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Question 136:

A global media company wants to deliver live video streams to millions of users worldwide. The solution must provide low-latency delivery, automatic scaling, and protection against DDoS attacks. Which AWS services should be used?

A) Amazon CloudFront, AWS Elemental MediaLive, and AWS Shield
B) Amazon S3 static website hosting with EC2 instances
C) Amazon RDS with custom streaming application
D) Amazon Elastic Beanstalk with a single instance

Answer:

A) Amazon CloudFront, AWS Elemental MediaLive, and AWS Shield

Explanation:

Delivering live video streams to a global audience requires a solution that can handle high-volume, low-latency data delivery, scale automatically, and provide security against potential threats. AWS Elemental MediaLive is a fully managed live video processing service that enables broadcasters and media companies to create high-quality live video streams. MediaLive ingests raw video content, encodes it in real time, and outputs it in multiple formats suitable for different devices, resolutions, and bandwidth conditions. By automating the video encoding process and supporting multiple bitrates, MediaLive ensures viewers receive an optimal experience regardless of network conditions.

Amazon CloudFront is a content delivery network (CDN) designed to deliver data, videos, applications, and APIs to users globally with low latency and high transfer speeds. CloudFront leverages a network of edge locations worldwide to cache content closer to end-users. For live video streaming, CloudFront reduces the latency between MediaLive output and the viewer, providing a seamless streaming experience. CloudFront integrates with MediaLive, MediaPackage, and other media services to enable scalable, low-latency delivery for millions of concurrent viewers. Additionally, CloudFront supports real-time metrics, logging, and the ability to enforce geographic restrictions to comply with content licensing or regional regulations.

AWS Shield is a managed Distributed Denial of Service (DDoS) protection service that safeguards applications running on AWS. Live video streaming platforms are often targets for DDoS attacks due to the high value of media content and potential revenue impact. AWS Shield Standard automatically protects against common network and transport layer attacks at no additional cost, while AWS Shield Advanced provides enhanced detection, mitigation, and 24/7 support from the AWS DDoS Response Team. Integrating AWS Shield with CloudFront ensures that streaming content remains available and resilient, even under attack, preventing downtime and maintaining user trust.

Option B, using S3 static website hosting with EC2 instances, is unsuitable for live streaming due to latency, scaling limitations, and lack of built-in live encoding capabilities. S3 is ideal for static content delivery but cannot process or encode live video streams efficiently. Option C, RDS with a custom streaming application, is not designed for high-volume media delivery and cannot handle the low-latency requirements of live streaming to a global audience. Option D, Elastic Beanstalk with a single instance, cannot scale to millions of viewers and does not provide the specialized media processing or global content delivery needed for live streaming.

Security, compliance, and operational monitoring are crucial. MediaLive integrates with AWS Identity and Access Management (IAM) to control access to streams, CloudFront enforces HTTPS for secure delivery, and CloudWatch provides detailed metrics, alarms, and logging for operational visibility. The architecture allows operators to monitor streaming performance, detect anomalies, and trigger automated responses if necessary. This design ensures the media company can focus on content creation and delivery without worrying about infrastructure bottlenecks, latency issues, or security threats.

Question 137:

A healthcare provider must store and analyze electronic medical records (EMRs) while ensuring HIPAA compliance. The solution must allow fast querying of structured and semi-structured data with minimal operational overhead. Which AWS services combination is most appropriate?

A) Amazon S3, AWS Glue, and Amazon Athena
B) Amazon RDS in a single availability zone
C) EC2 instances running MySQL
D) Amazon DynamoDB only

Answer:

A) Amazon S3, AWS Glue, and Amazon Athena

Explanation:

Healthcare providers must manage sensitive patient data in compliance with HIPAA regulations, requiring encryption, audit trails, and secure access controls. Amazon S3 provides highly durable and scalable storage for both structured and semi-structured data. EMRs can be stored in S3 using formats such as Parquet, ORC, JSON, or CSV, ensuring cost efficiency and ease of integration with analytics services. S3 supports encryption at rest using server-side encryption (SSE) and in transit using SSL/TLS, meeting HIPAA security and compliance requirements.

AWS Glue is a serverless data integration service that prepares and transforms data for analytics. Glue can crawl EMR data in S3, infer schemas, and catalog metadata into the Glue Data Catalog. This enables healthcare analysts and applications to query the data without manual schema management. Glue also allows the creation of ETL workflows to transform, clean, or anonymize sensitive data before analysis, ensuring compliance with privacy regulations while maintaining operational efficiency.

Amazon Athena is a serverless interactive query service that allows fast analysis of data stored in S3 using standard SQL. Athena eliminates the need for managing database infrastructure and enables healthcare providers to perform complex queries on EMR datasets efficiently. With Athena, analysts can join structured and semi-structured data, run aggregation queries, and generate reports with minimal operational overhead. Athena integrates with AWS Identity and Access Management (IAM) for fine-grained access control, ensuring that only authorized personnel can query sensitive EMR data.

