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Question 166:

A media company needs to design a content delivery system for its video-on-demand service. The system must provide global low-latency access, automatically scale during peak demand, and support caching for popular videos while storing original media files durably. Which architecture should the company implement?

A) Amazon CloudFront, Amazon S3, and AWS Lambda@Edge
B) Amazon EC2 Auto Scaling with Amazon RDS and Amazon S3
C) Amazon SQS, AWS Lambda, and Amazon S3
D) Amazon API Gateway, AWS Lambda, and Amazon DynamoDB

Answer:

A) Amazon CloudFront, Amazon S3, and AWS Lambda@Edge

Explanation:

Designing a scalable content delivery system for a video-on-demand platform requires addressing multiple considerations: global reach, low latency, caching of frequently accessed content, and durable storage of original video files. Amazon CloudFront, a content delivery network (CDN), is purpose-built to deliver content with low latency to viewers globally by caching content at edge locations near the users. This significantly reduces load on the origin and provides consistent performance, even during peak demand periods.

Amazon S3 provides highly durable storage for original video files, ensuring that content is preserved reliably and can be accessed for long-term use. S3’s integration with CloudFront allows the media company to serve content efficiently while leveraging the durability, scalability, and cost-effectiveness of object storage. Additionally, S3 lifecycle policies and intelligent tiering can optimize costs by automatically moving less frequently accessed content to lower-cost storage classes.

AWS Lambda@Edge extends serverless compute capabilities to CloudFront edge locations. Lambda@Edge allows the company to run custom logic close to users, such as modifying requests and responses, implementing authorization, personalizing content, or dynamically generating content based on viewer preferences. This further improves latency and responsiveness without deploying and managing global infrastructure manually.

Option B, EC2 Auto Scaling with RDS and S3, requires managing server instances and relational databases, which increases operational complexity and does not provide global low-latency caching. Option C, SQS, Lambda, and S3, is more suited for asynchronous processing rather than real-time content delivery. Option D, API Gateway, Lambda, and DynamoDB, supports serverless APIs but does not address global low-latency video delivery and caching requirements.

By leveraging CloudFront, S3, and Lambda@Edge, the media company can implement a globally distributed, low-latency content delivery system that automatically scales to handle spikes in viewership, caches popular content at edge locations, and maintains durable storage for original videos. This architecture ensures an optimal viewing experience for users worldwide, reduces operational overhead, and provides cost efficiency through edge caching and serverless processing.

Question 167:

A healthcare provider wants to implement a HIPAA-compliant system for storing and analyzing patient medical imaging data. The system must provide high durability, encryption at rest and in transit, access logging, and support for large-scale analytics. Which AWS services and configuration are most appropriate?

A) Amazon S3 with SSE-KMS, AWS CloudTrail, AWS Key Management Service, and Amazon Athena
B) Amazon EBS volumes with default encryption, Amazon RDS, and AWS Config
C) Amazon DynamoDB with server-side encryption and AWS Lambda
D) Amazon EC2 with local instance storage and AWS CloudWatch

Answer:

A) Amazon S3 with SSE-KMS, AWS CloudTrail, AWS Key Management Service, and Amazon Athena

Explanation:

Storing and analyzing medical imaging data in a HIPAA-compliant manner requires careful attention to data security, access control, auditing, and analytics capabilities. Amazon S3 provides highly durable and scalable object storage suitable for large medical imaging files such as DICOM images. By using server-side encryption with AWS Key Management Service (SSE-KMS), each object is encrypted at rest using customer-managed or AWS-managed keys. This meets HIPAA encryption requirements while enabling fine-grained access control.

AWS CloudTrail records API calls made to AWS services, providing detailed audit logs for access and modifications to the data. CloudTrail is essential for compliance, enabling the healthcare provider to demonstrate who accessed patient data and when, satisfying HIPAA logging requirements. AWS KMS manages cryptographic keys centrally, allowing secure key rotation, granular access control, and integration with S3 for automatic encryption and decryption.

Amazon Athena provides a serverless, interactive query service for analyzing data directly in S3. Healthcare providers can run large-scale analytics on imaging metadata or associated structured data without moving data out of S3. Athena integrates with AWS Glue for data cataloging, supporting schema management and simplifying complex queries on large datasets.

Option B, EBS with RDS and Config, is more suited for block storage and relational databases but does not provide scalable analytics for large unstructured imaging data. Option C, DynamoDB with Lambda, is optimized for key-value or document-based data but is inefficient for storing and analyzing large image files. Option D, EC2 with local instance storage and CloudWatch, lacks durable storage and integrated encryption and would require extensive operational management.

