Merge pull request #106335 from MicrosoftDocs/repo_sync_working_branch

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

Author: huypub

articles/active-directory-b2c/custom-policy-configure-user-input.md

@@ -259,7 +259,7 @@ The following elements are used to define the claim:
 1. Sign in to the [Azure portal](https://portal.azure.com).
 2. Make sure you're using the directory that contains your Azure AD tenant by selecting the **Directory + subscription** filter in the top menu and choosing the directory that contains your Azure AD tenant.
 3. Choose **All services** in the top-left corner of the Azure portal, and then search for and select **App registrations**.
-4. Select **Identity Experience Framework (Preview)**.
+4. Select **Identity Experience Framework**.
 5. Select **Upload Custom Policy**, and then upload the two policy files that you changed.
 2. Select the sign-up or sign-in policy that you uploaded, and click the **Run now** button.
 3. You should be able to sign up using an email address.

articles/azure-monitor/learn/tutorial-resource-logs.md

@@ -41,7 +41,7 @@ A Log Analytics workspace in Azure Monitor collects and indexes log data from a
    - **Subscription**: Select the subscription to store the workspace. This does not need to be the same subscription same as the resource being monitored.
    - **Resource Group**: Select an existing resource group or click **Create new** to create a new one. This does not need to be the same resource group same as the resource being monitored.
    - **Location**: Select an Azure region or create a new one. This does not need to be the same location same as the resource being monitored.
-   - **Pricing tier**: Select *Free* which will retain 7 days of data. You can change this pricing tier later. Click the **Log Analytics pricing** link to learn more about different pricing tiers.
+   - **Pricing tier**: Select *Pay-as-you-go* as the pricing tier. You can change this pricing tier later. Click the **Log Analytics pricing** link to learn more about different pricing tiers.
 
     ![New workspace](media/tutorial-resource-logs/new-workspace.png)
 

