Amazon AWS Certified Solutions Architect – Professional SAP-C02 Exam Dumps and Practice Test Questions Set 7 Q91-105

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Question 91:

A global e-commerce company wants to migrate its microservices-based architecture to AWS. The solution must ensure high availability, fault tolerance, seamless deployment of microservices, and support for event-driven processing. Which AWS architecture best addresses these requirements?

A) Amazon ECS with Fargate for container orchestration, Application Load Balancer, Amazon RDS Multi-AZ, Amazon SQS, and EventBridge for event-driven workflows
B) EC2 instances with manual deployment of microservices, Elastic Load Balancer, and RDS Single-AZ
C) Lambda functions for all microservices, S3 for data storage, and API Gateway for exposure
D) On-premises servers using Kubernetes for container orchestration and VPN to AWS

Answer:

A) Amazon ECS with Fargate for container orchestration, Application Load Balancer, Amazon RDS Multi-AZ, Amazon SQS, and EventBridge for event-driven workflows

Explanation:

Migrating a microservices-based e-commerce architecture to AWS requires designing for high availability, fault tolerance, scalability, and efficient management of microservices. Amazon ECS with Fargate provides a fully managed container orchestration service, allowing teams to deploy containers without managing underlying EC2 instances. Fargate eliminates server provisioning and patching overhead while supporting fine-grained scaling based on traffic demands. This ensures that microservices can handle sudden spikes during peak shopping periods, such as seasonal sales or flash events, without impacting performance.

An Application Load Balancer (ALB) distributes incoming requests across multiple microservice containers, providing high availability and fault tolerance. ALB supports path-based and host-based routing, enabling multiple microservices to share a single domain while allowing each service to scale independently. This setup minimizes downtime and provides redundancy across Availability Zones.

Amazon RDS with Multi-AZ deployments ensures database high availability and failover support. Multi-AZ RDS automatically replicates data synchronously across different Availability Zones, reducing the risk of data loss and ensuring transactional integrity during failures. Combined with read replicas, it can also improve read performance and scale workloads.

Event-driven processing is critical for microservices to communicate efficiently and decouple dependencies. Amazon SQS provides reliable message queuing for asynchronous communication, while EventBridge enables event-driven architecture by routing events between services. This allows the microservices to react to changes in real time, such as order placements, inventory updates, and payment confirmations, without tight coupling or polling overhead.

Option B relies on manually managed EC2 instances and Single-AZ RDS, which introduces operational complexity, single points of failure, and scaling limitations. Option C, using Lambda for all microservices, may not support complex stateful services or long-running tasks efficiently and could lead to cold-start latency issues for high-traffic endpoints. Option D, keeping on-premises servers with Kubernetes, reduces the benefits of AWS-managed services, increases management overhead, and limits scalability, global reach, and integration with other AWS services.

The architecture in Option A also supports observability and monitoring through Amazon CloudWatch, providing metrics, logs, and alarms for microservices and database performance. Security can be enforced with IAM roles, VPC configurations, and security groups, ensuring controlled access to resources. Automated CI/CD pipelines can be implemented using AWS CodePipeline and CodeBuild for seamless deployment of microservices across environments. This architecture ensures scalability, resilience, security, and operational efficiency while minimizing operational overhead, making it ideal for a global e-commerce company that must maintain continuous uptime, support rapid growth, and respond quickly to market demands.

Question 92:

A manufacturing company wants to implement a real-time quality control system using cameras on production lines. The system should detect defects, send alerts, and store the inspection data for analytics. Which AWS architecture is most suitable?

A) AWS IoT Greengrass for edge processing, Amazon Rekognition for image analysis, Kinesis Data Streams for event ingestion, and S3 for storage
B) EC2 instances with custom image processing scripts, S3 for storage, and manual alerting
C) Lambda functions with S3 triggers for batch processing, DynamoDB for metadata storage
D) On-premises image processing servers with VPN to AWS for storage

Answer:

A) AWS IoT Greengrass for edge processing, Amazon Rekognition for image analysis, Kinesis Data Streams for event ingestion, and S3 for storage

Explanation:

Implementing a real-time quality control system on a production line requires low-latency image processing, scalable analytics, and automated alerting. AWS IoT Greengrass extends AWS capabilities to edge devices, allowing image processing to occur locally on the production line. This reduces latency, ensures immediate detection of defects, and prevents defective products from advancing further along the manufacturing process. Greengrass can pre-process images, filter irrelevant data, and transmit only necessary insights to the cloud, optimizing bandwidth and reducing processing costs.

Amazon Rekognition is a fully managed computer vision service that can analyze images and videos to detect defects, anomalies, or deviations from standard product specifications. Rekognition supports object and label detection, enabling automated identification of issues such as scratches, misalignments, or missing components. By integrating Rekognition with Greengrass, defect detection can occur at the edge with minimal delay, while results are streamed to AWS for storage, analytics, and action.

Kinesis Data Streams provides real-time ingestion of detection events, enabling downstream processing and automated alerting. Streamed data can trigger Amazon Lambda functions or workflows orchestrated via Step Functions to notify operators, update dashboards, or initiate corrective actions. This real-time event-driven design ensures immediate response and minimizes defective product throughput.

Amazon S3 provides durable and scalable storage for raw images, inspection logs, and processed metadata. Data stored in S3 can be leveraged for historical analysis, predictive maintenance, process optimization, and compliance reporting. S3’s integration with Athena and QuickSight enables ad hoc queries and visualization of inspection trends across production lines, facilitating continuous improvement and operational insights.

Option B, relying solely on EC2 instances with custom scripts, introduces operational complexity, single points of failure, and challenges in scaling to multiple production lines or facilities. Option C, using Lambda with S3 triggers, may lead to delayed processing for high-volume streams and is better suited for batch workflows than real-time inspection. Option D, using on-premises servers, introduces latency, limits scalability, and complicates centralized analytics and monitoring.

The architecture in Option A provides an end-to-end, scalable, and secure solution. IoT Greengrass ensures edge processing security and device authentication, while Rekognition provides AI-driven inspection. Event streaming via Kinesis enables automated and real-time operational workflows. Cloud storage in S3 ensures durability, accessibility, and integration with analytics services. Overall, this design supports real-time quality control, minimizes defect propagation, improves operational efficiency, and allows data-driven process improvement in a scalable and secure manner, suitable for a global manufacturing environment.

Question 93:

A healthcare organization wants to implement a HIPAA-compliant electronic health record (EHR) system on AWS. The system must provide high availability, secure data storage, audit logging, and automated failover while supporting concurrent access from multiple hospitals. Which AWS architecture best meets these requirements?

