[2026年01月最新リリース]CNPA試験問題はあなたをパスさせる
Linux Foundation CNPA試験基本問題とアンサー
Linux Foundation CNPA 認定試験の出題範囲：
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質問 # 17
What is the fundamental difference between a CI/CD and a GitOps deployment model for Kubernetes application deployments?
- A. GitOps is predominantly a pull model, with a controller reconciling desired state.
- B. CI/CD is predominantly a push model, with the user providing the desired state.
- C. CI/CD is predominantly a pull model, with the container image providing the desired state.
- D. GitOps is predominantly a push model, with an operator reflecting the desired state.
正解：A
解説：
The fundamental difference between a traditional CI/CD model and a GitOps model lies in how changes are applied to the Kubernetes cluster-whether they are "pushed" to the cluster by an external system or "pulled" by an agent running inside the cluster.
CI/CD (Push Model)In a typical CI/CD pipeline for Kubernetes, the CI/CD server (like Jenkins, GitLab CI, or GitHub Actions) is granted credentials to access the cluster. When a pipeline runs, it executes commands like kubectl apply or helm upgrade to push the new application configuration and image versions directly to the Kubernetes API server.
* Actor: The CI/CD pipeline is the active agent initiating the change.
* Direction: Changes flow from the CI/CD system to the cluster.
* Security: Requires giving cluster credentials to an external system.
In a GitOps model, a Git repository is the single source of truth for the desired state of the application. An agent or controller (like Argo CD or Flux) runs inside the Kubernetes cluster. This controller continuously monitors the Git repository.
When it detects a difference between the desired state defined in Git and the actual state of the cluster, it pulls the changes from the repository and applies them to the cluster to bring it into the desired state. This process is called reconciliation.
* Actor: The in-cluster controller is the active agent initiating the change.
* Direction: The cluster pulls its desired state from the Git repository.
* Security: The cluster's credentials never leave its boundary. The controller only needs read-access to the Git repository.
質問 # 18
In the context of Agile methodology, which principle aligns best with DevOps practices in platform engineering?
- A. Development and operations teams should remain separate to maintain clear responsibilities.
- B. Teams should strictly adhere to initial project plans without making adjustments during development.
- C. Teams should continuously gather feedback and iterate on their work to improve outcomes.
- D. Customer involvement should be limited during the development process to avoid disruptions.
正解：C
解説：
Agile and DevOps share the principle of continuous improvement through rapid feedback and iteration.
Option B is correct because gathering feedback continuously and iterating aligns directly with DevOps practices such as CI/CD, observability-driven development, and platform engineering's focus on developer experience. This ensures platforms and applications evolve quickly in response to real-world conditions.
Option A contradicts Agile, which emphasizes active customer collaboration. Option C reflects rigid waterfall methodologies, not Agile or DevOps. Option D enforces silos, which is the opposite of DevOps principles of cross-functional collaboration.
By embracing continuous feedback loops, both Agile and platform engineering accelerate delivery, improve resilience, and ensure that platforms deliver real value to developers and end users. This cultural alignment ensures both speed and quality in cloud native environments.
References:- Agile Manifesto Principles- CNCF Platforms Whitepaper- Cloud Native Platform Engineering Study Guide
質問 # 19
How can an internal platform team effectively support data scientists in leveraging complex AI/ML tools and infrastructure?
- A. Offer workflows and easy access to specialized AI/ML tools, data, and compute.
- B. Implement strict resource quotas and isolation for AI/ML workloads for stability.
- C. Focus the portal on UI-driven execution of predefined AI/ML jobs via abstraction.
- D. Integrate AI/ML steps into standard developer CI/CD systems for maximum reuse
正解：A
解説：
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
質問 # 20
What is the most effective approach to architecting a platform for extensibility in cloud native environments?
- A. Creating a platform with a flexible governance model that requires all capability changes to be reviewed by specialized teams before being approved, ensuring consistent implementation across all platform areas.
