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Question 211:

A financial institution wants to deploy a high-performance trading application on AWS. The application requires extremely low latency for processing transactions, must maintain data consistency across multiple regions, ensure security for sensitive financial data, and provide full audit capabilities. Which AWS services should be used to meet these requirements?

A) Amazon EC2 instances in multiple regions with Amazon ElastiCache, AWS Key Management Service, Amazon RDS Multi-AZ, AWS CloudTrail, and AWS Config
B) Amazon S3 with versioning, AWS Lambda, and Amazon DynamoDB
C) Amazon Redshift, Amazon Athena, and AWS Glue
D) Amazon EC2 with EBS volumes and a self-managed database cluster

Answer:

A) Amazon EC2 instances in multiple regions with Amazon ElastiCache, AWS Key Management Service, Amazon RDS Multi-AZ, AWS CloudTrail, and AWS Config

Explanation:

Designing a high-performance trading application for a financial institution requires a comprehensive understanding of latency-sensitive workloads, multi-region consistency, security, and regulatory compliance. Trading applications operate on milliseconds-level timeframes, so infrastructure must be optimized for extremely low latency while ensuring high throughput, fault tolerance, and secure handling of sensitive financial data.

Amazon EC2 instances provide the flexibility to choose instance types optimized for high-performance computing, networking, and memory. For trading applications, compute instances with enhanced networking capabilities, such as those using Elastic Network Adapter (ENA) or Elastic Fabric Adapter (EFA), can significantly reduce latency by providing high bandwidth and low jitter. Deploying EC2 instances in multiple regions allows traders and clients in different geographic locations to access the application with minimal latency. Multi-region deployments also enhance resilience by ensuring that failures in one region do not impact global operations.

Amazon ElastiCache supports in-memory caching, which is critical for latency-sensitive applications like trading platforms. ElastiCache can be deployed using Redis or Memcached to store frequently accessed market data, session states, and temporary computation results. This reduces the need to query relational databases for every transaction, lowering response times and improving overall system performance. ElastiCache also supports replication across regions for high availability and disaster recovery scenarios.

Amazon RDS with Multi-AZ deployment ensures relational data consistency while providing automated backups, patching, and failover mechanisms. Multi-AZ RDS deployments create a synchronous standby in another Availability Zone, ensuring that in the event of an infrastructure failure, the standby instance can take over seamlessly. This is crucial for trading systems, where even minimal downtime can result in financial loss. Encryption at rest with AWS Key Management Service ensures that sensitive financial data, including transaction records and user credentials, is protected against unauthorized access. KMS enables fine-grained access control, audit logging, and automated key rotation to meet regulatory requirements.

Security in trading applications is multi-layered. EC2 instances can be secured with IAM roles, security groups, and network ACLs. AWS Key Management Service provides centralized control over cryptographic keys, allowing for consistent enforcement of encryption policies. AWS CloudTrail records all API calls, enabling detailed auditing of user and system activities. Coupled with AWS Config, which continuously monitors and records configuration changes, financial institutions can maintain complete compliance with financial regulations, including PCI DSS, SOC, and regional standards such as MiFID II.

Option B using S3, Lambda, and DynamoDB is serverless and scalable, but it is not ideal for extremely low-latency, transactional trading systems requiring strong consistency and deterministic response times. Option C with Redshift, Athena, and Glue is geared towards analytical workloads rather than real-time transactional processing. Option D with EC2 and self-managed databases increases operational overhead and introduces potential risks in configuration, failover, encryption, and auditing.

By combining EC2, ElastiCache, RDS Multi-AZ, KMS, CloudTrail, and Config, the financial institution can create a highly performant, low-latency trading platform that is resilient, secure, and compliant. The solution ensures that transactions are processed quickly across regions, sensitive data is protected both at rest and in transit, and audit logs provide full traceability for regulatory purposes. It also supports scalability to handle peak trading volumes, enabling the institution to respond to market demands efficiently while maintaining operational excellence and regulatory adherence.

Question 212:

A media streaming company wants to deliver live video streams to millions of global users. The solution must provide adaptive bitrate streaming, support concurrent high-traffic events, encrypt content, and collect analytics for viewer engagement and quality metrics. Which AWS services should be used?

A) Amazon CloudFront, AWS Elemental MediaLive, AWS Elemental MediaPackage, Amazon S3, Amazon Kinesis Data Analytics, and AWS CloudTrail
B) Amazon S3, Amazon RDS, and AWS Lambda
C) Amazon EC2, Amazon EBS, and Amazon Redshift
D) AWS Elastic Beanstalk, Amazon SQS, and Amazon QuickSight

Answer:

A) Amazon CloudFront, AWS Elemental MediaLive, AWS Elemental MediaPackage, Amazon S3, Amazon Kinesis Data Analytics, and AWS CloudTrail

Explanation:

Delivering live video streams to a global audience requires a combination of media processing, scalable content distribution, security, and analytics. Adaptive bitrate streaming ensures that users with varying network conditions receive optimal video quality without buffering, which is critical for maintaining engagement. AWS provides fully managed services that address the end-to-end requirements of live streaming.

AWS Elemental MediaLive encodes live video streams in real-time into multiple bitrates and formats, enabling adaptive streaming. MediaLive integrates with other AWS media services to deliver content reliably to viewers across multiple devices. MediaLive supports standard streaming protocols such as HLS, DASH, and CMAF, ensuring compatibility with web browsers, mobile devices, and connected TVs.

AWS Elemental MediaPackage packages the output from MediaLive into formats required for delivery and supports dynamic packaging and encryption. MediaPackage provides DRM support to protect video content from unauthorized access. It also supports just-in-time packaging, reducing storage requirements and enabling efficient delivery to viewers worldwide.

Amazon CloudFront, AWS’s content delivery network, caches content at edge locations, reducing latency and improving streaming performance for users globally. CloudFront ensures high availability and can scale automatically to support millions of concurrent users during peak events. CloudFront integrates with MediaPackage and MediaLive to deliver adaptive streams with minimal latency.

Amazon S3 provides durable storage for pre-recorded or archive video content. It integrates with MediaPackage for content distribution and with analytics services to capture engagement metrics. Kinesis Data Analytics processes streaming data from MediaLive and CloudFront, providing near real-time insights into viewer behavior, quality metrics, and engagement statistics. This allows the company to optimize streaming quality, identify issues, and make data-driven decisions to enhance viewer experience.

Security and compliance are critical for media content. CloudTrail provides detailed logs of API activity, enabling auditing of media operations, user access, and content changes. MediaPackage and CloudFront support encryption in transit, ensuring content is securely delivered to viewers. Access controls and DRM further prevent unauthorized content distribution, protecting licensing agreements and revenue streams.

