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Question 151:
A financial services company wants to automate credit risk analysis using historical transaction data stored on-premises. The solution must handle high volumes of data, allow predictive analytics, and integrate with machine learning models for real-time risk scoring. Which AWS architecture is most appropriate?
A) Amazon S3 for data storage, AWS Glue for ETL, Amazon SageMaker for machine learning, and Amazon EMR for batch processing
B) Amazon RDS Multi-AZ for database storage and AWS Lambda for processing
C) Amazon DynamoDB with global tables and AWS Fargate for containerized workloads
D) Amazon Redshift for storage only, with manual ML integration
Answer:
A) Amazon S3 for data storage, AWS Glue for ETL, Amazon SageMaker for machine learning, and Amazon EMR for batch processing
Explanation:
The financial services company requires a solution that can handle high volumes of historical transaction data, support batch and streaming analytics, integrate with machine learning models, and provide real-time risk scoring for credit evaluation. Amazon S3 serves as a highly scalable, durable, and secure storage solution for storing historical transaction data. It supports encryption at rest and in transit, fine-grained access control, and integration with auditing services, which are essential for financial workloads that require regulatory compliance.
AWS Glue is used to extract, transform, and load (ETL) the data into formats suitable for analytics and machine learning. It simplifies data preparation by automating schema discovery, cataloging data, and generating ETL scripts, reducing operational overhead and accelerating the time to insight. Glue also integrates seamlessly with S3, SageMaker, and EMR, allowing a unified workflow from raw data ingestion to analytical and machine learning pipelines.
Amazon SageMaker is used for building, training, and deploying machine learning models. SageMaker supports preprocessing, feature engineering, model training, hyperparameter tuning, and deployment of endpoints for real-time inference. By leveraging SageMaker, the company can deploy predictive models for real-time credit risk scoring, allowing automated decisions based on historical and streaming data. SageMaker also integrates with data sources in S3 and can consume transformed datasets from Glue to train highly accurate models.
Amazon EMR enables batch processing of large datasets in a distributed computing environment using Apache Spark, Hadoop, and other big data frameworks. EMR processes terabytes of historical transaction data efficiently, producing aggregated metrics, features for ML models, and insights for downstream applications. This combination allows the financial services company to analyze historical patterns, detect anomalies, and feed engineered features into SageMaker models for predictive analytics.
Option B, RDS with Lambda, is suitable for transactional workloads but does not scale effectively for large-scale analytics or feature extraction from massive datasets. Option C, DynamoDB with Fargate, provides high-speed NoSQL storage but lacks the complex data transformation, analytics, and batch processing required for historical data and predictive modeling. Option D, Redshift alone, provides analytical storage but does not include an integrated ETL and ML pipeline, increasing operational complexity and limiting real-time inference capabilities.
By using S3, Glue, SageMaker, and EMR, the financial services company can create a fully managed, scalable, and secure data processing and machine learning environment. This architecture allows seamless integration of large-scale batch processing with predictive analytics, supports real-time inference for risk scoring, and ensures compliance with industry regulations. Additionally, leveraging AWS services reduces operational overhead and improves time to deployment for complex ML-based financial applications.
Question 152:
A global e-commerce company wants to implement a hybrid architecture where sensitive user data remains on-premises, but analytical workloads are moved to AWS. The solution must allow secure, low-latency access to AWS services while supporting elasticity for seasonal traffic spikes. Which combination of AWS services is most suitable?
A) AWS Direct Connect, Amazon VPC, AWS Transit Gateway, and Amazon Redshift
B) AWS Site-to-Site VPN only and Amazon S3
C) Amazon CloudFront, Route 53, and AWS Lambda
D) Amazon DynamoDB with AWS Fargate containers
Answer:
A) AWS Direct Connect, Amazon VPC, AWS Transit Gateway, and Amazon Redshift
Explanation:
A hybrid architecture for a global e-commerce platform requires secure, reliable, and low-latency connectivity between on-premises data centers and AWS, while enabling scalable analytics for seasonal workloads. AWS Direct Connect provides a dedicated, private network connection from on-premises infrastructure to AWS, ensuring low-latency, consistent bandwidth, and high security compared to standard internet VPN connections.
Amazon VPC allows the company to create isolated, secure cloud environments for running analytics workloads. VPC enables fine-grained network segmentation, private subnets, routing, and integration with Direct Connect to ensure data remains private and secure during transfer.
AWS Transit Gateway simplifies network management by centralizing connectivity between multiple VPCs and on-premises locations. It ensures scalable routing and enables the company to efficiently handle traffic across multiple regions or business units. Transit Gateway reduces operational complexity, provides high availability, and supports secure peering connections across the hybrid environment.
Amazon Redshift provides a fast, fully managed, petabyte-scale data warehouse for analytics. Data from on-premises systems can be securely loaded into Redshift using AWS Glue or AWS Data Migration Service (DMS). Redshift supports complex queries, joins, aggregations, and integration with BI tools for reporting and real-time decision-making. Its elasticity allows the company to scale clusters during seasonal spikes and pause or resize resources during off-peak periods to optimize costs.
Option B, VPN-only and S3, is suitable for small workloads but does not provide the dedicated bandwidth, low-latency performance, or fully managed data warehouse capabilities required for large-scale analytics. Option C, CloudFront, Route 53, and Lambda, focuses on content delivery and serverless computing but does not provide direct connectivity for sensitive on-premises data. Option D, DynamoDB with Fargate, is suitable for NoSQL storage and microservices but lacks analytical depth and hybrid connectivity.
