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Design and implement build and release pipelinesmediumMultiple ChoiceObjective-mapped

AZ-400 Practice Question: Design and implement build and release pipelines

You are designing a release pipeline for a containerized application deployed to Azure Kubernetes Service (AKS). You want to implement a strategy where the new version is deployed to a small subset of pods first, and if healthy, gradually rolled out to all pods. Which Kubernetes deployment strategy should you use?

⚠ Common exam trap

A common mix-up: candidates confuse a rolling update with a canary deployment; while both are gradual, a rolling update does not provide traffic splitting or controlled subset exposure, which is the key requirement for a canary strategy.

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

A canary deployment is the correct strategy because it allows you to route a small percentage of traffic (e.g., 5%) to the new version of the application running on a subset of pods in AKS. If the canary is healthy (e.g., passes liveness and readiness probes), you can gradually increase the traffic percentage until 100% of pods run the new version, providing controlled rollout and rollback capabilities.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Canary deployment

    Why this is correct

    Canary deployment is the correct choice because it deploys the new containerized app version to a small subset of users or servers first, then gradually shifts traffic based on health checks and metrics. This allows you to validate the new version in production with minimal risk, automatically rolling back if errors or performance degradation are detected, before exposing it to the full user base.

  • Rolling update

    Why it's wrong here

    A rolling update gradually replaces old pods with new ones in a controlled manner, typically using a strategy like maxUnavailable and maxSurge, but it does not provide true traffic splitting between distinct versions for canary analysis. Because the same service selector routes traffic to both old and new pods during the process, you cannot precisely control the percentage of traffic each version receives, making it unsuitable for fine-grained canary validation.

  • Blue-green deployment

    Why it's wrong here

    Blue-green deployment runs two identical environments (blue and green) side by side, with only one receiving live traffic at a time; a switch moves all traffic from the old version to the new version at once. This lacks gradual traffic shifting or health-based canary analysis, so any latent issue in the new version will instantly affect all users, making it less safe than a canary deployment for progressive exposure.

  • Recreate deployment

    Why it's wrong here

    Recreate deployment terminates all existing pods before creating the new ones, which means the application experiences complete downtime during the transition and there is no possibility of rolling back gradually or routing a subset of traffic to the new version. It is the simplest strategy but is entirely unsuitable for a release pipeline that requires zero-downtime, incremental validation or canary-style risk mitigation.

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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

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