Question 921 of 1,170
Monitor and Maintain Azure ResourcesmediumMultiple ChoiceObjective-mapped

AZ-104 Monitor and Maintain Azure Resources Practice Question

This AZ-104 practice question tests your understanding of monitor and maintain azure resources. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.

Exhibit

Alert rule name: CPUSpikeAlert
Scope: /subscriptions/11111111-1111-1111-1111-111111111111/resourceGroups/RG-App/providers/Microsoft.Compute/virtualMachines/VM1
Condition: Average Percentage CPU > 80
Window size: 5 minutes
Evaluation frequency: 1 minute
Action group: Ops-Email
Recent alert history: 6 alerts between 23:10 and 23:25 during patching

Based on the exhibit, the operations team says the alert is too noisy because short CPU spikes after nightly maintenance trigger notifications. They want an alert only when VM1's average CPU stays above 80% for at least 10 minutes. What should you change?

Clue words in this question

Noticing these words before you look at the options changes how you read each choice.

  • Clue: "least"

    Why it matters: You want the option with minimum overhead, fewest steps, or lowest impact — not the most feature-rich or comprehensive answer.

Exhibit

Alert rule name: CPUSpikeAlert
Scope: /subscriptions/11111111-1111-1111-1111-111111111111/resourceGroups/RG-App/providers/Microsoft.Compute/virtualMachines/VM1
Condition: Average Percentage CPU > 80
Window size: 5 minutes
Evaluation frequency: 1 minute
Action group: Ops-Email
Recent alert history: 6 alerts between 23:10 and 23:25 during patching

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

Increase the window size to 10 minutes and keep the evaluation frequency at 1 minute.

Option B is correct because increasing the window size to 10 minutes while keeping the evaluation frequency at 1 minute means the alert will only fire when the average CPU over the last 10 minutes exceeds 80%. This filters out transient spikes from nightly maintenance, as the alert requires sustained high CPU for the full duration. The evaluation frequency of 1 minute ensures the alert is checked every minute, but the condition is based on the 10-minute rolling average.

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.

  • Lower the threshold to 70% so the alert becomes less sensitive.

    Why it's wrong here

    Reducing the threshold makes the alert fire sooner, not later. It does not prevent brief spikes from triggering notifications, so it does not solve the noise problem.

    When this WOULD be correct

    In a scenario where the operations team wants earlier warning of potential performance issues and is willing to accept more alerts, lowering the threshold could be correct. For example, if the goal is to detect any CPU usage above 70% to proactively address issues before they reach critical levels.

  • Increase the window size to 10 minutes and keep the evaluation frequency at 1 minute.

    Why this is correct

    A longer evaluation window requires CPU to remain elevated over a longer period before the rule triggers. That directly addresses short maintenance spikes while still checking frequently enough to detect sustained pressure.

    Clue confirmation

    The clue word "least" in the question point toward this answer.

    Related concept

    Read the scenario before looking for a memorised answer.

  • Replace the metric alert with a Log Analytics query alert against the activity log.

    Why it's wrong here

    CPU usage is a platform metric, so a metric alert is the simplest and most direct choice. An activity log query would not be the normal way to evaluate VM CPU behavior.

    When this WOULD be correct

    This option would be correct if the question asked for an alert based on a specific event in the activity log, such as when a VM is started or stopped, and the alert needed to trigger on that event rather than on performance metrics.

  • Move the alert scope from the VM to the resource group.

    Why it's wrong here

    Changing scope broadens what resources are watched, but it does not change how long CPU must stay high. The issue is threshold duration, not target scope.

    When this WOULD be correct

    If the question required monitoring the average CPU of all VMs in a resource group and alerting when the group-wide average exceeds a threshold, then moving the alert scope to the resource group would be correct.

Option-by-option analysis

Why each answer is right or wrong

Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The AZ-104 exam frequently reuses these exact scenarios with slightly different constraints.

