Merge pull request #185065 from MicrosoftDocs/repo_sync_working_branch

Confirm merge from repo_sync_working_branch to master to sync with https://github.com/MicrosoftDocs/azure-docs (branch master)

Author: huypub

articles/machine-learning/how-to-troubleshoot-deployment.md

@@ -113,6 +113,12 @@ After the image is successfully built, the system attempts to start a container
 
 Use the info in the [Inspect the Docker log](how-to-troubleshoot-deployment-local.md#dockerlog) article.
 
+## Container azureml-fe-aci launch fails
+
+When deploying a service to an Azure Container Instance compute target, Azure Machine Learning attempts to create a front-end container that has the name `azureml-fe-aci` for the inference request. If `azureml-fe-aci` crashes, you can see logs by running `az container logs --name MyContainerGroup --resource-group MyResourceGroup --subscription MySubscription --container-name azureml-fe-aci`. You can follow the error message in the logs to make the fix. 
+
+The most common failure for `azureml-fe-aci` is that the provided SSL certificate or key is invalid.
+
 ## Function fails: get_model_path()
 
 Often, in the `init()` function in the scoring script, [Model.get_model_path()](/python/api/azureml-core/azureml.core.model.model#get-model-path-model-name--version-none---workspace-none-) function is called to locate a model file or a folder of model files in the container. If the model file or folder cannot be found, the function fails. The easiest way to debug this error is to run the below Python code in the Container shell:
@@ -227,4 +233,4 @@ Learn more about deployment:
 
 * [How to deploy and where](how-to-deploy-and-where.md)
 * [Tutorial: Train & deploy models](tutorial-train-deploy-notebook.md)
-* [How to run and debug experiments locally](./how-to-debug-visual-studio-code.md)
+* [How to run and debug experiments locally](./how-to-debug-visual-studio-code.md)

articles/machine-learning/tutorial-designer-automobile-price-train-score.md

@@ -100,7 +100,7 @@ There are several sample datasets included in the designer for you to experiment
 
 You can visualize the data to understand the dataset that you'll use.
 
-1. Right-click the **Automobile price data (Raw)** and select **Visualize** > **Dataset output**.
+1. Right-click the **Automobile price data (Raw)** and select **Preview Data**.
 
 1. Select the different columns in the data window to view information about each one.
 
@@ -281,7 +281,7 @@ Now that your pipeline is all setup, you can submit a pipeline run to train your
 
 After the run completes, you can view the results of the pipeline run. First, look at the predictions generated by the regression model.
 
-1. Right-click the **Score Model** component, and select **Visualize** > **Scored dataset** to view its output.
+1. Right-click the **Score Model** component, and select **Preview data** > **Scored dataset** to view its output.
 
     Here you can see the predicted prices and the actual prices from the testing data.
 
@@ -291,7 +291,7 @@ After the run completes, you can view the results of the pipeline run. First, lo
 
 Use the **Evaluate Model** to see how well the trained model performed on the test dataset.
 
-1. Right-click the **Evaluate Model** component and select **Visualize** > **Evaluation results** to view its output.
+1. Right-click the **Evaluate Model** component and select **Preview data** > **Evaluation results** to view its output.
 
 The following statistics are shown for your model:
 

articles/synapse-analytics/database-designer/concepts-database-templates.md

@@ -46,7 +46,7 @@ A foreign key is a column or a combination of columns whose values match a prima
 
 ## Composite key
 
-A composite key is one that is composed of two or more columns that are together required to uniquely identify a table. For example, in an Order table, both OrderNumber and ProductId may be required to uniquely identify a record.
+A composite key is one that is composed of two or more columns that are together required to uniquely identify a record in a table. For example, in an Order table, both OrderNumber and ProductId may be required to uniquely identify a record.
 
