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.
A DevOps team wants to autoscale a GKE Deployment based on a custom metric exposed by the application. The metric is available via an HTTP endpoint. Which approach should they use to integrate this metric with the Horizontal Pod Autoscaler (HPA)?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Deploy a Prometheus Operator with the kube-state-metrics adapter and configure the HPA to use the custom metric.
Option A is correct because the Prometheus Operator, combined with the kube-state-metrics adapter (or the prometheus-adapter), allows HPA to consume custom metrics from a Prometheus server that scrapes the application's HTTP endpoint. The adapter exposes these metrics via the custom.metrics.k8s.io API, which HPA natively queries. This is the standard approach for integrating application-specific HTTP metrics into Kubernetes autoscaling.
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 a Prometheus Operator with the kube-state-metrics adapter and configure the HPA to use the custom metric.
Why this is correct
Prometheus adapter can scrape custom endpoints and expose metrics to the custom.metrics.k8s.io API used by HPA.
Related concept
Read the scenario before looking for a memorised answer.
✗
Expose the metric via an Ingress and configure HPA to read from the Ingress metrics.
Why it's wrong here
Ingress does not expose application custom metrics for HPA.
✗
Use the standard CPU-based HPA and map the custom metric to CPU usage via a script.
Why it's wrong here
Custom metrics cannot be mapped to CPU; HPA requires native metrics.
✗
Configure the Stackdriver Metrics Adapter to collect the metric from the endpoint.
Why it's wrong here
Stackdriver adapter requires metrics to be published to Stackdriver; it cannot scrape arbitrary HTTP endpoints.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that any HTTP endpoint can be directly plugged into HPA, but the trap here is that HPA requires a metrics API adapter (like prometheus-adapter or Stackdriver adapter) to bridge the gap between the raw metric source and the Kubernetes custom metrics API.
Detailed technical explanation
How to think about this question
The prometheus-adapter (part of the Prometheus Operator ecosystem) implements the custom.metrics.k8s.io API by querying Prometheus and translating metric names into HPA-compatible resources. Under the hood, it uses a configuration file (often a ConfigMap) to define metric discovery rules and label mappings. A real-world nuance is that the adapter must be configured to match the exact metric name and label structure expected by the HPA, and if the metric is not scraped by Prometheus (e.g., due to network policies), the HPA will fall back to an unknown metric state.
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.
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: Deploy a Prometheus Operator with the kube-state-metrics adapter and configure the HPA to use the custom metric. — Option A is correct because the Prometheus Operator, combined with the kube-state-metrics adapter (or the prometheus-adapter), allows HPA to consume custom metrics from a Prometheus server that scrapes the application's HTTP endpoint. The adapter exposes these metrics via the custom.metrics.k8s.io API, which HPA natively queries. This is the standard approach for integrating application-specific HTTP metrics into Kubernetes autoscaling.
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.
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Question Discussion
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