KCNA시험덤프 - KCNA유효한최신버전덤프

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Linux Foundation KCNA (Kubernetes and Cloud Native Associate) 인증 시험은 Kubernetes 및 Cloud-Native Technologies와의 협력에 대한 지식과 숙련도를 검증하는 업계 인식 인증입니다. 이 시험은 Kubernetes 및 Cloud-Native Architectures의 기본 개념과 주요 구성 요소에 대한 개인의 이해를 평가하기위한 것입니다.

>> KCNA시험덤프 <<

KCNA시험덤프 완벽한 시험대비 덤프자료

Linux Foundation인증 KCNA시험을 패스하여 자격증을 취득하는게 꿈이라구요? KoreaDumps에서 고객님의Linux Foundation인증 KCNA시험패스꿈을 이루어지게 지켜드립니다. KoreaDumps의 Linux Foundation인증 KCNA덤프는 가장 최신시험에 대비하여 만들어진 공부자료로서 시험패스는 한방에 끝내줍니다.

Linux Foundation KCNA (Kubernetes and Cloud Native Associate) 인증 시험은 Cloud-Native 컴퓨팅 분야에서 개인의 지식과 기술을 테스트하기 위해 설계된 포괄적 인 시험입니다. 이 시험에는 Kubernetes, 컨테이너화, 마이크로 서비스 및 클라우드 네이티브 아키텍처를 포함한 다양한 주제가 다릅니다. 이 인증은 클라우드 네이티브 애플리케이션 설계, 배포 및 관리에 대한 전문 지식을 입증하려는 개인에게 이상적입니다.

최신 Kubernetes Cloud Native Associate KCNA 무료샘플문제 (Q279-Q284):

질문 # 279
During a team meeting, a developer mentions the significance of open collaboration in the cloud native ecosystem. Which statement accurately reflects principles of collaborative development and community stewardship?

정답:A

설명:
Collaborative development in the cloud native ecosystem is built on open collaboration, where community events and working groups enable knowledge sharing, collective problem solving, and relationship building, all of which are essential for healthy, sustainable open source projects.


질문 # 280
Which of the following is a recommended security habit in Kubernetes?

정답:D

설명:
Disallowing privilege escalation within containers by default helps reduce the attack surface and enhances Kubernetes security best practices.


질문 # 281
Which of the following is a good habit for cloud native cost efficiency?

정답:C

설명:
The correct answer is A. In cloud-native environments, costs are highly dynamic: autoscaling changes compute footprint, ephemeral environments come and go, and usage-based billing applies to storage, network egress, load balancers, and observability tooling. Because of this variability, automation is the most sustainable way to achieve cost efficiency. Automated visibility (dashboards, chargeback/showback), anomaly detection, and forecasting help teams understand where spend is coming from and how it changes over time. Automated optimization actions can include right-sizing requests/limits, enforcing TTLs on preview environments, scaling down idle clusters, and cleaning unused resources.
Manual processes (B) don't scale as complexity grows. By the time someone reviews a spreadsheet or dashboard weekly, cost spikes may have already occurred. Automation enables fast feedback loops and guardrails, which is essential for preventing runaway spend caused by misconfiguration (e.g., excessive log ingestion, unbounded autoscaling, oversized node pools).
Option C is not a cost-efficiency "habit." Single-provider strategies may simplify some billing views, but they can also reduce leverage and may not be feasible for resilience/compliance; it's a business choice, not a best practice for cloud-native cost management. Option D is counterproductive: keeping legacy workloads unchanged often wastes money because cloud efficiency typically requires adapting workloads-right-sizing, adopting autoscaling, and using managed services appropriately.
In Kubernetes specifically, cost efficiency is tightly linked to resource management: accurate CPU/memory requests, limits where appropriate, cluster autoscaler tuning, and avoiding overprovisioning. Observability also matters because you can't optimize what you can't measure. Therefore, the best habit is an automated cost optimization approach with strong visibility and forecasting-A.
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질문 # 282
What components are common in a service mesh?

정답:C

설명:
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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질문 # 283
What can be used to create a job that will run at specified times/dates or on a repeating schedule?

정답:D

설명:
A CronJob in Kubernetes is designed to run jobs on a specified schedule, similar to the Unix cron utility, allowing execution at defined times or intervals.


질문 # 284
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