Google PCA Practice Question: Analysing and Optimising Technical and Business Processes
Your company runs a microservices application on GKE. The development team wants to adopt a progressive delivery strategy to reduce the risk of new releases. They need to route a small percentage of production traffic to a new version, monitor key metrics, and automatically roll back if errors increase. Which approach should you recommend?
⚠ Common exam trap
The trap here is thinking that a rolling update or blue/green deployment provides canary-style traffic splitting and automated rollback, which they do not.
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
✓
Implement a canary deployment using Istio with traffic splitting and Prometheus-based analysis.
A canary deployment with Istio allows you to route a small percentage of traffic to the new version and monitor metrics. Prometheus can feed analysis into an automated process that rolls back if error rates exceed a threshold. This provides the progressive delivery and automated risk mitigation the team needs, unlike blue/green, rolling updates, or basic Ingress.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Implement a canary deployment using Istio with traffic splitting and Prometheus-based analysis.
Why this is correct
Istio provides fine-grained traffic splitting to route a percentage of traffic to the canary. Combined with Prometheus metrics and analysis, you can automatically promote or roll back based on error rates. This directly supports progressive delivery with automated rollback, meeting the requirement for risk reduction and monitoring.
- ✗
Configure a rolling update on the Kubernetes Deployment with a maxSurge and maxUnavailable setting.
Why it's wrong here
Rolling updates replace pods incrementally but do not control the percentage of user traffic directed to the new version. All traffic goes to whichever pods are ready, so a small canary percentage is not possible. It also lacks automated analysis and rollback based on business metrics; it only ensures availability during the update.
- ✗
Use a blue/green deployment by creating a full second environment and switching all traffic at once after testing.
Why it's wrong here
Blue/green switches all traffic at once, which does not gradually expose the new version to production traffic. It reduces risk compared to in-place upgrades, but it lacks the ability to route a small percentage and monitor before full cutover. Automated rollback is also not inherent; it requires manual intervention or additional tooling.
- ✗
Deploy the new version to a separate namespace and use a Kubernetes Ingress with weight-based routing.
Why it's wrong here
Kubernetes Ingress does not natively support weight-based traffic splitting between services. You would need a service mesh or an advanced ingress controller. Even then, automated rollback based on metrics is not provided out of the box. This approach adds complexity without fulfilling the automated analysis requirement.
Go deeper
Related to this question
Learn chapter
Deployment Manager and Infrastructure as Code
Key term
CAN
A CAN (Controller Area Network) is a robust vehicle bus standard designed to allow microcontrollers and devices to communicate with each other without a host computer.
Key term
GKE
GKE is Google's managed Kubernetes service that automates deploying, scaling, and managing containerized applications in the cloud.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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.