By using Direct Connect, VPC, Transit Gateway, and Redshift, the e-commerce company achieves a secure, scalable, and high-performance hybrid architecture that preserves on-premises control over sensitive data while leveraging the elasticity and analytics power of AWS. This architecture supports complex queries, real-time insights, and operational efficiency while maintaining compliance and reducing latency.
Question 153:
A gaming company needs to deploy a global multiplayer game backend that must provide low-latency connections, automatic scaling, and multi-region failover. The backend requires session state management and secure communication between clients and servers. Which AWS architecture should be implemented?
A) Amazon GameLift, Amazon DynamoDB, Amazon CloudFront, and AWS Global Accelerator
B) Amazon EC2 Auto Scaling, Elastic Load Balancer, and RDS Multi-AZ
C) AWS Lambda with API Gateway and S3
D) Amazon Lightsail with regional replication
Answer:
A) Amazon GameLift, Amazon DynamoDB, Amazon CloudFront, and AWS Global Accelerator
Explanation:
Global multiplayer game backends require extremely low-latency connections, automatic scaling for varying player loads, reliable session state management, and global failover capabilities. Amazon GameLift is a fully managed service designed for deploying, operating, and scaling game servers. It automatically provisions server infrastructure, balances player sessions, and provides matchmaking services. GameLift ensures low-latency access and regional failover by deploying servers close to players across multiple AWS regions.
Amazon DynamoDB provides fast, highly available NoSQL storage for storing session states, player profiles, and game metadata. DynamoDB global tables enable multi-region replication, ensuring data is available close to players worldwide, reducing latency and supporting disaster recovery. It provides strong consistency and seamless scaling to handle millions of concurrent users, which is critical for real-time gaming workloads.
Amazon CloudFront accelerates content delivery for static assets, updates, and game patches by caching content at edge locations near players. This reduces latency for downloads and improves user experience globally. AWS Global Accelerator provides static anycast IP addresses that route traffic to the optimal regional endpoints, improving performance, reliability, and security. It handles failover automatically, ensuring the game remains accessible even if a regional service fails.
Option B, EC2 Auto Scaling with ELB and RDS Multi-AZ, provides general-purpose infrastructure but lacks game-specific session management, matchmaking, and real-time latency optimization. Option C, Lambda with API Gateway and S3, is suitable for stateless serverless applications but does not support persistent game state or low-latency multiplayer requirements. Option D, Lightsail, is a simplified deployment option and cannot handle multi-region failover or real-time scaling at global scale.
By using GameLift, DynamoDB, CloudFront, and Global Accelerator, the gaming company achieves a globally distributed, low-latency multiplayer backend that scales automatically, manages player sessions, and provides resilience against regional failures. This architecture ensures smooth gameplay, reduces latency for players worldwide, simplifies operational management, and supports rapid scaling for seasonal or promotional spikes in traffic.
Question 154:
A media company wants to process large volumes of user-generated video content for live streaming and on-demand playback. The solution must allow dynamic scaling, handle video transcoding, and store processed content for global delivery with low latency. Which combination of AWS services is most appropriate?
A) Amazon S3 for storage, AWS Elemental MediaConvert for transcoding, Amazon CloudFront for global delivery, and AWS Lambda for automation
B) Amazon EBS for storage, AWS Batch for processing, and Amazon EC2 Auto Scaling
C) Amazon DynamoDB for video metadata, AWS Fargate for processing, and Amazon S3
D) Amazon RDS for storage, AWS Step Functions for orchestration, and Amazon CloudFront
Answer:
A) Amazon S3 for storage, AWS Elemental MediaConvert for transcoding, Amazon CloudFront for global delivery, and AWS Lambda for automation
Explanation:
Media companies that deal with user-generated content face the challenge of processing massive video files efficiently while delivering them to global audiences with minimal latency. Amazon S3 serves as the backbone for storage, providing virtually unlimited, durable, and secure object storage. Videos uploaded by users can be stored directly in S3, which automatically scales to accommodate spikes in uploads without requiring provisioning of infrastructure. S3 also integrates with other AWS services to trigger processing pipelines automatically upon object creation, allowing a fully automated workflow.
AWS Elemental MediaConvert provides a managed, scalable video transcoding service, converting videos into multiple formats and bitrates suitable for on-demand streaming across different devices and network conditions. MediaConvert supports standard codecs, adaptive bitrate streaming, and DRM integration, ensuring videos are compatible with a wide range of platforms while maintaining high quality. By leveraging MediaConvert, the media company can reduce operational overhead associated with building and managing custom transcoding pipelines while ensuring reliability and scalability.
Amazon CloudFront, AWS’s global content delivery network, distributes the processed videos with low latency by caching content at edge locations worldwide. This ensures that end users experience minimal buffering and fast load times, even under heavy traffic. CloudFront integrates with S3 and MediaConvert outputs directly, automatically serving optimized content based on the user’s geographic location. In addition, CloudFront supports HTTPS, signed URLs, and signed cookies, allowing secure delivery of premium content while preventing unauthorized access.
AWS Lambda enables automation within the pipeline. For example, Lambda functions can be triggered when new content is uploaded to S3 to start transcoding, update metadata in databases, or generate notifications for workflow completion. This serverless approach allows the company to scale compute tasks dynamically, paying only for execution time and avoiding the cost and complexity of managing persistent infrastructure.
