The answer is that the Cloud Monitoring API query returns no data because the filter is missing required resource labels such as project_id, location, cluster_name, and namespace_name. For the kubernetes.io/container/cpu/core_usage_time metric type, these labels are mandatory to uniquely identify a GKE container’s time series; without them, the query cannot match any data points, even if the container is actively emitting CPU metrics. On the Google Professional Cloud DevOps Engineer exam, this tests your understanding of how Cloud Monitoring’s resource hierarchy and label requirements directly affect query results—a common trap is assuming a metric name alone suffices, when in fact missing resource labels silently produce empty responses. To avoid this, always verify that your filter includes all mandatory labels for the specific metric type. A helpful memory tip: think “P-L-C-N” for Project, Location, Cluster, Namespace—the four labels you must include to get container data back.
PCDOE Managing service incidents Practice Question
This PCDOE practice question tests your understanding of managing service incidents. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
Refer to the exhibit. You are investigating a performance issue where the api-server container is using excessive CPU. You run a Cloud Monitoring API query and receive the JSON configuration shown. However, the query returns no data points. What is the most likely cause?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The filter is missing required resource labels such as project_id, location, cluster_name, and namespace_name, causing no time series to match.
Option C is correct because the Cloud Monitoring API query for the 'kubernetes.io/container/cpu/core_usage_time' metric type requires mandatory resource labels—specifically 'project_id', 'location', 'cluster_name', and 'namespace_name'—to uniquely identify the time series for a GKE container. Without these labels in the filter, the query cannot match any time series, resulting in no data points returned, even if the metric is actively emitting data.
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 time interval specified is too short and falls outside the data retention period.
Why it's wrong here
The interval is one hour, which is well within the retention period (typically 6 weeks for Cloud Monitoring).
✗
The metric type 'kubernetes.io/container/cpu/core_usage_time' is deprecated and no longer available.
Why it's wrong here
The metric type is valid and commonly used for GKE container CPU usage.
✓
The filter is missing required resource labels such as project_id, location, cluster_name, and namespace_name, causing no time series to match.
Why this is correct
Resource labels must be fully specified in the filter to match the specific container; otherwise the query may not return data.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
The aggregation perSeriesAligner 'ALIGN_MEAN' is incompatible with the metric type, which requires 'ALIGN_RATE'.
Why it's wrong here
'ALIGN_MEAN' is compatible with cumulative metrics when used correctly; the issue is not the aligner.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that a missing filter causes an error or that the metric is deprecated, when in fact the API silently returns no data points because the required resource labels are absent from the filter.
Detailed technical explanation
How to think about this question
In Cloud Monitoring, metric descriptors for GKE container metrics are automatically created with a set of monitored resource labels (e.g., project_id, location, cluster_name, namespace_name, pod_name, container_name) that must be included in the filter to scope the query to a specific time series. Omitting any of these labels results in a query that matches zero time series because the metric is multi-dimensional and the API requires exact label matching to retrieve data. This is distinct from a query that returns no data due to a time range issue, which would still show the metric descriptor but with empty points.
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.
Managing service incidents — This question tests Managing service incidents — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The filter is missing required resource labels such as project_id, location, cluster_name, and namespace_name, causing no time series to match. — Option C is correct because the Cloud Monitoring API query for the 'kubernetes.io/container/cpu/core_usage_time' metric type requires mandatory resource labels—specifically 'project_id', 'location', 'cluster_name', and 'namespace_name'—to uniquely identify the time series for a GKE container. Without these labels in the filter, the query cannot match any time series, resulting in no data points returned, even if the metric is actively emitting data.
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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