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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Cloud Native Architecture | 16% | - Cloud Native Landscape
|
| Topic 2: Cloud Native Application Delivery | 8% | - CI/CD
|
| Topic 3: Container Orchestration | 22% | - Security
|
| Topic 4: Cloud Native Observability | 8% | - Tracing
|
| Topic 5: Kubernetes Fundamentals | 46% | - Kubernetes API
|
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NEW QUESTION # 114
Which of the following is a feature Kubernetes provides by default as a container orchestration tool?
Answer: C
Explanation:
Kubernetes provides automated rollouts and rollbacks for workloads by default (via controllers like Deployments), so D is correct. In Kubernetes, application delivery is controller-driven: you declare the desired state (new image, new config), and controllers reconcile the cluster toward that state. Deployments implement rolling updates, gradually replacing old Pods with new ones while respecting availability constraints. Kubernetes tracks rollout history and supports rollback to previous ReplicaSets when an update fails or is deemed unhealthy.
This is a core orchestration capability: it reduces manual intervention and makes change safer. Rollouts use readiness checks and update strategies to avoid taking the service down, and kubectl rollout status/history
/undo supports day-to-day release operations.
The other options are not "default Kubernetes orchestration features":
* Kubernetes is not a portable operating system (A). It's a platform for orchestrating containers on top of an OS.
* Kubernetes does not provide filesystem redundancy by itself (B). Storage redundancy is handled by underlying storage systems and CSI drivers (e.g., replicated block storage, distributed filesystems).
* Kubernetes does not include a built-in container image registry (C). You use external registries (Docker Hub, ECR, GCR, Harbor, etc.). Kubernetes pulls images but does not host them as a core feature.
So the correct "provided by default" orchestration feature in this list is the ability to safely manage application updates via automated rollouts and rollbacks.
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NEW QUESTION # 115
What is a sidecar container?
Answer: B
Explanation:
A sidecar container is an additional container that runs alongside the main application container within the same Pod, sharing network and storage context. That matches option C, so C is correct. The sidecar pattern is used to add supporting capabilities to an application without modifying the application code. Because both containers are in the same Pod, the sidecar can communicate with the main container over localhost and share volumes for files, sockets, or logs.
Common sidecar examples include: log forwarders that tail application logs and ship them to a logging system, proxies (service mesh sidecars like Envoy) that handle mTLS and routing policy, config reloaders that watch ConfigMaps and signal the main process, and local caching agents. Sidecars are especially powerful in cloud-native systems because they standardize cross-cutting concerns-security, observability, traffic policy- across many workloads.
Options A and D incorrectly describe "a Pod running next to ..." which is not how sidecars work; sidecars are containers, not separate Pods. Running separate Pods "next to" each other in a namespace does not give the same shared network namespace and tightly coupled lifecycle. Option B is also incorrect for the same reason:
a sidecar is not a separate Pod; it is a container in the same Pod.
Operationally, sidecars share the Pod lifecycle: they are scheduled together, scaled together, and generally terminated together. This is both a benefit (co-location guarantees) and a responsibility (resource requests
/limits should include the sidecar's needs, and failure modes should be understood). Kubernetes is increasingly formalizing sidecar behavior (e.g., sidecar containers with ordered startup semantics), but the core definition remains: a helper container in the same Pod.
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NEW QUESTION # 116
Which GitOps engine can be used to orchestrate parallel jobs on Kubernetes?
Answer: D
Explanation:
Argo Workflows (D) is the correct answer because it is a Kubernetes-native workflow engine designed to define and run multi-step workflows-often with parallelization-directly on Kubernetes. Argo Workflows models workflows as DAGs (directed acyclic graphs) or step-based sequences, where each step is typically a Pod. Because each step is expressed as Kubernetes resources (custom resources), Argo can schedule many tasks concurrently, control fan-out/fan-in patterns, and manage dependencies between steps (e.g., "run these 10 jobs in parallel, then aggregate results").
The question calls it a "GitOps engine," but the capability being tested is "orchestrate parallel jobs." Argo Workflows fits because it is purpose-built for running complex job orchestration, including parallel tasks, retries, timeouts, artifacts passing, and conditional execution. In practice, many teams store workflow manifests in Git and apply GitOps practices around them, but the distinguishing feature here is the workflow orchestration engine itself.
