KCNA Cloud Native Architecture Practice Test 3 — 15 Questions

Question 1

The exhibit shows pod status and logs. The web pod lmn34 has restarted 3 times. What is the root cause of the liveness probe failure?

Accepted Answer

The database service is not reachable, causing the application to fail its health check.. The liveness probe failure is caused by the database service being unreachable, which prevents the application from completing its health check. When the database is down or network connectivity is lost, the application's health endpoint returns a non-200 status code, causing Kubernetes to restart the container. The 3 restarts indicate repeated probe failures, and the logs show connection errors to the database, confirming this as the root cause.

Answer

The container is hitting a memory limit and being OOMKilled.

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A network policy is blocking traffic to the database.

Answer

The readiness probe is misconfigured and not allowing traffic.

Question 2

A development team is designing a new microservices application to run on a Kubernetes cluster. They want to ensure that each microservice can be developed, deployed, and scaled independently. Which cloud native architecture principle are they primarily applying?

Accepted Answer

Loose coupling. The principle of loose coupling ensures that each microservice can be developed, deployed, and scaled independently by minimizing dependencies between services. In Kubernetes, this is achieved through well-defined APIs and service boundaries, allowing teams to update or scale one service without affecting others. This directly supports the team's goal of independent lifecycle management for each microservice.

Answer

Immutable infrastructure

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Statelessness

Answer

Service discovery

Question 3

You are a platform engineer at a fast-growing startup. The company runs a Kubernetes cluster with 50 worker nodes for its production microservices. Recently, the operations team has been struggling with manual configuration drift: developers SSH into nodes to install debugging tools, and some nodes have different kernel parameters or installed packages. This has caused intermittent outages when a pod is scheduled onto a non-standard node. The CTO wants a solution that ensures each node is identical, immutable, and reproducible. The cluster uses kubeadm for bootstrapping and runs on AWS EC2. Which approach best achieves the goal of immutable nodes?

Accepted Answer

Create a golden AMI using Packer with all required configurations, then use Auto Scaling groups with a launch template that references the AMI and enable instance refresh for updates.. It uses a golden AMI built with Packer to create identical, immutable nodes that are reproducible via Auto Scaling groups and launch templates. This approach ensures that every EC2 instance launched has the exact same kernel parameters, packages, and configuration, eliminating configuration drift. Instance refresh allows rolling updates to the AMI without manual intervention, aligning with the goal of immutable infrastructure.

Answer

Use a configuration management tool like Ansible to enforce desired state on each node via periodic runs.

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Apply Kubernetes node labels and taints to categorize nodes and prevent workloads from running on non-standard nodes.

Answer

Deploy a DaemonSet that runs a privileged container to enforce node configuration and remove debugging tools.

Question 4

Which TWO of the following are key principles of cloud native architecture?

Accepted Answer

Infrastructure automation. Infrastructure automation (B) is a key principle of cloud native architecture because it enables consistent, repeatable, and error-free provisioning and management of infrastructure through code (e.g., Terraform, AWS CloudFormation, Ansible). This aligns with the cloud native goal of reducing manual toil and enabling rapid, reliable deployments. Microservices (C) is also a core principle, as it structures applications as a collection of loosely coupled, independently deployable services that can be scaled and updated individually, which is fundamental to cloud native agility and resilience.

Answer

Immutable infrastructure

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Monolithic design

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Manual scaling

Question 5

A developer creates the Pod manifest shown. When the Pod runs, the liveness probe fails and the container is restarted repeatedly. What is the most likely cause?

Accepted Answer

The liveness probe port (8080) does not match the container port (80).. The liveness probe is configured to check TCP on port 8080, but the container exposes port 80 for Nginx. Since the probe will never successfully connect to port 8080, it always fails, causing the container to be restarted repeatedly. The probe must target the same port that the application is listening on.

Answer

The initialDelaySeconds of 3 is too short for Nginx to start.

Answer

The periodSeconds of 5 causes too frequent probing.

Answer

The image nginx:latest does not have a /healthz endpoint.

Question 6

Your organization runs a multi-service application on a Kubernetes cluster. Each service is deployed as a set of Pods managed by a Deployment. The application experiences intermittent slowdowns during peak traffic. Monitoring shows that the database service Pods have high CPU usage, but the HorizontalPodAutoscaler (HPA) configured for the database Deployment does not scale. The HPA is based on average CPU utilization across Pods, with target 70%. The database Deployment has resource requests and limits set: requests.cpu: 500m, limits.cpu: 1000m. During peak, CPU usage reaches 800m per Pod. The HPA has a cooldown period of 3 minutes. The cluster has ample capacity. What is the most likely reason the HPA is not scaling?

Accepted Answer

The HPA cooldown period of 3 minutes prevents scaling during the short peak duration.. The HPA cooldown period of 3 minutes prevents scaling during short peak durations. The scenario states 'intermittent slowdowns during peak traffic,' implying the high CPU usage is not sustained long enough to trigger a scale-up event. After a previous scale operation (possibly due to an earlier peak), the cooldown period must elapse before the HPA can initiate another scaling action. Since the peaks are short and intermittent, they may fall within the cooldown window, preventing the HPA from scaling despite high CPU usage.

