Google PCA Practice Question: Managing Implementation and Ensuring Solution and Operations Reliability
Your organization runs a microservices application on Google Kubernetes Engine (GKE). You need to ensure that the application can be rolled back quickly if a new deployment causes errors. You want to use a deployment strategy that allows you to shift traffic back to the previous version with minimal downtime. Which approach should you use?
⚠ Common exam trap
The trap here is assuming that rolling updates with kubectl rollout undo provide instant rollback, but they require recreating old pods, which takes time.
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 a blue/green deployment by creating a new deployment with the new version, then switch the Service selector to point to the new version's pods. To roll back, switch the selector back.
Blue/green deployment allows you to have both versions running simultaneously. By switching the Service selector, you can instantly direct traffic to the new version or back to the old version. This provides minimal downtime and quick rollback. Other strategies like rolling update or canary may involve gradual traffic shifts and take longer to roll back, while Recreate causes downtime.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use a blue/green deployment by creating a new deployment with the new version, then switch the Service selector to point to the new version's pods. To roll back, switch the selector back.
Why this is correct
Blue/green deployment involves running two identical environments (blue and green). You deploy the new version to the green environment, test it, then switch traffic by updating the Service selector. If issues arise, you can quickly revert by switching the selector back to the blue environment. This provides near-instant rollback with minimal downtime, as both environments are already running.
- ✗
Use a canary deployment by creating a new deployment with a small number of replicas, then gradually increase traffic to the new version using Istio. To roll back, delete the canary deployment.
Why it's wrong here
Canary deployments are great for gradually testing a new version with a subset of traffic, but rollback involves deleting the canary deployment and ensuring all traffic returns to the stable version. This can be fast if traffic shifting is immediate, but it requires a service mesh like Istio and careful configuration. It may not be as instantaneous as blue/green, and if the canary is receiving production traffic, errors could affect users before rollback.
- ✗
Use a rolling update with a large maxSurge and maxUnavailable, and set the revisionHistoryLimit to a high value so you can roll back using kubectl rollout undo.
Why it's wrong here
Rolling updates gradually replace old pods with new ones. While you can roll back with kubectl rollout undo, the process is not instantaneous because it must recreate the old pods. If the new version is faulty, the rolling update might have already replaced many pods, causing degradation. Rollback takes time to scale up the old version and scale down the new, leading to potential downtime.
- ✗
Use a Recreate strategy by setting the deployment strategy to Recreate, which terminates all old pods before creating new ones. To roll back, redeploy the previous version.
Why it's wrong here
The Recreate strategy causes downtime because all old pods are terminated before new ones are created. This is not suitable for minimal downtime. Rollback would also require recreating the old pods, causing another period of downtime. This strategy is typically used for development environments or when applications cannot run multiple versions concurrently.
Go deeper
Related to this question
Learn chapter
Deployment Manager and Infrastructure as Code
Key term
Anthos
Anthos is a Google Cloud platform that lets you run applications consistently across different computing environments, like on-premises data centers and multiple public clouds.
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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