Top 20 AWS Interview Questions And Answers 2026 | AWS Interview Preparation | Simplilearn

[music] Hello and welcome to this video on the top AWS interview questions. In this video, we'll be covering 20 most important AWS interview questions moving from very basics to advanced. So whether you're just starting out or preparing for a cloud role, this will help you revise in a clear and practical way. AWS is one of the strongest cloud platform to learn because it offers more than 240 fully featured services and run on a massive global infrastructures with 39 regions and 123 availability zone. Which is one big reason so many companies use it for building, deploying, and scaling applications. And as a cloud adoption keeps growing, AWS knowledge continues to be valuable skills for developers, cloud engineers, and DevOps professionals. Especially in a market where enterprise cloud infrastructure spending reached $419 billion in 2025. So if you want to strengthen your AWS basics, understand how AWS services are used in real interview, and prepare with confidence, let's get started. Now before we move forward, there is something you should definitely check out. If you want to build strong cloud skills beyond just interview preparation, you can explore Simply Learn's Cloud Architect Master Program. This is a 4-month online program which is designed to help you learn AWS, Azure, and Google Cloud in a structured way. With official AWS authored self-learning content, Microsoft aligned courses, hands-on exposure to 40 plus in-demand skills, and 25 plus services, capstone learning, and career support through job assist. So if you're serious about cloud roles and want a guided path that combines practical learning with industry recognized credential, this can be a very good strong option for you. So without wasting any time, let's get started with the very first question. So let's start with the easy set of questions. Question number one, what is Amazon Web Service and why is it widely used? Now so basically when we talk about Amazon Web Service or AWS, we are referring to a cloud computing platform that allows you to use computing resources like servers, storage, databases, and networking over the internet instead of setting them up physically on your own. Now what this really means is you don't need to buy expensive hardware or maintain data centers. You can just go online, choose what you need, and start using it instantly. Moving ahead, AWS is widely used because of a few very important reason. The first is scalability, meaning you can increase or decrease your resources anytime depending on your needs. For example, if your app suddenly gets a lot of user, AWS can handle that without crashing. Second is pay-as-you-go model. Basically, you only pay for what you use, which makes it very cost-efficient, specially for startup and beginners. Another big reason is reliability. AWS has data centers all over the world, so your application stays up and running even if something fails in one location. And finally, security and flexibility. AWS provides strong security features and support almost every kind of application from simple website to advanced AI system. So in simple terms, we can say AWS is widely used because it makes building and running application much more faster. Coming to the next question, which is what is the difference between an AWS region and an availability zone? All right. So basically when we talk about AWS infrastructure, two very important terms comes up, region and availability zone. So the first, region is a simply a geographical location. You can think of it like a specific area in the world. For example, Mumbai or Ireland. Each region is completely separate from others, which help in keeping application isolated and reliable. Moving ahead, inside each region, we have something called as availability zone. These are basically individual data center within that region. And the important thing here is that the AZ is independent. It has its own power, networking, and infrastructure. Now how do they work together? This is where things get interesting. All these availability zone inside a region are connected through very fast and low latency networks. So what you can do is instead of running your own application in just one data center, you can spread it across multiple AZs. Meaning if one availability zone goes down for some reason, your application is still running in the other. Now coming to our question number three, which is what is Amazon EC2 and how does it work? Now Amazon EC2 is basically a service from Amazon Web Services that lets you run virtual servers in the cloud instead of using physical machines. The way it works is you first choose an AMI, which is like a template with an operating system and some pre-installed software. Then you select an instance and the type depending on how much CPU or memory you need. After that, you configure things like networking and security and launch the instance. Once it is running, it behaves just like a normal computer where you can install application and run your workloads. And the best part is you can scale it up or down anytime and only pay for what you use. Coming to our next question, which is what is Amazon S3 and what makes it highly durable? Now Amazon S3 is a cloud storage service from Amazon Web Services that lets you store and retrieve any type of data like images, videos, backups, or even large data set. Now you can think of it like a very advanced online storage system where data is stored in something called as bucket and each file is stored as an object. Now what really makes this S3 powerful is its durability. AWS doesn't store your data in just one place. Instead, it automatically copies your data across multiple data centers within a region. So even if one data center fails, your data is still safe. AWS mentioned 11 9 durability, which basically means your data is almost never lost. And on the top of that, S3 