Interview Questions – AWS Compute Services – Detailed Explanation

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Table of Contents

🔧 1. Design a Highly Available and Scalable Web Application with Auto Scaling and Load Balancing

Implementation Steps:

  • Use EC2 Auto Scaling Groups (ASG) :
    • Create an ASG that spans multiple Availability Zones (AZs).
    • Configure minimum, maximum, and desired capacity.
    • Use launch templates or launch configurations with appropriate AMI and instance types.
  • Load Balancer :
    • Use Application Load Balancer (ALB) for HTTP/HTTPS traffic.
    • Configure health checks on the ALB to ensure only healthy instances receive traffic.
  • Auto Scaling Policies :
    • Use dynamic scaling based on CPU utilization or custom metrics.
    • Optionally, use predictive scaling for predictable traffic patterns.
  • Security Considerations :
    • Use security groups to restrict access to the web servers.
    • Enable CloudTrail and CloudWatch Logs for auditing and monitoring.

🔧 2. Implementing a Serverless Architecture with AWS Lambda

Implementation Steps:

  • Lambda Functions :
    • Write functions in Python, Node.js, or Java.
    • Use AWS SAM or Serverless Framework for deployment.
  • Trigger Sources :
    • Connect Lambda to API Gateway , S3 , DynamoDB Streams , or EventBridge .
  • Optimization :
    • Set memory allocation and timeout values based on function needs.
    • Use provisioned concurrency to reduce cold starts.
  • Monitoring :
    • Use CloudWatch Metrics and X-Ray for tracing and debugging.
    • Enable logging via console.log() or CloudWatch Logs.

🔧 3. Containerized Applications with ECS and EKS

Implementation Steps:

  • ECS (Elastic Container Service) :
    • Use Fargate for serverless container orchestration.
    • Define tasks and services in the ECS console or with CloudFormation.
    • Use ECR (Elastic Container Registry) to store Docker images.
  • EKS (Elastic Kubernetes Service) :
    • Create a managed Kubernetes cluster.
    • Deploy workloads using Kubernetes manifests or Helm charts.
    • Use Amazon VPC CNI for networking.
  • Hybrid Setup :
    • Use ECS on EC2 for stateful workloads and EKS for stateless microservices.
    • Ensure consistent logging and monitoring across both environments.

ECS (Elastic Container Service) , EKS (Elastic Kubernetes Service) , and Hybrid Setup are all ways to run containerized applications on AWS. Each has its strengths, use cases, and trade-offs. Let’s break them down in detail.

🧩 Overview

ServiceDescription
ECS (Elastic Container Service)AWS-native container orchestration service with two launch types:EC2andFargate.
EKS (Elastic Kubernetes Service)Fully managed Kubernetes service that runs the Kubernetes control plane on AWS.
Hybrid SetupCombines on-premises infrastructure with cloud resources (e.g., AWS) for a mixed environment.

📊 Comparison Table

FeatureECSEKSHybrid Setup
OrchestrationNative AWS orchestrationKubernetes-basedHybrid (can use both)
Control PlaneManaged by AWSManaged by AWS (Kubernetes)On-premises or third-party
ComplexityEasier to useMore complex (Kubernetes)Most complex
FlexibilityLess flexibleHighly flexibleVery flexible
PricingPay per task (Fargate), EC2 costsPay for control plane + EC2/spot instancesVaries based on setup
Use CaseSimple apps, serverless workloadsComplex apps, microservices, multi-cloudLegacy systems, compliance, data sovereignty
Community & EcosystemSmaller than KubernetesLarge and activeDepends on hybrid tools used

🔍 Detailed Breakdown

1. ECS (Elastic Container Service)

✅ Pros:

  • Easy to use – Great for developers who want a simple, managed solution.
  • Integrated with AWS – Works well with other AWS services like S3, RDS, CloudWatch, etc.
  • Two launch types :
    • EC2 – Full control over the underlying infrastructure.
    • Fargate – Serverless, no need to manage EC2 instances.
  • Cost-effective for small to medium workloads.

❌ Cons:

  • Less flexibility compared to Kubernetes.
  • Limited customization of the runtime environment.
  • Not as mature in terms of community and ecosystem compared to Kubernetes.

