30 Cloud Interview Questions & Answers (AWS, Azure, GCP)

Last Updated: February 2026

I’ll never forget my first cloud computing interview. The interviewer asked me, “What’s the difference between EC2 and Lambda?” and I completely blanked. I knew I’d used both services, but explaining the fundamental difference under pressure? My brain just… stopped working.

Quick Summary: This guide covers the most asked cloud interview questions across AWS, Azure, and GCP, with beginner-friendly explanations and real-world examples.

That interview didn’t go well, but it taught me something important: knowing how to use cloud services and being able to explain them clearly are two very different skills.

If you’re preparing for a cloud interview—whether it’s for AWS, Google Cloud Platform, or Microsoft Azure—you’re in the right place. I’m going to walk you through the most common questions interviewers ask, not with textbook definitions, but with explanations that actually make sense.

We’ll cover all three major cloud providers because here’s the reality: most companies use multiple clouds, and understanding the concepts across platforms makes you way more valuable as a candidate.

Note:- I have written one blog on How DevOps Works: Complete CI/CD Flow from Code Commit to Production . This blog will help you understand the real flow of CI/CD in live actual Production Environment.

Why Cloud Computing Skills Matter in 2026

Before we jump into questions, let’s talk about why every tech interview seems to include cloud questions these days.

The shift to cloud isn’t slowing down—it’s accelerating. Companies that were hesitant five years ago are now cloud-first. The pandemic pushed the stragglers over the edge. Remote work, scaling challenges, and the need for flexibility made cloud computing a necessity, not a nice-to-have.

According to recent data, over 90% of enterprises use multiple cloud providers. That means whether you’re interviewing for a startup or a Fortune 500 company, cloud knowledge is expected.

But here’s the good news: you don’t need to be a certified cloud architect to pass entry-level or mid-level interviews. You need to understand the fundamentals and be able to explain them clearly.

Understanding the Big Three: AWS vs GCP vs Azure

Let’s start with the landscape. There are three giants in cloud computing, and they all do basically the same things with different names and slightly different approaches.

Amazon Web Services (AWS)

AWS is the granddaddy of cloud computing. They launched in 2006 and had a massive head start on everyone else. They’ve got the biggest market share—around 32% of the cloud market.

Pros: Most mature platform, widest range of services, biggest community, most job opportunities.

Cons: Can be more expensive, UI is sometimes clunky, so many services it’s overwhelming.

Best for: Companies that need every possible service, want the most mature ecosystem, or are already heavily invested in AWS.

Google Cloud Platform (GCP)

Google launched their cloud platform later but brought their expertise in data, machine learning, and Kubernetes (they literally invented it).

Pros: Best for data analytics and machine learning, great Kubernetes support, cleaner UI, often cheaper than AWS.

Cons: Smaller market share, fewer services than AWS, smaller community.

Best for: Data-heavy applications, machine learning projects, companies using Kubernetes extensively.

Microsoft Azure

Azure is the go-to for enterprises already using Microsoft products. If you’ve got Windows servers, Active Directory, or Office 365, Azure integration is seamless.

Pros: Best integration with Microsoft ecosystem, strong enterprise support, good hybrid cloud solutions.

Cons: Can be complex, historically had reliability issues (though they’ve improved).

Best for: Enterprises with existing Microsoft investments, hybrid cloud scenarios.

Now that you know the landscape, let’s dive into the actual interview questions.

30 Essential Cloud Interview Questions (Across All Platforms)

I’ve organized these by topic, starting with fundamentals and building to more advanced concepts. Most questions apply to all three cloud providers—I’ll note when there are important platform-specific differences.

Cloud Computing Fundamentals (Questions 1-5)

Question 1: “What is cloud computing? Explain it like I’m not a technical person.”

This is often the first question, and it’s a test of whether you actually understand the concepts or just memorized definitions.

Here’s how I’d answer:

“Cloud computing means using someone else’s computers over the internet instead of buying and maintaining your own. Think of it like Netflix—you don’t need to own a movie theater or buy DVDs. You just stream what you need, when you need it, and pay for what you use.

The same idea applies to computing resources. Instead of buying servers, installing them in your office, and hiring people to maintain them, you rent computing power from companies like Amazon, Google, or Microsoft. You can scale up when you need more capacity and scale down when you don’t.”

The “explain it to a non-technical person” framing shows you can communicate, which is huge in interviews.

Question 2: “What are the main service models in cloud computing?”

This is about IaaS, PaaS, and SaaS. Don’t just recite definitions—use examples.

“There are three main service models:

Infrastructure as a Service (IaaS) gives you virtual machines, storage, and networking. You manage everything from the operating system up. It’s like renting an apartment—you get the space and utilities, but you furnish it and maintain everything inside. Examples: AWS EC2, Google Compute Engine, Azure Virtual Machines.