Option B, EBS with Batch and EC2 Auto Scaling, would require extensive management and does not integrate seamlessly with a global delivery network, making it less suitable for media workloads with unpredictable traffic patterns. Option C, DynamoDB with Fargate, is appropriate for metadata and containerized processing but does not provide a robust managed transcoding service and complicates the pipeline for large video files. Option D, RDS with Step Functions and CloudFront, could handle orchestration but lacks the scalable storage and managed video processing needed for large-scale media workflows.
Using S3, MediaConvert, CloudFront, and Lambda allows the media company to implement a fully automated, scalable, and low-latency video processing pipeline. This architecture ensures videos are processed efficiently, delivered globally with minimal latency, and handled in a secure, compliant, and cost-effective manner. The flexibility and integration of these services reduce operational overhead while enabling rapid scaling for peak user activity, ensuring a seamless user experience across devices and regions.
Question 155:
A multinational retail company needs a solution to manage its inventory across multiple regions. The solution should provide real-time inventory updates, high availability, automatic scaling, and low-latency access for both regional warehouses and the central office. Which AWS services combination is most appropriate?
A) Amazon DynamoDB global tables, Amazon API Gateway, AWS Lambda, and Amazon CloudFront
B) Amazon RDS Multi-AZ, Elastic Load Balancer, and Amazon EC2 Auto Scaling
C) Amazon S3 for storage, AWS Glue for ETL, and Amazon Redshift for analytics
D) Amazon ElastiCache Redis cluster, AWS Fargate, and Amazon S3
Answer:
A) Amazon DynamoDB global tables, Amazon API Gateway, AWS Lambda, and Amazon CloudFront
Explanation:
Managing inventory across multiple regions requires a highly available, low-latency, and scalable data architecture. Amazon DynamoDB global tables are specifically designed for applications that span multiple regions. Global tables provide multi-master replication, enabling inventory data to be read and written locally in each region while maintaining eventual consistency across all locations. This ensures that updates from regional warehouses are immediately reflected across the system and the central office without the need for complex replication logic.
API Gateway allows the retail company to expose RESTful APIs for applications and internal systems to interact with the inventory data securely and efficiently. Through API Gateway, requests can be routed to Lambda functions that perform CRUD operations on DynamoDB tables, implement business logic, or trigger notifications when stock levels reach critical thresholds. API Gateway provides features like throttling, caching, authentication, and request validation, ensuring secure and reliable access to inventory data from multiple endpoints.
AWS Lambda complements DynamoDB and API Gateway by enabling serverless processing of inventory events. Lambda functions can automatically handle updates, aggregate metrics, or perform validation logic whenever inventory changes occur. By leveraging serverless functions, the company achieves automatic scaling to accommodate spikes in updates during peak business periods, such as holiday seasons, without managing underlying infrastructure.
Amazon CloudFront can be used to cache API responses or static content related to inventory data near end users, reducing latency for users accessing the system from global locations. By combining CloudFront with DynamoDB global tables, the company ensures that regional offices and warehouses have rapid access to critical inventory information, improving operational efficiency and decision-making.
Option B, RDS Multi-AZ with ELB and EC2, provides high availability but cannot offer global, low-latency updates across multiple regions without additional complex replication mechanisms, increasing operational overhead. Option C, S3 with Glue and Redshift, is suitable for analytics but not for real-time transactional inventory management. Option D, ElastiCache with Fargate and S3, could provide caching for frequently accessed data but does not natively provide persistent global storage or real-time updates.
By using DynamoDB global tables, API Gateway, Lambda, and CloudFront, the retail company achieves a modern, serverless, and globally distributed inventory management system. This architecture reduces latency, improves scalability, enhances resilience, and ensures consistent data availability for warehouses and the central office, supporting both operational efficiency and customer satisfaction. The integration of managed AWS services minimizes operational overhead and provides a cost-effective, maintainable solution for global inventory management
Question 156:
A healthcare provider wants to implement a secure and compliant system for storing patient medical records. The system must provide fine-grained access control, encryption at rest and in transit, auditing capabilities, and integration with machine learning for predictive analytics. Which AWS services are best suited for this scenario?
A) Amazon S3 with AWS Key Management Service (KMS), AWS Identity and Access Management (IAM), AWS CloudTrail, and Amazon SageMaker
B) Amazon RDS with standard encryption and IAM, and Amazon EC2 for ML processing
C) Amazon DynamoDB with server-side encryption and AWS Lambda
D) Amazon EFS with IAM and AWS Batch for processing
Answer:
A) Amazon S3 with AWS Key Management Service (KMS), AWS Identity and Access Management (IAM), AWS CloudTrail, and Amazon SageMaker
Explanation:
Healthcare data, particularly patient medical records, is highly sensitive and regulated under compliance standards such as HIPAA. Amazon S3 provides durable, highly available storage for medical records while supporting encryption at rest using AWS Key Management Service (KMS). S3 can also enforce encryption in transit using HTTPS and TLS, ensuring sensitive data is always protected during transfer. Fine-grained access control is achievable via AWS IAM policies, bucket policies, and S3 Access Points, allowing healthcare providers to define who can read, write, or delete records at a granular level.
