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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Container Orchestration | 22% | - Orchestration Fundamentals
|
| Topic 2: Cloud Native Architecture | 16% | - Architecture Concepts
|
| Topic 3: Cloud Native Observability | 8% | - Logging
|
| Topic 4: Kubernetes Fundamentals | 46% | - Kubernetes Architecture
|
| Topic 5: Cloud Native Application Delivery | 8% | - GitOps
|
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NEW QUESTION # 14
Which of these commands is used to retrieve the documentation and field definitions for a Kubernetes resource?
Answer: A
Explanation:
kubectl explain is the command that shows documentation and field definitions for Kubernetes resource schemas, so A is correct. Kubernetes resources have a structured schema: top-level fields like apiVersion, kind, and metadata, and resource-specific structures like spec and status. kubectl explain lets you explore these structures directly from your cluster's API discovery information, including field types, descriptions, and nested fields.
For example, kubectl explain deployment describes the Deployment resource, and kubectl explain deployment.spec dives into the spec structure. You can continue deeper, such as kubectl explain deployment.spec.template.spec.containers to discover container fields. This is especially useful when writing or troubleshooting manifests, because it reduces guesswork and prevents invalid YAML fields that would be rejected by the API server. It also helps when APIs evolve: you can confirm which fields exist in your cluster's current version and what they mean.
The other commands do different things. kubectl api-resources lists resource types and their shortnames, whether they are namespaced, and supported verbs-useful discovery, but not detailed field definitions. kubectl get --help shows CLI usage help for kubectl get, not the Kubernetes object schema. kubectl show is not a standard kubectl subcommand.
From a Kubernetes "declarative configuration" perspective, correct manifests are critical: controllers reconcile desired state from spec, and subtle field mistakes can change runtime behavior. kubectl explain is a built-in way to learn the schema and write manifests that align with the Kubernetes API's expectations. That's why it's commonly recommended in Kubernetes documentation and troubleshooting workflows.
NEW QUESTION # 15
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 # 16
You want to implement a system that ensures that all your container images are scanned for vulnerabilities before being deployed to your Kubernetes cluster. Which of the following open standards would you use?
Answer: A,C
Explanation:
Both Open Container Initiative (OCI) and Kubernetes Admission Controllers play a role in ensuring image security. OCI provides a standard for container images, enabling tools to analyze and scan them for vulnerabilities. Kubernetes Admission Controllers allow you to intercept the image deployment process and use tools like OCI-compliant scanners to check the image for vulnerabilities before it's deployed.
NEW QUESTION # 17
What command is often used to troubleshoot a failing Pod?
Answer: B
Explanation:
The kubectl describe command provides detailed information about a Pod, including events, status conditions, and error messages, making it a primary tool for troubleshooting Pod failures.
NEW QUESTION # 18
What is the main purpose of etcd in Kubernetes?
Answer: C
Explanation:
The main purpose of etcd in Kubernetes is to store the cluster's state as a distributed key-value store, so A is correct. Kubernetes is API-driven: objects like Pods, Deployments, Services, ConfigMaps, Secrets, Nodes, and RBAC rules are persisted by the API server into etcd. Controllers, schedulers, and other components then watch the API for changes and reconcile the cluster accordingly. This makes etcd the "source of truth" for desired and observed cluster state.
Options B, C, and D are misconceptions. etcd does not store the running containers; that's the job of the kubelet/container runtime on each node, and container state is ephemeral. etcd does not store /etc configuration file copies. And while you may author objects as YAML manifests, Kubernetes stores them internally as API objects (serialized) in etcd-not as "YAML definitions for all components." The data is structured key/value entries representing Kubernetes resources and metadata.
Because etcd is so critical, its performance and reliability directly affect the cluster. Slow disk I/O or poor network latency increases API request latency and can delay controller reconciliation, leading to cascading operational problems (slow rollouts, delayed scheduling, timeouts). That's why etcd is typically run on fast, reliable storage and in an HA configuration (often 3 or 5 members) to maintain quorum and tolerate failures.
Backups (snapshots) and restore procedures are also central to disaster recovery: if etcd is lost, the cluster loses its state.
Security is also important: etcd can contain sensitive information (especially Secrets unless encrypted at rest).
Proper TLS, restricted access, and encryption-at-rest configuration are standard best practices.
So, the verified correct answer is A: etcd stores all cluster data/state in a key-value store.
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NEW QUESTION # 19
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