The combination of S3, SSE-KMS, CloudTrail, KMS, and Athena ensures HIPAA compliance by providing secure, encrypted storage, detailed auditing, access control, and scalable analytics. This solution supports large-scale medical imaging workflows, allows cost-effective storage with S3 tiering, and ensures that sensitive patient data remains protected while enabling actionable insights through analytics.

Question 168:

A global software company wants to deploy a multi-region web application with high availability, low latency, and automatic failover. Users must be routed to the nearest region, and the system should replicate session and application data across regions. Which AWS architecture satisfies these requirements?

A) Amazon Route 53 with latency-based routing, Amazon CloudFront, DynamoDB Global Tables, and Amazon S3
B) Amazon EC2 Auto Scaling groups with RDS Multi-AZ and Elastic Load Balancer
C) AWS Elastic Beanstalk with single-region RDS and CloudFront
D) Amazon API Gateway with Lambda and DynamoDB Streams

Answer:

A) Amazon Route 53 with latency-based routing, Amazon CloudFront, DynamoDB Global Tables, and Amazon S3

Explanation:

A multi-region web application requires routing users to the nearest region for low latency, maintaining high availability during regional failures, and synchronizing session and application data across regions. Amazon Route 53 with latency-based routing ensures that users are directed to the AWS region providing the lowest latency, improving user experience and responsiveness.

Amazon CloudFront serves static content globally through its CDN, reducing latency by caching content at edge locations near users. CloudFront also integrates with origin failover, which allows automatic redirection to backup regions if the primary region becomes unavailable.

DynamoDB Global Tables provide active-active replication of application and session data across multiple AWS regions. This enables consistent and synchronized session information for users globally, ensuring high availability and fault tolerance without the need for manual replication management. Amazon S3 stores static content, media, and backups, providing highly durable storage that is accessible from all regions.

Option B, EC2 Auto Scaling with RDS Multi-AZ, provides regional high availability but lacks multi-region active-active capabilities and latency-based routing. Option C, Elastic Beanstalk with single-region RDS, cannot achieve global low-latency distribution and is limited to a single region’s resources. Option D, API Gateway with Lambda and DynamoDB Streams, supports serverless APIs but does not handle global routing and active-active replication effectively.

By combining Route 53, CloudFront, DynamoDB Global Tables, and S3, the software company can deploy a fully multi-region web application that delivers low latency to global users, ensures high availability with automatic failover, and maintains consistent application data across regions. This architecture reduces operational complexity, improves resilience against regional failures, and provides a scalable, globally distributed platform for modern web applications.

Question 169:

A financial services company needs to deploy a secure, highly available data lake that stores sensitive customer transaction data. The system must encrypt data at rest and in transit, support fine-grained access controls, integrate with analytics tools, and allow auditing of all data access. Which solution should the company implement?

A) Amazon S3 with server-side encryption (SSE-KMS), AWS Lake Formation, AWS CloudTrail, and Amazon Athena
B) Amazon EBS with default encryption, Amazon RDS, and Amazon QuickSight
C) Amazon DynamoDB with server-side encryption and AWS Lambda
D) Amazon Redshift with local snapshots and IAM policies

Answer:

A) Amazon S3 with server-side encryption (SSE-KMS), AWS Lake Formation, AWS CloudTrail, and Amazon Athena

Explanation:

Designing a secure, highly available data lake for financial transactions requires addressing multiple critical considerations: data security, access management, auditing, and analytics integration. Amazon S3 is a highly durable and scalable object storage service capable of storing structured and unstructured data. By enabling server-side encryption using AWS Key Management Service (SSE-KMS), each object in S3 is encrypted at rest using keys managed either by AWS or by the customer. This meets strict compliance and regulatory requirements common in the financial industry.

AWS Lake Formation simplifies the process of building a secure data lake on top of S3 by providing centralized access control, fine-grained permissions, and integration with AWS Identity and Access Management (IAM). Lake Formation allows the financial company to define row- and column-level permissions, ensuring that only authorized users can access sensitive fields such as account balances or personally identifiable information.

AWS CloudTrail captures all API calls and data access events across the AWS environment, providing immutable audit logs essential for regulatory compliance. CloudTrail ensures that every interaction with the data lake is logged and can be reviewed for unauthorized access attempts or operational audits.

Amazon Athena provides serverless interactive SQL queries directly on S3 data, allowing analysts and data scientists to perform complex analytics without moving data or managing clusters. Athena integrates seamlessly with Lake Formation, ensuring that access control policies are enforced during query execution.