AWS CloudTrail is used to maintain auditing and logging of all operations on S3, providing a complete history of access and modifications. This feature is critical for meeting regulatory compliance and supporting forensic investigations or auditing requirements. CloudTrail records include API calls, user identity, IP addresses, and timestamps, ensuring traceability of all interactions with sensitive medical data.
Amazon SageMaker enables the healthcare provider to build, train, and deploy predictive machine learning models securely. By analyzing historical patient data stored in S3, healthcare professionals can gain insights for predictive diagnostics, treatment recommendations, or resource allocation. SageMaker integrates with S3 directly, allowing seamless data preprocessing, feature engineering, model training, and deployment without moving sensitive data outside the secure environment. SageMaker also supports VPC endpoints, ensuring that machine learning operations remain within the healthcare provider’s private network.
Option B, RDS with standard encryption and IAM, provides relational storage but does not scale as effectively for large, unstructured medical records like imaging data or PDFs. EC2-based ML processing would require managing instances, adding operational complexity and security risks. Option C, DynamoDB with Lambda, is suitable for key-value storage and lightweight serverless processing but lacks the full-featured ML pipeline and analytics integration. Option D, EFS with IAM and AWS Batch, is less suitable for HIPAA-compliant workflows because it does not provide the same level of integrated ML capabilities and fine-grained access as S3 combined with SageMaker.
By combining S3, KMS, IAM, CloudTrail, and SageMaker, the healthcare provider achieves a secure, compliant, and scalable architecture for managing sensitive patient data. This architecture supports fine-grained access, end-to-end encryption, auditing, and advanced analytics, enabling predictive healthcare insights while meeting strict regulatory requirements. Automated integration with SageMaker ensures the provider can leverage machine learning securely and efficiently, improving patient outcomes and operational effectiveness.
Question 157:
A global e-commerce company wants to design a system to analyze millions of customer transactions in near real-time to detect fraud patterns. The system must provide high throughput, low latency, and scalability to handle seasonal spikes. Which combination of AWS services is most suitable?
A) Amazon Kinesis Data Streams, AWS Lambda, Amazon DynamoDB, and Amazon SageMaker
B) Amazon S3, AWS Glue, Amazon Redshift, and Amazon EMR
C) Amazon RDS Multi-AZ, Amazon EC2 Auto Scaling, and AWS Batch
D) Amazon ElastiCache Redis cluster, Amazon SQS, and Amazon EC2
Answer:
A) Amazon Kinesis Data Streams, AWS Lambda, Amazon DynamoDB, and Amazon SageMaker
Explanation:
Detecting fraud in real-time for a high-volume global e-commerce system requires a combination of streaming data ingestion, real-time processing, scalable storage, and machine learning analytics. Amazon Kinesis Data Streams provides a fully managed, scalable, and durable platform for streaming massive amounts of transaction data. Each transaction is ingested into a stream in real-time, enabling multiple consumers to process and analyze the data simultaneously. Kinesis automatically scales to match throughput demand, accommodating seasonal spikes, flash sales, and unexpected traffic surges without manual intervention.
AWS Lambda integrates with Kinesis to provide serverless processing of transaction events. Lambda functions can run custom logic to filter, enrich, and pre-process transaction data in real-time. For instance, a Lambda function can check for unusual transaction patterns such as high-value purchases, rapid successive transactions, or mismatched geolocations. By leveraging serverless computing, the company avoids provisioning or managing EC2 instances, reducing operational overhead and allowing automatic scaling to handle peaks in traffic. Lambda executes only when events occur, optimizing cost-efficiency.
Amazon DynamoDB serves as a high-performance, low-latency NoSQL database to store transaction metadata, fraud detection flags, and aggregated results. DynamoDB’s single-digit millisecond response times make it ideal for storing and retrieving data in near real-time. DynamoDB can also be configured with global tables, allowing multi-region replication, which ensures that fraud detection information is consistent and available for regional offices and global applications, enhancing operational efficiency and resiliency.
Amazon SageMaker enables predictive analytics and machine learning-based fraud detection. Historical transaction data stored in DynamoDB or S3 can be used to train supervised learning models that identify unusual behavior patterns indicative of fraud. SageMaker provides an integrated workflow for building, training, tuning, and deploying machine learning models. Real-time inference endpoints allow the system to predict the probability of fraudulent activity on each incoming transaction. By integrating Kinesis, Lambda, and DynamoDB with SageMaker, the e-commerce company establishes a robust, end-to-end real-time fraud detection pipeline that is secure, scalable, and cost-efficient.
Option B, using S3, Glue, Redshift, and EMR, is suitable for batch analytics rather than real-time fraud detection. It would introduce latency that is unacceptable for near real-time decisions. Option C, RDS Multi-AZ with EC2 Auto Scaling and AWS Batch, is not optimized for continuous streaming processing and would struggle with throughput during spikes. Option D, ElastiCache with SQS and EC2, could provide caching and queueing but does not offer a managed streaming or machine learning solution for predictive fraud detection.
By combining Kinesis, Lambda, DynamoDB, and SageMaker, the company can detect fraudulent transactions in near real-time, provide actionable insights immediately, and scale efficiently across global operations. This solution ensures minimal latency, high reliability, cost efficiency, and the ability to adapt to changing transaction patterns, making it highly suitable for global e-commerce fraud prevention.
Question 158:
A financial services organization needs a secure, scalable, and compliant solution to archive sensitive transaction records for regulatory compliance. The solution must provide encryption at rest and in transit, fine-grained access controls, retention policies, and easy retrieval. Which AWS services combination should be used?
