Cloud computing interviews test your grasp of fundamental concepts, major provider services, architecture patterns, security, cost management, and real-world trade-offs. This guide covers the 50 most common questions — with concise answers, comparisons, and practical examples.
Quick reference
| Topic | Most asked questions |
|---|---|
| Core concepts | IaaS/PaaS/SaaS, deployment models, cloud vs on-prem |
| Scalability | Horizontal vs vertical, auto-scaling, load balancing |
| Storage | Object vs block vs file, S3, EBS, lifecycle policies |
| Networking | VPC, subnets, CDN, DNS, VPN vs Direct Connect |
| Compute | VMs, containers, serverless, spot/reserved instances |
| Databases | RDS, DynamoDB, multi-region, read replicas |
| Security | IAM, encryption, shared responsibility model |
| Cost | Reserved vs on-demand, cost optimisation patterns |
| Architecture | High availability, DR, multi-region, CAP theorem |
| DevOps | CI/CD in cloud, IaC, monitoring, observability |
Core Concepts
1. What is cloud computing?
Cloud computing is the delivery of computing resources — servers, storage, databases, networking, software, analytics, and intelligence — over the internet ("the cloud") on a pay-as-you-go basis.
Key characteristics (NIST definition):
- On-demand self-service — provision resources without human interaction
- Broad network access — accessible from any device over the network
- Resource pooling — shared infrastructure serves multiple tenants
- Rapid elasticity — scale up or down quickly
- Measured service — usage metered and billed accordingly
2. What are the three main cloud service models?
| Model | Definition | You manage | Provider manages | Examples |
|---|---|---|---|---|
| IaaS (Infrastructure as a Service) | Rent raw compute, storage, networking | OS, runtime, apps, data | Physical hardware, hypervisor, network | AWS EC2, Azure VMs, GCP Compute Engine |
| PaaS (Platform as a Service) | Rent a platform to build and deploy apps | App code, data | OS, runtime, middleware, scaling | Heroku, Google App Engine, AWS Elastic Beanstalk |
| SaaS (Software as a Service) | Use software over the internet | Nothing (just use it) | Everything | Gmail, Salesforce, Dropbox, Slack |
Memory aid: IaaS = renting land; PaaS = renting a house; SaaS = renting a hotel room.
3. What are the four cloud deployment models?
| Model | Description | Who uses it | Example |
|---|---|---|---|
| Public cloud | Resources owned and operated by a third-party provider, shared among customers | Startups, most businesses | AWS, Azure, GCP |
| Private cloud | Cloud infrastructure dedicated to a single organisation, on-prem or hosted | Banks, government, healthcare | VMware vSphere, OpenStack |
| Hybrid cloud | Mix of public and private cloud, connected by technology | Enterprises with legacy systems | AWS Outposts + AWS, Azure Arc |
| Multi-cloud | Using services from multiple cloud providers | Risk mitigation, best-of-breed | AWS + GCP + Azure together |
4. What is the difference between cloud and on-premises?
| Dimension | Cloud | On-Premises |
|---|---|---|
| Capital expense | Low (OpEx model) | High (servers, data centres) |
| Scalability | Elastic — scale in minutes | Slow — buy hardware |
| Maintenance | Provider handles hardware | Your team handles everything |
| Geographic reach | Global in minutes | Requires physical expansion |
| Security control | Shared responsibility | Full control |
| Compliance | Requires due diligence | Easier to control |
5. What is the shared responsibility model?
The shared responsibility model defines what the cloud provider secures versus what you secure:
Provider is responsible for:
- Physical security of data centres
- Hardware (servers, networking equipment)
- Hypervisor and virtualisation layer
- Core cloud services infrastructure
You are responsible for:
- Data encryption and classification
- Identity and access management (IAM)
- Operating system patching (IaaS)
- Application code security
- Network configuration (security groups, firewalls)
- Compliance and regulatory requirements
The line moves depending on the service model: in SaaS the provider owns far more; in IaaS you own more.
