Cloud Computing MCQ Questions and Answers

Cloud Computing MCQ Questions and Answers

81. What is cloud computing’s pay-as-you-go pricing model?

  1. A model where cloud providers pay customers to use their excess computing capacity
  2. A consumption-based billing model where customers only pay for the computing resources they actually use — measured and billed in fine granularity (per hour, per second, per request, per GB) — eliminating upfront capital expenditure on hardware
  3. A monthly subscription where customers pay a fixed fee regardless of actual usage
  4. A payment model where customers pay in advance for computing capacity not yet used

Answer : B
Explanation: Pay-as-you-go (OpEx model) is one of the most transformative aspects of cloud computing. Traditional IT: large upfront CapEx (capital expenditure) to purchase servers — paying for full capacity even when utilization is low (typically 5-15%). Cloud: OpEx (operating expenditure) — pay only for what you use, billed in granular increments (EC2 billed per second, Lambda per millisecond and request, S3 per GB per month). Benefits: No upfront investment, OPEX vs CapEx accounting treatment, experiment without risk (spin up, test, shut down), match costs to actual usage, and no waste from idle hardware. AWS billing examples: EC2 t3.micro ~$0.0104/hour, S3 ~$0.023/GB/month, Lambda first 1M requests/month free. Reserved Instances and Savings Plans offer significant discounts (up to 72%) for committed long-term usage.

82. What is cloud computing’s availability zone and region?

  1. Geographic regions where cloud services are only available to specific countries
  2. A Region is a geographic area containing multiple isolated Availability Zones (AZs); each AZ consists of one or more discrete data centers with independent power, cooling, and networking — enabling fault-tolerant, highly available architectures
  3. A cloud provider’s operational time zones defining when technical support is available
  4. Specific IP address ranges (zones) assigned to each geographic region for routing

Answer : B
Explanation: Cloud providers organize their global infrastructure hierarchically: Region: a large geographic area (e.g., us-east-1 in North Virginia, eu-west-1 in Ireland). Each region is independent — data does not leave a region unless explicitly configured. Regions have multiple AZs for resilience. Choosing a region: latency to users, data sovereignty requirements, service availability. Availability Zone (AZ): one or more discrete data centers within a region with independent power, cooling, networking, and physical security. AZs are connected by low-latency links. Example: us-east-1 has 6 AZs (us-east-1a through 1f). Deploying across multiple AZs provides fault tolerance — if one AZ fails, the application continues running in other AZs. AWS has 31+ Regions and 99+ AZs. Google Cloud calls these “Zones” within “Regions.” Understanding regions and AZs is fundamental to cloud architecture design.

83. What is cloud computing’s edge computing concept?

  1. Cloud computing performed only at the geographic edges (border regions) of countries
  2. A distributed computing paradigm that processes data closer to where it is generated (at the network edge — on devices, local servers, or regional data centers) rather than sending it to a centralized cloud data center — reducing latency and bandwidth usage
  3. The cutting-edge (most advanced) computing services offered by top cloud providers
  4. A security feature that monitors activity at the edges (perimeters) of cloud networks

Answer : B
Explanation: Edge Computing brings computation and data storage closer to where data is generated rather than relying on a central cloud. Why edge? Latency-sensitive applications (autonomous vehicles, industrial robots, real-time gaming) cannot afford the 50-200ms round trip to a distant cloud data center. Bandwidth: processing video locally avoids transmitting terabytes of data to the cloud. Privacy: sensitive data processed locally never leaves the device. Examples: IoT sensors processing data at the factory floor, smart cameras detecting defects locally, autonomous vehicles making split-second decisions on-board, retail checkout kiosks, content caching at CDN PoPs. Cloud provider edge services: AWS Wavelength (5G edge), AWS Outposts (AWS rack in your data center), Azure Stack Edge, Google Distributed Cloud. Edge computing complements (not replaces) cloud — process time-sensitive data at the edge, send insights and aggregated data to cloud for long-term storage and analysis.