Option B, RDS in a single availability zone, lacks scalability and multi-region redundancy, and it may not efficiently query large volumes of semi-structured data. Option C, EC2 instances running MySQL, requires significant operational overhead, including patch management, backups, scaling, and performance tuning, which can be cumbersome for large datasets. Option D, DynamoDB only, is optimized for key-value workloads and is not ideal for ad-hoc complex queries or analytics on semi-structured data.

Security and compliance considerations include enabling S3 bucket policies and encryption, using IAM roles for controlled access, enabling CloudTrail for auditing all data access, and configuring AWS Config to monitor compliance. Athena queries can be logged to CloudWatch or S3 for traceability. The architecture supports data lifecycle management with S3 Object Lifecycle policies, allowing automatic transition to lower-cost storage tiers or deletion according to retention policies.

Question 138:

An e-commerce company wants to implement a data lake to centralize structured and unstructured data from multiple sources. The solution should allow analytics, machine learning, and secure access for different teams without managing servers. Which AWS services combination is most appropriate?

A) Amazon S3, AWS Lake Formation, and Amazon Athena
B) Amazon RDS with daily ETL scripts
C) Amazon DynamoDB with custom analytics
D) EC2 instances with Hadoop cluster

Answer:

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

Explanation:

A modern data lake centralizes diverse datasets, including structured, semi-structured, and unstructured data, providing a unified repository for analytics, machine learning, and reporting. Amazon S3 is the foundational storage layer, offering virtually unlimited scalability, high durability, and cost-effective storage for diverse data types. S3 supports multiple storage classes, including standard, intelligent tiering, and Glacier, allowing cost optimization for frequently and infrequently accessed datasets.

AWS Lake Formation simplifies the process of building, securing, and managing a data lake. Lake Formation allows administrators to define fine-grained access policies, catalog datasets, and enforce governance across multiple accounts and teams. Data from S3 can be crawled and classified automatically, providing a consistent schema and metadata for analytics and machine learning applications. Lake Formation also enables centralized security management, such as encryption at rest and in transit, role-based access control, and integration with AWS CloudTrail for audit logging.

Amazon Athena provides serverless interactive query capabilities on data stored in S3. Teams can run SQL queries on the data lake without provisioning or managing infrastructure. Athena integrates seamlessly with Lake Formation, respecting access policies and providing secure, role-based querying. Analysts can perform complex joins, aggregations, and transformations on structured and semi-structured datasets, while machine learning pipelines can access curated datasets for training models.

Option B, RDS with daily ETL scripts, lacks scalability, cannot efficiently handle unstructured data, and introduces operational overhead with batch ETL processes. Option C, DynamoDB with custom analytics, is optimized for key-value and document data and does not support ad-hoc analytics on diverse datasets. Option D, EC2 instances with a Hadoop cluster, increases operational complexity, requires manual scaling and maintenance, and is less flexible compared to a serverless, fully managed data lake solution.

Security, compliance, and governance are critical for a multi-team environment. Lake Formation enforces column-level and row-level security, S3 encryption ensures data confidentiality, and IAM roles control access to datasets. Integration with AWS CloudTrail and CloudWatch provides operational visibility and audit trails. Data can be cataloged and tagged to support data discovery, lineage tracking, and regulatory compliance.

Question 139:

A multinational retail company wants to migrate its existing on-premises data warehouse to AWS. The solution must provide high performance for complex analytical queries, automatic scaling, and integration with existing BI tools. Which AWS service is the most appropriate?

A) Amazon Redshift
B) Amazon RDS for PostgreSQL
C) Amazon DynamoDB
D) Amazon Aurora MySQL

Answer:

A) Amazon Redshift

Explanation:

Migrating a large on-premises data warehouse to AWS requires a solution that supports high-performance analytics on large datasets, complex queries, and integration with business intelligence tools. Amazon Redshift is a fully managed, petabyte-scale data warehouse service designed to handle large-scale analytics workloads. Redshift uses columnar storage and data compression to optimize storage efficiency and query performance. Its massively parallel processing (MPP) architecture allows it to distribute complex queries across multiple nodes, significantly reducing query execution time for large datasets.

Redshift integrates seamlessly with popular BI tools, including Tableau, Power BI, and Amazon QuickSight, enabling analysts to generate reports, dashboards, and visualizations without modifying existing workflows. It supports standard SQL and provides advanced analytics capabilities such as window functions, aggregations, and joins. Redshift also includes features such as materialized views, result caching, and automatic query optimization to enhance performance for recurring queries.

The service supports elastic resize and concurrency scaling, allowing the data warehouse to automatically handle increases in query volume or data size. For example, during peak reporting periods, Redshift can temporarily add additional compute resources to maintain low latency and high throughput. This elasticity reduces the need for manual intervention and ensures consistent performance without over-provisioning.

Data security is also critical for enterprises migrating sensitive data. Redshift supports encryption at rest using AWS Key Management Service (KMS), SSL encryption for data in transit, and fine-grained access control through AWS Identity and Access Management (IAM) and Redshift user roles. Additionally, Redshift integrates with AWS CloudTrail and AWS Config to provide audit logs and governance capabilities, ensuring compliance with organizational and regulatory requirements.