also gives features like versioning, you can recover deleted or the older version, life cycle policies, automatically moving old data to cheaper storage, and security, which is encryption and access control. Moving ahead to the fifth question, which is what is AWS IAM and why is it important? AWS IAM or identity and access management is a service in Amazon Web Server that control access to AWS resources. In simple term, it answers two question. Firstly, who are you? And secondly, what are you allowed to do? For example, in a company, a developer might need access to servers but not billing. A manager might needs report but not system level control. IAM helps you define these permissions properly. Now it has got three main components. The first one is users, which is individual people or application. The second is group, collection of user with similar permission. And finally, we have roles, which are temporary access given to services like EC2 or Lambda. The reason IAM is so important is security. Now without proper access control, anyone could access or modify your resources. It also follows the principle of least privilege, meaning you only give the minimum permission required and nothing extra. Coming to the next question, which is what is Amazon Machine Image? An Amazon Machine Image or AMI is a template used to launch virtual servers, EC2 instances, in Amazon. Think of it like as a pre-configured setup. Instead of installing an operating system and software every time manually, you create one image and then reuse it. An AMI typically includes operating system like Linux or Windows, pre-installed application or tools, configuration settings. So when you launch an EC2 instance using an AMI, everything is already good to go. This is very useful because it saves times, it ensures consistency across multiple servers, it makes scaling easy, you can launch many identical instances quickly. For example, if you have a web application, you can create an AMI with everything installed and then use it to launch multiple servers whenever traffic increases. Coming to the next question, which is explain the AWS Shared Responsibility Model. So the AWS Shared Responsibility Model basically means that AWS and the customer both have their own role when it comes to security and management in cloud. You can think of it like this, AWS takes care of the cloud itself and the customer takes care of whatever they put inside the cloud. Now when we say AWS responsible for security of the cloud, it means AWS handles the physical infrastructure. So things like data centers, hardware, networking, storage, and virtualization layer are all managed by AWS. In simple terms, AWS makes sure that actual cloud platform is secure, stable, and available. On the other hand, the customer is responsible for security in the cloud. That includes things like data, user access, IAM permission, encryption setting, application security, and network configuration. And in some services like EC2, the customer is also responsible for things like updating the operating system and securing the instances. For example, let us say I launch an EC2 instance, AWS will take care of the physical server, the hardware, and the underlying infrastructure. But I would be responsible for patching the OS, configuring security groups, managing user access, and protecting the data inside that instance. Similarly, if I use S3, AWS secures the storage infrastructure, but I still need to decide who can access the bucket, whether the data should be encrypted, and how the data should be managed securely. So yes, the main idea is pretty much simple. AWS secures the cloud and the customer secures what they run in the cloud, what they run and store in the cloud. Now moving ahead with the next question, which is what is Amazon VPC and what are its core components? So Amazon VPC stands for Amazon Virtual Private Cloud. Basically, it is like your own private network inside the AWS. It allows to us to launch AWS resources like EC2 instance in a secure and isolated environment. You can think of it as creating your own network in the cloud where we get control over things like IP address range, subnet, routing, and security. So, overall it helps us build a secure, isolated, and customizable network for our cloud resources. Now, coming to its core component, firstly we have VPC, which is the main network boundary. Then we have subnets, which are smaller section inside the VPC where we place our resources. These can be public or private depending on the use case. After that, we have route tables, which decide how traffic should move within the network or outside it. Then for the internet connectivity, we use an internet gateway when we want resource in public subnet to communicate with the internet. And if resources in a private subnet need only outbound internet access like for software updates, we use a NAT gateway. For security, we mainly use security group and network ACLs. Security group work at the instance level and act like a virtual firewall. While network ACLs work at the subnet level and provide an extra layer of traffic control. So, in simple terms, Amazon VPC gives us a full control over networking, communication, and security in AWS. So, moving ahead with our next question, which is actually built on the top of Amazon VPC. Because once we understand VPC and its component, the next important thing is understanding how security is managed inside the network, which brings me to the next difference between security group and network ACL. So, basically the main network ACLs is where applied and how they control traffic. Starting with security groups, they work at the instance level, meaning they are attached directly to resources like EC2 instances. So, basically they decide what kind of inbound and outbound traffic is allowed for that specific instance. One more important point here is that security groups are stateful, meaning if you allow incoming