A) Amazon RDS for PostgreSQL Multi-AZ deployment with encrypted storage, EC2 instances behind an Application Load Balancer, S3 with KMS encryption for backups, and CloudTrail for audit logging
B) EC2 instances with local storage, manual replication between hospitals, and VPN connections
C) Amazon DynamoDB for all patient records, S3 for backups, and Lambda for access control
D) On-premises EHR servers with AWS Direct Connect for offsite backups

Answer:

A) Amazon RDS for PostgreSQL Multi-AZ deployment with encrypted storage, EC2 instances behind an Application Load Balancer, S3 with KMS encryption for backups, and CloudTrail for audit logging

Explanation:

Deploying a HIPAA-compliant EHR system requires a highly available, secure, and auditable architecture. Amazon RDS with PostgreSQL Multi-AZ deployments provides managed database services with synchronous replication across Availability Zones, ensuring automatic failover and high availability. Multi-AZ deployment guarantees continuity of operations during hardware or AZ failures while providing strong data consistency for concurrent access from multiple hospitals. Encrypted storage using AWS KMS ensures that sensitive patient data is protected both at rest and in transit, meeting HIPAA compliance requirements.

EC2 instances behind an Application Load Balancer provide scalable compute resources for handling concurrent access to the EHR application. ALB supports SSL/TLS termination for secure communication and distributes traffic evenly across multiple EC2 instances, ensuring both high availability and low latency. Auto Scaling policies can be configured to dynamically adjust compute resources based on demand, maintaining consistent performance even during peak usage periods.

Amazon S3, integrated with KMS for encryption, provides durable storage for backups, audit logs, and archival data. Regular snapshots and versioning in S3 ensure point-in-time recovery and compliance with data retention policies. S3 also integrates with lifecycle policies to move infrequently accessed data to cost-optimized storage classes without sacrificing security or accessibility.

AWS CloudTrail enables audit logging of all API calls and administrative actions, providing a comprehensive audit trail necessary for HIPAA compliance. Combined with CloudWatch and AWS Config, the architecture ensures continuous monitoring, detection of anomalous access patterns, and compliance reporting. Role-based access control using IAM and fine-grained policies ensures that only authorized personnel have access to patient records.

Option B, using EC2 with local storage and VPNs, introduces operational overhead, lacks automated failover, and complicates audit and compliance reporting. Option C, using DynamoDB, may not support complex transactional requirements and multi-hospital consistency for EHR systems. Option D, relying on on-premises servers, increases operational complexity, reduces scalability, and limits high availability.

The architecture in Option A supports security, reliability, and compliance while enabling global access across multiple hospitals. It leverages managed services to reduce operational overhead, provides robust disaster recovery, and integrates with AWS security and monitoring tools. This ensures secure, compliant, and highly available access to patient records while maintaining operational efficiency and adherence to HIPAA standards.

Question 94:

A financial services company wants to implement a fraud detection system for real-time transactions. The system should ingest transaction data from multiple sources, detect anomalies in near real-time, and provide alerts to downstream systems for immediate action. Which AWS architecture is most suitable?

A) Amazon Kinesis Data Streams for ingestion, AWS Lambda for processing, Amazon SageMaker for anomaly detection, Amazon SNS for alerts, and S3 for archival storage
B) EC2 instances with a batch script to process daily transaction files and send alerts via email
C) AWS Glue for ETL, S3 for storage, and Athena queries run daily to detect anomalies
D) On-premises servers running legacy fraud detection software, with VPN to AWS for backup

Answer:

A) Amazon Kinesis Data Streams for ingestion, AWS Lambda for processing, Amazon SageMaker for anomaly detection, Amazon SNS for alerts, and S3 for archival storage

Explanation:

Designing a real-time fraud detection system in a financial services environment requires addressing several critical requirements: high throughput ingestion of streaming transaction data, low-latency anomaly detection, automated alerting, scalability, fault tolerance, and compliance with financial regulations. The architecture in Option A addresses these needs comprehensively using fully managed AWS services.

Data Ingestion: Amazon Kinesis Data Streams provides a high-throughput, low-latency service to ingest real-time transaction data from multiple sources, including POS terminals, mobile applications, ATM networks, and online banking platforms. Kinesis supports sharding, allowing the system to scale horizontally to handle peaks in transaction volume without data loss. Kinesis Data Streams ensures ordering of transactions, which is essential for sequential analysis of transactional patterns, enabling accurate anomaly detection.

Processing: AWS Lambda allows serverless real-time processing of transaction events as they arrive in Kinesis. Lambda provides an event-driven compute model, scaling automatically based on the number of incoming transactions. Using Lambda, transactions can be pre-processed, normalized, and enriched with contextual information such as customer profiles, historical behavior, and geolocation data. This processing is critical to prepare data for the anomaly detection model while keeping latency minimal, which is essential for real-time alerting.

Anomaly Detection: Amazon SageMaker enables the deployment of machine learning models capable of detecting anomalies indicative of potential fraud. Models can be trained on historical transaction data to recognize patterns of legitimate versus fraudulent activity. By integrating SageMaker endpoints with Lambda functions, the architecture achieves real-time inference, allowing the system to flag suspicious transactions immediately. SageMaker provides model monitoring capabilities to detect model drift, ensuring ongoing accuracy and reliability of fraud detection algorithms.

Alerting: Amazon SNS provides a fully managed pub/sub service to distribute alerts generated by the anomaly detection model. Alerts can be delivered to downstream systems, including internal risk management dashboards, mobile notifications to fraud analysts, or automated workflows that suspend suspicious transactions for further verification. SNS ensures reliable delivery with retry mechanisms, ensuring no critical alerts are lost in high-volume environments.

Data Storage and Compliance: Amazon S3 provides durable, cost-effective storage for raw and processed transaction data. S3 ensures compliance with regulatory requirements by enabling versioning, server-side encryption with KMS, and access logging. Archival of historical data is crucial for audit purposes, model retraining, and regulatory reporting. Integration with services like AWS Lake Formation or Athena can allow secure analytics and querying of historical transaction patterns without impacting real-time processing.

Option B, using EC2 instances with batch processing, is unsuitable for real-time fraud detection because it introduces unacceptable latency. Fraud detection requires immediate response, and batch processing on daily transaction files would delay alerts and allow fraudulent activity to go unchecked. Option C, using Glue, S3, and Athena with daily queries, also suffers from delayed detection, making it unsuitable for real-time intervention. Option D, relying on on-premises servers, introduces high operational complexity, limited scalability, and longer response times.

The architecture in Option A ensures high availability, with Kinesis Data Streams replicating data across multiple Availability Zones, Lambda providing serverless auto-scaling, and SageMaker endpoints supporting fault-tolerant inference. The decoupled design ensures resilience, with each component independently scalable. Security and compliance are maintained via IAM roles, VPC configurations, encryption in transit and at rest, and audit logging via CloudTrail.