A) Amazon S3 with S3 Object Lock, AWS Key Management Service, AWS IAM, and AWS CloudTrail
B) Amazon RDS with Multi-AZ deployment and manual snapshots
C) Amazon EFS with lifecycle management and AWS Lambda
D) Amazon DynamoDB with server-side encryption and IAM
Answer:
A) Amazon S3 with S3 Object Lock, AWS Key Management Service, AWS IAM, and AWS CloudTrail
Explanation:
Financial organizations must store transaction records securely while meeting strict compliance and regulatory requirements such as SEC, FINRA, and SOX. Amazon S3 provides highly durable, scalable, and secure object storage, capable of storing millions of financial records without performance degradation. S3 Object Lock enables write-once-read-many (WORM) storage, ensuring that records cannot be altered or deleted during a defined retention period. This feature is critical for regulatory compliance, providing legal and audit-proof data retention policies.
AWS Key Management Service (KMS) enables encryption of S3 objects at rest with customer-managed keys, giving fine-grained control over who can access and decrypt records. S3 also enforces encryption in transit via HTTPS, ensuring end-to-end protection for sensitive financial data. IAM policies and S3 bucket policies provide role-based access control, enabling administrators to define which users or systems have permissions to read, write, or delete objects. Fine-grained permissions can be applied to individual objects or object prefixes, ensuring least-privilege access models are adhered to for compliance purposes.
AWS CloudTrail records API activity, capturing all access events, modifications, and configuration changes for S3 and KMS. This provides an immutable audit trail, which is essential for regulatory reporting and compliance validation. CloudTrail logs can be used to reconstruct events, detect unauthorized access attempts, and generate alerts for suspicious activity, supporting risk management and internal audit requirements.
Option B, RDS with Multi-AZ and snapshots, is suitable for transactional databases but lacks WORM capabilities, making it less compliant for long-term archiving and regulatory retention requirements. Option C, EFS with lifecycle management and Lambda, is not optimized for regulatory-compliant WORM storage and introduces complexity for access control and retention enforcement. Option D, DynamoDB with server-side encryption, offers encryption and low-latency access but does not provide Object Lock or native compliance features required for secure archival.
Using S3 with Object Lock, KMS, IAM, and CloudTrail provides a secure, compliant, and highly scalable solution for archiving sensitive transaction records. The solution allows financial institutions to maintain regulatory compliance, ensure data integrity, control access at a granular level, and retrieve archived records efficiently when needed. Automated logging and encryption reduce operational risk while simplifying governance, making this architecture ideal for financial services with strict compliance mandates.
Question 159:
A logistics company wants to build a global package tracking system that provides near real-time location updates to customers. The solution should scale automatically based on the number of tracked packages, provide low latency for global users, and enable analytics on package movement. Which AWS services combination is appropriate?
A) Amazon DynamoDB global tables, AWS IoT Core, Amazon Kinesis Data Firehose, and Amazon QuickSight
B) Amazon RDS Multi-AZ, Amazon SQS, and AWS Lambda
C) Amazon S3, AWS Batch, and Amazon Redshift
D) Amazon ElastiCache Redis cluster, AWS Fargate, and Amazon CloudFront
Answer:
A) Amazon DynamoDB global tables, AWS IoT Core, Amazon Kinesis Data Firehose, and Amazon QuickSight
Explanation:
Building a global package tracking system requires real-time data ingestion, low-latency updates, scalable storage, and analytics capabilities. AWS IoT Core provides a managed platform to securely ingest telemetry data from GPS devices attached to packages. IoT Core supports billions of devices, enabling the logistics company to handle massive numbers of simultaneous updates from shipments across multiple continents. Messages from devices are transmitted securely via MQTT or HTTPS and can be filtered or routed for processing.
Amazon DynamoDB global tables store the location and status of each package, providing fast, consistent, and highly available access across multiple regions. This ensures that both regional offices and end customers experience minimal latency when querying package status. DynamoDB’s managed scaling allows the system to handle millions of simultaneous updates without performance degradation, accommodating spikes during peak shipping seasons.
Amazon Kinesis Data Firehose provides a managed streaming data delivery pipeline, enabling near real-time aggregation and delivery of telemetry data to analytics or storage destinations. Firehose can stream data directly to Amazon S3 for durable storage or Amazon Redshift for analytics, allowing the company to analyze delivery trends, optimize routes, and predict delivery times. Firehose handles scaling automatically, reducing operational overhead and ensuring the system remains performant during high-demand periods.
Amazon QuickSight can visualize package tracking data, providing dashboards and analytics to internal teams or customers. Integration with DynamoDB and S3 allows real-time insights into package movement, regional delivery performance, and operational efficiency. Decision-makers can monitor KPIs, identify bottlenecks, and optimize logistics using intuitive visualizations.
Option B, RDS Multi-AZ with SQS and Lambda, lacks global low-latency updates and scalable device telemetry ingestion. Option C, S3 with Batch and Redshift, is batch-oriented, unsuitable for real-time tracking. Option D, ElastiCache with Fargate and CloudFront, provides caching and web delivery but does not support large-scale IoT data ingestion or analytics pipelines natively.
By combining IoT Core, DynamoDB global tables, Kinesis Data Firehose, and QuickSight, the logistics company can deliver a globally scalable, low-latency package tracking system. This architecture supports real-time updates, efficient analytics, operational insights, and customer-facing applications, ensuring timely delivery information and actionable insights for decision-making across the supply chain.