Platform as a Service (PaaS) provides a platform for deploying applications without managing the underlying infrastructure. You just write your code and deploy it. It’s like a serviced apartment—furniture and cleaning included. Examples: AWS Elastic Beanstalk, Google App Engine, Azure App Service.

Software as a Service (SaaS) is ready-to-use software accessed over the internet. You’re just using the application. It’s like a hotel—everything’s done for you. Examples: Gmail, Salesforce, Office 365.”

Question 3: “What’s the difference between public, private, and hybrid cloud?”

“A public cloud is when you use shared infrastructure from providers like AWS, GCP, or Azure. Your virtual machines might run on the same physical hardware as other companies’ VMs, but they’re isolated. It’s the most cost-effective option.

A private cloud is dedicated infrastructure just for your organization. It might be on-premises or hosted by a provider, but you’re not sharing resources. Banks and healthcare companies often use private clouds for sensitive data. It’s more expensive but gives you more control.

A hybrid cloud combines both. You might keep sensitive data in a private cloud but use public cloud for less critical workloads or for bursting when you need extra capacity. It’s the most flexible but also the most complex to manage.”

Question 4: “What is virtualization and why does it matter for cloud computing?”

“Virtualization is the technology that makes cloud computing possible. It lets you run multiple virtual machines on a single physical server, each acting like it’s a separate computer.

Without virtualization, if you rented a server, you’d get the whole physical machine—wasteful if you only need 10% of its capacity. With virtualization, the cloud provider can carve up that physical server into multiple virtual machines and rent them to different customers.

It’s why cloud is so cost-effective and flexible. You can spin up a VM in seconds, resize it on demand, or delete it when you’re done. The hypervisor (software that manages virtualization) handles all the complexity of sharing hardware securely.”

Question 5: “What are the main benefits of cloud computing?”

Don’t just list generic benefits. Be specific and use examples.

“The main benefits are:

Cost savings: You avoid upfront hardware costs and only pay for what you use. If your website has 100 users today and 10,000 tomorrow, you only pay for the resources you actually consumed.

Scalability: You can scale up during high demand and scale down during quiet periods. Black Friday for an e-commerce site? Spin up 50 more servers. January doldrums? Scale back down.

Reliability: Cloud providers have multiple data centers. If one fails, your application automatically fails over to another. Building that yourself would cost millions.

Speed: You can provision resources in minutes instead of weeks. No waiting for hardware procurement and installation.

Global reach: Deploy your application in multiple regions worldwide with a few clicks. Users in Tokyo and London get fast response times.”

AWS-Specific Questions (Questions 6-12)

Question 6: “What is EC2 and what are EC2 instance types?”

“EC2 stands for Elastic Compute Cloud. It’s Amazon’s virtual machine service—you rent virtual servers and run your applications on them.

Instance types are different configurations of CPU, memory, storage, and networking capacity. They’re organized into families:

General Purpose (t3, m5): Balanced resources, good for web servers and small databases.

Compute Optimized (c5): High CPU performance, good for batch processing or gaming servers.

Memory Optimized (r5): High memory, good for databases and caching.

Storage Optimized (i3): Fast local storage, good for data warehousing.

GPU instances (p3, g4): For machine learning and graphics rendering.

You pick the instance type based on your workload. Running a simple web server? A t3.small might cost you $15/month. Training a machine learning model? You might need a p3.8xlarge costing $12/hour.”

Question 7: “What’s the difference between EC2 and Lambda?”

This is a classic question that trips up beginners.

“EC2 gives you virtual servers that run continuously until you shut them down. You’re responsible for managing the operating system, installing software, and maintaining everything. You pay by the hour, even if your application is idle.

Lambda is serverless—you just upload your code and it runs in response to events. AWS manages all the infrastructure. You don’t think about servers, operating systems, or scaling. You pay only for the compute time your code actually uses, measured in milliseconds.

Example: If you have a web API that gets 1000 requests per day, with EC2 you’d pay for a server running 24/7. With Lambda, you’d only pay for those few seconds when your code is actually executing.

Lambda is cheaper and simpler for event-driven workloads. EC2 gives you more control and works better for applications that need to run continuously.”

Question 8: “What is S3 and what are its use cases?”

“S3 stands for Simple Storage Service. It’s object storage—you store files (objects) in containers called buckets.

It’s not like a hard drive. You can’t mount it and browse files like a normal filesystem. You interact with it through APIs or the web interface.

Common use cases:

Static website hosting: Serve HTML, CSS, images directly from S3. Cheap and scales infinitely.

Backup and archival: Store database backups, log files, or old data you rarely access.