Option B, EBS with RDS and QuickSight, provides durable storage and analytics for relational data but lacks the scalability, global reach, and native fine-grained access controls required for a full-featured data lake. Option C, DynamoDB with Lambda, is suitable for transactional key-value workloads but does not provide efficient analytics or support large-scale object storage. Option D, Redshift with snapshots and IAM policies, is optimized for structured data warehousing but does not provide the object storage scalability or integration flexibility needed for a data lake.

Using S3, SSE-KMS, Lake Formation, CloudTrail, and Athena ensures a fully secure, highly available, and scalable financial data lake. This combination enforces encryption, centralized access control, and robust auditing while supporting a wide range of analytics workloads without operational overhead. It also allows the organization to meet regulatory requirements such as PCI DSS, SOX, and GDPR while providing a platform that can scale as data volumes grow.

Question 170:

An e-commerce company wants to deploy a microservices-based architecture that provides global low-latency access, automatic scaling, and fault tolerance. Each service must communicate asynchronously, handle intermittent failures, and persist state. Which AWS services should be used to meet these requirements?

A) Amazon SQS, Amazon SNS, AWS Lambda, and Amazon DynamoDB
B) Amazon EC2, Elastic Load Balancer, and Amazon RDS
C) AWS AppSync, Amazon API Gateway, and AWS Step Functions
D) Amazon Kinesis Data Streams, Amazon EMR, and Amazon S3

Answer:

A) Amazon SQS, Amazon SNS, AWS Lambda, and Amazon DynamoDB

Explanation:

Designing a microservices-based architecture for an e-commerce platform with global low-latency access and fault tolerance requires decoupling services, handling asynchronous communication, and ensuring reliable state persistence. Amazon SQS (Simple Queue Service) provides a fully managed message queuing service that allows services to communicate asynchronously. Messages can be reliably stored until they are processed by downstream services, decoupling producers and consumers and providing resiliency against intermittent failures.

Amazon SNS (Simple Notification Service) complements SQS by providing a publish-subscribe mechanism for fan-out communication. SNS can notify multiple microservices simultaneously when an event occurs, ensuring that updates are propagated quickly and reliably. This is particularly useful for broadcasting order updates, inventory changes, or payment confirmations.

AWS Lambda provides serverless compute for processing messages from SQS or SNS. Lambda automatically scales to match the incoming workload, reducing operational overhead and providing fault tolerance, as functions are retried automatically in case of transient errors. It also allows developers to focus on business logic rather than infrastructure management.

Amazon DynamoDB serves as the persistent storage layer, offering low-latency, fully managed, and highly available NoSQL storage. DynamoDB supports global tables for multi-region replication, ensuring that state data such as inventory, user sessions, and shopping carts are consistently available worldwide.

Option B, EC2 with RDS and ELB, requires manual scaling and maintenance, making it less suitable for highly dynamic workloads with variable demand. Option C, AppSync, API Gateway, and Step Functions, supports serverless workflows but does not provide the same level of message decoupling and high-throughput asynchronous processing as SQS/SNS. Option D, Kinesis, EMR, and S3, is optimized for streaming analytics rather than microservices orchestration.

By combining SQS, SNS, Lambda, and DynamoDB, the e-commerce company can implement a globally distributed, asynchronous microservices architecture that scales automatically, handles intermittent failures, maintains persistent state, and ensures low-latency access for users around the world. This design improves resilience, reduces coupling between services, and simplifies operational management while providing a highly responsive platform for e-commerce operations.

Question 171:

A logistics company is building an IoT solution to track shipment vehicles in real time. The system must ingest high-velocity telemetry data, process it for anomaly detection, and store historical data for long-term analysis. The solution should scale automatically and provide near real-time dashboards. Which architecture meets these requirements?

A) AWS IoT Core, Amazon Kinesis Data Streams, AWS Lambda, Amazon S3, and Amazon QuickSight
B) Amazon API Gateway, AWS Lambda, Amazon DynamoDB, and Amazon CloudWatch
C) Amazon SQS, Amazon SNS, AWS Lambda, and Amazon RDS
D) Amazon MQ, Amazon EC2, and Amazon Elasticsearch Service

Answer:

A) AWS IoT Core, Amazon Kinesis Data Streams, AWS Lambda, Amazon S3, and Amazon QuickSight

Explanation:

Designing an IoT solution for real-time vehicle tracking involves ingesting large volumes of telemetry data, processing it in near real time, and storing historical records for analytics. AWS IoT Core provides a secure and scalable platform to connect devices and ingest telemetry data. IoT Core supports MQTT and HTTPS protocols, allowing vehicles to send data continuously with low latency.