📌 Best For:

  • Short-lived tasks
  • Microservices
  • Applications where you don’t need full Kubernetes features

2. EKS (Elastic Kubernetes Service)

✅ Pros:

  • Full Kubernetes support – Runs the Kubernetes control plane on AWS.
  • Highly flexible – Supports custom configurations, advanced networking, and storage.
  • Multi-cloud ready – Can be used with other cloud providers.
  • Large community and ecosystem – Rich set of tools, operators, and integrations.

❌ Cons:

  • More complex to set up and manage.
  • Higher cost due to control plane and EC2/spot instance usage.
  • Steeper learning curve if you’re not familiar with Kubernetes.

📌 Best For:

  • Enterprise-grade applications
  • Microservices architectures
  • Applications requiring high availability and scalability
  • Teams already using Kubernetes or planning to adopt it

3. Hybrid Setup

A hybrid setup combines on-premises infrastructure with cloud resources (like AWS). It’s often used when:

  • You have legacy systems that can’t be moved to the cloud.
  • You need data sovereignty or compliance with local regulations.
  • You want to scale into the cloud without fully migrating.

✅ Pros:

  • Greater control over your data and infrastructure.
  • Flexibility to choose where to run which workload.
  • Improved security and compliance.

❌ Cons:

  • More complex to manage and maintain.
  • Higher operational overhead .
  • Increased cost due to managing both environments.

📌 Best For:

  • Organizations with regulatory requirements
  • Enterprises with legacy systems
  • Workloads that require low latency or specific hardware

🧠 When to Choose Which?

ScenarioRecommended Option
Simple app, fast deploymentECS (Fargate)
Complex microservices, enterprise needsEKS
Need to keep some data on-premisesHybrid Setup
Want to avoid managing KubernetesECS
Already using KubernetesEKS
Need GPU or specialized hardwareHybrid or EC2-based EKS/ECS

📚 Additional Notes

  • ECS vs EKS: If you’re new to containers and want a simpler experience, go with ECS . If you need the power and flexibility of Kubernetes, go with EKS.
  • Fargate is great for serverless container workloads, while EC2 gives more control.
  • Hybrid setups are becoming more common with AWS Outposts, VMware Cloud on AWS, and on-premises Kubernetes clusters.

🧑‍💻 Example Use Cases

Use CaseService
A small web app with a databaseECS (Fargate + RDS)
A large-scale e-commerce platformEKS + Kubernetes Operators
A financial application with strict complianceHybrid (on-prem + EKS)
A batch processing jobECS (Fargate)
A machine learning model training workloadEKS (with GPU support)

🧠 Summary

FeatureECSEKSHybrid
Ease of UseHighMediumLow
FlexibilityLowHighHigh
ControlLimitedFullFull
CostModerateHighHigh
Best ForSmall to mid-sized appsEnterprise, microservicesCompliance, legacy, multi-cloud

🔧 4. Cost Optimization of EC2 Instances

Implementation Steps:

  • Instance Types :
    • Choose the right-sized instances based on workload (e.g., t3.medium for small apps, c5.large for compute-heavy).
  • Reserved Instances (RI) :
    • Purchase RIs for long-term, predictable workloads.
  • Spot Instances :
    • Use for batch jobs or non-critical workloads.
    • Implement fallback mechanisms (e.g., on-demand instances) for reliability.
  • Savings Plans :
    • Use for consistent usage over time (e.g., 1-year commitment).
  • Tools :
    • Use Cost Explorer, AWS Trusted Advisor, and AWS Budgets for cost analysis.

🧩 Instance Types

✅ What are Instance Types?

EC2 instance types define the hardware configuration (CPU, memory, storage, networking) of a virtual server in AWS. Each type is optimized for specific workloads.

🔍 Common Instance Families:

FamilyUse CaseExample
t3/t4gGeneral-purpose, cost-effectivet3.medium,t4g.small
c5/c6gCompute-optimizedc5.large,c6g.xlarge
m5/m6gBalancedm5.large,m6g.medium
r5/r6gMemory-optimizedr5.large,r6g.medium
p3/p4dGPU-acceleratedp3.2xlarge,p4d.24xlarge

📌 Best Practices:

  • Choose the right size : Match instance type to your workload.
    • Small apps: t3.medium
    • Compute-heavy: c5.large or c6g.xlarge
    • Memory-heavy: r5.large
  • Use burstable instances (T3/T4g) for light to moderate workloads with variable usage.
  • Avoid over-provisioning : Use monitoring tools like CloudWatch to track CPU/memory usage and scale accordingly.

