Using new linkspector

README.md

@@ -61,7 +61,7 @@ This has two version of the solution:
 
 This project welcomes contributions and suggestions. Most contributions require you to agree to a
 Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
-the rights to use your contribution. For details, visit [https://cla.opensource.microsoft.com](https://cla.opensource.microsoft.com/broken-link).
+the rights to use your contribution. For details, visit [https://cla.opensource.microsoft.com](https://cla.opensource.microsoft.com/).
 
 When you submit a pull request, a CLA bot will automatically determine whether you need to provide
 a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions

docs/parking_sensors_troubleshooting.md

@@ -124,7 +124,7 @@ Steps:
 
 1. Verify: echo the env variables that you set previously, if you can get the values that you set in PS/CMD session in WSL session, then it means successfully setup shareable environment variables between WSL and Windows environments.
 
-Reference: [Share Environment Vars between WSL and Windows](https://devblogs.microsoft.com/commandline/share-environment-vars-between-wsl-and-windows/broken-link)
+Reference: [Share Environment Vars between WSL and Windows](https://devblogs.microsoft.com/commandline/share-environment-vars-between-wsl-and-windows/)
 
 ## Release pipeline fails on rerun with: "ERROR: A workspace package with name already exists."
 

e2e_samples/parking_sensors/README.md

@@ -134,7 +134,7 @@ The following summarizes key learnings and best practices demonstrated by this s
 ### 7. Monitor infrastructure, pipelines and data
 
 - A proper monitoring solution should be in-place to ensure failures are identified, diagnosed and addressed in a timely manner. Aside from the base infrastructure and pipeline runs, data quality should also be monitored. A common area that should have data monitoring is the malformed record store.
-- As an example this repository showcases how to use open source framework [Great Expectations](https://docs.greatexpectations.io/docs/) to define, measure and report data quality metrics at different stages of the data pipeline. Captured Data Quality metrics are reported to Azure Monitor for further visualizing and alerting. Take a look at sample [Data Quality report](docs/images/data_quality_report.png) generated with Azure Monitor workbook. Great Expectations can be configured to generate HTML reports and host directly as static site on Azure Blob Storage. Read more on [How to host and share Data Docs on Azure Blob Storage](https://docs.greatexpectations.io/docs/oss/guides/setup/configuring_data_docs/host_and_share_data_docs/broken-link).
+- As an example this repository showcases how to use open source framework [Great Expectations](https://docs.greatexpectations.io/docs/) to define, measure and report data quality metrics at different stages of the data pipeline. Captured Data Quality metrics are reported to Azure Monitor for further visualizing and alerting. Take a look at sample [Data Quality report](docs/images/data_quality_report.png) generated with Azure Monitor workbook. Great Expectations can be configured to generate HTML reports and host directly as static site on Azure Blob Storage. Read more on [How to host and share Data Docs on Azure Blob Storage](https://docs.greatexpectations.io/docs/oss/guides/setup/configuring_data_docs/host_and_share_data_docs/).
 
 ## Key Concepts