Cloud Engineer Interview Questions India 2026: AWS, Azure, GCP, and Cloud Architecture

Cloud engineer salary in India (2024-25):

Junior cloud engineer (0-2 years, AWS/Azure associate certification): 8-18 LPA. Mid-level cloud engineer (3-5 years, multiple certifications + production experience): 18-45 LPA. Senior cloud architect (5-8 years): 35-75 LPA. Principal cloud architect (8+ years): 60-120 LPA TC.

Cloud specialisation salary premium in India:

• DevOps/Cloud roles at product companies (Flipkart, Swiggy, Razorpay): 20-40% more than equivalent-experience backend engineers because cloud/infra expertise is rarer
• IT services cloud practices (Wipro Cloud Studio, Infosys Cobalt, TCS Cloud): 15-30% higher than non-cloud IT roles
• GCP-specific roles are slightly higher-paying than AWS roles in India (fewer GCP engineers available)
• Multi-cloud experience (AWS + Azure) commands a 15-20% premium over single-cloud specialists

Cloud provider comparison questions:

1. Market share and demand in India: AWS: largest market share globally and in India; most certifications in demand; most IT services companies have AWS partnerships. Azure: dominant in enterprise BFSI and companies already using Microsoft 365 (Office, Teams); growing rapidly. GCP: preferred for data-heavy workloads (BigQuery, Dataflow, Vertex AI); popular at Indian data and ML teams.

2. AWS vs Azure vs GCP equivalents: - Virtual machines: EC2 (AWS) / Azure VMs / Compute Engine (GCP) - Object storage: S3 (AWS) / Azure Blob Storage / Google Cloud Storage - Managed Kubernetes: EKS (AWS) / Azure AKS / Google GKE - Serverless functions: Lambda (AWS) / Azure Functions / Google Cloud Functions - Managed relational DB: RDS/Aurora (AWS) / Azure SQL Database / Cloud SQL (GCP) - Data warehouse: Redshift (AWS) / Azure Synapse / BigQuery (GCP)

3. Which certification to pursue first: For most Indian engineers: AWS SA Associate (SAA-C03) is the highest-ROI first certification because of the widest job market fit. If you are already working at a Microsoft-stack company or targeting BFSI: Azure Administrator (AZ-104) or Azure Solutions Architect (AZ-305). If you are targeting data engineering or ML: GCP Professional Data Engineer or Professional ML Engineer.

IaC questions in cloud engineering interviews:

1. What is Infrastructure as Code (IaC)? IaC: define and provision infrastructure using code files rather than manual console clicks. Benefits: version control (your infrastructure changes are tracked in git), repeatability (the same configuration deploys identical infrastructure in any environment), disaster recovery (re-deploy an entire environment from code), and collaboration (infrastructure changes go through code review).

2. Terraform (used at most Indian product companies): - Cloud-agnostic: works with AWS, Azure, GCP, and 100+ providers - HCL (HashiCorp Configuration Language): declarative configuration language - Core concepts: provider (AWS, Azure), resource (an awsinstance, an awss3_bucket), data source (reference existing resources), module (reusable group of resources), state (Terraform tracks actual vs desired state in a .tfstate file; store remotely in S3 or Terraform Cloud) - Workflow: terraform init, terraform plan (shows what will change), terraform apply (makes the changes), terraform destroy (tears down infrastructure)

3. CloudFormation (AWS-native, used at AWS-committed companies): - AWS-only; tightly integrated with AWS services - YAML or JSON templates; stack = a deployed CloudFormation template - Change sets: preview changes before applying (like terraform plan)

4. Pulumi (emerging, growing adoption): - Uses TypeScript, Python, Go, or C# instead of HCL - Strong type safety; can use same language for infra and application code - Popular at companies with strong engineering cultures

Cloud cost optimisation questions:

1. Common cost drivers in Indian AWS deployments: - Over-provisioned EC2 instances (common: teams size for peak load and never right-size) - Data transfer costs (AWS charges for data transfer OUT of AWS to the internet; keep processing close to data) - Underused RDS instances (a db.r5.2xlarge running at 5% CPU utilisation) - Unattached EBS volumes and old snapshots (deleted EC2 instances leave orphaned storage) - Development environments running 24/7 (should be stopped outside business hours; use AWS Instance Scheduler)

2. Cost optimisation strategies: - Right-sizing: use AWS Compute Optimizer or Azure Advisor to identify over-provisioned instances; downsize accordingly - Reserved Instances / Savings Plans: commit 1-3 years for 40-60% discount on predictable compute workloads - Spot Instances: 70-90% cheaper than On-Demand for fault-tolerant workloads (CI/CD agents, batch processing, ML training) - Auto-scaling: scale in during off-peak hours; set minimum capacity to zero for non-production environments - S3 lifecycle policies: move objects to cheaper storage tiers (S3 Infrequent Access after 30 days; S3 Glacier after 90 days) - Tagging: tag all resources with team, environment, and project; use AWS Cost Explorer or Azure Cost Management to attribute costs to teams

3. FinOps culture: FinOps (Financial Operations): the practice of shared financial responsibility for cloud spend. Engineers, finance, and product managers share cloud cost visibility and accountability. Key tool: AWS Cost Explorer, Azure Cost Management, Google Cloud Cost Management, or third-party tools like Apptio Cloudability or CloudHealth.