AWS Interview Questions 2026
AWS questions show up everywhere now, not just in dedicated cloud or DevOps interviews, but folded into backend rounds and system design discussions too, since so much of the industry actually runs on it. The tricky part is that AWS has an enormous number of services, and interviewers usually aren't testing whether you've memorized all of them, they're testing whether you understand the small set of core services well enough to reason about real tradeoffs: when to use RDS versus DynamoDB, why a NAT Gateway exists, what actually happens during a Lambda cold start.
This page focuses on that core set, compute, storage, databases, networking, IAM, and serverless, along with the kind of "how would you design this on AWS" questions that come up once you're past the definitions and into actual architecture decisions.
Why AWS Questions Come Up So Often
AWS is the default cloud provider at a huge share of companies, from early-stage startups to large enterprises, so interviewers use AWS questions to check something practical:
- Whether you understand the actual tradeoffs between services that sound similar (RDS vs DynamoDB, ALB vs NLB, SQS vs SNS)
- Whether you can reason about cost, availability, and security, not just "does it work"
- Whether you've actually deployed something real, not just clicked through the console once
- Whether you can sketch a reasonable architecture for a given requirement instead of just naming services
Who This Page Is For
- Backend and full stack developers interviewing for roles that touch cloud infrastructure
- DevOps and platform engineers prepping for AWS-specific rounds
- Anyone prepping for System Design interviews where AWS services come up as implementation details
Related Resources
AWS Interview Questions and Answers (2026)
AWS Fundamentals and Core Concepts
1. What's the difference between a Region, an Availability Zone, and an Edge Location?
A Region is a large geographic area (like ap-south-1 for Mumbai) that contains multiple, isolated data centers. An Availability Zone (AZ) is one of those individual data centers (or clusters of them) within a Region, each with independent power, cooling, and networking, so a failure in one AZ shouldn't take down another AZ in the same Region. An Edge Location is a smaller site, part of AWS's CDN and DNS infrastructure (CloudFront, Route 53), positioned close to end users to cache content and reduce latency, it's not a full Region and doesn't run general-purpose compute.
2. What's the actual difference between IaaS, PaaS, and SaaS, and where do EC2 and Lambda fall on that spectrum?
IaaS (Infrastructure as a Service) gives you raw compute, storage, and networking, and you're responsible for the OS, runtime, and everything above it, EC2 is the classic example. PaaS (Platform as a Service) manages the underlying infrastructure and OS for you, you just deploy your application code, something like Elastic Beanstalk fits here. SaaS is a fully finished, ready-to-use application, you don't manage or deploy anything, just use it. Lambda sits closer to a category sometimes called FaaS (Functions as a Service), an even more abstracted version of PaaS where you don't manage servers at all, just individual functions that run in response to events.
3. What is the AWS Shared Responsibility Model, and where's the actual line between what AWS secures and what you secure?
AWS is responsible for "security of the cloud," the physical data centers, the underlying hardware, the network infrastructure, the hypervisor. You're responsible for "security in the cloud," your data, your IAM configuration, your application code, your network configuration within your VPC, patching your own operating systems (for something like EC2, where you control the OS). The exact line shifts depending on the service, for a fully managed service like Lambda or DynamoDB, AWS takes on more of that responsibility than it does for a raw EC2 instance where you manage everything above the hypervisor.
4. What's the difference between the AWS Management Console, the CLI, and the SDK, when would you reach for each?
The Console is the web-based GUI, good for exploring, learning, and one-off manual tasks, but it doesn't scale well for repeatable work. The CLI lets you script and automate AWS operations from a terminal, useful for deployment scripts, CI/CD pipelines, and quick automation. The SDK (available for languages like Python's boto3, JavaScript, Java) lets you interact with AWS services directly from application code, which is what you'd use when your actual running application needs to, say, read from S3 or write to DynamoDB as part of its normal operation, not just for deployment tooling.
5. What is an AWS account, and why do most real organizations use multiple accounts instead of one big account with everything in it?
