Ace your cloud engineering interview with 10 targeted questions on cloud architecture, IaC, networking, and multi-cloud strategies.
I would deploy across multiple Availability Zones with an Application Load Balancer distributing traffic to an Auto Scaling group of EC2 instances or ECS Fargate containers. The database layer would use RDS Multi-AZ with read replicas. Static assets go to S3 behind CloudFront. I would implement Route 53 health checks for DNS failover. All infrastructure defined in Terraform with state stored in S3 with DynamoDB locking for team collaboration.
IaaS provides raw compute, storage, and networking - like EC2 instances where you manage the OS and everything above it. PaaS abstracts the infrastructure, letting you deploy code directly - like AWS Elastic Beanstalk or Azure App Service. SaaS is fully managed software accessed via browser, like Salesforce. The choice depends on how much operational control you need versus how much you want the provider to manage. Most modern architectures use a mix of all three.
I use Terraform with workspaces or separate state files per environment. Modules encapsulate reusable infrastructure patterns, with environment-specific variables defined in tfvars files. I enforce code review for all infrastructure changes through pull requests and run terraform plan in CI before any apply. State is stored remotely with locking to prevent concurrent modifications. Drift detection runs daily to catch manual changes that bypass the IaC pipeline.
I start with visibility by implementing tagging strategies and using cost allocation reports. Then I right-size instances based on actual utilization metrics, not requested capacity. Reserved Instances or Savings Plans cover steady-state workloads, while Spot instances handle fault-tolerant batch jobs. I implement auto-scaling to match capacity with demand, schedule non-production environments to shut down outside business hours, and review storage tiers monthly to move cold data to cheaper options.
I use GitHub Actions or GitLab CI with separate stages for linting, security scanning, plan preview, and apply. Infrastructure changes go through the same review rigor as application code. I integrate tools like tfsec and Checkov for security policy enforcement. The pipeline runs plan on pull requests so reviewers see exactly what will change. Apply only triggers on merge to main with manual approval gates for production. Rollback procedures are documented and tested regularly.
I design VPCs with public, private, and isolated subnets across multiple AZs. Public subnets only contain load balancers and NAT gateways. Application servers live in private subnets with no direct internet access. Security groups follow least privilege with specific port and source restrictions. NACLs provide an additional defense layer. VPC Flow Logs feed into our SIEM for monitoring. VPN or AWS PrivateLink handles connectivity to on-premises systems rather than exposing services publicly.
I have deployed production workloads on both EKS and ECS Fargate. For EKS, I manage cluster upgrades, configure horizontal pod autoscaling, implement network policies with Calico, and use Helm charts for application deployment. I prefer Fargate for simpler workloads because it eliminates node management overhead. I use ECR for container registries with image scanning enabled, and implement pod security standards to enforce least-privilege container execution.
I classify workloads by RTO and RPO requirements and design DR strategies accordingly. For critical systems, I implement active-active multi-region with database replication and global load balancing. For less critical workloads, pilot light or warm standby approaches reduce costs while meeting recovery targets. I automate DR runbooks so failover can execute in minutes, and we conduct quarterly DR drills to validate procedures. Backups are cross-region with encryption and regular restoration testing.
I implement a comprehensive observability stack with metrics, logs, and traces. CloudWatch or Datadog collects infrastructure and application metrics with custom dashboards and alerts. Distributed tracing with X-Ray or Jaeger identifies latency bottlenecks across microservices. I set up anomaly detection alerts rather than just static thresholds. When troubleshooting, I follow a systematic approach: check recent deployments, review metrics for correlation, examine logs for errors, and validate network connectivity layer by layer.
Serverless like Lambda excels for event-driven, bursty workloads with unpredictable traffic patterns and short execution times. Containers are better for long-running processes, workloads needing consistent performance, or applications with complex dependency chains. I also consider cold start latency requirements, cost at scale (serverless can become expensive at high consistent throughput), team familiarity, and vendor lock-in tolerance. Many architectures benefit from a hybrid approach using both.
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Download PrepPilot FreeAWS Solutions Architect, Azure Solutions Architect Expert, and Google Cloud Professional Cloud Architect are most sought-after. Kubernetes certifications are also highly valued.
Many companies include whiteboard architecture design or live infrastructure provisioning tasks. Practice designing scalable systems and writing Terraform templates.
Python and Go are most common. Familiarity with Bash scripting, Terraform HCL, and at least one general-purpose language is expected.