Google PCA Practice Question: Managing Implementation and Ensuring Solution and Operations Reliability
Your team is deploying a new version of a microservices application on Google Kubernetes Engine (GKE). You want to gradually shift traffic to the new version while monitoring key performance indicators (KPIs) such as error rate and latency. If KPIs degrade, you need to automatically roll back. Which approach should you use?
⚠ Common exam trap
A common mix-up: candidates confuse rolling updates with canary deployments; rolling updates replace pods but do not control traffic splitting or provide automated rollback based on metrics.
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 Flagger, which automates traffic shifting and rollback based on Prometheus metrics.
Flagger with Istio automates canary deployments by incrementally shifting traffic, analyzing Prometheus metrics, and rolling back automatically if KPIs degrade. This provides safe, gradual rollout with minimal manual intervention. Other options either lack automation, do not support gradual traffic shifting, or require manual monitoring.
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 GKE rolling updates with maxSurge and maxUnavailable set to 25%, and monitor KPIs manually.
Why it's wrong here
Rolling updates replace pods incrementally but do not provide traffic splitting or automated rollback based on KPIs. Manual monitoring is error-prone and slow. This approach lacks the canary analysis and automated rollback required to safely shift traffic while monitoring.
- ✓
Implement a canary deployment using Istio with Flagger, which automates traffic shifting and rollback based on Prometheus metrics.
Why this is correct
Flagger is a progressive delivery tool that integrates with Istio to automate canary releases. It gradually shifts traffic, monitors Prometheus metrics for KPIs, and automatically rolls back if metrics breach thresholds. This matches the requirement for automated rollback based on KPIs.
- ✗
Configure a GKE Ingress with two backends and use traffic splitting based on weights, then manually adjust weights based on monitoring.
Why it's wrong here
GKE Ingress supports traffic splitting, but manual adjustment of weights does not provide automated rollback. It requires constant human intervention and does not react to KPI degradation automatically. The scenario demands automated rollback, which this approach lacks.
- ✗
Use Blue/Green deployment by creating a second deployment and switching the Service selector to the new version after manual testing.
Why it's wrong here
Blue/Green deployment switches all traffic at once, which does not allow gradual traffic shifting. It also requires manual testing and does not automate rollback based on KPIs. This approach increases risk because any issues affect all users immediately.
Go deeper
Related to this question
Learn chapter
Virtual Machine Instances in Compute Engine
Key term
Latency
Latency is the time delay between a request being sent over a network and the response being received, often measured in milliseconds.
Key term
Microservices
Microservices is an architectural style where a software application is built as a collection of small, independent services, each handling a specific business function and communicating over a network.
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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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