An AWS account is the top-level container for your resources, billing, and access control. Real organizations typically use multiple accounts (often managed together through AWS Organizations) to create hard isolation boundaries, separate accounts for dev, staging, and production mean a mistake or a security breach in one environment can't accidentally reach another, billing and cost tracking become cleaner per team or project, and IAM permission boundaries are naturally enforced by account separation instead of relying entirely on careful IAM policy writing within one shared account.
6. What's the difference between a managed service like RDS and self-managing the same software yourself on EC2?
With RDS, AWS handles the underlying server provisioning, OS patching, database software installation, automated backups, and failover, you interact with it mostly through configuration, not server administration. Running the same database yourself on EC2 gives you full control, you can tune the OS, install any extension or version, configure anything you want, but you're also responsible for every bit of that operational overhead yourself, patching, backup scripts, failover logic. The tradeoff is control and flexibility versus operational burden, and most teams default to managed services unless they hit a specific limitation that requires the extra control.
Compute: EC2, Lambda and Auto Scaling
1. What's the difference between an EC2 instance type and an instance family, and how do you actually pick one for a given workload?
An instance family (like m5, c5, r5) represents a general category optimized for a particular resource balance, m for general purpose, c for compute-optimized, r for memory-optimized, and so on. An instance type (like m5.large or c5.2xlarge) is a specific size within that family, specifying the actual amount of vCPU and memory you get. You pick a family based on what your workload actually needs more of, a CPU-heavy batch job fits c family, an in-memory cache or database fits r family, then pick the smallest size within that family that comfortably handles your load, since oversizing just wastes money.
2. What's the difference between On-Demand, Reserved, Spot, and Savings Plan pricing for EC2?
On-Demand is pay-as-you-go with no commitment, most expensive per hour but maximally flexible, good for unpredictable or short-term workloads. Reserved Instances commit to a specific instance type for 1 or 3 years in exchange for a significant discount, good for steady, predictable baseline workloads. Spot Instances let you bid for spare AWS capacity at a steep discount, but AWS can reclaim them with short notice, which fits fault-tolerant, interruptible workloads like batch processing, not anything that needs to stay up continuously. Savings Plans offer similar discounts to Reserved Instances but with more flexibility, you commit to a certain amount of compute spend rather than a specific instance type, which can shift across instance families and even between EC2 and Lambda.
3. What is an AMI, and why does having a well-maintained custom AMI matter for scaling quickly?
An AMI (Amazon Machine Image) is a template that defines the initial state of an EC2 instance, the OS, installed software, configuration, everything baked in at launch. A well-maintained custom AMI (with your application's dependencies and configuration already baked in, rather than installing everything via a startup script every time) matters because it lets new instances come up and become ready to serve traffic much faster, which is critical when Auto Scaling needs to add capacity quickly in response to a traffic spike, waiting several minutes for a generic AMI to install everything from scratch defeats the purpose of fast autoscaling.
4. What is AWS Lambda, and what's the actual tradeoff between running something on Lambda versus a regular EC2 instance?
Lambda runs your code in response to events without you provisioning or managing any servers at all, you're billed only for the actual compute time your function uses, down to the millisecond, and it scales automatically, even to zero when there's no traffic. The tradeoff is that Lambda functions have execution time limits, less control over the runtime environment, and can suffer from cold start latency, while an EC2 instance gives you a persistently running server with full control but you pay for it continuously whether it's handling traffic or sitting idle. Lambda tends to fit bursty, event-driven, or infrequent workloads well, EC2 tends to fit steady, long-running, or highly customized workloads better.
5. What is cold start in Lambda, and what can you actually do to reduce its impact?
A cold start happens when Lambda needs to initialize a brand new execution environment for your function, downloading your code, starting the runtime, running any initialization code outside your handler, before it can actually process the request, which adds noticeable latency compared to a "warm" invocation reusing an already-initialized environment. You can reduce its impact by keeping deployment packages small (less to download and initialize), minimizing work done outside the handler function, choosing a faster-starting runtime, and using Provisioned Concurrency, which keeps a specified number of execution environments pre-initialized and ready, at an added cost, for latency-sensitive use cases where cold starts are unacceptable.
