Pass4sure Certified Cloud Native Platform Engineering Associate certification - Linux Foundation CNPA sure exam practice
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Linux Foundation Certified Cloud Native Platform Engineering Associate Sample Questions (Q17-Q22):
NEW QUESTION # 17
In a cloud native environment, how do policy engines facilitate a unified approach for teams to consume platform services?
Answer: A
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
A development team is struggling to find and connect to various services within a cloud platform. What is the primary benefit of implementing an API-driven service catalog for this team?
Answer: C
Explanation:
An API-driven service catalog provides a centralized and standardized interface where developers can discover and provision platform services. Option A is correct because it simplifies service discovery, allowing teams to connect to databases, messaging systems, and other infrastructure without needing in-depth platform knowledge. This improves productivity and developer experience by reducing cognitive load and ensuring consistent, governed access.
Option B is the opposite of the benefit-catalogs accelerate provisioning. Option C is incorrect because catalogs do not bypass security; they enforce guardrails and compliance. Option D is also incorrect because service catalogs abstract away provisioning details rather than forcing developers to manage them.
By providing golden paths and API-driven self-service, service catalogs ensure developers focus on building applications while platform teams maintain consistency and compliance.
References:- CNCF Platforms Whitepaper- CNCF Platform Engineering Maturity Model- Cloud Native Platform Engineering Study Guide
NEW QUESTION # 19
Why might a platform allow different resource limits for development and production environments?
Answer: D
Explanation:
Resource allocation varies between environments to balance cost, performance, and reliability. Option D is correct because development environments usually require fewer resources and are optimized for speed and cost efficiency, while production environments require stricter limits to ensure stability, scalability, and resilience under real user traffic.
Option A (identical settings) may simplify management but wastes resources and fails to account for different needs. Option B (maximizing usage in all environments) increases costs unnecessarily. Option C (strict parity) may be used in testing scenarios but is impractical as a universal rule.
By tailoring resource limits per environment, platforms ensure cost efficiency in dev/staging and robust performance in production. This practice is central to cloud native engineering, as it allows teams to innovate quickly while maintaining governance and operational excellence in production.
References:- CNCF Platforms Whitepaper- Kubernetes Resource Management Guidance- Cloud Native Platform Engineering Study Guide
NEW QUESTION # 20
Which tool is commonly used to automate environment provisioning?
Answer: C
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
Which of the following is a primary benefit of using Kubernetes Custom Resource Definitions (CRDs) in a self-service platform model?
Answer: D
Explanation:
Kubernetes Custom Resource Definitions (CRDs) extend the Kubernetes API by allowing platform teams to create and expose custom APIs without modifying the core Kubernetes API server code. Option C is correct because this extensibility enables teams to define new abstractions (e.g., Database, Application, or Environment resources) tailored to organizational needs, which developers can consume through a self- service model.
Option A is incorrect because scaling and failover are handled by controllers or operators, not CRDs themselves. Option B is wrong because RBAC is still required for access control over custom resources.
Option D is misleading because multi-cloud support depends on how CRDs and their controllers are implemented, not a built-in CRD feature.
By leveraging CRDs, platform teams can standardize workflows, hide complexity, and implement guardrails, all while presenting developers with simplified abstractions. This is central to platform engineering, as it empowers developers with self-service APIs while maintaining operational control.
References:- CNCF Platforms Whitepaper- Kubernetes Extensibility Documentation- Cloud Native Platform Engineering Study Guide
NEW QUESTION # 22
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