Merge pull request #125322 from ArieHein/Spelling-54

Court72 · web-flow · commit 992470d37029 · 2024-12-30T08:41:10.000-07:00
Spelling Fixes
diff --git a/articles/governance/machine-configuration/how-to/create-policy-definition.md b/articles/governance/machine-configuration/how-to/create-policy-definition.md
@@ -116,14 +116,14 @@ Parameters of the `New-GuestConfigurationPolicy` cmdlet:
 - **Category**: Sets the category metadata field in the policy definition.
 - **LocalContentPath**: The path to the local copy of the `.zip` Machine Configuration package
   file. This parameter is required if you're using a User Assigned Managed Identity to provide
-  access to an Azure Storge blob.
+  access to an Azure Storage blob.
 - **ManagedIdentityResourceId**: The `resourceId` of the User Assigned Managed Identity that has
   read access to the Azure Storage blob containing the `.zip` Machine Configuration package file.
   This parameter is required if you're using a User Assigned Managed Identity to provide access to
-  an Azure Storge blob.
+  an Azure Storage blob.
 - **ExcludeArcMachines**: Specifies that the Policy definition should exclude Arc machines. This
   parameter is required if you are using a User Assigned Managed Identity to provide access to an
-  Azure Storge blob.
+  Azure Storage blob.
 
 > [!IMPORTANT]
 > Unlike Azure VMs, Arc-connected machines currently do not support User Assigned Managed
@@ -189,7 +189,7 @@ New-GuestConfigurationPolicy @PolicyConfig3 -ExcludeArcMachines
 ```
 
 > [!NOTE]
-> You can retrieve the resorceId of a managed identity using the `Get-AzUserAssignedIdentity`
+> You can retrieve the resourceId of a managed identity using the `Get-AzUserAssignedIdentity`
 > PowerShell cmdlet.
 
 The cmdlet output returns an object containing the definition display name and path of the policy
diff --git a/articles/governance/machine-configuration/whats-new/psdsc-in-machine-configuration.md b/articles/governance/machine-configuration/whats-new/psdsc-in-machine-configuration.md
@@ -146,7 +146,7 @@ string value for the **Phrase** property.
 ```powershell
 $reasons = @()
 $reasons += @{
-  Code   = 'Name:Name:ReasonIdentifer'
+  Code   = 'Name:Name:ReasonIdentifier'
   Phrase = 'Explain why the setting is not compliant'
 }
 return @{
diff --git a/articles/governance/resource-graph/samples/alerts-samples.md b/articles/governance/resource-graph/samples/alerts-samples.md
@@ -39,7 +39,7 @@ This query filters virtual machines that need to be monitored.
 ```kusto
 let RuleGroupTags = dynamic(['Linux']);
 Perf | where ObjectName == 'Processor' and CounterName == '% Idle Time' and (InstanceName in ('Total','total'))
-| extend CpuUtilisation = (100 - CounterValue)   
+| extend CpuUtilization = (100 - CounterValue)   
 | join kind=inner hint.remote=left (arg("").Resources
     | where type =~ 'Microsoft.Compute/virtualMachines'
     | project _ResourceId=tolower(id), tags
diff --git a/articles/hdinsight-aks/required-outbound-traffic.md b/articles/hdinsight-aks/required-outbound-traffic.md
@@ -42,7 +42,7 @@ You need to configure the following network and application security rules in yo
 | ** FQDN|API Server FQDN (available once AKS cluster is created)|TCP|443|Network security rule| Required as the running pods/deployments use it to access the API Server. You can get this information from the AKS cluster running behind the cluster pool. For more information, see [how to get API Server FQDN](secure-traffic-by-firewall-azure-portal.md#get-aks-cluster-details-created-behind-the-cluster-pool) using Azure portal.|
 
 > [!NOTE]
-> ** This configiration isn't required if you enable private AKS. 
+> ** This configuration isn't required if you enable private AKS. 