Option B, Amazon RDS for PostgreSQL, is suitable for transactional workloads but is not optimized for large-scale analytical queries or complex joins across massive datasets. Option C, DynamoDB, is a NoSQL service optimized for key-value and document workloads, making it unsuitable for traditional SQL-based analytics and BI integration. Option D, Amazon Aurora MySQL, is a high-performance relational database for transactional workloads but lacks columnar storage and MPP architecture necessary for high-performance analytical queries on large datasets.

By migrating to Redshift, the retail company can achieve a scalable, high-performance, and managed analytics environment that supports complex queries, integrates with existing BI tools, and reduces operational overhead compared to managing on-premises infrastructure. Redshift’s features, such as automatic backups, snapshots, and cross-region replication, provide reliability and disaster recovery capabilities. In addition, Redshift Spectrum allows querying data directly in S3 without moving it into the warehouse, supporting a hybrid architecture for cold or archival data. Overall, Redshift provides a comprehensive solution for migrating and modernizing the company’s data warehouse with minimal operational complexity while maximizing analytics performance and flexibility.

Question 140:

A financial services company must store transaction logs from multiple sources in a central repository. The data must be encrypted, highly available, and queryable without managing any servers. Which AWS service combination is most appropriate?

A) Amazon S3, AWS KMS, and Amazon Athena
B) Amazon RDS with replication across multiple AZs
C) Amazon DynamoDB with global tables
D) EC2 instances running MySQL cluster

Answer:

A) Amazon S3, AWS KMS, and Amazon Athena

Explanation:

Financial services organizations must ensure that sensitive transaction data is securely stored, highly available, and easily accessible for analysis and regulatory compliance. Amazon S3 provides durable and highly available storage with a 99.999999999% durability SLA, making it ideal for centralizing logs from multiple sources. S3 supports multiple storage classes, allowing cost optimization based on access frequency. It also integrates seamlessly with AWS Key Management Service (KMS) to encrypt data at rest and ensures that access is controlled via IAM policies.

AWS KMS enables the creation, rotation, and management of cryptographic keys, allowing secure encryption of transaction logs. KMS supports both customer-managed keys and AWS-managed keys, providing flexibility for organizations that require full control over key management. Encryption in transit is achieved using SSL/TLS, protecting data during transmission from log sources to S3. This security architecture ensures compliance with financial regulations and industry standards, such as PCI DSS and SOX.

Amazon Athena allows serverless querying of S3-stored data using standard SQL. Organizations can run ad-hoc queries, generate reports, and analyze trends without provisioning or managing infrastructure. Athena integrates with AWS Glue to catalog metadata, making it easy to define table schemas, classify structured and semi-structured data, and enforce data governance policies. Athena’s serverless nature allows automatic scaling to accommodate varying query workloads, providing cost efficiency since organizations only pay for the queries they run.

Option B, RDS with multi-AZ replication, provides transactional database capabilities but introduces operational overhead and may not scale efficiently for large volumes of transaction logs. Option C, DynamoDB with global tables, is optimized for key-value workloads but is not suitable for complex analytical queries. Option D, EC2 instances with a MySQL cluster, requires significant management effort, including scaling, patching, backup, and high availability configurations, making it less practical for serverless log analytics.

By combining S3, KMS, and Athena, the financial services company can build a secure, highly available, and serverless analytics platform for transaction logs. S3 ensures durability and scalability, KMS provides strong encryption, and Athena enables efficient querying without managing servers. The architecture supports audit logging, retention policies, and integration with monitoring tools like CloudWatch to maintain operational oversight. It also allows cost optimization, as data can be tiered into lower-cost S3 storage classes for infrequently accessed logs. This approach ensures compliance, operational efficiency, and flexibility for analytics and reporting needs.

Question 141:

A technology company wants to implement a machine learning pipeline that reads raw sensor data from IoT devices, processes it, and trains predictive models. The solution must scale automatically and minimize operational overhead. Which AWS service combination is most appropriate?

A) AWS IoT Core, Amazon Kinesis Data Firehose, Amazon SageMaker
B) Amazon S3 with manual batch scripts
C) Amazon RDS with custom ETL processes
D) EC2 instances running TensorFlow

Answer:

A) AWS IoT Core, Amazon Kinesis Data Firehose, Amazon SageMaker

Explanation:

IoT applications generate continuous streams of data from connected devices, such as sensors, requiring real-time ingestion, processing, and analytics. AWS IoT Core is a managed service that securely connects IoT devices to AWS, allowing ingestion of data streams at massive scale. It supports MQTT and HTTP protocols, device authentication, and message routing to AWS endpoints such as Kinesis, S3, or Lambda. IoT Core provides secure device registration, certificate management, and policy enforcement, ensuring only authorized devices can transmit data.

