The answer is to compare memory usage metrics before and after the deployment using Cloud Monitoring, enable detailed memory profiling with tools like Cloud Profiler, and temporarily increase the container’s memory limit in the pod spec to prevent OOM kills. Comparing metrics before and after the deployment isolates the regression, while Cloud Profiler pinpoints the specific code path consuming excess memory. Raising `resources.limits.memory` provides immediate headroom, buying time for root-cause analysis without crashing the service. On the Google Professional Cloud DevOps Engineer exam, this scenario tests your ability to balance rapid mitigation with systematic investigation—a common trap is jumping to a permanent code fix before confirming the regression window. Remember the three-phase approach: **Compare, Profile, Raise**—first verify the change caused the leak, then profile the heap, then raise the limit as a temporary shield.
PCDOE Managing service incidents Practice Question
This PCDOE practice question tests your understanding of managing service incidents. 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.
An incident is declared for a production service running on GKE. The on-call engineer suspects a recent code change may have introduced a memory leak. Which THREE actions should the engineer take to investigate and mitigate?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the memory limit for the container as a temporary mitigation
Option A is correct because increasing the memory limit for the container provides a temporary mitigation to prevent the service from being killed by the Out of Memory (OOM) killer while the root cause is investigated. In GKE, the container's memory limit is defined in the pod spec under `resources.limits.memory`, and raising it gives the application more headroom to continue serving requests without immediate termination. This is a standard incident response practice to buy time for deeper analysis, such as reviewing logs and metrics, before applying a permanent fix.
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.
✓
Increase the memory limit for the container as a temporary mitigation
Why this is correct
Temporary increase buys time for a permanent fix.
Related concept
Read the scenario before looking for a memorised answer.
✗
Scale down the number of replicas to reduce memory pressure
Why it's wrong here
Scaling down reduces total memory but each container still leaks, causing crashes.
✗
Roll back the deployment immediately without further investigation
Why it's wrong here
Rollback is mitigation, but the question asks for investigation and mitigation steps.
✓
Check container logs for Out of Memory (OOM) killed messages
Why this is correct
OOM messages confirm memory exhaustion.
Related concept
Read the scenario before looking for a memorised answer.
✓
Compare memory usage metrics before and after the deployment using Cloud Monitoring
Why this is correct
Identifies if memory usage increased after the change.
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 misconception that scaling down replicas reduces memory pressure, when in fact it reduces total available memory and can worsen the impact of a memory leak.
Detailed technical explanation
How to think about this question
Memory leaks in containerized applications on GKE often manifest as a gradual increase in resident set size (RSS) until the container hits its `limits.memory` threshold, triggering an OOM kill by the Linux kernel's out-of-memory killer. Cloud Monitoring can track `container/memory/used_bytes` and `container/memory/limit_bytes` to pinpoint the exact time of the leak relative to the deployment. Checking container logs for 'OOMKilled' messages via `kubectl logs` or the GKE Logs Explorer confirms the kill event and helps correlate it with the suspect deployment.
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.
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: Increase the memory limit for the container as a temporary mitigation — Option A is correct because increasing the memory limit for the container provides a temporary mitigation to prevent the service from being killed by the Out of Memory (OOM) killer while the root cause is investigated. In GKE, the container's memory limit is defined in the pod spec under `resources.limits.memory`, and raising it gives the application more headroom to continue serving requests without immediate termination. This is a standard incident response practice to buy time for deeper analysis, such as reviewing logs and metrics, before applying a permanent fix.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A team uses Google Kubernetes Engine (GKE) with cluster telemetry enabled. During an incident, they notice that a deployment's pods are repeatedly crashing with Exit Code 137. The team wants to investigate the root cause. Which two Google Cloud services should they use together to correlate resource usage and logs?
medium
✓ A.Cloud Monitoring and Cloud Logging
B.Security Command Center and Cloud Logging
✓ C.Cloud Trace and Cloud Monitoring
D.Cloud Error Reporting and Cloud Logging
Why A: Exit Code 137 indicates that a container was killed by SIGKILL (signal 9), typically due to an out-of-memory (OOM) condition. Cloud Monitoring provides metrics such as memory usage and OOM kill counts, while Cloud Logging captures the container's termination logs and system events. By correlating these two services, the team can identify when memory usage spiked and confirm that the pod was OOM-killed, enabling root cause analysis.
Last reviewed: Jun 30, 2026
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This PCDOE practice question is part of Courseiva's free Google Cloud 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 PCDOE exam.
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