Q.36 Describe the process of setting up a Kubernetes ingress controller.

An ingress controller in Kubernetes handles external traffic routing to services within your cluster. Here’s a typical setup process:

  1. Choose an Ingress Controller: Several options exist (NGINX, Traefik, HAProxy, etc.). Evaluate based on features, performance, and integration with your environment.
  2. Deployment: Deploy the selected ingress controller as a Kubernetes deployment. This usually involves a pod running the controller software.
  3. Configuration: Create Kubernetes Ingress resources. These resources define:
    • Routing Rules: How external traffic (based on URLs, hostnames) should be mapped to internal services.
    • TLS Termination: (Optional) If you want to enable HTTPS.
    • Other Features: Load balancing, path-based routing, etc., may be specified depending on the chosen controller.
  4. Expose the Controller: Expose the ingress controller’s service, typically using a LoadBalancer service type, to make it accessible from outside the cluster.

Use Cases:

  • Exposing Web Applications: Centralized point for handling web traffic (HTTP/HTTPS) to internally hosted web applications.
  • Multiple Domains/Paths: Routing different domains or URL paths to separate backend services within the cluster.
  • Load Balancing: Distributing incoming traffic across multiple pods of your services.


A basic NGINX Ingress resource:

Q.37 What is the role of a Kubernetes network policy, and how does it enhance security?

Kubernetes network policies act like firewall rules within your cluster, controlling pod-to-pod communication and communication between pods and external endpoints. They improve security by:

  • Micro-segmentation: Isolating pods to allow communication only with pods that require interaction. This limits the spread of threats within the cluster.
  • Policy-Based Control: Network policies provide fine-grained, declarative rules based on labels, namespaces, IP ranges, and ports. This offers more flexibility than traditional, IP-based firewalls.
  • Zero-Trust Approach: Enabling a default-deny stance where traffic is blocked unless explicitly permitted by a network policy.

Use Cases:

  • Multi-Tenant Environments: Isolating workloads from different tenants to prevent unintended resource access.
  • Sensitive Workloads: Protecting databases and critical services by strictly controlling who can communicate with them.
  • Compliance: Assist in meeting regulatory requirements that often mandate network segmentation controls.


Network policy to allow ingress traffic only from web-frontend pods to database pods:

Q.38 How can you horizontally autoscale a Kubernetes deployment based on CPU utilization?

Horizontal scaling in Kubernetes means automatically adding or removing pods from a Deployment based on a defined metric. Here’s how to set up horizontal autoscaling with CPU utilization:

  1. Horizontal Pod Autoscaler (HPA): Deploy a Horizontal Pod Autoscaler (HPA) resource in Kubernetes. This resource continuously monitors metrics.
  2. Target Metric: Specify the target CPU utilization for your pods. For example, you might target an average CPU usage of 50%.
  3. Scaling Thresholds: Define the minimum and maximum number of pods you want the deployment to scale between.
  4. Monitoring: The HPA uses the Kubernetes Metrics Server (or compatible) to access CPU utilization data of pods.
  5. Automatic Scaling: If CPU usage exceeds the target, the HPA instructs Kubernetes to create more pods. When the usage drops, it reduces the number of pods.

Use Cases:

  • Handling Traffic Spikes: Automating efficient response to traffic bursts.
  • Resource Optimization: Ensuring your application has enough resources when needed, and avoids unnecessary costs during low-load periods.


HPA to scale a deployment based on 80% target CPU utilization:

Q.39 Explain the concept of Kubernetes affinity and anti-affinity rules.

Affinity and anti-affinity rules influence pod scheduling decisions, making the Kubernetes scheduler  prioritize or avoid certain nodes.

  • Node Affinity: Attracts pods to nodes with specific labels or attributes (e.g., nodes with SSDs, GPU capacity).
  • Pod Affinity: Encourages co-location of pods (e.g., keeping the frontend and backend of an application together).
  • Pod Anti-Affinity: Spreads pods across nodes or zones to distribute risk and boost resilience.

Use Cases:

  • Performance: Place compute-intensive pods on high-performance nodes.
  • Resource-specific needs: Match pods with nodes offering required resources.
  • High Availability: Prevent a single point of failure by distributing replicas.
  • Co-location: Improve network performance for interdependent pods.


Node affinity to schedule pods on nodes with an ‘ssd’ label:

Q.40 How do you troubleshoot and diagnose performance issues in a Kubernetes cluster?

Troubleshooting Kubernetes performance is a systematic process of:

  1. Metrics: Leverage tools like Prometheus, Grafana for cluster-wide resource monitoring (CPU, memory, network).
  2. Logging: Utilize container and application logging to trace errors and potential bottlenecks.
  3. Profiling: (if needed) Employ profiling tools to analyze application code for performance hotspots.
  4. Node-level issues: Examine host-level metrics for disk I/O or system bottlenecks.
  5. Networking: Check for routing problems, load balancer errors, or service misconfigurations.
  6. Kubernetes Resources: Examine if limits, requests, or configurations are causing resource contention.

Use Cases:

  • Slow Applications: Identify the root cause of performance degradation.
  • Resource Bottlenecks: Pinpoint areas where resources are strained.
  • Error Detection: Uncover problems indicated by logs or unusual metrics.


  • Using kubectl describe pod <pod-name> to inspect resource requests, limits, and container states.
  • Using kubectl top nodes and kubectl top pods to identify nodes/pods with excessive resource 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)

Hope you find this post helpful.

Telegram: https://t.me/LearnDevOpsForFree

Twitter: https://twitter.com/techyoutbe

Youtube: https://www.youtube.com/@T3Ptech

Leave a Reply

Your email address will not be published. Required fields are marked *

DevOps Lifecycle Simplified Cybersecurity Lifecycle Top 10 Technical Roles for 2023 7 Tips to become Data Scientist