🧾 Reserved Instances (RIs)

✅ What are Reserved Instances?

RIs are a commitment to use a specific instance type for a period (1 or 3 years), offering significant discounts compared to On-Demand pricing.

💰 Cost Savings:

  • Up to 75% off On-Demand prices.
  • Best for predictable, long-term workloads (e.g., databases, servers).

📌 Best Practices:

  • Purchase RIs for steady-state workloads (like production databases).
  • Use RI Marketplace for unused capacity from other users.
  • Consider Convertible RIs if you might change instance types later.

🌐 Spot Instances

✅ What are Spot Instances?

Spot Instances allow you to bid on unused EC2 capacity at up to 90% discount. They’re ideal for fault-tolerant, non-critical workloads.

⚠️ Limitations:

  • Can be interrupted by AWS when capacity is needed.
  • Not suitable for stateful or mission-critical applications .

📌 Best Practices:

  • Use Spot Fleet or Spot Instances with On-Demand fallback .
  • Pair with On-Demand or Reserved Instances for reliability.
  • Ideal for:
    • Batch processing
    • Data analysis
    • CI/CD pipelines
    • Render farms

🛡️ Fallback Mechanisms

✅ Why Fallbacks Matter:

Even with Spot Instances, you need redundancy to avoid service disruptions.

📌 Best Practices:

  • Use On-Demand or Reserved Instances as fallback for critical workloads.
  • Use Auto Scaling Groups with mixed instances (Spot + On-Demand).
  • Use ECS Task Definitions or Kubernetes Pods that can restart automatically.

💰 Savings Plans

✅ What are Savings Plans?

Savings Plans are a flexible commitment model where you pay a fixed hourly rate for compute usage over a 1 or 3-year term. They offer lower rates than On-Demand , but no upfront payment .

📌 Best Practices:

  • Use for consistent usage patterns (e.g., 24/7 web servers).
  • Compare with RIs and On-Demand to find the best option.
  • Ideal for workloads with predictable but not fully static usage .

🛠️ Tools for Cost Management

1. AWS Cost Explorer

  • Visualize costs over time.
  • Identify trends and anomalies.
  • Forecast future spending.

2. AWS Trusted Advisor

  • Get recommendations on cost optimization, performance, and security.
  • Includes checks for underutilized resources, RI purchases, and more.

3. AWS Budgets

  • Set custom budgets and receive alerts when you exceed them.
  • Helps enforce cost controls and stay within financial limits.

4. AWS Cost and Usage Report (CUR)

  • Detailed report of all your AWS charges.
  • Useful for advanced analytics and reporting.

📊 Summary Table

ComponentDescriptionBenefit
Instance TypesChoose based on workloadOptimize performance & cost
Reserved InstancesCommit to long-term usageSave up to 75%
Spot InstancesLow-cost, interruptibleGreat for batch jobs
Fallback MechanismsEnsure reliabilityPrevent downtime
Savings PlansFlexible, long-term commitmentLower rates than On-Demand
ToolsCost Explorer, Trusted Advisor, BudgetsMonitor, analyze, and control costs

🧑‍💻 Example Scenario

Scenario : You’re running a web application with steady traffic and batch processing jobs.

Optimized Setup :

  • Web App :
    • Use m5.large instances with Reserved Instances for 1 year.
  • Batch Jobs :
    • Use c5.xlarge Spot Instances with On-Demand fallback .
  • Cost Monitoring :
    • Use AWS Budgets to monitor spending.
    • Run Trusted Advisor checks monthly.

🎯 Final Tips

  • Monitor regularly : Use CloudWatch and Cost Explorer to track resource usage.
  • Right-size instances : Don’t overprovision; start small and scale as needed.
  • Automate where possible : Use Auto Scaling, Spot Fleets, and Cost Explorer automation.
  • Review annually : Re-evaluate your instance types, RI, and Savings Plan commitments.

