- A
Create a Smart Detection rule for anomalous failures.
Why wrong: Smart Detection analyzes telemetry over time and raises alerts for anomalies, but it does not allow you to set a fixed threshold like 5%. It is AI-based and not configurable with specific percentages.
- B
Create a metric alert on the 'Failed requests' metric with a threshold of 5%.
Why wrong: Metric alerts work on single metrics (e.g., count of failed requests), not on a ratio. To monitor percentage of failed requests, you need a log alert that calculates the ratio.
- C
Create a log alert using a Kusto query that calculates the percentage of failed requests over the last 5 minutes, with an alert condition when the result exceeds 0.05.
Log alerts allow complex queries. For example: 'requests | where timestamp > ago(5m) | summarize total=count(), failures=countif(success == false) | extend percent = failures * 100.0 / total | where percent > 5'. This triggers an alert when the condition is met.
- D
Create an availability test that checks for HTTP 200 responses and alert on failures.
Why wrong: Availability tests are for probing endpoints from external locations. They do not measure error rates within the application's traffic.
Quick Answer
The correct answer is to configure a log alert using a Kusto query. This is the only feature that allows you to calculate the exact percentage of failed requests over a rolling 5-minute window and trigger when that percentage exceeds 0.05, meeting the requirement for a dynamic, percentage-based threshold rather than an absolute count. Metric alerts on 'Failed requests' measure raw numbers, not rates, and Smart Detection lacks custom percentage controls, making the Kusto query essential for this scenario. On the AZ-204 exam, this tests your understanding of how to translate a business requirement—like "alert when error rate exceeds 5%"—into the correct Application Insights feature, with a common trap being to confuse metric alerts (which track counts) with log alerts (which handle calculations). Remember the tip: "Log alerts love percentages; metric alerts count absolutes."
AZ-204 Practice Question: Monitor, troubleshoot, and optimize Azure solutions
This AZ-204 practice question tests your understanding of monitor, troubleshoot, and optimize azure solutions. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 monitoring a web application with Application Insights. The application occasionally returns HTTP 500 errors. You want to be notified immediately when the error rate exceeds 5% of all requests in a rolling 5-minute window. Which feature of Application Insights should you configure?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"immediately / without restart"Why it matters: Time or reboot constraint — the correct answer must take effect right away without requiring a reboot or reload.
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
Create a log alert using a Kusto query that calculates the percentage of failed requests over the last 5 minutes, with an alert condition when the result exceeds 0.05.
Option C is correct because a log alert using a Kusto query allows you to calculate the exact percentage of failed requests over a rolling 5-minute window and trigger when that percentage exceeds 0.05 (5%). This is the only option that supports a dynamic, percentage-based threshold on a rolling time window, which is required for the stated condition. Metric alerts on 'Failed requests' measure absolute counts, not percentages, and Smart Detection does not allow custom percentage thresholds.
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.
- ✗
Create a Smart Detection rule for anomalous failures.
Why it's wrong here
Smart Detection analyzes telemetry over time and raises alerts for anomalies, but it does not allow you to set a fixed threshold like 5%. It is AI-based and not configurable with specific percentages.
- ✗
Create a metric alert on the 'Failed requests' metric with a threshold of 5%.
Why it's wrong here
Metric alerts work on single metrics (e.g., count of failed requests), not on a ratio. To monitor percentage of failed requests, you need a log alert that calculates the ratio.
- ✓
Create a log alert using a Kusto query that calculates the percentage of failed requests over the last 5 minutes, with an alert condition when the result exceeds 0.05.
Why this is correct
Log alerts allow complex queries. For example: 'requests | where timestamp > ago(5m) | summarize total=count(), failures=countif(success == false) | extend percent = failures * 100.0 / total | where percent > 5'. This triggers an alert when the condition is met.
Clue confirmation
The clue word "immediately / without restart" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Create an availability test that checks for HTTP 200 responses and alert on failures.
Why it's wrong here
Availability tests are for probing endpoints from external locations. They do not measure error rates within the application's traffic.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse metric alerts (which work on absolute counts or rates) with log alerts (which can compute custom ratios like percentages), leading them to choose Option B without realizing that the 'Failed requests' metric cannot be configured to alert on a percentage threshold.
Detailed technical explanation
How to think about this question
Under the hood, a log alert in Application Insights uses a Kusto query against the 'requests' table, where you can compute the failure percentage with an expression like `let total = count(); let failed = countif(success == false); failed / total`. The alert fires when the result exceeds 0.05, and the query is evaluated every minute over the last 5 minutes, giving a true rolling window. This approach is essential when you need to alert on a ratio rather than an absolute count, as absolute counts can be misleading during low-traffic periods.
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.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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Monitor, troubleshoot, and optimize Azure solutions — study guide chapter
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FAQ
Questions learners often ask
What does this AZ-204 question test?
Monitor, troubleshoot, and optimize Azure solutions — This question tests Monitor, troubleshoot, and optimize Azure solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Create a log alert using a Kusto query that calculates the percentage of failed requests over the last 5 minutes, with an alert condition when the result exceeds 0.05. — Option C is correct because a log alert using a Kusto query allows you to calculate the exact percentage of failed requests over a rolling 5-minute window and trigger when that percentage exceeds 0.05 (5%). This is the only option that supports a dynamic, percentage-based threshold on a rolling time window, which is required for the stated condition. Metric alerts on 'Failed requests' measure absolute counts, not percentages, and Smart Detection does not allow custom percentage thresholds.
What should I do if I get this AZ-204 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "immediately / without restart". Time or reboot constraint — the correct answer must take effect right away without requiring a reboot or reload.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jun 11, 2026
This AZ-204 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-204 exam.
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