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| Certification Vendor: | Linux Foundation |
|---|---|
| Exam Name: | Kubernetes and Cloud Native Associate (KCNA) Exam |
| Exam Number: | KCNA |
| Exam Duration: | 90 minutes |
| Passing Score: | 75% |
| Available Languages: | English |
| Related Certifications: | Certified Kubernetes Application Developer (CKAD) Certified Kubernetes Administrator (CKA) Certified Kubernetes Security Specialist (CKS) |
| Exam Price: | $250 USD |
| Certificate Validity Period: | 3 years |
| Exam Format: | Online proctored exam, Multiple select, Multiple choice |
| Real Exam Qty: | Approximately 60 |
| Recommended Training: | Introduction to Kubernetes (LFS158) Cloud Native Fundamentals / KCNA Preparation courses |
| Exam Registration: | Linux Foundation Certification Portal KCNA Exam Page |
| Sample Questions: | Linux Foundation KCNA Sample Questions |
| Exam Way: | Online proctored exam |
| Pre Condition: | No formal prerequisites; basic understanding of cloud, containers, and Kubernetes concepts is recommended |
| Official Syllabus URL: | https://training.linuxfoundation.org/certification/kubernetes-cloud-native-associate-kcna/ |
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Linux Foundation KCNA Certification Exam is a vendor-neutral certification program, which means that it is not tied to any specific cloud platform or technology vendor. This makes it an excellent choice for IT professionals who want to demonstrate their expertise in Kubernetes and cloud native technologies without being limited by proprietary technologies or vendor lock-in.
NEW QUESTION # 69
Which are the core features provided by a service mesh?
Answer: B
Explanation:
A is the correct answer because a service mesh primarily focuses on securing and managing service-to- service communication, and a core part of that is authentication and authorization. In microservices architectures, internal ("east-west") traffic can become a complex web of calls. A service mesh introduces a dedicated communication layer-commonly implemented with sidecar proxies or node proxies plus a control plane-to apply consistent security and traffic policies across services.
Authentication in a mesh typically means service identity: each workload gets an identity (often via certificates), enabling mutual TLS (mTLS) so services can verify each other and encrypt traffic in transit.
Authorization then builds on identity to enforce "who can talk to whom" via policies (for example: service A can call service B only on certain paths or methods). These capabilities are central because they reduce the need for every development team to implement and maintain custom security libraries correctly.
Why the other answers are incorrect:
* B (data distribution/replication) is a storage/database concern, not a mesh function.
* C (vulnerability scanning) is typically part of CI/CD and supply-chain security tooling, not service-to- service runtime traffic management.
* D (configuration management) is broader (GitOps, IaC, Helm/Kustomize); a mesh does have configuration, but "configuration management" is not the defining core feature tested here.
Service meshes also commonly provide traffic management (timeouts, retries, circuit breaking, canary routing) and telemetry (metrics/traces), but among the listed options, authentication and authorization best matches "core features." It captures the mesh's role in standardizing secure communications in a distributed system.
So, the verified correct answer is A.
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NEW QUESTION # 70
Which of the following would fall under the responsibilities of an SRE?
Answer: B
NEW QUESTION # 71
What kubectl command is used to retrieve the resource consumption (CPU and memory) for nodes or Pods?
Answer: A
Explanation:
To retrieve CPU and memory consumption for nodes or Pods, you use kubectl top, so C is correct. kubectl top nodes shows per-node resource usage, and kubectl top pods shows per-Pod (and optionally per-container) usage. This data comes from the Kubernetes resource metrics pipeline, most commonly metrics-server, which scrapes kubelet/cAdvisor stats and exposes them via the metrics.k8s.io API.
It's important to recognize that kubectl top provides current resource usage snapshots, not long-term historical trending. For long-term metrics and alerting, clusters typically use Prometheus and related tooling. But for quick operational checks-"Is this Pod CPU-bound?" "Are nodes near memory saturation?"-kubectl top is the built-in day-to-day tool.
Option A (kubectl cluster-info) shows general cluster endpoints and info about control plane services, not resource usage. Option B (kubectl version) prints client/server version info. Option D (kubectl api-resources) lists resource types available in the cluster. None of those report CPU/memory usage.
In observability practice, kubectl top is often used during incidents to correlate symptoms with resource pressure. For example, if a node is high on memory, you might see Pods being OOMKilled or the kubelet evicting Pods under pressure. Similarly, sustained high CPU utilization might explain latency spikes or throttling if limits are set. Note that kubectl top requires metrics-server (or an equivalent provider) to be installed and functioning; otherwise it may return errors like "metrics not available." So, the correct command for retrieving node/Pod CPU and memory usage is kubectl top.
NEW QUESTION # 72
Which role is responsible of creating service level indicator 'SLI', service level objective 'SLO', & Service Level Agreements 'SLA'
Answer: A
Explanation:
https://www.atlassian.com/incident-management/kpis/sla-vs-slo-vs-sli
NEW QUESTION # 73
Which of the following would fall under the responsibilities of an SRE?
Answer: B
Explanation:
Site Reliability Engineering (SRE) focuses on reliability, availability, performance, and operational excellence using engineering approaches. Among the options, creating a monitoring baseline for an application is a classic SRE responsibility, so B is correct. A monitoring baseline typically includes defining key service-level signals (latency, traffic, errors, saturation), establishing dashboards, setting sensible alert thresholds, and ensuring telemetry is complete enough to support incident response and capacity planning.
In Kubernetes environments, SRE work often involves ensuring that workloads expose health endpoints for probes, that resource requests/limits are set to allow stable scheduling and autoscaling, and that observability pipelines (metrics, logs, traces) are consistent. Building a monitoring baseline also ties into SLO/SLI practices: SREs define what "good" looks like, measure it continuously, and create alerts that notify teams when the system deviates from those expectations.
Option A is primarily an application developer task-SREs may contribute to reliability features, but core product feature development is usually owned by engineering teams. Option C is more aligned with finance, FinOps, or management responsibilities, though SRE data can inform costs. Option D is closer to governance, platform policy, or developer experience/process ownership; SREs might influence processes, but "policy on how to submit code change" is not the defining SRE duty compared to monitoring and reliability engineering.
Therefore, the best verified choice is B, because establishing monitoring baselines is central to operating reliable services on Kubernetes.
NEW QUESTION # 74
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