- B. Designing a platform with centralized configuration management that can quickly implement organization-wide changes through a single control plane operated by platform specialists.
- C. Implementing a modular architecture with well-defined APIs and interfaces that allows platform capabilities to be independently added, updated, or removed without disrupting the entire system.
- D. Building a monolithic platform with comprehensive documentation that provides complete instructions for users to modify internal components when new capabilities need to be added or removed.
正解：C
解説：
Extensibility in cloud native platform engineering depends on modular design with well-defined APIs and interfaces. Option A is correct because modular, API-driven architecture allows new capabilities (e.g., observability, self-service provisioning, policy engines) to be added, updated, or replaced independently, without disrupting the entire system. This enables innovation, adaptability, and continuous improvement.
Option B emphasizes governance, but relying solely on specialist approvals slows agility and reduces scalability. Option C (monolithic architecture) restricts flexibility and increases cognitive load for developers.
Option D (centralized configuration) provides consistency but risks bottlenecks and does not inherently enable extensibility.
Modularity and APIs are fundamental to platform engineering because they support composability, golden paths, and integration of open-source/cloud-native tools. This ensures that platforms evolve continuously while preserving developer experience and governance.
References:- CNCF Platforms Whitepaper- CNCF Platform Engineering Maturity Model- Cloud Native Platform Engineering Study Guide
質問 # 21
Which IaC approach ensures Kubernetes infrastructure maintains its desired state automatically?
- A. Manual
- B. Declarative
- C. Hybrid
- D. Imperative
正解：B
解説：
The declarative approach to Infrastructure as Code (IaC) is the foundation of Kubernetes and GitOps practices. Option A is correct because declarative IaC defines the desired state of the infrastructure (e.g., Kubernetes YAML manifests) and relies on controllers or reconciliation loops to ensure the actual state matches the declared one. This allows for automation, consistency, and drift correction without manual intervention.
Option B (imperative) requires explicit step-by-step instructions, which are not automatically enforced after execution. Option C (hybrid) can combine both methods but does not guarantee reconciliation. Option D (manual) is error-prone and eliminates the benefits of IaC entirely.
Declarative IaC reduces cognitive load, improves reproducibility, and ensures compliance through automated drift detection and reconciliation, which are essential in platform engineering for multi-cluster and multi-team environments.
References:- CNCF GitOps Principles- Kubernetes Declarative Model- Cloud Native Platform Engineering Study Guide
質問 # 22
What is the primary purpose of using multiple environments (e.g., development, staging, production) in a cloud native platform?
- A. Isolates different stages of application development and deployment
- B. Reduces cloud costs by running applications in different locations.
- C. Ensures all applications use the same infrastructure.
- D. Increases application performance by distributing traffic.
正解：A
解説：
The primary reason for implementing multiple environments in cloud native platforms is to isolate the different phases of the software development lifecycle. Option A is correct because environments such as development, staging, and production enable testing and validation at each stage without impacting end users. Development environments allow rapid iteration, staging environments simulate production for integration and performance testing, and production environments serve real users.
Option B (reducing costs) may be a side effect but is not the main purpose. Option C (distributing traffic) relates more to load balancing and high availability, not environment separation. Option D is the opposite of the goal-different environments often require tailored infrastructure to meet their distinct purposes.
Isolation through multiple environments is fundamental to reducing risk, supporting continuous delivery, and ensuring stability. This practice also allows for compliance checks, automated testing, and user acceptance validation before changes reach production.
References:- CNCF Platforms Whitepaper- Team Topologies & Platform Engineering Guidance- Cloud Native Platform Engineering Study Guide
質問 # 23
A platform engineering team is building an Internal Developer Platform (IDP). Which of the following enables application teams to manage infrastructure resources independently, without requiring direct platform team support?
- A. Self-service resource provisioning APIs.
- B. Manual infrastructure deployment services.