Scalability and Availability
6. What is the difference between horizontal and vertical scaling?
| Type | Definition | Pros | Cons |
|---|---|---|---|
| Vertical (Scale Up) | Add more CPU/RAM to an existing server | Simple, no app changes needed | Has a physical ceiling; single point of failure |
| Horizontal (Scale Out) | Add more servers and distribute load | No ceiling; high availability | Requires stateless apps, load balancer, distributed data |
Cloud-native architectures favour horizontal scaling because it provides both capacity and fault tolerance.
7. What is auto-scaling?
Auto-scaling automatically adjusts the number of compute instances based on defined metrics or schedules.
Types:
- Target tracking — maintain a metric at a target value (e.g., keep CPU at 60%)
- Step scaling — add/remove capacity in steps based on alarm thresholds
- Scheduled scaling — scale based on predictable patterns (e.g., Monday mornings)
AWS example — Auto Scaling Group with target tracking:
Desired: 2 instances
Min: 1 Max: 10
Policy: Keep average CPU < 60%
→ CPU hits 80% → ASG launches 2 more instances
→ CPU drops to 30% → ASG terminates 1 instance
8. What is a load balancer and what types exist?
A load balancer distributes incoming traffic across multiple servers to ensure no single server bears too much load.
| Type | Layer | Use case | AWS service |
|---|---|---|---|
| Application Load Balancer (ALB) | Layer 7 (HTTP/HTTPS) | Path-based routing, microservices | ALB |
| Network Load Balancer (NLB) | Layer 4 (TCP/UDP) | Ultra-high performance, static IP | NLB |
| Classic Load Balancer (CLB) | Layer 4/7 | Legacy | CLB (deprecated) |
| Gateway Load Balancer | Layer 3 | Third-party virtual appliances | GWLB |
9. What does "high availability" mean in cloud architecture?
High availability (HA) means designing systems to minimise downtime, typically measured as uptime percentage:
| SLA | Downtime per year |
|---|---|
| 99% | ~3.65 days |
| 99.9% ("three nines") | ~8.76 hours |
| 99.99% ("four nines") | ~52.6 minutes |
| 99.999% ("five nines") | ~5.26 minutes |
HA patterns:
- Multi-AZ deployment — run instances in multiple availability zones
- Health checks + auto-replace — detect and replace failed instances
- Load balancing — route traffic away from unhealthy instances
- Database replication — standby replica ready for failover
- Circuit breaker — prevent cascade failures
10. What is the difference between high availability and disaster recovery?
| High Availability | Disaster Recovery | |
|---|---|---|
| Goal | Prevent downtime | Recover after major failure |
| Scope | Hardware/instance failures, AZ failures | Region-level outage, data corruption |
| RTO | Seconds to minutes | Minutes to hours |
| Cost | Moderate (multi-AZ) | Higher (multi-region standby) |
| Examples | Multi-AZ RDS, ALB + ASG | S3 cross-region replication, Route 53 failover |
RTO (Recovery Time Objective) — maximum tolerable downtime.
RPO (Recovery Point Objective) — maximum tolerable data loss (in time).
Storage
11. What are the three types of cloud storage?
| Type | Description | Access | Best for | AWS example |
|---|---|---|---|---|
| Object storage | Flat namespace, files stored as objects with metadata | HTTP API | Unstructured data, backups, media | S3 |
| Block storage | Low-level storage volumes, raw blocks | Attached to VM | Databases, OS volumes, high IOPS workloads | EBS |
| File storage | Hierarchical file system, shared across instances | NFS/SMB | Shared home directories, legacy apps | EFS, FSx |
12. What is Amazon S3 and what are its storage classes?
Amazon S3 (Simple Storage Service) is an object storage service with 99.999999999% (11 nines) durability.
