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Title: CNPA Latest Exam Test, Exam CNPA Format [Print This Page]
Author: gregkin423    Time: yesterday 01:16
Title: CNPA Latest Exam Test, Exam CNPA Format
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Linux Foundation CNPA Exam Syllabus Topics:
TopicDetails
Topic 1
  • IDPs and Developer Experience: This section of the exam measures the skills of Supplier Management Consultants and focuses on improving developer experience. It covers simplified access to platform capabilities, API-driven service catalogs, developer portals for platform adoption, and the role of AI
  • ML in platform automation.
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
  • Continuous Delivery & Platform Engineering: This section measures the skills of Supplier Management Consultants and focuses on continuous integration pipelines, the fundamentals of the CI
  • CD relationship, and GitOps basics. It also includes knowledge of workflows, incident response in platform engineering, and applying GitOps for application environments.

Linux Foundation Certified Cloud Native Platform Engineering Associate Sample Questions (Q22-Q27):NEW QUESTION # 22
Which approach is effective for scalable Kubernetes infrastructure provisioning?
Answer: A
Explanation:
The most effective approach for scalable Kubernetes infrastructure provisioning is Crossplane compositions.
Option D is correct because compositions let platform teams define custom CRDs (Composite Resources) that abstract infrastructure details while embedding organizational policies and guardrails. Developers then consume these abstractions through simple Kubernetes-native APIs, enabling self-service at scale.
Option A (Helm with values.yaml) is useful for application deployment but not for scalable infrastructure provisioning across multiple clouds. Option B (imperative scripts) lacks scalability, repeatability, and governance. Option C (static YAML with kubectl apply) is manual and not suited for dynamic, multi-team environments.
Crossplane compositions allow platform teams to curate golden paths while giving developers autonomy. This reduces complexity, ensures compliance, and supports multi-cloud provisioning-all key aspects of platform engineering.
References:- CNCF Crossplane Project Documentation- CNCF Platforms Whitepaper- Cloud Native Platform Engineering Study Guide

NEW QUESTION # 23
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?
Answer: C
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 # 24
How can an internal platform team effectively support data scientists in leveraging complex AI/ML tools and infrastructure?
Answer: A
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 # 25
A platform team is deciding whether to invest engineering time into automating cluster autoscaling. Which of the following best justifies making this automation a priority?
Answer: D
Explanation:
Automation in platform engineering is primarily about reducing repetitive manual work, or toil, which consumes engineering capacity and increases the risk of human error. Option A is correct because cluster autoscaling-adjusting resources to meet workload demand-is a repetitive, ongoing task that is better handled through automation. Automating this process ensures scalability, efficiency, and reliability while freeing platform teams to focus on higher-value work.
Option B may provide learning opportunities but is not a sustainable justification. Option C is subjective and inefficient, while Option D is overly broad-automation should be applied thoughtfully to tasks that bring measurable benefits.
Automating autoscaling aligns with cloud native best practices, ensuring workloads can respond elastically to demand changes while maintaining cost efficiency. This reduces manual overhead, improves resiliency, and supports the developer experience by ensuring resource availability.
References:- CNCF Platforms Whitepaper- SRE Principles on Eliminating Toil- Cloud Native Platform Engineering Study Guide

NEW QUESTION # 26
In a GitOps approach, how should the desired state of a system be managed and integrated?
Answer: A
Explanation:
The GitOps model is built on the principle that the desired state of infrastructure and applications must be stored in Git as the single source of truth. Option D is correct because Git provides versioning, immutability, and auditability, while reconciliation controllers (e.g., Argo CD or Flux) pull the desired state into the system continuously. This ensures that actual cluster state always matches the declared Git state.
Option A is partially correct but fails because GitOps eliminates manual push workflows-automation ensures changes are pulled and reconciled. Option B describes Kubernetes CRDs, which may be part of the system but do not embody GitOps on their own. Option C contradicts GitOps principles, which rely on pull- based reconciliation, not centralized push.
Storing desired state in Git provides full traceability, automated rollbacks, and continuous reconciliation, improving reliability and compliance. This makes GitOps a core practice for cloud native platform engineering.
References:- CNCF GitOps Principles- CNCF Platforms Whitepaper- Cloud Native Platform Engineering Study Guide

NEW QUESTION # 27
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Practicing with Linux Foundation CNPA Exam questions will help you to become an expert in and acquire the Linux Foundation CNPA. Linux Foundation CNPA Exam Questions allow you to verify your skills as a professional. You have to pass the Linux Foundation CNPA to achieve the associate-level certification.
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