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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Cloud Native Observability | 8% | - Tracing
|
| Topic 2: Cloud Native Application Delivery | 8% | - GitOps
|
| Topic 3: Cloud Native Architecture | 16% | - Infrastructure and Practices
|
| Topic 4: Container Orchestration | 22% | - Container Runtimes
|
| Topic 5: Kubernetes Fundamentals | 46% | - Kubernetes Architecture
|
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NEW QUESTION # 87
A platform engineer wants to ensure that a new microservice is automatically deployed to every cluster registered in Argo CD. Which configuration best achieves this goal?
Answer: D
Explanation:
Argo CD is a declarative GitOps continuous delivery tool designed to manage Kubernetes applications across one or many clusters. When the requirement is to automatically deploy a microservice to every cluster registered in Argo CD, the most appropriate and scalable solution is to use an ApplicationSet.
The ApplicationSet controller extends Argo CD by enabling the dynamic generation of multiple Argo CD Applications from a single template. One of its most powerful features is the cluster generator, which automatically discovers all clusters registered with Argo CD and creates an Application for each of them. By combining this generator with a Git repository containing the microservice manifests, the platform engineer ensures that the microservice is consistently deployed to all existing clusters-and any new clusters added in the future-without manual intervention.
This approach aligns perfectly with GitOps principles. The desired state of the microservice is defined once in Git, and Argo CD continuously reconciles that state across all target clusters. Any updates to the microservice manifests are automatically rolled out everywhere in a controlled and auditable manner. This provides strong guarantees around consistency, scalability, and operational simplicity.
Option A is incorrect because a CronJob introduces imperative redeployment logic and does not integrate with Argo CD's reconciliation model. Option B is not scalable or maintainable, as it requires manual configuration for each cluster and increases the risk of configuration drift. Option D, while useful for packaging applications, still results in a single Application object and does not natively handle multi-cluster fan-out by itself.
Therefore, the correct and verified answer is Option C: creating an Argo CD ApplicationSet backed by a Git repository, which is the recommended and documented solution for multi-cluster application delivery in Argo CD.
NEW QUESTION # 88
A Kubernetes _____ is an abstraction that defines a logical set of Pods and a policy by which to access them.
Answer: D
Explanation:
A Kubernetes Service is the abstraction that defines a logical set of Pods and the policy for accessing them, so C is correct. Pods are ephemeral: their IPs change as they are recreated, rescheduled, or scaled. A Service solves this by providing a stable endpoint (DNS name and virtual IP) and routing rules that send traffic to the current healthy Pods backing the Service.
A Service typically uses a label selector to identify which Pods belong to it. Kubernetes then maintains endpoint data (Endpoints/EndpointSlice) for those Pods and uses the cluster dataplane (kube-proxy or eBPF- based implementations) to forward traffic from the Service IP/port to one of the backend Pod IPs. This is what the question means by "logical set of Pods" and "policy by which to access them" (for example, round-robin- like distribution depending on dataplane, session affinity options, and how ports map via targetPort).
Option A (Selector) is only the query mechanism used by Services and controllers; it is not itself the access abstraction. Option B (Controller) is too generic; controllers reconcile desired state but do not provide stable network access policies. Option D (Job) manages run-to-completion tasks and is unrelated to network access abstraction.
Services can be exposed in different ways: ClusterIP (internal), NodePort, LoadBalancer, and ExternalName.
Regardless of type, the core Service concept remains: stable access to a dynamic set of Pods. This is foundational to Kubernetes networking and microservice communication, and it is why Service discovery via DNS works effectively across rolling updates and scaling events.
Thus, the correct answer is Service (C).
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NEW QUESTION # 89
What are the two steps performed by the kube-scheduler to select a node to schedule a pod?
Answer: C
Explanation:
The kube-scheduler selects a node in two main phases: filtering and scoring, so C is correct. First, filtering identifies which nodes are feasible for the Pod by applying hard constraints. These include resource availability (CPU/memory requests), node taints/tolerations, node selectors and required affinities, topology constraints, and other scheduling requirements. Nodes that cannot satisfy the Pod's requirements are removed from consideration.
