Merge pull request #112760 from craigcaseyMSFT/vcraic0425a

PRMerger19 · web-flow · commit fb5c61d15551 · 2020-04-24T20:52:54.000-07:00
fix broken links from OPS report
diff --git a/articles/frontdoor/front-door-custom-domain-https.md b/articles/frontdoor/front-door-custom-domain-https.md
@@ -20,7 +20,7 @@ ms.author: sharadag
 
 This tutorial shows how to enable the HTTPS protocol for a custom domain that's associated with your Front Door under the frontend hosts section. By using the HTTPS protocol on your custom domain (for example, https:\//www.contoso.com), you ensure that your sensitive data is delivered securely via TLS/SSL encryption when it is sent across the internet. When your web browser is connected to a web site via HTTPS, it validates the web site's security certificate and verifies it's issued by a legitimate certificate authority. This process provides security and protects your web applications from attacks.
 
-Azure Front Door supports HTTPS on a Front Door default hostname, by default. For example, if you create a Front Door (such as https:\//contoso.azurefd.net), HTTPS is automatically enabled for requests made to https://contoso.azurefd.net. However, once you onboard the custom domain 'www.contoso.com' you will need to additionally enable HTTPS for this frontend host.   
+Azure Front Door supports HTTPS on a Front Door default hostname, by default. For example, if you create a Front Door (such as `https://contoso.azurefd.net`), HTTPS is automatically enabled for requests made to `https://contoso.azurefd.net`. However, once you onboard the custom domain 'www.contoso.com' you will need to additionally enable HTTPS for this frontend host.   
 
 Some of the key attributes of the custom HTTPS feature are:
 
diff --git a/articles/iot-accelerators/iot-accelerators-remote-monitoring-customize-flyout.md b/articles/iot-accelerators/iot-accelerators-remote-monitoring-customize-flyout.md
@@ -130,7 +130,7 @@ If the web UI is not already running locally, run the following command in the r
 npm start
 ```
 
-The previous command runs the UI locally at [http://localhost:3000/dashboard](http://localhost:3000/dashboard). Navigate to the **Example** page and click **Open Flyout**.
+The previous command runs the UI locally at `http://localhost:3000/dashboard`. Navigate to the **Example** page and click **Open Flyout**.
 
 ## Next steps
 
diff --git a/articles/iot-accelerators/iot-accelerators-remote-monitoring-customize-grid.md b/articles/iot-accelerators/iot-accelerators-remote-monitoring-customize-grid.md
@@ -179,7 +179,7 @@ If the web UI is not already running locally, run the following command in the r
 npm start
 ```
 
-The previous command runs the UI locally at [http://localhost:3000/dashboard](http://localhost:3000/dashboard). Navigate to the **Example** page to see the grid display data from the service.
+The previous command runs the UI locally at `http://localhost:3000/dashboard`. Navigate to the **Example** page to see the grid display data from the service.
 
 ## Select rows
 
diff --git a/articles/iot-accelerators/iot-accelerators-remote-monitoring-customize-page.md b/articles/iot-accelerators/iot-accelerators-remote-monitoring-customize-page.md
@@ -139,7 +139,7 @@ npm install
 npm start
 ```
 
-The previous command runs the UI locally at [http://localhost:3000/dashboard](http://localhost:3000/dashboard).
+The previous command runs the UI locally at `http://localhost:3000/dashboard`.
 
 Without connecting your local instance of the web UI to a deployed instance of the solution accelerator, you see errors on the dashboard. These errors don't affect your ability to test your new page.
 
diff --git a/articles/iot-accelerators/iot-accelerators-remote-monitoring-customize-panel.md b/articles/iot-accelerators/iot-accelerators-remote-monitoring-customize-panel.md
@@ -87,7 +87,7 @@ If the web UI is not already running locally, run the following command in the r
 npm start
 ```
 
-The previous command runs the UI locally at [http://localhost:3000/dashboard](http://localhost:3000/dashboard). Navigate to the **Dashboard** page to view the new panel.
+The previous command runs the UI locally at `http://localhost:3000/dashboard`. Navigate to the **Dashboard** page to view the new panel.
 
