checkpoint

Author: HeidiSteen

articles/search/media/search-synapseml-cognitive-services/cluster-green-dot.png

@@ -10,7 +10,7 @@ ms.service: azure-ai-search
 ms.custom:
   - ignite-2023
 ms.topic: tutorial
-ms.date: 04/22/2024
+ms.date: 01/23/2025
 ---
 
 # Tutorial: Index large data from Apache Spark using SynapseML and Azure AI Search
@@ -37,14 +37,14 @@ You need the `synapseml` library and several Azure resources. If possible, use t
 
 + [SynapseML package](https://microsoft.github.io/SynapseML/docs/Get%20Started/Install%20SynapseML/#python) <sup>1</sup> 
 + [Azure AI Search](search-create-service-portal.md) (any tier) <sup>2</sup> 
-+ [Azure AI services](/azure/ai-services/multi-service-resource?pivots=azportal) (any tier) <sup>3</sup> 
++ [Azure AI multi-service account](/azure/ai-services/multi-service-resource?pivots=azportal) (any tier) <sup>3</sup> 
 + [Azure Databricks](/azure/databricks/scenarios/quickstart-create-databricks-workspace-portal?tabs=azure-portal) (any tier) <sup>4</sup>
 
 <sup>1</sup> This link resolves to a tutorial for loading the package.
 
 <sup>2</sup> You can use the free search tier to index the sample data, but [choose a higher tier](search-sku-tier.md) if your data volumes are large. For billable tiers, provide the [search API key](search-security-api-keys.md#find-existing-keys) in the [Set up dependencies](#step-2-set-up-dependencies) step further on.
 
-<sup>3</sup> This tutorial uses Azure AI Document Intelligence and Azure AI Translator. In the instructions that follow, provide a [multi-service](/azure/ai-services/multi-service-resource?pivots=azportal) key and the region. The same key works for both services.
+<sup>3</sup> This tutorial uses Azure AI Document Intelligence and Azure AI Translator. In the instructions that follow, provide a [multi-service account](/azure/ai-services/multi-service-resource?pivots=azportal) key and the region. The same key works for both services.
 
 <sup>4</sup> In this tutorial, Azure Databricks provides the Spark computing platform. We used the [portal instructions](/azure/databricks/scenarios/quickstart-create-databricks-workspace-portal?tabs=azure-portal) to set up the workspace.
 
@@ -63,6 +63,10 @@ In this section, create a cluster, install the `synapseml` library, and create a
 
 1. Accept the default configuration. It takes several minutes to create the cluster.
 
+1. Verify the cluster is operational and running. A green dot by the cluster name confirms its status.
+
+   :::image type="content" source="media/search-synapseml-cognitive-services/cluster-green-dot.png" alt-text="Screenshot of a Data Bricks compute page with a green dot by the cluster name.":::
+
 1. Install the `synapseml` library after the cluster is created:
 
    1. Select **Libraries** from the tabs at the top of the cluster's page.
@@ -73,7 +77,7 @@ In this section, create a cluster, install the `synapseml` library, and create a
 
    1. Select **Maven**.
 
-   1. In Coordinates, enter `com.microsoft.azure:synapseml_2.12:1.0.4`
+   1. In Coordinates, search for or type `com.microsoft.azure:synapseml_2.12:1.0.9`
 
    1. Select **Install**.
 
@@ -85,15 +89,15 @@ In this section, create a cluster, install the `synapseml` library, and create a
 
 1. Give the notebook a name, select **Python** as the default language, and select the cluster that has the `synapseml` library.
 
-1. Create seven consecutive cells. Paste code into each one.
+1. Create seven consecutive cells. You use these to paste in code in the following sections.
 
    :::image type="content" source="media/search-synapseml-cognitive-services/create-seven-cells.png" alt-text="Screenshot of the notebook with placeholder cells." border="true":::
 
 ## Step 2: Set up dependencies
 
 Paste the following code into the first cell of your notebook. 
 
-Replace the placeholders with endpoints and access keys for each resource. Provide a name for a new search index. No other modifications are required, so run the code when you're ready.
+Replace the placeholders with endpoints and access keys for each resource. Provide a name for a new search index that's created for you. No other modifications are required, so run the code when you're ready.
 
 This code imports multiple packages and sets up access to the Azure resources used in this workflow.
 
@@ -103,12 +107,12 @@ from pyspark.sql.functions import udf, trim, split, explode, col, monotonically_
 from pyspark.sql.types import StringType
 from synapse.ml.core.spark import FluentAPI
 
-cognitive_services_key = "placeholder-cognitive-services-multi-service-key"
-cognitive_services_region = "placeholder-cognitive-services-region"
+cognitive_services_key = "placeholder-azure-ai-services-multi-service-key"
+cognitive_services_region = "placeholder-azure-ai-services-region"
 
 search_service = "placeholder-search-service-name"
-search_key = "placeholder-search-service-api-key"
-search_index = "placeholder-search-index-name"
+search_key = "placeholder-search-service-admin-api-key"
+search_index = "placeholder-for-new-search-index-name"
 ```
 
 ## Step 3: Load data into Spark