- C. Centralized logging and monitoring interfaces.
- D. A comprehensive platform knowledge center.
正解：A
解説：
The defining capability of an IDP is enabling self-service so developers can independently access infrastructure and platform resources. Option D is correct because self-service resource provisioning APIs allow developers to provision resources such as namespaces, databases, or environments without relying on manual intervention from the platform team. These APIs embed governance, compliance, and organizational guardrails while giving autonomy to development teams.
Option A (manual deployment services) defeats the purpose of self-service. Option B (knowledge centers) improve documentation but do not provide automation. Option C (logging/monitoring interfaces) are observability tools, not resource provisioning mechanisms.
Self-service APIs empower developers, reduce cognitive load, and minimize bottlenecks. They also align with the platform engineering principle of "treating the platform as a product," where developers are customers, and the platform offers curated golden paths to simplify consumption of infrastructure and services.
References:- CNCF Platforms Whitepaper- CNCF Platform Engineering Maturity Model- Cloud Native Platform Engineering Study Guide
質問 # 24
Which approach is an effective method for securing secrets in CI/CD pipelines?
- A. Storing secrets as plain-text environment variables managed through config files.
- B. Storing secrets and encrypting them in a secrets manager.
- C. Storing secrets in configuration files with restricted access.
- D. Encoding secrets in the source code using base64.
正解：B
解説：
The most secure and scalable method for handling secrets in CI/CD pipelines is to use a secrets manager with encryption. Option B is correct because solutions like HashiCorp Vault, AWS Secrets Manager, or Kubernetes Secrets (backed by KMS) securely store, encrypt, and control access to sensitive values such as API keys, tokens, or credentials.
Option A (restricted config files) may protect secrets but lacks auditability and rotation capabilities. Option C (plain-text environment variables) exposes secrets to accidental leaks through logs or misconfigurations.
Option D (base64 encoding) is insecure because base64 is an encoding, not encryption, and secrets can be trivially decoded.
Using a secrets manager ensures secure retrieval, audit trails, access policies, and secret rotation. This aligns with supply chain security and zero-trust practices, reducing risks of credential leakage in CI/CD pipelines.
References:- CNCF Security TAG Best Practices- CNCF Platforms Whitepaper- Cloud Native Platform Engineering Study Guide
質問 # 25
During a platform engineering meeting, a team discusses the importance of automating deployment processes to enhance collaboration and efficiency. What is the primary benefit of implementing automation in DevOps practices within platform engineering?
- A. It accelerates deployments, enabling faster iterations and continuous delivery.
- B. It creates dependencies on specific tools and platforms.
- C. It reduces the need for communication between team members.
- D. It eliminates the need for any manual intervention.
正解：A
解説：
Automation in DevOps practices is central to platform engineering because it enables faster, reliable, and repeatable deployments. Option D is correct: automation accelerates deployments, reduces bottlenecks, and enables continuous delivery and rapid iterations. By automating build, test, and deployment pipelines, teams can deliver new features quickly while maintaining high quality and compliance.
Option A is incorrect because automation does not reduce the need for communication-it complements collaboration by removing friction. Option B is unrealistic: some manual oversight may remain (e.g., in production approvals for sensitive workloads). Option C is not a primary benefit-while tools may be involved, the focus is on outcomes, not tool dependency.
By embedding automation, teams reduce toil, enforce consistency, and free developers to focus on value creation rather than repetitive tasks. This results in shorter lead times, higher deployment frequency, and overall improved developer experience, which aligns with DORA metrics.
References:- CNCF Platforms Whitepaper- Continuous Delivery Foundation Guidance- Cloud Native Platform Engineering Study Guide
質問 # 26
A Platform Team is adopting the HEART framework to measure user experience of their developer portal.
Which of the following aspects does the HEART framework primarily focus on to help improve developer experience and platform performance?
- A. HEART focuses on Happiness, Engagement, Adoption, Retention, and Task success, enabling teams to track both qualitative and quantitative metrics for user experience.