traffic, the return traffic is automatically allowed. You do not have to define that separately. Now, moving ahead to network ACLs, these work at subnet level, meaning they control traffic for the entire subnet and not just for one individual instance. Basically, network ACLs are stateless, which means inbound and outbound traffic are handled separately. So, if you allow traffic in one direction, you still need to explicitly allow the response traffic in the other direction as well. Now, coming to another key difference, which is security groups only support allow rules, whereas network ACLs support both allow and deny rule. Meaning if you want to block a specific IP address directly, then basically network ACLs are more suitable for that. So, overall I would say that security groups provide security at the instance level, while network ACLs provide security at the subnet level. Now, moving ahead with our next question, which is what is elastic load balancer and what are its type? So, elastic load balancer or ELB is a service in AWS that distributes incoming traffic across multiple targets like EC2 instance, containers, or IP addresses. Meaning instead of all requests going to just one server, it spreads the traffic across multiple healthy resources. Because of that, it helps improve availability, scalability, and fault tolerance. It also keeps checking the health of the target, so if one target becomes unhealthy, the traffic is redirected to the healthy one. Now, going ahead to its type, basically there are four type of elastic load balancer in AWS. The first one is application load balancer or ALB. This works at application layer and is mainly used for HTTP and HTTPS traffic. Basically, it is useful when we need advanced routing features like path-based or host-based routing. The second one is network load balancer or NLB, which works at transport layer and used for handling high performance traffic with low latency. The third one is gateway load balancer or GWLB. This is used for deploying and scaling security appliances like firewalls or intrusion detection system. And then we also have classic load balancer, which is the older generation load balancer in AWS. So, overall all I would say elastic load balancer in AWS service that distribute incoming traffic across multiple healthy target to make application more reliable, scalable, and highly available. Moving ahead with our next difference between vertical and horizontal scaling in AWS or server? Starting with vertical scaling, it means we increase the power of existing machine. We upgrade the same server by adding more CPU, RAM, or storage. For example, in AWS, if an EC2 instance is not able to handle the workload, we can move it from smaller instance type to the bigger one. So, basically vertical scaling means scaling up. Now, moving to the horizontal scaling, it means we increase capacity by adding more servers or instances behind of making one big server. So, if one EC2 instance is not enough, we can launch multiple EC2 instances and distribute traffic across them using a load balancer. This is called scaling out. One more important point is that vertical scaling has a limit because a single machine can only be upgraded to a certain level. But horizontal scaling is more flexible because we can keep adding more instances based on demand. Also, vertical scaling is usually simpler to implement in the beginning, but horizontal scaling is better for high availability, fault tolerance, and handling large-scale traffic. So, Amazon CloudWatch is a monitoring service in AWS that helps us keep track of the health, performance, and activity of AWS resources and application. Now, in simple terms, it gives us visibility into what is happening inside our AWS environment. For example, if you want to monitor CPU usage, system performance, logs, request count, or application errors, CloudWatch help us to do that. Now, if I talk about how it works, CloudWatch collects data in the form of metrics, logs, and events from different AWS services. Services like EC2, RDS, and other can send this data to CloudWatch automatically. And if you want more detailed monitoring, we can also configure additional logs or custom metrics. Going ahead, CloudWatch displays this information through dashboard, graphs, and monitoring views, which makes it easier to understand the current state of our infrastructure. Another important feature is CloudWatch alarms. So, if a metric crosses a defined threshold like CPU usage becoming too high, CloudWatch can alert us immediately or even trigger an automated response. It is also very useful for troubleshooting because it helps us analyze log and identify performance or application-related issues. Moving ahead with our next question, which is what is auto scaling and how does it work in AWS? So, auto scaling in AWS is used to automatically increase or decrease the number of EC2 instances based on the application load. So, basically the main purpose is to maintain performance when traffic is high and at the same time reduce cost when the traffic is low. The way it works, we create something called as auto scaling group where you know we define the minimum, maximum, and desired number of instances. So, AWS tries to maintain the desired capacity. If the load increases, it can automatically launch more instances. And if the load decreases, it can terminate the extra ones. Now, the scaling happen based on scaling policies and monitoring metrics is like CPU utilization, request count, or sometimes memory usage. For example, if CPU usage goes above a certain threshold, then AWS can you add more instances. And when the usage come down, it can remove extra ones. Now, coming to another important point is that auto scale helps with high availability. Meaning if one instance become unhealthy, AWS can automatically replace it with a new healthy instance. Now, moving ahead with our next question, how do you design a highly