Data lakes: Store huge amounts of raw data for analytics.

Content distribution: Store images and videos, then serve them through CloudFront CDN.

Application assets: Store user uploads, generated reports, or configuration files.

S3 is incredibly durable—99.999999999% durability. That means if you store 10 million files, you’d statistically lose one file every 10,000 years. And it’s cheap—about $0.023 per GB per month for standard storage.”

Question 9: “Explain the different S3 storage classes”

“S3 has multiple storage classes for different access patterns:

S3 Standard: For frequently accessed data. Most expensive but fastest access.

S3 Intelligent-Tiering: Automatically moves data between tiers based on access patterns. Good when you’re not sure how often you’ll access data.

S3 Standard-IA (Infrequent Access): For data accessed less than once a month. Cheaper storage but you pay a fee when you retrieve data.

S3 One Zone-IA: Like Standard-IA but stored in only one availability zone. Cheaper but less resilient.

S3 Glacier: For archival data you rarely need. Retrieval takes minutes to hours. Very cheap.

S3 Glacier Deep Archive: For data you might never retrieve. Takes 12+ hours to retrieve. Cheapest option.

You’d use Standard for your website’s images, Standard-IA for monthly reports, and Glacier for legal documents you need to keep but will probably never access.”

Question 10: “What is a VPC?”

“VPC stands for Virtual Private Cloud. It’s your own isolated network within AWS.

Think of it like this: AWS is a huge office building. A VPC is your private floor. You control who can enter, what they can access, and how different areas connect.

A VPC includes:

Subnets: Subdivisions of your network, like different departments on your floor. Public subnets have internet access, private subnets don’t.

Route tables: Rules for directing network traffic.

Internet Gateway: The door to the internet.

NAT Gateway: Lets private subnets access the internet without exposing them to incoming internet traffic.

Security groups: Firewall rules for your EC2 instances.

Network ACLs: Additional firewall at the subnet level.

You’d create a VPC, put your web servers in public subnets, your databases in private subnets, and configure security groups so only your web servers can talk to your databases.”

Question 11: “What are security groups and how do they work?”

“Security groups are virtual firewalls that control traffic to your EC2 instances.

They work with rules:

Inbound rules: What traffic can reach your instance. Example: Allow HTTP (port 80) from anywhere, allow SSH (port 22) only from your office IP.

Outbound rules: What traffic your instance can send. By default, everything is allowed outbound.

Security groups are stateful—if you allow incoming traffic, the response is automatically allowed out, even if there’s no outbound rule.

Example security group for a web server:

Inbound: Allow port 80 (HTTP) from 0.0.0.0/0 (anyone)
Inbound: Allow port 443 (HTTPS) from 0.0.0.0/0
Inbound: Allow port 22 (SSH) from 203.0.113.0/24 (only your office)
Outbound: Allow all

You can reference security groups within other security groups. So your web server security group can allow inbound from load balancer security group, creating a layered security model.”

Question 12: “What is EBS and how is it different from S3?”

“EBS stands for Elastic Block Storage. It’s like a hard drive for your EC2 instances.

Key differences from S3:

EBS is block storage attached to EC2 instances. You format it with a filesystem and use it like a local disk. It’s fast and used for operating systems, databases, or application data that needs frequent access.

S3 is object storage accessed via API. It’s for files you store and retrieve as whole objects—images, videos, backups, logs.

EBS is limited to one EC2 instance at a time (mostly—there’s multi-attach for some types). S3 can be accessed by unlimited clients simultaneously.

EBS lives in one availability zone. If that zone fails, your volume is unavailable. S3 is automatically replicated across zones.

Cost: EBS is more expensive per GB but provides better performance for databases and applications that need low-latency storage.

You’d use EBS for your database server’s storage and S3 for storing database backups.”

Google Cloud Platform Questions (Questions 13-18)

Question 13: “What is Compute Engine and how does it compare to AWS EC2?”

“Compute Engine is GCP’s virtual machine service—it’s Google’s equivalent to AWS EC2.

Similarities: Both provide virtual machines with various CPU, memory, and storage configurations. Both let you choose operating systems and install whatever software you need.

Key differences:

Pricing: GCP offers sustained use discounts automatically. The longer your VM runs, the cheaper it gets, up to 30% discount. AWS requires you to commit to reserved instances for discounts.

Per-second billing: GCP bills per second (after first minute). AWS bills per second for Linux, per hour for Windows.

Live migration: GCP can migrate your VM to different hardware during maintenance without downtime. AWS usually requires a restart.

Machine types: GCP lets you create custom machine types with exactly the CPU and memory you need. AWS makes you choose from preset instance types.

For most workloads, they’re functionally equivalent. GCP might be slightly cheaper if you run VMs continuously. AWS has more instance type options for specialized workloads.”

