Declarative resource management is a core principle in Kubernetes and cloud native platforms. Option C is correct because declarative systems define the desired state of resources (e.g., YAML manifests for Deployments, Services, or ConfigMaps), and controllers reconcile the actual state to match the desired state.
This provides consistency, automation, and resilience, while also allowing dynamic adjustments like scaling.
Option A (imperative management) requires step-by-step commands, which are error-prone and not scalable.
Option B (manual tracking) adds overhead and risk of drift. Option D (static allocation) wastes resources and does not adapt to changing workloads.
Declarative management enables GitOps workflows, automated scaling, and consistent application of policies.
This approach aligns with platform engineering principles by combining automation with governance, enabling efficiency and reliability at scale.
References:- CNCF GitOps Principles- Kubernetes Design Principles- Cloud Native Platform Engineering Study Guide

Helm provides native support for managing versioned releases, allowing easy rollback in case of issues.
Option A is correct because the helm rollback <release_name> <revision> command reverts the deployment to a previously known stable release without requiring a redeployment from scratch. This ensures fast recovery and minimizes downtime after a faulty upgrade.
Option B (helm upgrade --force) attempts to reapply an upgrade but does not restore the previous version.
Option C (helm template) only renders Kubernetes manifests from charts and does not affect running releases.
Option D (helm uninstall) removes the release entirely, which is not suitable for quick recovery.
Rollback functionality is essential in platform engineering for resilience and rapid mitigation of production issues. By using helm rollback, teams align with best practices for safe, controlled release management in Kubernetes environments.
References:- CNCF Helm Documentation- CNCF Platforms Whitepaper- Cloud Native Platform Engineering Study Guide

Prometheus is the de facto standard for collecting and monitoring metrics in Kubernetes and other cloud native environments. Option D is correct because Prometheus is a CNCF graduated project designed for multi- dimensional data collection, time-series storage, and powerful querying using PromQL. It integrates seamlessly with Kubernetes, automatically discovering targets such as Pods and Services through service discovery.
Option A (Grafana) is widely used for visualization but relies on Prometheus or other data sources to collect metrics. Option B (ELK Stack) is better suited for log aggregation rather than real-time metrics. Option C (OpenTelemetry) provides standardized instrumentation but is focused on generating and exporting metrics, logs, and traces rather than storage, querying, and alerting.
Prometheus plays a central role in platform observability strategies, often paired with Alertmanager for notifications and Grafana for dashboards. Together, they enable proactive monitoring, SLO/SLI measurement, and incident detection, making Prometheus indispensable in cloud native platform engineering.
References:- CNCF Observability Whitepaper- Prometheus CNCF Project Documentation- Cloud Native Platform Engineering Study Guide

An incident management system's primary function is to coordinate response during outages, ensuring that alerts are centralized, on-call personnel are notified, communication is managed, and visibility is maintained.
Option B is correct because it emphasizes the core responsibilities of incident management systems like PagerDuty, Opsgenie, or ServiceNow. These systems streamline response efforts, reducing mean time to recovery (MTTR).
Option A (incident documentation) is valuable but usually a secondary outcome of incident management.
Option C (root cause analysis) is part of post-incident reviews, not the primary function during active response. Option D (automated remediation) may be supported by runbooks but is not the core role of incident management systems.
By centralizing and standardizing incident response, these systems enhance collaboration, reduce confusion, and provide stakeholders with up-to-date information on incident status, which is critical for maintaining trust and operational resilience.
References:- CNCF Platforms Whitepaper- SRE Incident Management Practices- Cloud Native Platform Engineering Study Guide

The primary purpose of multiple environments in software delivery pipelines is to isolate changes and test them before they reach production. Option A is correct because development, staging, and production environments provide controlled phases where teams can validate functionality, integration, performance, and security without impacting end users.
Option B (team collaboration) is facilitated by source control and workflows, not environment separation.
Option C (testing only in staging) is a risky practice and not recommended. Option D is a partial benefit- testing with different datasets helps-but the broader purpose is risk isolation.
By maintaining environment separation, organizations reduce the likelihood of bugs or misconfigurations reaching production. This practice aligns with DevOps and platform engineering principles, ensuring safer, more reliable continuous delivery.
References:- CNCF Platforms Whitepaper- Continuous Delivery Foundation Best Practices- Cloud Native Platform Engineering Study Guide