Amazon Kinesis Data Firehose is a fully managed service for delivering real-time streaming data to destinations such as S3, Redshift, and Elasticsearch. Firehose automatically scales to match the volume of incoming data, handles buffering, batching, compression, and encryption, and ensures near-real-time delivery to downstream storage or analytics platforms. This allows sensor data to be efficiently ingested and persisted without operational overhead or manual infrastructure management. Firehose can also transform data using AWS Lambda functions before delivery, providing data cleaning or enrichment capabilities.

Amazon SageMaker enables building, training, and deploying machine learning models at scale. SageMaker provides managed Jupyter notebooks, pre-built algorithms, automatic model tuning, distributed training, and one-click deployment of endpoints for inference. Sensor data ingested through Kinesis Firehose can be stored in S3 and automatically processed using SageMaker processing jobs to prepare features for model training. SageMaker’s automatic scaling and managed infrastructure reduce operational complexity while ensuring models can train on large volumes of streaming data efficiently.

Option B, S3 with manual batch scripts, requires operational effort for data ingestion, transformation, and model training, introducing latency and scalability limitations. Option C, RDS with custom ETL processes, is optimized for structured transactional data rather than streaming sensor data and would require significant operational overhead to scale. Option D, EC2 instances running TensorFlow, offers flexibility for machine learning but requires manual management of clusters, scaling, and infrastructure, increasing operational complexity.

By integrating IoT Core, Kinesis Data Firehose, and SageMaker, the technology company can implement a fully managed, scalable, and serverless machine learning pipeline for real-time IoT data. IoT Core handles secure data ingestion, Firehose provides near-real-time delivery and processing, and SageMaker manages the training and deployment of predictive models. This architecture supports rapid experimentation, scaling to millions of devices, and automated feature engineering and model training, allowing data scientists to focus on model development rather than infrastructure management. Additionally, the solution supports secure, auditable, and compliant handling of data, leveraging IAM, encryption, and logging capabilities to maintain operational governance and regulatory compliance.

Question 142:

A healthcare company needs to store and analyze large volumes of patient records while ensuring compliance with HIPAA regulations. The solution must scale automatically, encrypt data at rest and in transit, and allow for querying using standard SQL. Which AWS service combination is most appropriate?

A) Amazon S3, AWS KMS, and Amazon Athena
B) Amazon RDS for MySQL with read replicas
C) Amazon DynamoDB with on-demand capacity
D) EC2 instances running PostgreSQL

Answer:

A) Amazon S3, AWS KMS, and Amazon Athena

Explanation:

Healthcare organizations are required to handle sensitive patient data under strict compliance frameworks like HIPAA. Therefore, any data storage and analytics solution must provide high levels of security, encryption, access control, and compliance features while remaining scalable and cost-efficient.

Amazon S3 is a fully managed object storage service designed to provide durability of 99.999999999% and virtually unlimited storage capacity. It supports server-side encryption, versioning, and fine-grained access control through IAM policies and bucket policies. S3’s durability and high availability make it ideal for storing massive volumes of patient records securely.

AWS Key Management Service (KMS) is used to manage encryption keys, enabling secure encryption of data at rest. With KMS, organizations can control key rotation, auditing, and access permissions, ensuring that only authorized users and services can decrypt sensitive data. Encryption in transit is achieved via SSL/TLS, preventing interception during data transfer. Together, S3 and KMS provide a robust encryption framework that meets HIPAA requirements.

Amazon Athena is a serverless query service that enables running SQL queries directly against data stored in S3. Athena does not require provisioning or managing servers, automatically scales to handle varying workloads, and integrates with AWS Glue Data Catalog for schema management. This allows healthcare analysts to perform complex queries and generate reports without operational overhead. Athena supports querying structured, semi-structured, and unstructured data, making it flexible for diverse healthcare datasets such as CSV, JSON, or Parquet.

Option B, Amazon RDS with read replicas, is suitable for transactional workloads but requires manual scaling and does not handle massive volumes of semi-structured or unstructured data efficiently. Option C, DynamoDB, is optimized for NoSQL workloads and provides key-value and document storage, but it does not support standard SQL queries or ad-hoc analytics natively. Option D, EC2 instances running PostgreSQL, introduces operational complexity for managing scaling, patching, high availability, and compliance, which could increase the risk of misconfiguration and regulatory violations.

By leveraging S3, KMS, and Athena, the healthcare company achieves a secure, scalable, and serverless analytics solution. This combination supports data encryption at rest and in transit, ensures compliance with HIPAA, and reduces operational overhead. Analysts can generate insights rapidly, and the infrastructure scales automatically with data growth, minimizing costs while maintaining compliance and security. Additional features such as S3 Lifecycle policies allow data tiering to lower-cost storage for archival data, while Athena’s integration with QuickSight provides visual analytics capabilities. This architecture provides a fully managed, auditable, and compliant solution for healthcare data analytics.

Question 143:

A gaming company wants to deliver global content with low latency and high availability. The company’s game assets include large media files and frequently updated dynamic content. Which AWS service combination should the company use?

