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資格考試和一年級Linux Foundation Certified Cloud Native Platform Engineering Associate的100%合格率新版CNPA題庫和領先提供者
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最新的 Cloud and Containers CNPA 免費考試真題 (Q13-Q18):
問題 #13
In what way does an internal platform impact developers' cognitive load?
- A. It increases cognitive load by requiring knowledge of all the underlying tools involved.
- B. It reduces cognitive load by hiding complex infrastructure details and providing simple interfaces.
- C. It has no impact on the mental effort required from developers, ensuring their cognitive load remains unchanged.
- D. It shifts all operational complexity onto developers, making them fully responsible for managing the process.
答案:B
解題說明:
The primary role of an Internal Developer Platform (IDP) is to reduce cognitive load for developers by abstracting away infrastructure complexity and providing simple, self-service interfaces. Option B is correct because platforms deliver curated golden paths, service catalogs, and APIs that allow developers to focus on application logic instead of learning every underlying infrastructure tool.
Option A is incorrect-platforms are specifically designed to reduce mental overhead. Option C contradicts the platform engineering principle of shifting complexity away from developers. Option D also misrepresents the intent of platforms, which aim to unify and simplify rather than complicate.
By lowering cognitive load, platforms improve productivity, enable faster onboarding, and reduce the likelihood of errors. This aligns with the "platform as a product" model, where developers are treated as customers and the platform is designed to optimize their experience.
References:- CNCF Platforms Whitepaper- Team Topologies (Cognitive Load Principle)- Cloud Native Platform Engineering Study Guide
問題 #14
In assessing the effectiveness of platform engineering initiatives, which DORA metric most directly correlates to the time it takes for code from its initial commit to be deployed into production?
- A. Change Failure Rate
- B. Mean Time to Recovery
- C. Lead Time for Changes
- D. Deployment Frequency
答案:C
解題說明:
Lead Time for Changes is a DORA (DevOps Research and Assessment) metric that measures the time from code commit to successful deployment in production. Option A is correct because it directly reflects how quickly the platform enables developers to turn ideas into delivered software. Shorter lead times indicate an efficient delivery pipeline, streamlined workflows, and effective automation.
Option B (Deployment Frequency) measures how often code is deployed, not how long it takes to reach production. Option C (Mean Time to Recovery) measures operational resilience after failures. Option D (Change Failure Rate) indicates stability by measuring the percentage of deployments causing incidents.
While all DORA metrics are valuable, only Lead Time for Changes measures end-to-end speed of delivery.
In platform engineering, improving lead time often involves automating CI/CD pipelines, implementing GitOps, and reducing manual approvals. It is a core measurement of developer experience and platform efficiency.
References:- CNCF Platforms Whitepaper- Accelerate: State of DevOps Report (DORA Metrics)- Cloud Native Platform Engineering Study Guide
問題 #15
In a cloud native environment, what is one of the security benefits of implementing a service mesh?
- A. Limiting network access to services based on IP allowlisting.
- B. Enabling encryption of communication between services using mTLS.
- C. Using a centralized logging system to monitor service interactions.
- D. Automatically scaling services to handle increased traffic.
答案:B
解題說明:
A key advantage of using a service mesh is its ability to secure service-to-service communication transparently, without requiring application code changes. Option A is correct because service meshes (e.g., Istio, Linkerd) provide mutual TLS (mTLS) by default, ensuring both encryption in transit and authentication between services. This establishes a zero-trust networking model inside the cluster.
Option B (scaling) is managed by Kubernetes (Horizontal Pod Autoscaler), not service mesh. Option C (logging) may be supported as an observability feature, but it is not the primary security benefit. Option D (IP allowlisting) is an outdated, less flexible mechanism compared to identity-based policies that meshes provide.
Service meshes enforce security consistently across all services, support fine-grained policies, and ensure compliance without burdening developers with complex configurations. This makes mTLS a foundational benefit in cloud native platform security.
References:- CNCF Service Mesh Whitepaper- CNCF Platforms Whitepaper- Cloud Native Platform Engineering Study Guide
問題 #16
Which provisioning strategy ensures efficient resource scaling for an application on Kubernetes?
- A. Using an imperative approach to script resource changes in response to traffic spikes.
- B. Using a declarative approach with Infrastructure as Code (IaC) tools to define resource requirements.
- C. Manual provisioning of resources based on predicted traffic.
- D. Implementing a fixed resource allocation that does not change regardless of demand.
答案:B
解題說明:
The most efficient and scalable strategy is to use a declarative approach with Infrastructure as Code (IaC)
. Option B is correct because declarative definitions specify the desired state (e.g., resource requests, limits, autoscaling policies) in code, allowing Kubernetes controllers and autoscalers to reconcile and enforce them dynamically. This ensures that applications can scale efficiently based on actual demand.
Option A (fixed allocation) is inefficient, leading to wasted resources during low usage or insufficient capacity during high demand. Option C (manual provisioning) introduces delays, risk of error, and operational overhead. Option D (imperative scripting) is not sustainable for large-scale or dynamic workloads, as it requires constant manual intervention.
Declarative IaC aligns with GitOps workflows, enabling automated, version-controlled scaling decisions.
Combined with Kubernetes' Horizontal Pod Autoscaler (HPA) and Cluster Autoscaler, this approach allows platforms to balance cost efficiency with application reliability.
References:- CNCF GitOps Principles- Kubernetes Autoscaling Documentation- Cloud Native Platform Engineering Study Guide
問題 #17
A platform team is implementing an API-driven approach to enable development teams to consume platform capabilities more effectively. Which of the following examples best illustrates this approach?
- A. Providing a documented process for developers to submit feature requests for the platform.
- B. Implementing a CI/CD pipeline that automatically deploys updates to the platform based on developer requests.
- C. Developing a dashboard that visualizes platform usage statistics without exposing any APIs.
- D. Allowing developers to request and manage development environments on demand through an internal tool.
答案:D
解題說明:
An API-driven approach in platform engineering enables developers to interact with the platform programmatically through self-service capabilities. Option C is correct because giving developers the ability to request and manage environments on demand via APIs or internal tooling exemplifies the API-first model. This approach abstracts infrastructure complexity, reduces manual intervention, and ensures automation and repeatability-all key goals of platform engineering.
Option A is a traditional request/response workflow but does not empower developers with real-time, self- service capabilities. Option B provides visibility but does not expose APIs for consumption or management.
Option D focuses on automating platform updates rather than enabling developer interaction with platform services.
By exposing APIs for services such as provisioning environments, databases, or networking, the platform team empowers developers to operate independently while maintaining governance and consistency. This improves developer experience and accelerates delivery, aligning with internal developer platform (IDP) practices.
References:- CNCF Platforms Whitepaper- CNCF Platform Engineering Maturity Model- Cloud Native Platform Engineering Study Guide
問題 #18
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