AZ-400 Designing and Implementing Microsoft DevOps Solutions Exam

Configure Azure Monitor and Log Analytics to integrate with DevOps tools

Azure Monitor and Log Analytics provide end-to-end visibility into application and infrastructure performance. Azure Monitor collects and analyzes telemetry across your Azure resources, while Log Analytics stores and processes large volumes of logs. By integrating these tools with DevOps pipelines, teams can identify issues faster and maintain consistent quality.

To integrate with DevOps tools, you link your Log Analytics workspace to Azure Boards and Azure Pipelines. This connection lets you surface logs and metrics directly in work items and release pipelines. You can configure diagnostic settings to route resource logs and metrics to the workspace.

Once data flows into Log Analytics, you use Kusto Query Language (KQL) to explore and analyze the information. Queries help teams pinpoint errors, track performance trends, and uncover root causes. Dashboards built on these queries give stakeholders real-time views into system health.

A key benefit of this integration is proactive monitoring. Teams receive alerts based on query results, enabling quick responses to failures. This approach drives continuous improvement and fosters reliable software delivery.

Configure collection of telemetry by using Application Insights, VMInsights, Container Insights, Storage Insights, and Network Insights

Telemetry collection lets you track how applications and infrastructure behave in production. Application Insights focuses on application performance and user behavior, capturing rich metrics like response times and dependency calls. Meanwhile, VM Insights gathers operating system and guest-level diagnostics from virtual machines.

Container Insights monitors containerized workloads in Kubernetes or Service Fabric, offering metrics on CPU usage, memory, and pod health. It visualizes container maps and node status to help with capacity planning. Similarly, Storage Insights collects data on capacity, latency, and transaction details for storage accounts.

For network resources, Network Insights uses Network Watcher to capture traffic flows, inspect packet captures, and generate topological maps. This collection highlights communication bottlenecks and security vulnerabilities. All Insights tools feed into the same Log Analytics workspace, enabling unified analysis.

After data is ingested, teams can apply retention policies and set up export to long-term storage. They build queries to correlate data across application, compute, and network layers. This holistic view supports informed decision-making and helps maintain robust systems.

Configure monitoring in GitHub, including enabling insights and creating and configuring charts

GitHub offers built-in monitoring features that help teams track repository health and workflow performance. By enabling GitHub Insights, you unlock analytics on pull requests, issue trends, and code review times. This visibility supports continuous improvement of development practices.

To start, go to the repository’s Insights tab and activate the metrics you need. You can track community activity, code frequency, and contributor statistics. Enabling these features helps your team understand collaboration patterns and identify process bottlenecks.

Creating and configuring charts in GitHub allows you to visualize trends such as open issues over time or average merge times. You use YAML or the GitHub UI to define chart types and data sources. Charts can be embedded in project boards or dashboards, making it easy for stakeholders to review performance at a glance.

Alerts in GitHub can also notify your team about unexpected changes in these metrics. By linking charts to notification rules, you ensure that anomalies trigger immediate action. This setup promotes data-driven development and faster resolution of process issues.

Configure alerts for events in GitHub Actions and Azure Pipelines

Setting up alerts helps teams respond quickly to build or deployment failures. In GitHub Actions, you configure notifications through workflow files and repository settings. You can send alerts via email, Slack, or Microsoft Teams when a job fails or a security scan detects vulnerabilities.

To define GitHub Action alerts, include the on: workflow_run or on: workflow_dispatch triggers in your YAML file. Then add notification steps using community actions or custom scripts. This approach ensures that failures or unexpected behavior are immediately surfaced to the right team members.

For Azure Pipelines, use service hooks or pipeline triggers in Azure DevOps to send alerts when builds succeed, fail, or when deployments are complete. You can also create Azure Monitor alerts that target pipeline logs and metrics. These alerts use conditions—like failure counts or duration thresholds—to fire notifications.

Best practices include grouping related alerts, setting clear severity levels, and linking alerts back to runbooks or knowledge base articles. This structure reduces alert fatigue and guides responders through standard remediation steps.

Conclusion

In this section, we covered how to integrate Azure Monitor and Log Analytics with DevOps tools to collect and analyze telemetry seamlessly. We saw how Application Insights, VM, container, storage, and network insights feed into a unified workspace for holistic monitoring.

We also explored enabling GitHub Insights, creating charts for repository metrics, and configuring alerts in both GitHub Actions and Azure Pipelines. These practices ensure teams receive timely notifications and maintain high software quality.

By mastering these monitoring and alerting techniques, DevOps teams can proactively detect issues, measure performance, and drive continuous improvements in their delivery pipelines.