| Storage class | Use case | Retrieval | Cost |
|---|---|---|---|
| S3 Standard | Frequently accessed data | Milliseconds | Highest |
| S3 Standard-IA | Infrequently accessed, still fast | Milliseconds | Lower storage cost, retrieval fee |
| S3 One Zone-IA | IA data, one AZ only | Milliseconds | Cheaper, less redundant |
| S3 Glacier Instant | Archive, occasional access | Milliseconds | Low |
| S3 Glacier Flexible | Archive | 1–5 minutes to 12 hours | Very low |
| S3 Glacier Deep Archive | Long-term archive | Up to 48 hours | Cheapest |
| S3 Intelligent-Tiering | Unknown/changing access | Milliseconds | Auto-moves between tiers |
13. What is the difference between S3 and EBS?
| S3 | EBS | |
|---|---|---|
| Type | Object storage | Block storage |
| Access | HTTP API from anywhere | Mounted to one EC2 instance |
| Use case | Backups, static files, data lake | Database volumes, OS disks |
| Durability | 11 nines (multi-AZ) | 99.999% (replicated within AZ) |
| Sharing | Multiple clients simultaneously | One instance (Multi-Attach limited) |
| Pricing | Per GB stored + requests | Per GB provisioned + IOPS |
14. What is a CDN and how does it work?
A Content Delivery Network (CDN) is a globally distributed network of edge servers that cache content closer to users.
How it works:
- User requests
https://example.com/image.jpg - DNS routes to nearest CDN edge server
- If cached (cache hit) — return immediately (low latency)
- If not cached (cache miss) — fetch from origin, cache, return
- Next user in same region gets cached copy
Benefits: lower latency, reduced origin load, DDoS protection.
AWS CloudFront is the AWS CDN; Cloudflare and Akamai are provider-agnostic options.
15. What is a data lake vs a data warehouse?
| Data Lake | Data Warehouse | |
|---|---|---|
| Data type | Raw, unstructured + structured | Cleaned, structured |
| Schema | Schema on read | Schema on write |
| Users | Data scientists, ML engineers | Analysts, business users |
| Storage | Cheap object storage (S3) | Columnar storage (Redshift, BigQuery) |
| Query speed | Slower (needs transformation) | Fast (optimised for analytics) |
| Cost | Low storage cost | Higher, optimised for queries |
Networking
16. What is a VPC?
A Virtual Private Cloud (VPC) is a logically isolated section of a cloud provider's network where you launch resources.
Key VPC components (AWS):
- Subnets — subdivide the VPC CIDR block into ranges (public and private)
- Route tables — define where traffic is directed
- Internet Gateway — allows public subnets to access the internet
- NAT Gateway — allows private subnets to initiate outbound internet traffic
- Security Groups — stateful firewall at the instance level
- Network ACLs — stateless firewall at the subnet level
17. What is the difference between a public and private subnet?
| Public Subnet | Private Subnet | |
|---|---|---|
| Internet access | Directly via Internet Gateway | Via NAT Gateway (outbound only) |
| Inbound from internet | Yes (with security group rules) | No |
| Typical resources | Load balancers, bastion hosts | App servers, databases |
| Route table | Has route to Internet Gateway | Routes to NAT Gateway only |
18. What is the difference between a Security Group and a Network ACL?
| Security Group | Network ACL | |
|---|---|---|
| Level | Instance/ENI | Subnet |
| Statefulness | Stateful (return traffic auto-allowed) | Stateless (must allow both directions) |
| Rules | Allow only (no deny) | Allow and deny |
| Evaluation | All rules evaluated | Rules evaluated in order (numbered) |
| Default | Deny all inbound, allow all outbound | Allow all inbound and outbound |
19. What is VPN vs Direct Connect (private connectivity)?
| VPN | Direct Connect | |
|---|---|---|
| Connection type | Encrypted tunnel over public internet | Dedicated private fibre connection |
| Latency | Variable (internet-dependent) | Consistent, low latency |
| Bandwidth | Up to ~1.25 Gbps | 1 Gbps to 100 Gbps |
| Setup time | Minutes to hours | Weeks to months |
| Cost | Lower | Higher (port + partner fees) |
| Use case | Remote access, dev environments | Production, large data transfers |
20. What is DNS and how does Route 53 work?
DNS (Domain Name System) translates human-readable domain names to IP addresses.
