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[General] CNPA Test Vce | Study CNPA Demo

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【General】 CNPA Test Vce | Study CNPA Demo

Posted at yesterday 16:13      View：11 | Replies：1        Print      Only Author   [Copy Link] 1#
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Linux Foundation CNPA Exam Syllabus Topics:
TopicDetails
Topic 1
  • Platform Engineering Core Fundamentals: This section of the exam measures the skills of Supplier Management Consultants and covers essential foundations such as declarative resource management, DevOps practices, application environments, platform architecture, and the core goals of platform engineering. It also includes continuous integration fundamentals, delivery approaches, and GitOps principles.
Topic 2
  • 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 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.
Topic 4
  • 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 (Q18-Q23):NEW QUESTION # 18
As a platform engineer, how do you automate application deployments across multiple Kubernetes clusters using GitOps, Helm, and Crossplane, ensuring a consistent application state?
  • A. Leverage Git for configuration storage, with manual application of Helm and Crossplane.
  • B. Integrate Helm and Crossplane into a GitOps-enabled CI/CD pipeline.
  • C. Use Helm and Crossplane, with manual GUI-based configuration updates.
  • D. Employ a GitOps controller to synchronize Git-stored Helm and Crossplane configurations.
Answer: D
Explanation:
The most effective way to achieve consistent, automated deployments across multiple Kubernetes clusters is to combine GitOps controllers (e.g., Argo CD, Flux) with declarative configurations managed by Helm and Crossplane. Option A is correct because the GitOps controller continuously reconciles the desired state stored in Git-Helm charts for applications and Crossplane manifests for infrastructure-ensuring consistency across clusters.
Option B and D rely on manual updates, which are error-prone and not scalable. Option C mischaracterizes GitOps by suggesting push-based pipelines rather than the core GitOps model of pull-based reconciliation.
This combination leverages Helm for application packaging, Crossplane for cloud infrastructure provisioning, and GitOps for declarative, version-controlled delivery. It ensures applications remain in sync with Git, providing auditability, automation, and resilience in multi-cluster environments.
References:- CNCF GitOps Principles- CNCF Platforms Whitepaper- Cloud Native Platform Engineering Study Guide

NEW QUESTION # 19
What is the primary purpose of Kubernetes runtime security?
  • A. Encrypts the sensitive data stored in etcd.
  • B. Protects workloads against threats during execution.
  • C. Manages the access control to the Kubernetes API.
  • D. Scans container images before deployment.
Answer: B
Explanation:
The main purpose of Kubernetes runtime security is to protect workloads during execution. Option B is correct because runtime security focuses on monitoring active Pods, containers, and processes to detect and prevent malicious activity such as privilege escalation, anomalous network connections, or unauthorized file access.
Option A (etcd encryption) addresses data at rest, not runtime. Option C (image scanning) occurs pre- deployment, not during execution. Option D (API access control) is enforced through RBAC and IAM, not runtime security.
Runtime security solutions (e.g., Falco, Cilium, or Kyverno) continuously observe system calls, network traffic, and workload behaviors to enforce policies and detect threats in real time. This ensures compliance, strengthens defenses in zero-trust environments, and provides critical protection for cloud native workloads in production.
References:- CNCF Security TAG Guidance- CNCF Platforms Whitepaper- Cloud Native Platform Engineering Study Guide

NEW QUESTION # 20
A platform engineering team needs to provide comprehensive cost visibility for Kubernetes workloads to optimize infrastructure utilization. Which tool is recommended to achieve this goal?
  • A. OpenCost for real-time, granular Kubernetes cost allocation and analysis.
  • B. Kubernetes resource usage metrics paired with cloud provider billing data.
  • C. Application performance monitoring tools with limited resource cost tracking.
  • D. Cloud provider cost estimation tools with basic Kubernetes integration.
Answer: A
Explanation:
OpenCost is the CNCF-supported open-source project designed specifically for Kubernetes cost visibility and optimization. Option B is correct because OpenCost provides granular, real-time allocation of Kubernetes costs across namespaces, workloads, and teams. This allows organizations to understand true cost drivers and optimize resource utilization effectively.
Option A (APM tools) may track performance but usually lack deep integration with Kubernetes cost allocation. Option C provides partial visibility but requires complex manual correlation of resource usage with billing data. Option D (cloud provider estimators) typically offer limited or high-level insights and do not map costs down to Kubernetes workloads.
By adopting OpenCost, platform teams can align financial accountability with engineering usage, a practice known as FinOps. This supports sustainable scaling, cost efficiency, and transparency-critical aspects of measuring platform success.
References:- CNCF OpenCost Project- CNCF Platforms Whitepaper- Cloud Native Platform Engineering Study Guide

NEW QUESTION # 21
What does the latest tag usually represent in a container image registry?
  • A. A system-generated version number based on Git history.
  • B. The most recently built image unless otherwise specified.
  • C. A signed image that has passed all security validations.
  • D. The only image tag that can be deployed to production systems.
Answer: B
Explanation:
In most container registries, the latest tag is simply an alias pointing to whichever image was most recently built and pushed, unless explicitly overridden. Option A is correct because the latest tag does not carry any semantic guarantee beyond being the most recently tagged version.
Option B is incorrect-latest does not imply security validation or attestation. Option C is false because production systems should not rely on latest; instead, immutable, versioned tags or digests should be used for reproducibility. Option D is misleading, as latest is not tied to Git history but rather to tag assignment during the build/push process.
While convenient for testing or local development, relying on latest in production pipelines is discouraged.
Platform engineering best practices emphasize explicit versioning and image immutability to ensure consistency, reproducibility, and traceability. Using signed images with SBOM attestation is recommended for security and compliance, while latest should only be used in controlled, non-production workflows.
References:- CNCF Supply Chain Security Whitepaper- CNCF Platforms Whitepaper- Cloud Native Platform Engineering Study Guide

NEW QUESTION # 22
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. Offer workflows and easy access to specialized AI/ML tools, data, and compute.
  • D. Integrate AI/ML steps into standard developer CI/CD systems for maximum reuse
Answer: C
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 # 23
......
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