available architecture on AWS? So, if I had to design a highly available in architecture on AWS, the first thing I would focus is on removing single point of failure. The application should continue running if one server or one service even goes down. Availability zone goes down. A common approach I would follow is to deploy the application across multiple availability zones within the same region. For example, I would place the application servers in auto scaling group spread across at least two or more availability zones. In front of them, I would use an elastic load balancer, so traffic is automatically distributed only to the healthy instances. For the database layer, I would use a managed service like Amazon RDS with multi-AZ deployment. So, if the primary database fails, AWS can automatically fail over to the standby instance. If the workload is read heavy, then you know I can also use read replicas to improve performance and reduce load on the main database. For the network design, I would create a VPC with public and private subnet across multiple availability zones. Public subnets would typically host components like load balancer, and private subnet would host the application and database layer for better security. Then going a bit further, I would also think about data durability and backup. For static content, I would use S3 because it is highly durable. And for the backup, I would enable automated snapshot and backup policies. If low latency and better global performance are needed, I can place CloudFront in front of the application. Now, monitoring is also very important in a highly available design. So, I would use CloudWatch for metrics, logs, and alarms, and set up health checks and notification. And yes, wherever possible, I would design the application in a stateless manner because that makes scaling and failover much easier. If the requirement is even stronger like disaster recovery at a larger level, then I would also consider a multi-region strategy where critical data is replicated across regions and Route 53 can help with DNS failover. Now, moving ahead to the next question, which is when would you choose Amazon RDS versus Amazon DynamoDB? So, for me the choice between Amazon RDS and Amazon DynamoDB really depends on the type of application, the data model, and the query pattern. Now, if my application is dealing with structured data, and I need things like SQL query, table, relationship, joins, and strong transactional support, then I would choose Amazon RDS. It is more suitable for traditional business application where data is relational in nature. For example, systems like banking application, order management platform, HR system, or ERP application are usually a better fit for RDS. Because in those cases, the relationship between data is very important. On the other hand, if the application needs very high scalability, low latency, performance, and a flexible NoSQL data model, then I would go with Amazon DynamoDB. It is better choice when we are dealing with massive traffic, very fast read and write operation, and use cases where we do not want to manage complex relation structures. For example, things like real-time application, gaming leaderboards, session stores, shopping carts, or large-scale mobile and web apps are often better suited for DynamoDB. Now, moving ahead with our next question, which is how does AWS Lambda compare to EC2, and when we should use each? So, AWS Lambda and EC2 are both compute services in AWS, but honestly, they are used in very different situations. Now, if we talk about Lambda first, it is a serverless service. That means, you know, there is no need to manage the actual server. The code is just deployed. The AWS handles things like provisioning, scaling, and execution in the background. So, this is really useful when the workload is event-driven and does not need a server to keep running all the time. Now, for example, things like uploads, API requests, notification, or scheduled jobs are very common use cases of Lambda. Now, EC2 is different. EC2 is more like getting a virtual machine in the cloud. So, here there is much more control. The operating system can be chosen, software can be installed, can be repeat configuration can be managed, and the environment can be customized as needed. That is why EC2 is usually preferred for application that are long-running, needs full server control, or depend on a custom setup. So, if the different has to be explained in a simple way, then Lambda is more about convenience and automatic scaling, whereas EC2 is more about flexibility and full control. Now, a very good way to decide between them is if the requirement is something lightweight, event-based, and there is no need to manage infra, then Lambda is a very good choice. But if the application needs to run continuously or needs specific software, OS level access, or deeper customization, then EC2 is a better fit. Now, coming to our next question, which how would you design a serverless API architecture on AWS? All right. So, to design a serverless API architecture on AWS, the first thing would be to make the API entry point Amazon API Gateway. That would act as the front door for the application, meaning all the client requests from a web app or mobile app would first come through API Gateway. Then, behind API Gateway, the business logic would be handled using AWS Lambda. So, instead of running dedicated servers, each API request would trigger a Lambda function to process the request. For example, one Lambda can handle user creation, another can fetch records, and another can process updates. This keeps the design modular and easy to manage. For the data layer, the choice would depend on use case. If the application needs a NoSQL, highly scalable database, then DynamoDB would be a strong fit. And if the application needs to store file, image, or document, then Amazon S3 can be used along with it. Now, while designing this, security