In addition, this architecture supports continuous improvement. Historical transaction data stored in S3 can be used for retraining machine learning models, improving detection accuracy over time. Integration with Amazon CloudWatch allows monitoring of throughput, processing latency, and error rates, enabling proactive operational management. Alerts can trigger automated workflows using Step Functions, enhancing operational efficiency.

By leveraging AWS managed services, this architecture minimizes operational overhead while providing scalable, secure, fault-tolerant, and real-time fraud detection capabilities, critical for a financial services company operating in a highly regulated and dynamic environment.

Question 95:

A retail company wants to implement a personalized recommendation system for its online platform. The system must process clickstream data in real-time, provide recommendations to users dynamically, and update recommendations based on user behavior. Which AWS services should be combined to achieve this solution?

A) Amazon Kinesis Data Streams for clickstream ingestion, Amazon Personalize for recommendations, Lambda for real-time updates, and DynamoDB for storing user interaction data
B) S3 for storing clickstream logs, Athena for batch analysis, and EC2 instances serving precomputed recommendations
C) Amazon RDS for storing clickstream data, manual machine learning model training on EC2, and S3 for storing results
D) On-premises Hadoop cluster for clickstream processing and recommendation generation

Answer:

A) Amazon Kinesis Data Streams for clickstream ingestion, Amazon Personalize for recommendations, Lambda for real-time updates, and DynamoDB for storing user interaction data

Explanation:

Delivering personalized recommendations in real-time requires a combination of event-driven architecture, machine learning capabilities, scalable storage, and low-latency data processing. Option A integrates AWS services to meet these requirements efficiently.

Clickstream Data Ingestion: Amazon Kinesis Data Streams captures user interactions, including clicks, page views, and purchases, in real-time. This streaming capability ensures that user behavior data is continuously fed into the recommendation system, enabling immediate reflection of changes in user preferences. Kinesis supports horizontal scaling via shards to handle high-volume traffic, ensuring no data loss during peak periods, such as holiday sales or flash promotions.

Recommendation Engine: Amazon Personalize is a fully managed machine learning service optimized for building real-time personalization and recommendation systems. Personalize leverages historical and streaming user behavior data to train models for item-to-item, personalized ranking, and real-time recommendations. By integrating Personalize with Kinesis, the system continuously updates the recommendation models to reflect the most recent interactions, providing a dynamic and adaptive user experience.

Real-Time Updates: AWS Lambda enables serverless, event-driven processing of clickstream data. As Kinesis streams events arrive, Lambda functions can preprocess data, update user interaction records in DynamoDB, and call Personalize endpoints to fetch updated recommendations. This ensures near-instant personalization for users, reducing latency between a user action and the corresponding update in recommendations.

User Data Storage: Amazon DynamoDB provides a fast, scalable, and fully managed NoSQL database for storing user interactions, session history, and other metadata required for generating recommendations. DynamoDB supports high read/write throughput and low latency access, essential for delivering personalized content quickly. It also allows integration with AWS IAM for secure access control, ensuring compliance with data privacy regulations.

Option B relies on batch processing with S3 and Athena, which cannot provide real-time updates and would result in stale recommendations. Option C requires manual model training and EC2 management, adding operational complexity and latency. Option D, using on-premises Hadoop, lacks the elasticity, real-time capabilities, and managed ML services offered by AWS, making it unsuitable for responsive personalization at scale.

The architecture in Option A also supports scalability and resilience. Kinesis Data Streams is multi-AZ by design, ensuring durability and fault tolerance. Lambda automatically scales with incoming events, while DynamoDB provides predictable performance under high loads. Security best practices, including encryption at rest (KMS) and in transit (TLS), ensure data protection. CloudWatch enables monitoring of system metrics, alerting, and operational visibility.

Furthermore, this architecture enables continuous learning. Interaction data stored in DynamoDB and historical clickstream records can be used to retrain Personalize models, improving recommendation accuracy over time. Real-time insights into user behavior allow marketers to implement adaptive campaigns, A/B testing, and personalized promotions. Automated pipelines using AWS Step Functions can orchestrate preprocessing, model updates, and recommendation delivery efficiently.

By leveraging AWS managed services, Option A delivers scalable, secure, real-time, and adaptive personalization, ensuring enhanced user engagement, increased conversion rates, and operational efficiency for a retail company with dynamic online traffic and diverse user behavior patterns.

Question 96:

A media company wants to implement a scalable video streaming platform on AWS. The platform should deliver high-quality video globally, provide low latency, and automatically scale to handle large spikes in traffic. Which AWS architecture best supports these requirements?

A) Amazon CloudFront for global content delivery, AWS Elemental MediaConvert for video processing, Amazon S3 for storage, Elastic Load Balancer with Auto Scaling EC2 instances for serving dynamic content
B) EC2 instances serving videos directly from local storage without a CDN
C) On-premises media servers with AWS Direct Connect for hybrid delivery
D) S3 static hosting with daily batch video processing and manual scaling of EC2 instances

Answer:

Explanation:

Delivering a high-quality, scalable, and low-latency video streaming platform requires an architecture that addresses global content distribution, automated scaling, efficient video processing, and durability of storage. Option A leverages AWS managed services to meet these requirements comprehensively.

Content Delivery: Amazon CloudFront is a globally distributed content delivery network (CDN) that caches video content at edge locations close to viewers. This reduces latency, optimizes streaming performance, and handles large spikes in concurrent users without impacting origin servers. CloudFront supports adaptive bitrate streaming, which adjusts video quality dynamically based on network conditions, ensuring a smooth viewing experience.

Video Processing: AWS Elemental MediaConvert enables on-demand transcoding of video files into multiple formats and resolutions suitable for different devices and bandwidth conditions. MediaConvert integrates with S3 for input and output storage, automating the preparation of videos for streaming. It supports features like captions, DRM encryption, and adaptive bitrate packaging, essential for delivering a professional streaming experience.

Storage: Amazon S3 provides durable and scalable storage for original and processed video content. S3 ensures high availability and durability across multiple Availability Zones, and integrates with CloudFront for efficient content delivery. Lifecycle policies can manage storage costs by moving older or less frequently accessed videos to Glacier or S3 Intelligent-Tiering.

Dynamic Content Serving: Elastic Load Balancer (ALB) combined with Auto Scaling EC2 instances handles dynamic content, such as user authentication, recommendations, and playback analytics. Auto Scaling ensures that compute capacity automatically adjusts based on traffic patterns, maintaining performance during peak streaming events.