6. What is an Auto Scaling Group, and how does it decide when to add or remove instances?
An Auto Scaling Group (ASG) manages a collection of EC2 instances, automatically launching new ones or terminating existing ones to maintain a desired capacity, and to keep at least a minimum and at most a maximum number of instances running at all times. Scaling decisions are typically driven by CloudWatch alarms tied to metrics, like average CPU utilization crossing a threshold, or a target tracking policy that tries to keep a specific metric (like requests per instance) close to a target value, adding instances when the metric climbs and removing them when it drops, so capacity actually follows real demand instead of being fixed.
Storage: S3, EBS and EFS
1. What's the difference between S3, EBS, and EFS, and how do you decide which one fits a given use case?
S3 is object storage, accessed over HTTP, ideal for storing files, backups, static assets, and data at any scale, but it's not something you can mount as a traditional filesystem for an application expecting normal file I/O. EBS is block storage, attached to a single EC2 instance at a time, behaving like a regular hard drive, used for things like a database's data files that need low-latency, POSIX-style file access. EFS is a managed, elastic network filesystem that multiple EC2 instances can mount and access simultaneously, fitting use cases like shared configuration or content that multiple servers need to read and write concurrently.
2. What are S3 storage classes, and why would you use something like S3 Glacier instead of just Standard for everything?
S3 offers multiple storage classes with different cost and retrieval-speed tradeoffs: Standard for frequently accessed data, Standard-IA (Infrequent Access) for data accessed less often but still needing fast retrieval when it is, and Glacier (and Glacier Deep Archive) for long-term archival data that's rarely accessed and can tolerate retrieval times ranging from minutes to hours. Using Glacier for old backups or compliance data instead of Standard for everything can cut storage costs dramatically, since you're paying a fraction of the price in exchange for slower retrieval, which is a fine tradeoff for data you genuinely don't expect to need quickly.
3. What's the difference between S3 bucket versioning and just keeping backups yourself?
Versioning, once enabled on a bucket, automatically keeps every version of an object whenever it's overwritten or deleted, rather than actually removing the old data, a "delete" just adds a delete marker while the previous version remains recoverable. This protects against accidental overwrites and deletions without you having to build and maintain a separate manual backup process, though it's worth noting it does mean you're storing every version, which affects storage costs if you don't pair it with a lifecycle policy to eventually clean up old versions.
4. How does S3 achieve its extremely high durability, and what does "11 nines" actually mean?
S3 is designed for 99.999999999% durability, meaning if you stored 10 million objects, you'd statistically expect to lose one object roughly once every 10,000 years. It achieves this by automatically and redundantly storing copies of your data across multiple devices in multiple Availability Zones within a Region, so the loss of an entire data center wouldn't cause data loss. It's worth distinguishing durability (will the data survive and not get corrupted or lost) from availability (can you actually access it right now), S3 also has strong availability, but the specific "11 nines" figure specifically refers to durability.
5. What's the difference between EBS and instance store volumes, and why does that distinction matter if an instance stops?
EBS volumes are network-attached, persistent storage that exists independently of any specific EC2 instance's lifecycle, if the instance is stopped or even terminated (depending on configuration), the EBS volume and its data can persist and be reattached elsewhere. Instance store volumes are physically attached to the specific underlying host hardware and are genuinely ephemeral, if the instance is stopped or the underlying hardware fails, that data is gone permanently, it's only appropriate for temporary data like caches or buffers that you can afford to lose, never for anything that needs to survive an instance stop.
Databases: RDS, DynamoDB and Aurora
1. What's the difference between RDS and DynamoDB, and how do you decide which one fits a given application?
RDS is a managed relational database service, running actual engines like PostgreSQL, MySQL, or SQL Server, with a fixed schema, SQL queries, joins, and strong transactional consistency, it's the right fit when your data is genuinely relational and you need those relational guarantees. DynamoDB is a managed NoSQL key-value/document database, built for extremely high scale and consistent low-latency performance at any size, with a flexible schema, but no joins and a query pattern that has to be designed around your access patterns up front. You'd pick RDS for complex, relational data with evolving query needs, and DynamoDB for massive scale, simple access patterns, and workloads where you know upfront exactly how you'll be querying the data.