Option B with S3, RDS, and Lambda supports content storage and serverless processing but cannot handle real-time live streaming or adaptive bitrate packaging. Option C with EC2, EBS, and Redshift is suitable for infrastructure management and analytics but does not provide a managed media processing pipeline. Option D with Elastic Beanstalk, SQS, and QuickSight is better suited for web application deployment and reporting rather than high-scale live video delivery.

By integrating MediaLive, MediaPackage, CloudFront, S3, Kinesis Data Analytics, and CloudTrail, the media streaming company can deploy a highly scalable, secure, and global live streaming solution. This architecture ensures adaptive streaming quality, supports millions of concurrent viewers, provides detailed engagement analytics, and maintains secure, compliant delivery of premium content. It enables the company to respond to user demand dynamically, reduce latency, and optimize the viewing experience while minimizing operational overhead through fully managed AWS services.

Question 213:

A pharmaceutical research company needs to run large-scale genomic data processing workflows. The workflows require distributed computation, large data storage, fault tolerance, and secure handling of sensitive research data. The company also wants to reduce operational overhead and integrate with machine learning pipelines for analysis. Which AWS service combination is best suited?

A) AWS Batch, Amazon S3, Amazon EC2 Spot Instances, AWS Lambda, Amazon SageMaker, and AWS Key Management Service
B) Amazon RDS, Amazon SQS, and Amazon CloudWatch
C) Amazon Redshift, AWS Glue, and Amazon QuickSight
D) AWS Elastic Beanstalk, Amazon DynamoDB, and Amazon Athena

Answer:

A) AWS Batch, Amazon S3, Amazon EC2 Spot Instances, AWS Lambda, Amazon SageMaker, and AWS Key Management Service

Explanation:

Processing large-scale genomic data requires significant compute power, high-performance storage, scalability, and fault tolerance. Genomic workflows typically involve multiple steps including alignment, variant calling, annotation, and analysis. Each step may involve processing terabytes or petabytes of data, so distributed computation and cost optimization are critical. AWS provides managed services to handle compute orchestration, storage, and integration with machine learning.

AWS Batch is ideal for orchestrating large-scale, parallelizable computational workloads. It dynamically provisions compute resources, schedules jobs, retries failed jobs, and optimizes resource allocation based on workload demand. By using AWS Batch, the company can submit thousands of genomic analysis jobs, manage dependencies, and monitor progress without manual intervention.

Amazon S3 provides durable and highly available storage for raw genomic datasets, intermediate files, and results. S3’s lifecycle policies, versioning, and cross-region replication ensure data durability and compliance with research data retention requirements. S3 supports encryption at rest using KMS, and encryption in transit using SSL/TLS, ensuring sensitive genomic data is protected.

EC2 Spot Instances reduce cost for high-throughput workloads. Batch can automatically provision Spot Instances, taking advantage of unused EC2 capacity at significant discounts while providing fault-tolerant execution. Spot interruption handling ensures that jobs are retried or rescheduled, providing resilience without manual operational effort.

AWS Lambda can be used to automate workflow orchestration, triggering downstream analysis jobs, and integrating with S3 events for data ingestion. Lambda provides serverless, event-driven processing to streamline pipeline execution.

Amazon SageMaker enables machine learning analysis on processed genomic data. It allows training, tuning, and deploying predictive models to identify patterns, anomalies, or correlations in the genomic datasets. SageMaker integrates seamlessly with S3 and Batch outputs, providing a cohesive pipeline for end-to-end data processing and analysis.

AWS Key Management Service ensures all sensitive data is encrypted and access is controlled. Researchers can define granular access policies, rotate keys automatically, and log all key usage for compliance auditing.

Option B with RDS, SQS, and CloudWatch is insufficient for large-scale parallel genomic computation. Option C with Redshift, Glue, and QuickSight is optimized for analytics, not high-throughput batch genomic pipelines. Option D with Elastic Beanstalk, DynamoDB, and Athena is suitable for web applications and querying data but not large-scale distributed computation or machine learning integration.

By combining AWS Batch, S3, EC2 Spot Instances, Lambda, SageMaker, and KMS, the pharmaceutical company can build a scalable, secure, fault-tolerant, and cost-efficient genomic data processing workflow. This architecture minimizes operational overhead, allows integration with machine learning pipelines, and ensures compliance with data protection requirements while enabling high-performance genomic analysis and insights.

Question 214:

A global e-commerce company is designing a multi-region architecture on AWS to handle traffic spikes during seasonal events. The system must provide low-latency access to users worldwide, maintain data consistency for orders, ensure high availability, and comply with PCI DSS standards for payment processing. Which AWS services and architecture pattern should the company implement?

A) Amazon Route 53 for global DNS, Amazon CloudFront for content delivery, Amazon Aurora Global Database for orders, AWS WAF, and AWS Shield Advanced
B) Amazon S3 with cross-region replication, AWS Lambda, Amazon DynamoDB, and AWS Config
C) Amazon EC2 with Auto Scaling groups, Elastic Load Balancers, and a self-managed MySQL cluster
D) AWS Elastic Beanstalk for multi-region deployment, Amazon RDS for order database, and Amazon SNS

Answer:

A) Amazon Route 53 for global DNS, Amazon CloudFront for content delivery, Amazon Aurora Global Database for orders, AWS WAF, and AWS Shield Advanced

Explanation:

Designing a multi-region e-commerce platform for a global audience involves addressing several critical aspects: latency optimization, high availability, data consistency, security, and compliance. Each component of the architecture must work seamlessly to provide a fast, secure, and reliable shopping experience, especially during high-traffic periods like seasonal sales events.

Global DNS and routing is achieved with Amazon Route 53. It allows for low-latency routing using latency-based routing policies to direct users to the nearest available region. This ensures minimal response times, improving user experience across continents. Route 53 can also use health checks to reroute traffic away from regions experiencing issues, maintaining availability even during localized failures.

Content delivery is enhanced with Amazon CloudFront, which caches static assets such as images, CSS, JavaScript, and videos at edge locations globally. By offloading requests to edge caches, the origin servers are relieved from heavy traffic, reducing latency and improving performance for users. CloudFront integrates seamlessly with AWS WAF and Shield Advanced, providing protection against web attacks, DDoS threats, and ensuring compliance with security best practices. This layer addresses both availability and security concerns while maintaining a fast and responsive user experience.