Option B, serving videos directly from EC2 local storage without a CDN, introduces latency for global users and cannot efficiently handle spikes in demand. Option C, using on-premises servers, lacks elasticity, global reach, and requires high operational effort. Option D, relying on S3 static hosting with batch processing, cannot deliver real-time streaming or scale dynamically for live traffic spikes.

This architecture ensures high availability, low latency, operational efficiency, and scalability. CloudFront provides global reach, MediaConvert ensures compatibility with various devices and formats, S3 offers durability, and ALB with Auto Scaling EC2 ensures application logic scales with demand. Security measures, including encryption, IAM access controls, and HTTPS streaming, protect content and user data.

Additionally, monitoring and analytics are facilitated through CloudWatch metrics, CloudTrail logging, and integration with AWS Kinesis for real-time analytics of playback behavior. This allows the media company to optimize performance, detect anomalies, and continuously improve the streaming experience.

By leveraging AWS managed services, Option A delivers a fully scalable, globally accessible, high-quality video streaming platform capable of handling dynamic demand, ensuring low latency and high availability, while minimizing operational complexity and cost.

Question 97:

A healthcare provider needs to build a secure and scalable data lake for storing patient records, imaging data, and lab results. The solution should provide fine-grained access control, allow analytics using AWS services, and ensure compliance with healthcare regulations. Which AWS architecture best meets these requirements?

A) Amazon S3 for centralized storage, AWS Lake Formation for data lake management and access control, AWS Glue for ETL, and Amazon Athena and Amazon Redshift Spectrum for analytics
B) EC2 instances running Hadoop HDFS with manual IAM policies for access control and Spark for analytics
C) Amazon RDS for storing all patient data, with S3 for backups
D) On-premises storage arrays integrated with AWS Direct Connect for analytics

Answer:

A) Amazon S3 for centralized storage, AWS Lake Formation for data lake management and access control, AWS Glue for ETL, and Amazon Athena and Amazon Redshift Spectrum for analytics

Explanation:

Designing a secure and compliant healthcare data lake involves several architectural considerations, including data security, fine-grained access control, scalability, durability, regulatory compliance, and analytics capabilities. Option A leverages managed AWS services to meet these needs effectively.

Amazon S3 serves as the foundation for a centralized, highly durable, and scalable storage layer capable of handling large volumes of structured, semi-structured, and unstructured healthcare data, including patient records, imaging files, and laboratory results. S3 provides 99.999999999 percent durability, ensuring data is resilient to hardware failures, and integrates with encryption features such as server-side encryption with AWS KMS to protect sensitive data at rest. Data can also be encrypted in transit using HTTPS, which is essential for maintaining HIPAA compliance and safeguarding patient privacy.

AWS Lake Formation simplifies the creation and management of a secure data lake on S3. It provides centralized, fine-grained access control at the table, column, and row levels, enabling different user groups, such as clinicians, researchers, and administrators, to access only the data they are authorized to view. Lake Formation enforces consistent security policies across multiple analytics services, including Athena, Redshift Spectrum, and EMR. Additionally, Lake Formation supports auditing and logging via AWS CloudTrail, helping meet compliance requirements for healthcare data access and usage monitoring.

AWS Glue enables ETL (Extract, Transform, Load) workflows to prepare and cleanse data for analytics. Healthcare data is often heterogeneous, coming from multiple sources with varying formats, such as HL7 messages, DICOM images, CSV lab results, and JSON API responses. Glue provides a serverless environment to automate schema discovery, data cataloging, and transformation, ensuring that analytics services can query the data efficiently. Glue also integrates with Lake Formation to maintain access control policies consistently during data transformation processes.

For analytics, Amazon Athena allows querying structured and semi-structured data directly in S3 using standard SQL, without requiring data movement. Redshift Spectrum extends Amazon Redshift capabilities to query large datasets stored in S3, allowing complex analytics and BI reporting. By combining Athena and Redshift Spectrum, organizations can perform scalable analytics on petabyte-scale datasets while maintaining fine-grained security policies defined in Lake Formation.

Option B, using EC2 and Hadoop, introduces significant operational complexity, requires manual implementation of access controls, and increases the risk of misconfigurations that can compromise sensitive healthcare data. Option C, using RDS, is unsuitable for unstructured data such as medical imaging and does not scale cost-effectively for petabyte-scale datasets. Option D, relying on on-premises storage, reduces elasticity, increases operational overhead, and lacks the integrated analytics and managed security services available in AWS.

This architecture supports scalability, as S3 scales automatically to accommodate growing healthcare datasets, and Glue provides serverless processing. Security is enforced through encryption, IAM policies, and Lake Formation permissions. Monitoring and auditing are supported through CloudWatch, CloudTrail, and AWS Config.

The solution also supports continuous improvement and compliance. Historical and current healthcare datasets can be used for research analytics, predictive modeling, and operational insights. Data scientists can build ML models using Amazon SageMaker to predict patient outcomes, optimize workflows, and enhance decision-making. Regulatory compliance, including HIPAA and GDPR, is maintained through encryption, access control, logging, and audit capabilities, ensuring patient data privacy and governance.

Overall, Option A provides a scalable, secure, compliant, and highly available architecture for healthcare organizations seeking a modern data lake to support analytics, research, and operational efficiency, while minimizing administrative overhead and ensuring regulatory adherence.

Question 98:

A gaming company wants to deploy a multiplayer game backend that can handle millions of concurrent players globally. The system must provide low latency, auto-scaling, and session state management. Which architecture should the company implement on AWS?

A) Amazon GameLift for game session management, Amazon DynamoDB for player state, Amazon CloudFront for global content delivery, and AWS Lambda for event processing
B) EC2 instances in a single region with manual load balancing and local databases
C) On-premises servers distributed across multiple data centers
D) S3 hosting of game assets and batch processing of player events

Answer:

A) Amazon GameLift for game session management, Amazon DynamoDB for player state, Amazon CloudFront for global content delivery, and AWS Lambda for event processing

Explanation:

Building a highly scalable, low-latency multiplayer gaming backend requires addressing session management, state persistence, global delivery, and event-driven processing. Option A leverages managed AWS services designed to meet these requirements at scale.

Amazon GameLift is a fully managed service specifically for deploying, operating, and scaling session-based multiplayer game servers. GameLift automatically provisions and scales game servers based on player demand, distributes sessions across available servers, and provides matchmaking and player session management. Using GameLift reduces operational complexity and ensures that global gaming traffic can be handled efficiently without server bottlenecks.

Player state, such as progress, inventory, or real-time stats, is stored in Amazon DynamoDB. DynamoDB provides single-digit millisecond latency for reads and writes, essential for real-time game interactions. It is fully managed, scales horizontally without downtime, and provides high availability through multi-AZ replication. DynamoDB streams enable near real-time event processing, allowing backend logic to respond to changes in player state.