would also be a major part. So, authentication and authorization can be handled using Amazon Cognito or API Authorizers. So, that only valid user can access the endpoint. Along with that, each Lambda function should have a proper IAM role with only required permissions. Then, for monitoring and troubleshooting, CloudWatch would be included for logs, metrics, and alarms that helps in tracking errors, performance, and unusual act. And yes, one more good design practice would be to keep the Lambda function small, focused, instead of putting all the logic into one big function. That makes the system easier to scale, test, and maintain. So, overall, the design would be API Gateway as the entry point, Lambda for processing, DynamoDB or S3 for storage, Cognito for security, and CloudWatch for monitoring. ahead with our next question, which is your AWS bill suddenly doubled, how would you investigate and reduce the cost? Now, if an AWS bill suddenly doubled, the first thing would be to avoid guessing and start by finding exactly what changed. The practical way to do that would be to check the billing breakdown and identify which service, region, account, or usage type caused the spike. Once that is clear, the next step would be to trace it back to the actual resources. Now, for example, if the increase is coming from EC2, then it is important to check whether extra instances were launched, whether instance sizes were increased, or whether some machines were left running by mistake. If the spike is from RDS, then it would be due to high instance size, storage growth, backup retention, or multi-AZ changes. If it is from S3 or networking, then storage growth, request volume, or data transfer charges would need to be checked very carefully, because those can increase cost quite fast. Now, after identifying the source, the next part would be understanding why it happened. In many practical cases, sudden billing jumps come from things like recent deployment, an auto scaling issue, test environment left active, unattached EBS volumes, old snapshots, unused old load balancer, excessive logging, or unexpected traffic growth. So, the idea would be to connect the billing spike with some actual technical or operational changes in the environment. Now, once the root cause is found, the cost reduction becomes easier and more targeted. Unused resource can be stopped or removed, oversize compute can be right-sized, old storage and snapshots can be cleaned up, and if workload is stable, cost-saving options like saving plans or reserved instance can be considered. If the issue is data transfer, then the architecture may need to be reviewed to reduce unnecessary traffic between service or across availability zones. Now, coming to our next question, which is how would you troubleshoot high latency or 5xx error in an application hosted on AWS? So, when troubleshooting high latency or 5xx error in an application hosted on AWS, the first thing would be to avoid jumping to conclusions and identify where the problem is happening. Now, those error can come from different layers like load balancer, the application server, the databases, or even a recent deployment. So, the right approach would be systematic. I would start by checking monitoring data in CloudWatch to see when the latency increased, whether the error rate also increased at the same time, and whether there were any spikes in CPU, memory, request count, or response time. After that, I would check whether the 5xx error are being generated by the load balancer itself or by the backend application, because that immediately helps narrow down the problem. Now, once that is clear, the next step would be to verify the health of backend targets. If the application is behind an application load balancer, I would check target health, health check failures, timeout setting, and whether any instances are unhealthy or overloaded. Then, I would move to the application side and review the application logs carefully to see if there are expectations, dependency failure, timeout issues, connection pool exhaustion, or configuration problem. If the application depends on a database like RDS, I would also check whether the latency is actually coming from slow query, high DB connections, locking issues, or resource bottlenecks at the database layer. In many cases, what looks like an application issue is actually a database or downstream dependency issue. Now, I would also compare the issue against recent changes, because sometimes the root cause is fresh deployment, a configuration update, or auto scaling event, or a sudden traffic spike. Now, coming to our last question, which is how would you design a secure architecture on AWS following best practices? Now, if I had to design a secure architecture on AWS, I would follow a layered security approach, meaning I would not depend on just one control. I would secure the environment at the identity level, network level, data level, and monitoring level. First, I would control access properly using IAM roles, least privilege, and MFA, so only the required users and services have the right permission. Then, at the network level, I would place public-facing resources in public subnets and application servers or database in private subnets, and protect them from using security groups and network ACLs. For data security, I would make sure the data is encrypted at rest and in transit. I would store secrets like password or API key securely. Then, for visibility, I would enable services like CloudTrail, CloudWatch, and GuardDuty, so that I can monitor activity. And finally, I would also include backup, high availability, and recovery planning, because security is not just about prevention, it is also about being prepared if something goes wrong. So, that is all for this video. If you guys have liked this video, kindly hit the like and subscribe buttons for Simply Learn YouTube channel. Thank you, and see you in the next video.