84. What is a Virtual Private Cloud (VPC) in cloud computing?

  1. A private cloud built using virtual reality technology for immersive user interaction
  2. An isolated, private virtual network within a public cloud provider’s infrastructure — allowing users to define their own IP address ranges, subnets, routing tables, and network gateways, similar to a traditional on-premise network but hosted in the cloud
  3. A virtual private server (VPS) used for hosting personal cloud storage systems
  4. A cloud provider’s private internal network used only for internal company operations

Answer : B
Explanation: A VPC provides a logically isolated section of the cloud provider’s network. Key components: Subnets (divide VPC into public and private zones), Internet Gateway (allows public subnet resources to access the internet), NAT Gateway (allows private subnet resources to access the internet without being publicly accessible), Security Groups (stateful firewall for EC2 instances — allow/deny inbound and outbound traffic), Network ACLs (stateless firewall at the subnet level), Route Tables (control traffic routing between subnets and gateways), and VPC Peering (connect two VPCs). Example: web servers in a public subnet (internet-accessible), databases in a private subnet (no direct internet access). AWS VPC is automatically created per account per region. Azure calls this Virtual Network (VNet). GCP uses Global VPC spanning all regions. VPC is fundamental to cloud security architecture.

85. What is cloud monitoring and observability?

  1. A physical security system that monitors cloud provider data center premises with cameras
  2. The practice of collecting, analyzing, and visualizing metrics, logs, and traces from cloud systems to understand their health and performance — enabling proactive detection and resolution of issues before they impact users
  3. A cloud service that monitors employee productivity when working remotely from home
  4. A tool that observes and records all customer interactions with cloud applications

Answer : B
Explanation: Cloud monitoring and observability is based on three pillars (the three pillars of observability): Metrics — numerical measurements over time (CPU usage, request latency, error rate, memory). Logs — timestamped records of discrete events (application errors, security events, access logs). Traces — records of requests as they flow through distributed microservices (helps identify bottlenecks in complex systems). Cloud monitoring tools: AWS CloudWatch (metrics, logs, alarms, dashboards), Azure Monitor (metrics, logs, Application Insights), Google Cloud Operations (formerly Stackdriver). Third-party observability platforms: Datadog, New Relic, Prometheus + Grafana (open-source stack), Elastic APM, Jaeger (distributed tracing). Key use cases: alerting when CPU > 90%, identifying slow database queries, tracing a failed payment through 12 microservices, and capacity planning based on growth trends.

86. What is disaster recovery (DR) in cloud computing?

  1. A cloud service for recovering lost or deleted files from cloud storage
  2. A set of policies, tools, and procedures enabling the recovery of critical IT systems and data after a disruptive event — cloud provides faster, cheaper DR through geographically distributed infrastructure, automated failover, and pay-per-use DR environments
  3. A cloud security service that prevents disasters by blocking all incoming attack traffic
  4. A physical disaster preparedness plan for cloud provider data center employees

Answer : B
Explanation: Cloud DR provides business continuity when disasters occur (hardware failure, natural disaster, ransomware attack, power outage). Key DR metrics: RTO (Recovery Time Objective) — maximum acceptable downtime before systems must be restored. RPO (Recovery Point Objective) — maximum acceptable data loss (how old can recovered data be?). Cloud DR strategies (in order of cost and recovery speed): Backup and Restore (cheapest, slowest — hours to days RTO/RPO), Pilot Light (minimal core infrastructure always running in DR region — hours RTO), Warm Standby (scaled-down version of production running in DR — minutes RTO), Active-Active/Multi-Site (full production running in multiple regions simultaneously — near-zero RTO). Cloud advantages for DR: no need to maintain idle DR hardware (pay only during actual DR), geographic distribution (multi-region), automation (infrastructure-as-code rebuilds environments automatically), and global replication services.

87. What is Function as a Service (FaaS) and how does it differ from traditional cloud services?

  1. A service that provides mathematical functions as cloud-hosted computation units
  2. A cloud execution model (also called serverless) where individual functions are deployed and executed in response to events — without managing any server infrastructure, scaling automatically to zero when idle
  3. A cloud feature that maps business functions (HR, Finance) to specific cloud services
  4. A service model where cloud providers offer consulting functions to enterprise customers

Answer : B
Explanation: FaaS (Function as a Service) is the serverless execution model. Developers deploy individual functions (small units of business logic) that are triggered by events. The provider handles all infrastructure management. Key differences from traditional services: Granular billing (per invocation + execution duration in milliseconds vs. per hour for VMs), True zero-scale (no cost when idle — vs. always-on VMs), Instant auto-scale (from 0 to thousands of concurrent executions in seconds), and Stateless by design. FaaS examples: AWS Lambda (triggers: S3 events, API Gateway, SQS, DynamoDB streams), Azure Functions (triggers: HTTP, timer, blob storage, Service Bus), Google Cloud Functions. Common use cases: real-time file processing, REST API backends, chatbots, scheduled jobs, IoT event processing, webhook handlers. FaaS is part of the broader “serverless” ecosystem alongside managed databases, queues, and storage.