 
 ## Cluster specific traffic
 
diff --git a/articles/hdinsight/hadoop/apache-hadoop-use-sqoop-mac-linux.md b/articles/hdinsight/hadoop/apache-hadoop-use-sqoop-mac-linux.md
@@ -38,7 +38,7 @@ Learn how to use Apache Sqoop to import and export between an Apache Hadoop clus
 
 
     export SERVER_CONNECT="jdbc:sqlserver://$SQL_SERVER.database.windows.net:1433;user=sqluser;password=$PASSWORD"
-    export SERVER_DB_CONNECT="jdbc:sqlserver://$SQL_SERVER.database.windows.net:1433;user=sqluser;password=$PASSWORD;database=$DABATASE"
+    export SERVER_DB_CONNECT="jdbc:sqlserver://$SQL_SERVER.database.windows.net:1433;user=sqluser;password=$PASSWORD;database=$DATABASE"
     ```
 
 ## Sqoop export
diff --git a/articles/hdinsight/hdinsight-administer-use-powershell.md b/articles/hdinsight/hdinsight-administer-use-powershell.md
@@ -86,13 +86,13 @@ $clusterName = "<HDInsight Cluster Name>"
 
 $clusterInfo = Get-AzHDInsightCluster -ClusterName $clusterName
 $storageInfo = $clusterInfo.DefaultStorageAccount.split('.')
-$defaultStoreageType = $storageInfo[1]
+$defaultStorageType = $storageInfo[1]
 $defaultStorageName = $storageInfo[0]
 
 echo "Default Storage account name: $defaultStorageName"
-echo "Default Storage account type: $defaultStoreageType"
+echo "Default Storage account type: $defaultStorageType"
 
-if ($defaultStoreageType -eq "blob")
+if ($defaultStorageType -eq "blob")
 {
     $defaultBlobContainerName = $cluster.DefaultStorageContainer
     $defaultStorageAccountKey = (Get-AzStorageAccountKey -ResourceGroupName $resourceGroupName -Name $defaultStorageAccountName)[0].Value
diff --git a/articles/hdinsight/hdinsight-apache-kafka-spark-structured-streaming.md b/articles/hdinsight/hdinsight-apache-kafka-spark-structured-streaming.md
@@ -237,7 +237,7 @@ This example demonstrates how to use Spark Structured Streaming with Kafka on HD
 1. Declare a schema. The following command demonstrates how to use a schema when reading JSON data from kafka. Enter the command in your next Jupyter cell.
 
     ```scala
-    // Import bits useed for declaring schemas and working with JSON data
+    // Import bits used for declaring schemas and working with JSON data
     import org.apache.spark.sql._
     import org.apache.spark.sql.types._
     import org.apache.spark.sql.functions._
diff --git a/articles/hdinsight/hdinsight-multiple-clusters-data-lake-store.md b/articles/hdinsight/hdinsight-multiple-clusters-data-lake-store.md
@@ -47,7 +47,7 @@ Some key points to consider.
 
     |Folder  |Permissions  |Owning user  |Owning group  | Named user | Named user permissions | Named group | Named group permissions |
     |---------|---------|---------|---------|---------|---------|---------|---------|
-    |/clusters/finanace/ fincluster01 | rwxr-x---  |Service Principal |FINGRP  |- |-  |-   |-  |
+    |/clusters/finance/ fincluster01  | rwxr-x---  |Service Principal |FINGRP  |- |-  |-   |-  |
 
 ## Recommendations for job input and output data
 
diff --git a/articles/hdinsight/hdinsight-release-notes-archive.md b/articles/hdinsight/hdinsight-release-notes-archive.md
@@ -1398,7 +1398,7 @@ This release applies for both HDInsight 3.6 and HDInsight 4.0. HDInsight release
 
 ### New features
 #### Auto key rotation for customer managed key encryption at rest
-Starting from this release, customers can use Azure KeyValut version-less encryption key URLs for customer managed key encryption at rest. HDInsight will automatically rotate the keys as they expire or replaced with new versions. Learn more details [here](./disk-encryption.md).
+Starting from this release, customers can use Azure KeyVault version-less encryption key URLs for customer managed key encryption at rest. HDInsight will automatically rotate the keys as they expire or replaced with new versions. Learn more details [here](./disk-encryption.md).