Route 53 is AWS's scalable DNS service. Key routing policies:
| Policy | Description | Use case |
|---|---|---|
| Simple | Single resource | Basic websites |
| Weighted | Split traffic by percentage | A/B testing, gradual rollouts |
| Latency | Route to lowest-latency region | Global apps |
| Failover | Primary + standby | Disaster recovery |
| Geolocation | Route by user's geographic location | Compliance, localisation |
| Multi-value | Return multiple healthy IPs | Simple load balancing |
| Geoproximity | Route by distance + bias | Traffic shaping |
Compute
21. What is the difference between a VM, a container, and a serverless function?
| Virtual Machine | Container | Serverless | |
|---|---|---|---|
| Isolation | Full OS isolation | Process isolation | Per-invocation |
| Boot time | Minutes | Seconds | Milliseconds |
| Resource unit | vCPU + RAM | CPU shares + memory | Function execution time |
| OS | Full OS (Guest OS) | Shared host OS kernel | Provider managed |
| Density | Low (1 VM per guest OS) | High (hundreds per host) | Infinite (abstracted) |
| Pricing | Per hour | Per second (ECS Fargate) | Per invocation + GB-seconds |
| Use case | Lift-and-shift, general workloads | Microservices, CI/CD | Event-driven, spiky workloads |
22. What is serverless computing?
Serverless computing lets you run code without provisioning or managing servers. The provider automatically handles scaling, patching, and availability.
Key characteristics:
- Event-driven — functions triggered by events (HTTP request, S3 upload, queue message)
- Stateless — no persistent process state between invocations
- Auto-scaling — scales to zero or to millions of concurrent executions
- Pay-per-use — billed in milliseconds of execution time
Examples: AWS Lambda, Azure Functions, Google Cloud Functions.
Limitations: cold starts, execution time limits (15 min for Lambda), no persistent local storage.
23. What are EC2 instance purchasing options?
| Option | Payment | Savings vs On-Demand | Best for |
|---|---|---|---|
| On-Demand | Per hour/second | Baseline | Short-term, unpredictable |
| Reserved (1yr) | Upfront/monthly | ~40% | Steady-state baseline load |
| Reserved (3yr) | Upfront | ~60% | Long-term predictable workloads |
| Savings Plans | Commit to $/hour | ~66% | Flexible compute commitment |
| Spot | Bid market price | Up to 90% | Fault-tolerant, flexible workloads |
| Dedicated Host | Per host | 0% (compliance premium) | Licensing, compliance |
24. What is containers-as-a-service (CaaS)?
CaaS is a cloud service model that provides a managed platform for deploying, running, and scaling containers.
| Service | Provider | Managed | Self-managed |
|---|---|---|---|
| ECS (Fargate) | AWS | Fargate manages nodes | EC2 launch type: you manage nodes |
| EKS | AWS | Control plane managed | Worker nodes managed by you |
| GKE Autopilot | GCP | Fully managed | — |
| Azure AKS | Azure | Control plane managed | Worker nodes managed by you |
25. What is a managed service vs a self-managed service?
| Managed | Self-managed | |
|---|---|---|
| Patching | Provider patches | You patch |
| Backups | Provider handles | You configure |
| Scaling | Auto or simpler | Manual configuration |
| HA/replication | Built-in or one checkbox | You configure |
| Control | Less | More |
| Examples | RDS, ElastiCache, DynamoDB | MySQL on EC2, Redis on EC2 |
Managed services cost more but reduce operational burden. Self-managed gives more control but requires more expertise.
Databases
26. What is the difference between RDS and DynamoDB?
| RDS | DynamoDB | |
|---|---|---|
| Type | Relational (SQL) | NoSQL (key-value / document) |
| Schema | Fixed schema | Schema-less |
| Query language | SQL | DynamoDB API / PartiQL |
| Scaling | Vertical + read replicas | Auto, horizontal, unlimited |
| Performance | Good for complex queries | Sub-millisecond at any scale |
| Consistency | Strong (ACID) | Eventually consistent (default) or strongly consistent |
| Best for | Complex queries, transactions, reporting | High throughput, IoT, gaming, user profiles |
27. What are read replicas and multi-AZ in RDS?
| Read Replica | Multi-AZ | |
|---|---|---|
| Purpose | Scale read operations | High availability / failover |
| Replication | Asynchronous | Synchronous |
| Failover | No automatic failover | Automatic failover in ~60–120s |
| Readable | Yes | No (standby only) |
| Cross-region | Yes | No (same region) |
| Use case | Heavy read workloads, reporting | Production HA requirement |
28. What is database sharding?
Sharding is a technique to horizontally partition a database by distributing rows across multiple database servers (shards), each holding a subset of the data.