articles/azure-monitor/platform/manage-access.md

@@ -84,6 +84,7 @@ if ($_.Properties.features.enableLogAccessUsingOnlyResourcePermissions -eq $null
 else
     { $_.Properties.features.enableLogAccessUsingOnlyResourcePermissions = $true }
 Set-AzResource -ResourceId $_.ResourceId -Properties $_.Properties -Force
+}
 ```
 
 ### Using a Resource Manager template

articles/cosmos-db/sql-api-nodejs-get-started.md

@@ -433,7 +433,7 @@ Azure Cosmos DB supports rich queries against JSON documents stored in each cont
         ]
     };
 
-    const { resources } = await client.database(databaseId).container(containerId).items.query(querySpec, {enableCrossPartitionQuery:true}).fetchAll();
+    const { resources } = await client.database(databaseId).container(containerId).items.query(querySpec).fetchAll();
     for (var queryResult of resources) {
         let resultString = JSON.stringify(queryResult);
         console.log(`\tQuery returned ${resultString}\n`);

articles/hdinsight/kafka/apache-kafka-producer-consumer-api.md

@@ -214,5 +214,5 @@ To remove the resource group using the Azure portal:
 
 In this document, you learned how to use the Apache Kafka Producer and Consumer API with Kafka on HDInsight. Use the following to learn more about working with Kafka:
 
-> [!div class="nextstepaction"]
-> [Analyze Apache Kafka logs](apache-kafka-log-analytics-operations-management.md)
+* [Use Kafka REST Proxy](rest-proxy.md)
+* [Analyze Apache Kafka logs](apache-kafka-log-analytics-operations-management.md)

articles/iot-edge/iot-edge-certs.md

@@ -64,7 +64,7 @@ Because manufacturing and operation processes are separated, consider the follow
 
 * With any certificate-based process, the root CA certificate and all intermediate CA certificates should be secured and monitored during the entire process of rolling out an IoT Edge device. The IoT Edge device manufacturer should have strong processes in place for proper storage and usage of their intermediate certificates. In addition, the device CA certificate should be kept in as secure storage as possible on the device itself, preferably a hardware security module.
 
-* The IoT Edge hub server certificate is presented by IoT Edge hub to the connecting client devices and modules. The common name (CN) of the device CA certificate **must not be** the same as the "hostname" that will be used in config.yaml on the IoT Edge device. The name used by clients to connect to IoT Edge (for example, via the GatewayHostName parameter of the connection string or the CONNECT command in MQTT) **can't be** the same as common name used in the device CA certificate. This restriction is because the IoT Edge hub presents its entire certificate chain for verification by clients. If the IoT Edge hub server certificate and the device CA certificate both have the same CN, you get in a verification loop and the certificate invalidates.
+* The IoT Edge hub server certificate is presented by IoT Edge hub to the connecting client devices and modules. The common name (CN) of the device CA certificate **must not be** the same as the "hostname" that will be used in config.yaml on the IoT Edge device. The name used by clients to connect to IoT Edge (for example, via the GatewayHostName parameter of the connection string or the CONNECT command in MQTT) **can't be** the same as the common name used in the device CA certificate. This restriction is because the IoT Edge hub presents its entire certificate chain for verification by clients. If the IoT Edge hub server certificate and the device CA certificate both have the same CN, you get in a verification loop and the certificate invalidates.
 
 * Because the device CA certificate is used by the IoT Edge security daemon to generate the final IoT Edge certificates, it must itself be a signing certificate, meaning it has certificate signing capabilities. Applying "V3 Basic constraints CA:True" to the device CA certificate automatically sets up the required key usage properties.
 

articles/machine-learning/data-science-virtual-machine/linux-dsvm-walkthrough.md

@@ -184,6 +184,8 @@ To deploy the decision tree code from the preceding section, sign in to Azure Ma
    ![The Azure Machine Learning Studio (classic) primary authorization token](./media/linux-dsvm-walkthrough/workspace-token.png)
 1. Load the **AzureML** package, and then set values of the variables with your token and workspace ID in your R session on the DSVM:
 
+        if(!require("devtools")) install.packages("devtools")
+        devtools::install_github("RevolutionAnalytics/AzureML")
         if(!require("AzureML")) install.packages("AzureML")
         require(AzureML)
         wsAuth = "<authorization-token>"
@@ -203,9 +205,23 @@ To deploy the decision tree code from the preceding section, sign in to Azure Ma
         return(colnames(predictDF)[apply(predictDF, 1, which.max)])
         }
 
+1. Create a settings.json file for this workspace:
+
+        vim ~/.azureml/settings.json
+
+1. Make sure the following contents are put inside settings.json:
+
+         {"workspace":{
+           "id": "<workspace-id>",
+           "authorization_token": "<authorization-token>",
+           "api_endpoint": "https://studioapi.azureml.net",
+           "management_endpoint": "https://management.azureml.net"
+         }
+
 
 1. Publish the **predictSpam** function to AzureML by using the **publishWebService** function:
 
+        ws <- workspace()
         spamWebService <- publishWebService(ws, fun = predictSpam, name="spamWebService", inputSchema = smallTrainSet, data.frame=TRUE)
 
 1. This function takes the **predictSpam** function, creates a web service named **spamWebService** that has defined inputs and outputs, and then returns information about the new endpoint.

articles/machine-learning/how-to-track-experiments.md

@@ -36,7 +36,7 @@ The following metrics can be added to a run while training an experiment. To vie
 |Lists|Function:<br>`run.log_list(name, value, description='')`<br><br>Example:<br>run.log_list("accuracies", [0.6, 0.7, 0.87]) | Log a list of values to the run with the given name.|
 |Row|Function:<br>`run.log_row(name, description=None, **kwargs)`<br>Example:<br>run.log_row("Y over X", x=1, y=0.4) | Using *log_row* creates a metric with multiple columns as described in kwargs. Each named parameter generates a column with the value specified.  *log_row* can be called once to log an arbitrary tuple, or multiple times in a loop to generate a complete table.|
 |Table|Function:<br>`run.log_table(name, value, description='')`<br><br>Example:<br>run.log_table("Y over X", {"x":[1, 2, 3], "y":[0.6, 0.7, 0.89]}) | Log a dictionary object to the run with the given name. |
-|Images|Function:<br>`run.log_image(name, path=None, plot=None)`<br><br>Example:<br>`run.log_image("ROC", plt)` | Log an image to the run record. Use log_image to log an image file or a matplotlib plot to the run.  These images will be visible and comparable in the run record.|
+|Images|Function:<br>`run.log_image(name, path=None, plot=None)`<br><br>Example:<br>`run.log_image("ROC", plot=plt)` | Log an image to the run record. Use log_image to log an image file or a matplotlib plot to the run.  These images will be visible and comparable in the run record.|
 |Tag a run|Function:<br>`run.tag(key, value=None)`<br><br>Example:<br>run.tag("selected", "yes") | Tag the run with a string key and optional string value.|
 |Upload file or directory|Function:<br>`run.upload_file(name, path_or_stream)`<br> <br> Example:<br>run.upload_file("best_model.pkl", "./model.pkl") | Upload a file to the run record. Runs automatically capture file in the specified output directory, which defaults to "./outputs" for most run types.  Use upload_file only when additional files need to be uploaded or an output directory is not specified. We suggest adding `outputs` to the name so that it gets uploaded to the outputs directory. You can list all of the files that are associated with this run record by called `run.get_file_names()`|
 

articles/storage/queues/storage-python-how-to-use-queue-storage.md

@@ -38,7 +38,7 @@ The [Azure Storage SDK for Python](https://github.com/azure/azure-storage-python
 To install via the Python Package Index (PyPI), type:
 
 ```bash
-pip install azure-storage-blob==2.1.0
+pip install azure-storage-queue==2.1.0
 ```
 
 > [!NOTE]