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| Section | Objectives |
|---|---|
| Cloud Native Observability | - Monitoring and logging
|
| Kubernetes Fundamentals | - Core Kubernetes concepts
|
| Cloud Native Application Delivery | - CI/CD and deployment strategies
|
| Cloud Security and Governance | - Security fundamentals in cloud native systems
|
| Cloud Native Architecture | - Microservices and distributed systems
|
| Container Orchestration | - Container lifecycle and scheduling
|
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NEW QUESTION # 308
What are the 3 pillars of Observability?
Answer: D
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 # 309
What fields must exist in any Kubernetes object (e.g. YAML) file?
Answer: C
Explanation:
Any Kubernetes object manifest must include apiVersion, kind, and metadata, which makes A correct. This comes directly from how Kubernetes resources are represented and processed by the API server.
apiVersion tells Kubernetes which API group and version should be used to interpret the object (for example v1, apps/v1, batch/v1). This matters because schemas and available fields can change between versions.
kind specifies the type of object you are creating (for example Pod, Service, Deployment, ConfigMap). Kubernetes uses this to route the request to the correct API endpoint and schema.
metadata contains identifying and organizational information such as name, namespace (when namespaced), labels, and annotations. At minimum, most objects require a name; labels and annotations are optional but extremely common for selection and tooling.
A common point of confusion is spec. Many Kubernetes objects include spec because they define desired state (like a Deployment's replica count, Pod template, update strategy). However, the question asks what fields must exist in any Kubernetes object file. Not all objects require a spec in the same way (and some objects include other top-level sections like data for ConfigMaps/Secrets or rules for RBAC objects). The truly universal top-level requirements are the trio in option A.
Options B, C, and D include fields that are not universally required (namespace is not required for cluster-scoped objects, and data only applies to certain kinds like ConfigMaps/Secrets). Therefore, apiVersion + kind + metadata is the correct, general rule and matches Kubernetes object structure.
NEW QUESTION # 310
What framework allows developers to write code without worrying about the servers and operating systems they will run on?
Answer: B
NEW QUESTION # 311
A ________ is an application running on kubernetes.
Answer: C
Explanation:
https://kubernetes.io/docs/concepts/workloads/
NEW QUESTION # 312
You're using Prometheus and Grafana for monitoring your Kubernetes cluster. How can you utilize these tools to identify potential cost-saving opportunities?
Answer: A,B,C,D
Explanation:
Monitoring tools like Prometheus and Grafana are essential for identifying cost-saving opportunities. Visualizing resource utilization metrics (CPU, memory) in Grafana provides an overview of resource consumption. Alerts based on resource utilization thresholds notify you of potential overprovisioning. Analyzing historical metrics helps identify underutilized workloads for downscaling or optimization. Prometheus queries can identify issues like frequent pod restarts or errors, potentially indicating resource constraints that can be addressed. While integrating billing data can provide valuable insights, it is not directly supported by Prometheus or Grafana.
NEW QUESTION # 313
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