- A
Enable Horizontal Pod Autoscaling (HPA) on the transcoding service based on CPU utilization, targeting 70% utilization.
Why wrong: CPU-based HPA may not be responsive enough to queue buildup, and the transcoding service still blocks the frontend, causing latency.
- B
Upgrade the transcoding service to a larger machine type with more CPU and memory.
Why wrong: Larger machines may improve per-pod performance but are more expensive and still need to handle queue buildup; they also don't address the blocking issue.
- C
Increase the number of replicas of the transcoding service to 10 and keep it static.
Why wrong: Static scaling leads to either over-provisioning (waste) or under-provisioning (bottleneck) and does not adapt to traffic patterns; also, cost may be higher than needed.
- D
Refactor the frontend to push transcoding tasks to a Cloud Pub/Sub topic, and create a separate deployment of workers that subscribe to the topic and perform transcoding. Configure HPA on the worker deployment based on the Pub/Sub subscription backlog.
This decouples the frontend from the transcoding, preventing blocking. Workers can scale based on queue depth, optimizing cost and performance.
Quick Answer
The correct answer is to refactor the frontend to push transcoding tasks to a Cloud Pub/Sub topic and configure a separate worker deployment with HPA based on the Pub/Sub subscription backlog. This decouples the transcoding service from user-facing requests, allowing the workers to scale independently and cost-efficiently only when the backlog grows, which directly addresses the CPU bottleneck and rising latency without over-provisioning replicas. On the Google Professional Cloud DevOps Engineer exam, this scenario tests your understanding of event-driven architecture and custom metrics for HPA—a common trap is to simply increase static replicas or enable CPU-based autoscaling on the existing deployment, which fails to decouple the workload. The key insight is that CPU metrics lag behind queue depth, making Pub/Sub backlog a more responsive scaling signal. Memory tip: “Decouple to scale—backlog, not CPU, tells the tale.”
PCDOE Optimizing service performance Practice Question
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.
You are a DevOps engineer at a media streaming company. Your application runs on Google Kubernetes Engine (GKE) and serves video content to users worldwide. The application uses a microservices architecture with a frontend service that handles user requests and a backend transcoding service that converts video files. Recently, you noticed that the transcoding service is causing performance bottlenecks during peak hours, leading to increased latency for users. You have enabled Cloud Monitoring and Cloud Trace and observed that the transcoding service's CPU utilization is consistently above 90% during peak times, and the queue of video transcoding tasks is growing. The current deployment has 5 replicas of the transcoding service with no autoscaling. You need to optimize the performance of the transcoding service to reduce latency. Your company has a limited budget and wants to minimize costs. What should you do?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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
Refactor the frontend to push transcoding tasks to a Cloud Pub/Sub topic, and create a separate deployment of workers that subscribe to the topic and perform transcoding. Configure HPA on the worker deployment based on the Pub/Sub subscription backlog.
Option D is correct because it decouples the transcoding workload from user-facing requests using Cloud Pub/Sub, allowing the worker deployment to scale independently based on the backlog of tasks. This pattern reduces latency by preventing the frontend from being blocked by transcoding, and HPA on Pub/Sub backlog ensures cost-efficient scaling only when demand increases, aligning with the limited budget.
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 Horizontal Pod Autoscaling (HPA) on the transcoding service based on CPU utilization, targeting 70% utilization.
Why it's wrong here
CPU-based HPA may not be responsive enough to queue buildup, and the transcoding service still blocks the frontend, causing latency.
- ✗
Upgrade the transcoding service to a larger machine type with more CPU and memory.
Why it's wrong here
Larger machines may improve per-pod performance but are more expensive and still need to handle queue buildup; they also don't address the blocking issue.
- ✗
Increase the number of replicas of the transcoding service to 10 and keep it static.
Why it's wrong here
Static scaling leads to either over-provisioning (waste) or under-provisioning (bottleneck) and does not adapt to traffic patterns; also, cost may be higher than needed.
- ✓
Refactor the frontend to push transcoding tasks to a Cloud Pub/Sub topic, and create a separate deployment of workers that subscribe to the topic and perform transcoding. Configure HPA on the worker deployment based on the Pub/Sub subscription backlog.
Why this is correct
This decouples the frontend from the transcoding, preventing blocking. Workers can scale based on queue depth, optimizing cost and performance.
Clue confirmation
The clue word "minimum / minimize" 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
Google Cloud often tests the misconception that CPU-based HPA is sufficient for all performance bottlenecks, but the trap here is that CPU-bound services with growing queues require decoupling and backlog-based scaling, not just more replicas or larger machines.
Detailed technical explanation
How to think about this question
Cloud Pub/Sub provides a managed, asynchronous messaging service that decouples services, and the HPA can be configured with custom metrics from Cloud Monitoring, such as the 'pubsub.googleapis.com/subscription/num_undelivered_messages' metric, to scale workers based on backlog depth. This pattern, often called the 'Keda' or 'event-driven autoscaling' approach, ensures that workers scale to zero when idle, minimizing costs, and scale up rapidly when tasks accumulate, directly addressing the latency caused by queue growth.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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.
- →
Optimizing service performance — study guide chapter
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FAQ
Questions learners often ask
What does this PCDOE question test?
Optimizing service performance — This question tests Optimizing service performance — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Refactor the frontend to push transcoding tasks to a Cloud Pub/Sub topic, and create a separate deployment of workers that subscribe to the topic and perform transcoding. Configure HPA on the worker deployment based on the Pub/Sub subscription backlog. — Option D is correct because it decouples the transcoding workload from user-facing requests using Cloud Pub/Sub, allowing the worker deployment to scale independently based on the backlog of tasks. This pattern reduces latency by preventing the frontend from being blocked by transcoding, and HPA on Pub/Sub backlog ensures cost-efficient scaling only when demand increases, aligning with the limited budget.
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: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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 30, 2026
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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