CNPA Linux Foundation Certified Cloud Native Platform Engineering Associate Free Practice Exam Questions (2025 Updated)

Lead Time for Changes is the DORA metric that best measures the efficiency and impact of platform initiatives. Option A is correct because it tracks the time from code commit to successful production deployment, directly reflecting how effectively a platform enables developers to deliver software.

Option B (MTTR) measures resilience and recovery speed, not efficiency. Option C (Change Failure Rate) measures deployment stability, while Option D (SLAs) are contractual agreements, not engineering performance metrics.

By reducing lead time, platform engineering demonstrates its ability to provide self-service, automation, and streamlined CI/CD workflows. This makes Lead Time for Changes a critical measurement of platform efficiency and developer experience improvements.

The most effective stage to generate a Software Bill of Materials (SBOM) is during the build process. Option C is correct because the build phase is when dependencies are resolved and artifacts (e.g., container images, binaries) are created. Generating an SBOM at this point provides a complete, accurate inventory of all included libraries and components, which is critical for vulnerability scanning, license compliance, and supply chain security.

Option A (testing) is too late to capture all dependencies reliably. Option B (before committing code) cannot provide a full SBOM because builds often introduce additional dependencies. Option D (after deployment) delays insights until production, missing the opportunity to detect and remediate issues early.

Integrating SBOM generation into CI/CD pipelines enables shift-left security, ensuring vulnerabilities are detected early and allowing remediation before artifacts reach production. This aligns with CNCF supply chain security practices and platform engineering goals.

GitOps uses a pull-based approach, where controllers inside the cluster continuously reconcile the desired state stored in Git with the actual cluster state. Option A is correct because GitOps sync agents (e.g., Argo CD, Flux) poll or watch Git repositories for changes and automatically apply updates to the cluster.

Option B reverses the model—clusters do not send updates to Git; Git is the source of truth. Option C is partially misleading: webhooks can trigger faster syncs but reconciliation is still pull-based. Option D misrepresents GitOps—Git never pushes directly to clusters.

This pull-based approach ensures greater security (clusters pull changes rather than exposing themselves to pushes), consistency (Git as source of truth), and continuous reconciliation (drift correction).

Mutual TLS (mTLS) is a core feature of service meshes, such as Istio or Linkerd, that enhances security in cloud native environments by ensuring that both communicating services authenticate each other and that the communication channel is encrypted. Option A is correct because mTLS delivers two critical benefits: authentication (verifying the identity of both client and server services) and encryption (protecting data in transit from interception or tampering).

Option B is incorrect because mTLS does not bypass security—it enforces it. Option C is partly true in that service meshes often support observability and logging, but that is not the primary purpose of mTLS. Option D relates to scaling, which is outside the scope of mTLS.

In platform engineering, mTLS is a fundamental security mechanism that provides zero-trust networking between microservices, ensuring secure communication without requiring application-level changes. It strengthens compliance with security and data protection requirements, which are crucial in regulated industries.