Cloud & DevOps Interview Questions

Question 1

What is DevOps?

Accepted Answer

DevOps is a culture and set of practices that unifies development and operations, emphasizing automation, CI/CD, and collaboration. Core principles: (1) Culture: shared responsibility, no silos. (2) Automation: CI/CD pipelines, IaC, automated testing. (3) Measurement: metrics, monitoring, feedback loops. (4) Sharing: knowledge sharing, blameless postmortems. Practices: continuous integration, continuous delivery, infrastructure as code, monitoring, microservices. Tools: Git, Jenkins/GitHub Actions, Docker, Kubernetes, Terraform, Prometheus. DORA metrics: deployment frequency, lead time, change failure rate, MTTR.

Question 2

What is CI/CD?

Accepted Answer

CI (Continuous Integration) auto-builds and tests code on every commit; CD (Continuous Delivery/Deployment) auto-deploys to production. CI: developers merge frequently → automated build → automated tests → fast feedback. CD (Delivery): code is always deployable, manual release approval. CD (Deployment): fully automated deployment to production. Pipeline: commit → build → unit tests → integration tests → staging → production. Tools: GitHub Actions, GitLab CI, Jenkins, CircleCI. Best practices: small commits, fast tests, trunk-based development, feature flags.

Question 3

What is Docker and containerization?

Accepted Answer

Docker packages applications with their dependencies into containers — lightweight, portable, isolated units that run consistently anywhere. Concepts: Image (blueprint), Container (running instance), Dockerfile (build instructions), Registry (Docker Hub). Benefits: consistent environments, fast startup, resource efficient (share OS kernel), portable. Dockerfile: FROM, RUN, COPY, CMD. docker build/run/push commands. Docker Compose: multi-container apps (docker-compose.yml). Best practices: small base images (Alpine), multi-stage builds, non-root user, .dockerignore.

Question 4

What is cloud computing and its service models?

Accepted Answer

Cloud computing delivers IT resources on-demand. Models: IaaS (infrastructure), PaaS (platform), SaaS (software). IaaS: raw compute, storage, networking. You manage: OS, runtime, apps. Examples: AWS EC2, Azure VMs, GCP Compute Engine. PaaS: managed platform, focus on code. You manage: apps and data. Examples: Heroku, AWS Elastic Beanstalk, Google App Engine. SaaS: complete software. You manage: nothing. Examples: Gmail, Salesforce, Slack. Also: FaaS/Serverless (AWS Lambda), CaaS (ECS, GKE). Deployment: public, private, hybrid, multi-cloud.

Question 5

What is Kubernetes?

Accepted Answer

Kubernetes is an open-source container orchestration platform that automates deployment, scaling, and management of containerized applications. Core concepts: Pod (smallest unit, one or more containers), Deployment (desired state), Service (networking/load balancing), Ingress (external access), ConfigMap/Secret (configuration), PersistentVolume (storage). Control plane: API server, scheduler, controller manager, etcd. Features: auto-scaling (HPA), self-healing, rolling updates, service discovery. kubectl: CLI tool. Managed: EKS (AWS), GKE (Google), AKS (Azure). Helm: package manager for K8s.

Question 6

What is Infrastructure as Code (IaC)?

Accepted Answer

IaC manages infrastructure through code files instead of manual processes, enabling version control, automation, and reproducibility. Tools: Terraform (multi-cloud, declarative), CloudFormation (AWS), Pulumi (general-purpose languages), Ansible (configuration management). Benefits: reproducible environments, version controlled, code review for infra changes, automated provisioning. Terraform workflow: write (.tf files) → plan (preview changes) → apply (create resources). State management: track what's deployed. Modules: reusable infrastructure components. GitOps: Git as single source of truth for infrastructure.

Question 7

What is monitoring and observability?

Accepted Answer

Monitoring tracks predefined metrics; observability enables understanding system behavior through logs, metrics, and traces. Three pillars: (1) Metrics: numerical measurements (CPU, memory, request rate). Tools: Prometheus, Grafana, CloudWatch. (2) Logs: event records. Tools: ELK Stack (Elasticsearch, Logstash, Kibana), Loki. (3) Traces: request journey across services. Tools: Jaeger, Zipkin, OpenTelemetry. Alerting: PagerDuty, OpsGenie. SLIs (indicators), SLOs (objectives), SLAs (agreements). Key metrics: latency, error rate, throughput, saturation (RED/USE methods).

Question 8

What is a service mesh?

Accepted Answer

A service mesh manages service-to-service communication with features like load balancing, encryption, and observability without code changes. Implementation: sidecar proxy (Envoy) deployed alongside each service. Features: (1) mTLS encryption between services. (2) Load balancing and retry logic. (3) Circuit breaking. (4) Traffic management (canary, blue-green). (5) Observability (distributed tracing). Tools: Istio, Linkerd, Consul Connect. Control plane manages configuration, data plane handles traffic. Use when: many microservices, complex traffic patterns, security requirements. Adds complexity and resource overhead.

Question 9

Your production deployment caused an outage. How do you handle it?

Accepted Answer

Roll back immediately, communicate status, investigate root cause, implement fixes, and conduct blameless postmortem. Response: (1) Detect: alerting triggers. (2) Assess severity. (3) Roll back: revert to last known good version. (4) Communicate: status page, stakeholder updates. (5) Investigate: check logs, metrics, recent changes. (6) Fix: apply targeted fix if rollback insufficient. (7) Verify: confirm system healthy. (8) Postmortem: timeline, root cause, contributing factors, action items. Prevention: canary deployments, feature flags, automated rollbacks, chaos engineering, pre-production testing.

Question 10

Netflix: How does Netflix handle deployments at scale?

Accepted Answer

Netflix uses Spinnaker for continuous delivery, canary deployments, chaos engineering, and immutable infrastructure on AWS. Netflix's practices: (1) Spinnaker: open-source CD platform for multi-cloud. (2) Canary deployments: deploy to small percentage, compare metrics. (3) Chaos engineering: Chaos Monkey randomly kills instances. (4) Immutable infrastructure: never patch, always replace. (5) Microservices: hundreds of services deployed independently. (6) Full AWS deployment. (7) Custom tools: Zuul (routing), Eureka (service discovery), Hystrix (circuit breaker). Key lesson: build for failure, automate everything.

Cloud Computing & DevOps Course

What You'll Learn

Career Opportunities

Course Completion Certificate

Course Modules Overview

Cloud Computing Fundamentals

Infrastructure as Code

CICD Pipelines

Container Orchestration

Monitoring and Observability

Multi-Cloud & Hybrid Cloud

Security & Compliance

Advanced DevOps Practices

Site Reliability Engineering

Advanced Kubernetes & Microservices

Cloud Security & Compliance

Cloud Architecture and Cost Optimization

Course Curriculum

Cloud Computing Fundamentals

Infrastructure as Code

CICD Pipelines

Container Orchestration

Monitoring and Observability

Multi-Cloud & Hybrid Cloud

Security & Compliance

Advanced DevOps Practices

Site Reliability Engineering

Advanced Kubernetes & Microservices

Cloud Security & Compliance

Cloud Architecture and Cost Optimization

Your Learning Path

Beginner

Intermediate

Advanced

Tools & Technologies

AWS

Docker

Kubernetes

Jenkins

Terraform

Prometheus

Ready to Start Learning?

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