A) Amazon CloudFront with S3 and Lambda@Edge
B) Amazon RDS Multi-AZ with Elastic Load Balancer
C) Amazon EC2 with Auto Scaling and EBS volumes
D) Amazon DynamoDB Global Tables with API Gateway

Answer:

A) Amazon CloudFront with S3 and Lambda@Edge

Explanation:

Delivering global content with low latency requires a content delivery network (CDN) that caches data close to end-users. Amazon CloudFront is a globally distributed CDN that delivers both static and dynamic content with low latency and high transfer speeds. CloudFront caches static game assets such as images, textures, and videos at edge locations worldwide, reducing the distance between users and content.

Amazon S3 is ideal for storing large static media files, providing durability, high availability, and integration with CloudFront. S3 allows versioning, lifecycle policies, and secure access through IAM policies or signed URLs, ensuring that content updates are controlled and secure. Frequent updates to content can be propagated to edge caches using cache invalidation, ensuring users receive the latest game assets without delay.

Lambda@Edge is a serverless compute feature that enables running custom logic at CloudFront edge locations. It allows the gaming company to modify requests and responses in real time, handle authentication, route requests dynamically, or implement custom content personalization without impacting latency. Lambda@Edge scales automatically with traffic, removing the need to manage servers and ensuring consistent performance for a global audience.

Option B, RDS Multi-AZ with Elastic Load Balancer, is appropriate for database high availability but does not optimize content delivery or reduce latency for globally distributed static assets. Option C, EC2 with Auto Scaling and EBS, requires management overhead, and while scalable, it does not provide a global caching mechanism to minimize latency. Option D, DynamoDB Global Tables with API Gateway, provides highly available NoSQL database capabilities but is not designed for efficiently delivering large media files to users worldwide.

By combining CloudFront, S3, and Lambda@Edge, the gaming company can achieve low-latency, highly available global content delivery. CloudFront ensures edge caching and optimized routing, S3 provides durable and scalable storage, and Lambda@Edge allows dynamic content handling and customization. This architecture minimizes latency, improves user experience, and reduces operational overhead while providing the scalability needed for peak gaming traffic. Security features include SSL/TLS encryption, signed URLs, and AWS WAF integration to protect against malicious traffic and ensure safe content delivery.

Question 144:

A media analytics company collects large volumes of video streams from thousands of cameras worldwide. The solution must process streams in real-time, perform analytics, and store results for long-term analysis. Which AWS services combination is most appropriate?

A) Amazon Kinesis Video Streams, AWS Lambda, and Amazon S3
B) Amazon S3 with manual processing scripts on EC2
C) Amazon RDS with batch ingestion jobs
D) Amazon CloudFront with DynamoDB

Answer:

A) Amazon Kinesis Video Streams, AWS Lambda, and Amazon S3

Explanation:

Real-time video processing requires a scalable, high-throughput, and managed platform that can ingest, process, and store video streams efficiently. Amazon Kinesis Video Streams is a fully managed service designed to ingest, buffer, and store video streams securely from millions of devices. It provides time-ordered delivery and supports live and batch analytics by integrating with AWS services such as Lambda, Rekognition Video, and SageMaker.

AWS Lambda allows serverless processing of video streams as they are ingested. Lambda functions can perform frame extraction, metadata generation, event detection, or trigger machine learning inference without provisioning servers. Lambda scales automatically with the number of streams, ensuring consistent processing throughput and minimizing operational overhead. Lambda’s integration with Kinesis Video Streams allows near real-time analytics, such as motion detection, object recognition, and anomaly detection.

Amazon S3 provides durable and scalable storage for processed video clips, extracted frames, and analytics results. S3 supports lifecycle management for archiving older data, encryption with KMS, and fine-grained access control through IAM policies. Analytics results stored in S3 can be queried using Amazon Athena or processed further using AWS Glue, Redshift, or SageMaker for predictive modeling, reporting, or long-term trend analysis.

Option B, S3 with manual scripts on EC2, requires operational management of compute clusters and scaling, introducing complexity and potential latency. Option C, RDS with batch ingestion, is suitable for structured transactional data, not unstructured video streams. Option D, CloudFront with DynamoDB, is designed for content delivery and NoSQL workloads, not real-time video ingestion and analytics.

By combining Kinesis Video Streams, Lambda, and S3, the media analytics company can implement a fully managed, scalable, and serverless video processing pipeline. The system supports ingestion from global camera networks, real-time processing, storage of raw and processed data, and integration with analytics and machine learning tools. This architecture ensures high availability, scalability, compliance, and cost efficiency while providing a platform for near real-time insights and long-term analytics from massive video datasets. It eliminates operational overhead of server management and provides flexibility to implement new analytics workflows or machine learning models as business needs evolve.

Question 145:

A global e-commerce company wants to migrate its on-premises relational database workloads to AWS while minimizing downtime and ensuring data consistency. The solution must support multi-region disaster recovery and allow read scaling for reporting workloads. Which AWS service combination should the company use?