Amazon CloudFront serves static game assets, patches, and downloadable content to players globally. By caching content at edge locations, CloudFront reduces latency and improves the player experience, particularly during peak traffic periods or in regions far from primary game servers. CloudFront also integrates with AWS Shield and WAF to provide DDoS protection and secure content delivery.

AWS Lambda provides serverless event processing for game events, analytics, and notifications. Lambda functions can process real-time events such as achievements, in-game purchases, and leaderboard updates, without the need to manage underlying servers. Lambda scales automatically based on the number of events and integrates with other AWS services such as SNS, SQS, and Kinesis for asynchronous workflows and alerts.

Option B, using EC2 in a single region with manual load balancing, cannot scale efficiently to millions of concurrent players, introduces higher latency for global users, and increases operational overhead. Option C, on-premises servers, lacks the elasticity and global reach of AWS, making it difficult to handle sudden spikes in traffic and leading to high capital expenditure. Option D, relying on S3 and batch processing, cannot provide the low-latency, real-time gameplay experience required for multiplayer games.

The architecture in Option A ensures high availability, global reach, and operational efficiency. GameLift handles session placement and scaling, DynamoDB ensures fast state management, CloudFront delivers low-latency content worldwide, and Lambda processes events in real time. Security is maintained via IAM roles, encryption at rest and in transit, and integration with AWS Shield and WAF.

Additionally, analytics and monitoring can be integrated using CloudWatch, X-Ray, and Kinesis Data Firehose, providing insights into player behavior, server health, and performance metrics. This allows continuous optimization of the game experience and backend infrastructure.

Overall, Option A provides a fully managed, scalable, low-latency, globally distributed architecture for multiplayer gaming, capable of handling millions of concurrent players while minimizing operational complexity and ensuring an immersive player experience.

Question 99:

An e-commerce company wants to implement a serverless architecture for processing user orders. The system should handle high traffic during peak shopping periods, integrate with payment gateways, and send order notifications. Which combination of AWS services provides the best solution?

A) AWS Lambda for order processing, Amazon API Gateway for API endpoints, Amazon SQS for decoupling order events, Amazon DynamoDB for order state, and Amazon SNS for notifications
B) EC2 instances hosting a monolithic application with RDS for order storage and email servers for notifications
C) On-premises servers running order processing applications, connected via VPN to payment gateways
D) S3 static website hosting with batch scripts to process orders daily

Answer:

A) AWS Lambda for order processing, Amazon API Gateway for API endpoints, Amazon SQS for decoupling order events, Amazon DynamoDB for order state, and Amazon SNS for notifications

Explanation:

Designing a serverless order processing system requires handling dynamic traffic, ensuring fault tolerance, decoupling components, integrating with external services, and maintaining low operational overhead. Option A leverages AWS serverless services to achieve these objectives effectively.

AWS Lambda provides event-driven, serverless compute for processing orders in real time. It scales automatically in response to incoming traffic, ensuring the system can handle high traffic during peak shopping periods without manual intervention. Lambda functions can validate orders, call payment gateways, update order state in DynamoDB, and trigger notifications.

Amazon API Gateway exposes REST or HTTP APIs for submitting orders from web, mobile, or third-party applications. API Gateway integrates seamlessly with Lambda, providing authentication, throttling, request validation, and monitoring, enabling secure and scalable API endpoints.

Amazon SQS decouples order submission from processing, ensuring that high traffic does not overwhelm backend functions. SQS provides durable message storage with configurable retry policies, enabling asynchronous processing and fault tolerance. This ensures reliable delivery of order events even in the case of transient downstream service failures.

Amazon DynamoDB stores order state and metadata, providing fast, consistent, and scalable access to order information. DynamoDB streams allow integration with Lambda for real-time processing of order events, such as updating inventory or triggering notifications.

Amazon SNS distributes order notifications to customers, operations teams, or third-party systems. SNS supports multiple delivery protocols, including email, SMS, and HTTP endpoints, ensuring timely communication of order status.

Option B, using EC2 and RDS, increases operational complexity, does not scale as seamlessly for peak traffic, and requires manual scaling. Option C, on-premises servers, lacks elasticity, resilience, and the ability to handle dynamic spikes efficiently. Option D, using S3 and batch scripts, introduces high latency and cannot provide real-time order processing or notifications.

This architecture ensures high availability, fault tolerance, and operational simplicity. Lambda and API Gateway automatically scale to handle traffic, SQS provides buffering and decoupling, DynamoDB ensures low-latency data access, and SNS delivers real-time notifications. Security is maintained through IAM policies, encryption in transit and at rest, and API Gateway authentication mechanisms.

Monitoring and analytics can be achieved through CloudWatch, enabling tracking of request rates, function latency, error rates, and system health. Integration with X-Ray provides distributed tracing, allowing developers to identify bottlenecks and optimize performance.

Overall, Option A provides a scalable, fully managed, serverless solution for e-commerce order processing, capable of handling high traffic, integrating with external systems, providing real-time notifications, and minimizing operational overhead, making it ideal for modern online retail environments.

Question 100:

A financial services company needs a highly available, secure, and low-latency environment for processing real-time trading transactions. The system should ensure data integrity, disaster recovery, and compliance with financial regulations. Which AWS architecture provides the most robust solution?

A) Amazon VPC with multi-AZ deployment, Amazon RDS Multi-AZ for transaction data, Amazon ElastiCache for low-latency caching, AWS Direct Connect for private network connectivity, and Amazon CloudWatch for monitoring
B) Single EC2 instance hosting a relational database in a public subnet with periodic backups to S3
C) On-premises data center with VPN to AWS for backup and analytics only
D) Amazon S3 with batch processing using AWS Lambda to process transaction data daily

Answer:

Explanation:

Designing a financial services system for real-time trading requires careful attention to availability, latency, disaster recovery, security, and regulatory compliance. Option A provides a comprehensive architecture leveraging multiple AWS services to meet these requirements effectively.

Amazon VPC provides an isolated network environment, allowing fine-grained control over network access and routing. Multi-AZ deployment ensures high availability and resilience, distributing resources across multiple availability zones to mitigate the impact of a zone failure. This is critical for financial applications where downtime can lead to significant operational and financial losses.

Amazon RDS Multi-AZ deployments provide a managed relational database with synchronous replication to a standby instance in a different availability zone. This ensures high availability and automatic failover in case of an outage. RDS also integrates with AWS KMS for encryption at rest, ensuring that sensitive financial data is protected. Automated backups, snapshots, and point-in-time recovery enable disaster recovery and data integrity, which are crucial for financial systems where transactional consistency is mandatory.

