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The Linux Foundation KCNA exam is suitable for individuals who have a basic understanding of Linux and containers and want to deepen their knowledge of Kubernetes and cloud native technologies. It covers a range of topics, including Kubernetes architecture, deployment and maintenance, networking, security, and troubleshooting. KCNA Exam is conducted online and consists of multiple-choice questions that test the candidate's understanding of these concepts.
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The KCNA Exam is a performance-based exam that involves completing a set of tasks in a real-world environment. KCNA exam is conducted online and requires candidates to have access to a Linux environment with Kubernetes and other cloud-native tools installed. KCNA Exam is designed to test the candidate's ability to deploy and manage containerized applications using Kubernetes and other cloud-native technologies.
NEW QUESTION # 116
Which statement best describes observability in a cloud native environment?
Answer: B
Explanation:
Observability involves collecting and analyzing telemetry data -- logs, metrics, and traces -- to gain insight into system behavior, diagnose issues, and understand how applications perform in a cloud native environment.
NEW QUESTION # 117
What is FinOps?
Answer: A
Explanation:
https://www.servicenow.com/products/it-asset-management/what-is-finops.html
NEW QUESTION # 118
What components are common in a service mesh?
Answer: B
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 # 119
DNS issues are suspected inside a Pod. Which command is most useful to verify the problem?
Answer: C
Explanation:
Executing nslookup inside the Pod tests DNS resolution from the Pod's own network namespace, directly verifying whether service names can be resolved as expected.
NEW QUESTION # 120
Which of the following is a valid PromQL query?
Answer: B
Explanation:
Prometheus Query Language (PromQL) uses a function-and-selector syntax, not SQL. A valid query typically starts with a metric name and optionally includes label matchers in curly braces. In the simplified quiz syntax given, the valid PromQL-style selector is best represented by D: http_requests_total(job="apiserver"), so D is correct.
Conceptually, what this query means is "select time series for the metric http_requests_total where the job label equals apiserver." In standard PromQL formatting you most often see this as: http_requests_total{job="apiserver"}. Many training questions abbreviate braces and focus on the idea of filtering by labels; the key is that PromQL uses label matchers rather than SQL WHERE clauses.
Options A and C are invalid because they use SQL (SELECT * FROM ...) which is not PromQL. Option B is also invalid because PromQL does not use the keyword WHERE. PromQL filtering is done by applying label matchers directly to the metric selector.
In Kubernetes observability, PromQL is central to building dashboards and alerts from cluster metrics. For example, you might compute rates from counters: rate(http_requests_total{job="apiserver"}[5m]), aggregate by labels: sum by (code) (...), or alert on error ratios. Understanding the selector and label-matcher model is foundational because Prometheus metrics are multi-dimensional-labels define the slices you can filter and aggregate on.
So, within the provided options, D is the only one that follows PromQL's metric+label-filter style and therefore is the verified correct answer.
NEW QUESTION # 121
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