Question 14: “What is Google Kubernetes Engine (GKE)?”

“GKE is Google’s managed Kubernetes service. Since Google created Kubernetes, their implementation is considered the gold standard.

What it does: You define your containerized applications, and GKE handles running them, scaling, updating, and managing the underlying infrastructure.

Why it matters: Running Kubernetes yourself is complex—you need to manage master nodes, upgrade versions, configure networking, and monitor everything. GKE does all that for you.

How it works: You create a cluster (a group of machines), deploy your containers, and GKE ensures they stay running. If a container crashes, GKE restarts it. If you need more capacity, GKE adds nodes. If you deploy a new version, GKE rolls it out gradually.

Comparison to alternatives:

AWS EKS: Similar managed Kubernetes, but GKE is generally considered easier to use and better integrated with Google services.
Azure AKS: Microsoft’s managed Kubernetes, comparable features.

GKE also has Autopilot mode where Google manages everything including node provisioning and scaling. You literally just deploy containers and forget about infrastructure.”

Question 15: “What is BigQuery?”

“BigQuery is Google’s fully managed data warehouse for analytics. It’s designed for analyzing massive datasets—we’re talking petabytes.

What makes it special:

Serverless: No infrastructure to manage. You just load your data and write SQL queries.

Insane speed: It can scan terabytes of data in seconds using massively parallel processing.

Pay-per-query: You pay for the data scanned by your queries, not for running servers. A query scanning 1TB costs about $5.

Real-time analysis: You can query streaming data as it arrives.

Use cases:

Analyzing clickstream data from millions of users
Processing IoT sensor data
Business intelligence and reporting
Machine learning on large datasets

Example: A retail company could dump all their sales transactions, website clicks, and inventory data into BigQuery, then write a SQL query to find out which products customers view together but don’t buy. Traditional databases would choke on that volume, but BigQuery handles it easily.”

Question 16: “What is Cloud Storage and how does it compare to AWS S3?”

“Cloud Storage is GCP’s object storage service—their equivalent to AWS S3. Functionally, they’re very similar.

Both provide:

Unlimited storage capacity
High durability (99.999999999%)
Multiple storage classes
Versioning, lifecycle policies, access controls

Key differences:

Naming: S3 uses ‘buckets’ and ‘objects’. Cloud Storage uses ‘buckets’ and ‘objects’ too, but the terminology is slightly different in some areas.

Storage classes:

Standard: For frequently accessed data (same as S3 Standard)
Nearline: For data accessed less than once per month (like S3 Standard-IA)
Coldline: For data accessed less than once per quarter
Archive: For long-term archival (like S3 Glacier)

Pricing: GCP is often slightly cheaper, especially for egress (downloading data out of the cloud).

Integration: Cloud Storage integrates better with GCP services like BigQuery. S3 integrates better with AWS services.

Performance: Both are fast. GCP has slight advantages in some regions.

For most uses, they’re interchangeable. Choose based on which cloud platform you’re already using.”

Question 17: “What is Cloud Functions?”

“Cloud Functions is GCP’s serverless compute service—their version of AWS Lambda.

You write individual functions in Node.js, Python, Go, or Java, and they run in response to events:

HTTP requests (like an API endpoint)
Cloud Storage changes (when a file is uploaded)
Pub/Sub messages (events from other services)
Firestore database changes

How it works: You upload your code, configure a trigger, and GCP handles everything else. When the trigger fires, your function executes. You’re billed only for execution time, measured in 100ms increments.

Example use case: User uploads a profile photo to Cloud Storage → Cloud Function triggers → Function resizes the image and saves thumbnails → User sees their resized photo.

Differences from AWS Lambda:

GCP supports more built-in triggers
Simpler cold start handling
Slightly different pricing model
Better integration with GCP services

Both are great for event-driven architectures, scheduled tasks, or APIs that get occasional traffic.”

Question 18: “What is a VPC in GCP and how does it differ from AWS?”

“GCP VPCs work similarly to AWS but with some important differences.

GCP VPC characteristics:

Global by default: A single VPC can span all GCP regions. In AWS, VPCs are regional—you need separate VPCs or VPC peering to connect regions.

Subnets are regional: Unlike AWS where subnets are per availability zone, GCP subnets span all zones in a region automatically.

Shared VPC: You can share a VPC across multiple GCP projects. This is cleaner than AWS’s VPC peering for multi-account setups.

Firewall rules: Instead of security groups attached to instances, GCP uses VPC-level firewall rules that apply based on tags or service accounts.

Practical difference: In AWS, if you want high availability across two availability zones, you need two subnets. In GCP, one subnet automatically covers all zones in the region.