Without sharding: With sharding:
[All users: 1B rows] [Shard 1: users A-M]
[Shard 2: users N-Z]
Benefits: each shard handles smaller data volume, queries are faster.
Challenges: cross-shard queries are expensive, re-sharding is complex, hotspot shards if key distribution is uneven.
29. What is eventual consistency vs strong consistency?
| Strong Consistency | Eventual Consistency | |
|---|---|---|
| Reads after write | Always returns latest write | May return stale data temporarily |
| Latency | Higher (wait for replication) | Lower (no wait) |
| Availability | Lower (per CAP theorem) | Higher |
| Example | RDS synchronous replica, DynamoDB strong reads | S3, DynamoDB default reads |
CAP Theorem: a distributed system can guarantee only two of three properties:
- Consistency — every read gets the latest write
- Availability — every request gets a response
- Partition tolerance — system continues despite network partitions
30. What is a connection pool in cloud databases?
A connection pool maintains a set of reusable database connections rather than opening and closing a new connection for each request.
Problem: each DB connection has overhead (~5–10ms setup, memory on DB server). At high scale, thousands of new connections per second overwhelm the DB.
Solution: pool (e.g., RDS Proxy, PgBouncer) maintains open connections, hands them to app functions, returns them after use.
AWS RDS Proxy is particularly useful with Lambda (which opens many short-lived connections).
Security
31. What is IAM and what are its key components?
IAM (Identity and Access Management) controls who can access what in your cloud account.
| Component | Description | Example |
|---|---|---|
| User | Individual identity with long-term credentials | alice@example.com |
| Group | Collection of users sharing permissions | Developers, Admins |
| Role | Identity assumed temporarily by services or federated users | EC2 instance role, Lambda execution role |
| Policy | JSON document defining permissions | AllowS3ReadAccess |
IAM best practices:
- Enable MFA for root and all users
- Use roles instead of access keys where possible
- Principle of least privilege (grant minimum required permissions)
- Rotate access keys regularly
- Use IAM Access Analyzer to detect over-permissive policies
32. What is the principle of least privilege?
Grant only the permissions required to perform a specific task — nothing more.
Bad practice:
{ "Action": "*", "Resource": "*", "Effect": "Allow" }
Good practice:
{
"Action": ["s3:GetObject", "s3:PutObject"],
"Resource": "arn:aws:s3:::my-specific-bucket/*",
"Effect": "Allow"
}
33. What is encryption at rest vs encryption in transit?
| Encryption at Rest | Encryption in Transit | |
|---|---|---|
| What | Data stored on disk | Data moving over networks |
| Protects against | Physical theft, storage compromise | Network eavesdropping, MITM attacks |
| Protocols | AES-256, KMS-managed keys | TLS 1.2/1.3, HTTPS, SSL |
| AWS tools | S3 SSE, EBS encryption, RDS encryption | HTTPS endpoints, ACM certificates |
Both are required for compliance (HIPAA, PCI-DSS, SOC 2).
34. What is AWS KMS?
AWS Key Management Service (KMS) is a managed service for creating and controlling encryption keys.
Key concepts:
- CMK (Customer Master Key) — your encryption key; stored in KMS, never exported
- Data key — used to encrypt actual data; itself encrypted by CMK (envelope encryption)
- Key rotation — automatic annual rotation of CMK
- Audit trail — every key usage logged in CloudTrail
Envelope encryption:
Data → encrypted with Data Key
Data Key → encrypted with CMK (stored in KMS)
Only the CMK can decrypt the Data Key
35. What is DDoS protection in cloud?
| Layer | Attack type | AWS protection |
|---|---|---|
| Layer 3/4 | Volumetric (UDP floods, SYN floods) | AWS Shield Standard (free) |
| Layer 7 | HTTP floods, slow attacks | AWS WAF + Shield Advanced |
| DNS | DNS amplification | Route 53 (anycast, rate limiting) |
| App layer | SQL injection, XSS | AWS WAF rules |
AWS Shield Standard is automatically applied to all AWS customers. Shield Advanced ($3,000/month) adds 24/7 DDoS response team and financial protection.
