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Linux Foundation Kubernetes and Cloud Native Associate Sample Questions (Q45-Q50):

NEW QUESTION # 45
Which of the following observability data streams would be most useful when desiring to plot resource consumption and predicted future resource exhaustion?

Answer: C

Explanation:
The correct answer is D: Metrics. Metrics are numeric time-series measurements collected at regular intervals, making them ideal for plotting resource consumption over time and forecasting future exhaustion. In Kubernetes, this includes CPU usage, memory usage, disk I/O, network throughput, filesystem usage, Pod restarts, and node allocatable vs requested resources. Because metrics are structured and queryable (often with Prometheus), you can compute rates, aggregates, percentiles, and trends, and then apply forecasting methods to predict when a resource will run out.
Logs and traces have different purposes. Logs are event records (strings) that are great for debugging and auditing, but they are not naturally suited to continuous quantitative plotting unless you transform them into metrics (log-based metrics). Traces capture end-to-end request paths and latency breakdowns; they help you find slow spans and dependency bottlenecks, not forecast CPU/memory exhaustion. stdout is just a stream where logs might be written; by itself it's not an observability data type used for capacity trending.
In Kubernetes observability stacks, metrics are typically scraped from components and workloads: kubelet
/cAdvisor exports container metrics, node exporters expose host metrics, and applications expose business
/system metrics. The metrics pipeline (Prometheus, OpenTelemetry metrics, managed monitoring) enables dashboards and alerting. For resource exhaustion, you often alert on "time to fill" (e.g., predicted disk fill in < N hours), high sustained utilization, or rapidly increasing error rates due to throttling.
Therefore, the most appropriate data stream for plotting consumption and predicting exhaustion is Metrics, option D.
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NEW QUESTION # 46
Your application requires specific network configurations for its pods, including custom DNS settings and network namespaces. How can you achieve this in Kubernetes?

Answer: A

Explanation:
The correct answer is 'Create a custom network plugin and integrate it with Kubernetes'. Kubernetes allows you to extend its networking functionality by developing and integrating custom network plugins. These plugins can provide advanced network configurations, including custom DNS settings, network namespaces, and other specific network requirements. Options A, B, C, and E are not suitable for this scenario. 'NetworkPolicy' is used for network access control, 'Pod security context' defines security settings for a pod, 'DaemonSet' is used for deploying agents on nodes, and modifying the API server's network settings can affect the entire cluster's network configuration.


NEW QUESTION # 47
A Pod has been created, but when checked with kubectl get pods, the READY column shows 0/1. What Kubernetes feature causes this behavior?

Answer: A

Explanation:
The READY column in the output of kubectl get pods indicates how many containers in a Pod are currently considered ready to serve traffic, compared to the total number of containers defined in that Pod. A value of 0
/1 means that the Pod has one container, but that container is not yet marked as ready. The Kubernetes feature responsible for determining this readiness state is the readiness probe.
Readiness probes are used by Kubernetes to decide when a container is ready to accept traffic. These probes can be configured to perform HTTP requests, execute commands, or check TCP sockets inside the container.
If a readiness probe is defined and it fails, Kubernetes marks the container as not ready, even if the container is running successfully. As a result, the READY column will show 0/1, and the Pod will be excluded from Service load balancing until the probe succeeds.
Option A (Node Selector) is incorrect because node selectors influence where a Pod is scheduled, not whether its containers are considered ready after startup. Option C (DNS Policy) affects how DNS resolution works inside a Pod and has no direct impact on readiness reporting. Option D (Security Contexts) define security- related settings such as user IDs, capabilities, or privilege levels, but they do not control the READY status shown by kubectl.
Readiness probes are particularly important for applications that take time to initialize, load configuration, or warm up caches. By using readiness probes, Kubernetes ensures that traffic is only sent to containers that are fully prepared to handle requests. This improves reliability and prevents failed or premature connections.
According to Kubernetes documentation, a container without a readiness probe is considered ready by default once it is running. However, when a readiness probe is defined, its result directly controls the READY state.
Therefore, the presence and behavior of readiness probes is the verified and correct reason why a Pod may show 0/1 in the READY column, making option B the correct answer.


NEW QUESTION # 48
You are managing a large Kubernetes cluster with multiple namespaces. You want to control access to resources within different namespaces. Which of the following mechanisms can be used to achieve fine-grained access control?

Answer: B,C

Explanation:
Both Role-Based Access Control (RBAC) and Pod Security Policies (PSP) are used for managing access to resources within Kubernetes. RBAC provides fine-grained permissions based on roles and users, while PSPs define security constraints for Pods, limiting their capabilities and access to resources.


NEW QUESTION # 49
Which of the following are tasks performed by a container orchestration tool?

Answer: B

Explanation:
A container orchestration tool (like Kubernetes) is responsible for scheduling, scaling, and health management of workloads, making A correct. Orchestration sits above individual containers and focuses on running applications reliably across a fleet of machines. Scheduling means deciding which node should run a workload based on resource requests, constraints, affinities, taints/tolerations, and current cluster state. Scaling means changing the number of running instances (replicas) to meet demand (manually or automatically through autoscalers). Health management includes monitoring whether containers and Pods are alive and ready, replacing failed instances, and maintaining the declared desired state.
Options B and D include "create images" and "store images," which are not orchestration responsibilities. Image creation is a CI/build responsibility (Docker/BuildKit/build systems), and image storage is a container registry responsibility (Harbor, ECR, GCR, Docker Hub, etc.). Kubernetes consumes images from registries but does not build or store them. Option C includes "debug applications," which is not a core orchestration function. While Kubernetes provides tools that help debugging (logs, exec, events), debugging is a human/operator activity rather than the orchestrator's fundamental responsibility.
In Kubernetes specifically, these orchestration tasks are implemented through controllers and control loops: Deployments/ReplicaSets manage replica counts and rollouts, kube-scheduler assigns Pods to nodes, kubelet ensures containers run, and probes plus controller logic replace unhealthy replicas. This is exactly what makes Kubernetes valuable at scale: instead of manually starting/stopping containers on individual hosts, you declare your intent and let the orchestration system continually reconcile reality to match. That combination-placement + elasticity + self-healing-is the core of container orchestration, matching option A precisely.


NEW QUESTION # 50
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