Data consistency for orders is managed using Amazon Aurora Global Database. Aurora is a fully managed relational database with high performance, reliability, and the ability to replicate data across multiple regions with minimal lag. The global database architecture enables near real-time replication, ensuring that users in different regions can view consistent order data. Aurora also supports Multi-AZ deployments for high availability within each region and offers automated backups, failover mechanisms, and encrypted storage to meet PCI DSS requirements for handling payment information.

Security and compliance are essential for PCI DSS adherence. AWS WAF enables application-layer protection against common exploits such as SQL injection and cross-site scripting. AWS Shield Advanced provides enhanced DDoS protection to mitigate volumetric attacks that could disrupt service during traffic spikes. Together with CloudTrail logging, KMS encryption, and IAM policies, these services create a robust security posture, ensuring sensitive payment and user data is handled securely.

Option B, using S3, Lambda, DynamoDB, and Config, provides a serverless and scalable approach but does not inherently address multi-region relational database consistency or PCI DSS-specific transactional requirements. Option C with EC2 and self-managed MySQL introduces high operational complexity, risk of misconfiguration, and challenges in cross-region replication. Option D with Elastic Beanstalk and RDS is insufficient for global low-latency access without a specialized global database and lacks integrated CDN capabilities.

By integrating Route 53, CloudFront, Aurora Global Database, WAF, and Shield Advanced, the company can achieve a high-performing, secure, multi-region architecture that provides consistent order data, low latency, protection against attacks, and adherence to PCI DSS standards. This design supports peak traffic events, allows operational agility through managed services, and ensures that end-users experience fast and secure transactions regardless of their geographic location. The architecture also scales automatically to accommodate sudden increases in traffic, reducing downtime and optimizing operational efficiency.

Question 215:

A healthcare company needs to implement a system to store, process, and analyze sensitive patient data in compliance with HIPAA regulations. The system should allow secure sharing with authorized users, support analytics and machine learning on anonymized data, and minimize operational overhead. Which AWS services provide the best solution?

A) Amazon S3 with encryption, AWS Glue for ETL, Amazon SageMaker for ML, AWS IAM, AWS KMS, and Amazon Macie
B) Amazon DynamoDB for storage, AWS Lambda for processing, and Amazon CloudWatch for monitoring
C) Amazon RDS for database storage, Amazon QuickSight for analytics, and Amazon S3 for backup
D) AWS Elastic Beanstalk, Amazon S3, and AWS CloudTrail

Answer:

A) Amazon S3 with encryption, AWS Glue for ETL, Amazon SageMaker for ML, AWS IAM, AWS KMS, and Amazon Macie

Explanation:

Healthcare data is highly sensitive and subject to strict regulatory frameworks like HIPAA. Designing an AWS-based architecture to store, process, and analyze such data requires careful attention to security, access control, encryption, auditability, and compliance, while also providing scalable compute and analytics capabilities.

Data storage begins with Amazon S3, which provides durable, highly available object storage. S3 supports encryption at rest using AWS KMS-managed keys, ensuring that sensitive patient data is protected. Encryption in transit is enforced through SSL/TLS, securing data during transfer between systems. S3’s access control policies, bucket policies, and fine-grained IAM permissions enable secure data sharing with authorized users, ensuring that only appropriate personnel can access specific datasets. Versioning and logging further enhance auditability, which is critical for compliance.

Data processing and transformation is handled by AWS Glue, a fully managed extract-transform-load (ETL) service. Glue allows the company to clean, normalize, and anonymize sensitive patient data before analysis. This is particularly important to comply with HIPAA guidelines while enabling secondary use of data for research, analytics, and machine learning. Glue also integrates with S3, Redshift, and other services to orchestrate complex workflows without requiring manual server management, minimizing operational overhead.

Machine learning and advanced analytics are implemented using Amazon SageMaker. SageMaker provides a managed environment for model development, training, and deployment. By working with anonymized patient data, SageMaker allows researchers and data scientists to build predictive models, identify trends, or detect anomalies without exposing personally identifiable information. SageMaker’s built-in security controls, integration with IAM, and support for encrypted data ensure compliance with regulatory requirements.

Identity and access management is critical to maintain security and compliance. AWS IAM provides role-based access control, multi-factor authentication, and fine-grained permission policies. Integration with KMS ensures that only authorized users can access encryption keys and sensitive datasets. Amazon Macie provides automated discovery, classification, and monitoring of sensitive data in S3. It detects potential privacy risks, unencrypted data, or unauthorized access, providing alerts to maintain security posture and regulatory compliance.

Option B with DynamoDB and Lambda supports scalable, serverless processing but lacks the robust ETL, analytics, and ML integration needed for sensitive healthcare datasets. Option C with RDS, QuickSight, and S3 provides relational storage and reporting but does not provide automated data anonymization, ETL orchestration, or secure machine learning workflows. Option D with Elastic Beanstalk is more suited for web application deployment and does not address the complex requirements of secure data processing and analytics for healthcare datasets.

By integrating S3, Glue, SageMaker, IAM, KMS, and Macie, the healthcare company can implement a fully managed, secure, HIPAA-compliant architecture. The system enables encrypted storage, secure sharing, automated data processing, and advanced analytics while minimizing operational overhead. It also allows researchers to leverage anonymized patient data for insights, predictions, and machine learning workflows without compromising privacy or compliance. The architecture ensures that sensitive data remains protected, audit-ready, and accessible only to authorized users, while enabling scalable and cost-efficient analytics and machine learning capabilities.

Question 216:

A large enterprise wants to migrate its on-premises data warehouse to AWS while maintaining near real-time reporting capabilities. The solution should support petabyte-scale data, high concurrency for business intelligence queries, and integration with machine learning pipelines. Which AWS services should be used?

A) Amazon Redshift RA3 nodes, Amazon S3 for storage, AWS Glue for ETL, Amazon SageMaker for ML, and Amazon QuickSight for analytics
B) Amazon RDS Multi-AZ for database storage, Amazon Athena for queries, and AWS Lambda for processing
C) Amazon DynamoDB for storage, AWS Lambda for processing, and Amazon Kinesis for streaming data
D) Amazon Aurora for transactional data, Amazon S3 for backup, and Amazon EMR for analytics

Answer:

A) Amazon Redshift RA3 nodes, Amazon S3 for storage, AWS Glue for ETL, Amazon SageMaker for ML, and Amazon QuickSight for analytics

Explanation:

Migrating a large on-premises data warehouse to AWS requires a strategy that ensures scalability, performance, and integration with modern analytics and machine learning pipelines. The architecture must handle massive datasets, provide high concurrency for BI users, support complex queries, and enable data-driven insights.