A) Amazon Aurora Global Database with read replicas
B) Amazon RDS Multi-AZ with automated backups
C) Amazon DynamoDB Global Tables
D) Amazon EC2 with PostgreSQL and custom replication

Answer:

A) Amazon Aurora Global Database with read replicas

Explanation:

Migrating critical relational database workloads to AWS while minimizing downtime and ensuring data consistency requires a solution that supports high availability, global distribution, read scaling, and seamless disaster recovery. Amazon Aurora, a fully managed relational database compatible with MySQL and PostgreSQL, is specifically designed to meet these requirements for enterprise-scale workloads.

Aurora Global Database enables a single database to span multiple AWS regions, supporting disaster recovery and global read scalability. The primary region handles write operations while up to five secondary regions support low-latency read operations. Data is replicated across regions using dedicated infrastructure with typical latency under a second, ensuring strong consistency while maintaining high performance. This architecture allows the e-commerce company to serve read-heavy reporting workloads from geographically closer replicas, reducing latency and improving user experience.

Aurora automatically handles replication between the primary and secondary regions. In the event of a regional failure, failover can be performed to a secondary region within minutes, ensuring business continuity. Aurora also integrates with AWS backup and monitoring services, including Amazon CloudWatch, AWS CloudTrail, and AWS Config, providing auditing, metrics, and operational visibility.

Option B, RDS Multi-AZ with automated backups, provides high availability within a single region and supports failover for disaster recovery, but it does not inherently support cross-region replication or global read scaling. Option C, DynamoDB Global Tables, is ideal for key-value and document NoSQL workloads but is not suitable for complex relational queries, joins, or transactional consistency required by relational databases. Option D, EC2 with PostgreSQL and custom replication, introduces significant operational overhead and increases the risk of human error while requiring ongoing monitoring, patching, and scaling management.

By choosing Aurora Global Database with read replicas, the e-commerce company can migrate its on-premises relational databases to a highly available, globally distributed architecture with minimal downtime. This architecture supports transactional consistency, multi-region disaster recovery, and scalable read operations without requiring extensive manual intervention. Additionally, Aurora’s managed capabilities such as automated backups, patching, monitoring, and replication simplify database operations while maintaining performance, security, and compliance. This solution reduces operational complexity, lowers risk during migration, and ensures the company can continue to meet its global business needs without sacrificing performance or reliability.

Question 146:

A financial services company wants to build a serverless architecture for processing real-time transactions. The system must provide low-latency processing, store processed data in a secure and durable manner, and allow downstream analytics. Which AWS services should be used?

A) Amazon Kinesis Data Streams, AWS Lambda, and Amazon S3
B) Amazon SQS, Amazon EC2, and Amazon RDS
C) Amazon DynamoDB Streams, AWS Fargate, and Amazon Redshift
D) Amazon MQ, AWS Lambda, and Amazon EBS

Answer:

A) Amazon Kinesis Data Streams, AWS Lambda, and Amazon S3

Explanation:

Processing real-time transactions in a secure, serverless, and low-latency environment requires a managed streaming service, serverless compute for processing, and durable storage for analytics and compliance. Amazon Kinesis Data Streams is designed to handle real-time streaming data with high throughput and low latency, allowing ingestion of millions of events per second from multiple sources. Each transaction is captured and made available to consumers in near real time.

AWS Lambda provides serverless compute capabilities to process data in real time as it arrives in Kinesis Data Streams. Lambda functions can perform validation, enrichment, transformation, or aggregation of transactions with automatic scaling based on incoming traffic. This eliminates the need to provision or manage servers and ensures consistent performance even during traffic spikes. Lambda also integrates with IAM and KMS to enforce secure processing and encryption of sensitive financial data.

Amazon S3 serves as durable and highly available storage for processed transaction data, ensuring long-term retention, compliance, and integration with analytics tools. S3 supports versioning, lifecycle policies, encryption at rest and in transit, and fine-grained access controls. Analytics can be performed directly on S3 using Amazon Athena or loaded into Amazon Redshift for advanced reporting and machine learning workflows.

Option B, SQS with EC2 and RDS, introduces server management overhead and does not inherently provide real-time streaming or low-latency processing. Option C, DynamoDB Streams with Fargate and Redshift, can process data asynchronously but may introduce higher latency for high-volume transactional workloads. Option D, Amazon MQ with Lambda and EBS, is more suited for traditional message queuing and not optimized for high-throughput, low-latency streaming at global scale.

By combining Kinesis Data Streams, AWS Lambda, and S3, the financial services company can implement a fully serverless, low-latency, and scalable architecture for real-time transaction processing. Kinesis ensures data durability and ordering, Lambda provides instant processing without server management, and S3 offers secure, cost-effective long-term storage. This architecture supports high reliability, security, and compliance for financial transactions while enabling analytics and reporting downstream. Additionally, Kinesis Data Streams supports encryption, monitoring, and shard scaling to match varying transaction volumes, ensuring the architecture adapts automatically to business growth.

Question 147:

A video streaming company wants to implement a highly available, scalable, and cost-efficient solution for storing user-generated content. The solution must provide global access with low latency and enable processing for video transcoding and analytics. Which AWS service combination is best suited?

