- A
Enable the Vertical Pod Autoscaler (VPA) in update mode to automatically adjust memory requests.
Why wrong: VPA adjusts resource requests but requires pod restarts and does not address the immediate scale-out need; it also does not prevent OOM if memory leak persists.
- B
Switch the HPA to use the default 'container/cpu/utilization' metric instead of the custom metric.
Why wrong: The HPA already uses a CPU-based custom metric; switching to the default CPU metric would not help because CPU is not the bottleneck.
- C
Increase the memory request and limit for the pods to allow more memory usage.
Why wrong: Increasing limits temporarily masks the problem but does not solve the underlying memory leak; pods will eventually hit the new limit.
- D
Add a custom metric for memory utilization to the HPA and configure the target to scale when memory exceeds 70%.
This allows the HPA to react to memory pressure, scaling out pods to distribute memory load and reduce OOM errors.
PCD Managing application performance monitoring Practice Question
This PCD practice question tests your understanding of managing application performance monitoring. 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.
You are a site reliability engineer for a fintech company that runs a latency-sensitive trading application on Google Kubernetes Engine (GKE). The application is instrumented with OpenTelemetry and exports traces and metrics to Cloud Monitoring and Cloud Logging. Recently, the team observed a gradual increase in p99 latency from 50ms to 500ms over the past week, and error rates have spiked to 5% from a baseline of 0.1%. You review the Cloud Monitoring dashboards and notice that the 'container/cpu/utilization' metric shows normal usage, but the 'container/memory/bytes_used' metric shows a steady climb, reaching 90% of the memory limit on several pods. The application logs contain many 'OutOfMemoryError' exceptions and 'GC overhead limit exceeded' messages. You also see that the HPA (Horizontal Pod Autoscaler) has not triggered any scale-up events because the 'custom/googleapis.com|container/cpu/utilization' metric is below the target utilization threshold. The cluster autoscaler is enabled and has sufficient node pool capacity. What is the most likely root cause and the best immediate action to resolve the issue?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"best"Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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 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
Add a custom metric for memory utilization to the HPA and configure the target to scale when memory exceeds 70%.
The gradual memory increase and OutOfMemoryError exceptions indicate that the application is memory-bound, not CPU-bound. Since the HPA is configured to scale only on CPU utilization, it never triggers scale-up despite memory pressure. Adding a custom memory utilization metric to the HPA (option D) directly addresses the root cause by scaling pods when memory exceeds 70%, preventing OOM errors and reducing latency.
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.
- ✗
Enable the Vertical Pod Autoscaler (VPA) in update mode to automatically adjust memory requests.
Why it's wrong here
VPA adjusts resource requests but requires pod restarts and does not address the immediate scale-out need; it also does not prevent OOM if memory leak persists.
- ✗
Switch the HPA to use the default 'container/cpu/utilization' metric instead of the custom metric.
Why it's wrong here
The HPA already uses a CPU-based custom metric; switching to the default CPU metric would not help because CPU is not the bottleneck.
- ✗
Increase the memory request and limit for the pods to allow more memory usage.
Why it's wrong here
Increasing limits temporarily masks the problem but does not solve the underlying memory leak; pods will eventually hit the new limit.
- ✓
Add a custom metric for memory utilization to the HPA and configure the target to scale when memory exceeds 70%.
Why this is correct
This allows the HPA to react to memory pressure, scaling out pods to distribute memory load and reduce OOM errors.
Clue confirmation
The clue words "best", "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that CPU is the only metric for HPA scaling, or that increasing resource limits alone solves memory pressure, when in fact memory-bound applications require scaling based on memory utilization to avoid OOM and latency degradation.
Detailed technical explanation
How to think about this question
GKE HPA supports custom metrics via the custom.metrics.k8s.io API, which can ingest container/memory/bytes_used from Cloud Monitoring. When memory utilization exceeds the target, the HPA calculates the desired replica count as ceil(currentReplicas * (currentMetricValue / targetMetricValue)). This ensures proactive scaling before OOM occurs, reducing GC pressure and p99 latency. The cluster autoscaler then adds nodes if needed, but the immediate fix is to trigger pod-level scaling.
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.
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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: Add a custom metric for memory utilization to the HPA and configure the target to scale when memory exceeds 70%. — The gradual memory increase and OutOfMemoryError exceptions indicate that the application is memory-bound, not CPU-bound. Since the HPA is configured to scale only on CPU utilization, it never triggers scale-up despite memory pressure. Adding a custom memory utilization metric to the HPA (option D) directly addresses the root cause by scaling pods when memory exceeds 70%, preventing OOM errors and reducing latency.
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.
Are there clue words in this question I should notice?
Yes — watch for: "best", "most likely". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jun 25, 2026
This PCD 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 PCD exam.
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