Your company runs a mission-critical application on Azure Virtual Machines in a single region. You need to design a monitoring solution that provides proactive alerts for performance degradation and allows the operations team to analyze historical trends. The solution must minimize cost and operational overhead. You have an existing Log Analytics workspace. What should you include in the design?
This provides proactive alerts and historical analysis with low overhead.
Why this answer
Option D is correct because it uses the Azure Monitor agent to collect performance data from VMs, enabling metric alerts for proactive notification of high CPU and memory usage, while leveraging the existing Log Analytics workspace for cost-effective historical analysis. This approach minimizes operational overhead by using a single agent and native Azure Monitor features without additional services or complex configurations.
Exam trap
The trap here is that candidates may confuse VM insights (which offers rich visualizations but limited historical analysis) with the full monitoring solution required, or mistakenly think Application Insights is appropriate for VM-level performance monitoring when it is designed for application telemetry.
How to eliminate wrong answers
Option A is wrong because VM insights provides live map and performance views for real-time monitoring but is not designed for deep historical trend analysis, and its prebuilt performance charts have limited retention without Log Analytics. Option B is wrong because Azure Autoscale is for automatically scaling VM instances based on metrics, not for monitoring performance degradation or analyzing historical trends; it also does not address the requirement for proactive alerts and historical analysis. Option C is wrong because Application Insights is primarily for application-level monitoring (e.g., web apps, APIs) and requires instrumenting each application, which adds cost and complexity; it is not suitable for OS-level performance metrics like CPU and memory on VMs.