Amazon Kinesis Data Streams serves as the ingestion and streaming layer, capable of handling high-velocity data. Kinesis allows real-time processing of streaming data, which is essential for anomaly detection such as identifying vehicles that deviate from expected routes or speeds. Kinesis also integrates with AWS Lambda to process streaming records with minimal delay, allowing immediate reactions to anomalies or triggering notifications.

AWS Lambda acts as the compute layer, executing code in response to Kinesis events without requiring server management. This ensures automatic scaling as the data volume increases, handling bursts in telemetry data without manual intervention. Lambda functions can also perform data transformation, aggregation, or enrichment before storing results.

Amazon S3 provides durable, cost-effective storage for historical telemetry data, enabling long-term analysis, reporting, and compliance requirements. S3 lifecycle policies can manage storage costs by transitioning older data to lower-cost storage classes.

Amazon QuickSight allows creation of real-time dashboards and visualizations, providing operational insights for fleet managers. QuickSight connects directly to processed data in S3 or streaming outputs from Kinesis, enabling interactive analytics and anomaly visualization.

Option B, API Gateway with Lambda and DynamoDB, is suitable for transactional APIs but does not handle high-velocity streaming or near real-time analytics effectively. Option C, SQS/SNS with RDS, decouples messages but is not optimized for high-volume telemetry or streaming processing. Option D, Amazon MQ with EC2 and Elasticsearch, provides messaging and search capabilities but introduces unnecessary operational overhead and lacks native real-time processing scalability.

The combination of IoT Core, Kinesis, Lambda, S3, and QuickSight ensures a fully managed, scalable, and real-time IoT analytics solution. Vehicles can transmit telemetry continuously, anomalies are detected immediately, historical data is preserved efficiently, and dashboards provide actionable insights to optimize fleet operations.

Question 172:

A healthcare company wants to migrate its on-premises electronic health records (EHR) system to AWS. The system must maintain HIPAA compliance, encrypt sensitive patient data, support high availability, and allow secure access for authorized personnel globally. Which solution provides the most appropriate architecture?

A) Amazon EC2 with EBS encryption, Amazon RDS for PostgreSQL with encryption at rest, Multi-AZ deployment, AWS IAM, and AWS CloudTrail
B) Amazon S3 with default encryption, Amazon DynamoDB, and AWS Lambda
C) Amazon Redshift with snapshots and IAM policies
D) Amazon EMR cluster with local HDFS and EC2 instances

Answer:

A) Amazon EC2 with EBS encryption, Amazon RDS for PostgreSQL with encryption at rest, Multi-AZ deployment, AWS IAM, and AWS CloudTrail

Explanation:

Migrating an electronic health records (EHR) system to AWS requires a focus on security, regulatory compliance, availability, and accessibility. Healthcare data is highly sensitive and subject to HIPAA regulations, which mandate secure storage, controlled access, and auditing of protected health information (PHI).

Amazon EC2 provides virtual servers that allow the EHR application to be lifted and shifted to the cloud while maintaining application architecture and dependencies. Encrypting EBS volumes ensures that all data stored at rest is secure and compliant. EBS encryption integrates with AWS KMS, enabling centralized key management and audit capabilities.

Amazon RDS for PostgreSQL provides a managed relational database service that supports encryption at rest and in transit. Enabling Multi-AZ deployment ensures high availability and automatic failover in case of hardware or availability zone failures, providing continuous access to patient data without downtime.

AWS IAM manages secure access by defining granular permissions for personnel, ensuring that only authorized users can read or modify sensitive records. IAM integrates with multi-factor authentication (MFA) to add an additional layer of security.

AWS CloudTrail provides auditing of all API calls and data access operations, ensuring that the healthcare organization can maintain a complete record of all interactions with the system. CloudTrail is critical for HIPAA compliance, supporting operational oversight and regulatory reporting requirements.

Option B, S3 with DynamoDB and Lambda, lacks the relational structure necessary for transactional EHR data and does not provide sufficient operational continuity guarantees for mission-critical healthcare workloads. Option C, Redshift with snapshots, is designed for analytics and reporting but does not meet the operational and transactional requirements of EHR systems. Option D, EMR clusters with local HDFS, introduces unnecessary operational complexity and does not provide native high availability or managed relational storage required for transactional data.

By using EC2, RDS, IAM, EBS encryption, and CloudTrail, the healthcare company can migrate its EHR system while maintaining HIPAA compliance, providing secure global access, high availability, and full auditability of sensitive patient data. This architecture also allows future scalability and integration with analytics or machine learning tools for population health management while keeping compliance and security at the core.