A) Amazon S3, Amazon CloudFront, and AWS Lambda
B) Amazon EBS, EC2 Auto Scaling, and Elastic Load Balancer
C) Amazon RDS with Multi-AZ, and Amazon SQS
D) Amazon DynamoDB Global Tables with AWS Fargate

Answer:

A) Amazon S3, Amazon CloudFront, and AWS Lambda

Explanation:

Storing user-generated content such as videos at scale requires a solution that is globally accessible, highly durable, cost-efficient, and capable of supporting processing workloads like transcoding and analytics. Amazon S3 is designed for high durability, unlimited storage capacity, and integration with a wide range of AWS services. S3 supports server-side encryption, versioning, lifecycle management, and access controls, ensuring content security and compliance.

Amazon CloudFront, a global content delivery network, caches video content at edge locations to reduce latency and provide high-speed access for users worldwide. CloudFront ensures that frequently accessed content is delivered efficiently while maintaining the scalability needed for global user bases. It also supports features like signed URLs and cookies for secure access control.

AWS Lambda can be triggered by S3 events to perform serverless processing such as video transcoding, thumbnail generation, or metadata extraction. Lambda scales automatically based on the number of incoming videos, eliminating the need to manage servers and ensuring cost efficiency. The processed data can be stored back in S3 or fed into analytics pipelines using services like Amazon Athena, AWS Glue, or Amazon Redshift for insights into user engagement or content performance.

Option B, EBS with EC2 Auto Scaling and ELB, requires manual server management and does not provide a global content distribution mechanism. Option C, RDS Multi-AZ with SQS, is suitable for relational data but not for storing large unstructured video files. Option D, DynamoDB Global Tables with Fargate, is ideal for key-value workloads but does not efficiently handle large media files or global low-latency delivery.

By combining S3, CloudFront, and Lambda, the video streaming company achieves a highly available, scalable, cost-efficient, and globally accessible solution. S3 provides durable storage, CloudFront delivers content with low latency worldwide, and Lambda handles processing workloads without infrastructure management. This architecture supports efficient video delivery, dynamic processing, analytics, and cost optimization while maintaining high availability and durability. Security features, including encryption and IAM access policies, ensure that user-generated content is protected, and integration with monitoring services like CloudWatch provides operational insights and anomaly detection for continuous improvement.

Question 148:

A healthcare company wants to migrate its on-premises patient records database to AWS while ensuring HIPAA compliance, high availability, and scalability. The solution must encrypt data at rest and in transit and allow analytics on the data without compromising security. Which AWS service combination is most appropriate?

A) Amazon RDS for PostgreSQL with Multi-AZ deployment and AWS KMS encryption
B) Amazon EC2 with MySQL and self-managed replication
C) Amazon DynamoDB with global tables
D) Amazon S3 with EC2-based MySQL instances

Answer:

A) Amazon RDS for PostgreSQL with Multi-AZ deployment and AWS KMS encryption

Explanation:

Migrating sensitive patient data to the cloud requires a solution that ensures compliance, high availability, security, and analytical capabilities. Amazon RDS for PostgreSQL provides a fully managed relational database service that supports encryption at rest using AWS Key Management Service (KMS) and SSL/TLS encryption for data in transit, making it suitable for HIPAA-regulated workloads. Multi-AZ deployments provide high availability by automatically replicating data synchronously to a standby instance in a different Availability Zone. This ensures that the system can failover seamlessly in the event of hardware or network failure, minimizing downtime and preserving data integrity.

RDS also allows automated backups, point-in-time recovery, monitoring, and maintenance operations, which are critical for healthcare applications that require strict data integrity and auditing. It integrates with AWS CloudTrail, AWS Config, and Amazon CloudWatch to provide logging, auditing, and monitoring for security and operational insights. This integration ensures that administrators can monitor access patterns, detect unusual behavior, and maintain compliance with regulatory standards.

In addition to operational capabilities, PostgreSQL in RDS supports analytical workloads through features such as materialized views, read replicas, and integration with analytics services like Amazon Redshift, Amazon Athena, and AWS Glue. This enables the healthcare company to perform reporting and data analysis on patient records without compromising security. By creating read replicas, organizations can offload analytics queries from the primary database, reducing latency for transactional workloads while ensuring the analytical data remains consistent and secure.

Option B, EC2 with MySQL and self-managed replication, introduces operational complexity, higher risk of misconfiguration, and increased maintenance overhead. Option C, DynamoDB global tables, is suitable for high-availability NoSQL workloads but does not provide the relational capabilities, complex query support, or transactional integrity required for healthcare data. Option D, S3 with EC2 MySQL, does not provide a managed, highly available database environment and increases operational and security management burden.

By selecting RDS for PostgreSQL with Multi-AZ deployment and KMS encryption, the healthcare company achieves a secure, highly available, and compliant database solution that simplifies management, ensures data protection, and supports analytics. This architecture balances operational efficiency with compliance requirements and enables the healthcare provider to focus on patient care rather than database operations.