Amazon Redshift RA3 nodes provide a petabyte-scale, fully managed data warehouse solution. RA3 nodes separate compute and storage, allowing independent scaling for query performance and storage capacity. Redshift supports columnar storage, data compression, and parallel query execution to deliver fast query performance even with high concurrency. For near real-time reporting, Redshift can use streaming ingestion from S3 or Kinesis, ensuring that updated data is immediately available for analytics.

Amazon S3 serves as the scalable, durable storage layer for raw and transformed data. Redshift RA3 nodes use Redshift Managed Storage (RMS) to automatically offload data to S3 while maintaining query performance. S3 enables cost-effective storage for historical datasets and integrates with ETL and analytics workflows.

AWS Glue provides a serverless ETL solution to transform, clean, and catalog data before loading it into Redshift. Glue supports scheduling, triggers, and integration with S3 and Redshift, reducing operational overhead and enabling near real-time ingestion of updated datasets.

Machine learning integration is achieved through Amazon SageMaker, which can access data in S3 or Redshift to build predictive models. For example, business forecasting, anomaly detection, or recommendation systems can be implemented using historical and near real-time warehouse data.

Analytics and reporting are delivered via Amazon QuickSight, which connects directly to Redshift and provides interactive dashboards, visualizations, and self-service analytics. QuickSight can handle high-concurrency queries, ensuring multiple stakeholders can access insights simultaneously.

Option B with RDS, Athena, and Lambda is suitable for smaller datasets or serverless querying, but not for petabyte-scale, high-concurrency analytics. Option C with DynamoDB and Kinesis supports real-time streaming workloads but lacks the robust analytical querying needed for data warehouse workloads. Option D with Aurora and EMR is better suited for transactional workloads and big data batch processing rather than a fully managed, scalable, high-concurrency data warehouse.

By combining Redshift RA3, S3, Glue, SageMaker, and QuickSight, the enterprise can migrate its on-premises warehouse to a highly scalable, near real-time AWS solution. This architecture provides fast, concurrent query performance, seamless integration with machine learning workflows, and cost-efficient storage management while supporting complex analytics and business intelligence operations.

Question 217:

A financial services company wants to deploy a highly available, fault-tolerant application on AWS that processes financial transactions. The application must maintain strong consistency for critical data across multiple regions and ensure end-to-end encryption. Which AWS architecture is the most appropriate?

A) Amazon Route 53, AWS Global Accelerator, Amazon Aurora Global Database, AWS Key Management Service, and AWS WAF
B) Amazon S3, Amazon DynamoDB, AWS Lambda, and Amazon API Gateway
C) Amazon EC2 with Auto Scaling, Elastic Load Balancer, and self-managed MySQL with cross-region replication
D) AWS Elastic Beanstalk, Amazon RDS Multi-AZ, and Amazon CloudFront

Answer:

A) Amazon Route 53, AWS Global Accelerator, Amazon Aurora Global Database, AWS Key Management Service, and AWS WAF

Explanation:

Financial services applications are among the most sensitive workloads in terms of availability, fault tolerance, security, and compliance. Handling transactions requires strong consistency across regions, secure storage, and robust mechanisms to maintain uninterrupted service even during regional failures. Selecting an appropriate AWS architecture involves analyzing several critical requirements:

High availability and fault tolerance are achieved by deploying resources across multiple AWS regions. Amazon Route 53 enables global DNS routing and health checks to direct users to the nearest healthy endpoint, minimizing latency while maintaining service availability. AWS Global Accelerator further improves global performance by providing static anycast IP addresses and intelligent routing to optimal AWS endpoints. Together, these services ensure that users worldwide experience low-latency, resilient access to the application.

Strong consistency for transactional data is provided by Amazon Aurora Global Database. Aurora offers cross-region replication with minimal lag, allowing applications to read data from local replicas while writing to a primary region. This guarantees consistency for financial transactions and ensures that even in the event of a regional failure, the application can failover to another region without data loss. Aurora also supports Multi-AZ deployments within a region for added fault tolerance and automated failover, which is critical for financial applications that require high uptime and reliability.

Security and compliance are paramount. AWS Key Management Service (KMS) enables encryption at rest for sensitive financial data. Combined with SSL/TLS for encryption in transit, KMS ensures end-to-end encryption of data. AWS WAF protects against common web attacks, including SQL injection and cross-site scripting, which could compromise sensitive financial information. In regulated industries, logging, monitoring, and auditing through AWS CloudTrail and AWS Config further enforce compliance with standards such as PCI DSS, ensuring that all access and changes are traceable.

Option B with S3, DynamoDB, Lambda, and API Gateway provides a highly scalable and serverless architecture but lacks transactional consistency required for financial applications. DynamoDB supports eventual consistency by default, which is not ideal for strong consistency requirements. Option C with EC2 and a self-managed MySQL cluster introduces operational complexity, manual replication, and higher risk of misconfiguration, increasing potential downtime. Option D with Elastic Beanstalk and RDS Multi-AZ does not inherently provide cross-region consistency and may not meet stringent low-latency requirements for global users.

By combining Route 53, Global Accelerator, Aurora Global Database, KMS, and WAF, the financial services company achieves a resilient, high-performance, and secure architecture. The system ensures consistent transactional data across regions, fast and reliable access for users, end-to-end encryption, and protection against attacks. Managed services reduce operational overhead, automate failover, and allow the organization to focus on business logic rather than infrastructure maintenance. The architecture also supports monitoring and auditing, enabling adherence to regulatory compliance while providing a scalable solution that can handle spikes in transaction volume during peak periods.

Question 218:

A media company wants to build a live video streaming platform on AWS that can deliver streams globally, scale automatically to millions of viewers, and provide real-time analytics on user engagement. Which AWS services and design pattern should be used?

A) Amazon CloudFront, AWS Elemental MediaLive, AWS Elemental MediaPackage, Amazon Kinesis Data Analytics, and Amazon DynamoDB
B) Amazon S3, AWS Lambda, Amazon API Gateway, and Amazon RDS
C) Amazon EC2 Auto Scaling, Elastic Load Balancer, and self-managed video transcoding servers
D) AWS Elastic Beanstalk, Amazon CloudFront, and Amazon S3

Answer:

A) Amazon CloudFront, AWS Elemental MediaLive, AWS Elemental MediaPackage, Amazon Kinesis Data Analytics, and Amazon DynamoDB

Explanation:

Building a global live video streaming platform requires addressing three major requirements: low-latency content delivery, scalable streaming infrastructure, and real-time analytics. Each of these aspects must be handled efficiently to deliver a high-quality experience for millions of viewers simultaneously.