 ## Next steps
 
diff --git a/articles/iot-accelerators/iot-accelerators-remote-monitoring-deploy-local-java.md b/articles/iot-accelerators/iot-accelerators-remote-monitoring-deploy-local-java.md
@@ -185,10 +185,10 @@ Each web service opens a Command Prompt window and web browser window. At the co
 
 To access the status of the services, go to the following URLs:
 
-* IoT-Hub Manager: [http://localhost:9002/v1/status](http://localhost:9002/v1/status)
-* Device Telemetry: [http://localhost:9004/v1/status](http://localhost:9004/v1/status)
-* config: [http://localhost:9005/v1/status](http://localhost:9005/v1/status)
-* storage-adapter: [http://localhost:9022/v1/status](http://localhost:9022/v1/status)
+* IoT-Hub Manager: `http://localhost:9002/v1/status`
+* Device Telemetry: `http://localhost:9004/v1/status`
+* config: `http://localhost:9005/v1/status`
+* storage-adapter: `http://localhost:9022/v1/status`
 
 ### Start the Stream Analytics job
 
@@ -208,7 +208,7 @@ npm install
 npm start
 ```
 
-When the **start** command is completed, your browser displays the page at the address [http://localhost:3000/dashboard](http://localhost:3000/dashboard). The errors on this page are expected. To view the application without errors, complete the following steps.
+When the **start** command is completed, your browser displays the page at the address `http://localhost:3000/dashboard`. The errors on this page are expected. To view the application without errors, complete the following steps.
 
 ### Configure and run Nginx
 
@@ -221,7 +221,7 @@ For more information about running Nginx, see [nginx for Windows](https://nginx.
 
 ### Connect to the dashboard
 
-To access the Remote Monitoring solution dashboard, go to http://localhost:9000 in your browser.
+To access the Remote Monitoring solution dashboard, go to `http://localhost:9000` in your browser.
 
 ## Clean up
 
diff --git a/articles/key-vault/general/access-behind-firewall.md b/articles/key-vault/general/access-behind-firewall.md
@@ -21,7 +21,7 @@ To access a key vault, your key vault client application has to access multiple
 
 * Authentication via Azure Active Directory (Azure AD).
 * Management of Azure Key Vault. This includes creating, reading, updating, deleting, and setting access policies through Azure Resource Manager.
-* Accessing and managing objects (keys and secrets) stored in Key Vault itself, going through the Key Vault-specific endpoint (for example, [https://yourvaultname.vault.azure.net](https://yourvaultname.vault.azure.net)).  
+* Accessing and managing objects (keys and secrets) stored in Key Vault itself, going through the Key Vault-specific endpoint (for example, `https://yourvaultname.vault.azure.net`).  
 
 Depending on your configuration and environment, there are some variations.
 
diff --git a/articles/machine-learning/data-science-virtual-machine/reference-ubuntu-vm.md b/articles/machine-learning/data-science-virtual-machine/reference-ubuntu-vm.md
@@ -50,7 +50,7 @@ Some example notebooks are available in JupyterHub.
 
 H2O is a fast, in-memory, distributed machine learning and predictive analytics platform. A Python package is installed in both the root and py35 Anaconda environments. An R package is also installed. 
 
-To open H2O from the command line, run `java -jar /dsvm/tools/h2o/current/h2o.jar`. There are various [command-line options](http://docs.h2o.ai/h2o/latest-stable/h2o-docs/starting-h2o.html#from-the-command-line) that you might want to configure. You can access the Flow web UI by browsing to http://localhost:54321 to get started. Sample notebooks are also available in JupyterHub.
+To open H2O from the command line, run `java -jar /dsvm/tools/h2o/current/h2o.jar`. There are various [command-line options](http://docs.h2o.ai/h2o/latest-stable/h2o-docs/starting-h2o.html#from-the-command-line) that you might want to configure. You can access the Flow web UI by browsing to `http://localhost:54321` to get started. Sample notebooks are also available in JupyterHub.
 
 ### Keras
 
@@ -64,7 +64,7 @@ MXNet is a deep learning framework designed for both efficiency and flexibility.
 
 The NVIDIA Deep Learning GPU Training System, known as DIGITS, is a system to simplify common deep learning tasks. These tasks include managing data, designing and training neural networks on GPU systems, and monitoring performance in real time with advanced visualization.
 
-DIGITS is available as a service called *digits*. Start the service and browse to http://localhost:5000 to get started.
