In my previous role at a fintech startup, we evaluated hosting options for a new analytics platform.

I needed to recommend the most suitable service model based on cost, control, and time‑to‑market.

I compared IaaS (AWS EC2) for full control, PaaS (AWS Elastic Beanstalk) for managed runtime, and SaaS (Snowflake) for a fully managed data warehouse, outlining pros/cons for each.

We selected PaaS for the analytics API to reduce operational overhead while retaining scalability, cutting deployment time by 40%.

During a migration project for a retail client, we needed isolated networking.

Explain the concept of a Virtual Private Cloud (VPC) to stakeholders.

I described a VPC as a logically isolated section of the cloud where you define IP ranges, subnets, route tables, and security groups, similar to an on‑premise data center.

Stakeholders approved the design, enabling secure segmentation of public‑facing web servers and private databases.

Our e‑commerce platform expected a flash‑sale event with unpredictable traffic.

Create an architecture that scales automatically and remains fault‑tolerant.

I proposed an Elastic Load Balancer front‑ending Auto Scaling groups of EC2 instances across multiple AZs, Amazon RDS Multi‑AZ for the database, Amazon CloudFront CDN for static assets, and Route 53 health‑checked DNS failover. I added S3 for asset storage and Lambda@Edge for request routing.

During the event, traffic grew 5× without downtime, and latency stayed under 200 ms, meeting SLA.

A media company needed a central repository for raw video files and analytics data.

Architect a data lake on AWS that is cost‑effective, performant for analytics, and meets security standards.

I selected Amazon S3 as the storage tier with Intelligent‑Tiering for cost control, enabled S3 Object Lock for immutability, and applied bucket policies with IAM roles for fine‑grained access. For analytics, I integrated AWS Glue crawlers and Athena for serverless querying, and used Lake Formation to enforce column‑level security. I added CloudTrail logging and KMS encryption at rest and in transit.

The solution reduced storage costs by 30% versus a hot‑tier only approach, delivered sub‑second query latency for analysts, and passed the company’s compliance audit.

Our organization managed workloads on AWS and Azure and wanted consistent provisioning.

Establish an IaC pipeline that works across both clouds.

I chose Terraform as the declarative tool, stored modules in a private Git repo, and used separate workspaces for each environment. CI/CD was built with GitHub Actions to run plan and apply stages, with policy checks via Sentinel. Secrets were managed via HashiCorp Vault, and state files were stored in an encrypted S3 bucket with DynamoDB locking for AWS and Azure Blob with lease for Azure.

Provisioning time dropped from days to minutes, and drift was eliminated, leading to a 25% reduction in operational incidents.

A payment microservice in our GKE cluster started showing 2‑3× higher response times during peak hours.

Identify root cause and restore performance.

I started with Prometheus metrics to check CPU/memory usage, then examined pod logs for errors. I discovered a spike in GC pauses due to a memory leak in the Java service. I scaled the deployment temporarily, rolled out a hotfix to address the leak, and added resource limits. I also reviewed network policies and found no bottlenecks. Finally, I updated the CI pipeline to include a memory‑leak detection test.

Latency returned to baseline within an hour, and the new test prevented similar regressions.

We were moving a legacy CRM system to Azure.

Create a migration plan that protects data at rest and in transit.

I performed a data classification, encrypted data at rest using Azure Storage Service Encryption, used Azure Key Vault for key management, and enforced TLS 1.2 for all network traffic. I leveraged Azure Site Recovery for lift‑and‑shift, validated encryption post‑migration, and conducted a penetration test on the new environment. I also updated IAM roles to follow least‑privilege principles.

The migration completed with zero data breaches, and the client passed their external security audit.

In a multi‑tenant SaaS platform, we needed to restrict access to resources per tenant.

Design IAM policies that grant only necessary permissions.

I created role‑based policies using AWS IAM with scoped resource ARNs, applied condition keys (aws:SourceVpc, aws:RequestedRegion), and used permission boundaries for cross‑account access. I also employed AWS Organizations SCPs to enforce organization‑wide constraints and regularly reviewed permissions with Access Analyzer.

Unauthorized access attempts dropped to zero, and audit reports showed compliance with the least‑privilege principle.