The global nature of GCP VPCs makes multi-region deployments simpler, but the concepts are similar enough that understanding one helps you learn the other.”

Azure-Specific Questions (Questions 19-24)

Question 19: “What is Azure and when would you choose it over AWS or GCP?”

“Azure is Microsoft’s cloud platform. While AWS leads in market share, Azure is growing fast—especially in enterprise.

You’d choose Azure when:

Existing Microsoft investments: If you’re already using Windows Server, SQL Server, Active Directory, or Office 365, Azure integration is seamless. Licensing costs are often lower if you’re already a Microsoft customer.

Hybrid cloud: Azure has the best hybrid cloud story with Azure Arc and Azure Stack, letting you manage on-premises and cloud resources together.

Enterprise support: Microsoft has long-standing relationships with enterprises and offers extensive support contracts.

Development stack: If you’re a .NET shop, Azure App Service and Visual Studio integration are excellent.

Specific services: Azure has some unique services like Azure DevOps (really good CI/CD) and better integration with enterprise tools.

When to choose AWS: More mature services, biggest ecosystem, most job opportunities.

When to choose GCP: Data analytics, machine learning, Kubernetes, generally cheaper for compute.

Reality? Many companies use multiple clouds. Understanding all three makes you more valuable.”

Question 20: “What is Azure Virtual Machines?”

“Azure VMs are Microsoft’s IaaS offering—equivalent to AWS EC2 and GCP Compute Engine.

Key concepts:

VM sizes: Like AWS instance types, Azure has different VM sizes:

B-series: Burstable VMs for workloads with variable CPU needs
D-series: General purpose
E-series: Memory optimized
F-series: Compute optimized
N-series: GPU-enabled

Availability Sets: You place VMs in availability sets to ensure they’re distributed across different hardware. If one rack fails, other VMs stay up.

Availability Zones: Like AWS availability zones, physically separate data centers within a region.

Scale Sets: Automatically create and manage groups of identical VMs for load balancing and auto-scaling.

Unique features:

Spot instances: Like AWS Spot instances, cheap VMs that can be evicted but great for batch processing.

Azure Hybrid Benefit: Reuse existing Windows Server licenses on Azure VMs, saving up to 40%.

Reserved instances: Commit to 1-3 years for significant discounts.

Functionally similar to EC2, but better integrated with Microsoft ecosystem and licensing.”

Question 21: “What is Azure Blob Storage?”

“Blob Storage is Azure’s object storage service—the equivalent of AWS S3 and GCP Cloud Storage.

Storage tiers:

Hot: For frequently accessed data. Most expensive storage, cheapest access.

Cool: For data accessed less than once per month. Cheaper storage, but you pay to access data. 30-day minimum storage period.

Archive: For rarely accessed data. Cheapest storage but retrieval takes hours. 180-day minimum.

Key features:

Blob types:

Block blobs: For files, documents, media. Most common type.
Append blobs: Optimized for append operations, good for logs.
Page blobs: For random access, used by Azure VM disks.

Lifecycle management: Automatically move blobs between tiers or delete old data based on rules.

Versioning and soft delete: Recover from accidental deletions or changes.

Example: Store user-uploaded photos in Hot tier, move to Cool after 30 days if not accessed, archive after a year, delete after 7 years. All automatic.”

Question 22: “What is Azure Functions?”

“Azure Functions is Microsoft’s serverless compute offering—their version of AWS Lambda and GCP Cloud Functions.

Supported languages: C#, JavaScript, Python, PowerShell, Java, TypeScript.

Triggers: HTTP requests, timers, Azure Storage events, Cosmos DB changes, Service Bus messages, Event Grid events.

Hosting plans:

Consumption Plan: Pay-per-execution like Lambda. Functions can be cold started. Cheapest for sporadic workloads.

Premium Plan: Pre-warmed instances for no cold starts. Better for production APIs.

Dedicated Plan: Run on dedicated App Service VMs. For workloads that need guaranteed resources.

Durable Functions: A unique Azure feature that lets you write stateful workflows in code. You can have long-running processes, wait for external events, or orchestrate complex workflows—all with simple code.

Example: Order processing workflow—charge card (wait for confirmation), update inventory (wait for confirmation), send email (wait for delivery), update analytics. Durable Functions handles all the state management and retries automatically.”

Question 23: “What is Azure Resource Manager (ARM)?”

“ARM is Azure’s infrastructure management layer. Every Azure resource (VMs, databases, networks) is created and managed through ARM.

Why it matters:

Declarative templates: You define what you want in JSON templates, and ARM creates it. Like Terraform but Azure-native.

Resource groups: You organize resources into groups. A web application might have a resource group containing VMs, a database, a load balancer, and storage. You can manage, monitor, or delete them all together.

