Q.41 Explain the concept of a Kubernetes custom resource definition (CRD).

  • A Custom Resource Definition (CRD) allows you to extend the Kubernetes API to manage your own custom objects (resources) within the Kubernetes environment.
  • Think of CRDs as a way to add new “nouns” to the Kubernetes vocabulary. Kubernetes has built-in resources like Pods, Deployments, etc., and CRDs let you define resources specific to your applications or infrastructure.

Use Cases:

  • Representing complex application configurations: A CRD can store the structure and state of a complex application, simplifying configuration management.
  • Integrating external systems: Use CRDs to interact with databases, message queues, or other systems outside of Kubernetes.
  • Automating operational tasks: Define a CRD for a task like database backups, then have a controller watch for those CRDs and automate the backup processes.


You could create a CRD called “Website” to define all the elements of a website deployment (Deployment, Service, Ingress, ConfigMaps). Then a single “Website” object would manage that entire website setup.

Q.42 How do you handle rolling back a failed deployment in Kubernetes?

Kubernetes provides several mechanisms for rolling back failed deployments:

  • kubectl rollout undo: This is the simplest method. It reverts a deployment to the previous revision stored in Kubernetes’ revision history.
  • Revision history: Kubernetes automatically stores a history of revisions for Deployments. You can manually revert to an older revision if needed.
  • Canary deployments or blue/green deployments: These strategies involve gradually rolling out updates and allow for quick rollback if issues arise.

Use Cases:

  • Rapid recovery from errors: Quickly restore a working version if a new deployment introduces bugs.
  • Minimize downtime: Ensuring service availability during the rollback process.
  • Testing updates: Rollbacks support safe experimentation with new versions.


kubectl rollout undo deployment/my-deployment

Q.43 Discuss the challenges of managing stateful applications in Kubernetes.

  • Persistent storage: Stateful applications (like databases) require persistent storage that survives Pod restarts and failures. Kubernetes provides Persistent Volumes (PVs) and Persistent Volume Claims (PVCs) to address this.
  • Data consistency: Ensuring data remains consistent across replicas or during failovers is crucial in distributed stateful applications.
  • Networking: Stateful applications often require stable network identities and predictable ways for services to discover each other.
  • Orchestration: Tasks like replicating data, handling failovers, and scaling stateful sets require more complex orchestration than stateless applications.

Use Cases:

Stateful applications are involved whenever data needs to persist, which includes:

  • Running databases in Kubernetes: Understanding persistent storage options and potentially complex configuration setups are needed.
  • Deploying applications with strong consistency requirements: Careful attention to data replication and failover strategies.

Q.44 What is the role of a Kubernetes operator?

  • A Kubernetes Operator is a type of controller that leverages CRDs and custom logic to manage complex applications or infrastructure components within Kubernetes.
  • Operators work by encapsulating operational knowledge and automating tasks such as installation, upgrades, backups, scaling, and failure recovery.
  • They act as extensions of a human operating complex systems.

Use Cases:

  • Managing stateful applications: Operators are ideal for stateful sets, automating tasks that would otherwise require manual intervention.
  • Deploying and maintaining external systems: Use operators to integrate with databases, cloud services, or other systems outside of Kubernetes.
  • Enforcing custom policies: An operator can ensure applications adhere to specific security, compliance, or configuration rules.


A PostgreSQL Operator could handle creating a PostgreSQL cluster, provisioning storage, configuring backups, and handling failovers—activities beyond standard Kubernetes object management.

Q.45 How can you optimize resource utilization in a large-scale Kubernetes cluster?

  • Right-sizing requests and limits: Ensure Pods request the CPU and memory they need, but not excessively more (limits). This prevents resources from sitting idle.
  • Autoscaling: Use the Horizontal Pod Autoscaler (HPA) and Cluster Autoscaler to dynamically adjust the number of Pods and nodes based on demand.
  • Bin packing: Kubernetes’ scheduler tries to efficiently “pack” Pods onto nodes. More advanced scheduling strategies can further optimize this.
  • Monitoring: Use tools like Prometheus to track resource usage over time and identify bottlenecks or unused capacity.
  • Node selection: Match Pods to nodes that best suit their requirements (e.g., Pods needing GPUs get scheduled on nodes with GPUs).

Use Cases:

  • Cost savings: Utilize hardware resources efficiently to reduce the number of required nodes.
  • Improved performance: Ensure applications have sufficient resources to run without unnecessary waiting.
  • Scalability: Efficiently accommodate bursts in traffic or fluctuating application workloads.


  • Define resource requests and limits for each Pod.
  • Configure the HPA to scale a Deployment based on CPU usage.

Part 1- Kubernetes Interview Q & A (Q1-Q5)

Part 2- Kubernetes Interview Q & A (Q6-Q10)

Part 3 – Kubernetes Interview Questions & Answers (Q.11 to Q.15)

Part 4 – Kubernetes Interview Questions & Answers (Q.16 to Q.20)

Part 5 – Kubernetes Interview Questions & Answers (Q.21 to Q.25)

Part 6 – Kubernetes Interview Questions & Answers (Q.26 to Q.30)

Part 7 – Kubernetes Interview Questions & Answers (Q.31 to Q.35)

Part 8 – Kubernetes Interview Questions & Answers (Q.36 to Q.40)

Hope you find this post helpful.

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