Why the other options are not best:
Flux (C) is a GitOps controller that reconciles cluster state from Git; it doesn't orchestrate parallel job graphs as its core function.
Flagger (B) is a progressive delivery operator (canary/blue-green) often paired with GitOps and service meshes/Ingress; it's not a general workflow orchestrator for parallel batch jobs.
Jenkins X (A) is CI/CD-focused (pipelines), not primarily a Kubernetes-native workflow engine for parallel job DAGs in the way Argo Workflows is.
So, the Kubernetes-native tool specifically used to orchestrate parallel jobs and workflows is Argo Workflows (D).
NEW QUESTION # 117
In Kubernetes, which command is the most efficient way to check the progress of a Deployment rollout and confirm if it has completed successfully?
Answer: D
Explanation:
When performing rolling updates in Kubernetes, it is important to have a clear and efficient way to track the progress of a Deployment rollout and determine whether it has completed successfully. The most direct and purpose-built command for this task is kubectl rollout status deployment/my-deployment, making option D the correct answer.
The kubectl rollout status command is specifically designed to monitor the state of rollouts for resources such as Deployments, StatefulSets, and DaemonSets. It provides real-time feedback on the rollout process, including whether new Pods have been created, old Pods are being terminated, and if the desired number of updated replicas has become available. The command blocks until the rollout either completes successfully or fails, which makes it especially useful in automation and CI/CD pipelines.
Option A is incorrect because kubectl get deployments only provides a snapshot view of deployment status fields and does not actively track rollout progress. Option B can provide detailed information and events, but it is verbose and not optimized for quickly confirming rollout completion. Option C is incorrect because Deployment objects themselves do not produce logs; logs are generated by Pods and containers, not higher- level workload resources.
The rollout status command also integrates with Kubernetes' revision history, ensuring that it accurately reflects the current state of the Deployment's update strategy. If a rollout is stuck due to failed Pods, readiness probe failures, or resource constraints, the command will indicate that the rollout is not progressing, helping operators quickly identify issues.
In summary, kubectl rollout status deployment/my-deployment is the most efficient and reliable way to check rollout progress and confirm success. It is purpose-built for rollout tracking, easy to interpret, and widely used in production Kubernetes workflows, making Option D the correct and verified answer.
NEW QUESTION # 118
In Kubernetes, which abstraction defines a logical set of Pods and a policy by which to access them?
Answer: A
Explanation:
The correct answer is C: Service. A Kubernetes Service is an abstraction that provides stable access to a logical set of Pods. Pods are ephemeral: they can be rescheduled, recreated, and scaled, which changes their IP addresses over time. A Service solves this by providing a stable identity-typically a virtual IP (ClusterIP) and a DNS name-and a traffic-routing policy that directs requests to the current set of backend Pods.
Services commonly select Pods using labels via a selector (e.g., app=web). Kubernetes then maintains the backend endpoint list (Endpoints/EndpointSlices). The cluster networking layer routes traffic sent to the Service IP/port to one of the Pod endpoints, enabling load distribution across replicas. This is fundamental to microservices architectures: clients call the Service name, not individual Pods.
Why the other options are incorrect:
A ServiceAccount is an identity for Pods to authenticate to the Kubernetes API; it doesn't define a set of Pods nor traffic access policy.
A NetworkPolicy defines allowed network flows (who can talk to whom) but does not provide stable addressing or load-balanced access to Pods. It is a security policy, not an exposure abstraction.
A CustomResourceDefinition extends the Kubernetes API with new resource types; it's unrelated to service discovery and traffic routing for a set of Pods.
Understanding Services is core Kubernetes fundamentals: they decouple backend Pod churn from client connectivity. Services also integrate with different exposure patterns via type (ClusterIP, NodePort, LoadBalancer, ExternalName) and can be paired with Ingress/Gateway for HTTP routing. But the essential definition in the question-"logical set of Pods and a policy to access them"-is exactly the textbook description of a Service.
Therefore, the verified correct answer is C.
NEW QUESTION # 119
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