 
 #### Ability to select different Zookeeper virtual machine sizes for Spark, Hadoop, and ML Services
 HDInsight previously didn't support customizing Zookeeper node size for Spark, Hadoop, and ML Services cluster types. It defaults to A2_v2/A2 virtual machine sizes, which are provided free of charge. From this release, you can select a Zookeeper virtual machine size that is most appropriate for your scenario. Zookeeper nodes with virtual machine size other than A2_v2/A2 will be charged. A2_v2 and A2 virtual machines are still provided free of charge.
diff --git a/articles/hdinsight/spark/apache-spark-perf.md b/articles/hdinsight/spark/apache-spark-perf.md
@@ -93,7 +93,7 @@ In each of the following articles, you can find information on different aspects
 
 ### Optimize Spark SQL partitions
 
-- `spark.sql.shuffle.paritions` is 200 by default. We can adjust based on the business needs when shuffling data for joins or aggregations.
+- `spark.sql.shuffle.partitions` is 200 by default. We can adjust based on the business needs when shuffling data for joins or aggregations.
 - `spark.sql.files.maxPartitionBytes` is 1G by default in HDI. The maximum number of bytes to pack into a single partition when reading files. This configuration is effective only when using file-based sources such as Parquet, JSON and ORC.
 - AQE in Spark 3.0. See [Adaptive Query Execution](https://spark.apache.org/docs/latest/sql-performance-tuning.html#adaptive-query-execution)
 
diff --git a/articles/hdinsight/spark/apache-spark-use-bi-tools.md b/articles/hdinsight/spark/apache-spark-use-bi-tools.md
@@ -100,9 +100,9 @@ The first steps in working with Spark are to connect to the cluster in Power BI
 
         :::image type="content" source="./media/apache-spark-use-bi-tools/apache-spark-bi-area-graph-sum.png " alt-text="Screenshot showing area graph sum." border="true":::
 
-        By default the visualization shows the sum for **ActualTemp** and **TargetTemp**. Select the down arrow next to **ActualTemp** and **TragetTemp** in the Visualizations pane, you can see **Sum** is selected.
+        By default the visualization shows the sum for **ActualTemp** and **TargetTemp**. Select the down arrow next to **ActualTemp** and **TargetTemp** in the Visualizations pane, you can see **Sum** is selected.
 
-    3. Select the down arrows next to **ActualTemp** and **TragetTemp** in the Visualizations pane, select **Average** to get an average of actual and target temperatures for each building.
+    3. Select the down arrows next to **ActualTemp** and **TargetTemp** in the Visualizations pane, select **Average** to get an average of actual and target temperatures for each building.
 
         :::image type="content" source="./media/apache-spark-use-bi-tools/apache-spark-bi-average-of-values.png " alt-text="Screenshot showing average of values." border="true":::
 
diff --git a/articles/healthcare-apis/azure-api-for-fhir/autoscale-azure-api-fhir.md b/articles/healthcare-apis/azure-api-for-fhir/autoscale-azure-api-fhir.md
@@ -75,7 +75,7 @@ The data size is one of several factors used in calculating the total throughput
 
 You should be able to see the Max data collection size over the time period selected. Change the "Time Range" if necessary, for example from "Last 30 minutes" to "Last 48 Hours".
 
-:::image type="content" source="media/cosmosdb/cosmosdb-collection-size.png" alt-text="Screenshot of cosdmosdb_collection_size":::
+:::image type="content" source="media/cosmosdb/cosmosdb-collection-size.png" alt-text="Screenshot of cosmosdb_collection_size":::
 
 Use the following formulas to calculate required RU/s.