Global low-latency content delivery is achieved with Amazon CloudFront, a content delivery network that caches live streaming segments at edge locations worldwide. CloudFront reduces latency for end users, offloads traffic from origin servers, and integrates seamlessly with MediaLive and MediaPackage for video delivery. CloudFront also provides features such as geo-restriction, signed URLs, and HTTPS support to secure content and manage access.

Video encoding and packaging are handled by AWS Elemental MediaLive and MediaPackage. MediaLive performs live transcoding of video inputs into multiple adaptive bitrate streams suitable for different devices and network conditions. MediaPackage packages streams into formats such as HLS, DASH, and CMAF, providing reliable delivery to a variety of client devices. MediaPackage can also cache and serve streams efficiently through CloudFront, reducing buffering and improving user experience.

Real-time analytics is enabled through Amazon Kinesis Data Analytics and DynamoDB. Kinesis can ingest streaming data such as viewer metrics, engagement statistics, and performance logs in real time. Kinesis Data Analytics processes this streaming data to produce actionable insights, which can be stored in DynamoDB for fast lookups, dashboards, and real-time reporting. This setup allows media companies to analyze trends, detect issues, and respond immediately to changes in viewer behavior.

Option B using S3, Lambda, API Gateway, and RDS is suitable for serverless static content or batch processing but cannot handle live video streams at scale. Option C with EC2 and self-managed transcoding servers requires extensive operational management, lacks seamless global scalability, and is less cost-efficient. Option D with Elastic Beanstalk and CloudFront does not address real-time transcoding or analytics for live streaming, limiting functionality and scalability.

By integrating CloudFront, MediaLive, MediaPackage, Kinesis Data Analytics, and DynamoDB, the media company can deliver a scalable, low-latency, and fully managed live streaming solution. The architecture supports millions of concurrent viewers, provides global content distribution, enables real-time engagement insights, and minimizes operational complexity. It leverages managed services to ensure reliability, security, and elasticity while maintaining the ability to innovate and respond to audience demands quickly. This design also reduces the risk of bottlenecks and provides a foundation for monetization strategies through analytics-driven personalization and targeted content delivery.

Question 219:

An enterprise wants to implement a disaster recovery (DR) solution on AWS for its critical applications hosted in a primary region. The solution must ensure minimal downtime and data loss while optimizing costs for the standby region. Which AWS DR strategy and services should be selected?

A) Pilot Light using AWS CloudFormation, Amazon RDS with cross-region read replicas, Amazon S3, and Amazon Route 53
B) Backup and restore using Amazon S3, Amazon Glacier, and AWS Backup
C) Multi-site active-active using Amazon EC2, Amazon Aurora, and AWS Global Accelerator
D) Warm Standby using AWS Elastic Beanstalk, Amazon RDS Multi-AZ, and Amazon S3

Answer:

A) Pilot Light using AWS CloudFormation, Amazon RDS with cross-region read replicas, Amazon S3, and Amazon Route 53

Explanation:

Disaster recovery strategies require careful consideration of Recovery Time Objective (RTO), Recovery Point Objective (RPO), and cost optimization. AWS provides multiple approaches ranging from backup and restore to multi-site active-active deployments. The Pilot Light strategy balances cost efficiency with fast recovery, making it suitable for critical workloads that cannot afford extended downtime.

Pilot Light implementation involves maintaining a minimal version of the infrastructure in the standby region, which includes essential core components and databases. AWS CloudFormation templates automate the provisioning of infrastructure, ensuring consistent configuration and enabling rapid scaling during failover events. This approach reduces the ongoing operational cost since only a small set of resources is running in the standby region, and the majority of the environment is dormant until needed.

Database replication is critical to maintaining up-to-date data. Amazon RDS supports cross-region read replicas, which continuously replicate data from the primary region to the standby. In the event of a disaster, these replicas can be promoted to read-write, minimizing data loss and ensuring business continuity. Amazon S3 stores critical backups and assets with high durability, providing an additional layer of data protection.

Global traffic management is handled with Amazon Route 53. Health checks and failover routing policies ensure that user traffic is automatically redirected to the standby region during outages, minimizing downtime and providing seamless continuity to end-users.

Option B, backup and restore using S3, Glacier, and AWS Backup, provides the most cost-efficient solution but has high RTO and RPO, making it unsuitable for critical applications requiring minimal downtime. Option C, multi-site active-active with EC2, Aurora, and Global Accelerator, offers the lowest RTO and RPO but is cost-intensive since resources are continuously active in multiple regions. Option D, warm standby using Elastic Beanstalk and Multi-AZ RDS, reduces costs compared to active-active but still incurs higher operational overhead than pilot light.

By combining Pilot Light with CloudFormation, RDS cross-region replication, S3, and Route 53, enterprises achieve a cost-optimized DR solution that ensures critical applications can recover quickly with minimal data loss. The architecture provides automated provisioning, consistent configuration, real-time data replication, and traffic failover mechanisms. It allows organizations to comply with business continuity objectives, maintain operational efficiency, and scale rapidly in response to disasters without paying for full active resources in multiple regions. The Pilot Light approach is ideal for enterprises looking to balance cost and reliability while meeting regulatory and operational requirements.

Question 220:

A multinational e-commerce company needs to deploy a global, highly available architecture for its online store. The solution must provide low-latency access to users in multiple continents, secure customer payment data, and scale dynamically to handle sudden spikes in traffic during promotions. Which AWS architecture should be implemented?

A) Amazon CloudFront, AWS Global Accelerator, Amazon Aurora Global Database, AWS WAF, and AWS Key Management Service
B) Amazon S3, Amazon DynamoDB, AWS Lambda, and Amazon API Gateway
C) Amazon EC2 Auto Scaling with Elastic Load Balancer and self-managed MySQL
D) AWS Elastic Beanstalk, Amazon RDS Multi-AZ, and Amazon CloudFront

Answer:

A) Amazon CloudFront, AWS Global Accelerator, Amazon Aurora Global Database, AWS WAF, and AWS Key Management Service

Explanation:

Designing a global, highly available e-commerce architecture requires careful consideration of performance, data consistency, security, and scalability. Users accessing the online store from multiple continents must experience minimal latency, while the platform must protect sensitive customer data, particularly payment information. Additionally, the system should scale elastically to accommodate spikes in traffic during promotional events such as Black Friday or Cyber Monday.

Low-latency global access can be achieved through Amazon CloudFront and AWS Global Accelerator. CloudFront caches static and dynamic content at edge locations, distributing traffic across the globe to reduce latency and improve user experience. Dynamic requests are routed intelligently to the optimal region based on health checks and network performance. AWS Global Accelerator further enhances global performance by providing static anycast IP addresses and routing user requests to the nearest healthy endpoint, reducing the round-trip time and ensuring consistent performance even during peak traffic. Together, these services form the foundation for low-latency, resilient global access.

