KCNA Practice Test 48 — 15 Questions

Question 1

A Kubernetes cluster has a single control plane node and two worker nodes. The control plane node fails. What is the immediate impact on the workloads running on the worker nodes?

Accepted Answer

Existing workloads continue running, but no new pods can be scheduled. In Kubernetes, the control plane is responsible for scheduling new pods and maintaining desired state via the API server, controller manager, and scheduler. When the control plane fails, the kubelets on worker nodes continue to run existing pods based on their local state, but the scheduler cannot assign new pods to nodes, and the API server is unavailable for updates or scaling operations.

Answer

All workloads will stop immediately

Answer

The kubelet on worker nodes will restart all pods

Answer

Workloads will be automatically migrated to another cluster

Question 2

Which TWO of the following are benefits of using Helm for managing Kubernetes applications?

Accepted Answer

Templating engine for parameterizing Kubernetes manifests. Helm's templating engine (option C) lets you parameterize Kubernetes manifests with values files and Go templates, so the same chart can render environment-specific YAML instead of maintaining duplicated static manifests. Helm also tracks each install/upgrade as a numbered revision, so `helm rollback ` can restore a previous release (option D), which is a core release-management benefit. The other options describe capabilities Helm does not provide natively: automatic CPU-based pod scaling is the Horizontal Pod Autoscaler (option A), service-mesh traffic splitting is handled by tools like Istio or Linkerd (option B), and canary deployments require additional controllers or progressive-delivery tools such as Argo Rollouts or Flagger (option E).

Answer

Automatic scaling of pods based on CPU usage

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Native integration with service mesh for traffic splitting

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Built-in support for canary deployments

Question 3

A DevOps engineer wants to update a Deployment's container image from 'v1' to 'v2' with zero downtime. Which kubectl command should they use?

Accepted Answer

kubectl set image deployment/ =:v2. `kubectl set image` directly updates the container image in a Deployment's pod template, triggering a rolling update that replaces pods incrementally with zero downtime. Kubernetes Deployments manage ReplicaSets to ensure availability during the update, making this the simplest and most reliable command for a controlled image change.

Answer

kubectl rollout restart deployment/<name>

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kubectl patch deployment -p '{"spec":{"template":{"spec":{"containers":[{"name":"","image":":v2"}]}}}}'

Answer

kubectl edit deployment <name>

Question 4

Which command would you use to apply a manifest file 'deployment.yaml' to a Kubernetes cluster?

Accepted Answer

kubectl apply -f deployment.yaml. kubectl apply -f deployment.yaml is the declarative command that creates or updates resources from a manifest file, reconciling the desired state in the file with the cluster's current state. The -f flag specifies the file, and apply is idempotent — running it repeatedly produces the same result. This is the standard way to deploy manifests in Kubernetes.

Answer

kubectl run deployment.yaml

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kubectl set image deployment.yaml

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kubectl create -f deployment.yaml

Question 5

Based on the exhibit, why is the pod web-pod not running?

Accepted Answer

The container image is not available in the registry.. The pod's status indicates an ImagePullBackOff error, which occurs when the kubelet fails to pull the specified container image from the registry. This typically means the image name or tag is incorrect, the registry is unreachable, or the image does not exist in the registry. The exhibit shows the pod is stuck in a waiting state with the reason 'ErrImagePull' or 'ImagePullBackOff', directly pointing to a missing or inaccessible image.

Answer

A network policy is blocking the image pull.

Answer

The node does not have enough memory.

Answer

The pod was not scheduled onto a node.

Question 6

What is Helm's role in Kubernetes?

Accepted Answer

A package manager for Kubernetes applications. Helm is widely recognized as the package manager for Kubernetes, analogous to apt or yum for Linux. It allows you to define, install, and upgrade even the most complex Kubernetes applications using reusable templates called charts. Charts bundle all necessary Kubernetes manifests and dependencies, enabling consistent deployments across environments.

Answer

A CI/CD server

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A security scanner for container images

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A monitoring and logging tool

Question 7

Which of the following is a key benefit of container orchestration compared to running containers manually?

Accepted Answer

Automatic scaling and self-healing. Container orchestration platforms like Kubernetes provide automatic scaling and self-healing capabilities that are not available when running containers manually. Self-healing automatically restarts failed containers, reschedules them when nodes fail, and replaces containers that fail health checks, while scaling adjusts the number of replicas based on CPU/memory metrics or custom metrics. Manual container management requires operators to monitor and intervene for each failure or load change, making orchestration essential for production-grade reliability and elasticity.

Answer

Containers use less memory

Answer

Orchestration eliminates the need for container images

Answer

Containers run faster when orchestrated

Question 8

In OpenTelemetry, what is the purpose of the Collector component?