Cost Optimisation
36. What are common cloud cost optimisation strategies?
| Strategy | Description | Typical savings |
|---|---|---|
| Right-sizing | Match instance size to actual usage | 20–40% |
| Reserved Instances / Savings Plans | Commit to 1–3 years | 40–66% |
| Spot Instances | Use spare capacity | Up to 90% |
| Auto-scaling | Scale down during low demand | 20–40% |
| Delete unused resources | Idle EIPs, unattached EBS volumes | Variable |
| S3 lifecycle policies | Move old data to Glacier | 70–90% on archived data |
| Natgateway optimization | VPC endpoints instead of NAT | Significant for high traffic |
| Data transfer | Keep traffic in same region/AZ | Avoid egress costs |
37. What is the difference between CapEx and OpEx in cloud?
| CapEx (Capital Expenditure) | OpEx (Operational Expenditure) | |
|---|---|---|
| Definition | Upfront investment in physical assets | Ongoing pay-as-you-go costs |
| On-premises | Buy servers, data centre | Power, cooling, staff |
| Cloud | Reserved instances (upfront) | On-demand usage billing |
| Accounting | Depreciated over years | Expensed immediately |
| Advantage | Predictable long-term cost | No upfront investment, flexible |
Cloud's OpEx model is a primary reason organisations migrate — convert large upfront capital costs to predictable monthly bills.
38. What is cloud cost allocation and tagging?
Cost allocation tags let you categorise cloud resources and attribute costs to teams, projects, or environments.
Example tag strategy:
Environment: production | staging | dev
Team: platform | data | frontend
Project: checkout-service | analytics-pipeline
CostCenter: 12345
Enforce tagging via:
- AWS Tag Policies (prevent untagged resource creation)
- AWS Cost Explorer (filter cost by tag)
- AWS Budgets (alert when team cost exceeds threshold)
Architecture Patterns
39. What is a microservices architecture in cloud?
Microservices is an architectural style that structures an application as a collection of small, independently deployable services, each running in its own process and communicating via APIs.
| Monolith | Microservices | |
|---|---|---|
| Deployment | Single unit | Independent per service |
| Scaling | Scale the whole app | Scale individual services |
| Failure | One bug can crash all | Isolated failures |
| Technology | One stack | Polyglot (each service chooses) |
| Complexity | Lower operationally | Higher (distributed systems) |
Cloud-native microservices: each service in containers (ECS/EKS), communicating via REST/gRPC/events (SQS, SNS, Kafka).
40. What is an event-driven architecture?
Event-driven architecture (EDA) decouples services by having producers emit events and consumers react to them asynchronously.
[Order Service] → publishes "order.placed" → [SQS Queue]
↓
[Inventory Service]
[Notification Service]
[Analytics Service]
Benefits: loose coupling, independent scaling, replay events, easy to add new consumers.
AWS services: SNS (pub/sub), SQS (queuing), EventBridge (event routing), Kinesis (streaming).
41. What is the difference between SQS and SNS?
| SQS | SNS | |
|---|---|---|
| Type | Message queue (pull) | Pub/sub notification (push) |
| Consumers | One consumer per message | Many subscribers (fan-out) |
| Retention | Up to 14 days | No retention |
| Delivery | At-least-once (standard), exactly-once (FIFO) | Best-effort |
| Use case | Work queues, decoupling producers/consumers | Fan-out to multiple systems, mobile push |
Fan-out pattern: SNS topic → multiple SQS queues → each queue consumed by different services.