Strong consistency for transactional data is critical for e-commerce platforms. Amazon Aurora Global Database supports multi-region replication with minimal lag, providing fast read access locally while maintaining a primary write region to ensure strong consistency. This is essential for handling critical transactions, inventory updates, and payment processing. Multi-AZ deployment within a region provides additional fault tolerance, allowing automatic failover and reducing the risk of downtime during regional outages. Aurora’s managed service model eliminates the operational burden of replication management and scaling, allowing the company to focus on business logic rather than infrastructure.

Security and compliance are addressed with AWS Key Management Service and AWS WAF. KMS encrypts sensitive data at rest, including customer payment details and personally identifiable information, while TLS/SSL encrypts data in transit. AWS WAF protects the application from common web exploits, such as SQL injection or cross-site scripting, ensuring that malicious traffic is blocked before it reaches the application. The architecture can also integrate AWS CloudTrail and AWS Config to provide audit trails and compliance reporting, which is essential for PCI DSS compliance and regulatory requirements across multiple regions.

Option B, which relies on S3, DynamoDB, Lambda, and API Gateway, provides a serverless approach that scales automatically but does not guarantee strong consistency for critical transactional data. DynamoDB offers eventual consistency by default, which could result in incorrect inventory counts or payment discrepancies in high-concurrency scenarios. Option C, EC2 Auto Scaling with self-managed MySQL, introduces operational complexity and increases the risk of human error during replication and failover processes, reducing reliability. Option D, Elastic Beanstalk with RDS Multi-AZ and CloudFront, does not provide cross-region replication and may not deliver optimal latency for global users, potentially impacting the customer experience.

By combining CloudFront, Global Accelerator, Aurora Global Database, WAF, and KMS, the e-commerce platform achieves a resilient, secure, and scalable architecture capable of handling sudden traffic spikes while maintaining high availability. Managed services reduce operational overhead, improve disaster recovery readiness, and provide performance and security optimizations automatically. Real-time monitoring with Amazon CloudWatch, alerts, and analytics ensures the company can detect anomalies and adjust resources proactively. This architecture also provides a foundation for personalized user experiences and global expansion, ensuring that customers around the world receive fast, reliable, and secure service.

Question 221:

A healthcare provider wants to build a HIPAA-compliant analytics platform on AWS to analyze patient data in real-time for early disease detection. The solution must store sensitive data securely, provide controlled access, and scale to support millions of patient records. Which AWS services and architecture pattern are most suitable?

A) Amazon S3, AWS Glue, Amazon Redshift, AWS IAM, and AWS Key Management Service
B) Amazon DynamoDB, AWS Lambda, Amazon Athena, and Amazon API Gateway
C) Amazon EC2 Auto Scaling, self-managed Hadoop cluster, and Amazon RDS
D) AWS Elastic Beanstalk, Amazon Aurora Multi-AZ, and Amazon S3

Answer:

A) Amazon S3, AWS Glue, Amazon Redshift, AWS IAM, and AWS Key Management Service

Explanation:

Healthcare analytics platforms must prioritize security, scalability, compliance, and real-time data processing. Patient records are highly sensitive and protected under HIPAA regulations, which require encryption, auditing, and access control. The platform must also support analytical workloads on massive datasets for early disease detection and insights.

Data storage and security are foundational for compliance. Amazon S3 provides scalable, durable, and encrypted storage for structured and unstructured patient data. Using S3 server-side encryption with AWS KMS ensures that all data at rest is encrypted using customer-managed or AWS-managed keys. Fine-grained access control using IAM policies and bucket policies allows administrators to grant role-based access to datasets, ensuring that only authorized users or applications can access sensitive records. Logging access requests with AWS CloudTrail provides auditability, which is crucial for HIPAA compliance.

Data ingestion and transformation are handled with AWS Glue, a managed ETL (extract, transform, load) service. Glue can securely read encrypted data from S3, transform it, and load it into analytics stores such as Amazon Redshift. Glue provides job scheduling, serverless execution, and schema management, allowing the platform to handle continuous streams of patient data efficiently while maintaining compliance with regulatory requirements.

Analytical storage and querying is supported by Amazon Redshift. Redshift allows petabyte-scale data storage and high-performance querying for complex analytics. Redshift integrates with Amazon QuickSight for visualization and reporting, enabling healthcare professionals to gain real-time insights into patient trends. With Redshift Spectrum, the platform can query data directly in S3 without moving large datasets, improving efficiency and reducing costs.

Option B with DynamoDB, Lambda, Athena, and API Gateway is suitable for serverless analytics on unstructured data but may not provide the advanced query performance and relational analytical capabilities required for healthcare data. Option C, EC2 with a self-managed Hadoop cluster and RDS, introduces operational overhead, high management complexity, and potential compliance risks. Option D, Elastic Beanstalk with Aurora and S3, lacks a fully managed, scalable analytics layer capable of handling petabyte-scale queries and real-time insights.

By combining S3, Glue, Redshift, IAM, and KMS, the healthcare provider can build a HIPAA-compliant analytics platform capable of scaling to millions of patient records. The architecture supports real-time data ingestion, transformation, secure storage, and analytical querying while enforcing strict access controls and encryption standards. Managed services reduce operational complexity, improve scalability, and ensure compliance while enabling advanced analytics for early disease detection, predictive modeling, and research initiatives. The architecture can integrate with other AWS services like SageMaker for machine learning and Comprehend Medical for natural language processing on unstructured clinical notes, further enhancing the platform’s analytical capabilities.

Question 222:

A global SaaS company needs a multi-region architecture to ensure its application can survive regional outages while maintaining low-latency access for users worldwide. The company also wants automated failover and strong consistency for its transactional database. Which design and services should be chosen?

A) Amazon Aurora Global Database, Amazon Route 53, AWS Global Accelerator, AWS WAF, and AWS KMS
B) Amazon S3, AWS Lambda, Amazon DynamoDB Global Tables, and Amazon API Gateway
C) Amazon EC2 Auto Scaling with MySQL replication across regions
D) AWS Elastic Beanstalk with Amazon RDS Multi-AZ

Answer:

A) Amazon Aurora Global Database, Amazon Route 53, AWS Global Accelerator, AWS WAF, and AWS KMS

Explanation:

For SaaS platforms with a global user base, designing a multi-region architecture is critical to ensure availability, low latency, and strong transactional consistency. Users expect uninterrupted access and fast response times, while businesses require data integrity, security, and automated failover to prevent service disruptions during regional failures.