2. What is Aurora, and how is it actually different from just running MySQL or PostgreSQL on RDS?
Aurora is AWS's own database engine, compatible with the MySQL and PostgreSQL wire protocols (so your existing drivers and tools still work), but built on a custom, distributed storage architecture designed specifically for the cloud, decoupling compute from a highly available, self-healing storage layer that automatically replicates across multiple Availability Zones. In practice this generally gives Aurora better throughput and faster failover than standard RDS running the same engine, at a somewhat higher cost, and it adds Aurora-specific features like fast cloning and a serverless scaling mode that standard RDS doesn't have.
3. What's the difference between a Multi-AZ RDS deployment and a Read Replica? People mix these up constantly.
Multi-AZ is purely for high availability, it maintains a synchronously replicated standby copy of your database in a different Availability Zone, which automatically takes over if the primary fails, but that standby isn't used for serving read traffic, it just sits ready as a failover target. A Read Replica is for scaling read throughput, it's an asynchronously replicated copy that you can actually query directly to offload read traffic from the primary, but it's not automatically used for failover (though you can manually promote one if needed). They solve different problems and it's genuinely common to use both together, Multi-AZ for resilience and Read Replicas for read scaling.
4. What is DynamoDB's partition key, and why does a poorly chosen one cause real performance problems at scale?
The partition key determines which physical partition (a unit of DynamoDB's underlying distributed storage) an item is stored on, and DynamoDB distributes your data and throughput capacity across partitions based on it. If your partition key has low cardinality or is unevenly accessed, say, most of your traffic hits just a few key values, you get what's called a "hot partition," where that specific partition's throughput limit gets hammered while the rest of your provisioned capacity sits unused, causing throttling even though your table's overall capacity looks fine on paper. Choosing a partition key with high cardinality and a genuinely even access pattern is one of the most important DynamoDB design decisions you make up front.
5. What's the difference between DynamoDB's on-demand and provisioned capacity modes?
Provisioned capacity requires you to specify how many read and write capacity units you expect to need in advance, it's cheaper per-request if your traffic is predictable and you can size it accurately, but you either pay for unused capacity if you overprovision, or get throttled if you underprovision and traffic exceeds it (unless auto scaling is configured on top). On-demand mode charges purely per actual request with no capacity planning required at all, and scales instantly with your traffic, which is simpler and safer for unpredictable or spiky workloads, though typically more expensive per request than well-tuned provisioned capacity.
Networking: VPC, Security Groups and Load Balancers
1. What is a VPC, and what's the relationship between a VPC, a subnet, and an Availability Zone?
A VPC (Virtual Private Cloud) is your own logically isolated network within AWS, where you control the IP address range, routing, and connectivity. A subnet is a subdivision of that VPC's IP range, and critically, each subnet exists entirely within a single Availability Zone, it can't span multiple AZs. This means a highly available architecture typically has multiple subnets, one or more per AZ, so if you want resources spread across multiple AZs for resilience, you need to actually create and use subnets in each of those AZs, not just one subnet for the whole VPC.
2. What's the difference between a public subnet and a private subnet, and what actually makes a subnet "public"?
The distinction comes entirely down to routing, specifically, whether the subnet's route table has a route sending internet-bound traffic (0.0.0.0/0) to an Internet Gateway. A public subnet has that route, so resources in it (with a public IP) can directly reach and be reached from the internet. A private subnet doesn't have that direct route, so resources there have no direct path to or from the internet, they typically reach the internet indirectly through a NAT Gateway if they need outbound access at all, which is common for backend servers and databases that should never be directly internet-facing.
3. What's the difference between a Security Group and a Network ACL?
A Security Group operates at the instance level, it's stateful, meaning if you allow inbound traffic on a port, the corresponding outbound response traffic is automatically allowed too, without needing a separate matching rule, and it only supports allow rules, nothing can be explicitly denied, just left out. A Network ACL operates at the subnet level, it's stateless, so you need explicit rules for both inbound and outbound traffic independently, and it supports both allow and deny rules, evaluated in order by rule number, which makes it useful for explicitly blocking specific IPs or ranges at the subnet boundary, something a Security Group alone can't do.
