Cloud-Based System Design Interview Questions for AWS, Azure, GCP

Free Coding Questions Catalog

Boost your coding skills with our essential coding questions catalog. Take a step towards a better tech career now!

Cloud computing is a cornerstone of modern software architecture, so it’s no surprise that cloud-based system design is now common in tech interviews.

Interviewers want to see that you can apply classic design principles to cloud infrastructure – in other words, that you understand distributed systems and can leverage the managed services of Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP).

While the big three clouds offer similar core services, each has unique strengths – AWS is known for a large global presence and broad scalability, Azure excels at enterprise integration and security, and GCP stands out with advanced data analytics and machine learning features.

Fundamentals of Cloud System Design

Before tackling specific designs, make sure you grasp these key cloud architecture concepts:

Scalability & Elasticity: Scalability is a system’s ability to handle increased load by adding resources, while elasticity is the automatic adjustment of resources to match demand. An auto-scaling group can launch more server instances during peak traffic and shut them down during lulls.
High Availability & Disaster Recovery: High availability (HA) means designing systems to stay online with minimal downtime, eliminating single points of failure (e.g. deploy servers across multiple zones so one outage doesn’t take down the entire system). Disaster recovery (DR) is about preparing for catastrophic failures – using backups and cross-region replication so you can restore data or fail over to a secondary region if the primary one fails.
Multi-Region Architecture: Deploying your application in multiple geographic regions improves fault tolerance and latency. Even if an entire region goes down, others can keep the service running. Serving users from the region closest to them also reduces latency.
Security Best Practices: Implement least privilege access (restrict permissions with IAM roles), encrypt data at rest and in transit (manage keys with AWS KMS, Azure Key Vault, etc.), isolate networks (VPCs, subnets) and use firewalls (security groups, ACLs).

Common Cloud-Based System Design Interview Questions

Interviewers often pose open-ended scenarios. Common cloud system design questions include:

Design a scalable e-commerce system. How would you architect an online store to handle millions of users? Consider load balancing for web traffic, scaling databases, using caches, and auto-scaling for traffic spikes. Learn how to design an e-commerce system.
Real-time chat application. How to design a chat app for instant messaging? Think about using WebSockets or a pub/sub messaging service for real-time updates, a fast datastore for messages, and scaling to thousands of concurrent connections.
Video streaming service architecture. How to build a video streaming platform? Consider storing and transcoding videos, using a CDN for global content delivery, and designing for high bandwidth and low latency.
Highly available database setup. How to design a fault-tolerant database? Discuss replicating data across zones or regions, automatic failover mechanisms, and whether to use SQL or NoSQL based on consistency requirements.

Recommended Courses

Sample Answers and Approaches

How might you approach these design problems on AWS, Azure, or GCP? Here are brief sample solutions:

AWS: For a scalable web application, use an Elastic Load Balancer with auto-scaling EC2 instances across multiple AZs to handle the front-end. Store persistent data in managed databases (e.g. Amazon RDS for relational data or DynamoDB for NoSQL), and serve static assets from Amazon S3 via CloudFront CDN to deliver content globally with low latency.
Azure: For a real-time application (e.g. a chat app), use Azure Web PubSub (or Azure’s SignalR service) to handle live messaging to clients. Run back-end processing on Azure Functions, and store data in Azure Cosmos DB, which automatically replicates data across regions for high availability.
GCP: For a global service like video streaming, store content in Google Cloud Storage and put Cloud CDN in front of it to cache videos at edge locations worldwide. Run your application logic on Cloud Run (Google’s serverless containers platform) which scales out to handle incoming requests, and use a globally distributed database like Cloud Spanner to keep user data and metadata consistent across regions.

Best Practices for Cloud System Design Interviews

Keep these best practices in mind to impress your interviewer:

Optimize cost and performance: Use elasticity to your advantage – auto-scale resources to meet demand (avoiding idle costs), use managed services/serverless to minimize maintenance, and cache data (e.g. with CDNs or Redis) to reduce load and latency.
Choose the right storage and compute: Match services to the workload. For example, use a NoSQL database for simple key-value or document data, but a relational database when you need transactions and strong consistency. Similarly, decide between serverless, containers, or VMs for compute – a short-lived event-driven task fits well on a function, whereas a steady, long-running process might run better on a container or VM.
Handle data consistency and replication: Show that you understand distributed data trade-offs. Be familiar with the CAP theorem – many NoSQL systems favor availability (with eventual consistency) over strict consistency, whereas SQL systems often prioritize consistency over availability. Describe how data will replicate across zones/regions and what happens on failover. For example, you might use a primary-secondary setup with failover for strong consistency, or a multi-master cluster if the system can tolerate eventual consistency.

Real-World Examples

Real-world systems can illustrate these concepts.

For instance, Netflix’s architecture on AWS is a prime example – Netflix runs on thousands of AWS servers, enabling millions of users to stream content worldwide with high availability. Meanwhile, many enterprises adopt a multi-cloud strategy.

About 89% of companies use multiple clouds.

For example, a business might run its core application on AWS but integrate Azure Databricks for a specialized analytics project – leveraging AWS’s robust infrastructure with Azure’s specialized capabilities.