42. What is Infrastructure as Code (IaC)?
IaC means managing and provisioning infrastructure through machine-readable configuration files rather than manual processes.
| Tool | Language | Provider | Style |
|---|---|---|---|
| Terraform | HCL | Multi-cloud | Declarative |
| CloudFormation | JSON/YAML | AWS only | Declarative |
| CDK | TypeScript/Python/Java | AWS | Imperative (compiles to CF) |
| Pulumi | TypeScript/Python/Go | Multi-cloud | Imperative |
| Ansible | YAML | Multi-cloud | Procedural |
Benefits: version control, reproducible environments, peer review, automated testing, consistent deployments.
43. What is blue-green deployment?
Blue-green deployment runs two identical production environments (blue = current, green = new):
Traffic → [Blue: v1.0] (100%)
Deploy v1.1 to [Green] (idle)
Test Green
Switch DNS/load balancer:
Traffic → [Green: v1.1] (100%)
Blue stays up for rollback, then decommissioned
Benefits: zero downtime, instant rollback (flip traffic back to blue).
Cloud implementation: Route 53 weighted routing, ALB target group swap, Lambda aliases with traffic shifting.
44. What is canary deployment?
Canary deployment gradually shifts traffic to a new version:
v1.0: 95% traffic
v1.1: 5% traffic (canary)
→ monitor metrics
→ if healthy: increase to 25%, 50%, 100%
→ if problems: rollback canary to 0%
Named after canary birds used in coal mines to detect gas. AWS CodeDeploy, Lambda aliases, and API Gateway canary releases support this natively.
45. What is the CAP theorem and how does it apply to cloud databases?
CAP theorem states a distributed system can only guarantee two of three:
| Consistent | Available | Partition Tolerant | |
|---|---|---|---|
| CP (Consistent + Partition tolerant) | ✓ | ✗ (may reject requests) | ✓ |
| AP (Available + Partition tolerant) | ✗ (may return stale) | ✓ | ✓ |
| CA (Consistent + Available) | ✓ | ✓ | ✗ |
In cloud (where network partitions are inevitable), you choose between CP and AP. DynamoDB is AP by default; you can request strongly consistent reads for CP behaviour at higher latency cost.
DevOps and Operations
46. What is CI/CD in the cloud?
CI/CD (Continuous Integration / Continuous Delivery) automates building, testing, and deploying applications.
| Stage | What happens | AWS service |
|---|---|---|
| Source | Code push triggers pipeline | CodeCommit, GitHub |
| Build | Compile, run unit tests, create artifact | CodeBuild |
| Test | Integration tests, SAST scans | CodeBuild, third-party |
| Deploy staging | Deploy to staging environment | CodeDeploy, ECS, Lambda |
| Approval | Manual or automated gate | CodePipeline approval action |
| Deploy production | Blue-green or canary release | CodeDeploy |
47. What is observability in cloud systems?
Observability is the ability to understand a system's internal state from its external outputs. It consists of three pillars:
| Pillar | What | AWS service |
|---|---|---|
| Logs | Timestamped records of events | CloudWatch Logs |
| Metrics | Numeric measurements over time (CPU, latency, error rate) | CloudWatch Metrics |
| Traces | End-to-end request flow across services | AWS X-Ray |
Why all three? Metrics tell you something is wrong, logs tell you what happened, traces tell you where it happened in your distributed system.
48. What is a service mesh?
A service mesh is a dedicated infrastructure layer that manages service-to-service communication in a microservices architecture.
Handles:
- Traffic management — load balancing, retries, circuit breakers
- Security — mutual TLS (mTLS) between services
- Observability — automatic metrics, tracing, logging for every request
How: sidecar proxy (Envoy) injected into each pod/container intercepts all traffic.
Examples: AWS App Mesh (Envoy-based), Istio, Linkerd.
49. What is chaos engineering?
Chaos engineering is the practice of intentionally introducing failures into a system to build confidence in its ability to withstand unexpected conditions.
Famous example: Netflix's Chaos Monkey — randomly terminates production EC2 instances to ensure the system is resilient enough to survive instance loss.
Failure types to test:
- Random instance termination
- Network latency injection
- Region or AZ failure simulation
- CPU/memory pressure
- Database failover
AWS Fault Injection Service (FIS) is the managed chaos engineering tool on AWS.