Question 173:

A media company needs to implement a content distribution solution that can deliver video streams globally with low latency, scale automatically during peak demand, and provide detailed metrics on viewership. Which architecture should be deployed?

A) Amazon CloudFront with S3 origin, AWS Elemental MediaConvert, and Amazon CloudWatch
B) Amazon S3 with DynamoDB and Amazon Athena
C) Amazon API Gateway with Lambda and Amazon RDS
D) Amazon Kinesis Data Streams, Amazon EMR, and Amazon QuickSight

Answer:

A) Amazon CloudFront with S3 origin, AWS Elemental MediaConvert, and Amazon CloudWatch

Explanation:

Delivering video content globally requires a solution optimized for low latency, high throughput, automatic scaling, and real-time monitoring. Amazon CloudFront is a content delivery network (CDN) that caches video content at edge locations around the world, reducing latency and providing a consistent playback experience for viewers regardless of location. CloudFront also scales automatically to handle spikes in traffic, such as during live events or viral content distribution.

Amazon S3 acts as the origin for video files, providing highly durable and cost-effective storage. Using S3 with CloudFront allows the media company to serve large volumes of static video content reliably without managing infrastructure for storage or delivery.

AWS Elemental MediaConvert allows for transcoding video into multiple formats and bitrates suitable for various devices, including mobile phones, tablets, and smart TVs. This ensures adaptive streaming, providing smooth playback even under fluctuating network conditions. MediaConvert integrates with S3 and CloudFront, automating the conversion and delivery pipeline.

Amazon CloudWatch collects metrics and logs related to content delivery and system performance. CloudWatch dashboards and alarms allow the company to monitor viewership patterns, identify performance bottlenecks, and optimize delivery. Metrics such as cache hit ratios, latency, and errors provide actionable insights for maintaining an optimal user experience.

Option B, S3 with DynamoDB and Athena, is primarily suitable for storage and analytics rather than low-latency global content delivery. Option C, API Gateway with Lambda and RDS, is designed for API-based services and transactional workloads, not high-throughput streaming media. Option D, Kinesis with EMR and QuickSight, is optimized for streaming analytics rather than content delivery, making it less appropriate for video streaming applications.

Using CloudFront with S3, MediaConvert, and CloudWatch ensures that video content is delivered with minimal latency, adapts to user bandwidth, scales automatically during peak demand, and provides detailed operational metrics. This architecture balances performance, cost, and operational simplicity while supporting a global audience with high-quality video streaming.

Question 174:

A retail company is designing an event-driven architecture for order processing. Orders can be submitted via web, mobile, and in-store systems. The system must process orders asynchronously, ensure exactly-once processing, and allow for integration with multiple downstream services such as inventory, shipping, and billing. Which architecture fulfills these requirements?

A) Amazon SQS FIFO queues, Amazon SNS, AWS Lambda, and Amazon DynamoDB
B) Amazon EC2 instances with Elastic Load Balancing and RDS
C) AWS Step Functions with API Gateway and S3
D) Amazon Kinesis Data Firehose, Amazon Redshift, and Amazon QuickSight

Answer:

A) Amazon SQS FIFO queues, Amazon SNS, AWS Lambda, and Amazon DynamoDB

Explanation:

An event-driven architecture for order processing requires handling asynchronous events, guaranteeing order delivery, and coordinating multiple downstream systems. Amazon SQS FIFO queues provide exactly-once processing and preserve the order of messages, ensuring that each order is processed once and in sequence, which is critical for retail operations to prevent duplication or missed transactions.

Amazon SNS allows broadcasting events to multiple subscribers. When an order is placed, SNS can notify inventory, shipping, and billing services simultaneously, ensuring that all systems are updated consistently. This pub/sub model decouples services, allowing independent scaling and reducing interdependencies.

AWS Lambda processes incoming SQS messages and SNS notifications, executing business logic such as updating order status, calculating totals, or triggering notifications. Lambda’s serverless nature provides automatic scaling and resilience against sudden spikes in order volume, reducing operational overhead.

Amazon DynamoDB stores persistent state such as order metadata, inventory levels, and transaction records. DynamoDB provides low-latency, high-throughput access with global tables to support multiple regions. This ensures that the system can scale to handle large volumes of concurrent orders while maintaining consistency.

Option B, EC2 with RDS and ELB, requires manual scaling and does not provide guaranteed exactly-once message processing. Option C, Step Functions with API Gateway and S3, is suitable for orchestrated workflows but lacks native asynchronous message queuing with exactly-once semantics. Option D, Kinesis Data Firehose with Redshift and QuickSight, is optimized for analytics pipelines rather than transactional event processing.

