Fixed blocking issues

Author: HeidiSteen

articles/search/cognitive-search-concept-intro.md

@@ -15,7 +15,7 @@ ms.custom: references_regions
 
 In Azure Cognitive Search, AI enrichment refers to a pipeline process that adds machine learning to [indexer-based indexing](search-indexer-overview.md). Steps in the pipeline create new information where none previously existed: extracting information from images, detecting sentiment or key phrases from chunks of text, and recognizing entities, to name a few. All of these processes result in making previously unsearchable content available to full text search and knowledge mining scenarios.
 
-[**Azure Blob Storage**(../storage/blobs/storage-blobs-overview.md) is the most commonly used input, but any indexer-supported data source can provide the initial content. A [**skillset**](cognitive-search-working-with-skillsets.md), attached to an indexer, adds the AI processing. The indexer extracts content and sets up the pipeline, while the skillset identifies, analyzes, and creates new information out of blobs, images, and raw text. Output is a [**search index**](search-what-is-an-index.md) or optional [**knowledge store**](knowledge-store-concept-intro.md).
+[**Azure Blob Storage**](../storage/blobs/storage-blobs-overview.md) is the most commonly used input, but any indexer-supported data source can provide the initial content. A [**skillset**](cognitive-search-working-with-skillsets.md), attached to an indexer, adds the AI processing. The indexer extracts content and sets up the pipeline, while the skillset identifies, analyzes, and creates new information out of blobs, images, and raw text. Output is a [**search index**](search-what-is-an-index.md) or optional [**knowledge store**](knowledge-store-concept-intro.md).
 
 ![Enrichment pipeline diagram](./media/cognitive-search-intro/cogsearch-architecture.png "enrichment pipeline overview")
 

articles/search/search-howto-indexing-azure-tables.md

@@ -78,19 +78,19 @@ For more information on storage shared access signatures, see [Using shared acce
 
 1. [Create or update an index](/rest/api/searchservice/create-index) to define search fields that will store content from entities:
 
-```http
-POST https://[service name].search.windows.net/indexes?api-version=2020-06-30
-Content-Type: application/json
-api-key: [admin key]
-
-{
-        "name" : "my-target-index",
-        "fields": [
-        { "name": "key", "type": "Edm.String", "key": true, "searchable": false },
-        { "name": "SomeColumnInMyTable", "type": "Edm.String", "searchable": true }
-        ]
-}
-```
+    ```http
+    POST https://[service name].search.windows.net/indexes?api-version=2020-06-30
+    Content-Type: application/json
+    api-key: [admin key]
+    
+    {
+            "name" : "my-target-index",
+            "fields": [
+            { "name": "key", "type": "Edm.String", "key": true, "searchable": false },
+            { "name": "SomeColumnInMyTable", "type": "Edm.String", "searchable": true }
+            ]
+    }
+    ```
 
 1. Check for field correspondence between entity fields and search fields. If names and types don't match, [add field mappings](search-indexer-field-mappings.md) to the indexer definition to ensure the source-to-destination path is clear.