Answer

The HPA is configured to use a different metric (e.g., memory) instead of CPU.

Answer

The CPU limit of 1000m restricts the Pods from using more than 1000m, but the HPA bases scaling on requests, not limits.

Answer

The cluster does not have enough nodes to schedule additional Pods.

Question 7

Drag and drop the steps to configure a Kubernetes Service of type LoadBalancer in a cloud environment into the correct order.

Accepted Answer

Step 1: Deploy the application. Step 2: Define and create the LoadBalancer service. Step 3: Retrieve the external IP. Step 4: Access the application using the IP.. First deploy the app, then define and create the LoadBalancer service, retrieve the IP, and access it.

Answer

Step 1: Define and create the LoadBalancer service. Step 2: Deploy the application. Step 3: Retrieve the external IP. Step 4: Access the application.

Answer

Step 1: Retrieve the external IP. Step 2: Deploy the application. Step 3: Define and create the LoadBalancer service. Step 4: Access the application.

Answer

Step 1: Deploy the application. Step 2: Retrieve the external IP. Step 3: Define and create the LoadBalancer service. Step 4: Access the application.

Question 8

Match each Kubernetes object to its typical use case.

Accepted Answer

Deployment: Manage stateless applications with rolling updates and rollbacks. Correct matches: Deployment manages stateless apps with rolling updates; StatefulSet handles stateful apps with stable identities; DaemonSet runs a pod on every node. Common confusions: mixing Deployment with DaemonSet (one per node vs. stateless management) and StatefulSet with Jobs (stateful vs. batch).

Answer

Deployment: Ensure one pod per node in the cluster

Answer

StatefulSet: Run a batch job to completion

Question 9

Which of the following best describes the purpose of the Cloud Native Computing Foundation (CNCF)?

Accepted Answer

To promote cloud native technologies and host open source projects. The CNCF's primary purpose is to foster the adoption of cloud native technologies by hosting and governing open source projects like Kubernetes, Prometheus, and Envoy. It provides a neutral home for these projects, ensuring they are developed collaboratively under a vendor-neutral governance model. This aligns directly with option A, which captures both the promotional and hosting roles of the foundation.

Answer

To define the 12-factor app methodology

Answer

To provide cloud infrastructure services for enterprises

Answer

To develop and maintain the Kubernetes project exclusively

Question 10

Which of the following is a core principle of cloud native architecture as defined by the CNCF?

Accepted Answer

Microservices packaged in containers. The CNCF defines cloud native architecture as using microservices, containers, dynamic orchestration, and DevOps.

Answer

Monolithic application design

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Manual scaling of applications

Answer

Static infrastructure provisioning

Question 11

In the 12-factor app methodology, which factor describes the practice of storing configuration in environment variables?

Accepted Answer

Config. The Config factor (Factor III) of the 12-factor app methodology mandates that configuration—such as database URLs, credentials, or feature flags—be stored in environment variables. This decouples configuration from code, allowing the same build to be deployed across different environments without code changes, and prevents accidental commits of secrets to version control.

Answer

Backing services

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Processes

Answer

Build, release, run

Question 12

A team wants to deploy a serverless function that scales to zero when not in use. Which CNCF project is specifically designed for this purpose?

Accepted Answer

Knative. Knative is the correct answer because it is a CNCF project built on Kubernetes that provides a serverless platform specifically designed to scale workloads to zero when not in use. It achieves this through its Serving component, which automatically scales pods down to zero replicas based on traffic, and scales up from zero on the first request, enabling true serverless behavior.

Answer

Helm

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Prometheus

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Envoy

Question 13

Which component in a service mesh is responsible for handling traffic management, security, and observability as a sidecar proxy?

Accepted Answer

Envoy. Envoy is the most common sidecar proxy used in service meshes like Istio.

Answer

Pilot

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Mixer

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Citadel

Question 14

An application experiences intermittent failures when calling an external API. Which resilience pattern should be implemented to handle transient faults?

Accepted Answer

Retry. (Retry) is correct because intermittent failures when calling an external API are typically transient faults (e.g., network glitches, temporary service unavailability). The Retry pattern automatically reattempts the failed operation a configured number of times, often with exponential backoff, to overcome these short-lived issues without changing the application's overall architecture. This directly addresses the scenario's requirement to handle transient faults.

Answer

Bulkhead

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Circuit breaker

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Timeout

Question 15

Which of the following best describes the GitOps pattern?

Accepted Answer

Declarative configuration stored in Git, with automated deployment via a controller. GitOps is a pattern where the entire system's desired state is declared in a Git repository, and an automated controller (such as Argo CD or Flux) continuously reconciles the live cluster state with that declarative configuration. This ensures that Git is the single source of truth, and any drift from the declared state is automatically corrected without manual intervention.

Answer

Imperative commands applied to the cluster and tracked in Git

Answer

Using Git as a backup for Kubernetes manifests

Answer

Using Git hooks to trigger CI/CD pipelines