- A
Deploy the Metrics Server in the cluster to expose custom metrics via the Custom Metrics API.
The Metrics Server provides the Custom Metrics API, enabling HPA to use custom metrics.
- B
Modify the application to expose custom metrics via an endpoint and configure the HPA to reference the custom metric.
The application must expose the metric, and the HPA must be configured to use it.
- C
Enable the Cloud Monitoring API and create a custom dashboard to track pending requests.
Why wrong: Cloud Monitoring API is for monitoring, not for custom metrics scaling.
- D
Configure a HorizontalPodAutoscaler (HPA) with the target average CPU utilization set to 80%.
Why wrong: CPU utilization is a resource metric, not a custom metric.
- E
Enable GKE Autopilot mode to automatically manage scaling based on custom metrics.
Why wrong: Autopilot does not support custom metrics scaling.
Quick Answer
The correct steps are to modify the application to expose custom metrics via an endpoint and configure the HorizontalPodAutoscaler (HPA) to reference that custom metric. This is necessary because GKE’s HPA relies on the Custom Metrics API, which requires the application to serve a metrics endpoint (e.g., `/metrics` with a format like Prometheus) and the HPA manifest to specify the metric name and target value, such as `pending-requests-per-pod`. On the Google Professional Cloud Architect exam, this scenario tests your understanding of how custom metrics scaling differs from CPU/memory-based scaling, which uses the Metrics Server automatically. A common trap is assuming the Metrics Server alone is sufficient—it is required, but the application must first expose the metric, and the HPA must explicitly reference it. Memory tip: “Expose then reference” – the app must expose the metric endpoint first, then the HPA references it by name.
Google PCA Manage implementation of cloud architecture Practice Question
This PCA practice question tests your understanding of manage implementation of cloud architecture. 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.
A company is deploying a microservices application on Google Kubernetes Engine (GKE). They want to ensure that the cluster can automatically scale based on custom metrics, such as the number of pending requests per pod. Which two steps should they take? (Choose TWO)
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
Deploy the Metrics Server in the cluster to expose custom metrics via the Custom Metrics API.
Option A is correct because the Metrics Server is required to expose custom metrics via the Custom Metrics API in GKE. Without it, the HorizontalPodAutoscaler (HPA) cannot retrieve the custom metrics needed for scaling decisions. Option B is correct because the application must expose custom metrics (e.g., pending requests) through an endpoint, and the HPA must be configured to reference that custom metric name to trigger scaling based on that specific value.
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.
- ✓
Deploy the Metrics Server in the cluster to expose custom metrics via the Custom Metrics API.
Why this is correct
The Metrics Server provides the Custom Metrics API, enabling HPA to use custom metrics.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Modify the application to expose custom metrics via an endpoint and configure the HPA to reference the custom metric.
Why this is correct
The application must expose the metric, and the HPA must be configured to use it.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Enable the Cloud Monitoring API and create a custom dashboard to track pending requests.
Why it's wrong here
Cloud Monitoring API is for monitoring, not for custom metrics scaling.
- ✗
Configure a HorizontalPodAutoscaler (HPA) with the target average CPU utilization set to 80%.
Why it's wrong here
CPU utilization is a resource metric, not a custom metric.
- ✗
Enable GKE Autopilot mode to automatically manage scaling based on custom metrics.
Why it's wrong here
Autopilot does not support custom metrics scaling.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is confusing the Metrics Server (which exposes resource metrics) with the need for a custom metrics adapter; candidates often think the Metrics Server alone handles custom metrics, but it only serves CPU/memory, not application-level custom metrics like pending requests.
Detailed technical explanation
How to think about this question
The Custom Metrics API is an extension of the Kubernetes API server that allows the HPA to query custom metrics from sources like Prometheus or Stackdriver. Under the hood, the Metrics Server collects resource metrics (CPU/memory) via the resource metrics API, but for custom metrics, you need an adapter (e.g., the Stackdriver Metrics Adapter) that translates Cloud Monitoring data into the custom.metrics.k8s.io API. A real-world scenario is an e-commerce site scaling based on queue depth; without the adapter, the HPA cannot read the custom metric.
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
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FAQ
Questions learners often ask
What does this PCA question test?
Manage implementation of cloud architecture — This question tests Manage implementation of cloud architecture — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Deploy the Metrics Server in the cluster to expose custom metrics via the Custom Metrics API. — Option A is correct because the Metrics Server is required to expose custom metrics via the Custom Metrics API in GKE. Without it, the HorizontalPodAutoscaler (HPA) cannot retrieve the custom metrics needed for scaling decisions. Option B is correct because the application must expose custom metrics (e.g., pending requests) through an endpoint, and the HPA must be configured to reference that custom metric name to trigger scaling based on that specific value.
What should I do if I get this PCA 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.
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Last reviewed: Jun 11, 2026
This PCA 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 PCA exam.
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