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| Certification Vendor: | Linux Foundation |
|---|---|
| Exam Name: | Kubernetes and Cloud Native Associate (KCNA) Exam |
| Exam Number: | KCNA |
| Certificate Validity Period: | 3 years |
| Exam Price: | $250 USD |
| Exam Format: | Multiple select, Online proctored exam, Multiple choice |
| Passing Score: | 75% |
| Related Certifications: | Certified Kubernetes Security Specialist (CKS) Certified Kubernetes Application Developer (CKAD) Certified Kubernetes Administrator (CKA) |
| Real Exam Qty: | Approximately 60 |
| Available Languages: | English |
| Exam Duration: | 90 minutes |
| Recommended Training: | Introduction to Kubernetes (LFS158) Cloud Native Fundamentals / KCNA Preparation courses |
| Exam Registration: | KCNA Exam Page Linux Foundation Certification Portal |
| 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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NEW QUESTION # 45
Which kubectlcommand is useful for collecting information about any type of resource that is active in a Kubernetes cluster?
Answer: A
NEW QUESTION # 46
What happens if only a limit is specified for a resource and no admission-time mechanism has applied a default request?
Answer: B
Explanation:
If a container specifies a resource limit but no request and no admission-time default is applied, Kubernetes automatically sets the request equal to the specified limit, ensuring the scheduler has a defined resource request to use for placement decisions.
NEW QUESTION # 47
What fields must exist in any Kubernetes object (e.g. YAML) file?
Answer: D
Explanation:
Any Kubernetes object manifest must include apiVersion, kind, and metadata, which makes A correct. This comes directly from how Kubernetes resources are represented and processed by the API server.
* apiVersion tells Kubernetes which API group and version should be used to interpret the object (for example v1, apps/v1, batch/v1). This matters because schemas and available fields can change between versions.
* kind specifies the type of object you are creating (for example Pod, Service, Deployment, ConfigMap).
Kubernetes uses this to route the request to the correct API endpoint and schema.
* metadata contains identifying and organizational information such as name, namespace (when namespaced), labels, and annotations. At minimum, most objects require a name; labels and annotations are optional but extremely common for selection and tooling.
A common point of confusion is spec. Many Kubernetes objects include spec because they define desired state (like a Deployment's replica count, Pod template, update strategy). However, the question asks what fields must exist in any Kubernetes object file. Not all objects require a spec in the same way (and some objects include other top-level sections like data for ConfigMaps/Secrets or rules for RBAC objects). The truly universal top-level requirements are the trio in option A.
Options B, C, and D include fields that are not universally required (namespace is not required for cluster- scoped objects, and data only applies to certain kinds like ConfigMaps/Secrets). Therefore, apiVersion + kind + metadata is the correct, general rule and matches Kubernetes object structure.
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NEW QUESTION # 48
What does vertical scaling an application deployment describe best?
Answer: B
Explanation:
Vertical scaling means changing the resources allocated to a single instance of an application (more or less CPU/memory), which is why C is correct. In Kubernetes terms, this corresponds to adjusting container resource requests and limits (for CPU and memory). Increasing resources can help a workload handle more load per Pod by giving it more compute or memory headroom; decreasing can reduce cost and improve cluster packing efficiency.
This differs from horizontal scaling, which changes the number of instances (replicas). Option D describes horizontal scaling: adding/removing replicas of the same workload, typically managed by a Deployment and often automated via the Horizontal Pod Autoscaler (HPA). Option B describes scaling the infrastructure layer (nodes) which is cluster/node autoscaling (Cluster Autoscaler in cloud environments). Option A is not a standard scaling definition.
In practice, vertical scaling in Kubernetes can be manual (edit the Deployment resource requests/limits) or automated using the Vertical Pod Autoscaler (VPA), which can recommend or apply new requests based on observed usage. A key nuance is that changing requests/limits often requires Pod restarts to take effect, so vertical scaling is less "instant" than HPA and can disrupt workloads if not planned. That's why many production teams prefer horizontal scaling for traffic-driven workloads and use vertical scaling to right-size baseline resources or address memory-bound/cpu-bound behavior.
From a cloud-native architecture standpoint, understanding vertical vs horizontal scaling helps you design for elasticity: use vertical scaling to tune per-instance capacity; use horizontal scaling for resilience and throughput; and combine with node autoscaling to ensure the cluster has sufficient capacity. The definition the question is testing is simple: vertical scaling = change resources per application instance, which is option C.
NEW QUESTION # 49
What is Serverless computing?
Answer: A
NEW QUESTION # 50
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