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| Certification Vendor: | Linux Foundation / CNCF |
|---|---|
| Exam Name: | Kubernetes and Cloud Native Associate (KCNA) Exam |
| Exam Number: | KCNA |
| Related Certifications: | CKA (Certified Kubernetes Administrator) CKS (Certified Kubernetes Security Specialist) KCSA (Kubernetes and Cloud Native Security Associate) CKAD (Certified Kubernetes Application Developer) |
| Exam Format: | Multiple choice, Online proctored |
| Available Languages: | Japanese, English |
| Passing Score: | 75% |
| Exam Duration: | 90 minutes |
| Real Exam Qty: | 60 |
| Exam Price: | $250 USD |
| Certificate Validity Period: | 2 years |
| 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/ |
Linux FoundationのKCNA認定試験がIT業界には極めて重要な地位があるがよく分かりましょう。試験に合格するのは簡単ではないもよくわかりましょう。“簡単に合格できる方法がありますか？”答えはもちろんですよ。GoShikenはこの問題を着々解決できますよ。IT専門家がLinux FoundationのKCNA認定試験に関する特別な問題集を開発しています。それをもって、試験は問題になりませんよ。
Linux Foundation KCNA（Kubernetes and Cloud Native Associate）認定試験は、クラウドネイティブコンピューティング技術における専門知識を確立したい個人にとって高く求められる認定資格です。Kubernetesは、コンテナ化されたアプリケーションを管理するオープンソースのプラットフォームであり、クラウドネイティブコンピューティングは、クラウドコンピューティングモデルを最大限に活用するアプリケーションの構築と実行のアプローチです。この認定試験は、クラウドネイティブコンピューティングにキャリアを求める専門家のスキルと知識を検証するために設計されています。
質問 # 215
Which of the following is a primary use case of Istio in a Kubernetes cluster?
正解：B
解説：
Istio is a widely adopted service mesh for Kubernetes that focuses on managing service-to-service communication in distributed, microservices-based architectures. Its primary use case is to provide advanced traffic management, observability, and security capabilities between services, making option D the correct answer.
In a Kubernetes cluster, applications often consist of many independent services that communicate over the network. Managing this communication using application code alone becomes complex and error-prone as systems scale. Istio addresses this challenge by inserting a transparent data plane-typically based on Envoy proxies-alongside application workloads. These proxies intercept all inbound and outbound traffic, enabling consistent policy enforcement without requiring code changes.
Istio's traffic management features include fine-grained routing, retries, timeouts, circuit breaking, fault injection, and canary or blue-green deployments. These capabilities allow operators to control how traffic flows between services, test new versions safely, and improve overall system resilience. For observability, Istio provides detailed telemetry such as metrics, logs, and distributed traces, giving deep insight into service performance and behavior. On the security front, Istio enables mutual TLS (mTLS) for service-to-service communication, strong identity, and access policies to secure traffic within the cluster.
Option A is incorrect because container runtime management is handled at the node and cluster level by Kubernetes and the underlying operating system, not by Istio. Option B is incorrect because Istio does not provide database management functionality. Option C is incorrect because persistent storage provisioning is handled by Kubernetes storage APIs and CSI drivers, not by service meshes.
By abstracting networking concerns away from application code, Istio helps teams operate complex microservices environments more safely and efficiently. Therefore, the correct and verified answer is Option D, which accurately reflects Istio's core purpose and documented use cases in Kubernetes ecosystems.
質問 # 216
At which layer would distributed tracing be implemented in a cloud native deployment?
正解：B
解説：
Distributed tracing is implemented primarily at the application layer, so B is correct. The reason is simple: tracing is about capturing the end-to-end path of a request as it traverses services, libraries, queues, and databases. That "request context" (trace ID, span ID, baggage) must be created, propagated, and enriched as code executes. While infrastructure components (proxies, gateways, service meshes) can generate or augment trace spans, the fundamental unit of tracing is still tied to application operations (an HTTP handler, a gRPC call, a database query, a cache lookup).
In Kubernetes-based microservices, distributed tracing typically uses standards like OpenTelemetry for instrumentation and context propagation. Application frameworks emit spans for key operations, attach attributes (route, status code, tenant, retry count), and propagate context via headers (e.g., W3C Trace Context). This is what lets you reconstruct "Service A → Service B → Service C" for one user request and identify the slow or failing hop.
Why other layers are not the best answer:
Network focuses on packets/flows, but tracing is not a packet-capture problem; it's a causal request-path problem across services.
Database spans are part of traces, but tracing is not "implemented in the database layer" overall; DB spans are one component.
Infrastructure provides the platform and can observe traffic, but without application context it can't fully represent business operations (and many useful attributes live in app code).