 
diff --git a/articles/iot-edge/how-to-configure-multiple-nics.md b/articles/iot-edge/how-to-configure-multiple-nics.md
@@ -75,7 +75,7 @@ Once you successfully assign the virtual switch to the EFLOW VM, create a networ
         Add-EflowVmEndpoint -vSwitchName "{switchName}" -vEndpointName "{EndpointName}" -ip4Address "{staticIp4Address}" -ip4GatewayAddress "{gatewayIp4Address}" -ip4PrefixLength "{prefixLength}"
         ```
 
-        For example, if you wanted to create and assign the **OnlineEndpoint** endpoint with the external virtual switch named **OnlineExt**, and Static IP configurations (_ip4Address=192.168.0.103, ip4GatewayAddress=192.168.0.1, ip4PrefixLenght=24_) you should use the following command:
+        For example, if you wanted to create and assign the **OnlineEndpoint** endpoint with the external virtual switch named **OnlineExt**, and Static IP configurations (_ip4Address=192.168.0.103, ip4GatewayAddress=192.168.0.1, ip4PrefixLength=24_) you should use the following command:
         ```powershell
         Add-EflowVmEndpoint -vSwitchName "OnlineExt" -vEndpointName "OnlineEndpoint" -ip4Address "192.168.0.103" -ip4GatewayAddress "192.168.0.1" -ip4PrefixLength "24"
         ```
diff --git a/articles/iot-edge/how-to-configure-proxy-support.md b/articles/iot-edge/how-to-configure-proxy-support.md
@@ -323,7 +323,7 @@ With the environment variables included, your module definition should look like
 }
 ```
 
-If you included the **UpstreamProtocol** environment variable in the confige.yaml file on your IoT Edge device, add that to the IoT Edge agent module definition too.
+If you included the **UpstreamProtocol** environment variable in the config.yaml file on your IoT Edge device, add that to the IoT Edge agent module definition too.
 
 ```json
 "env": {
diff --git a/articles/iot-edge/how-to-share-windows-folder-to-vm.md b/articles/iot-edge/how-to-share-windows-folder-to-vm.md
@@ -45,7 +45,7 @@ Before starting with the adding and removing share mechanisms, let's define four
 The following steps provide example EFLOW PowerShell commands to share one or more Windows host OS folders with the EFLOW virtual machine. 
 
 >[!NOTE]
->If you're using Windows 10, ensure to reboot your Windows host OS after your fresh MSI instlalation or update before adding the Windows shared folders to the EFLOW VM.
+>If you're using Windows 10, ensure to reboot your Windows host OS after your fresh MSI installation or update before adding the Windows shared folders to the EFLOW VM.
 
 1. Start by creating a new root shared folder. Go to **File Explorer** and choose a location for the *root folder* and create the folder. 
 
diff --git a/articles/iot-operations/deploy-iot-ops/howto-prepare-cluster.md b/articles/iot-operations/deploy-iot-ops/howto-prepare-cluster.md
@@ -192,7 +192,7 @@ Connect your cluster to Azure Arc so that it can be managed remotely.
 
 1. Save the file and exit the nano editor.
 
-1. Prepare for enabling the Azure Arc service, custom location, on your Arc cluster by getting the custom location object ID and saving it as the environment variable, OBJECT_ID. You must be logged into Azure CLI with a Microsoft Entra user account to sucessfully run the command, not a service principal. Run the following command **exactly as written**, without changing the GUID value. 
+1. Prepare for enabling the Azure Arc service, custom location, on your Arc cluster by getting the custom location object ID and saving it as the environment variable, OBJECT_ID. You must be logged into Azure CLI with a Microsoft Entra user account to successfully run the command, not a service principal. Run the following command **exactly as written**, without changing the GUID value. 
 
    ```azurecli
    export OBJECT_ID=$(az ad sp show --id bc313c14-388c-4e7d-a58e-70017303ee3b --query id -o tsv)

Original file line number	Diff line number	Diff line change
`@@ -146,7 +146,7 @@ string value for the Phrase property.`
`146`	`146`	```powershell
`147`	`147`	`$reasons = @()`
`148`	`148`	`$reasons += @{`
`149`		`- Code = 'Name:Name:ReasonIdentifer'`
	`149`	`+ Code = 'Name:Name:ReasonIdentifier'`
`150`	`150`	`Phrase = 'Explain why the setting is not compliant'`
`151`	`151`	`}`
`152`	`152`	`return @{`