Question 160:
A healthcare provider needs to store large volumes of patient imaging data for research purposes. The data must be encrypted at rest and in transit, and the system should allow fine-grained access control and lifecycle management to move older data to lower-cost storage. Which combination of AWS services is most appropriate?
A) Amazon S3 with S3 Object Lock, AWS Key Management Service, AWS IAM, and Amazon S3 Glacier
B) Amazon EBS with encryption, Amazon EC2, and AWS Backup
C) Amazon RDS with Multi-AZ deployment and snapshots
D) Amazon DynamoDB with server-side encryption and IAM
Answer:
A) Amazon S3 with S3 Object Lock, AWS Key Management Service, AWS IAM, and Amazon S3 Glacier
Explanation:
Healthcare organizations face stringent data security, privacy, and compliance requirements such as HIPAA, HITECH, and GDPR. Storing sensitive patient imaging data securely while maintaining accessibility and cost-efficiency requires a combination of encrypted, durable, and scalable storage with lifecycle management. Amazon S3 provides a highly durable and scalable object storage solution suitable for storing petabytes of unstructured imaging data. S3 supports encryption at rest using AWS Key Management Service (KMS) keys and encryption in transit via HTTPS, ensuring complete data protection throughout its lifecycle.
S3 Object Lock provides write-once-read-many (WORM) functionality to enforce immutability of stored images, which is essential for research and compliance purposes. With Object Lock, imaging records cannot be modified or deleted during the retention period, ensuring regulatory compliance and audit readiness. AWS IAM policies enable fine-grained access control, allowing administrators to restrict access to specific S3 buckets, prefixes, or even individual objects. IAM roles and policies can be tailored to specific research teams, clinicians, or applications, enforcing the principle of least privilege.
Lifecycle policies in S3 allow automatic transition of older imaging data to lower-cost storage classes such as S3 Glacier or Glacier Deep Archive. This ensures that storage costs remain optimized without sacrificing durability or compliance. Glacier provides secure, durable, and cost-effective long-term storage with retrieval options suited for research workflows that do not require immediate access.
Option B, EBS with EC2 and AWS Backup, is suitable for block storage but lacks native scalability for massive datasets and fine-grained object-level access. Option C, RDS Multi-AZ, is optimized for structured relational data rather than large unstructured imaging files. Option D, DynamoDB with server-side encryption, supports low-latency transactional data but does not provide native lifecycle management or cost-effective long-term storage for large binary files.
By leveraging S3, Object Lock, KMS, IAM, and Glacier, the healthcare provider can securely store imaging data, enforce compliance with retention policies, optimize storage costs, and provide controlled access to authorized personnel and researchers. This architecture ensures long-term data durability, secure access, and regulatory compliance while maintaining cost efficiency and scalability.
Question 161:
A media company wants to deliver a video streaming platform that automatically adjusts to traffic spikes during popular events. The system should provide low latency, global reach, and high availability while minimizing operational overhead. Which combination of AWS services should the company use?
A) Amazon CloudFront, AWS Elemental MediaPackage, Amazon S3, and AWS Lambda@Edge
B) Amazon EC2 Auto Scaling, Elastic Load Balancer, and Amazon RDS
C) Amazon S3, AWS Batch, and Amazon Redshift
D) Amazon ElastiCache Redis cluster, AWS Fargate, and Amazon CloudFront
Answer:
A) Amazon CloudFront, AWS Elemental MediaPackage, Amazon S3, and AWS Lambda@Edge
Explanation:
Delivering a high-quality video streaming service with global reach and automatic scaling requires a combination of content storage, packaging, content delivery, and serverless edge processing. Amazon S3 provides durable, cost-effective storage for media assets, supporting seamless scalability as the platform grows. High durability ensures that video content remains reliably available, and S3 integrates with other AWS services for content delivery and processing.
AWS Elemental MediaPackage enables the preparation and packaging of video streams in real-time for adaptive bitrate delivery. This ensures that viewers receive the best possible video quality based on their network conditions and device capabilities. MediaPackage supports multiple streaming formats such as HLS, DASH, and CMAF, providing flexibility for different client devices. It also integrates seamlessly with DRM solutions for content protection.
Amazon CloudFront, a global content delivery network (CDN), caches video content at edge locations worldwide, reducing latency for end-users and offloading traffic from the origin servers. CloudFront automatically scales to handle sudden spikes in demand, such as during live events or popular content releases, ensuring uninterrupted playback without the need for manual intervention.
AWS Lambda@Edge extends serverless computing to CloudFront edge locations, allowing for custom logic execution closer to the end-users. Lambda@Edge can be used for dynamic content manipulation, URL rewrites, authentication, and authorization, ensuring personalized and secure video delivery without additional infrastructure management.
Option B, EC2 Auto Scaling with ELB and RDS, provides scaling but does not offer global content caching or adaptive streaming capabilities. Option C, S3 with Batch and Redshift, is suitable for batch analytics, not real-time video delivery. Option D, ElastiCache, Fargate, and CloudFront, provides caching and compute but lacks end-to-end media packaging and adaptive streaming capabilities.
By combining S3, MediaPackage, CloudFront, and Lambda@Edge, the media company can deliver a globally available, low-latency, adaptive video streaming platform. This architecture ensures high availability, operational efficiency, scalability during peak traffic events, and a rich user experience while minimizing the need for manual server management.
