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Linux Foundation CNPA Exam Syllabus Topics:

Topic
Details

Topic 1

Platform APIs and Provisioning Infrastructure: This part of the exam evaluates Procurement Specialists on the use of Kubernetes reconciliation loops, APIs for self-service platforms, and infrastructure provisioning with Kubernetes. It also assesses knowledge of the Kubernetes operator pattern for integration and platform scalability.

Topic 2

Platform Observability, Security, and Conformance: This part of the exam evaluates Procurement Specialists on key aspects of observability and security. It includes working with traces, metrics, logs, and events while ensuring secure service communication. Policy engines, Kubernetes security essentials, and protection in CI
CD pipelines are also assessed here.

Topic 3

Measuring your Platform: This part of the exam assesses Procurement Specialists on how to measure platform efficiency and team productivity. It includes knowledge of applying DORA metrics for platform initiatives and monitoring outcomes to align with organizational goals.

Linux Foundation Certified Cloud Native Platform Engineering Associate Sample Questions (Q16-Q21):

NEW QUESTION # 16
How can an internal platform team effectively support data scientists in leveraging complex AI/ML tools and infrastructure?

A. Focus the portal on UI-driven execution of predefined AI/ML jobs via abstraction.
B. Implement strict resource quotas and isolation for AI/ML workloads for stability.
C. Integrate AI/ML steps into standard developer CI/CD systems for maximum reuse
D. Offer workflows and easy access to specialized AI/ML tools, data, and compute.

Answer: D

Explanation:
The best way for platform teams to support data scientists is by enabling easy access to specialized AI/ML workflows, tools, and compute resources. Option C is correct because it empowers data scientists to experiment, train, and deploy models without worrying about the complexities of infrastructure setup. This aligns with platform engineering's principle of self-service with guardrails.
Option A (integrating into standard CI/CD) may help, but AI/ML workflows often require specialized tools like MLflow, Kubeflow, or TensorFlow pipelines. Option B (strict quotas) ensures stability but does not improve usability or productivity. Option D (UI-driven execution only) restricts flexibility and reduces the ability of data scientists to adapt workflows to evolving needs.
By offering AI/ML-specific workflows as golden paths within an Internal Developer Platform (IDP), platform teams improve developer experience for data scientists, accelerate innovation, and ensure compliance and governance.
References:- CNCF Platforms Whitepaper- CNCF Platform Engineering Maturity Model- Cloud Native Platform Engineering Study Guide

NEW QUESTION # 17
In a cloud native environment, how do policy engines facilitate a unified approach for teams to consume platform services?

A. Integrates with CI/CD pipelines to streamline service provisioning.
B. Provides centralized reusable policies to ensure security and compliance.
C. Enforces service-level agreements (SLAs) across all teams.
D. Enforces strict compliance policies with security standards.

Answer: B

Explanation:
Policy engines (such as Open Policy Agent - OPA or Kyverno) play a critical role in enforcing governance, security, and compliance consistently across cloud native platforms. Option D is correct because policy engines provide centralized, reusable policies that can be applied across clusters, services, and environments. This ensures that developers consume platform services in a compliant and secure manner, without needing to manage these controls manually.
Option A is partially correct but too narrow, as policies extend beyond compliance to include operational, security, and cost-control measures. Option B is not the primary function of policy engines, though integration with CI/CD is possible. Option C is incorrect because SLAs are business agreements, not enforced by policy engines directly.
Policy engines enforce guardrails like image signing, RBAC rules, resource quotas, and network policies automatically, reducing cognitive load for developers while giving platform teams confidence in compliance.
This supports the platform engineering principle of combining self-service with governance.
References:- CNCF Platforms Whitepaper- CNCF Security TAG (OPA, Kyverno)- Cloud Native Platform Engineering Study Guide

NEW QUESTION # 18
Development teams frequently raise support tickets for short-term access to staging clusters, creating a growing burden on the platform team. What's the best long-term solution to balance control, efficiency, and developer experience?

A. Provide pre-approved kubeconfigs to trusted developers so they can access staging clusters without platform intervention.
B. Dedicate one Cloud Native Platform Engineer to triage and fulfill all access requests to maintain fast turnaround times.
C. Use GitOps to manage RBAC roles and allow teams to request access via pull requests with automatic approval for non-sensitive environments.
D. Set up scheduled access windows and batch all requests into specific time slots managed by the platform team.

Answer: C

Explanation:
The most sustainable solution for managing developer access while balancing governance and self-service is to adopt GitOps-based RBAC management. Option A is correct because it leverages Git as the source of truth for access permissions, allowing developers to request access through pull requests. For non-sensitive environments such as staging, approvals can be automated, ensuring efficiency while still maintaining auditability. This approach aligns with platform engineering principles of self-service, automation, and compliance.
Option B places the burden entirely on one engineer, which does not scale. Option C introduces bottlenecks, delays, and reduces developer experience. Option D bypasses governance and auditability, potentially creating security risks.
GitOps for RBAC not only improves developer experience but also ensures all changes are versioned, reviewed, and auditable. This model supports compliance while reducing manual intervention from the platform team, thus enhancing efficiency.
References:- CNCF GitOps Principles- CNCF Platforms Whitepaper- Cloud Native Platform Engineering Study Guide

NEW QUESTION # 19
A platform team wants to let developers provision cloud services like S3 buckets and databases using Kubernetes-native APIs, without exposing cloud-specific details. Which tool is best suited for this?

A. Cluster API
B. Crossplane
C. OpenTofu
D. Helm

Answer: B

Explanation:
Crossplane is the CNCF project designed to extend Kubernetes with the ability to provision and manage cloud resources via Kubernetes-native APIs. Option B is correct because Crossplane lets developers use familiar Kubernetes manifests to request resources like S3 buckets, databases, or VPCs while abstracting provider-specific implementation details. Platform teams can define compositions and abstractions, providing developers with golden paths that include organizational guardrails.
Option A (Cluster API) is focused on provisioning Kubernetes clusters themselves, not cloud services. Option C (Helm) manages Kubernetes application deployments but does not provision external infrastructure. Option D (OpenTofu) is a Terraform fork that provides IaC but is not Kubernetes-native.
By leveraging Crossplane, platform teams achieve infrastructure as data and full GitOps integration, empowering developers to provision services declaratively while ensuring governance and compliance.
References:- CNCF Crossplane Project Documentation- CNCF Platforms Whitepaper- Cloud Native Platform Engineering Study Guide

NEW QUESTION # 20
Which tool is commonly used to automate environment provisioning?

A. OpenTofu
B. Kubernetes
C. Prometheus
D. Docker

Answer: A

Explanation:
OpenTofu (the open-source fork of Terraform) is one of the most widely used tools for automating environment provisioning. Option D is correct because OpenTofu allows teams to define infrastructure as code, supporting multiple cloud providers and services. It enables declarative, reusable, and version- controlled provisioning workflows, ensuring consistency across environments.
Option A (Kubernetes) orchestrates containers and workloads but does not provision infrastructure outside its cluster scope. Option B (Prometheus) is an observability tool, not an IaC tool. Option C (Docker) manages containers but does not provision full environments or infrastructure.
By using tools like OpenTofu/Terraform, platform engineers ensure scalable, repeatable environment provisioning integrated into CI/CD or GitOps workflows. This aligns with platform engineering's goals of reducing toil and enabling self-service with compliance.
References:- CNCF Platforms Whitepaper- Infrastructure as Code Best Practices- Cloud Native Platform Engineering Study Guide

NEW QUESTION # 21
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