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| Certification Vendor: | Linux Foundation / CNCF |
|---|---|
| Exam Name: | Kubernetes and Cloud Native Associate (KCNA) Exam |
| Exam Number: | KCNA |
| Exam Duration: | 90 minutes |
| Passing Score: | 75% |
| Exam Format: | Online proctored, Multiple choice |
| Available Languages: | Japanese, English |
| Certificate Validity Period: | 2 years |
| Exam Price: | $250 USD |
| Related Certifications: | KCSA (Kubernetes and Cloud Native Security Associate) CKS (Certified Kubernetes Security Specialist) CKAD (Certified Kubernetes Application Developer) CKA (Certified Kubernetes Administrator) |
| Real Exam Qty: | 60 |
| Recommended Training: | LFS250: Kubernetes and Cloud Native Essentials |
| Exam Registration: | Official Registration |
| Sample Questions: | Linux Foundation KCNA Sample Questions |
| Exam Way: | Online, remotely proctored; available worldwide |
| Pre Condition: | No mandatory prerequisites; basic IT knowledge and familiarity with command-line tools recommended |
| Official Syllabus URL: | https://training.linuxfoundation.org/certification/kubernetes-cloud-native-associate/ |
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The KCNA Certification is highly regarded in the IT industry and is recognized by many organizations as a benchmark for cloud-native expertise. Kubernetes and Cloud Native Associate certification is ideal for IT professionals who want to enhance their career prospects and demonstrate their knowledge and skills in the field of cloud computing and containerization. Kubernetes and Cloud Native Associate certification is also suitable for organizations that want to validate the skills of their employees in Kubernetes and other cloud-native technologies.
NEW QUESTION # 257
How to create a headless service?
Answer: D
NEW QUESTION # 258
Kubernetes ___ protect you against voluntary interruptions (such as deleting Pods, draining nodes) to run applications in a highly available manner.
Answer: D
Explanation:
The correct answer is B: Pod Disruption Budgets (PDBs). A PDB is a policy object that limits how many Pods of an application can be voluntarily disrupted at the same time. "Voluntary disruptions" include actions such as draining a node for maintenance (kubectl drain), cluster upgrades, or an administrator deleting Pods.
The core purpose is to preserve availability by ensuring that a minimum number (or percentage) of replicas remain running and ready while those planned disruptions occur.
A PDB is typically defined with either minAvailable (e.g., "at least 3 Pods must remain available") or maxUnavailable (e.g., "no more than 1 Pod can be unavailable"). Kubernetes uses this budget when performing eviction operations. If evicting a Pod would violate the PDB, the eviction is blocked (or delayed), which forces maintenance workflows to proceed more safely-either by draining more slowly, scaling up first, or scheduling maintenance in stages.
Why the other options are not correct: topology spread constraints (A) influence scheduling distribution across failure domains but don't directly protect against voluntary disruptions. Taints and tolerations (C) control where Pods can schedule, not how many can be disrupted. Resource requests/limits (D) control CPU
/memory allocation and do not guard availability during drains or deletions.
PDBs also work best when paired with Deployments/StatefulSets that maintain replicas and with readiness probes that accurately represent whether a Pod can serve traffic. PDBs do not prevent involuntary disruptions (node crashes), but they materially reduce risk during planned operations-exactly what the question is targeting.
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NEW QUESTION # 259
How are ReplicaSets and Deployments related?
Answer: C
NEW QUESTION # 260
What is a sidecar container?
Answer: C
Explanation:
A sidecar container is an additional container that runs alongside the main application container within the same Pod, sharing network and storage context. That matches option C, so C is correct. The sidecar pattern is used to add supporting capabilities to an application without modifying the application code. Because both containers are in the same Pod, the sidecar can communicate with the main container over localhost and share volumes for files, sockets, or logs.
Common sidecar examples include: log forwarders that tail application logs and ship them to a logging system, proxies (service mesh sidecars like Envoy) that handle mTLS and routing policy, config reloaders that watch ConfigMaps and signal the main process, and local caching agents. Sidecars are especially powerful in cloud-native systems because they standardize cross-cutting concerns-security, observability, traffic policy- across many workloads.
Options A and D incorrectly describe "a Pod running next to ..." which is not how sidecars work; sidecars are containers, not separate Pods. Running separate Pods "next to" each other in a namespace does not give the same shared network namespace and tightly coupled lifecycle. Option B is also incorrect for the same reason:
a sidecar is not a separate Pod; it is a container in the same Pod.
Operationally, sidecars share the Pod lifecycle: they are scheduled together, scaled together, and generally terminated together. This is both a benefit (co-location guarantees) and a responsibility (resource requests
/limits should include the sidecar's needs, and failure modes should be understood). Kubernetes is increasingly formalizing sidecar behavior (e.g., sidecar containers with ordered startup semantics), but the core definition remains: a helper container in the same Pod.
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NEW QUESTION # 261
What components are common in a service mesh?
Answer: A
Explanation:
A service mesh is an architectural pattern that manages service-to-service communication in a microservices environment by inserting a dedicated networking layer. The two most common building blocks you'll see across service mesh implementations are (1) a data plane of proxies and (2) a control plane that configures and manages those proxies-this aligns best with "service proxy and control plane," option D.
In practice, the data plane is usually implemented via sidecar proxies (or sometimes node/ambient proxies) that sit "next to" workloads and handle traffic functions such as mTLS encryption, retries, timeouts, load balancing policies, traffic splitting, and telemetry generation. These proxies can capture inbound and outbound traffic without requiring changes to application code, which is one of the defining benefits of a mesh.
The control plane provides the management layer: it distributes policy and configuration to the proxies (routing rules, security policies, identities/certificates), discovers services/endpoints, and often coordinates certificate rotation and workload identity. In Kubernetes environments, meshes typically integrate with the Kubernetes API for service discovery and configuration.
Option C is close in spirit but uses non-standard wording ("runtime plane" is not a typical service mesh term;
"control plane" is). Options A and B describe capabilities that may exist in a mesh ecosystem (telemetry, circuit breaking), but they are not the universal "core components" across meshes. Tracing/log storage, for example, is usually handled by external observability backends (e.g., Jaeger, Tempo, Loki) rather than being intrinsic "mesh components." So, the most correct and broadly accepted answer is D: service proxy and control plane.
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NEW QUESTION # 262
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