Detailed Exam Domain Coverage

This practice test repository is systematically organized to reflect the exact technical distribution and complex architectural scenarios found in modern cloud-native engineering interviews.

Container Orchestration (20%): Control plane and data plane Kubernetes Architecture, Pod Lifecycle states, ReplicaSet mechanics, advanced Deployment strategies, and automated Scaling (HPA/VPA).
Service Mesh (15%): Service mesh implementations via Istio and Linkerd, sophisticated Traffic Management, automated Canary Deployments, and enforcing zero-trust with Mutual TLS (mTLS).
Security (15%): Fine-grained Role-Based Access Control (RBAC), Container Image Security scanning, secure Secrets Management, runtime security, and tight Network Policies.
Networking (10%): Container Network Interface (CNI) plugins, Container Runtime Interface (CRI) layers, kube-proxy routing modes (IPVS/iptables), Service Discovery, and Cloud Load Balancing integration.
Troubleshooting (15%): Root-cause analysis for etcd cluster split-brain or corruption, diagnosing Pod OOMKilled states, fixing replication lag, debugging cluster-wide Network Connectivity, and resolving underlying node Performance Issues.
Storage and Data Management (10%): Dynamic provisioning with Persistent Volumes (PV/PVC), orchestrating stateful workloads using StatefulSets, ensuring Data Consistency, managing CSI Volume Snapshots, and establishing reliable Backup and Restore runs.
Monitoring and Logging (5%): Scraping metrics with Prometheus, visualizing performance via Grafana dashboards, cluster-wide centralized Logging Solutions, high-cardinality Metrics Collection, and fine-tuning Alerting and Notifications.
CI/CD and Automation (10%): GitOps pipelines via GitHub Actions and Jenkins, GitOps delivery, declarative cluster provisioning with Terraform, configuration management using Ansible, and writing resilient custom Automation Scripts.

About the Course

Cracking an enterprise DevOps or Kubernetes infrastructure interview demands more than just memorizing basic kubectl commands. Production clusters present complex, multi-layered challenges where networking, security, storage, and orchestration converge. Hiring managers do not look for people who can simply spin up a cluster; they look for engineers who can architect for high availability, secure a multi-tenant environment, trace ephemeral networking failures, and debug failing control plane components under pressure. I built this comprehensive question bank to provide the exact level of rigor required to match those high-stakes technical loops.

Featuring 550 highly detailed, original practice questions, this course focuses on deeply technical scenarios, architectural dilemmas, and real-world troubleshooting scenarios. Every single question comes paired with an exhaustive engineering breakdown that analyzes the core mechanics of the problem, pointing out exactly why the correct approach operates flawlessly and why the alternative architectural configurations or troubleshooting steps fail in a production cluster. Whether you are stepping up to a Cloud Engineer role, validating your field knowledge before a principal tech round, or looking for a robust benchmark to clear your cloud-native technical assessments, this practice material ensures you are prepared to clear your upcoming interviews confidently on your very first try.

Sample Practice Questions Preview

Review these three production-level sample questions to see the deep structural layout and technical depth included across this entire question bank.

Question 1: Root-Cause Analysis of Ephemeral Pod Termination Codes

A critical backend microservice running inside a memory-constrained namespace keeps failing intermittently during peak traffic hours. The command kubectl describe pod reveals that the container terminated with an Exit Code of 137. Which underlying mechanism triggered this specific cluster event?

A) The application binary threw an unhandled runtime exception that caused the container's primary process to exit naturally.
B) The operating system kernel on the worker node invoked the Out-Of-Memory (OOM) killer because the container exceeded its declared memory limit configuration.
C) The kubelet liveness probe failed continuously, causing the control plane to issue a standard SIGTERM signal that went unacknowledged.
D) The container network interface plugin lost its routing table entry for the pod, resulting in an automatic network-eviction timeout.
E) The underlying container runtime interface encountered a storage layer driver error while writing container logs to the host disk.
F) The Admission Controller revoked the pod's execution permissions dynamically because of an overlapping RBAC security policy update.

Correct Answer & Explanation:

Correct Answer: B
Why it is correct: Exit Code 137 specifically indicates that a process was terminated by the operating system using a standard SIGKILL signal ($128 + 9 = 137$). In a Kubernetes context, when a container's real-time memory usage breaches the threshold set in its resources.limits.memory block, the node kernel's OOM killer steps in and forcibly terminates the process to protect host stability, causing the pod status to show OOMKilled.
Why alternative options are incorrect:
- Option A is incorrect: Unhandled application exceptions typically lead to standard exit codes like 1 or 2, resulting in a CrashLoopBackOff without an explicit OOM killer invocation.
- Option C is incorrect: If a liveness probe fails, the kubelet kills the container using SIGTERM (Exit Code 143) first, moving to SIGKILL only if graceful shutdown periods expire.
- Option D is incorrect: CNI routing anomalies lead to network timeouts, connection drops, or CreateContainerConfigError statuses, not an immediate 137 termination code.
- Option E is incorrect: Storage driver or logging errors typically generate a FailedCreatePodSandBox status or disk pressure taints on the node.
- Option F is incorrect: Admission Controller rejections block the pod at the API validation step before scheduling, throwing a Forbidden error instead of terminating a running container process.