By combining SQS FIFO queues, SNS, Lambda, and DynamoDB, the retail company can implement a fully event-driven, asynchronous order processing system. This architecture guarantees exactly-once processing, decouples services for independent scaling, ensures data consistency across multiple downstream systems, and allows for future extensibility, such as integrating loyalty programs or predictive analytics, while maintaining operational resilience and reliability.

Question 175:

A financial services company needs to process large volumes of transactions in real time, detect potential fraud, and trigger alerts to security teams instantly. The solution must scale automatically with demand and minimize operational overhead. Which architecture fulfills these requirements?

A) Amazon Kinesis Data Streams, AWS Lambda, Amazon DynamoDB, and Amazon SNS
B) Amazon RDS Multi-AZ deployment with periodic queries
C) Amazon S3 batch processing with AWS Glue
D) Amazon Redshift with scheduled SQL queries

Answer:

A) Amazon Kinesis Data Streams, AWS Lambda, Amazon DynamoDB, and Amazon SNS

Explanation:

Processing real-time financial transactions at scale requires a robust event-driven architecture that can handle high throughput, low latency, and immediate response to anomalies. Amazon Kinesis Data Streams provides a scalable, fully managed service that can ingest and process hundreds of thousands of transactions per second. Each transaction becomes a stream record that can be processed in real time, ensuring minimal latency between data arrival and detection of potential fraud.

AWS Lambda integrates with Kinesis to process each record as it arrives. Lambda functions allow the implementation of fraud detection logic, such as pattern matching, anomaly detection, or validation against blacklists. Lambda automatically scales in response to the volume of incoming events, minimizing operational overhead and eliminating the need to manage servers.

Amazon DynamoDB stores transaction states, metadata, and historical patterns for reference. Its low-latency access allows Lambda to quickly validate new transactions against existing patterns or thresholds. DynamoDB’s scalability ensures the system can handle growing transaction volumes without compromising performance.

Amazon SNS provides immediate notifications to security teams when suspicious activity is detected. This allows teams to take swift action, reducing risk and ensuring compliance with financial regulations. SNS supports multiple delivery channels, including email, SMS, and mobile push notifications, ensuring rapid and reliable alerting.

Option B, RDS Multi-AZ with periodic queries, cannot handle real-time detection or instant alerting, as it relies on batch query execution, introducing latency and reducing responsiveness. Option C, S3 with Glue, is designed for batch ETL workflows, not real-time processing. Option D, Redshift with scheduled SQL queries, is suitable for analytics rather than immediate transactional fraud detection.

The combination of Kinesis, Lambda, DynamoDB, and SNS allows the financial services company to process transactions in real time, automatically scale according to demand, detect fraud efficiently, and alert teams instantly, all while minimizing operational complexity and ensuring regulatory compliance. This architecture also supports integration with machine learning models for predictive fraud analysis, enabling more sophisticated detection strategies as the system evolves.

Question 176:

A global e-commerce company is designing a data lake for analytics. The solution must ingest structured, semi-structured, and unstructured data from multiple regions, enforce fine-grained access control, and enable scalable query processing. Which architecture should be implemented?

A) Amazon S3 with AWS Lake Formation, AWS Glue for ETL, and Amazon Athena
B) Amazon RDS with cross-region read replicas and periodic backups
C) Amazon Redshift with snapshot replication to multiple regions
D) Amazon DynamoDB with global tables and Lambda

Answer:

A) Amazon S3 with AWS Lake Formation, AWS Glue for ETL, and Amazon Athena

Explanation:

Building a global data lake requires a solution that can store diverse data types, enforce security policies, and provide flexible, scalable querying capabilities. Amazon S3 provides highly durable, cost-effective storage for structured, semi-structured, and unstructured data. S3’s scalability ensures that the data lake can grow as the company collects more transactional, operational, and clickstream data across multiple regions.

AWS Lake Formation simplifies the creation of secure data lakes by centralizing data ingestion, cataloging, and access control. Lake Formation allows fine-grained access control at the table, column, and row levels, enabling compliance with data privacy regulations such as GDPR or CCPA. Integration with AWS IAM and AWS Key Management Service ensures secure identity and encryption management.

AWS Glue is a fully managed ETL service that prepares and transforms data from various sources for analytics. Glue can handle schema inference, cleansing, and enrichment, making raw data queryable while reducing the operational overhead of managing ETL pipelines manually. Glue integrates with S3 and Lake Formation, maintaining security and governance across the data lake.

