure scale, security, and reach
-+ Azure integration at the data layer, machine learning layer, Azure AI services and Azure OpenAI
++ A search engine for [vector search](vector-search-overview.md) and [full text](search-lucene-query-architecture.md) and [hybrid search](hybrid-search-overview.md) over a search index.
++ Rich indexing with the ability to content transformation. This includes [integrated data chunking and vectorization](vector-search-integrated-vectorization.md) for RAG, [lexical analysis](search-analyzers.md) for text, and [optional applied AI](cognitive-search-concept-intro.md) for content extraction and enrichment.
++ Rich query syntax for [vector queries](vector-search-how-to-query.md), text search, [hybrid queries](hybrid-search-how-to-query.md), fuzzy search, autocomplete, geo-search and others.
++ Relevance and query performance tuning with [semantic ranking](semantic-search-overview.md), [scoring profiles](index-add-scoring-profiles.md), [quantization for vector queries](vector-search-how-to-quantization.md), and parameters for controlling query behaviors at runtime.
++ Azure scale, security, and reach.
++ Azure integration at the data layer, machine learning layer, Azure AI services and Azure OpenAI.
 
 > [!div class="nextstepaction"]
 > [Create a search service](search-create-service-portal.md)
@@ -47,7 +47,7 @@ On the search service itself, the two primary workloads are *indexing* and *quer
 
 + [**Indexing**](search-what-is-an-index.md) is an intake process that loads content into your search service and makes it searchable. Internally, inbound text is processed into tokens and stored in inverted indexes, and inbound vectors are stored in vector indexes. The document format that Azure AI Search can index is JSON. You can upload JSON documents that you've assembled, or use an indexer to retrieve and serialize your data into JSON. 
 
-  [Applied AI](cognitive-search-concept-intro.md) through a [skillset](cognitive-search-working-with-skillsets.md) extends indexing with image and language models. If you have images or large unstructured text in source document, you can attach skills that perform OCR, analyze and describe images, infer structure, translate text and more. Output is text that can be serialized into JSON and ingested into a search index.
+  [Applied AI](cognitive-search-concept-intro.md) through a [skillset](cognitive-search-working-with-skillsets.md) extends indexing with image and language models. If you have images or large unstructured text in source document, you can attach skills that perform OCR, analyze and describe images, infer structure, translate text, and more. Output is text that can be serialized into JSON and ingested into a search index.
 
   Skillsets can also perform [data chunking and vectorization during indexing](vector-search-integrated-vectorization.md). Skills that attach to Azure OpenAI, the model catalog in Azure AI Foundry portal, or custom skills that attach to any external chunking and embedding model can be used during indexing to create vector data. Output is chunked vector content that can be ingested into a search index.
 
@@ -139,8 +139,8 @@ Customers often ask how Azure AI Search compares with other search-related solut
 
 Key strengths include:
 
-+ Support for vector and nonvector (text) indexing and queries. With vector similarity search, you can find information that’s semantically similar to search queries, even if the search terms aren’t exact matches. Use hybrid search for the best of keyword and vector search.
-+ Ranking and relevance tuning through semantic ranking and scoring profiles. Query syntax supports term boosting and field prioritization.
++ Support for vector and nonvector (text) indexing and queries. With vector similarity search, you can find information that’s semantically similar to search queries, even if the search terms aren’t exact matches. Use hybrid search to combine the strengths of keyword and vector search.
++ Ranking and relevance tuning through semantic ranking and scoring profiles. You can also leverage query syntax that supports term boosting and field prioritization.
 + Azure data integration (crawlers) at the indexing layer.
 + Azure AI integration for transformations that make content text and vector searchable.
 + Microsoft Entra security for trusted connections, and Azure Private Link for private connections in no-internet scenarios.

@@ -2,7 +2,7 @@
 title: Azure Policy Regulatory Compliance controls for Azure AI Search
 description: Lists Azure Policy Regulatory Compliance controls available for Azure AI Search. These built-in policy definitions provide common approaches to managing the compliance of your Azure resources.
 ms.date: 02/06/2024
-ms.topic: sample
+ms.topic: reference
 author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search

@@ -10,7 +10,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 02/08/2024
+ms.date: 12/10/2024
 ---
 
 # Return a semantic answer in Azure AI Search

@@ -10,7 +10,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 06/25/2024
+ms.date: 12/10/2024
 ---
 
 # Add spell check to queries in Azure AI Search

@@ -414,7 +414,7 @@ items:
         href: search-normalizers.md
     - name: Search results 
       items:
-      - name: Paging, sorting, and formatting
+      - name: Page, sort, and shape results
         href: search-pagination-page-layout.md
       - name: Return a semantic answer
         href: semantic-answers.md

@@ -10,7 +10,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 02/20/2024
+ms.date: 12/10/2024
 ---
 
 # Troubleshoot issues with Shared Private Links in Azure AI Search

@@ -8,7 +8,7 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
 ms.topic: tutorial
-ms.date: 01/17/2024
+ms.date: 12/10/2024
 ms.custom:
   - devx-track-csharp
   - devx-track-dotnet