Question 2: Designing Secure Multi-Tenant Boundaries using Advanced Network Policies

An administrator wants to secure a multi-tenant cluster containing two sensitive namespaces: tenant-alpha and tenant-beta. The goal is to configure a declarative NetworkPolicy in tenant-alpha that permits incoming traffic only from pods labeled role: frontend that reside inside the tenant-beta namespace. Which structural design pattern must be implemented in the policy spec?

A) Define an ingress rule containing a single item that includes both the podSelector and namespaceSelector blocks as separate fields within a single array element.
B) Define an ingress rule containing a single namespaceSelector block and use a nested matchExpressions block that references the external pod labels directly.
C) Define an ingress rule with two separate list items: one item containing the namespaceSelector block and a separate item containing the podSelector block.
D) Define an egress rule inside the target namespace that references the external API server endpoints directly via a dedicated CIDR block.
E) Define a global ClusterNetworkPolicy that overrides the namespace isolation defaults using a wild-card service account binding.
F) Define an ingress rule that omits selectors entirely and relies exclusively on the container runtime's mutual TLS identity headers.

Correct Answer & Explanation:

Correct Answer: A
Why it is correct: When configuring Kubernetes NetworkPolicies, combining a namespaceSelector and a podSelector within the same array element creates an intersection (AND logic). This forces the policy engine to match only those pods that have the specified label and belong to namespaces that match the namespace label, creating a secure multi-tenant boundary.
Why alternative options are incorrect:
- Option B is incorrect: A namespaceSelector reads labels applied directly to the namespace objects themselves; it cannot traverse into the namespace to read individual pod labels within a single block.
- Option C is incorrect: Placing selectors in separate array elements creates a union (OR logic). This dangerous configuration allows traffic from any pod in the specified namespace, or any pod matching that label in any namespace across the cluster.
- Option D is incorrect: The goal requires controlling incoming traffic using an ingress rule, making an egress rule definition with static CIDR blocks completely irrelevant.
- Option E is incorrect: Standard Kubernetes API resources do not natively support a "ClusterNetworkPolicy" object without utilizing specific third-party CNI providers like Calico or Cilium.
- Option F is incorrect: Omitting selectors completely from an ingress rule creates a default-deny or default-allow behavior depending on the structure, ignoring the specific label requirements entirely.

Question 3: Traffic Management Routing Logic inside Istio Service Meshes

An engineering team deploys a new microservice version (v2) inside an Istio-managed service mesh. They want to set up a canary release strategy where 90% of production traffic targets the stable v1 version, and 10% routes to the new v2 version. Which combination of Istio custom resource definitions (CRDs) must be created to enforce this traffic split accurately?

A) A single Gateway resource that maps the physical port definitions directly to separate target cluster IP addresses.
B) A ServiceEntry resource that registers the endpoints combined with a PeerAuthentication policy to encrypt the transport layer.
C) A VirtualService resource outlining the percentage weight values alongside a DestinationRule resource that explicitly defines the v1 and v2 subsets.
D) An EnvoyFilter resource that modifies the raw upstream clusters combined with a standard Kubernetes cluster Service object.
E) A Telemetry resource that tracks the connection counts and a Sidecar configuration that overrides egress routing tables globally.
F) A WorkloadGroup resource mapping the pod templates to an external virtual machine instance running outside the cluster.

Correct Answer & Explanation:

Correct Answer: C
Why it is correct: In the Istio service mesh architecture, splitting traffic relies on two cooperative custom resources. The DestinationRule defines the actual destinations or subsets of workloads based on pod labels (e.g., version tags). The VirtualService then intercepts the traffic layer, using a weight field within its routing block to divide traffic proportionally (90/10) across those defined subsets.
Why alternative options are incorrect:
- Option A is incorrect: An Istio Gateway configures the edge load balancers to accept incoming HTTP/TCP connections; it does not manage fine-grained routing weights inside the internal mesh.
- Option B is incorrect: ServiceEntry is used to add external, non-mesh dependencies (like an external cloud database) to the internal service registry, not to route internal service traffic.
- Option D is incorrect: While EnvoyFilter allows low-level tuning of Envoy proxy configurations, using it for basic canary splits introduces massive complexity and bypasses standard traffic management primitives.
- Option E is incorrect: Telemetry and Sidecar resources control logging behavior and proxy network scopes; they do not manipulate the percentage distribution of application traffic.
- Option F is incorrect: A WorkloadGroup describes non-Kubernetes VM workloads onboarded into the mesh, which is completely unrelated to shifting traffic between internal pod deployments.

What to Expect

Welcome to the Interview Questions Tests to help you prepare for your Kubernetes Interview Questions Practice Test.
You can retake the exams as many times as you want
This is a huge original question bank
You get support from instructors if you have questions
Each question has a detailed explanation
Mobile-compatible with the Udemy app

We hope that by now you're convinced! And there are a lot more questions inside the course.