Question 181 of 500
Implementing service monitoring strategieshardMultiple ChoiceObjective-mapped

PCDOE Implementing service monitoring strategies Practice Question

This PCDOE practice question tests your understanding of implementing service monitoring strategies. 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 cloud_run_revision::https://googleapis.com/traces/span
| filter spans == "my-service"
| align delta(1m)
| every 1m
| group_by [span_id], [latency: percentile(99)]
```

The above MQL query is used in a Cloud Monitoring dashboard. What does it display?

Question 1hardmultiple choice
Full question →

Exhibit

```
fetch cloud_run_revision::https://googleapis.com/traces/span
| filter spans == "my-service"
| align delta(1m)
| every 1m
| group_by [span_id], [latency: percentile(99)]
```

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

The 99th percentile latency of each span_id, every minute.

The MQL query uses the `fetch` command to retrieve spans from the `my-service` service, then applies `percentile(99)` to the latency metric, and groups the result by `span_id` using the `into` clause. The `every 1m` parameter sets the alignment window to one minute. This produces a time series showing the 99th percentile latency for each distinct span_id, updated every minute.

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.

  • The total number of spans in 'my-service' each minute.

    Why it's wrong here

    The query uses percentile(99) on latency, not counting spans.

  • The maximum latency of spans grouped by span_id.

    Why it's wrong here

    The query uses percentile(99), not max.

  • The 99th percentile latency of all spans in the 'my-service' service, every minute.

    Why it's wrong here

    The group_by [span_id] breaks down per span_id, not aggregated per service.

  • The 99th percentile latency of each span_id, every minute.

    Why this is correct

    The group_by [span_id] combined with percentile(99) on latency gives per-span_id 99th percentile values.

    Related concept

    Read the scenario before looking for a memorised answer.

Common exam traps

Common exam trap: answer the scenario, not the keyword

Google Cloud often tests the distinction between aggregating across all spans (e.g., service-level percentile) versus grouping by a dimension like `span_id`, leading candidates to mistakenly choose a service-wide aggregation when the query explicitly groups by a finer granularity.

Detailed technical explanation

How to think about this question

The `percentile(99)` function in MQL computes the value below which 99% of the latency observations fall, ignoring outliers. Grouping by `span_id` with `into` creates separate time series for each unique span identifier, which is useful for identifying slow individual operations (e.g., a specific database query or HTTP endpoint) within a service. The `every 1m` aligns the percentile calculation to one-minute intervals, smoothing short-term 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

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.

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 PCDOE question test?

Implementing service monitoring strategies — This question tests Implementing service monitoring strategies — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: The 99th percentile latency of each span_id, every minute. — The MQL query uses the `fetch` command to retrieve spans from the `my-service` service, then applies `percentile(99)` to the latency metric, and groups the result by `span_id` using the `into` clause. The `every 1m` parameter sets the alignment window to one minute. This produces a time series showing the 99th percentile latency for each distinct span_id, updated every minute.

What should I do if I get this PCDOE 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 30, 2026

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