🔧 5. Secure and Isolated Environments with VPC and Security Groups

Implementation Steps:

  • VPC Design :
    • Create a private subnet for DBs, public subnets for load balancers, and isolated subnets for sensitive data.
    • Use NAT Gateway to access the internet from private subnets.
  • Security Groups :
    • Allow only necessary ports (e.g., 80/443 for web, 3306 for MySQL).
    • Restrict traffic between tiers (e.g., allow web to app, but not directly to DB).
  • Network ACLs :
    • Add an extra layer of security by controlling inbound/outbound traffic at the subnet level.

🔧 6. Disaster Recovery and Backup Strategy

Implementation Steps:

  • AMIs and Snapshots :
    • Regularly create AMI backups of critical instances.
    • Use EBS snapshots for persistent storage.
  • CloudFormation :
    • Store infrastructure as code for quick recovery.
  • Backups :
    • AWS Backup is used to automate the backup of EC2, RDS, and other resources.
  • DR Plan :
    • Replicate data to another region.
    • Test failover procedures periodically.

🔧 7. High Availability with EC2 Auto Scaling Groups

Implementation Steps:

  • Multi-AZ ASG :
    • Configure ASG to span multiple AZs.
    • Use health check grace period to avoid premature termination.
  • Health Checks :
    • Use ELB health checks or EC2 health checks .
  • Scaling Policies :
    • Set up target tracking or step scaling policies.
    • Monitor CPU, memory, and custom metrics .

🔧 8. Performance Optimization for EC2 Instances

Implementation Steps:

  • Instance Type Selection :
    • Choose c5, m5, r5 for compute/memory-intensive apps.
    • Use GPU instances (g4dn, p3) for ML or rendering.
  • Disk I/O :
    • Use SSD-backed EBS volumes (gp3 or io1) for high I/O.
  • Networking :
    • Use enhanced networking (ENA) and placement groups for low-latency communication.
  • Tuning :
    • Optimize OS and application-level settings (e.g., kernel parameters, TCP tuning).

🔧 9. Multi-Tier Application Architecture on AWS

Implementation Steps:

  • Web Tier :
    • Use EC2 instances behind an ALB .
  • App Tier :
    • Use ECS Fargate or EKS for microservices.
  • Database Tier :
    • Use RDS or Aurora for relational databases.
  • Communication :
    • Use VPC peering or PrivateLink for secure inter-tier communication.
    • Use security groups to limit access between tiers.

🔧 10. CI/CD Pipeline for EC2-Based Applications

Implementation Steps:

  • Tools :
    • Use AWS CodePipeline , CodeBuild , and CodeDeploy .
  • Infrastructure as Code (IaC) :
    • Use CloudFormation or Terraform to provision EC2 instances.
  • Deployment Strategy :
    • Use blue/green deployments to minimize downtime.
    • Automate testing and rollback using CodePipeline stages.

🔧 11. Hybrid Cloud Compute Architecture

Implementation Steps:

  • Connectivity :
    • Use AWS Direct Connect or VPN for secure connectivity.
    • Use AWS Outposts for on-premises AWS-like infrastructure.
  • Compute Services :
    • Run EC2 and ECS in AWS for scalable workloads.
    • Use on-premises VMs for legacy applications.
  • Data Sync :
    • Use S3 Transfer Acceleration or Snowball for large data transfers.

🔧 12. Managing Stateful Workloads on AWS

Implementation Steps:

  • Persistent Storage :
    • Use EBS volumes for EC2 instances.
    • Use EFS for shared file systems.
    • Use RDS or Aurora for relational databases.
  • State Management :
    • Avoid storing state on EC2 instances; use external storage.
    • Use DynamoDB or Redis for session storage.

🔧 13. Custom Compute Solutions with AWS Batch

Implementation Steps:

  • Batch Job Submission :
    • Submit jobs via AWS Batch API or CLI .
  • Job Definitions :
    • Define job definitions with container images and resource requirements.
  • Queue Configuration :
    • Set up job queues and compute environments (EC2 or Fargate).
  • Use Cases :
    • Use for batch processing , HPC , or data transformation .

🔧 14. Real-Time Data Processing with AWS Fargate

Implementation Steps:

  • Fargate Tasks :
    • Use Fargate for running containers without managing EC2 instances.
  • Real-Time Workloads :
    • Use Kafka , Kinesis , or Lambda as input sources.
  • Orchestration :
    • Use ECS or EKS to manage Fargate tasks.
  • Benefits :
    • No need to manage underlying infrastructure.