50. What questions should you ask when designing a cloud architecture?
A structured cloud architecture review covers:
| Category | Questions |
|---|---|
| Requirements | What are the RTO/RPO requirements? What SLA is needed? |
| Scalability | What is peak traffic? How fast must it scale? |
| Data | How much data? Read vs write ratio? Consistency requirements? |
| Security | What data classification? Compliance requirements (HIPAA, PCI)? |
| Cost | What is the budget? Is workload predictable enough for reserved capacity? |
| Operations | Who will operate this? What monitoring is required? |
| Reliability | What single points of failure exist? Multi-AZ? Multi-region? |
| Network | Public or private? VPN or Direct Connect? CDN needed? |
Well-architected framework pillars (AWS): Operational Excellence, Security, Reliability, Performance Efficiency, Cost Optimisation, Sustainability.
Common mistakes
| Mistake | Problem | Fix |
|---|---|---|
| Storing secrets in environment variables plaintext | Exposed in logs, process lists | Use Secrets Manager or Parameter Store |
| Opening 0.0.0.0/0 in security groups | Exposes services to internet | Restrict to specific IPs or use private subnets |
| Ignoring egress costs | Surprise bills from data transfer | Use VPC endpoints, same-AZ traffic |
| Using root account for daily work | High blast radius | Create IAM users/roles, lock root |
| No tagging strategy | Can't attribute costs | Define and enforce tag policy from day one |
| Single-AZ architecture | One AZ failure = downtime | Multi-AZ for databases, multi-AZ ASG |
| Over-provisioning "just in case" | Wasted spend | Right-size + auto-scaling |
| Ignoring S3 public access | Data breaches | Enable S3 Block Public Access account-wide |
Cloud computing vs related terms
| Term | Meaning |
|---|---|
| Cloud computing | Delivering IT resources over internet on pay-as-you-go basis |
| Edge computing | Processing data near where it's generated (IoT, low latency) |
| Fog computing | Intermediate layer between edge devices and cloud |
| Hybrid cloud | Mix of on-prem private cloud + public cloud |
| Multi-cloud | Using services from multiple cloud providers |
| Serverless | Cloud model where provider manages all infrastructure |
| Virtualisation | Technology underpinning cloud (hypervisors, VMs) |
| Kubernetes | Container orchestration — often runs on cloud |
| Cloud-native | Applications designed specifically to run in cloud |
FAQ
Q: Which cloud provider should I learn first?
AWS has the largest market share (~32%) and most job postings. Azure is dominant in enterprises using Microsoft stack. GCP excels in data/ML. Start with AWS for maximum job market coverage, but core concepts transfer across all three.
Q: Is cloud computing the same as the internet?
No. The internet is the global network infrastructure. Cloud computing runs on top of the internet — it's a model for delivering computing services (storage, compute, databases) over the internet from shared data centres.
Q: What is multi-tenancy in cloud?
Multiple customers (tenants) share the same underlying physical infrastructure (servers, storage, networking). Isolation is achieved through virtualisation, containers, and software-enforced boundaries. Public cloud is multi-tenant; private cloud and dedicated hosts are single-tenant.
Q: What is the Well-Architected Framework?
AWS's Well-Architected Framework is a set of best practices across six pillars (Operational Excellence, Security, Reliability, Performance Efficiency, Cost Optimisation, Sustainability). Use the AWS Well-Architected Tool to review architectures against these pillars.
Q: How is cloud computing priced?
Primarily on consumption: compute (per hour/second), storage (per GB/month), network egress (per GB transferred out), requests (per million API calls). Use the AWS Pricing Calculator, Azure Pricing Calculator, or GCP Pricing Calculator to estimate costs before deploying.
Q: What certifications are valuable for cloud computing?
AWS: Cloud Practitioner (entry) → Solutions Architect Associate → Solutions Architect Professional. Azure: AZ-900 → AZ-104 → AZ-305. GCP: Cloud Digital Leader → Associate Cloud Engineer → Professional Cloud Architect. AWS SAA-C03 is the most widely recognised mid-level cloud certification.