Amazon ElastiCache is used to store frequently accessed trading data in memory, providing sub-millisecond response times and reducing load on the primary database. Low-latency caching is critical in trading environments where even minor delays can impact transaction execution. ElastiCache supports both Redis and Memcached engines, allowing developers to implement caching strategies for session data, reference data, and market feeds.

AWS Direct Connect establishes a private network connection between the financial institution’s on-premises data centers and AWS. This reduces latency, increases bandwidth stability, and enhances security by avoiding public internet traffic. Direct Connect is often a regulatory requirement for financial institutions that handle sensitive trading data.

Monitoring and logging with Amazon CloudWatch provides real-time visibility into system health, transaction throughput, latency, and anomalies. CloudWatch alarms, metrics, and dashboards allow operational teams to quickly detect issues, optimize performance, and maintain compliance with audit and reporting requirements. Additionally, AWS CloudTrail captures all API calls, ensuring accountability and traceability for all actions performed within the environment, which is essential for regulatory compliance.

Option B, using a single EC2 instance, introduces a single point of failure and cannot meet high availability or low-latency requirements. Option C, relying solely on on-premises systems, lacks elasticity and scalability, and does not provide the same high availability or disaster recovery capabilities offered by AWS. Option D, processing transactions in batch using S3 and Lambda, is unsuitable for real-time trading where instantaneous processing is required.

This architecture ensures high availability, low latency, security, and compliance. Multi-AZ deployments and failover mechanisms protect against outages. Encryption in transit and at rest protects sensitive financial data. Operational visibility is achieved through CloudWatch and CloudTrail, enabling proactive management and auditability.

Additionally, the architecture supports scalability and performance optimization. During peak trading hours, RDS read replicas and ElastiCache can absorb increased load. Auto Scaling groups for EC2-based microservices, if needed, can dynamically adjust capacity. Integration with AWS Identity and Access Management (IAM) enforces least-privilege access control for developers, operators, and third-party systems.

Disaster recovery strategies are further strengthened with cross-region replication of RDS and backups stored in Amazon S3, ensuring that critical trading data is preserved even in case of regional disasters. Security and compliance are maintained through encryption, fine-grained access control, network isolation, logging, monitoring, and adherence to financial regulatory standards such as SOC 2, PCI DSS, and FINRA regulations.

Question 101:

A media company wants to stream live video content globally with minimal latency and high availability. The system should scale automatically with viewer demand and support analytics for viewer engagement. Which AWS services provide the optimal solution?

A) Amazon CloudFront for content delivery, AWS Elemental MediaLive for video encoding, AWS Elemental MediaStore for storage, Amazon CloudWatch for monitoring, and Amazon Kinesis Data Analytics for real-time viewer metrics
B) S3 static hosting with periodic video uploads and manual CDN configuration
C) EC2 instances running NGINX for video streaming with manual scaling
D) On-premises video servers connected via VPN to AWS for analytics only

Answer:

Explanation:

Global live video streaming requires architecture optimized for low latency, high availability, scalability, and real-time analytics. Option A leverages AWS managed services to meet these objectives while minimizing operational complexity.

Amazon CloudFront delivers content with low latency by caching video segments at edge locations globally, bringing content closer to viewers. This improves performance and reduces buffering. CloudFront integrates with AWS Shield and WAF, protecting against DDoS attacks and providing secure delivery. CloudFront supports live streaming protocols, including HLS and DASH, which are standard for adaptive bitrate streaming.

AWS Elemental MediaLive encodes live video streams in real time, converting raw inputs into multiple output formats and bitrates suitable for various devices. MediaLive automatically scales to handle increased stream volume, ensuring smooth delivery during peak events. Integration with MediaStore provides durable, low-latency storage for segmenting video content, essential for live streaming workflows. MediaStore also supports consistent read and write operations, ensuring real-time delivery to viewers.

Amazon Kinesis Data Analytics allows real-time analysis of streaming data, such as viewer engagement metrics, concurrent viewers, playback quality, and geographic distribution. These insights inform operational decisions, improve the viewer experience, and support monetization strategies like ad placement and targeted content recommendations. Kinesis integrates seamlessly with CloudWatch, enabling alerts and dashboards for proactive monitoring.

CloudWatch monitors the health and performance of the streaming workflow, including MediaLive encoding pipelines, MediaStore storage performance, CloudFront distribution metrics, and Kinesis stream performance. Operational teams can detect bottlenecks, latency issues, or anomalies and respond quickly.

Option B, S3 static hosting with manual CDN configuration, is unsuitable for real-time streaming, as it cannot handle live input or adaptive bitrate streaming. Option C, using EC2 with NGINX, introduces operational complexity, manual scaling, and single points of failure. Option D, relying on on-premises servers, lacks global scalability, introduces higher latency, and limits analytical capabilities.

The architecture in Option A ensures high availability through globally distributed edge locations, managed encoding, and resilient storage. It supports elastic scalability, automatically adjusting capacity to match viewer demand. Security is maintained through encryption at rest and in transit, IAM policies, WAF, and AWS Shield.

Additionally, this architecture allows for content personalization and dynamic ad insertion, enhancing engagement and monetization. The integration with Kinesis Data Analytics provides real-time insights, enabling rapid response to changes in viewer behavior and quality-of-experience optimization.

Question 102:

A retail company wants to implement a predictive inventory management system to optimize stock levels across multiple stores. The solution should forecast demand, integrate with ERP systems, and provide actionable insights to store managers. Which AWS services should the company use?

A) Amazon Forecast for demand prediction, Amazon S3 for historical data storage, AWS Glue for ETL, Amazon SageMaker for custom ML models, and Amazon QuickSight for visualization
B) EC2 instances running Excel macros to process sales data manually
C) On-premises database with batch reporting to generate weekly forecasts
D) S3 static storage with manual calculations performed by store managers

Answer:

A) Amazon Forecast for demand prediction, Amazon S3 for historical data storage, AWS Glue for ETL, Amazon SageMaker for custom ML models, and Amazon QuickSight for visualization

Explanation:

Predictive inventory management requires accurate demand forecasting, integration with operational systems, actionable insights, and scalability. Option A provides a fully managed, serverless architecture to address these requirements efficiently.

Amazon Forecast uses historical sales data, seasonality, and external factors like promotions or holidays to generate accurate demand forecasts using machine learning models. Forecasting reduces stockouts and overstock, optimizing working capital and improving customer satisfaction. Forecast integrates with S3 and Glue, allowing ingestion of structured and semi-structured historical data from ERP and POS systems.