Global traffic management is provided by Amazon Route 53 and AWS Global Accelerator. Route 53 supports health checks, latency-based routing, and failover, automatically redirecting user traffic to the nearest healthy region. AWS Global Accelerator complements Route 53 by providing a static anycast IP and routing traffic through the optimal network paths to minimize latency. Together, they ensure global users have fast, reliable access while automatically failing over to alternate regions during disruptions.

Transactional database consistency is critical. Amazon Aurora Global Database enables low-latency reads in multiple regions and provides strong consistency for writes in the primary region. In the event of a regional failure, failover to another region can be automated, minimizing downtime and ensuring the application continues processing transactions accurately. Aurora’s managed replication, automated failover, and Multi-AZ support ensure that the database remains reliable, consistent, and resilient without requiring complex operational procedures.

Security is addressed with AWS WAF and AWS KMS. WAF protects the SaaS platform from web-based attacks, while KMS encrypts sensitive data at rest. TLS/SSL ensures encryption in transit, and CloudTrail provides auditing and logging capabilities, which are critical for compliance and operational visibility. By leveraging managed services, the architecture reduces administrative overhead and operational risk while ensuring regulatory compliance.

Option B, using DynamoDB Global Tables and Lambda, provides global availability but eventually consistent reads and writes may not meet the strong consistency requirements for transactional workloads. Option C with EC2 and MySQL replication introduces operational complexity and slower failover during outages. Option D with Elastic Beanstalk and RDS Multi-AZ provides regional fault tolerance but does not provide cross-region consistency or failover, limiting global resiliency.

By implementing Aurora Global Database with Route 53, Global Accelerator, WAF, and KMS, the SaaS company achieves a multi-region architecture that ensures strong consistency, low-latency global access, automated failover, and secure data handling. This design provides a resilient, scalable, and cost-effective solution for global applications, allowing the company to deliver consistent user experiences, protect sensitive data, and meet business continuity requirements. Additionally, managed services provide monitoring, logging, and compliance support, enabling proactive management and rapid response to anomalies or regional failures.

Question 223:

A financial services company needs to deploy a high-availability database solution for storing customer transactions. The solution must provide strong consistency, cross-region replication for disaster recovery, and automated failover in the event of regional outages. Which AWS database service and architecture should be used?

A) Amazon Aurora Global Database with Multi-AZ deployment, Amazon Route 53, and AWS KMS
B) Amazon RDS MySQL Multi-AZ deployment with read replicas in another region
C) Amazon DynamoDB Global Tables with eventual consistency
D) Amazon Redshift with cross-region snapshots

Answer:

A) Amazon Aurora Global Database with Multi-AZ deployment, Amazon Route 53, and AWS KMS

Explanation:

Designing a high-availability, disaster-tolerant database solution for financial transactions requires meticulous attention to consistency, durability, performance, and regulatory compliance. Financial data is highly sensitive and must remain consistent across multiple regions to ensure accurate transaction records, proper reconciliation, and compliance with standards such as PCI DSS or local financial regulations. Additionally, automated failover is critical to minimize downtime in the event of a regional outage, which could otherwise disrupt customer operations or result in significant financial loss.

Amazon Aurora Global Database is specifically designed for scenarios requiring both high availability and global reach. It supports a single primary region for writes and multiple secondary regions for low-latency reads. This design ensures that all write operations are strongly consistent, while read operations in secondary regions are quickly propagated with minimal lag. For financial transactions, strong consistency is essential because eventual consistency could lead to discrepancies in account balances or transaction histories, which would be unacceptable in a banking or payment processing environment. Aurora automatically handles replication and failover management, reducing the operational burden on database administrators and increasing overall system reliability.

Multi-AZ deployment within the primary region provides additional fault tolerance against infrastructure failures. In a Multi-AZ setup, Aurora maintains synchronous standby replicas in another Availability Zone, ensuring that if the primary instance fails, failover occurs automatically without manual intervention. This capability is crucial for financial institutions that cannot tolerate downtime, as even brief service interruptions can have severe consequences for business operations and customer trust. Combined with Aurora Global Database, Multi-AZ ensures that the database remains operational during both regional and local outages, providing a highly resilient architecture.

Route 53 is used to implement cross-region failover and routing. By monitoring the health of the primary region, Route 53 can redirect application traffic to a secondary region in case of a failure. This automated failover mechanism ensures that applications remain available globally without manual intervention. Additionally, latency-based routing optimizes the user experience by directing traffic to the region with the lowest network latency, improving transaction speeds for international customers.

Security and compliance are addressed with AWS KMS for data encryption at rest and in transit. All customer data, including sensitive financial information, is encrypted using customer-managed or AWS-managed keys. This ensures regulatory compliance while preventing unauthorized access to transaction data. Integration with AWS CloudTrail and Amazon CloudWatch provides audit logging, monitoring, and alerting for suspicious or anomalous activity, which is critical for financial institutions subject to strict regulatory oversight.

Option B, RDS MySQL Multi-AZ with cross-region read replicas, provides redundancy and some cross-region disaster recovery, but failover across regions is not fully automated and requires manual intervention, making it less suitable for high-availability transactional systems. Option C, DynamoDB Global Tables, is eventually consistent by default, which could compromise the accuracy of financial records. While DynamoDB supports strong consistency for single-region reads, cross-region replication introduces eventual consistency, making it unsuitable for financial applications requiring strict transactional guarantees. Option D, Redshift with cross-region snapshots, is optimized for analytical workloads rather than transactional systems, and does not provide real-time, strongly consistent write capabilities required for financial data.

By choosing Aurora Global Database with Multi-AZ deployment, Route 53, and KMS, the company achieves a fully managed, globally distributed, highly available, and secure architecture. The system ensures strong consistency for all transactions, supports automated failover, meets regulatory compliance, and provides low-latency access for customers worldwide. This architecture reduces operational complexity while delivering high reliability and resilience, allowing the company to maintain business continuity and safeguard sensitive financial information. Advanced monitoring, alerting, and automated scaling further enhance operational efficiency, ensuring that the database can handle peak loads and transactional surges without performance degradation.

Question 224:

A retail company wants to build a serverless e-commerce application that can scale automatically during flash sales. The application must handle thousands of simultaneous users, store product and order data, and integrate with payment systems securely. Which AWS services and architecture are most appropriate?