+DIGITS is available as a service called *digits*. Start the service and browse to `http://localhost:5000` to get started.
 
 DIGITS is also installed as a Python module in the Conda root environment.
 
@@ -135,7 +135,7 @@ If you're on a graphical interface or have X11 forwarding set up, you can enter
 
 The Anaconda distribution also comes with a Jupyter notebook, an environment to share code and analysis. The Jupyter notebook is accessed through JupyterHub. You sign in by using your local Linux username and password.
 
-The Jupyter notebook server has been pre-configured with Python 2, Python 3, and R kernels. Use the **Jupyter Notebook** desktop icon to open the browser and access the notebook server. If you're on the VM via SSH or the X2Go client, you can also access the Jupyter notebook server at [https://localhost:8000/](https://localhost:8000/).
+The Jupyter notebook server has been pre-configured with Python 2, Python 3, and R kernels. Use the **Jupyter Notebook** desktop icon to open the browser and access the notebook server. If you're on the VM via SSH or the X2Go client, you can also access the Jupyter notebook server at `https://localhost:8000/`.
 
 > [!NOTE]
 > Continue if you get any certificate warnings.
diff --git a/articles/machine-learning/team-data-science-process/spark-overview.md b/articles/machine-learning/team-data-science-process/spark-overview.md
@@ -139,7 +139,7 @@ You can launch the Jupyter Notebook from the Azure portal. Find your Spark clust
 
 ![Cluster dashboards](./media/spark-overview/spark-jupyter-on-portal.png)
 
-You can also browse to ***https://CLUSTERNAME.azurehdinsight.net/jupyter*** to access the Jupyter Notebooks. Replace the CLUSTERNAME part of this URL with the name of your own cluster. You need the password for your admin account to access the notebooks.
+You can also browse to ***`https://CLUSTERNAME.azurehdinsight.net/jupyter`*** to access the Jupyter Notebooks. Replace the CLUSTERNAME part of this URL with the name of your own cluster. You need the password for your admin account to access the notebooks.
 
 ![Browse Jupyter Notebooks](./media/spark-overview/spark-jupyter-notebook.png)
 
diff --git a/articles/media-services/latest/offline-plaready-streaming-for-windows-10.md b/articles/media-services/latest/offline-plaready-streaming-for-windows-10.md
@@ -57,13 +57,13 @@ Below are two sets of test assets, the first one using PlayReady license deliver
 
 Asset #1:
 
-* Progressive download URL: [https://willzhanmswest.streaming.mediaservices.windows.net/8d078cf8-d621-406c-84ca-88e6b9454acc/20150807-bridges-2500_H264_1644kbps_AAC_und_ch2_256kbps.mp4](https://willzhanmswest.streaming.mediaservices.windows.net/8d078cf8-d621-406c-84ca-88e6b9454acc/20150807-bridges-2500_H264_1644kbps_AAC_und_ch2_256kbps.mp4")
-* PlayReady LA_URL (AMS): [https://willzhanmswest.keydelivery.mediaservices.windows.net/PlayReady/](https://willzhanmswest.keydelivery.mediaservices.windows.net/PlayReady/)
+* Progressive download URL: [https://willzhanmswest.streaming.mediaservices.windows.net/8d078cf8-d621-406c-84ca-88e6b9454acc/20150807-bridges-2500_H264_1644kbps_AAC_und_ch2_256kbps.mp4](https://willzhanmswest.streaming.mediaservices.windows.net/8d078cf8-d621-406c-84ca-88e6b9454acc/20150807-bridges-2500_H264_1644kbps_AAC_und_ch2_256kbps.mp4)
+* PlayReady LA_URL (AMS): `https://willzhanmswest.keydelivery.mediaservices.windows.net/PlayReady/`
 
 Asset #2:
 
 * Progressive download URL: [https://willzhanmswest.streaming.mediaservices.windows.net/7c085a59-ae9a-411e-842c-ef10f96c3f89/20150807-bridges-2500_H264_1644kbps_AAC_und_ch2_256kbps.mp4](https://willzhanmswest.streaming.mediaservices.windows.net/7c085a59-ae9a-411e-842c-ef10f96c3f89/20150807-bridges-2500_H264_1644kbps_AAC_und_ch2_256kbps.mp4)
-* PlayReady LA_URL (on premises): [https://willzhan12.cloudapp.net/playready/rightsmanager.asmx](https://willzhan12.cloudapp.net/playready/rightsmanager.asmx)
+* PlayReady LA_URL (on premises): `https://willzhan12.cloudapp.net/playready/rightsmanager.asmx`
 
 For playback testing, we used a Universal Windows Application on Windows 10. In [Windows 10 Universal samples](https://github.com/Microsoft/Windows-universal-samples), there is a basic player sample called [Adaptive Streaming Sample](https://github.com/Microsoft/Windows-universal-samples/tree/master/Samples/AdaptiveStreaming). All we have to do is to add the code for us to pick downloaded video and use it as the source, instead of adaptive streaming source. The changes are in button click event handler:
 