Amazon S3 provides highly durable, scalable storage for historical sales data, transactional data, and external datasets such as weather or economic indicators. S3 integrates with AWS Glue, enabling automated extraction, transformation, and loading workflows to prepare data for predictive analytics.

AWS Glue manages ETL processes and prepares the data for Forecast and SageMaker. It automates schema discovery, data cleansing, and transformation, ensuring data consistency and enabling high-quality predictive models. Glue also enforces security and governance through integration with AWS Lake Formation and IAM roles.

Amazon SageMaker allows the creation of custom machine learning models to complement Forecast predictions. For instance, specialized models can account for new product launches, regional demand variability, or promotional events. SageMaker provides managed training, tuning, and deployment capabilities, enabling scalable, cost-effective machine learning workflows.

Amazon QuickSight visualizes predictions, trends, and actionable insights for store managers and executives. Interactive dashboards highlight stock levels, predicted demand, and replenishment recommendations. Integration with mobile devices ensures store managers can act on insights promptly.

Option B, using Excel on EC2, is prone to errors, lacks scalability, and cannot handle real-time data efficiently. Option C, relying on on-premises batch reporting, introduces latency, limits scalability, and fails to support real-time decision-making. Option D, manual calculations, is inefficient, error-prone, and unsuitable for large-scale retail operations.

This architecture ensures scalability, accuracy, and operational efficiency. Forecast automatically adjusts models based on incoming data, Glue automates ETL tasks, SageMaker provides custom modeling capabilities, and QuickSight delivers actionable insights. Security is maintained via IAM, encryption, and audit logging. Integration with ERP and supply chain systems enables automated replenishment, reducing human intervention and improving efficiency.

Overall, Option A provides a modern, serverless, and scalable architecture for predictive inventory management, enabling retailers to optimize stock levels, reduce costs, improve customer satisfaction, and make data-driven operational decisions.

Question 103:

A global e-commerce company wants to migrate its monolithic application to AWS to improve scalability and reduce operational overhead. The company requires a microservices architecture, automatic scaling, and minimal downtime during migration. Which AWS services and approach provide the most suitable solution?

A) Amazon ECS with Fargate, Amazon RDS Multi-AZ, Amazon S3 for static assets, AWS Application Load Balancer, and AWS CodePipeline for CI/CD
B) Single EC2 instance running the monolithic application with EBS for storage
C) On-premises servers with VPN to AWS for backup only
D) S3 static hosting with Lambda functions for the entire application

Answer:

A) Amazon ECS with Fargate, Amazon RDS Multi-AZ, Amazon S3 for static assets, AWS Application Load Balancer, and AWS CodePipeline for CI/CD

Explanation:

Migrating a monolithic application to AWS requires careful planning to achieve scalability, high availability, operational efficiency, and minimal downtime. Option A offers a microservices-based architecture using fully managed services that reduce operational overhead while supporting automatic scaling and secure, resilient deployments.

Amazon ECS with Fargate allows the company to run containerized microservices without managing underlying servers or clusters. Fargate abstracts server provisioning and maintenance, enabling the team to focus on application logic instead of infrastructure management. This approach aligns with the goal of reducing operational overhead while supporting a scalable, microservices architecture. ECS integrates seamlessly with other AWS services such as IAM, CloudWatch, and CloudTrail, ensuring security, monitoring, and auditing of microservice operations.

Amazon RDS Multi-AZ provides a highly available, managed relational database solution that supports automatic failover in the event of an outage. Multi-AZ deployments ensure continuous availability and maintain data integrity during migrations or failures. RDS supports multiple database engines such as MySQL, PostgreSQL, and Oracle, allowing the company to choose a database engine compatible with the existing monolithic application while benefiting from managed backup, patching, and scaling.

Amazon S3 serves as a reliable, scalable storage solution for static assets such as images, scripts, and configuration files. S3 ensures high durability and availability while reducing operational complexity associated with on-premises storage. It also supports content distribution via Amazon CloudFront, enabling low-latency delivery of assets to a global user base. S3 versioning, replication, and lifecycle policies allow for efficient data management and compliance with retention requirements.

The AWS Application Load Balancer (ALB) distributes incoming traffic across ECS tasks, ensuring that requests are served efficiently and that services remain highly available. ALB supports path-based and host-based routing, enabling routing to different microservices within the architecture. It also provides SSL termination, integration with AWS WAF, and monitoring via CloudWatch, ensuring secure, reliable, and observable application traffic management.

AWS CodePipeline facilitates continuous integration and continuous delivery (CI/CD), automating build, test, and deployment processes for microservices. By implementing automated pipelines, the company can achieve minimal downtime during migration, quickly roll out updates, and enforce consistent deployment standards. Integration with AWS CodeBuild, CodeDeploy, and third-party tools enhances flexibility and accelerates delivery cycles.

Option B, relying on a single EC2 instance, introduces a single point of failure, lacks elasticity, and does not provide microservices support. Option C, using on-premises servers for backup, does not address scalability or migration challenges. Option D, using S3 and Lambda functions for the entire application, is suitable for serverless workloads but not for complex monolithic applications requiring stateful services and transactional databases.

The architecture in Option A enables a gradual migration strategy, often referred to as the “strangler pattern,” where components of the monolithic application are containerized and deployed incrementally. Each microservice can be independently developed, tested, deployed, and scaled. This reduces migration risk while improving fault isolation and operational agility.

Security and compliance are maintained through IAM policies, security groups, network segmentation via VPC, encryption of data at rest and in transit, and auditing through CloudTrail. ECS tasks can assume IAM roles to access resources securely, while ALB and CloudFront provide secure access to external clients. Monitoring and logging with CloudWatch enable proactive detection of performance issues, anomalies, or operational bottlenecks.

By leveraging managed services such as ECS, RDS, S3, ALB, and CodePipeline, the company achieves cost optimization through serverless or on-demand resource consumption. Fargate removes the need to manage EC2 instances, RDS handles patching and backups, and S3 reduces storage management complexity. This combination supports scalability and resilience while ensuring operational efficiency and minimal downtime during migration.

Question 104:

A healthcare provider wants to securely store and analyze electronic health records (EHR) for research purposes. The solution must comply with HIPAA regulations, ensure data encryption, access control, and audit logging. Which AWS services provide the optimal solution?

A) Amazon S3 with server-side encryption, AWS Key Management Service (KMS) for key management, Amazon Athena for analysis, AWS Identity and Access Management (IAM) for access control, and AWS CloudTrail for audit logging
B) EC2 instances with local disk storage and custom encryption scripts
C) On-premises database with VPN to AWS for occasional backups only
D) S3 public buckets with manual encryption using third-party tools

Answer:

Explanation:

Healthcare data, particularly electronic health records (EHR), requires strict security, regulatory compliance, encryption, access control, and auditability. Option A provides a fully managed architecture that meets HIPAA compliance requirements and supports secure, scalable analytics.