Question 149:

A media company wants to implement a scalable architecture for processing and analyzing millions of images uploaded by users daily. The system must provide automatic scaling, integrate with machine learning workflows, and minimize operational overhead. Which AWS services should be used?

A) Amazon S3, Amazon SQS, AWS Lambda, and Amazon Rekognition
B) Amazon EBS, EC2 Auto Scaling, and Elastic Load Balancer
C) Amazon DynamoDB, AWS Fargate, and Amazon SageMaker
D) Amazon RDS with Multi-AZ and Amazon EMR

Answer:

A) Amazon S3, Amazon SQS, AWS Lambda, and Amazon Rekognition

Explanation:

Processing millions of images daily requires a serverless, event-driven architecture that can scale automatically and integrate with machine learning for image analysis. Amazon S3 serves as a durable, highly available storage layer for images uploaded by users. S3 automatically scales to handle large volumes of data and provides features such as versioning, lifecycle policies, and encryption to ensure security and compliance.

Amazon SQS acts as a decoupling mechanism to buffer image processing requests. Each image upload triggers an event that is sent to an SQS queue, which ensures reliable delivery and allows downstream services to process requests asynchronously. This decoupling ensures that processing workloads do not overwhelm compute resources and provides resilience in the event of temporary failures.

AWS Lambda is used to process images serverlessly. Lambda functions can be triggered by S3 events or SQS messages, enabling automatic scaling to handle spikes in uploads without provisioning or managing servers. The functions can perform preprocessing, resizing, and data validation before passing the images to Amazon Rekognition, a fully managed computer vision service for image analysis. Rekognition can detect objects, faces, text, and other metadata in images, enabling the media company to generate insights for personalization, moderation, or content recommendations.

This architecture minimizes operational overhead because AWS handles scaling, provisioning, and maintenance of compute and storage resources. It also allows integration with analytics pipelines for further processing of image metadata using services like Amazon Athena, Amazon Redshift, or AWS Glue for machine learning workflows.

Option B, EC2 with EBS and ELB, requires manual server management and scaling, which increases operational complexity and cost. Option C, DynamoDB with Fargate and SageMaker, does not provide a native mechanism for image storage and may require additional integration work to handle large binary objects. Option D, RDS with Multi-AZ and EMR, is designed for relational and big data analytics workloads rather than real-time, event-driven image processing.

By using S3, SQS, Lambda, and Rekognition, the media company can build a scalable, automated, and cost-efficient pipeline for processing and analyzing millions of images daily. This architecture ensures durability, security, operational simplicity, and seamless integration with machine learning workflows, allowing the company to focus on insights and application features rather than infrastructure management.

Question 150:

A global retail company wants to implement a hybrid architecture connecting its on-premises ERP system to AWS. The architecture must support secure, low-latency access to AWS services, allow scaling for seasonal workloads, and maintain consistent network performance. Which AWS service combination should be used?

A) AWS Direct Connect, Amazon VPC with VPN, and Elastic Load Balancer
B) AWS Site-to-Site VPN only, connecting to a public VPC
C) Amazon CloudFront and Amazon Route 53
D) AWS Transit Gateway with AWS Lambda

Answer:

A) AWS Direct Connect, Amazon VPC with VPN, and Elastic Load Balancer

Explanation:

Building a hybrid architecture for global retail operations requires a solution that provides secure, reliable, and low-latency connectivity between on-premises infrastructure and AWS. AWS Direct Connect establishes a dedicated network connection between the company’s data centers and AWS, providing higher bandwidth, lower latency, and more consistent network performance than standard internet-based VPN connections. This is critical for ERP systems that require predictable response times and reliable connectivity.

An Amazon VPC (Virtual Private Cloud) provides an isolated, scalable environment in AWS to host cloud-based workloads. By connecting the on-premises network to the VPC via Direct Connect and VPN, the company can create a secure hybrid architecture that supports routing, private subnets, and controlled access to AWS services. The VPN acts as a backup in case the Direct Connect link experiences failure, ensuring business continuity.

Elastic Load Balancer distributes traffic across multiple EC2 instances or services within the VPC, providing automatic scaling for seasonal peaks in retail demand. It ensures high availability, fault tolerance, and seamless scaling without manual intervention. This combination of Direct Connect, VPC, VPN, and ELB creates a resilient hybrid network that balances performance, security, and scalability for global operations.

Option B, VPN-only, may be sufficient for small workloads but does not provide consistent low-latency connections for enterprise-scale ERP integration and can experience variable performance over the public internet. Option C, CloudFront and Route 53, are primarily for content delivery and DNS management and do not provide dedicated connectivity to on-premises systems. Option D, Transit Gateway with Lambda, simplifies routing between multiple VPCs but does not replace the need for dedicated connectivity and low-latency access to on-premises ERP systems.

By using Direct Connect with VPN, VPC, and ELB, the global retail company achieves a secure, high-performance, and scalable hybrid architecture. This allows seamless integration with AWS services while maintaining predictable network performance and availability for critical business workloads. The architecture supports seasonal scalability, disaster recovery, and operational flexibility while minimizing latency and ensuring secure data transfers.