AZ-400 Practice Question: Design and implement build and release pipelines
You are implementing a release pipeline for a containerized application using Azure Kubernetes Service (AKS). The pipeline should use canary deployments to gradually shift traffic from the stable version to the new version. Which strategy should you use to manage the traffic shift?
⚠ Common exam trap
Watch out — candidates often confuse canary deployments with blue-green or rolling updates, not realizing that only a service mesh (or similar traffic-splitting mechanism) provides the precise, percentage-based traffic shifting required for a true canary release in Kubernetes.
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
✓
Canary deployment using a service mesh (e.g., Istio)
A service mesh like Istio provides fine-grained traffic management capabilities (e.g., using VirtualService and DestinationRule resources) that allow you to route a specific percentage of traffic to the new canary version while the rest goes to the stable version. This enables gradual traffic shifting without modifying application code, and supports advanced routing rules based on headers or weights, which is essential for canary deployments in AKS.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Blue-green deployment strategy
Why it's wrong here
Blue-green deployment strategy is incorrect because it routes all live traffic to either the blue or green environment at once, with no gradual or percentage-based traffic shifting between the two versions. This all-or-nothing switch does not allow fine-grained canary analysis or incremental exposure of a new containerized release to a subset of users.
- ✗
Rolling update strategy
Why it's wrong here
Rolling update strategy is incorrect because although it incrementally replaces old pods with new ones to maintain availability, it does so sequentially and does not split live traffic between the old and new versions simultaneously. It lacks the fine-grained traffic routing controls needed to direct a small percentage of requests to a canary version for controlled validation.
- ✗
A/B testing with feature flags
Why it's wrong here
A/B testing with feature flags is incorrect because feature flags toggle functionality at the application layer, not route or shift network traffic between deployment versions. They are designed to enable or disable features for specific user segments, not to gradually distribute raw traffic across container instances, so they do not satisfy a traffic-based canary release requirement.
- ✓
Canary deployment using a service mesh (e.g., Istio)
Why this is correct
Canary deployment using a service mesh (e.g., Istio) is correct because Istio's traffic management rules (VirtualService and DestinationRule) allow precise percentage-based traffic splitting between the stable and canary versions of a containerized workload. This enables gradual exposure, real-time metrics collection, and automated or manual rollback, making it the ideal strategy for safely validating a new release in production.
Go deeper
Related to this question
Learn chapter
Introduction to DevOps and Azure DevOps
Key term
Pipeline
A pipeline is an automated series of steps that takes code from development to production, ensuring quality and speed.
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.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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