Role-based access control (RBAC): Grant permissions at the resource group or individual resource level.

Tags: Add metadata to resources for cost tracking, environment labeling, or organization.

Consistent management: Whether you use the portal, CLI, PowerShell, or REST API, it all goes through ARM.

Example: You create an ARM template for a 3-tier web application. One command deploys all resources (network, VMs, database, load balancer) with the correct configuration and relationships. Need another environment? Deploy the same template to a different resource group.”

Question 24: “What is Azure Active Directory?”

“Azure AD is Microsoft’s cloud-based identity and access management service. It’s not the same as traditional Active Directory, though they integrate.

What it does:

Single Sign-On (SSO): Users log in once and access multiple applications.

Multi-Factor Authentication: Add extra security with phone or app verification.

User management: Create and manage user accounts, groups, and permissions.

Application integration: Integrate thousands of SaaS applications (Salesforce, Slack, etc.).

B2B and B2C: Let partners or customers access your applications with their own credentials.

Key concepts:

Tenants: Your organization’s instance of Azure AD.

Users and groups: Manage who has access to what.

Service principals: Identity for applications to access Azure resources.

Managed identities: Azure resources can have identities and access other resources without storing credentials.

Enterprise use case: Company has 5,000 employees. Azure AD manages their identities. When they log in to their laptop, they automatically get access to Office 365, internal web apps, and cloud resources based on their role. IT manages everything centrally.”

Multi-Cloud Concepts (Questions 25-30)

Question 25: “What is object storage and why is it important?”

“Object storage is a way of storing data as objects rather than in traditional file hierarchies or database tables.

How it works: Each object contains the data itself, metadata (information about the data), and a unique identifier. You access objects via API, not by mounting a drive.

Why it’s important:

Scalability: Object storage scales infinitely. S3, Cloud Storage, and Blob Storage handle trillions of objects.

Durability: Data is automatically replicated across multiple locations. You don’t worry about disk failures.

Cost-effective: Cheaper than block storage for large amounts of data you don’t access frequently.

Global access: Access from anywhere via HTTP/HTTPS.

Common uses:

Media files (images, videos)
Backups and archives
Data lakes for analytics
Static website hosting
Content distribution

Limitations: Not suitable for databases or applications needing a filesystem. Can’t edit objects in place—you must download, modify, and re-upload.”

Question 26: “What is a load balancer and why do you need one?”

“A load balancer distributes incoming traffic across multiple servers to ensure no single server gets overwhelmed.

The problem it solves: If you have one web server and it goes down, your site is down. If it gets too much traffic, it becomes slow or crashes.

The solution: Put a load balancer in front of multiple web servers. Traffic gets distributed evenly. If one server fails, the load balancer stops sending traffic there.

Types:

Application Load Balancer (AWS ALB): Routes traffic based on content (URL path, HTTP headers). Can route /api/* to backend servers and /images/* to a different set.

Network Load Balancer (AWS NLB): Handles millions of requests per second with ultra-low latency. Routes based on IP protocol data.

Classic Load Balancer: Older AWS option, still works but ALB/NLB are better.

GCP Load Balancing: Global load balancing that can route users to the nearest region automatically.

Azure Load Balancer: Similar to NLB. Azure Application Gateway is similar to ALB.

Health checks: Load balancers constantly check if servers are healthy. Unhealthy servers don’t receive traffic until they recover.

Example: E-commerce site runs on 5 servers behind a load balancer. During Black Friday, they add 20 more servers. Load balancer automatically includes them. After Black Friday, they remove the extra servers. Users never notice—they just see fast response times.”

Question 27: “What is auto-scaling?”

“Auto-scaling automatically adjusts the number of servers based on demand.

How it works: You define rules:

When CPU usage > 70% for 5 minutes, add 2 servers
When CPU usage < 30% for 10 minutes, remove 1 server

The cloud platform monitors your metrics and adds/removes servers accordingly.

Why it’s essential:

Cost savings: You don’t pay for servers you don’t need. During low traffic, you might run 2 servers. During high traffic, 50 servers.

Performance: Your application stays fast even during traffic spikes.

Reliability: If a server fails, auto-scaling replaces it automatically.

Types of scaling:

Horizontal scaling (scale out): Add more servers. This is what auto-scaling typically does.

Vertical scaling (scale up): Use a bigger server. Requires downtime and has limits.

AWS: Auto Scaling Groups GCP: Managed Instance Groups
Azure: Virtual Machine Scale Sets

Example: News website normally gets 10,000 visitors/hour on 3 servers. Breaking news hits, traffic jumps to 100,000/hour. Auto-scaling adds 27 more servers in minutes. Traffic normalizes after 2 hours, scales back down. Cost for those 2 hours? Maybe $20. Cost of keeping 30 servers running 24/7 just in case? Thousands per month.”