Question 162:
An online retail company wants to implement a recommendation engine that suggests products in real-time based on user behavior, purchase history, and trending products. The solution should be scalable, support machine learning models, and integrate with existing web and mobile applications. Which AWS services combination is appropriate?
A) Amazon Personalize, Amazon DynamoDB, AWS Lambda, and Amazon API Gateway
B) Amazon S3, AWS Batch, and Amazon Redshift
C) Amazon RDS Multi-AZ, Amazon EC2, and AWS Glue
D) Amazon Kinesis Data Streams, AWS Glue, and Amazon SageMaker
Answer:
A) Amazon Personalize, Amazon DynamoDB, AWS Lambda, and Amazon API Gateway
Explanation:
Real-time product recommendations require a system that ingests user behavior data, processes it in near real-time, and delivers personalized suggestions through web and mobile applications. Amazon Personalize is a fully managed machine learning service that allows developers to create individualized recommendations based on user interactions, purchase history, and item popularity. Personalize abstracts the complexity of machine learning model development, training, and optimization, enabling rapid deployment of recommendation engines without requiring specialized ML expertise.
Amazon DynamoDB provides a low-latency, scalable data store to capture user behavior, item metadata, and precomputed recommendation results. DynamoDB global tables allow for multi-region replication, ensuring consistent performance and availability for users across different geographies. Its automatic scaling handles surges in traffic, particularly during sales or promotional events, without manual intervention.
AWS Lambda integrates with DynamoDB streams and other sources to trigger real-time processing of user interactions. Lambda can preprocess data, update recommendations, or trigger Amazon Personalize campaigns as user behavior changes. Serverless architecture reduces operational overhead and automatically scales to meet demand.
Amazon API Gateway exposes the recommendation service to web and mobile applications, providing a secure and scalable interface for retrieving product suggestions. API Gateway manages authentication, throttling, caching, and request routing, ensuring high availability and performance while simplifying integration with front-end applications.
Option B, S3 with Batch and Redshift, supports batch analytics rather than real-time recommendations. Option C, RDS with EC2 and Glue, lacks low-latency access for real-time personalization and requires significant operational management. Option D, Kinesis with Glue and SageMaker, provides streaming analytics and ML capabilities but requires custom ML model management and integration, whereas Personalize offers a managed end-to-end recommendation solution.
By using Personalize, DynamoDB, Lambda, and API Gateway, the retail company can deliver real-time, scalable, and personalized product recommendations to users across platforms. This architecture supports low-latency delivery, integrates easily with applications, and scales automatically as traffic and user interactions increase. It enables personalized marketing, improves user engagement, and enhances conversion rates, making it a highly effective solution for online retail.
Question 163:
A financial services company is designing a system to analyze real-time market data from multiple sources. The system must handle high-throughput streams, scale automatically, and allow near real-time analytics while ensuring fault tolerance. Which combination of AWS services is most suitable?
A) Amazon Kinesis Data Streams, AWS Lambda, Amazon S3, and Amazon Redshift
B) Amazon SQS, Amazon RDS Multi-AZ, and AWS Glue
C) Amazon S3, AWS Batch, and Amazon Athena
D) Amazon EC2 Auto Scaling with Amazon ElastiCache
Answer:
A) Amazon Kinesis Data Streams, AWS Lambda, Amazon S3, and Amazon Redshift
Explanation:
Financial services companies often require real-time analytics systems capable of ingesting large volumes of market data, processing it, and delivering actionable insights in near real-time. These systems must provide scalability, fault tolerance, and high availability while handling bursts of data during periods of high market activity.
Amazon Kinesis Data Streams is designed to ingest massive volumes of streaming data from multiple sources, including stock exchanges, financial news, and trading platforms. It provides scalable and durable stream storage that allows multiple consumers to process the same data concurrently. Kinesis automatically scales to accommodate spikes in data volume, ensuring no data loss and consistent throughput.
AWS Lambda complements Kinesis by providing serverless processing of streaming data. Lambda functions can transform, filter, and enrich incoming data in real-time, triggering subsequent processing or storage actions based on the analysis requirements. Using Lambda removes the need to manage servers, and it scales automatically with the volume of incoming data.
Amazon S3 acts as a durable storage layer for raw and processed market data. S3’s highly durable architecture ensures that historical data is safely stored and available for future analysis, auditing, or compliance purposes. Lifecycle policies can be applied to optimize costs by transitioning older data to lower-cost storage classes such as Glacier.
Amazon Redshift is used for near real-time analytics, enabling complex queries and aggregations over large datasets. Redshift Spectrum allows querying data directly in S3 without loading it into the cluster, which reduces latency and speeds up analysis. Combining Redshift with Kinesis and Lambda ensures that analysts and decision-makers can access up-to-date information for informed financial decisions.
Option B, SQS with RDS and Glue, is not optimized for real-time streaming analytics; it is more suited for batch processing and asynchronous messaging. Option C, S3 with Batch and Athena, supports batch queries and analysis rather than real-time insights. Option D, EC2 Auto Scaling with ElastiCache, provides compute and caching capabilities but lacks native support for scalable streaming ingestion and real-time processing.
By leveraging Kinesis Data Streams, Lambda, S3, and Redshift, the financial services company can build a fault-tolerant, scalable, and near real-time analytics platform. This architecture supports automated scaling, high availability, data durability, and fast access to actionable insights, making it ideal for the volatile and high-throughput nature of financial markets.
