Google PCA Manage implementation of cloud architecture Practice Question
You are deploying a new version of a microservices application to a GKE cluster. The deployment must be released to a small subset of users first, and if errors occur, traffic must automatically revert to the previous version. You also need to monitor the error rate and latency of the new version. Which approach should you use?
⚠ Common exam trap
The trap here is assuming that a Kubernetes rolling update or Ingress can perform canary releases with automatic rollback, but they lack native traffic splitting and metric-based automation.
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
✓
Use Anthos Service Mesh to implement a canary deployment with traffic splitting, and configure automatic rollback based on error rate metrics.
Anthos Service Mesh offers advanced traffic management, including canary deployments with precise traffic splitting and automated rollback triggered by monitoring metrics. This aligns with the need to release to a subset, monitor, and revert automatically. The other options lack either the fine-grained traffic control or the automation required.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Create two separate GKE clusters, one for the old version and one for the new version, and use a global load balancer to split traffic 50/50. Monitor errors and manually shift traffic back if needed.
Why it's wrong here
This approach requires managing two clusters, which adds operational overhead. Traffic splitting is at 50/50, not a small subset, and rollback is manual. It does not provide automatic revert based on error rates. While it allows monitoring, it lacks the fine-grained canary and automation required.
- ✗
Deploy the new version as a separate Kubernetes Service and use an Ingress with session affinity to route a percentage of users to the new version. Monitor errors and adjust the Ingress configuration manually.
Why it's wrong here
Ingress with session affinity can route based on cookies, but does not support percentage-based traffic splitting natively. It also requires manual intervention to roll back, and does not automatically react to error rates. This adds complexity and does not meet the automatic rollback requirement.
- ✗
Use a Kubernetes Deployment with a rolling update and configure readiness probes; use kubectl rollout undo if errors occur.
Why it's wrong here
A rolling update replaces pods gradually but does not allow directing traffic to a subset of users based on user identity or percentage. It also lacks automatic revert based on error rate; kubectl rollout undo is manual. Monitoring would require separate setup. This does not meet the requirement for canary testing and automatic rollback.
- ✓
Use Anthos Service Mesh to implement a canary deployment with traffic splitting, and configure automatic rollback based on error rate metrics.
Why this is correct
Anthos Service Mesh provides traffic splitting, allowing you to send a percentage of traffic to the new version. It integrates with Cloud Monitoring to automatically roll back if error rates exceed thresholds. This directly satisfies the canary release and automatic revert requirements, and provides observability for latency and errors.
Go deeper
Related to this question
Learn chapter
Identity and Access Management (IAM)
Key term
Service
A service is a software component or system that performs a specific function and is available to be used by other programs or users over a network.
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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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
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