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Deployment Patterns Flashcards

7 cards from real AZ-400 practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.

Read the first 7 Deployment Patterns flashcards as text

  1. When using the 'rolling' deployment strategy in an Azure Pipelines YAML deployment job, what does the 'maxParallel' property control?

    Answer: Maximum number of targets updated simultaneously in each rolling batch

    maxParallel defines how many targets (VMs, servers) are updated at the same time in each rolling wave, balancing speed and availability.

  2. You need to deploy a new API version while keeping the old version live for backward compatibility. Which Azure API Management feature supports this pattern?

    Answer: API versioning with multiple version sets and routing

    Azure API Management supports versioning via URL path, header, or query string, allowing multiple API versions to coexist and route independently.

  3. In a multi-stage Azure pipeline, what is the correct way to pass a secret output from one stage to a downstream stage?

    Answer: Use pipeline variables with isOutput=true and mark as secret, then reference via dependencies.

    Setting isOutput=true on a task variable and referencing it via the dependencies context in a later stage passes values across stages; secrets can be mapped as secret variables.

  4. A deployment fails halfway through a rolling update in Kubernetes, leaving some pods on v1 and some on v2. What is the fastest safe remediation?

    Answer: Run 'kubectl rollout undo deployment/' to revert to the previous ReplicaSet

    'kubectl rollout undo' reverts the deployment to its previous recorded state using the saved ReplicaSet, quickly restoring all pods to v1.

  5. Which deployment pattern involves routing production traffic to a new version while using the production data set — but never exposing results to users until manually promoted?

    Answer: Shadow (dark launch) deployment

    Shadow deployments send real production traffic to the new version silently; responses are discarded and users see only the live service until promotion.

  6. An organization uses Azure Pipelines with approval gates. A release is stuck waiting for approval and the approver is unavailable. Which built-in setting allows the release to auto-approve after a timeout?

    Answer: The approval policy 'Timeout' field auto-rejects or auto-approves the release after the specified duration

    Azure Pipelines approval configurations include a timeout value that, when reached, can either reject the deployment or allow it to proceed automatically based on the configured behavior.

  7. Which metric in DORA measures how long it takes to restore service after a production incident, and what is the Elite benchmark?

    Answer: Mean Time to Restore (MTTR); less than one hour

    MTTR (Mean Time to Restore) measures recovery time from incidents; Elite DORA performers restore service in less than one hour.