Second, scoring ranks the remaining feasible nodes using priority functions to choose the "best" placement.
Scoring can consider factors like spreading Pods across nodes/zones, packing efficiency, affinity preferences, and other policies configured in the scheduler. The node with the highest score is selected (with tie-breaking), and the scheduler binds the Pod by setting spec.nodeName.
Option B ("filtering and selecting") is close but misses the explicit scoring step that is central to scheduler design. The scheduler does "select" a node, but the canonical two-step wording in Kubernetes scheduling is filtering then scoring. Options A and D are not how scheduler internals are described.
Operationally, understanding filtering vs scoring helps troubleshoot scheduling failures. If a Pod can't be scheduled, it failed in filtering-kubectl describe pod often shows "0/... nodes are available" reasons (insufficient CPU, taints, affinity mismatch). If it schedules but lands in unexpected places, it's often about scoring preferences (affinity weights, topology spread preferences, default scheduler profiles).
So the verified correct answer is C: kube-scheduler uses Filtering and Scoring.
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NEW QUESTION # 90
In a serverless computing architecture:
Answer: B
Explanation:
Serverless architectures typically bill based on actual consumption, often measured as number of requests and execution duration (and sometimes memory/CPU allocated), so A is correct. The defining trait is that you don't provision or manage servers directly; the platform scales execution up and down automatically, including down to zero for many models, and charges you for what you use.
Option B is incorrect: many serverless platforms can run container-based workloads (and some are explicitly
"serverless containers"). The idea is the operational abstraction and billing model, not incompatibility with containers. Option C is incorrect because "making a reservation based on estimation" describes reserved capacity purchasing, which is the opposite of the typical serverless pay-per-use model. Option D is misleading: serverless systems aim to avoid charging for idle compute; while platforms may keep some warm capacity for latency reasons, the customer-facing model is not "containers running idle in the background." In cloud-native architecture, serverless is often chosen for spiky, event-driven workloads where you want minimal ops overhead and cost efficiency at low utilization. It pairs naturally with eventing systems (queues, pub/sub) and can be integrated with Kubernetes ecosystems via event-driven autoscaling frameworks or managed serverless offerings.
So the correct statement is A: charging is commonly based on requests (and usage), which captures the cost and operational model that differentiates serverless from always-on infrastructure.
=========
NEW QUESTION # 91
What native runtime is Open Container Initiative (OCI) compliant?
Answer: A
Explanation:
The Open Container Initiative (OCI) publishes open specifications for container images and container runtimes so that tools across the ecosystem remain interoperable. When a runtime is "OCI-compliant," it means it implements the OCI Runtime Specification (how to run a container from a filesystem bundle and configuration) and/or works cleanly with OCI image formats through the usual layers (image → unpack → runtime). runC is the best-known, widely used reference implementation of the OCI runtime specification and is the low-level runtime underneath many higher-level systems. In Kubernetes, you typically interact with a higher-level container runtime (such as containerd or CRI-O) through the Container Runtime Interface (CRI). That higher-level runtime then uses a low-level OCI runtime to actually create Linux namespaces/cgroups, set up the container process, and start it. In many default installations, containerd delegates to runC for this low-level "create/start" work.
The other options are related but differ in what they are: Kata Containers uses lightweight VMs to provide stronger isolation while still presenting a container-like workflow; gVisor provides a user-space kernel for sandboxing containers; these can be used with Kubernetes via compatible integrations, but the canonical "native OCI runtime" answer in most curricula is runC. Finally, "runV" is not a common modern Kubernetes runtime choice in typical OCI discussions. So the most correct, standards-based answer here is A (runC) because it directly implements the OCI runtime spec and is commonly used as the default low-level runtime behind CRI implementations.
NEW QUESTION # 92
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