Question 28: “What is a CDN (Content Delivery Network)?”

“A CDN caches your content in multiple locations worldwide so users get faster load times from a server near them.

The problem: Your web server is in Virginia. Users in Singapore have to wait 300ms for each request to travel across the world.

The solution: CDN caches your static content (images, CSS, JavaScript, videos) on servers in Singapore, London, Sydney, São Paulo, etc. Users get content from the nearest location.

How it works:

User in Tokyo requests an image
CDN checks if it has the image cached in Tokyo
If yes, serves it immediately (maybe 20ms)
If no, fetches from origin server, caches it, then serves it
Next user gets it from cache

Major CDNs:

AWS CloudFront: Integrates with S3, EC2, and other AWS services.

GCP Cloud CDN: Integrates with Cloud Storage and Compute Engine.

Azure CDN: Integrates with Blob Storage and Azure services.

Benefits:

Speed: Faster load times = better user experience
Reduced load: Your origin servers handle less traffic
DDoS protection: CDNs can absorb massive traffic spikes
Lower bandwidth costs: You pay less to transfer data from your servers

Example: Video streaming platform stores videos in S3 but serves them through CloudFront. User in India gets video from a nearby CloudFront edge location instead of waiting for it to transfer from US. Video starts playing in 1 second instead of 10.”

Question 29: “What is the shared responsibility model in cloud security?”

“The shared responsibility model defines what the cloud provider secures versus what you’re responsible for securing.

Cloud provider is responsible for:

Physical security of data centers
Hardware and infrastructure
Network infrastructure
Hypervisor and virtualization layer

You are responsible for:

Your data
Application code
User access management
Operating system (for IaaS)
Network configuration (firewalls, security groups)
Encryption

It varies by service model:

IaaS (EC2, Compute Engine, Azure VMs): You’re responsible for everything from the OS up. Security patches, firewall rules, application security, data encryption.

PaaS (Elastic Beanstalk, App Engine, App Service): Provider manages OS and runtime. You’re responsible for application code and data.

SaaS (Gmail, Office 365): Provider manages almost everything. You manage user access and data classification.

Common misconception: ‘It’s in the cloud so it’s secure.’ Wrong. If you misconfigure an S3 bucket to be public, that’s your fault, not AWS’s.

Interview tip: Understand this model. Show you know security is shared, not just the provider’s job.”

Question 30: “What is Infrastructure as Code (IaC)?”

“Infrastructure as Code means defining your infrastructure using code instead of clicking through consoles.

Why it matters:

Reproducibility: Deploy identical environments for dev, staging, and production.

Version control: Your infrastructure is in git. You can see who changed what and when.

Automation: Create entire environments with one command.

Documentation: The code IS the documentation.

Disaster recovery: If everything explodes, redeploy from code.

Popular IaC tools:

Terraform: Cloud-agnostic, works with AWS, GCP, Azure, and 100+ other providers. Most popular choice for multi-cloud.

AWS CloudFormation: AWS-specific, uses JSON or YAML templates.

Azure ARM Templates: Azure-specific JSON templates.

Google Cloud Deployment Manager: GCP-specific YAML templates.

Pulumi: Uses real programming languages (TypeScript, Python, Go) instead of YAML.

Example workflow:

Write Terraform code defining VPC, subnets, EC2 instances, RDS database
Commit to git
Run terraform apply
Entire infrastructure creates in 10 minutes
Need another region? Change one variable, apply again
Need to tear down? terraform destroy removes everything

Without IaC: Click through console for hours, forget a security group rule, wonder why staging doesn’t match production, spend days rebuilding after disaster.

With IaC: terraform apply, everything’s consistent, documented, and recoverable.”

Additional Resources for Learning Cloud

If you want to go deeper (and you should), here are the best resources I’ve found:

Official Documentation

Frequently Asked Questions

Which cloud platform should I learn first?

AWS if you want the most job opportunities and broadest skillset. It has the biggest market share and most companies use it.

GCP if you’re interested in data engineering or machine learning. Google’s tools in these areas are excellent.

Azure if you’re working in enterprise IT or already familiar with Microsoft technologies.

Honestly? Learn the concepts on one platform, and the others become much easier. They’re more similar than different.

Do I need certifications to get a cloud job?

Not necessarily, but they help. Certifications show you’re serious and validate your knowledge. For entry-level positions, they can make up for lack of experience.

But practical experience > certifications. A GitHub repo with cloud projects beats a certification with no hands-on work.

How long does it take to prepare for a cloud interview?

For entry-level: 2-4 weeks of focused study if you’re starting from scratch. Cover the basics, create a free tier account, build something simple.