Accepted Answer

Receive, process, and export telemetry data. The OpenTelemetry Collector is a vendor-agnostic agent or gateway that receives telemetry data (traces, metrics, logs) from instrumented applications, processes it (e.g., batching, filtering, sampling), and exports it to one or more backends (e.g., Jaeger, Prometheus, or any OTLP-compatible system). It decouples data generation from data export, enabling flexible pipeline management without modifying application code.

Answer

Instrument code automatically

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Visualize traces and metrics

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Aggregate logs from multiple sources

Question 9

A developer wants to deploy a stateless web application that should scale to 5 replicas. Each replica must be identical and should be automatically replaced if it fails. Which Kubernetes resource should be used?

Accepted Answer

Deployment. A Deployment is the correct resource because it manages a ReplicaSet to ensure the desired number of identical, stateless pod replicas (5) are running. It provides declarative updates, self-healing (automatic replacement of failed pods), and scaling capabilities, which directly match the requirement for a stateless web application.

Answer

StatefulSet

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DaemonSet

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ReplicationController

Question 10

What type of Prometheus metric is best suited to count the total number of HTTP requests received by a service?

Accepted Answer

Counter. A Counter is a cumulative metric that only increases (or resets to zero on restart), making it ideal for tracking the total number of events like HTTP requests. Since the total request count is monotonically increasing, a Counter directly represents this without additional processing. Other metric types like Gauge, Histogram, and Summary serve different purposes: Gauge can go up and down, Histogram and Summary are for distributions (e.g., request durations).

Answer

Histogram

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Gauge

Answer

Summary

Question 11

A pod is running but its container exits with code 137. The pod logs show 'Killed'. What is the most likely cause?

Accepted Answer

The container was OOMKilled due to memory limit. Exit code 137 (128 + 9) indicates the container was terminated by SIGKILL. Combined with 'Killed' in logs, this is the definitive signature of an OOMKill event, where the Linux kernel's Out-Of-Memory (OOM) killer terminates the container process because it exceeded its memory limit (specified in the pod's resource limits). Kubernetes enforces memory limits via cgroups, and when the container's memory usage surpasses the limit, the OOM killer sends SIGKILL, resulting in exit code 137.

Answer

The container's CPU limit was exceeded

Answer

The container's liveness probe failed

Answer

The node ran out of disk space

Question 12

Which TWO statements about container images are correct? (Choose two.)

Accepted Answer

Each layer is identified by a unique hash. Option B is correct because container image layers are content-addressable: each layer is identified by a unique cryptographic digest (hash) of its content, which is how registries and runtimes verify integrity and deduplicate layers. Option D is correct because an image is constructed as a stack of read-only layers, typically defined by Dockerfile instructions, with each layer adding or changing files on top of the previous one. Option A is incorrect because images can be pulled from public registries such as Docker Hub as well as private registries, so 'always' is false. Option C is incorrect because writing at runtime happens in the writable container layer created on top of the image, not by modifying the image layers themselves. Option E is incorrect because after a pull, image layers are stored in the container runtime's local storage area (for example, /var/lib/docker or /var/lib/containers) rather than simply as arbitrary files on the host filesystem.

Answer

Images are always pulled from a private registry

Answer

Images can be modified at runtime by writing to the container layer

Answer

Images are stored on the host filesystem after being pulled

Question 13

An SRE team defines an SLO that 99.9% of requests to a service should complete in under 500ms over a 30-day rolling window. If the service receives 10 million requests in a month, what is the maximum number of requests that can exceed the latency threshold while still meeting the SLO?

Accepted Answer

10,000. An SLO of 99.9% means 0.1% of requests may violate the latency threshold. With 10,000,000 requests, 0.1% equals 10,000 requests. This is the error budget for the 30-day rolling window.

Answer

5,000

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1,000

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100,000

Question 14

An organization wants to manage infrastructure using code to ensure consistent and repeatable deployments across multiple cloud providers. Which tool is MOST suitable for this multi-cloud Infrastructure as Code approach?

Accepted Answer

Terraform. Terraform is provider-agnostic and supports AWS, Azure, GCP, and hundreds of other providers through a plugin architecture, making it the most suitable tool for defining infrastructure as code across multiple clouds with a single workflow and state model. Its HCL configuration and plan/apply cycle give consistent, repeatable deployments regardless of the target cloud.

Answer

Kuberntes manifests

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AWS CloudFormation

Answer

Azure Resource Manager Templates

Question 15

What is the Open Container Initiative (OCI) responsible for?

Accepted Answer

Defining standards for container images and runtimes. The Open Container Initiative (OCI) is a Linux Foundation project that defines open industry standards for container formats and runtimes. Specifically, it maintains the OCI Image Specification (which standardizes the container image format, including layers and configuration) and the OCI Runtime Specification (which defines the lifecycle and interface for container runtimes like runc). This ensures interoperability between different container tools and platforms.

Answer

Certifying Kubernetes administrators

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Providing a hosted container registry

Answer

Managing the Kubernetes source code