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| Certification Vendor: | Linux Foundation / CNCF |
|---|---|
| Exam Name: | Kubernetes and Cloud Native Associate (KCNA) Exam |
| Exam Number: | KCNA |
| Certificate Validity Period: | 2 years |
| Passing Score: | 75% |
| Real Exam Qty: | 60 |
| Exam Price: | $250 USD |
| Exam Format: | Online proctored, Multiple choice |
| Exam Duration: | 90 minutes |
| Available Languages: | English, Japanese |
| Related Certifications: | CKA (Certified Kubernetes Administrator) CKS (Certified Kubernetes Security Specialist) KCSA (Kubernetes and Cloud Native Security Associate) CKAD (Certified Kubernetes Application Developer) |
| Recommended Training: | LFS250: Kubernetes and Cloud Native Essentials |
| Exam Registration: | Official Registration |
| Sample Questions: | Linux Foundation KCNA Sample Questions |
| Exam Way: | Online, remotely proctored; available worldwide |
| Pre Condition: | No mandatory prerequisites; basic IT knowledge and familiarity with command-line tools recommended |
| Official Syllabus URL: | https://training.linuxfoundation.org/certification/kubernetes-cloud-native-associate/ |
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Linux Foundation has been providing certification exams to individuals who want to demonstrate their expertise in open source technologies. One of their newest offerings is the KCNA (Kubernetes and Cloud Native Associate) Certification Exam. KCNA exam is designed for individuals who want to prove their knowledge and skills in cloud native computing and Kubernetes.
Linux Foundation KCNA (Kubernetes and Cloud Native Associate) Exam is a certification program that is designed to provide individuals with the skills and knowledge required to work with Kubernetes and cloud-native technologies. Kubernetes and Cloud Native Associate certification is ideal for individuals who are interested in pursuing a career in the field of cloud computing or for those who want to enhance their skills in this area. The program covers various areas such as containerization, orchestration, networking, security, and storage.
NEW QUESTION # 89
You are developing a serverless application using Azure Functions that processes real-time streaming dat
a. How would you ensure reliable and efficient data ingestion from a Kafka topic to your Azure Functions?
Answer: C
Explanation:
Azure Event Hubs is the recommended approach for connecting Kafka to Azure Functions for real-time streaming data ingestion. It offers high throughput and scalability, making it ideal for handling large volumes of streaming events. Service Bus (B) is suitable for message queuing but not primarily designed for streaming data. Directly polling Kafka topics (C) can be inefficient and might not scale well. Custom connectors (D) can be complex and might lack the required functionality for streaming data ingestion. Data Factory (E) is more focused on data movement and transformation, not real-time streaming
NEW QUESTION # 90
Which component of the Kubernetes architecture is responsible for scheduling Pods to nodes and ensuring that they are running as intended?
Answer: D
Explanation:
The kube-scheduler is the component responsible for scheduling Pods to nodes. It takes into account resource availability node affinity, and other factors to ensure optimal resource utilization and application performance.
NEW QUESTION # 91
What is an advantage of using the Gateway API compared to Ingress in Kubernetes?
Answer: D
Explanation:
The Gateway API is a newer Kubernetes networking API designed to address several limitations of the traditional Ingress resource. One of its most significant advantages is the clear separation of roles and responsibilities between infrastructure providers (such as platform teams or cluster administrators) and application developers. This design principle is a core motivation behind the Gateway API and directly differentiates it from Ingress.
With Ingress, a single resource often combines concerns such as load balancer configuration, TLS settings, routing rules, and application-level details. This frequently leads to heavy reliance on annotations, which are controller-specific, non-standardized, and blur ownership boundaries. Application developers may need elevated permissions to modify Ingress objects, even when changes affect shared infrastructure, creating operational risk.
The Gateway API introduces multiple distinct resources-such as GatewayClass, Gateway, and route resources (e.g., HTTPRoute)-each aligned with a specific role. Infrastructure providers manage GatewayClass and Gateway resources, which define how traffic enters the cluster and what capabilities are available. Application developers interact primarily with route resources to define how traffic is routed to their Services, without needing access to the underlying infrastructure configuration. This separation improves security, governance, and scalability in multi-team environments.
Option A is incorrect because automatic scaling based on CPU and memory is handled by the Horizontal Pod Autoscaler, not by Gateway API or Ingress. Option C describes a characteristic of Ingress, not an advantage of Gateway API; in fact, Gateway API explicitly reduces reliance on annotations by using structured, portable fields. Option D is incorrect because exposing applications externally requires more than just a Service; traffic management resources like Ingress or Gateway are still necessary.
Therefore, the correct and verified answer is Option B, as the Gateway API's role-oriented design is a key advancement over Ingress and is clearly documented in Kubernetes networking architecture guidance.
NEW QUESTION # 92
Which of the following is a good habit for cloud native cost efficiency?
Answer: C
NEW QUESTION # 93
You have a Kubernetes cluster with a HorizontalPodAutoscaler (HPA) configured for your application. The HPA is set to scale the application based on CPU utilization. The application suddenly experiences a surge in traffic, causing high CPU usage. How does the HPA respond to this scenario?
Answer: D
Explanation:
The HPA typically has a defined threshold (e.g., 80% CPU utilization) and a scaling policy (e.g., add one Pod if CPU exceeds the threshold). The HPA won't immediately scale up upon a traffic surge; it will wait for the CPU utilization to surpass the specified threshold before triggering the scaling action.
NEW QUESTION # 94
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