Amazon S3 provides highly durable and available storage, supporting server-side encryption (SSE) to encrypt data at rest using AWS-managed or customer-managed keys. SSE ensures that sensitive healthcare information remains confidential, protecting against unauthorized access. S3 also offers versioning, lifecycle policies, and replication features, enabling robust data governance and disaster recovery.

AWS Key Management Service (KMS) allows organizations to create and manage cryptographic keys for encrypting EHR data. KMS provides centralized key management, auditing, and integration with other AWS services such as S3, Athena, and Redshift. Key rotation policies can be enforced automatically to enhance security, while detailed key usage logs provide transparency and compliance support.

Amazon Athena allows serverless querying of EHR data stored in S3 using standard SQL syntax. Athena supports ad hoc analysis without requiring data movement or provisioning of infrastructure. Researchers can run queries securely on encrypted datasets, integrating with IAM to enforce fine-grained access permissions. Athena’s integration with AWS Lake Formation can further manage data access, ensuring that only authorized personnel can query sensitive datasets.

IAM provides centralized access control, enabling organizations to enforce least privilege principles. Role-based access policies ensure that only authorized researchers, administrators, or analysts can access specific EHR datasets. Temporary credentials and multi-factor authentication (MFA) provide additional layers of security.

AWS CloudTrail records all API calls and management events, enabling comprehensive auditing of data access and modifications. CloudTrail logs support regulatory compliance, incident investigations, and operational transparency. Together with CloudWatch, CloudTrail allows real-time monitoring, alerting, and anomaly detection for sensitive data access patterns.

Option B, using EC2 with local disks, introduces operational complexity, potential single points of failure, and inconsistent compliance enforcement. Option C, on-premises databases with VPN, lacks scalability, elastic compute, and integrated analytics capabilities. Option D, using public S3 buckets, poses severe security risks and does not meet HIPAA compliance requirements.

Question 105:

A global e-commerce company needs to deploy a high-traffic web application on AWS. The application must handle sudden spikes in traffic, provide low-latency access to users worldwide, and ensure secure handling of customer data. Which architecture provides the most scalable, highly available, and secure solution?

A) Deploy the application across multiple AWS Regions using Amazon Route 53 latency-based routing, Amazon CloudFront for content delivery, Amazon EC2 Auto Scaling behind Application Load Balancers, and AWS WAF for security
B) Single EC2 instance in one Availability Zone with EBS-backed storage and a local firewall
C) On-premises servers with VPN connectivity to a single AWS Region for failover
D) Single Region deployment with Amazon EC2 instances behind a basic load balancer and manual scaling

Answer:

Explanation:

Designing a high-traffic global web application for e-commerce on AWS requires a combination of scalability, high availability, low latency, and robust security. Option A provides the most comprehensive solution that addresses all these requirements effectively.

The first key consideration is high availability across global users. Deploying the application in multiple AWS Regions ensures that the service remains operational even if an entire region experiences an outage. Each region contains multiple Availability Zones, which act as isolated failure domains. This multi-region deployment allows traffic to failover seamlessly from one region to another, minimizing downtime and maintaining continuous service availability. For an e-commerce platform, downtime can result in significant financial losses, customer dissatisfaction, and reputational damage, making multi-region architecture crucial.

Amazon Route 53 latency-based routing ensures that users are directed to the AWS Region that provides the lowest network latency. This is essential for global applications, as users accessing the website from different continents will experience optimized response times. Route 53 supports health checks and automatic failover, further improving reliability by ensuring traffic is directed only to healthy endpoints. Health checks monitor the availability of web application endpoints, and if a region becomes unhealthy, Route 53 automatically routes users to an operational region.

To deliver content efficiently worldwide, Amazon CloudFront serves as a global content delivery network (CDN) that caches static assets, images, scripts, and dynamic content at edge locations. This reduces latency and improves performance for users regardless of their geographic location. CloudFront integrates with AWS WAF for security protection, allowing rule sets to prevent web attacks, cross-site scripting, SQL injections, and other threats. CloudFront also supports HTTPS connections, ensuring that data is encrypted in transit and meets security requirements for sensitive customer information.

Elasticity and scalability are achieved using Amazon EC2 Auto Scaling groups combined with Application Load Balancers (ALBs). Auto Scaling automatically adjusts the number of EC2 instances based on demand, ensuring that the application can handle traffic spikes efficiently while optimizing costs during periods of low traffic. The ALB distributes incoming traffic across healthy instances, providing fault tolerance at the application layer and ensuring that no single instance is overwhelmed. Load balancers also support SSL/TLS termination, offloading encryption processing from backend instances and improving overall performance.

Security is a major concern for e-commerce applications due to the handling of sensitive customer data, including payment information. AWS WAF provides protection against common web exploits, while AWS Shield adds DDoS protection. IAM policies enforce least-privilege access, ensuring that both users and applications have only the permissions necessary for their functions. Logging and auditing via AWS CloudTrail and monitoring via Amazon CloudWatch provide visibility into all actions and metrics, which is crucial for compliance with regulations like PCI DSS. Encryption at rest using AWS KMS for EBS volumes and S3 buckets further protects customer data, ensuring that sensitive information is always encrypted and secure.

Option B, which uses a single EC2 instance, introduces a single point of failure and lacks both horizontal scalability and global availability. Traffic spikes could overwhelm the instance, and there is no automated failover in the event of instance or zone failure. Option C relies on on-premises servers with a VPN to a single AWS Region, which cannot meet global low-latency requirements or handle sudden traffic spikes efficiently. Option D, a single-region deployment with manual scaling, introduces operational complexity and risks performance degradation during demand surges, as scaling decisions are not automated.

From an operational standpoint, Option A leverages fully managed services that minimize manual intervention, reduce maintenance, and enhance reliability. Auto Scaling groups dynamically manage resources, reducing the need for manual capacity planning. CloudFront and Route 53 handle global traffic distribution automatically. AWS WAF and Shield provide proactive security without requiring dedicated security infrastructure. This allows development and operations teams to focus on business features rather than infrastructure management, ensuring agility and faster time-to-market for new e-commerce functionality.

In summary, Option A provides a highly available, globally performant, scalable, and secure architecture for a high-traffic e-commerce web application. It ensures low-latency access for users worldwide, supports traffic surges through Auto Scaling, delivers content efficiently via CloudFront, protects sensitive data with WAF, Shield, and encryption, and provides comprehensive monitoring and compliance capabilities. This architecture minimizes operational overhead, maximizes reliability, and meets both business and regulatory requirements.

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