- B. HEART focuses on Happiness, Efficiency, Availability, Reliability, and Throughput, enabling teams to assess both qualitative and quantitative aspects of platform performance.
- C. HEART tracks Happiness, Engagement, Adoption, Reliability, and Throughput, helping teams optimize infrastructure performance.
- D. HEART evaluates Hardware, Efficiency, Availability, Response times, and Throughput to measure and ensure platform reliability and performance.
正解：A
解説：
The HEART framework was developed by Google to measure user experience using both qualitative and quantitative indicators. Option C is correct because HEART stands for Happiness, Engagement, Adoption, Retention, and Task success. In platform engineering, this framework is applied to measure developer experience with internal developer portals (IDPs) and other platform components.
Option A and D misrepresent the acronym by replacing its original user-experience focus with infrastructure- oriented metrics. Option B substitutes Reliability for Retention, which is incorrect.
By applying HEART, platform teams can measure satisfaction (Happiness), frequency of use (Engagement), onboarding success (Adoption), long-term value (Retention), and ability to complete tasks effectively (Task success). This helps teams identify pain points, iterate on golden paths, and improve the usability of their platform.
References:- CNCF Platforms Whitepaper- Google HEART Framework for UX Measurement- Cloud Native Platform Engineering Study Guide
質問 # 27
During a CI/CD pipeline review, the team discusses methods to prevent insecure code from being introduced into production. Which practice is most effective for this purpose?
- A. Implementing security gates at key stages of the pipeline.
- B. Conducting A/B testing to validate secure code changes.
- C. Using caching strategies to control secure content delivery.
- D. Performing load balancing controls to manage traffic during deployments.
正解：A
解説：
The most effective way to prevent insecure code from reaching production is to integrate security gates directly into the CI/CD pipeline. Option A is correct because security gates involve automated scanning of dependencies, SBOM generation, code analysis, and policy enforcement during build and test phases. This ensures that vulnerabilities or policy violations are caught early in the development lifecycle.
Option B (load balancing) improves availability but is unrelated to code security. Option C (A/B testing) validates functionality, not security. Option D (caching strategies) affects performance, not code safety.
By embedding automated checks into CI/CD pipelines, teams adopt a shift-left security approach, ensuring compliance and minimizing risks of supply chain attacks. This practice directly supports platform engineering goals of combining security with speed and reducing developer friction through automation.
References:- CNCF Supply Chain Security Whitepaper- CNCF Platforms Whitepaper- Cloud Native Platform Engineering Study Guide
質問 # 28
In a GitOps approach, how should the desired state of a system be managed and integrated?
- A. By storing it so it is versioned and immutable, and pulled automatically into the system.
- B. By storing it in Git, and manually pushing updates through CI/CD pipelines.
- C. By using a centralized management tool to push changes immediately to all environments.
- D. As custom Kubernetes resources, stored and applied directly to the system.
正解：A
解説：
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
質問 # 29
Which of the following would be considered an advantage of using abstract APIs when offering cloud service provisioning and management as platform services?
- A. Abstractions curate cloud services with built-in guardrails for development teams.
- B. Abstractions enforce explicit platform team approval before any cloud resource is deployed.
- C. Development teams can arbitrarily deploy cloud services via abstractions.
- D. Abstractions allow customization of cloud services and resources without guardrails.
正解：A
解説：
Abstract APIs are an essential component of platform engineering, providing a simplified interface for developers to consume infrastructure and cloud services without deep knowledge of provider-specific details.
Option B is correct because abstractions allow platform teams to curate services with built-in guardrails, ensuring compliance, security, and operational standards are enforced automatically. Developers get the benefit of self-service and flexibility while the platform team ensures governance.
Option A would slow down the process, defeating the purpose of abstraction. Option C removes guardrails, which risks security and compliance violations. Option D allows uncontrolled deployments, which can create chaos and undermine platform governance.
