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| Section | Weight | Objectives |
|---|
| Topic 1: Cloud Native Architecture | 16% | - Architecture Concepts
- 1. Elasticity and Resilience
- 2. Microservices Architecture
- 3. Autoscaling (HPA, VPA, Cluster Autoscaler)
- 4. Serverless and FaaS
- Cloud Native Landscape
- 1. CNCF Project Categories (Sandbox, Incubating, Graduated)
- 2. CNCF Role and Governance
- Infrastructure and Practices
- 1. Infrastructure as Code (IaC)
- 2. DevOps Practices and Culture
- 3. Immutable Infrastructure
|
| Topic 2: Cloud Native Observability | 8% | - Monitoring and Metrics
- 1. Prometheus and Metrics Collection
- 2. Dashboards and Visualization (Grafana)
- Tracing
- 1. Distributed Tracing Concepts (OpenTelemetry, Jaeger)
- Logging
- 1. Kubernetes Logging Architecture
- 2. Centralized Logging (Fluentd, Elasticsearch, Kibana)
|
| Topic 3: Cloud Native Application Delivery | 8% | - CI/CD
- 1. Continuous Integration and Continuous Delivery Pipelines
- 2. Artifact Management and Image Registries
- GitOps
- 1. Tools (Argo CD, Flux)
- 2. GitOps Principles and Workflow
- Deployment Strategies
- 1. Blue/Green, Canary, Rolling Updates
|
| Topic 4: Kubernetes Fundamentals | 46% | - Scheduling
- 1. Taints and Tolerations
- 2. Node Selection and Affinity
- 3. Resource Requests and Limits
- Containers
- 1. Basic kubectl Commands
- 2. Container Images and Registries
- 3. Container Runtime Interface (CRI)
- Kubernetes API
- 1. Declarative Management (Manifests/YAML)
- 2. API Resource Structure and Versioning
- Kubernetes Resources
- 1. Configuration Resources (ConfigMaps, Secrets)
- 2. Workload Resources (Pods, Deployments, StatefulSets, DaemonSets, ReplicaSets, Jobs, CronJobs)
- 3. Networking Resources (Services, Ingress)
- Kubernetes Architecture
- 1. Control Plane Components (API Server, etcd, Scheduler, Controller Manager)
- 2. Worker Node Components (Kubelet, Kube-proxy, Container Runtime)
|
| Topic 5: Container Orchestration | 22% | - Storage
- 1. Volumes, PersistentVolumes (PV), PersistentVolumeClaims (PVC)
- 2. Storage Classes and Dynamic Provisioning
- Security
- 1. RBAC (Role-Based Access Control)
- 2. Pod Security Standards (Admission Control)
- 3. Network Policies
- Orchestration Fundamentals
- 1. Scheduling and Resource Management
- 2. Service Discovery and Load Balancing
- 3. Self-healing and Rolling Updates
- Container Runtimes
- 1. Docker, containerd, CRI-O
- Networking
- 1. Kubernetes Networking Model
- 2. CoreDNS and Service Networking
- Service Mesh
- 1. Service Mesh Concepts (Istio, Linkerd)
- 2. Sidecar Pattern and Traffic Management
|
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Linux Foundation Kubernetes and Cloud Native Associate Sample Questions (Q128-Q133):
NEW QUESTION # 128
You are running a resource-intensive application in Kubernetes. You want to reduce costs without impacting performance. Which of the following approaches would be most effective?
- A. Reduce the resource requests and limits for the application's pods.
- B. Optimize the application code to reduce resource consumption.
- C. Migrate the application to a different cloud provider with lower pricing.
- D. Implement vertical pod autoscaling to adjust the resource allocation based on actual usage.
- E. Decrease the number of replicas for the application's deployment.
