AZ-305 Practice Question: Design identity, governance, and monitoring solutions
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?
⚠ Common exam trap
Watch out — candidates often 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.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Enable Azure Monitor on all VMs using the Azure Monitor agent. Create metric alerts for high CPU and memory usage. Use Log Analytics to query and analyze historical performance data.
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.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable VM insights in Azure Monitor and use its live map and performance views for historical analysis.
Why it's wrong here
VM insights in Azure Monitor delivers interactive views of VM health and dependencies, but its live map and performance views are intended for real-time troubleshooting, not long-term historical trend analysis. To analyze past performance you'd need to query the Log Analytics workspace with KQL, which this option does not mention. Furthermore, VM insights does not by itself create proactive alerts; alert rules must be explicitly defined, so this approach lacks both critical capabilities required by the solution.
- ✗
Configure Azure Autoscale for the VMs based on CPU metrics and use Azure Monitor for logging.
Why it's wrong here
Configuring Azure Autoscale to scale VMs based on CPU metrics addresses infrastructure capacity changes but does nothing to analyze historical performance trends; autoscale rules only act on the current metric value. While Azure Monitor for logging can store operational data, simply logging without a Log Analytics query layer or alert rules cannot provide proactive notifications or trend analysis. This option conflates scaling actions with monitoring and analysis, leaving the mission-critical performance data unexamined.
- ✗
Deploy Application Insights on each VM and use its built-in alerts for performance.
Why it's wrong here
Application Insights is an application performance monitoring (APM) service that instruments the application code via SDKs or the Application Insights agent to track server-side application telemetry such as requests, dependencies, and exceptions. It does not collect VM-level guest OS metrics like overall CPU or memory utilization, which are the very metrics your solution requires. Adding Application Insights to each VM would monitor the app's behavior but would fail to provide alerts and historical analysis for the underlying virtual machine's resource consumption, so this option does not meet the stated monitoring requirements.
- ✓
Enable Azure Monitor on all VMs using the Azure Monitor agent. Create metric alerts for high CPU and memory usage. Use Log Analytics to query and analyze historical performance data.
Why this is correct
Enable the Azure Monitor agent on all VMs to collect guest OS performance counters (CPU, memory, disk) and forward them to a Log Analytics workspace. Metric alerts can then be configured on these performance counters for near-real-time proactive notification when thresholds are breached. The historical data in the workspace can be queried with Kusto Query Language to analyze long-term trends and capacity forecasting. This combination gives low-overhead, proactive alerting plus powerful historical analysis, and is the standard Azure Well-Architected approach for VM monitoring.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AZ-305 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AZ-305 exam.