You're reading from Cracking the Kubernetes Interview Discover expert tips and best practices to ace your Kubernetes technical interviews

Product type Paperback

Published in Oct 2025

Publisher Packt

ISBN-13 9781835460030

Length 560 pages

Edition 1st Edition

Tools

Kubernetes

Concepts

DevOps

Authors (2):

Viktor Vedmich

Alexander Dovnar

View More author details

Table of Contents (27) Chapters

Preface

1. Part 1: Introduction to Kubernetes

2. Understanding Kubernetes from Containers to Orchestration FREE CHAPTER

3. Kubernetes Key Components and Their Functions

4. Part 2: Schedulers, Workloads, and Scaling

5. Exploring Kubernetes Basics

6. Principles of Scaling: Horizontal vs Vertical

7. Autoscaling in Action

8. Part 3: Networking, Services, and Security

9. Essentials of Kubernetes Networking

10. Kubernetes Network Architecture: Services and Ingresses

11. Storage: Persistence and CSI Drivers

12. Security Best Practices in Kubernetes and RBAC

13. Part 4: Deployment, Application Management, and GitOps

14. Real-World Deployment Scenarios and Continuous Deployment

15. Understanding GitOps: Principles and Practices

16. Part 5: Reliability, Monitoring, and Maintenance

17. Custom Resource Definitions and Operators

18. Ensuring High Availability and Reliability

19. Monitoring Best Practices

20. Backup, Maintenance, and Disaster Recovery in Kubernetes

21. Part 6: Troubleshooting and Best Practices

22. Real-World Troubleshooting Scenarios

23. Common Mistakes and How to Avoid Them

24. Interview Stories from Authors

25. Other Books You May Enjoy

26. Index

Cluster Autoscaler

Let’s break down the Cluster Autoscaler (CA), a tool that automatically resizes your cluster to match workload demand. The CA’s job is to make sure every Pod has a place to run, which cuts waste and optimizes costs.

The CA is adept at managing the delicate act of scaling a cluster up or down. It takes action to scale up the cluster when it detects Pods that are unable to find a home on existing nodes due to a lack of resources. Conversely, it scales down the cluster when it notices nodes are being underutilized, thus conserving resources.

This Kubernetes feature doesn’t directly measure resource usage such as CPU or memory. Instead, it focuses on the Pods’ state, specifically looking for those that are pending and unable to schedule due to resource constraints. When such Pods are found, the CA triggers the addition of new nodes, allowing these pending Pods to be scheduled and run.