4. What's the difference between an Internet Gateway and a NAT Gateway?
An Internet Gateway allows direct, two-way internet connectivity for resources in a public subnet, they can both receive inbound and send outbound internet traffic. A NAT Gateway is one-directional in practical effect, it allows resources in a private subnet to initiate outbound connections to the internet (say, to download a package update), while still blocking any unsolicited inbound connections from reaching those private resources directly, which is exactly the behavior you want for backend servers that need outbound internet access but should never be directly reachable from outside.
5. What's the difference between an Application Load Balancer and a Network Load Balancer, and when would you pick each?
An Application Load Balancer (ALB) operates at Layer 7, it understands HTTP/HTTPS, and can route based on the actual request content, path, host header, headers, which makes it the right choice for typical web applications and microservices that need content-based routing. A Network Load Balancer (NLB) operates at Layer 4, it's built for extremely high throughput and low latency with minimal overhead, handling raw TCP/UDP traffic without inspecting application-layer content, which fits use cases needing extreme performance or static IP addresses, or protocols that aren't HTTP at all.
IAM and Security
1. What's the difference between an IAM user, an IAM group, and an IAM role?
An IAM user represents a specific person or application with long-term credentials, used for direct, ongoing access. An IAM group is just a way to bundle permissions and apply them to multiple users at once, rather than attaching the same policies to each user individually. An IAM role is different from both, it's an identity with permissions that's assumed temporarily, by an AWS service (like an EC2 instance needing to access S3), by a user needing elevated permissions temporarily, or by a user from another AWS account, and it issues short-lived temporary credentials rather than long-term ones, which is a meaningfully safer pattern than embedding permanent credentials anywhere.
2. What is the principle of least privilege, and how does IAM actually help you enforce it?
The principle of least privilege means granting only the exact permissions an identity actually needs to do its job, nothing more, so a compromised credential or a mistake has the smallest possible blast radius. IAM enforces it through granular policies, you can scope permissions down to specific actions on specific resources (not just "full S3 access" but "read-only access to this one specific bucket"), and by favoring roles with temporary credentials over long-lived user credentials wherever possible, since a role's permissions are also easier to scope tightly to a specific task or service.
3. What's the difference between an IAM policy attached to a user versus a role assumed by an EC2 instance?
A policy attached directly to a user grants that specific person permanent, standing permissions whenever they authenticate, which they carry with them across everything they do. A role attached to an EC2 instance (via an instance profile) grants permissions to whatever is running on that instance, and critically, no long-lived credentials are stored anywhere on the instance itself, AWS automatically provides temporary, rotating credentials to the instance, which is both more secure (nothing to leak if the instance is compromised long-term) and avoids the operational headache of manually rotating access keys on servers.
4. What are AWS access keys, and why is it considered bad practice to hardcode them into application code?
Access keys are a long-lived credential pair (an access key ID and secret access key) that let a program authenticate as an IAM user directly. Hardcoding them into application code or committing them to a repository is dangerous because they're long-lived and broad, if that code or repo is ever exposed, even briefly (a public GitHub repo, a leaked build artifact), anyone with the keys has standing access until you notice and manually revoke them. The safer pattern is using IAM roles wherever possible (for anything running inside AWS, like EC2 or Lambda), which sidesteps the need for embedded credentials entirely.
5. What is KMS, and what problem does it solve for encrypting data in AWS?
KMS (Key Management Service) is a managed service for creating, storing, and controlling access to encryption keys, without you having to build and secure your own key management infrastructure. It solves the problem of safely handling encryption keys at scale, integrating directly with services like S3, EBS, and RDS so you can enable encryption at rest with a few clicks or config lines, while KMS handles key rotation, access control (via IAM), and audit logging (via CloudTrail) for every time a key is actually used to encrypt or decrypt something.
