AWS powers a large share of the world's cloud workloads, and cloud fluency is now a baseline expectation for backend, DevOps, and platform engineering roles. Interviewers want to see that you understand the core building blocks — compute, storage, networking, and identity — and that you can reason about tradeoffs in availability, security, and cost. This guide covers the AWS and cloud computing questions that come up most often in 2026, with clear answers and practical examples. The concepts transfer to other providers too, so they strengthen any cloud interview.

Core Compute and Storage

Amazon EC2 (Elastic Compute Cloud) provides resizable virtual servers in the cloud. You choose an instance family and size based on the CPU, memory, storage, and network profile your workload needs. Pricing options let you optimize cost: On-Demand charges by the second with no commitment and suits variable or unpredictable workloads; Reserved Instances and Savings Plans offer large discounts for one- or three-year commitments on steady workloads; Spot Instances use spare capacity at up to around 90 percent off but can be reclaimed with short notice, making them ideal for fault-tolerant, interruptible jobs like batch processing.

Amazon S3 (Simple Storage Service) is durable, virtually unlimited object storage designed for eleven nines of durability. Objects live in buckets and are accessed over HTTP APIs by key. Storage classes let you match cost to access patterns: S3 Standard for frequently accessed data, S3 Standard-Infrequent Access for data accessed less often, S3 Intelligent-Tiering to move objects automatically between tiers based on usage, and S3 Glacier or Glacier Deep Archive for cheap long-term archival with slower retrieval. Lifecycle rules automate transitions and expirations, which is a common cost-optimization lever interviewers probe.

# Create a bucket, enable versioning, and block public access
aws s3api create-bucket \
  --bucket my-app-assets-2026 \
  --region us-east-1

aws s3api put-bucket-versioning \
  --bucket my-app-assets-2026 \
  --versioning-configuration Status=Enabled

aws s3api put-public-access-block \
  --bucket my-app-assets-2026 \
  --public-access-block-configuration \
  BlockPublicAcls=true,IgnorePublicAcls=true,BlockPublicPolicy=true,RestrictPublicBuckets=true

# Upload an object with server-side encryption
aws s3 cp ./logo.png s3://my-app-assets-2026/logo.png --sse AES256

Networking and Identity

A VPC (Virtual Private Cloud) is an isolated virtual network within AWS where you launch resources. You divide it into subnets, each tied to an Availability Zone. A public subnet has a route to an Internet Gateway, letting resources reach and be reached from the internet; a private subnet routes outbound traffic through a NAT Gateway so instances can download updates without being publicly reachable. Route tables control where traffic flows. Security groups act as stateful firewalls at the instance level, while Network ACLs provide stateless filtering at the subnet level. A well-designed VPC keeps databases in private subnets and exposes only load balancers publicly.

IAM (Identity and Access Management) controls who can do what in your AWS account through users, groups, roles, and policies. The foundational principle is least privilege: grant only the permissions a principal needs. Best practices include avoiding use of the root account for daily work, enforcing MFA, preferring IAM roles over long-lived access keys — especially for EC2, Lambda, and cross-account access — and using temporary credentials via STS. Policies should be scoped tightly with explicit resource ARNs and conditions rather than broad wildcards. Interviewers often ask you to critique an over-permissive policy.

Databases: RDS and DynamoDB

Amazon RDS is a managed relational database service supporting engines like PostgreSQL, MySQL, and Aurora. Choose it when you need ACID transactions, complex joins, and a fixed schema — typical for financial or transactional systems. DynamoDB is a fully managed NoSQL key-value and document store offering single-digit millisecond latency at any scale with no servers to manage. Choose it for high-throughput, predictable-access-pattern workloads such as session stores, shopping carts, or IoT ingestion. The key design difference is that DynamoDB requires you to model tables around access patterns and partition keys up front, whereas relational databases let you query flexibly with SQL after the fact.

