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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Cloud Native Architecture | 16% | - Infrastructure and Practices
|
| Topic 2: Container Orchestration | 22% | - Networking
|
| Topic 3: Kubernetes Fundamentals | 46% | - Kubernetes Resources
|
| Topic 4: Cloud Native Application Delivery | 8% | - GitOps
|
| Topic 5: Cloud Native Observability | 8% | - Logging
|
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NEW QUESTION # 83
You are running a distributed database service in Kubernetes. The database requires secure communication between its pods and external clients. What Kubernetes networking features can you leverage to achieve this?
Answer: A,B,C
Explanation:
The correct answers are A, D, and E. - A: Using a 'LoadBalancer' with TLS termination allows secure communication between external clients and the database service through an encrypted HTTPS connection. - D Implementing mutual TLS authentication between database pods and clients ensures that only authorized clients can access the database, further enhancing security. - E. Combining 'Services', 'Ingress', and TLS termination can provide comprehensive security for external access to the database service. This allows you to manage ingress traffic with
'Ingress' resources and secure the connection with TLS termination on the 'LoadBalancer' or ingress controller. While 'NetworkPolicies' can help control network traffic, they cannot directly enforce encryption. 'Custom network plugins' can implement specific encryption features, but they might require more complex configuration and integration. The best approach is to use a combination of Kubernetes features to achieve secure communication between pods and external clients.
NEW QUESTION # 84
Which Kubernetes component is the smallest deployable unit of computing?
Answer: A
Explanation:
In Kubernetes, the Pod is the smallest deployable and schedulable unit, making C correct. Kubernetes does not schedule individual containers directly; instead, it schedules Pods, each of which encapsulates one or more containers that must run together on the same node. This design supports both single-container Pods (the most common) and multi-container Pods (for sidecars, adapters, and co-located helper processes).
Pods provide shared context: containers in a Pod share the same network namespace (one IP address and port space) and can share storage volumes. This enables tight coupling where needed-for example, a service mesh proxy sidecar and the application container communicate via localhost, or a log-forwarding sidecar reads logs from a shared volume. Kubernetes manages lifecycle at the Pod level: kubelet ensures the containers defined in the PodSpec are running and uses probes to determine readiness and liveness.
StatefulSet and Deployment are controllers that manage sets of Pods. A Deployment manages ReplicaSets for stateless workloads and provides rollout/rollback features; a StatefulSet provides stable identities, ordered operations, and stable storage for stateful replicas. These are higher-level constructs, not the smallest units.
Option D ("Container") is smaller in an abstract sense, but it is not the smallest Kubernetes deployable unit because Kubernetes APIs and scheduling work at the Pod boundary. You don't "kubectl apply" a container; you apply a Pod template within a Pod object (often via controllers).
Understanding Pods as the atomic unit is crucial: Services select Pods, autoscalers scale Pods (replica counts), and scheduling decisions are made per Pod. That's why Kubernetes documentation consistently refers to Pods as the fundamental building block for running workloads.
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NEW QUESTION # 85
What is the resource type used to package sets of containers for scheduling in a cluster?
Answer: A
Explanation:
The Kubernetes resource used to package one or more containers into a schedulable unit is the Pod, so A is correct. Kubernetes schedules Pods onto nodes; it does not schedule individual containers. A Pod represents a single "instance" of an application component and includes one or more containers that share key runtime properties, including the same network namespace (same IP and port space) and the ability to share volumes.
Pods enable common patterns beyond "one container per Pod." For example, a Pod may include a main application container plus a sidecar container for logging, proxying, or configuration reload. Because these containers share localhost networking and volume mounts, they can coordinate efficiently without requiring external service calls. Kubernetes manages the Pod lifecycle as a unit: the containers in a Pod are started according to container lifecycle rules and are co-located on the same node.
Option B (ContainerSet) is not a standard Kubernetes workload resource. Option C (ReplicaSet) manages a set of Pod replicas, ensuring a desired count is running, but it is not the packaging unit itself. Option D (Deployment) is a higher-level controller that manages ReplicaSets and provides rollout/rollback behavior, again operating on Pods rather than being the container-packaging unit.
From the scheduling perspective, the PodSpec defines container images, commands, resources, volumes, security context, and placement constraints. The scheduler evaluates these constraints and assigns the Pod to a node. This "Pod as the atomic scheduling unit" is fundamental to Kubernetes architecture and explains why Kubernetes-native concepts (Services, selectors, readiness, autoscaling) all revolve around Pods.
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NEW QUESTION # 86
How can persistent volume be provisioned?
Answer: A
Explanation:
https://kubernetes.io/docs/concepts/storage/persistent-volumes/
NEW QUESTION # 87
What are the 3 pillars of Observability?
Answer: C
Explanation:
The correct answer is A: Metrics, Logs, and Traces. These are widely recognized as the "three pillars" because together they provide complementary views into system behavior:
Metrics are numeric time series collected over time (CPU usage, request rate, error rate, latency percentiles). They are best for dashboards, alerting, and capacity planning because they are structured and aggregatable. In Kubernetes, metrics underpin autoscaling and operational visibility (node/pod resource usage, cluster health signals).
Logs are discrete event records (often text) emitted by applications and infrastructure components. Logs provide detailed context for debugging: error messages, stack traces, warnings, and business events. In Kubernetes, logs are commonly collected from container stdout/stderr and aggregated centrally for search and correlation.
Traces capture the end-to-end journey of a request through a distributed system, breaking it into spans. Tracing is crucial in microservices because a single user request may cross many services; traces show where latency accumulates and which dependency fails. Tracing also enables root cause analysis when metrics indicate degradation but don't pinpoint the culprit.
Why the other options are wrong: a span is a component within tracing, not a top-level pillar; "data" is too generic; and "resources" are not an observability signal category. The pillars are defined by signal type and how they're used operationally.
In cloud-native practice, these pillars are often unified via correlation IDs and shared context: metrics alerts link to logs and traces for the same timeframe/request. Tooling like Prometheus (metrics), log aggregators (e.g., Loki/Elastic), and tracing systems (Jaeger/Tempo/OpenTelemetry) work together to provide a complete observability story.
Therefore, the verified correct answer is A.
NEW QUESTION # 88
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