Serverless and Event-Driven Architecture
1. What's the difference between SQS and SNS, and when would you use each?
SQS (Simple Queue Service) is a message queue, a message sent to a queue is delivered to and processed by exactly one consumer, which is the right fit for distributing work across a pool of workers, each message gets handled once. SNS (Simple Notification Service) is a pub/sub system, a message published to a topic gets delivered to every subscriber independently, which fits fan-out scenarios where multiple, different downstream systems all need to react to the same event, and it's common to combine them, SNS fanning out to multiple SQS queues, each queue feeding its own independent set of consumers.
2. How would you design an event-driven pipeline using S3, Lambda, and SQS together?
A common pattern: a file gets uploaded to S3, which triggers an S3 event notification that invokes a Lambda function directly, or, for better resilience against processing spikes, sends a message to an SQS queue instead, which a Lambda function then consumes at a controlled rate. Using SQS as a buffer between the event source and the processing Lambda smooths out bursts (S3 uploading 10,000 files at once won't try to invoke 10,000 Lambdas simultaneously), and gives you built-in retry behavior and a dead-letter queue for messages that repeatedly fail processing, instead of losing failed events entirely.
3. What is API Gateway, and what does it typically sit in front of?
API Gateway is a managed service for creating, publishing, and securing APIs, handling concerns like request routing, throttling, authentication, and request/response transformation. It commonly sits in front of Lambda functions, turning them into a proper REST or HTTP API that external clients can call, but it can also route to other backends like EC2-hosted services or other AWS services directly, acting as the single, managed entry point for an API instead of every backend service needing to handle these concerns itself.
4. What is EventBridge, and how is it different from SNS?
EventBridge is an event bus service designed for building event-driven architectures at a larger scale, it supports content-based filtering and routing (rules that only trigger for events matching specific patterns), and it can ingest events not just from your own applications but from a large number of native AWS services and even third-party SaaS integrations directly. SNS is comparatively simpler, more like a straightforward pub/sub topic, while EventBridge is built around more sophisticated event routing logic and a much broader range of built-in event sources, making it a better fit for more complex, multi-service event architectures.
5. What's the difference between a Step Function and just chaining Lambda functions together yourself?
Manually chaining Lambda functions (one invoking the next directly, or coordinating through your own queue logic) works for simple cases but gets messy fast, error handling, retries, and tracking the overall workflow's state become your responsibility to build and maintain. AWS Step Functions gives you a managed state machine, defined declaratively, that orchestrates a sequence of steps (which can be Lambda calls or other AWS service integrations), handling retries, error branching, parallel execution, and giving you built-in visibility into exactly where a given execution is or failed, without you writing that orchestration logic yourself.
Monitoring, Logging and Cost Management
1. What is CloudWatch, and what are the main things people actually use it for?
CloudWatch is AWS's core monitoring and observability service, collecting metrics (numeric time-series data like CPU utilization or request count), logs (actual application and service log output), and letting you set alarms that trigger actions (like notifications or Auto Scaling adjustments) when a metric crosses a threshold. Most teams use it for dashboards to visualize system health, alarms to get paged when something's wrong, and centralized log aggregation so they're not SSHing into individual servers to check logs.
2. What's the difference between a CloudWatch metric, a log, and an alarm?
A metric is a numeric data point tracked over time, like average CPU usage or number of 5xx errors per minute. A log is unstructured or semi-structured text output from your application or an AWS service, useful for debugging specific events in detail rather than tracking trends. An alarm watches a specific metric and triggers an action, like sending a notification or scaling out, when that metric crosses a threshold you define for a specified period, alarms are the "do something automatically" layer built on top of the metrics CloudWatch collects.
3. What is CloudTrail, and how is it different from CloudWatch?
CloudTrail records API calls made within your AWS account, who did what, when, from where, which service or action, essentially an audit log of account activity, critical for security investigations and compliance. CloudWatch focuses on operational monitoring, application and infrastructure metrics, logs, and alarms about how your systems are actually performing. The distinction that trips people up: CloudTrail answers "who changed this security group" or "who deleted this S3 bucket," CloudWatch answers "is my application healthy right now."
