The answer is a log alert. This is correct because the condition requires querying Application Insights data to calculate the percentage of requests with a server response time exceeding 5 seconds within a 5-minute window, which demands aggregation and threshold evaluation beyond simple metric values. A log alert runs a Kusto query against the `requests` table, allowing you to filter, count, and compute the percentage of slow requests, then trigger when that percentage exceeds 10%. On the AZ-204 exam, this scenario tests your understanding of when to choose log alerts over metric alerts—a common trap is selecting a metric alert, which cannot perform percentage calculations or multi-step aggregations. Remember that any alert condition involving a ratio, percentage, or custom Kusto logic (like “more than 10% of requests”) is a strong signal for a log alert. Memory tip: if you need to “ask a question” of your log data, think “log alert.”
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. 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 using Application Insights to monitor a web application. You need to create an alert that triggers when the server response time exceeds 5 seconds for more than 10% of requests in a 5-minute window. Which type of Azure Monitor alert should you create?
Why wrong: Metric alerts track pre-aggregated metrics like average server response time. They cannot easily compute a ratio of requests exceeding a threshold versus total requests.
B
Log alert
Log alerts run a Kusto query that can group and count requests, calculate the percentage with duration > 5000 ms of total requests, and trigger when that percentage exceeds 10 over the evaluation period.
C
Activity log alert
Why wrong: Activity log alerts trigger on resource-level events (e.g., creation, deletion) and are not suitable for performance metric thresholds.
D
Application Insights smart detection alert
Why wrong: Smart detection uses machine learning to detect anomalies automatically but does not allow custom threshold definition.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Log alert
A log alert is correct because the condition involves querying Application Insights trace data to calculate the percentage of requests with a server response time exceeding 5 seconds within a 5-minute window. Log alerts run a Kusto query against the `requests` table, allowing aggregation and threshold evaluation (e.g., >10% of requests), which is not possible with simple metric 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.
✗
Metric alert
Why it's wrong here
Metric alerts track pre-aggregated metrics like average server response time. They cannot easily compute a ratio of requests exceeding a threshold versus total requests.
✓
Log alert
Why this is correct
Log alerts run a Kusto query that can group and count requests, calculate the percentage with duration > 5000 ms of total requests, and trigger when that percentage exceeds 10 over the evaluation period.
Related concept
Read the scenario before looking for a memorised answer.
✗
Activity log alert
Why it's wrong here
Activity log alerts trigger on resource-level events (e.g., creation, deletion) and are not suitable for performance metric thresholds.
✗
Application Insights smart detection alert
Why it's wrong here
Smart detection uses machine learning to detect anomalies automatically but does not allow custom threshold definition.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume a metric alert can handle percentage-based conditions, but metric alerts only support simple aggregations (e.g., average, count, max) and cannot compute a ratio of requests meeting a custom condition without a log query.
Detailed technical explanation
How to think about this question
Under the hood, a log alert in Application Insights runs a Kusto query against the `requests` table, using operators like `where duration > 5000` and `summarize` to count total and slow requests, then computes a percentage. The alert fires when the result of the query (e.g., `slow_percentage > 10`) meets the threshold over the evaluation period. This approach is essential for scenarios where you need to monitor percentile-based SLIs, such as 'p95 response time' or 'error budget burn rate', which cannot be expressed as a single static metric.
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
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AZ-204 question in full detail.
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: Log alert — A log alert is correct because the condition involves querying Application Insights trace data to calculate the percentage of requests with a server response time exceeding 5 seconds within a 5-minute window. Log alerts run a Kusto query against the `requests` table, allowing aggregation and threshold evaluation (e.g., >10% of requests), which is not possible with simple metric 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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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