Answer: B,D
Explanation:
For resource-intensive applications, the most effective cost reduction strategies focus on optimizing resource usage and application efficiency. Vertical pod autoscaling (VPA) dynamically adjusts resource allocation based on actual usage, ensuring pods have sufficient resources without overprovisioning. Optimizing the application code itself to reduce resource consumption is crucial, as it can significantly impact overall cost. While decreasing replicas or reducing requests/limits might seem tempting, they can negatively impact performance. Migrating to a different cloud provider might offer cost advantages, but it's a complex and potentially disruptive process.
NEW QUESTION # 129
You are monitoring a Kubernetes cluster using Prometheus. You notice a sudden spike in the number of requests to a specific pod, followed by a corresponding increase in pod CPU utilization. Which Prometheus query can you use to identify the specific endpoint being heavily accessed?
- A. kube_http_server_requests_seconds_bucket{job="", method="GET", uri="/specific_endpoint"}
- B. kube_pod_container_status_restart_count{pod=""}
- C. kube_deployment_status_replicas{deployment=""}
- D. kube_pod_container_resource_requests_cpu_cores{pod=""}
- E. kube_pod_status_phase{pod=""}
Answer: A
Explanation:
The correct answer is E . The query 'kube_http_server_requests_seconds_bucket{job="", method="GET", uri="/specific_endpoint"}" targets HTTP requests made to a specific endpoint within a pod (identified by the •job', •method', and 'uri' labels). This query allows you to see the number of requests made to the '/specific_endpoint• and identify spikes in traffic to that endpoint. The other options are not relevant to identifying specific endpoints being heavily accessed. Option A shows CPU resource requests for a pod, option B shows the pod's phase, option C shows the number of replicas in a deployment, and option D shows the restart count for a pod's containers. These queries do not provide information about specific endpoints and traffic patterns.
NEW QUESTION # 130
What is the API that exposes resource metrics from the metrics-server?
- A. cadvisor.k8s.io
- B. custom.k8s.io
- C. resources.k8s.io
- D. metrics.k8s.io
Answer: D
Explanation:
he metrics-server exposes resource metrics through the metrics.k8s.io API for use by components like the Horizontal Pod Autoscaler.
NEW QUESTION # 131
How to get the logs of the previously terminated nginx container from the web pod?
- A. kubectl logs -f -c nginx web
- B. kubectl logs nginx
- C. kubectl logs -p -c web nginx
- D. kubectl logs -p -c nginx web
Answer: D
Explanation:
https://kubernetes.io/docs/reference/generated/kubectl/kubectl-commands#logs

NEW QUESTION # 132
Which of these is a valid container restart policy?
- A. On login
- B. On update
- C. On failure
- D. On start
Answer: C
Explanation:
The correct answer is D: On failure. In Kubernetes, restart behavior is controlled by the Pod-level field spec.
restartPolicy, with valid values Always, OnFailure, and Never. The option presented here ("On failure") maps to Kubernetes' OnFailure policy. This setting determines what the kubelet should do when containers exit:
* Always: restart containers whenever they exit (typical for long-running services)
* OnFailure: restart containers only if they exit with a non-zero status (common for batch workloads)
* Never: do not restart containers (fail and leave it terminated)
So "On failure" is a valid restart policy concept and the only one in the list that matches Kubernetes semantics.
The other options are not Kubernetes restart policies. "On login," "On update," and "On start" are not recognized values and don't align with how Kubernetes models container lifecycle. Kubernetes is declarative and event-driven: it reacts to container exit codes and controller intent, not user "logins." Operationally, choosing the right restart policy is important. For example, Jobs typically use restartPolicy:
OnFailure or Never because the goal is completion, not continuous uptime. Deployments usually imply
"Always" because the workload should keep serving traffic, and a crashed container should be restarted. Also note that controllers interact with restarts: a Deployment may recreate Pods if they fail readiness, while a Job counts completions and failures based on Pod termination behavior.
Therefore, among the options, the only valid (Kubernetes-aligned) restart policy is D.
=========
NEW QUESTION # 133
......
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