Acrolinx fixes

Author: GraemeMalcolm

learn-pr/wwl-data-ai/ai-knowldge-mining/8-knowledge-check.yml

@@ -29,13 +29,13 @@ quiz:
     choices:
     - content: "Azure Functions"
       isCorrect: false
-      explanation: "Incorrect. Azure Functions can be used to implement custom skills, but is not required for built-in skills."
+      explanation: "Incorrect. Azure Functions can be used to implement custom skills, but isn't required for built-in skills."
     - content: "Azure AI services"
       isCorrect: true
-      explanation: "Correct. Azure AI services is required to support built-in skills."
+      explanation: "Correct. An Azure AI services resource is required to support built-in skills."
     - content: "Azure Cosmos DB"
       isCorrect: false
-      explanation: "Incorrect. Azure Cosmos DB could be a data source fo Azure AI Search, but it is not required for built-in skills."
+      explanation: "Incorrect. Azure Cosmos DB could be a data source fo Azure AI Search, but it isn't required for built-in skills."
   - content: "Which kind of projection results in a relational data schema for extracted fields?"
     choices:
     - content: "File"
@@ -46,6 +46,6 @@ quiz:
       explanation: "Incorrect. Object projections are used to store extracted documents as JSON files."
     - content: "Table"
       isCorrect: true
-      explanation: "Correct. Use table projections to persists extracted data in a relational schema."
+      explanation: "Correct. Use table projections to persist extracted data in a relational schema."
 
 

learn-pr/wwl-data-ai/ai-knowldge-mining/includes/3-indexing.md

@@ -2,11 +2,11 @@ At the heart of Azure AI Search solutions is the creation of an *index* An index
 
 ![Diagram of an indexer extracting documents from a source and creating an index.](../media/indexer.png)
 
-The indexing process starts with a data source: the storage location of your original data artifacts; for example an Azure blob store container full of documents, a database, or some other store.
+The indexing process starts with a data source: the storage location of your original data artifacts; for example, an Azure blob store container full of documents, a database, or some other store.
 
 The Indexer automates the extraction and indexing of data *fields* through an *enrichment pipeline*, in which it applies *document cracking* to extract the contents of the source documents and applies incremental steps to create a hierarchical (JSON-based) document with the required fields for the index definition.
 
-The result is a populated index which can be queried to return specified fields from documents that match the query criteria.
+The result is a populated index, which can be queried to return specified fields from documents that match the query criteria.
 
 ## How documents are constructed during indexing
 

learn-pr/wwl-data-ai/ai-knowldge-mining/includes/4-ai-skills.md

@@ -18,7 +18,7 @@ To use the built-in skills, your indexer must have access to an Azure AI service
 
 ## Custom skills
 
-You can further extend the enrichment capabilities of your index by creating *custom* skills. As the name suggests, custom skills perform custom logic on input data from your index document to return new field values that can be incorporated into the index. Often, custom skills are "wrappers" around services that are specifically designed to extract data from documents. For example, you could implement a custom skill as an Azure Function, and use it to pass data from your index document to an Azure AI Document Intelligence model which can extract fields from a form.
+You can further extend the enrichment capabilities of your index by creating *custom* skills. As the name suggests, custom skills perform custom logic on input data from your index document to return new field values that can be incorporated into the index. Often, custom skills are "wrappers" around services that are specifically designed to extract data from documents. For example, you could implement a custom skill as an Azure Function, and use it to pass data from your index document to an Azure AI Document Intelligence model, which can extract fields from a form.
 
 > [!TIP]
 > To learn mode about using custom skills with Azure AI Search, see **[Add a custom skill to an Azure AI Search enrichment pipeline](/azure/search/cognitive-search-custom-skill-interface)** in the Azure AI Search documentation.

learn-pr/wwl-data-ai/ai-knowldge-mining/includes/5-search-index.md

@@ -11,11 +11,11 @@ Each index field can be configured with the following attributes:
 - **facetable**: Fields that can be used to determine values for *facets* (user interface elements used to filter the results based on a list of known field values).
 - **retrievable**: Fields that can be included in search results (*by default, all fields are retrievable unless this attribute is explicitly removed*).
 
-## Full text search
+## Full-text search
 
-While you could retrieve index entries based on simple field value matching, most search solutions use *full text search* semantics to query an index.
+While you could retrieve index entries based on simple field value matching, most search solutions use *full-text search* semantics to query an index.
 
-Full text search describes search solutions that parse text-based document contents to find query terms. Full text search queries in Azure AI Search are based on the *Lucene* query syntax, which provides a rich set of query operations for searching, filtering, and sorting data in indexes. Azure AI Search supports two variants of the Lucene syntax:
+Full-text search describes search solutions that parse text-based document contents to find query terms. Full-text search queries in Azure AI Search are based on the *Lucene* query syntax, which provides a rich set of query operations for searching, filtering, and sorting data in indexes. Azure AI Search supports two variants of the Lucene syntax:
 
 - **Simple** - An intuitive syntax that makes it easy to perform basic searches that match literal query terms submitted by a user.
 - **Full** - An extended syntax that supports complex filtering, regular expressions, and other more sophisticated queries.

learn-pr/wwl-data-ai/ai-knowldge-mining/includes/9-summary.md

@@ -1,4 +1,4 @@
-In this module, you've learned how Azure AI Search enables you to build intelligent search and knowledge mining solutions by indexing and enriching data from a variety of sources. You explored the indexing process, the use of AI skills for data enrichment, and how to persist enriched data in a knowledge store for further analysis and integration.
+In this module, you've learned how Azure AI Search enables you to build intelligent search and knowledge mining solutions by indexing and enriching data from various sources. You explored the indexing process, the use of AI skills for data enrichment, and how to persist enriched data in a knowledge store for further analysis and integration.
 
 With these skills, you're now equipped to design and implement solutions that unlock valuable insights from your data using Azure AI Search.