Abstract APIs strike the balance between developer experience and organizational control. They provide golden paths and opinionated defaults while maintaining the flexibility needed for developer productivity.
This approach ensures efficient service provisioning at scale with reduced cognitive load on developers.
References:- CNCF Platforms Whitepaper- CNCF Platform Engineering Maturity Model- Cloud Native Platform Engineering Study Guide
質問 # 30
In a software deployment pipeline, what is a common purpose of having different environments like production, staging, and development?
- A. Allows teams to isolate changes and catch issues before reaching production.
- B. Supports testing features against different datasets without impacting live users.
- C. Helps streamline deployments by limiting testing to staging environments only.
- D. Lets developers work together on the same codebase more effectively.
正解：A
解説：
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
質問 # 31
What is a key consideration during the setup of a Continuous Integration/Continuous Deployment (CI/CD) pipeline to ensure efficient and reliable software delivery?
- A. Manually approve each build before deployment to maintain control over quality.
- B. Implement automated testing at multiple points in the pipeline.
- C. Using a single development environment for all stages of the pipeline.
- D. Skip the packaging step to save time and reduce complexity.
正解：B
解説：
Automated testing throughout the pipeline is a key enabler of efficient and reliable delivery. Option B is correct because incorporating unit tests, integration tests, and security scans at different pipeline stages ensures that errors are caught early, reducing the risk of faulty code reaching production. This also accelerates delivery by providing fast, consistent feedback to developers.
Option A (single environment) undermines isolation and does not reflect real-world deployment conditions.
Option C (skipping packaging) prevents reproducibility and traceability of builds. Option D (manual approvals) adds delays and reintroduces human bottlenecks, which goes against DevOps and GitOps automation principles.
Automated testing, combined with immutable artifacts and GitOps-driven deployments, aligns with platform engineering's focus on automation, reliability, and developer experience. It reduces cognitive load for teams and enforces quality consistently.
References:- CNCF Platforms Whitepaper- Continuous Delivery Foundation Best Practices- Cloud Native Platform Engineering Study Guide
質問 # 32
Which Kubernetes feature allows you to control how Pods communicate with each other and external services?
- A. Network Policies
- B. Pod Security Standards
- C. Security Context
- D. Role-based access control (RBAC)
正解：A
解説：
Kubernetes Network Policies are the feature that controls how Pods communicate with each other and external services. Option B is correct because Network Policies define rules for ingress (incoming) and egress (outgoing) traffic at the Pod level, ensuring fine-grained control over communication pathways within the cluster.
Option A (Pod Security Standards) defines policies around Pod security contexts (e.g., privilege escalation, root access) but does not control network traffic. Option C (Security Context) is specific to Pod or container- level permissions, not networking. Option D (RBAC) governs access to Kubernetes API resources, not Pod-to- Pod traffic.
Network Policies are essential for implementing a zero-trust model in Kubernetes, ensuring that only authorized services communicate. This enhances both security and compliance, especially in multi-tenant clusters.
References:- CNCF Kubernetes Security Best Practices- CNCF Platforms Whitepaper- Cloud Native Platform Engineering Study Guide
質問 # 33
Which key observability signal helps detect real-time performance bottlenecks in a Kubernetes cluster?
- A. Traces
- B. Metrics
- C. Events
- D. Logs
正解：B
解説：
Metrics are the observability signal most effective at detecting real-time performance bottlenecks in Kubernetes. Option C is correct because metrics provide numerical, time-series data (e.g., CPU usage, memory consumption, request latency, pod restarts) that can be aggregated and monitored continuously. This makes them the best fit for identifying performance degradation and bottlenecks before they escalate into outages.
Option A (logs) capture detailed events but are better for debugging after issues occur. Option B (traces) provide request-level insights across distributed systems but focus on transaction flow rather than cluster-wide performance. Option D (events) record discrete system changes but are not designed for continuous performance monitoring.
