Quick Answer
The answer is to increase the memory limit and requests for the container. This is correct because frequent GC pauses with high memory usage but low CPU utilization indicate the JVM heap is too small, forcing the garbage collector to run constantly and causing the observed GKE microservice latency. By raising the memory limit, you give the JVM more headroom, reducing GC frequency and directly lowering latency. On the Google Professional Cloud DevOps Engineer exam, this scenario tests your understanding of resource tuning in containerized environments—a common trap is to scale CPU when the bottleneck is memory, not compute. Remember the mnemonic: “Low CPU, high mem, GC screams—raise the heap, not the streams.”
This PCDOE practice question tests your understanding of optimizing service performance. 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.
A team deploys a microservice on Google Kubernetes Engine (GKE) that processes user uploads. The service latency has increased over time. Monitoring shows that CPU utilization is low, but memory usage is high and garbage collection (GC) pauses are frequent. Which action is most likely to reduce latency?
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.
Scale out the deployment by increasing the number of replicas.
Why wrong: CPU is low, so more replicas won't reduce GC pauses.
Reduce the number of replicas to concentrate load.
Why wrong: Fewer replicas increase load per pod, worsening GC.
Increase the CPU limit to allow faster processing.
Why wrong: CPU is not the bottleneck.
Increase the memory limit and requests for the container.
More memory reduces GC frequency and pauses.
Answer choices
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 and requests for the container.
Frequent GC pauses and high memory usage with low CPU indicate the JVM heap is too small, causing the garbage collector to run more often. Increasing the memory limit and requests gives the JVM more headroom to reduce GC frequency, directly lowering latency. This is a classic JVM tuning scenario in containerized environments like GKE.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Scale out the deployment by increasing the number of replicas.
Why it's wrong here
CPU is low, so more replicas won't reduce GC pauses.
Reduce the number of replicas to concentrate load.
Why it's wrong here
Fewer replicas increase load per pod, worsening GC.
Increase the CPU limit to allow faster processing.
Why it's wrong here
CPU is not the bottleneck.
Increase the memory limit and requests for the container.
Why this is correct
More memory reduces GC frequency and pauses.
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.
Common exam traps
Google Cloud often tests the misconception that scaling out or adding CPU fixes all performance issues, but here the symptom of low CPU and high memory with GC pauses specifically points to a memory constraint, not a throughput or compute bottleneck.
Detailed technical explanation
Under the hood, the JVM's garbage collector (e.g., G1GC) triggers a pause when the heap approaches its maximum size. In Kubernetes, the container's memory limit is the cgroup memory.max, and the JVM uses this to set heap sizes via -XX:MaxRAMPercentage. If the limit is too low, the JVM cannot allocate enough heap, leading to frequent full GCs. A real-world scenario is a Java microservice processing large uploads where object allocation spikes; without enough heap, GC becomes the dominant latency factor.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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
Optimizing service performance — This question tests Optimizing service performance — Read the scenario before looking for a memorised answer..
The correct answer is: Increase the memory limit and requests for the container. — Frequent GC pauses and high memory usage with low CPU indicate the JVM heap is too small, causing the garbage collector to run more often. Increasing the memory limit and requests gives the JVM more headroom to reduce GC frequency, directly lowering latency. This is a classic JVM tuning scenario in containerized environments like GKE.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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