A) Amazon API Gateway, AWS Lambda, Amazon DynamoDB, AWS Step Functions, and AWS KMS
B) Amazon EC2 Auto Scaling, Elastic Load Balancer, Amazon RDS, and Amazon S3
C) AWS Elastic Beanstalk, Amazon Aurora Multi-AZ, and Amazon CloudFront
D) Amazon S3, Amazon Redshift, AWS Glue, and Amazon Athena

Answer:

A) Amazon API Gateway, AWS Lambda, Amazon DynamoDB, AWS Step Functions, and AWS KMS

Explanation:

For a serverless e-commerce application, scalability, availability, and security are the most critical factors. Flash sales or promotional events generate sudden, unpredictable spikes in user traffic, requiring automatic scaling without the operational burden of managing underlying infrastructure. Additionally, the application must handle transactional data reliably, integrate with external payment systems, and protect sensitive customer information such as payment card data and personal information.

AWS Lambda provides a fully serverless compute platform that automatically scales to handle incoming requests. During high-traffic periods, Lambda can instantiate thousands of concurrent executions in milliseconds, ensuring that users experience minimal latency even during flash sales. By decoupling compute from server management, the application reduces operational complexity and allows developers to focus on business logic and integration with payment gateways and order processing workflows.

Amazon API Gateway provides a fully managed API layer that routes user requests to Lambda functions. It supports throttling, caching, and authorization mechanisms, ensuring that the application remains responsive under load while protecting backend services from abuse. API Gateway integrates seamlessly with Lambda, enabling secure and scalable endpoints for e-commerce functionality such as browsing products, placing orders, and checking inventory.

Amazon DynamoDB serves as the primary data store for product and order information. DynamoDB provides low-latency read and write access and scales automatically to handle large numbers of concurrent requests. Its flexible schema accommodates evolving product catalogs and order data without the need for complex database migrations. For transactions, DynamoDB supports atomic writes and conditional updates, ensuring that stock levels and order records remain accurate even under high concurrency. This is particularly important during flash sales, where multiple users may attempt to purchase the same item simultaneously.

AWS Step Functions orchestrate workflows for order processing, payment verification, inventory updates, and notifications. By using state machines, the platform ensures that each step in the order lifecycle is executed reliably and in the correct sequence, with error handling and retry mechanisms built in. This guarantees that orders are processed accurately and efficiently, even when upstream systems, such as payment gateways, experience delays or temporary failures.

AWS KMS ensures that sensitive data, such as payment information or personally identifiable information, is encrypted both at rest and in transit. By using customer-managed keys, the company can meet compliance requirements such as PCI DSS while retaining control over encryption policies. Logging and auditing via CloudTrail and monitoring via CloudWatch provide additional visibility into application operations and security events.

Option B, using EC2 Auto Scaling with RDS, introduces operational complexity, requires provisioning and patching of servers, and may not scale efficiently during sudden traffic spikes. Option C, Elastic Beanstalk with Aurora Multi-AZ, provides managed deployment but lacks the seamless scalability of a fully serverless architecture and may result in slower response times during flash sales. Option D, S3 with Redshift, Glue, and Athena, is optimized for analytics workloads rather than real-time transactional processing required by an e-commerce platform.

By combining Lambda, API Gateway, DynamoDB, Step Functions, and KMS, the company achieves a fully serverless, highly scalable, secure, and resilient architecture. The system can handle unpredictable surges in traffic, maintain data consistency, and ensure secure integration with payment providers. This architecture reduces operational overhead, allows rapid iteration, and ensures that customers experience seamless shopping even during high-demand periods. The managed services provide monitoring, error handling, and compliance features out-of-the-box, enabling the company to focus on optimizing user experience, marketing campaigns, and business growth rather than infrastructure management.

Question 225:

A media company wants to build a global video streaming platform that can deliver content with low latency to viewers in multiple continents. The platform must handle dynamic scaling, DRM-protected content, and analytics on user engagement. Which AWS architecture is most suitable?

A) Amazon CloudFront, AWS Elemental MediaConvert, Amazon S3, Amazon DynamoDB, AWS Lambda, AWS KMS, and Amazon Redshift
B) Amazon EC2 Auto Scaling with Nginx, Amazon RDS MySQL, and Amazon CloudFront
C) AWS Elastic Beanstalk, Amazon Aurora Multi-AZ, and Amazon S3
D) Amazon S3, AWS Glue, Amazon Redshift, and Amazon Athena

Answer:

A) Amazon CloudFront, AWS Elemental MediaConvert, Amazon S3, Amazon DynamoDB, AWS Lambda, AWS KMS, and Amazon Redshift

Explanation:

Building a global video streaming platform involves multiple requirements: low-latency content delivery, dynamic scalability for millions of viewers, DRM protection for content, and analytics on user engagement. Each of these requirements can be met through a combination of AWS managed services that provide global reach, security, and operational efficiency.

Amazon CloudFront ensures low-latency delivery of video content worldwide by caching videos at edge locations close to viewers. This minimizes buffering and provides a smooth streaming experience. CloudFront integrates with Lambda@Edge for custom processing, including URL signing, authentication, and A/B testing, enhancing content delivery and user personalization. It also supports geo-restriction and DRM integration to protect content according to licensing agreements.

AWS Elemental MediaConvert is used for transcoding and packaging video content into multiple formats suitable for various devices and bandwidth conditions. This ensures that viewers on mobile, web, or smart TVs can access high-quality streams without interruptions. Integration with S3 as the origin store allows scalable, durable storage for original and processed video assets.

Amazon DynamoDB stores metadata for videos, user preferences, and session states. Its high throughput and low latency allow the platform to handle large numbers of simultaneous user requests, enabling real-time recommendations and personalized experiences. AWS Lambda orchestrates backend workflows, including triggering transcoding jobs, updating metadata, and responding to API requests.

AWS KMS encrypts video content, metadata, and user data, ensuring compliance with licensing agreements and security requirements. Analytics pipelines using Amazon Redshift provide insights into viewer engagement, content popularity, and performance metrics. This data enables the company to optimize recommendations, manage inventory of streaming licenses, and make business decisions based on actual user behavior.

Option B, EC2 with Nginx and RDS, requires significant operational effort and does not scale efficiently for global audiences. Option C, Elastic Beanstalk with Aurora, provides managed deployment but lacks real-time global content delivery optimization and DRM protection. Option D, S3 with Glue and Redshift, is designed for analytics and batch processing rather than low-latency streaming and interactive user experiences.

By combining CloudFront, Elemental MediaConvert, S3, DynamoDB, Lambda, KMS, and Redshift, the company achieves a globally scalable, secure, and low-latency streaming platform. This architecture supports dynamic scaling to handle peak viewership, ensures content protection through DRM and encryption, and provides detailed analytics for continuous improvement. Managed services reduce operational overhead and improve reliability, allowing the media company to focus on content creation and user engagement rather than infrastructure management.