The correct choice is an Azure Monitor metric alert on the dead-letter message count. This is the most efficient solution because Azure Monitor natively exposes the 'Dead-letter message count' metric for a Service Bus namespace or entity, allowing you to set a threshold-based alert that triggers automatically when the count exceeds 100—no Log Analytics queries or additional compute overhead required. On the AZ-305 exam, this scenario tests your ability to distinguish between native metric alerts and log-based alerts; a common trap is choosing a Log Analytics query alert, which adds latency and cost for real-time monitoring when a direct metric already exists. Remember that metric alerts are ideal for high-frequency, threshold-driven conditions like poison message detection, while log alerts are better for complex aggregations or cross-resource analysis. Memory tip: "Metric for the metric—if the metric exists, use the alert that matches it."
AZ-305 Practice Question: Design identity, governance, and monitoring solutions
This AZ-305 practice question tests your understanding of design identity, governance, and monitoring solutions. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
You are designing a monitoring solution for an Azure function app that processes messages from Azure Service Bus. The function app is critical and must be highly available. You need to monitor for poison messages and trigger an alert when the dead-letter queue count exceeds 100. What should you use?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Azure Monitor metric alert on the dead-letter message count
Azure Monitor metric alerts can directly monitor the 'Dead-letter message count' metric for a Service Bus namespace or entity. When this count exceeds 100, the alert triggers, enabling automated response to poison messages without additional query overhead. This is the most efficient and native monitoring solution for real-time threshold-based alerts on Service Bus metrics.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Azure Service Bus Explorer
Why it's wrong here
Service Bus Explorer is a management tool, not for alerts.
✓
Azure Monitor metric alert on the dead-letter message count
Why this is correct
Metric alerts can monitor the dead-letter queue count and trigger actions.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure Log Analytics workspace querying Service Bus logs
Why it's wrong here
Logs can be used, but metric alerts are more direct.
✗
Azure Application Insights availability tests
Why it's wrong here
Availability tests monitor web endpoints, not queue metrics.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may overthink and choose Log Analytics (Option C) for its querying flexibility, but the question specifically asks for a threshold-based alert on a single metric, which is exactly what Azure Monitor metric alerts are designed for.
Detailed technical explanation
How to think about this question
Azure Monitor collects Service Bus metrics at 1-minute granularity by default, including 'Dead-letter message count' for queues and subscriptions. A metric alert can be configured with a static threshold (e.g., >100) and an evaluation frequency as low as 1 minute, enabling near-real-time detection of poison message accumulation. This approach avoids the complexity and cost of Log Analytics ingestion, which is better suited for ad-hoc analysis or correlation across multiple resources.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AZ-305 question in full detail.
Design identity, governance, and monitoring solutions — This question tests Design identity, governance, and monitoring solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Azure Monitor metric alert on the dead-letter message count — Azure Monitor metric alerts can directly monitor the 'Dead-letter message count' metric for a Service Bus namespace or entity. When this count exceeds 100, the alert triggers, enabling automated response to poison messages without additional query overhead. This is the most efficient and native monitoring solution for real-time threshold-based alerts on Service Bus metrics.
What should I do if I get this AZ-305 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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