So the correct layer for "where tracing is implemented" is the application layer-even when a mesh or proxy helps, it's still describing application request execution across components.
質問 # 217
Which storage operator in Kubernetes can help the system to self-scale, self-heal, etc?
正解：A
解説：
Rook is a Kubernetes storage operator that helps manage and automate storage systems in a Kubernetes- native way, so A is correct. The key phrase in the question is "storage operator ... self-scale, self-heal." Operators extend Kubernetes by using controllers to reconcile a desired state. Rook applies that model to storage, commonly by managing storage backends like Ceph (and other systems depending on configuration).
With an operator approach, you declare how you want storage to look (cluster size, pools, replication, placement, failure domains), and the operator works continuously to maintain that state. That includes operational behaviors that feel "self-healing" such as reacting to failed storage Pods, rebalancing, or restoring desired replication counts (the exact behavior depends on the backend and configuration). The important KCNA-level idea is that Rook uses Kubernetes controllers to automate day-2 operations for storage in a way consistent with Kubernetes' reconciliation loops.
The other options do not match the question: "Kubernetes" is the orchestrator itself, not a storage operator.
"Helm" is a package manager for Kubernetes apps-it can install storage software, but it is not an operator that continuously reconciles and self-manages. "CSI" (Container Storage Interface) is an interface specification that enables pluggable storage drivers; CSI drivers provision and attach volumes, but CSI itself is not a "storage operator" with the broader self-managing operator semantics described here.
So, for "storage operator that can help with self-* behaviors," Rook is the correct choice.
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質問 # 218
Which group of container runtimes provides additional sandboxed isolation and elevated security?
正解：C
解説：
The runtimes most associated with sandboxed isolation are gVisor's runsc and Kata Containers, making C correct. Standard container runtimes (like containerd with runc) rely primarily on Linux namespaces and cgroups for isolation. That isolation is strong for many use cases, but it shares the host kernel, which can be a concern for multi-tenant or high-risk workloads.
gVisor (runsc) provides a user-space kernel-like layer that intercepts and mediates system calls, reducing the container's direct interaction with the host kernel. Kata Containers takes a different approach: it runs containers inside lightweight virtual machines, providing hardware-virtualization boundaries (or VM-like isolation) while still integrating into container workflows. Both are used to increase isolation compared to traditional containers, and both can be integrated with Kubernetes through compatible CRI/runtime configurations.
The other options are incorrect for the question's intent. "rune, cgroups" is not a meaningful pairing here (cgroups is a Linux resource mechanism, not a runtime). "docker, containerd" are commonly used container platforms/runtimes but are not specifically the "sandboxed isolation" category (containerd typically uses runc for standard isolation). "crun, cri-o" represents a low-level OCI runtime (crun) and a CRI implementation (CRI-O), again not specifically a sandboxed-isolation grouping.
So, when the question asks for the group that provides additional sandboxing and elevated security, the correct, well-established answer is runsc + Kata.
質問 # 219
Which of the following options is true about considerations for large Kubernetes clusters?
正解：C
解説：
The correct answer is C: Kubernetes scalability guidance commonly cites support up to 5000 nodes and recommends no more than 110 Pods per node. The "110 Pods per node" recommendation is a practical limit based on kubelet, networking, and IP addressing constraints, as well as performance characteristics for scheduling, service routing, and node-level resource management. It is also historically aligned with common CNI/IPAM defaults where node Pod CIDRs are sized for ~110 usable Pod IPs.
Why the other options are incorrect: A and D reference "containers per node," which is not the standard sizing guidance (Kubernetes typically discusses Pods per node). B's "500 Pods per node" is far above typical recommended limits for many environments and would stress IPAM, kubelet, and node resources significantly.
In large clusters, several considerations matter beyond the headline limits: API server and etcd performance, watch/list traffic, controller reconciliation load, CoreDNS scaling, and metrics/observability overhead. You must also plan for IP addressing (cluster CIDR sizing), node sizes (CPU/memory), and autoscaling behavior. On each node, kubelet and the container runtime must handle churn (starts/stops), logging, and volume operations. Networking implementations (kube-proxy, eBPF dataplanes) also have scaling characteristics.
Kubernetes provides patterns to keep systems stable at scale: request/limit discipline, Pod disruption budgets, topology spread constraints, namespaces and quotas, and careful observability sampling. But the exam-style fact this question targets is the published scalability figure and per-node Pod recommendation.
Therefore, the verified true statement among the options is C.
質問 # 220
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KCNA日本語認定対策: https://www.goshiken.com/Linux-Foundation/KCNA-mondaishu.html
無料でクラウドストレージから最新のGoShiken KCNA PDFダンプをダウンロードする：https://drive.google.com/open?id=1Sbb0bRMxXgTq8wso_vJNV1KmeqDV0lHQ