For RDS, enable Multi-AZ deployments: AWS maintains a synchronous standby replica in a different Availability Zone and fails over automatically if the primary becomes unhealthy, usually within a minute or two. Add read replicas to scale read traffic and offload reporting queries. Aurora extends this with a storage layer replicated across three Availability Zones and up to fifteen low-latency replicas. DynamoDB is inherently multi-AZ and can span regions with global tables for active-active replication. The general theme interviewers want is: spread across Availability Zones, automate failover, and back up regularly.

Serverless and Scaling

AWS Lambda runs your code in response to events — an HTTP request via API Gateway, an S3 upload, a queue message — without provisioning servers. You pay only for the compute time consumed, billed per millisecond, and AWS scales instances automatically with demand. The tradeoffs: cold starts add latency when a new execution environment spins up, functions have a maximum timeout (15 minutes) and memory limits, and long-running or stateful workloads fit poorly. Serverless shines for spiky, event-driven, and glue workloads where you want zero idle cost and automatic scaling, and it pairs naturally with DynamoDB and S3.

An Auto Scaling Group launches and terminates EC2 instances automatically to match demand, using scaling policies driven by metrics like average CPU or request count, and it replaces unhealthy instances to maintain a desired capacity. An Application Load Balancer distributes incoming traffic across instances in multiple Availability Zones, performs health checks, and routes only to healthy targets. Together they deliver both horizontal scalability and high availability: traffic spreads across zones, capacity grows and shrinks automatically, and failed instances are replaced without manual intervention.

# CloudFormation snippet: an Auto Scaling Group across two AZs
Resources:
  WebASG:
    Type: AWS::AutoScaling::AutoScalingGroup
    Properties:
      MinSize: "2"
      MaxSize: "6"
      DesiredCapacity: "2"
      HealthCheckType: ELB
      HealthCheckGracePeriod: 120
      VPCZoneIdentifier:
        - !Ref PrivateSubnetA
        - !Ref PrivateSubnetB
      LaunchTemplate:
        LaunchTemplateId: !Ref WebLaunchTemplate
        Version: !GetAtt WebLaunchTemplate.LatestVersionNumber
      TargetGroupARNs:
        - !Ref WebTargetGroup
  ScaleOnCpu:
    Type: AWS::AutoScaling::ScalingPolicy
    Properties:
      AutoScalingGroupName: !Ref WebASG
      PolicyType: TargetTrackingScaling
      TargetTrackingConfiguration:
        PredefinedMetricSpecification:
          PredefinedMetricType: ASGAverageCPUUtilization
        TargetValue: 60.0

Cost Optimization

Start with right-sizing: match instance types to actual CPU and memory usage using metrics rather than guesses. Commit to Savings Plans or Reserved Instances for steady baseline workloads, and use Spot Instances for interruptible batch jobs. Apply S3 lifecycle policies to tier cold data into Glacier and expire obsolete objects. Turn off idle non-production resources on a schedule, delete unattached EBS volumes and old snapshots, and consolidate NAT Gateways. Finally, use tagging plus AWS Cost Explorer and Budgets to attribute spend and catch anomalies early. The interview goal is to show you treat cost as an engineering constraint, not an afterthought.

Frequently Asked Questions

A certification like the AWS Solutions Architect Associate helps structure your learning and signals baseline knowledge, but it is not required. Interviewers care far more about whether you can reason about real tradeoffs — availability, security, and cost — and explain design decisions clearly. Hands-on experience building even small projects on AWS usually carries more weight than a certificate alone.

Expect core services (EC2, S3, Lambda, VPC, IAM, RDS, DynamoDB), plus scaling and high availability with Auto Scaling and load balancers, security best practices around IAM and encryption, serverless architecture, and cost optimization. Many interviews also include a lightweight design question, such as architecting a highly available web application across multiple Availability Zones.

Build a small project — for example a serverless API with Lambda, API Gateway, and DynamoDB — so you can speak from experience. Then practice explaining your architecture and its tradeoffs out loud, as you would to an interviewer. A voice-based AI mock interview gives you realistic spoken practice and instant feedback on clarity, which is exactly what live cloud interviews evaluate.