AZ-400 Question 25
Select 2You lead a development team that uses Azure DevOps to build and deploy a microservices application. Your stakeholders want to measure several key metrics: average lead time from code commit to production, the number of automated test runs per sprint, and the pass/fail rate of those tests. You plan to create queries and dashboards to display these metrics. Which two actions should you take to implement these metrics effectively?
- A
Configure Analytics views in Azure DevOps to track changes from commit to deployment, and reference these views in a custom dashboard widget to calculate lead time.
- B
Build new work item types to store deployment data in each sprint and manually update them when releases are completed.
- C
Use built-in test result widgets or Analytics views to gather automated testing data, including pass/fail rates, and display them on a shared dashboard.
- D
Export pipeline logs to a third-party solution each time a deployment completes, then manually calculate average lead time using external reporting tools.
Show answer and explanation
Correct answers: A, C
Explanation
Azure DevOps provides built-in Analytics views and dashboard widgets, making it straightforward to retrieve data on pipeline runs, work item changes, and test results. By leveraging these native features, you can automate calculations such as lead time and test pass rates, which reduces manual effort and improves accuracy. Refer to Microsoft documentation on Azure DevOps Analytics (https://learn.microsoft.com/azure/devops/report/analytics/overview) for best practices on designing metrics queries and dashboards that effectively track development progress.
- A. Correct.
Correct. Using Analytics views in Azure DevOps is a recommended approach to track pipeline and work item data automatically. By referencing these views in a custom dashboard widget, you can calculate average lead time from code commit to production without requiring manual intervention.
- B. Incorrect.
Incorrect. Creating new work item types to store deployment data manually is cumbersome and error-prone. Azure DevOps provides native tools (Analytics and pipelines data) to automate this process.
- C. Correct.
Correct. Built-in widgets or Analytics views for test results in Azure DevOps allow you to automatically gather metrics on test pass/fail rates. These widgets can be added to a team dashboard, making the data visible and actionable.
- D. Incorrect.
Incorrect. Although exporting pipeline logs to a third-party solution is possible, it requires additional effort, manual calculations, and risks data latency or duplication. It is more efficient and accurate to use Azure DevOps Analytics or native widgets for metrics.