20+ practice questions focused on Cloud Native Application Delivery — one of the most tested topics on the Kubernetes and Cloud Native Associate KCNA exam. Each question includes a detailed explanation so you learn why the right answer is correct.
Start Cloud Native Application Delivery PracticeA user reports that a ConfigMap update is not reflected in running pods. Which action should be taken to ensure pods receive the updated configuration?
Explanation: A is correct because ConfigMaps are mounted into pods as volumes or consumed via environment variables at pod creation time. Kubernetes does not automatically propagate ConfigMap updates to running pods; the only way to pick up the new configuration is to restart the pods. A rollout restart of the deployment (e.g., `kubectl rollout restart deployment`) triggers a new ReplicaSet, which creates fresh pods that read the updated ConfigMap.
Refer to the exhibit. The deployment myapp is updated from image myapp:1.0 to myapp:2.0. During the rollout, what is the maximum number of pods that will be unavailable at any given time?
Explanation: The deployment uses a RollingUpdate strategy configured with maxUnavailable=0 (as shown in the exhibit), meaning that during the update, no existing pods are terminated until new pods are ready. This ensures that the desired number of replicas is always maintained, resulting in zero unavailable pods at all times.
Your organization runs a microservices application on a Kubernetes cluster with 5 worker nodes (each with 4 vCPU, 16GB RAM). The application consists of 20 microservices, each deployed as a Deployment with 3 replicas. Recently, after a new microservice 'inventory' was deployed with resource requests of 2 CPU and 4GB memory per pod, the cluster started experiencing pod scheduling failures. Many existing pods are in 'Pending' state with events indicating 'Insufficient cpu' or 'Insufficient memory'. The cluster has cluster autoscaling enabled (node pool ranging from 3 to 10 nodes), but new nodes are not being added quickly enough, and the existing nodes are heavily utilized. You need to resolve the scheduling failures while ensuring the inventory service can scale. Which course of action should you take?
Explanation: Reducing the CPU request of the inventory deployment to 1 CPU per pod allows the scheduler to pack pods more efficiently on existing nodes, alleviating immediate 'Insufficient cpu' and 'Insufficient memory' failures while the cluster autoscaler provisions new nodes. This approach balances short-term scheduling needs with the ability to scale the inventory service later, as requests can be adjusted upward once the cluster has more capacity.
A financial services company runs a critical trading application on Kubernetes. The application is deployed as a Deployment with 3 replicas. Each pod exposes metrics on port 8080 and uses a ConfigMap to load configuration. Recently, after a configuration change via a ConfigMap update, two of the three pods started crashing with an out-of-memory (OOM) error, while the third pod continues to run fine. The team verified that the ConfigMap was updated correctly and that the application code did not change. The pods have resource limits set: memory limit of 512Mi and request of 256Mi. The application's memory usage before the change was around 200Mi. The new configuration increases the in-memory cache size. The team suspects the issue is related to the configuration change. What is the best course of action?
Explanation: The OOM errors are directly caused by the increased memory usage from the larger in-memory cache, which exceeds the current 512Mi memory limit. Increasing the limit to 1Gi accommodates the new cache size while preserving resource boundaries, and a rolling update applies the change without downtime. This aligns with Kubernetes best practices of setting realistic resource limits based on application requirements.
Refer to the exhibit. The deployment above is created, but the pods are not receiving traffic from the associated Service. The Service selector matches 'app: web'. The Service endpoints list is empty. What is the most likely cause?
Explanation: A readiness probe that fails (e.g., the /health endpoint does not exist in the nginx container) will cause the pod to be marked as not ready. Kubernetes removes pods with failing readiness probes from the Service's endpoints list, resulting in an empty endpoints list even though the Service selector matches the pod labels. This is a common misconfiguration where the probe endpoint is not actually served by the container.
+15 more Cloud Native Application Delivery questions available
Practice all Cloud Native Application Delivery questions1. Baseline your knowledge
Start with 10 questions to gauge your current understanding of Cloud Native Application Delivery. This tells you whether you need a concept refresher or just practice.
2. Review every explanation
For each question — right or wrong — read the full explanation. Understanding why an answer is correct is more valuable than knowing the answer itself.
3. Focus on exam traps
Cloud Native Application Delivery questions on the KCNA frequently use trap wording. Look for subtle differences in answers that test your precision, not just general knowledge.
4. Reach 80% consistently
Do repeated sessions until you score 80%+ three times in a row. Then move to mixed-mode practice to test cross-topic recall under realistic conditions.
The exact number varies per candidate. Cloud Native Application Delivery is tested as part of the Kubernetes and Cloud Native Associate KCNA blueprint. Practicing with targeted Cloud Native Application Delivery questions ensures you can handle any format or difficulty that appears.
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Difficulty is subjective, but Cloud Native Application Delivery is a high-priority exam concept tested in multiple ways — direct recall, scenario analysis, and command-output interpretation. Consistent practice is the best way to build confidence.
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