AZ-400 Designing and Implementing Microsoft DevOps Solutions Exam

Design and implement a dashboard, including flow of work, such as cycle times, time to recovery, and lead time

A dashboard gives teams a visual view of how work moves through the system. By collecting data on items like cycle time, time to recovery, and lead time, teams gain real-time insight into process efficiency. These metrics help spot bottlenecks early and guide decisions on improving flow.

Key flow-of-work metrics include:

• Cycle time: the time taken to complete a work item from start to finish
• Time to recovery: how long it takes to restore service after a failure
• Lead time: the duration from a request to its delivery

Using Azure DevOps, you can add widgets to a dashboard that pull data from work item queries, pipelines, and monitoring tools. By arranging charts and lists in one view, teams get an at-a-glance overview of both development health and service reliability.

Design and implement appropriate metrics and queries for project planning

Project planning metrics help teams predict delivery dates and manage scope. Tracking the number of work items, story points, and capacity provides a way to balance workload across sprints. These metrics inform whether a team can meet upcoming goals and where to adjust planning.

Common planning metrics include:

• Sprint burndown: remaining work over time
• Backlog health: number of items in each state
• Team capacity: available effort vs. planned tasks

In Azure Boards, you can create custom queries to filter work items by state, area path, or iteration. Then, embed query results in dashboards or export to Analytics views for deeper trend analysis. This data-driven approach helps teams refine their sprint processes and forecast more accurately.

Design and implement appropriate metrics and queries for development

Development metrics measure code quality and team productivity during software creation. Tracking pull request counts, code churn, and build success rates highlights where developers spend time and where automation can help. These metrics drive continuous improvement by showing which code areas need attention.

Key development metrics include:

• Pull request lead time: time from PR creation to merge
• Build success rate: percentage of successful automated builds
• Code churn: amount of code changed over a period

To capture these metrics in Azure DevOps, use Analytics views on repositories and pipelines. Queries can filter commits, PRs, and builds, providing charts that show trends in code stability and team performance. With this visibility, teams can pinpoint delays and streamline coding practices.

Design and implement appropriate metrics and queries for testing

Testing metrics indicate the quality and coverage of the application under test. By measuring test pass rate, defect density, and automated test duration, teams ensure they deliver stable and reliable software. These insights guide which tests to prioritize and where additional coverage is needed.

Important testing metrics include:

• Test pass rate: number of passed tests divided by total tests
• Defect density: defects found relative to lines of code
• Automated test duration: time spent running test suites

Azure Test Plans integrates with pipelines to collect these metrics automatically. You can query test runs and link them to work items, then display results in dashboards. Having consistent feedback on test health helps teams remediate failures quickly and maintain confidence in releases.

Design and implement appropriate metrics and queries for security

Security metrics bring DevSecOps practices into the pipeline by identifying vulnerabilities early. Tracking vulnerability counts, time to fix, and policy compliance helps maintain a strong security posture. These metrics also demonstrate how quickly the team addresses security issues.

Security-focused metrics include:

• Vulnerabilities found: number of issues flagged by scans
• Time to fix: average time to remediate a security flaw
• Compliance score: adherence to defined security standards

In Azure, you can use tools like Azure Security Center and GitHub Advanced Security to run code scans and policy checks. Queries on security results can feed dashboards, giving continuous visibility into risk areas. This ensures security is an integral part of the delivery process, not just an afterthought.

Design and implement appropriate metrics and queries for delivery

Delivery metrics track how often and how reliably code moves into production. Key indicators such as deployment frequency, change failure rate, and mean time to deploy reveal the efficiency of release pipelines. Collecting these metrics helps teams optimize their CI/CD processes.

Delivery metrics include:

• Deployment frequency: how often releases occur
• Change failure rate: percentage of deployments that cause issues
• Mean time to deploy: average time to complete a deployment

Using Azure Pipelines Analytics, you can query pipeline runs for duration, success, and failure rates. Visualizing these queries in dashboards highlights areas for automation and process improvement. By monitoring delivery performance, teams can reduce downtime and increase release confidence.

Design and implement appropriate metrics and queries for operations

Operations metrics focus on production reliability and system health. By monitoring availability, error rates, and resource utilization, teams gain visibility into live environments. These metrics aid in proactive maintenance and rapid response to incidents.

Essential operations metrics include:

• Availability: uptime percentage of services
• Error rate: frequency of failed requests
• Resource utilization: CPU, memory, and other resource usage

Azure Monitor and Application Insights collect telemetry from applications and infrastructure. You can write Log Analytics queries to filter events, performance counters, and traces. Displaying these results on operational dashboards ensures teams can detect anomalies and address them before they impact users.

Conclusion

In this section, we explored how to set up DevOps dashboards that track the flow of work using cycle time, lead time, and time to recovery. We then examined metrics and queries for each phase of development, from project planning through operations. By defining key indicators—such as sprint burndown, build success rate, test pass rate, vulnerability counts, deployment frequency, and service availability—teams gain actionable insights.

Using Azure DevOps tools like Boards, Repos, Pipelines, Test Plans, and Azure Monitor, you can implement these metrics and queries seamlessly. The resulting dashboards provide real-time visibility into process health, helping teams make data-driven decisions. Ultimately, designing and implementing the right metrics ensures continuous improvement across the DevOps lifecycle.