 ## Relationships
 

articles/synapse-analytics/get-started-analyze-spark.md

@@ -31,16 +31,19 @@ A serverless Spark pool is a way of indicating how a user wants to work with Spa
 ## Analyze NYC Taxi data with a Spark pool
 
 > [!NOTE]
-> Make sure you have [placed the sample data into the primary storage account](get-started-create-workspace.md#place-sample-data-into-the-primary-storage-account)
+> Make sure you have [placed the sample data in the primary storage account](get-started-create-workspace.md#place-sample-data-into-the-primary-storage-account).
+
+1. In Synapse Studio, go to the **Develop** hub.
+1. Create a new notebook.
+1. Create a new code cell and paste the following code in that cell:
 
-1. In Synapse Studio, go to the **Develop** hub
-2. Create a new Notebook
-3. Create a new code cell and paste the following code into that cell.
     ```py
     %%pyspark
     df = spark.read.load('abfss://[email protected]/NYCTripSmall.parquet', format='parquet')
     display(df.limit(10))
     ```
+
+1. Modify the load URI, so it references the sample file in your storage account according to the [abfss URI scheme](../storage/blobs/data-lake-storage-introduction-abfs-uri.md).
 1. In the notebook, in the **Attach to** menu, choose the **Spark1** serverless Spark pool that we created earlier.
 1. Select **Run** on the cell. Synapse will start a new Spark session to run this cell if needed. If a new Spark session is needed, initially it will take about two seconds to be created. 
 1. If you just want to see the schema of the dataframe run a cell with the following code:

articles/synapse-analytics/security/how-to-set-up-access-control.md

@@ -97,7 +97,7 @@ Identify the following information about your storage:
     | --- | --- |
     | Role | Storage Blob Data Contributor |
     | Assign access to |SERVICEPRINCIPAL |
-    | Members |workspace1_SynapseAdmins, workspace1_SynapseContributors, and workspace1_SynapseComputeOperators|
+    | Members |workspace1_SynapseAdministrators, workspace1_SynapseContributors, and workspace1_SynapseComputeOperators|
 
     ![Add role assignment page in Azure portal.](../../../includes/role-based-access-control/media/add-role-assignment-page.png)
 

articles/synapse-analytics/spark/apache-spark-secure-credentials-with-tokenlibrary.md

@@ -349,7 +349,7 @@ print(connection_string)
 ```csharp
 using Microsoft.Spark.Extensions.Azure.Synapse.Analytics.Utils;
 
-string connectionString = TokenLibrary.getSecret("<AZURE KEY VAULT NAME>", "<SECRET KEY>", "<LINKED SERVICE NAME>");
+string connectionString = TokenLibrary.GetSecret("<AZURE KEY VAULT NAME>", "<SECRET KEY>", "<LINKED SERVICE NAME>");
 Console.WriteLine(connectionString);
 ```
 

articles/synapse-analytics/sql-data-warehouse/what-is-a-data-warehouse-unit-dwu-cdwu.md

@@ -37,7 +37,7 @@ Increasing DWUs:
 
 - Linearly changes performance of the system for scans, aggregations, and CTAS statements
 - Increases the number of readers and writers for PolyBase load operations
-- Increases the maximum number of concurrent queries and concurrency slots.
+- Increases the maximum number of concurrent queries and concurrency slots
 
 ## Service Level Objective
 
@@ -67,8 +67,6 @@ Each performance tier uses a slightly different unit of measure for their data w
 
 Both DWUs and cDWUs support scaling compute up or down, and pausing compute when you don't need to use the data warehouse. These operations are all on-demand. Gen2 uses a local disk-based cache on the compute nodes to improve performance. When you scale or pause the system, the cache is invalidated and so a period of cache warming is required before optimal performance is achieved.
 
-Each SQL server (for example, myserver.database.windows.net) has a [Database Transaction Unit (DTU)](../../azure-sql/database/service-tiers-dtu.md) quota that allows a specific number of data warehouse units. For more information, see the [workload management capacity limits](sql-data-warehouse-service-capacity-limits.md#workload-management).
-
 ## Capacity limits
 
 Each SQL server (for example, myserver.database.windows.net) has a [Database Transaction Unit (DTU)](../../azure-sql/database/service-tiers-dtu.md?bc=%2fazure%2fsynapse-analytics%2fbreadcrumb%2ftoc.json&toc=%2fazure%2fsynapse-analytics%2ftoc.json) quota that allows a specific number of data warehouse units. For more information, see the [workload management capacity limits](sql-data-warehouse-service-capacity-limits.md#workload-management).