For mid-level: If you have some cloud exposure, 1-2 weeks reviewing common questions and practicing architecture design.

The key is hands-on practice, not just reading documentation.

What’s the hardest part of cloud interviews?

For beginners: Understanding when to use which service. Is this an EC2 or Lambda use case? S3 or EBS?

For everyone: Designing architectures on the fly. Practice common patterns: web applications, data pipelines, disaster recovery.

Also, cost awareness. Many candidates design solutions that would cost thousands monthly for a simple application.

Can I get a cloud job with no prior IT experience?

It’s tough but possible. Cloud roles usually want some technical background—whether that’s programming, system administration, or networking.

Realistic path: Learn programming basics (Python is popular in cloud), get AWS Cloud Practitioner or Azure Fundamentals certification, build several projects, contribute to open source, apply for junior DevOps or cloud support roles.

Timeline: 6-12 months of dedicated learning if starting from zero.

What programming languages should I know for cloud work?

Python is the most common for cloud automation and AWS Lambda.

JavaScript/TypeScript for Node.js Lambda functions and web frontends.

Go is becoming popular for cloud-native applications.

Bash for scripting in Linux environments.

PowerShell if working heavily with Azure.

You don’t need to be an expert programmer, but basic scripting ability is essential.

How do I explain cloud concepts to non-technical interviewers?

Use analogies:

Cloud computing = renting instead of buying Auto-scaling = calling in extra staff during rush hour Backup and disaster recovery = keeping copies of important documents Load balancer = multiple checkout lanes at a store

Practice explaining technical concepts simply. If you can make a recruiter understand, you’ll nail the technical interview.

What if I’ve only used one cloud provider?

Be honest. Say “I have hands-on experience with AWS, but I understand the concepts transfer to GCP and Azure. For example, AWS Lambda is similar to GCP Cloud Functions and Azure Functions.”

Then demonstrate you understand the underlying concepts, not just one vendor’s implementation.

Should I memorize pricing details?

No. But understand:

Pay-as-you-go vs reserved instances
Data transfer costs money
Different storage tiers have different costs
Some services are way more expensive than others

You should be able to have a cost-aware conversation, not quote prices per GB.

What’s the best way to stand out in a cloud interview?

Show you’ve built things. Have a portfolio of cloud projects you can discuss. Even simple projects show you can apply knowledge.

Ask good questions. About their cloud environment, challenges, what they’re trying to accomplish.

Demonstrate cost awareness. Most candidates design solutions without considering cost. If you mention “we could use spot instances for the batch processing to reduce costs,” you’ll stand out.

Know the trade-offs. Nothing is perfect. If you can explain “we could use RDS for managed databases, but we’d lose some control and flexibility compared to EC2 instances,” you sound experienced.

Wrapping Up: You’ve Got This

Cloud interviews can feel overwhelming. There’s so much to know across three major platforms, each with hundreds of services.

But here’s the thing: interviewers don’t expect you to know everything. They want to see that you understand the fundamentals, can reason through problems, and have the curiosity to learn.

If you’ve made it this far through this guide, you’re already ahead of most candidates. You know the difference between IaaS, PaaS, and SaaS. You understand what EC2, Compute Engine, and Azure VMs do. You can explain why someone would use Lambda over EC2, or when S3 makes sense versus EBS.

That’s the foundation. The specific services and features? You’ll learn those on the job.

My advice for your next cloud interview:

Focus on concepts over memorization. Understand why services exist and what problems they solve.

Get hands-on experience. Even a weekend of building something in the cloud teaches you more than weeks of reading.

Be honest about what you don’t know. Then show how you’d figure it out.

Think about real-world concerns. Cost, security, reliability—these matter in production.

And remember: everyone in cloud computing started by not knowing anything. That senior solutions architect interviewing you? They once struggled to understand what a VPC was.

You’ve got this. Now go build something.

Got questions about cloud interviews or want to share your experience? Drop a comment below. I read every one, and your question might help someone else preparing for their interview.

Good luck out there. See you in the cloud.

About the Author

Kedar Salunkhe

DevOps Engineer | Seven years of fixing things that break at 2am
Kubernetes • OpenShift • AWS • Coffee

I’ve spent almost 7 years keeping production systems running, often when everyone else is asleep. These days I’m working with Kubernetes and OpenShift deployments, automating everything that can be automated, and occasionally remembering to document the things I fix. When I’m not troubleshooting clusters, I’m probably trying out new DevOps tools or explaining to someone why we can’t just “restart everything” as a debugging strategy. You can usually find me where the coffee is strong and the error logs are confusing

Tags: Cloud Computing, AWS Interview Questions, GCP Interview, Azure Interview, Cloud Certification, IaaS, PaaS, SaaS, DevOps, Cloud Architecture