Metrics integrate with tools like Prometheus and Grafana, enabling SLO/SLI monitoring and alerting. They allow proactive capacity planning, scaling decisions, and real-time issue detection-critical aspects of cloud native observability.
References:- CNCF Observability Whitepaper- Prometheus CNCF Documentation- Cloud Native Platform Engineering Study Guide
質問 # 34
As a Cloud Native Platform Associate, which of the following is the best example of a self-service use case that should be implemented within a cloud platform?
- A. An automated resource provisioning system to spin up environments on demand.
- B. A centralized dashboard for monitoring application performance.
- C. An internal wiki for documenting best practices in cloud usage.
- D. A manual request process for acquiring additional storage resources.
正解：A
解説：
Self-service capabilities are a cornerstone of platform engineering, enabling developers to move quickly while reducing dependency on platform teams. Option C is correct because an automated resource provisioning system allows developers to spin up sandbox or test environments on demand, supporting experimentation and rapid iteration. This aligns with the principle of treating platforms as products, focusing on developer experience and productivity.
Option A (manual request process) creates bottlenecks and is the opposite of self-service. Option B (documentation) is helpful but does not enable automation or self-service. Option D (centralized monitoring) improves observability but is not a self-service capability by itself.
By implementing automated provisioning, developers gain autonomy while platform teams maintain governance through abstractions, golden paths, and policy enforcement. This fosters agility, consistency, and scalability, improving both developer experience and organizational efficiency.
References:- CNCF Platforms Whitepaper- CNCF Platform Engineering Maturity Model- Cloud Native Platform Engineering Study Guide
質問 # 35
A developer is struggling to access the necessary services on a cloud native platform due to complex Kubernetes configurations. What approach can best simplify their access to platform capabilities?
- A. Increase the number of required configurations to enhance security.
- B. Provide detailed documentation on Kubernetes configurations.
- C. Limit user access to only a few services.
- D. Implement a web portal that abstracts the Kubernetes complexities.
正解：D
解説：
One of the primary objectives of internal developer platforms (IDPs) is to improve developer experience by reducing cognitive load. Complex Kubernetes configurations often overwhelm developers who simply want to consume services and deploy code without worrying about infrastructure intricacies.
Option B is correct because implementing a self-service web portal (or developer portal) abstracts away Kubernetes complexities, providing developers with easy access to platform services through standardized workflows, templates, and golden paths. This aligns with platform engineering principles: empowering developers with self-service capabilities while maintaining governance, security, and compliance.
Option A increases burden unnecessarily and negatively impacts productivity. Option C limits access to services, reducing flexibility and developer autonomy, which goes against the core goal of IDPs. Option D, while helpful for education, does not remove complexity-it only shifts the responsibility back to the developer. By leveraging portals, APIs, and automation, platform teams allow developers to focus on building business value instead of managing infrastructure details.
References:- CNCF Platforms Whitepaper- Team Topologies and Platform Engineering Practices- Cloud Native Platform Engineering Study Guide
質問 # 36
In a cloud native environment, which approach is effective for managing resources to ensure a balance between defined states and dynamic adjustments?
- A. Imperative Resource Management
- B. Static Resource Allocation
- C. Manual Resource Tracking
- D. Declarative Resource Management
正解：D
解説：
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
質問 # 37
Which platform component enables one-click provisioning of sandbox environments, including both infrastructure and application code?
- A. Observability pipeline
- B. Service mesh
- C. CI/CD pipeline
- D. Service bus
正解：C
解説：
A CI/CD pipeline is the platform component that enables automated provisioning of sandbox environments with both infrastructure and application code. Option A is correct because modern pipelines integrate Infrastructure as Code (IaC) with application deployment, enabling "one-click" or self-service provisioning of complete environments. This capability is central to platform engineering because it empowers developers to spin up temporary or permanent sandbox environments quickly for testing, experimentation, or demos.