Amazon Athena allows scalable, serverless querying of data stored in S3 using standard SQL. Athena automatically scales to accommodate large query volumes, making it suitable for ad hoc analysis, dashboards, and reporting without provisioning or managing infrastructure. Integration with Lake Formation ensures queries respect access policies, maintaining security and compliance.

Option B, RDS with cross-region read replicas, provides relational database capabilities but cannot efficiently handle unstructured or semi-structured data at a global scale. Option C, Redshift with snapshot replication, supports structured data analytics but lacks native support for unstructured and semi-structured data types and requires more operational management. Option D, DynamoDB with Lambda, is optimized for transactional workloads rather than analytical queries across diverse datasets.

This architecture using S3, Lake Formation, Glue, and Athena allows the company to ingest diverse data types from multiple regions, maintain fine-grained access control, and perform scalable analytics without complex infrastructure management. It supports future integration with machine learning and AI workloads, enabling predictive analytics and personalized recommendations for global users while ensuring compliance and operational efficiency.

Question 177:

A company wants to implement a multi-region disaster recovery solution for its web application hosted in AWS. The solution must ensure minimal downtime, near-zero data loss, and allow read/write access from multiple regions for improved latency. Which architecture is the most appropriate?

A) Amazon Aurora Global Database with cross-region replication, Route 53 latency-based routing, and Multi-AZ deployments
B) Amazon S3 with cross-region replication and lifecycle policies
C) Amazon RDS with daily snapshots and manual failover
D) Amazon DynamoDB with on-demand capacity and global tables disabled

Answer:

A) Amazon Aurora Global Database with cross-region replication, Route 53 latency-based routing, and Multi-AZ deployments

Explanation:

Implementing a multi-region disaster recovery solution requires considerations of high availability, minimal downtime, data durability, and low-latency access for users in multiple regions. Amazon Aurora Global Database provides a fully managed relational database solution that allows read/write operations in the primary region and read-only operations in secondary regions. Cross-region replication is nearly synchronous, achieving low-latency data replication with typically under a second delay. This ensures minimal data loss and supports disaster recovery scenarios where failover to a secondary region is required.

Route 53 latency-based routing directs user requests to the region with the lowest latency, improving application responsiveness globally. Combining this with Aurora Multi-AZ deployments ensures that each region has high availability and automatic failover within the region itself. Multi-AZ deployments protect against infrastructure failures within an availability zone, while the global database protects against regional outages.

Option B, S3 with cross-region replication, provides durable storage but does not support transactional workloads or read/write access for databases. Option C, RDS with daily snapshots and manual failover, introduces higher recovery time objectives (RTO) and potential data loss because snapshots are not real-time. Option D, DynamoDB with global tables disabled, cannot provide multi-region write capability and would result in higher latency and potential data inconsistency.

Using Aurora Global Database, Route 53 latency-based routing, and Multi-AZ deployments ensures a highly available, globally distributed, resilient architecture. This setup supports near-zero downtime, minimal data loss, and low-latency access across regions while maintaining operational simplicity and scalability. It also allows seamless integration with analytics and application layers that require globally consistent and reliable transactional data.

Question 178:

A global video streaming company needs to deliver content with low latency to users worldwide. The solution must handle millions of requests per second, support caching, and provide secure content delivery. Which architecture should be implemented?

A) Amazon CloudFront with S3 origin, AWS WAF, and Lambda@Edge
B) Amazon S3 with versioning and lifecycle policies
C) Amazon RDS with Multi-AZ deployment
D) Amazon EC2 Auto Scaling group behind an Application Load Balancer

Answer:

A) Amazon CloudFront with S3 origin, AWS WAF, and Lambda@Edge

Explanation:

Delivering video content to a global audience with low latency requires a content delivery network (CDN) that can cache content close to end users, provide security, and scale to millions of requests per second. Amazon CloudFront is a fully managed CDN that integrates with other AWS services to optimize content delivery. By using S3 as the origin, static video files are stored in a highly durable and scalable storage solution. CloudFront caches this content at edge locations around the world, reducing latency by serving content from locations nearest to users rather than the origin region.

AWS WAF integrates with CloudFront to provide application layer security. It allows filtering of malicious traffic, protection against common web exploits such as SQL injection or cross-site scripting, and helps maintain the availability and reliability of the streaming service. Using Lambda@Edge, the company can run serverless code at CloudFront edge locations, enabling dynamic content manipulation, authentication, personalized recommendations, or real-time logging before content is served to end users.