4. What are common ways teams end up with a surprisingly large AWS bill, and how do you prevent it?
Common culprits include forgetting to terminate unused resources (test EC2 instances left running, unattached EBS volumes still being billed), over-provisioned instances sized far beyond what's actually needed, data transfer costs between services or across regions that weren't accounted for, and NAT Gateway costs, which charge per GB processed and can add up fast for high-traffic private subnets. Prevention typically involves tagging resources by team/project for cost visibility, setting up AWS Budgets with alerts, regularly reviewing Cost Explorer for unexpected spend patterns, and using tools like AWS Trusted Advisor to flag idle or underutilized resources.
5. What's the difference between AWS Budgets and Cost Explorer?
AWS Budgets is forward-looking and alert-driven, you set a spending threshold (overall, or scoped to specific services/tags), and it notifies you when actual or forecasted spend approaches or exceeds it, so you find out proactively before a bill surprises you. Cost Explorer is backward-looking and analytical, it lets you visualize and break down historical spend by service, region, tag, or time period, which is what you'd use to actually investigate why a bill was higher than expected or to spot spending trends over time, they complement each other, Budgets for alerts, Cost Explorer for analysis.
High Availability, Scaling and Real-World Scenarios
1. How would you design a highly available web application architecture on AWS from scratch?
At a high level: deploy EC2 instances (or containers) across multiple Availability Zones behind an Application Load Balancer, so the failure of a single AZ doesn't take the whole application down. Use an Auto Scaling Group to automatically maintain capacity and replace unhealthy instances. Use RDS with Multi-AZ for the database layer for automatic failover, and Read Replicas if read traffic needs to scale independently. Put static assets behind CloudFront and S3, and keep configuration and secrets out of the instances themselves, using Parameter Store or Secrets Manager, so instances stay stateless and easily replaceable.
2. What's the difference between scaling an application horizontally on AWS versus vertically?
Vertical scaling means moving to a bigger EC2 instance type, more vCPU and memory on the same single machine, which is simple but has a hard ceiling (the largest instance type available) and typically requires downtime to resize. Horizontal scaling means adding more instances behind a load balancer, which AWS's Auto Scaling Groups are specifically built to automate, and it has effectively no ceiling and adds redundancy as a side effect, if you're designing for real scale and availability on AWS, horizontal scaling is almost always the preferred long-term approach.
3. How would you design a system on AWS to handle a sudden, massive traffic spike?
Front static and cacheable content with CloudFront so a large share of requests never even reach your origin servers. Use Auto Scaling Groups with a target tracking policy so compute capacity actually grows with real demand, and consider pre-warming or setting a higher minimum capacity if the spike is predictable (like a planned sale event). Use SQS as a buffer in front of anything that can be processed asynchronously, so a burst of incoming work gets smoothed out and processed at a sustainable rate rather than overwhelming backend services directly. For the database layer, Read Replicas and caching (like ElastiCache) reduce load on the primary database, which is often the hardest component to scale quickly under sudden load.
4. What's a real strategy for disaster recovery on AWS, and how does it differ from just having backups?
Backups alone tell you what data you can restore, but disaster recovery is about how quickly and completely you can actually get the whole system running again, measured by RTO (Recovery Time Objective, how long until you're back up) and RPO (Recovery Point Objective, how much data loss is acceptable). Strategies range from simple backup-and-restore (cheap, but slow recovery) to pilot light (a minimal version of the environment always running, scaled up during a disaster) to warm standby (a scaled-down but fully functional copy running continuously) to multi-site active-active (full redundancy across regions, near-zero downtime, but the most expensive). The right strategy depends entirely on how much downtime and data loss the business can actually tolerate.
5. How would you reduce latency for users in different geographic regions using AWS?
CloudFront is the first line of defense, caching content at edge locations close to users worldwide so static (and some dynamic) content doesn't have to travel back to a single origin region every time. For genuinely global, latency-sensitive applications, you might deploy application infrastructure in multiple AWS Regions closer to major user populations, using Route 53 with latency-based or geolocation routing to send each user to the nearest healthy region, combined with a strategy for keeping data consistent (or acceptably eventually consistent) across those regions, which is a significantly more complex architecture than a single-region deployment, so it's only worth it once latency for distant users is a genuine, measured problem.
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