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| Section | Weight | Objectives |
|---|---|---|
| Cloud Native Architecture | 16% | - Cloud Native Principles
|
| Cloud Native Application Delivery | 8% | - Deployment Strategies
|
| Container Orchestration | 22% | - Orchestration Principles
|
| Kubernetes Fundamentals | 46% | - Scheduling and Administration
|
| Cloud Native Observability | 8% | - Monitoring & Metrics
|
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NEW QUESTION # 35
Which of the following observability data streams would be most useful when desiring to plot resource consumption and predicted future resource exhaustion?
Answer: B
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.
NEW QUESTION # 36
Which of the following workload require a headless service while deploying into the namespace?
Answer: A
NEW QUESTION # 37
What are the 3 pillars of Observability?
Answer: B
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 # 38
What service account does a Pod use in a given namespace when the service account is not specified?
Answer: B
Explanation:
D (default) is correct. In Kubernetes, if you create a Pod (or a controller creates Pods) without specifying spec.serviceAccountName, Kubernetes assigns the Pod the default ServiceAccount in that namespace. The ServiceAccount determines what identity the Pod uses when accessing the Kubernetes API (for example, via the in-cluster token mounted into the Pod, when token automounting is enabled).
Every namespace typically has a default ServiceAccount created automatically. The permissions associated with that ServiceAccount are determined by RBAC bindings. In many clusters, the default ServiceAccount has minimal permissions (or none) as a security best practice, because leaving it overly privileged would allow any Pod to access sensitive cluster APIs.
Why the other options are wrong: Kubernetes does not automatically choose "admin," "sysadmin," or "root" service accounts. Those are not standard implicit identities, and automatically granting admin privileges would be insecure. Instead, Kubernetes follows a predictable, least-privilege-friendly default: use the namespace's default ServiceAccount unless you explicitly request a different one.
Operationally, this matters for security and troubleshooting. If an application in a Pod is failing with
"forbidden" errors when calling the API, it often means it's using the default ServiceAccount without the necessary RBAC permissions. The correct fix is usually to create a dedicated ServiceAccount and bind only the required roles, then set serviceAccountName in the Pod template. Conversely, if you're hardening a cluster, you often disable automounting of service account tokens for Pods that don't need API access.
Therefore, the verified correct answer is D: default.
=========
NEW QUESTION # 39
In Kubernetes, what is the primary responsibility of the kubelet running on each worker node?
Answer: B
Explanation:
The kubelet is the primary node-level agent in Kubernetes and plays a critical role in ensuring that workloads run correctly on each worker node. Its main responsibility is to ensure that the containers described in Pod specifications are running and remain healthy on that node, which makes option C the correct answer.
Once the Kubernetes scheduler assigns a Pod to a node, the kubelet on that node takes over execution responsibilities. It watches the API server for Pod specifications that are scheduled to its node and then interacts with the container runtime to start, stop, and manage the containers defined in those Pods. The kubelet continuously monitors container health and reports Pod and node status back to the API server, enabling Kubernetes to make informed decisions about restarts, rescheduling, or remediation.
Health checks are another key responsibility of the kubelet. It executes liveness, readiness, and startup probes as defined in the Pod specification. Based on probe results, the kubelet may restart containers or update Pod status to reflect whether the application is ready to receive traffic. This behavior directly supports Kubernetes' self-healing capabilities.
Option A is incorrect because persistent storage allocation and data replication are handled by storage systems, CSI drivers, and controllers-not by the kubelet itself. Option B is incorrect because cluster state management and scheduling decisions are the responsibility of control plane components such as the API server, controller manager, and kube-scheduler. Option D is incorrect because DNS resolution and service traffic routing are handled by components like CoreDNS and kube-proxy.
In summary, the kubelet acts as the "node supervisor" for Kubernetes workloads. By ensuring containers are running as specified and continuously reporting their status, the kubelet forms the essential link between the Kubernetes control plane and the actual execution of applications on worker nodes. This clearly aligns with Option C as the correct and verified answer.
NEW QUESTION # 40
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