Option B, S3 with versioning and lifecycle policies, provides durable storage but cannot reduce latency or support global caching for millions of users. Option C, RDS with Multi-AZ, is a relational database solution not optimized for high-volume content delivery. Option D, EC2 Auto Scaling behind an ALB, provides compute scaling but cannot deliver static video files globally with low latency as efficiently as a CDN.

The combination of CloudFront, S3, WAF, and Lambda@Edge ensures the video streaming service can handle global traffic, minimize latency, enhance security, and support dynamic content processing, all without managing infrastructure manually. This architecture also supports scaling for future growth, integration with analytics to monitor performance, and compliance with content security policies.

Question 179:

A company wants to migrate a legacy on-premises application to AWS. The application uses a relational database and requires high availability with automated failover. Which AWS service combination is most appropriate?

A) Amazon RDS Multi-AZ deployment with read replicas
B) Amazon DynamoDB with on-demand capacity
C) Amazon S3 with versioning and lifecycle policies
D) Amazon Redshift with cross-region snapshots

Answer:

A) Amazon RDS Multi-AZ deployment with read replicas

Explanation:

Migrating a legacy relational database application to AWS requires a solution that maintains high availability, durability, and failover capabilities without requiring extensive manual configuration. Amazon RDS Multi-AZ deployments provide automated database replication across availability zones. The primary database synchronously replicates updates to a standby instance in a different availability zone. If the primary instance fails, RDS automatically fails over to the standby, minimizing downtime and ensuring business continuity.

Read replicas enhance scalability by allowing read operations to be distributed across multiple instances. This is especially useful for applications with heavy read workloads, as the primary instance is relieved of read traffic while still handling write operations. RDS supports automated backups, snapshots, and patching, further reducing operational overhead.

Option B, DynamoDB, is a NoSQL database and does not provide native relational database features, making it unsuitable for applications requiring complex transactions or relational queries. Option C, S3, is object storage, not a relational database, and cannot support transactional workloads or failover for an application relying on structured data. Option D, Redshift, is designed for analytics and data warehousing, not transactional workloads, and cannot replace a legacy OLTP system.

The combination of RDS Multi-AZ deployment with read replicas ensures the application maintains high availability, supports scaling for read-heavy workloads, minimizes downtime during failures, and simplifies operational management, allowing the company to migrate its legacy application with minimal disruption and compliance with enterprise SLAs.

Question 180:

A healthcare provider needs to store and analyze patient records securely. The solution must comply with regulatory requirements, provide encryption at rest and in transit, and allow analytics using SQL without managing servers. Which architecture meets these requirements?

A) Amazon S3 with AWS Lake Formation, AWS Glue, and Amazon Athena
B) Amazon RDS with Multi-AZ deployment and daily backups
C) Amazon DynamoDB with on-demand capacity and encryption
D) Amazon Redshift with single-node cluster and manual snapshots

Answer:

A) Amazon S3 with AWS Lake Formation, AWS Glue, and Amazon Athena

Explanation:

Healthcare data is highly sensitive and regulated under standards such as HIPAA. Storing and analyzing patient records requires encryption at rest and in transit, fine-grained access control, audit logging, and the ability to query large datasets securely. Amazon S3 provides highly durable and scalable storage, supporting server-side encryption using AWS KMS and transport layer security for in-transit encryption.

AWS Lake Formation simplifies the process of creating a secure data lake. Lake Formation provides centralized access control policies, allowing the healthcare provider to define fine-grained permissions at the table, column, and row levels. This ensures that only authorized users can access sensitive patient data while supporting regulatory compliance. It also integrates with AWS IAM and logging services for auditing purposes.

AWS Glue is used for data transformation and preparation. Glue ETL jobs clean, normalize, and structure patient records from multiple sources to make them queryable. By integrating with Lake Formation, Glue ensures that data transformation does not violate access policies.

Amazon Athena enables serverless SQL querying directly on data stored in S3, allowing analytics without managing database servers. Athena scales automatically to handle large datasets, supports standard SQL syntax, and integrates with Lake Formation to enforce access policies. This combination allows analytics teams to perform complex queries while maintaining compliance, security, and governance.

Option B, RDS with Multi-AZ, provides relational storage but requires ongoing server management and scaling to support large-scale analytics. Option C, DynamoDB, is suitable for NoSQL workloads but does not provide SQL query capabilities directly for analytics. Option D, Redshift with a single-node cluster, lacks redundancy, scalability, and integrated access control at the granular level required for patient data compliance.

This architecture provides a highly secure, scalable, and serverless solution for storing and analyzing healthcare data, ensuring compliance with regulations, protecting patient privacy, and enabling insights using standard SQL queries without complex infrastructure management.