88. What is cloud computing’s high availability (HA) concept?

  1. A cloud service that provides extra-fast processing speeds for premium customers
  2. The ability of a cloud system to remain operational and accessible for the vast majority of time — typically measured as a percentage (99.9%, 99.99%, 99.999%) — achieved through redundancy, failover mechanisms, load balancing, and multi-AZ deployments
  3. A cloud setting that makes applications visible to the highest number of users simultaneously
  4. A feature that allows cloud providers to access customer systems with high privileges

Answer : B
Explanation: High Availability (HA) ensures systems remain available even when individual components fail. HA uptime targets: 99% = 87.6 hours downtime/year (insufficient for most businesses), 99.9% (three nines) = 8.76 hours/year, 99.99% (four nines) = 52.6 minutes/year, 99.999% (five nines) = 5.26 minutes/year — used in telecommunications and critical systems. HA design principles: Eliminate single points of failure (deploy across multiple AZs/Regions), Use load balancers (distribute traffic, detect unhealthy instances), Implement auto-healing (automatically replace failed instances), Use managed services (AWS RDS Multi-AZ, Azure SQL with geo-redundancy automatically handles failover), and Design for failure (assume components will fail and build accordingly). AWS SLA for EC2 with multiple instances across AZs: 99.99%. Active-active vs. Active-passive failover: active-active keeps all instances serving traffic (better availability); active-passive keeps standby ready to activate on failure.

89. What is cloud computing’s managed service concept?

  1. A cloud service where the provider’s staff manages all customer business processes
  2. Cloud services where the provider handles the operational burden of running, patching, backing up, and scaling the underlying infrastructure — customers only configure and use the service (e.g., managed databases, managed Kubernetes)
  3. A consulting service where cloud experts manage a customer’s cloud migration project
  4. A premium support tier where a dedicated cloud engineer manages a customer’s account

Answer : B
Explanation: Managed Services shift operational responsibility to the cloud provider — reducing operational overhead for customers. Examples: Managed Database: AWS RDS, Azure SQL Database, Google Cloud SQL (provider handles installation, patching, backups, failover, scaling). Managed Kubernetes: AWS EKS, Azure AKS, GKE (provider handles the Kubernetes control plane). Managed Message Queue: AWS SQS, Azure Service Bus (provider handles scaling, replication, durability). Managed Analytics: AWS EMR (Hadoop/Spark), Google Dataproc. Benefits: reduced ops team workload, automatic patching and updates (security and performance), built-in high availability, SLA-backed reliability, and faster time to production. Trade-off: less control/customization than self-managed services. Most cloud-native architectures prefer managed services to minimize undifferentiated heavy lifting (operations that don’t differentiate the business but consume engineering time).

90. What is the difference between a public cloud and an on-premises (on-premise) data center?

  1. A public cloud uses wireless connections; on-premise uses wired connections only
  2. A public cloud provides shared, internet-accessible infrastructure owned by a third-party provider (pay-per-use, instant scalability, no hardware ownership); an on-premise data center is privately owned infrastructure within the organization’s facility (upfront CapEx, fixed capacity, full control)
  3. On-premise data centers are always more secure than public cloud environments
  4. Public clouds only store data in the USA; on-premise stores data anywhere in the world

Answer : B
Explanation: Key differences: Ownership — Public cloud: third-party (AWS, Azure, GCP); On-premise: customer owns hardware. Cost model — Cloud: OpEx (pay-per-use); On-premise: CapEx (large upfront purchase) + ongoing maintenance. Scalability — Cloud: instant, elastic; On-premise: requires months to procure new hardware. Control — Cloud: limited (shared responsibility); On-premise: full control of hardware and software. Maintenance — Cloud: provider handles hardware maintenance; On-premise: customer’s IT team manages everything. Availability — Cloud: global infrastructure with built-in redundancy; On-premise: requires separate DR investment. Time to deploy — Cloud: minutes; On-premise: months for new hardware. Most organizations today use a hybrid approach — migrating suitable workloads to cloud while keeping legacy systems, sensitive data, or latency-critical applications on-premise.

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