Increase the window size to 10 minutes and keep the evaluation frequency at 1 minute.Correct answer

Why this is correct

A longer evaluation window requires CPU to remain elevated over a longer period before the rule triggers. That directly addresses short maintenance spikes while still checking frequently enough to detect sustained pressure.

Lower the threshold to 70% so the alert becomes less sensitive.Wrong answer — click to see why

Why this is wrong here

Lowering the threshold to 70% would make the alert more sensitive, not less, and does not address the requirement to filter out short spikes by requiring sustained high CPU for 10 minutes.

★ When this WOULD be the correct answer

In a scenario where the operations team wants earlier warning of potential performance issues and is willing to accept more alerts, lowering the threshold could be correct. For example, if the goal is to detect any CPU usage above 70% to proactively address issues before they reach critical levels.

Why candidates choose this

Candidates may mistakenly think that lowering the threshold reduces alert frequency, but in reality it increases sensitivity, leading to more alerts. They might also confuse 'threshold' with 'sensitivity' in a way that seems intuitive but is technically incorrect.

Replace the metric alert with a Log Analytics query alert against the activity log.Wrong answer — click to see why

Why this is wrong here

The question requires a metric-based alert for CPU spikes, not a Log Analytics query alert. Activity logs track operational events, not performance metrics like CPU usage, so this change would not address the noisy alert issue.

★ When this WOULD be the correct answer

This option would be correct if the question asked for an alert based on a specific event in the activity log, such as when a VM is started or stopped, and the alert needed to trigger on that event rather than on performance metrics.

Why candidates choose this

Candidates may think Log Analytics offers more flexibility for complex conditions, or they confuse activity log alerts with metric alerts, assuming a query can filter CPU data from logs.

Move the alert scope from the VM to the resource group.Wrong answer — click to see why

Why this is wrong here

Moving the alert scope to the resource group does not address the alert noise from short CPU spikes; it would aggregate metrics across all VMs in the group, potentially increasing noise rather than reducing it.

★ When this WOULD be the correct answer

If the question required monitoring the average CPU of all VMs in a resource group and alerting when the group-wide average exceeds a threshold, then moving the alert scope to the resource group would be correct.

Why candidates choose this

Candidates may think that broadening the scope reduces sensitivity by averaging metrics across multiple resources, but this does not filter out short spikes on a single VM.

Analysis generated from the official AZ-104blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates often confuse 'window size' with 'evaluation frequency' and think increasing the evaluation frequency alone would solve the noise, but it is the window size that controls the duration over which the metric must remain above the threshold.

Detailed technical explanation

How to think about this question

Azure Monitor metric alerts use a sliding window aggregation: the 'window size' defines the period over which the metric values are aggregated (e.g., average), and the 'evaluation frequency' controls how often the condition is evaluated. For CPU metrics, the underlying data is collected every 30 seconds by the Azure Diagnostics extension, and the alert engine resamples this into 1-minute granularity. By setting a 10-minute window, the alert requires 10 consecutive data points (or 20 if using 30-second granularity) to exceed the threshold, effectively ignoring brief spikes.

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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FAQ

Questions learners often ask

What does this AZ-104 question test?

Monitor and Maintain Azure Resources — This question tests Monitor and Maintain Azure Resources — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: Increase the window size to 10 minutes and keep the evaluation frequency at 1 minute. — Option B is correct because increasing the window size to 10 minutes while keeping the evaluation frequency at 1 minute means the alert will only fire when the average CPU over the last 10 minutes exceeds 80%. This filters out transient spikes from nightly maintenance, as the alert requires sustained high CPU for the full duration. The evaluation frequency of 1 minute ensures the alert is checked every minute, but the condition is based on the 10-minute rolling average.

What should I do if I get this AZ-104 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: "least". You want the option with minimum overhead, fewest steps, or lowest impact — not the most feature-rich or comprehensive answer.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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Last reviewed: Jun 11, 2026

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This AZ-104 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-104 exam.