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The Kubernetes and Cloud Native Associate (KCNA) certification exam is offered by the Linux Foundation, a non-profit organization that aims to promote and support open source technology. Kubernetes and Cloud Native Associate certification exam is designed to test the knowledge and skills of individuals who are interested in working with Kubernetes and cloud native technologies. KCNA Exam covers a range of topics, including containerization, Kubernetes architecture, deployment, and management.

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Linux Foundation Kubernetes and Cloud Native Associate Sample Questions (Q140-Q145):

NEW QUESTION # 140
What are cluster-wide objects

Answer: B

Explanation:
https://kubernetes.io/docs/concepts/overview/working-with-objects/_print/


NEW QUESTION # 141
What is a probe within Kubernetes?

Answer: C

Explanation:
In Kubernetes, a probe is a health check mechanism that the kubelet executes against containers, so C is correct. Probes are part of how Kubernetes implements self-healing and safe traffic management. The kubelet runs probes periodically according to the configuration in the Pod spec and uses the results to decide whether a container is healthy, ready to receive traffic, or still starting up.
Kubernetes supports three primary probe types:
Liveness probe: determines whether the container should be restarted. If liveness fails repeatedly, kubelet restarts the container (subject to restartPolicy).
Readiness probe: determines whether the Pod should receive traffic via Services. If readiness fails, the Pod is removed from Service endpoints, preventing traffic from being routed to it until it becomes ready again.
Startup probe: used for slow-starting containers. It disables liveness/readiness failures until startup succeeds, preventing premature restarts during initialization.
Probe mechanisms can be HTTP GET, TCP socket checks, or exec commands run inside the container. These checks are performed by kubelet on the node where the Pod is running, not by the API server.
Options A and D incorrectly attribute probes to the Kubernetes API. While probe configuration is stored in the API as part of Pod specs, execution is node-local. Option B is not a Kubernetes concept.
So the correct definition is: a probe is a periodic diagnostic run by kubelet to assess container health/readiness, enabling reliable rollouts, traffic gating, and automatic recovery.


NEW QUESTION # 142
What are the 3 pillars of Observability?

Answer: B

Explanation:
The correct answer is A: Metrics, Logs, and Traces. These are widely recognized as the "three pillars" because together they provide complementary views into system behavior:
Metrics are numeric time series collected over time (CPU usage, request rate, error rate, latency percentiles). They are best for dashboards, alerting, and capacity planning because they are structured and aggregatable. In Kubernetes, metrics underpin autoscaling and operational visibility (node/pod resource usage, cluster health signals).
Logs are discrete event records (often text) emitted by applications and infrastructure components. Logs provide detailed context for debugging: error messages, stack traces, warnings, and business events. In Kubernetes, logs are commonly collected from container stdout/stderr and aggregated centrally for search and correlation.
Traces capture the end-to-end journey of a request through a distributed system, breaking it into spans. Tracing is crucial in microservices because a single user request may cross many services; traces show where latency accumulates and which dependency fails. Tracing also enables root cause analysis when metrics indicate degradation but don't pinpoint the culprit.
Why the other options are wrong: a span is a component within tracing, not a top-level pillar; "data" is too generic; and "resources" are not an observability signal category. The pillars are defined by signal type and how they're used operationally.
In cloud-native practice, these pillars are often unified via correlation IDs and shared context: metrics alerts link to logs and traces for the same timeframe/request. Tooling like Prometheus (metrics), log aggregators (e.g., Loki/Elastic), and tracing systems (Jaeger/Tempo/OpenTelemetry) work together to provide a complete observability story.
Therefore, the verified correct answer is A.


NEW QUESTION # 143
Which of these is a valid container restart policy?

Answer: B

Explanation:
The correct answer is D: On failure. In Kubernetes, restart behavior is controlled by the Pod-level field spec.restartPolicy, with valid values Always, OnFailure, and Never. The option presented here ("On failure") maps to Kubernetes' OnFailure policy. This setting determines what the kubelet should do when containers exit:
Always: restart containers whenever they exit (typical for long-running services) OnFailure: restart containers only if they exit with a non-zero status (common for batch workloads) Never: do not restart containers (fail and leave it terminated) So "On failure" is a valid restart policy concept and the only one in the list that matches Kubernetes semantics.
The other options are not Kubernetes restart policies. "On login," "On update," and "On start" are not recognized values and don't align with how Kubernetes models container lifecycle. Kubernetes is declarative and event-driven: it reacts to container exit codes and controller intent, not user "logins." Operationally, choosing the right restart policy is important. For example, Jobs typically use restartPolicy: OnFailure or Never because the goal is completion, not continuous uptime. Deployments usually imply "Always" because the workload should keep serving traffic, and a crashed container should be restarted. Also note that controllers interact with restarts: a Deployment may recreate Pods if they fail readiness, while a Job counts completions and failures based on Pod termination behavior.
Therefore, among the options, the only valid (Kubernetes-aligned) restart policy is D.


NEW QUESTION # 144
You are developing a serverless application using Azure Functions that processes real-time streaming dat
a. How would you ensure reliable and efficient data ingestion from a Kafka topic to your Azure Functions?

Answer: C

Explanation:
Azure Event Hubs is the recommended approach for connecting Kafka to Azure Functions for real-time streaming data ingestion. It offers high throughput and scalability, making it ideal for handling large volumes of streaming events. Service Bus (B) is suitable for message queuing but not primarily designed for streaming data. Directly polling Kafka topics (C) can be inefficient and might not scale well. Custom connectors (D) can be complex and might lack the required functionality for streaming data ingestion. Data Factory (E) is more focused on data movement and transformation, not real-time streaming


NEW QUESTION # 145
......

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