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Managing application performance monitoring →easyMultiple ChoiceObjective-mapped

PCD Managing application performance monitoring Practice Question

This PCD practice question tests your understanding of managing application performance monitoring. 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

fetch gce_instance
| metric 'compute.googleapis.com/instance/cpu/utilization'
| group_by [zone], 1m [mean_cpu: mean(value.cpu_utilization)]
| every 1m
| condition mean_cpu > 0.8

Refer to the exhibit. A team is using Cloud Monitoring with MQL to alert on CPU utilization per zone. They notice that the alert fires even when no single instance in a zone has CPU>80%, because the average across instances in the zone exceeds 80%. What change should they make to the MQL query to alert only when any individual instance exceeds 80%?

Question 1easymultiple choice

Full question →

Exhibit

fetch gce_instance
| metric 'compute.googleapis.com/instance/cpu/utilization'
| group_by [zone], 1m [mean_cpu: mean(value.cpu_utilization)]
| every 1m
| condition mean_cpu > 0.8

A
Remove the group_by and use a filter instead.
Why wrong: Filtering without grouping would still evaluate aggregated data, not per instance.
B
Add a filter for each instance individually.
Why wrong: Impractical and not scalable; MQL cannot filter per instance without known IDs.
C
Use a ratio instead of mean.
Why wrong: A ratio would compare two metrics, not address per-instance evaluation.
D
Change the group_by to group_by [instance_id] and remove zone grouping.
Correct: grouping by instance_id ensures each instance's CPU is evaluated individually.

Full breakdown with real-world context →

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

✓

Change the group_by to group_by [instance_id] and remove zone grouping.

Option D is correct because the current MQL query uses `group_by [zone]` to compute the mean CPU utilization per zone, which averages all instances in a zone together. By changing the grouping to `group_by [instance_id]` and removing the zone grouping, the alert will evaluate each instance individually, firing only when a single instance's CPU exceeds 80%, rather than when the zone-wide average exceeds the threshold.

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.

✗
Remove the group_by and use a filter instead.
Why it's wrong here
Filtering without grouping would still evaluate aggregated data, not per instance.
✗
Add a filter for each instance individually.
Why it's wrong here
Impractical and not scalable; MQL cannot filter per instance without known IDs.
✗
Use a ratio instead of mean.
Why it's wrong here
A ratio would compare two metrics, not address per-instance evaluation.
✓
Change the group_by to group_by [instance_id] and remove zone grouping.
Why this is correct
Correct: grouping by instance_id ensures each instance's CPU is evaluated individually.
Related concept
Read the scenario before looking for a memorised answer.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates assume the alert is already per-instance because they see a CPU utilization metric, but they overlook that the `group_by [zone]` clause is causing the average across all instances in the zone, triggering the alert on the zone average rather than on any single instance.

Detailed technical explanation

How to think about this question

In MQL, the `group_by` clause determines the granularity of the aggregation window; `group_by [zone]` collapses all time series within a zone into a single aggregated series (e.g., mean). To alert on individual instances, you must group by a unique identifier like `instance_id`, which preserves each VM's time series separately. A common real-world scenario is when a zone has many low-utilization instances masking a single overloaded instance, leading to missed alerts if only zone-level aggregation is used.

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 PCD question test?

Managing application performance monitoring — This question tests Managing application performance monitoring — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: Change the group_by to group_by [instance_id] and remove zone grouping. — Option D is correct because the current MQL query uses `group_by [zone]` to compute the mean CPU utilization per zone, which averages all instances in a zone together. By changing the grouping to `group_by [instance_id]` and removing the zone grouping, the alert will evaluate each instance individually, firing only when a single instance's CPU exceeds 80%, rather than when the zone-wide average exceeds the threshold.

What should I do if I get this PCD 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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Last reviewed: Jun 25, 2026

Question Discussion

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