Question 164:
A global e-commerce company wants to implement a secure and scalable system to manage customer session data across multiple regions. The system must ensure low latency, consistency, and support millions of concurrent sessions with automatic scaling. Which AWS service or combination should they use?
A) Amazon DynamoDB Global Tables with DAX and AWS Lambda
B) Amazon RDS Multi-AZ with read replicas
C) Amazon S3 with Lifecycle policies and S3 Transfer Acceleration
D) Amazon ElastiCache Redis cluster with Multi-AZ
Answer:
A) Amazon DynamoDB Global Tables with DAX and AWS Lambda
Explanation:
Managing session data for a global e-commerce platform requires a database solution that is globally distributed, low-latency, and capable of handling high concurrency. Customer sessions typically involve frequent reads and writes, requiring fast response times and automatic scalability to meet variable workloads during peak shopping periods.
Amazon DynamoDB provides a fully managed NoSQL database that can scale horizontally to support millions of concurrent requests while delivering single-digit millisecond latency. With DynamoDB Global Tables, the company can replicate session data across multiple AWS regions, ensuring low-latency access for users worldwide. Global Tables provide active-active replication, which enables seamless failover and disaster recovery without additional operational complexity.
DAX (DynamoDB Accelerator) is an in-memory caching service that significantly improves read performance, reducing latency for session retrieval. By caching frequently accessed data, DAX ensures that session lookups are fast, even under high traffic loads. This improves the user experience by keeping web and mobile applications responsive during peak demand periods.
AWS Lambda can be used to trigger custom logic for session management, such as updating user activity, expiring stale sessions, or synchronizing data with other services. Lambda’s serverless architecture ensures automatic scaling and eliminates the need for manual server provisioning, reducing operational overhead.
Option B, RDS Multi-AZ with read replicas, provides high availability for relational data but lacks the ultra-low latency and seamless horizontal scaling required for millions of concurrent sessions. Option C, S3 with lifecycle policies, is suitable for object storage but not real-time session management. Option D, ElastiCache Redis with Multi-AZ, provides fast in-memory storage but lacks the global replication capabilities of DynamoDB Global Tables and requires additional operational management for scaling and failover.
By using DynamoDB Global Tables with DAX and Lambda, the e-commerce company can achieve a globally distributed, low-latency, scalable session management system. This solution ensures consistent performance, high availability, and fault tolerance while minimizing operational complexity. It allows customers worldwide to have a seamless shopping experience, with rapid session updates and data replication across regions.
Question 165:
A logistics company wants to build a fleet tracking system that collects GPS data from thousands of vehicles in real-time. The system must process the data streams, store them for historical analysis, and provide near real-time visualization dashboards. Which AWS architecture is best suited for this scenario?
A) Amazon Kinesis Data Streams, AWS Lambda, Amazon S3, Amazon Timestream, and Amazon QuickSight
B) Amazon SQS, Amazon EC2, and Amazon RDS
C) Amazon DynamoDB with Streams, AWS Glue, and Amazon Redshift
D) Amazon ElastiCache Redis, Amazon RDS, and AWS Batch
Answer:
A) Amazon Kinesis Data Streams, AWS Lambda, Amazon S3, Amazon Timestream, and Amazon QuickSight
Explanation:
A real-time fleet tracking system requires the collection and processing of streaming GPS data, durable storage for historical analysis, and visualization of data on interactive dashboards. The system must handle high-frequency updates from thousands of vehicles while providing near real-time insights into fleet movements.
Amazon Kinesis Data Streams provides a scalable and fault-tolerant ingestion layer for GPS data. It supports high throughput and allows multiple consumers to process the same data stream concurrently. Kinesis can handle bursts in traffic during peak hours, ensuring continuous ingestion without data loss.
AWS Lambda functions can process each incoming GPS data point in real-time, performing transformations, filtering, or aggregations before storing the data. Lambda’s serverless architecture automatically scales with the volume of incoming data, eliminating the need for manual provisioning and management of servers.
Amazon S3 provides durable, cost-effective storage for raw and processed data, enabling long-term retention and historical analysis. Lifecycle policies can transition older data to Glacier or Glacier Deep Archive to optimize storage costs. Amazon Timestream, a purpose-built time-series database, stores processed GPS data for real-time querying and analysis. Timestream is optimized for time-series workloads, providing fast ingestion, scalable storage, and efficient queries for temporal data.
Amazon QuickSight integrates with Timestream to provide interactive dashboards, visualizing real-time fleet positions, historical routes, and fleet utilization metrics. This enables operations teams to make informed decisions regarding routing, scheduling, and resource allocation.
Option B, SQS with EC2 and RDS, is not designed for high-frequency real-time data ingestion and analytics. Option C, DynamoDB with Streams, Glue, and Redshift, is better suited for batch processing and analytical workloads but does not natively support high-throughput streaming. Option D, ElastiCache with RDS and Batch, provides caching and batch processing but lacks a scalable real-time streaming pipeline and integrated time-series analytics.
Using Kinesis, Lambda, S3, Timestream, and QuickSight allows the logistics company to implement a highly scalable, real-time fleet tracking system. This architecture supports continuous ingestion, near real-time processing, long-term storage, and interactive visualization, providing operational efficiency, improved decision-making, and enhanced fleet management capabilities.