🔧 15. Microservices Architecture with AWS

Implementation Steps:

  • Service Selection :
    • Use ECS Fargate for stateless services.
    • Use EKS for complex microservices.
    • Use Lambda for event-driven components.
  • Communication :
    • Use API Gateway or Service Mesh (App Mesh) .
  • CI/CD :
    • Use CodePipeline for deploying microservices independently.

🔧 16. Handling Sudden Traffic Spikes with Auto Scaling

Implementation Steps:

  • Auto Scaling Policies :
    • Use target tracking for consistent performance.
    • Use step scaling for more granular control.
  • Pre-Warming :
    • Pre-warm instances before expected traffic spikes.
  • Load Balancer :
    • Use ALB with sticky sessions if needed.

🔧 17. Monitoring and Observability for Compute Resources

Implementation Steps:

  • CloudWatch :
    • Monitor CPU, memory, disk, and network metrics.
  • X-Ray :
    • Trace requests across microservices.
  • CloudTrail :
    • Audit API calls and user actions.
  • Logs :
    • Use CloudWatch Logs for centralized log management.

🔧 18. Serverless vs. Traditional Compute Services

Implementation Steps:

  • When to Use Serverless :
    • For event-driven or short-lived tasks (e.g., image processing, data ingestion).
  • When to Use Traditional :
    • For long-running processes (e.g., web servers, background workers).
  • Limitations :
    • Lambda has execution time limits and cold start issues .
    • EC2 provides full control over the environment.

🔧 19. Secure Access to EC2 Instances

Implementation Steps:

  • SSH Access :
    • Use IAM roles for EC2 instances instead of hardcoded credentials.
    • Use bastion hosts or SSH tunnels for secure access.
  • Security Groups :
    • Restrict SSH access to specific IP ranges.
  • Key Pairs :
    • Use key pairs for authentication and rotate them regularly.

🔧 20. Migration of On-Premises Workloads to AWS

Implementation Steps:

  • Assessment :
    • Use AWS Migration Hub to track migration progress.
  • Replication :
    • Use VM Import/Export or AWS Snowball for large data.
  • Refactoring :
    • Migrate monolithic apps to microservices if needed.
  • Testing :
    • Perform disaster recovery drills and load testing .

🔧 21. Global Application Deployment with AWS Regions and Edge Locations

Implementation Steps:

  • Global Infrastructure :
    • Deploy applications in multiple AWS regions .
  • Global Load Balancing :
    • Use Route 53 with latency-based routing .
  • Edge Locations :
    • Use CloudFront for content delivery.
    • Cache static assets at edge locations.

🔧 22. Efficient Use of Spot Instances for Cost Savings

Implementation Steps:

  • Workloads :
    • Use for batch processing , data analytics , or rendering .
  • Fallback :
    • Use On-Demand or Reserved instances as fallback.
  • Tools :
    • Use Spot Instance Scheduler or Auto Scaling to manage spot instances.

🔧 23. Kubernetes Cluster Management with EKS

Implementation Steps:

  • Cluster Setup :
    • Use eksctl or CloudFormation to create an EKS cluster.
  • Node Groups :
    • Create managed node groups or self-managed node groups.
  • Networking :
    • Use Amazon VPC CNI for pod-to-pod communication.
  • Monitoring :
    • Use CloudWatch and Prometheus for monitoring.

🔧 24. Designing a Compute Infrastructure for Machine Learning

Implementation Steps:

  • Training :
    • Use GPU-enabled EC2 instances (g4dn, p3) or SageMaker .
  • Inference :
    • Use SageMaker endpoints or Lambda with SageMaker .
  • Storage :
    • Use S3 for data and EFS for shared models.
  • CI/CD :
    • Use SageMaker Pipelines for model training and deployment.

🔧 25. Compliance and Governance for Compute Resources

Implementation Steps:

  • Policies :
    • Use AWS Organizations and Service Control Policies (SCPs) .
  • Compliance Tools :
    • Use AWS Config to enforce compliance rules.
    • Use GuardDuty for threat detection.
  • Auditing :
    • Use CloudTrail and CloudWatch Events for audit trails.

✅ Summary

  • High availability & scalability
  • Serverless architecture
  • Container orchestration
  • Cost optimization
  • Disaster recovery
  • Security and compliance
  • Global deployment
  • Microservices and hybrid cloud

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