Option B (service mesh) focuses on secure, observable service-to-service communication but does not provision environments. Option C (service bus) is used for asynchronous communication between services, not environment provisioning. Option D (observability pipeline) deals with collecting telemetry data, not provisioning.
By leveraging CI/CD pipelines integrated with GitOps and IaC tools (such as Terraform, Crossplane, or Kubernetes manifests), platform teams ensure consistency, compliance, and automation. Developers benefit from reduced friction, faster feedback cycles, and a better overall developer experience.
References:- CNCF Platforms Whitepaper- CNCF GitOps Principles- Cloud Native Platform Engineering Study Guide
質問 # 38
In a cloud native environment, which factor most critically influences the need for customized CI pipeline configurations across different application types?
- A. The organizational practice of assigning unique pipeline configurations based on application priority levels.
- B. The requirement to visually distinguish between different application pipelines in monitoring dashboards.
- C. The technical differences in build tools, testing frameworks, and artifact formats across programming languages.
- D. The need to accommodate varying team sizes and developer expertise levels within the organization.
正解：C
解説：
The biggest driver for customizing CI pipeline configurations across application types is technical differences between programming languages, frameworks, and artifact formats. Option B is correct because applications written in Java, Python, Go, or Node.js require different build tools (e.g., Maven, pip, go build, npm), testing frameworks, and packaging mechanisms. These differences must be reflected in the CI pipeline to ensure successful builds, tests, and artifact generation.
Option A (priority-based pipelines) is more of an organizational practice, not a technical necessity. Option C (team sizes and expertise) may influence usability but does not drive pipeline configuration. Option D (visual distinction) relates to dashboards and observability, not pipeline functionality.
Platform engineers often provide pipeline templates or abstractions that encapsulate these differences while standardizing security and compliance checks. This balances customization with consistency, enabling developers to use pipelines suited to their technology stack without fragmenting governance.
References:- CNCF Platforms Whitepaper- Continuous Delivery Foundation Guidance- Cloud Native Platform Engineering Study Guide
質問 # 39
What is the goal of automating processes in platform teams?
- A. Increasing the number of tasks completed.
- B. Ensuring high-quality coding standards.
- C. Reducing time spent on repetitive tasks.
- D. Focusing on manual processes.
正解：C
解説：
Comprehensive and Detailed Explanation at least 150 to 200 words:
In platform engineering, automation's primary goal is to eliminate manual, repetitive toil by codifying repeatable workflows and guardrails so teams can focus on higher-value work. Authoritative Cloud Native Platform Engineering guidance emphasizes that platforms should provide consistent, reliable, and secure self- service capabilities-achieved by automating provisioning, configuration, policy enforcement, and delivery pipelines. This directly reduces cognitive load and handoffs, shortens lead time for changes, decreases error rates, and improves overall reliability. While automation often improves code quality indirectly (e.g., through automated testing, linting, and policy-as-code), the central, explicitly stated aim is to remove repetitive manual work and standardize operations, not to simply "do more tasks" or prioritize manual intervention.
Therefore, option A most accurately captures the intent. Options B and C misframe the objective: platform engineering seeks fewer manual steps and better outcomes, not just higher task counts. Option D is a beneficial consequence but not the core purpose. By systematizing common paths ("golden paths") and embedding security and compliance controls into automated workflows, platforms deliver predictable, compliant environments at scale while freeing engineers to focus on product value.
References:- CNCF Platforms Whitepaper (Platform Engineering)- CNCF Platform Engineering Maturity Model- Cloud Native Platform Engineering Study Guide
質問 # 40
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2026年最新のリアルな無料Linux Foundation CNPA試験問題集問題と解答：https://www.passtest.jp/Linux-Foundation/CNPA-shiken.html
CNPA練習テストエンジン購入前に試そう87試験問題：https://drive.google.com/open?id=11ERhhaNOR-BcBuoDmt_l_9IQfVE3O8Ax