Amazon AWS Certified Solutions Architect – Professional SAP-C02 Exam Dumps and Practice Test Questions Set 3 Q31-45

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Question 31:

A global retail company needs a highly available, fault-tolerant, and scalable architecture to process millions of real-time transactions from its online stores. The solution should allow multiple services to consume transaction data concurrently, ensure no message loss, and scale automatically during peak traffic periods. Which AWS architecture is most suitable?

A) Amazon SQS Standard queues with multiple EC2 consumers
B) Amazon Kinesis Data Streams with multiple Lambda consumers and Amazon DynamoDB for storage
C) Amazon SNS with a single SQS subscription and EC2 consumers
D) Amazon MQ with a single EC2 consumer

Answer:

B) Amazon Kinesis Data Streams with multiple Lambda consumers and Amazon DynamoDB for storage

Explanation:

Processing millions of real-time transactions requires a system that is highly scalable, fault-tolerant, and capable of concurrent consumption of messages. Amazon Kinesis Data Streams is a managed service that enables real-time ingestion of high-volume streaming data. By organizing the stream into shards, Kinesis allows multiple consumers to read and process data concurrently while preserving the order of events within each shard. Shards can scale dynamically to accommodate traffic spikes, ensuring the system remains responsive even during peak hours.

AWS Lambda functions act as serverless consumers of Kinesis streams. Multiple Lambda functions can process the same stream simultaneously for parallel processing of transactions, such as validation, enrichment, routing, and analytics. Lambda scales automatically in response to stream volume, eliminating the need for server management and providing cost efficiency since users pay only for the compute consumed.

Amazon DynamoDB serves as a durable, low-latency storage layer for processed transactions. DynamoDB provides on-demand scaling to handle fluctuating workloads and maintains consistent performance regardless of transaction volume. Its built-in encryption with AWS KMS and fine-grained IAM-based access control ensures secure handling of sensitive transaction data, complying with industry standards like PCI DSS.

Option A, SQS with EC2 consumers, does not efficiently support multiple consumers reading the same message concurrently and can introduce latency. Option C, SNS with a single SQS subscription, limits fan-out and parallel processing, reducing scalability. Option D, Amazon MQ with a single EC2 consumer, introduces a single point of failure and increases operational complexity, making it unsuitable for real-time, high-volume processing.

CloudWatch provides operational monitoring for Kinesis shard throughput, Lambda execution, and DynamoDB performance. CloudTrail logs all API activity for auditing and compliance. By combining Kinesis, Lambda, and DynamoDB, the company can implement a robust, fault-tolerant, and scalable architecture capable of processing millions of transactions in real-time while ensuring data durability, reliability, and regulatory compliance. This architecture is ideal for handling unpredictable spikes, supporting multiple downstream services, and maintaining high operational efficiency with minimal management overhead.

Question 32:

A healthcare provider wants to create a secure and scalable data lake for sensitive patient data, including electronic health records, imaging files, and unstructured physician notes. The solution must provide fine-grained access control, auditing, and compliance with HIPAA while supporting analytics and machine learning. Which AWS services combination is most appropriate?

A) Amazon S3, AWS Lake Formation, AWS Glue Data Catalog, AWS IAM
B) Amazon RDS, Amazon Redshift, AWS Lambda, AWS KMS
C) Amazon DynamoDB, AWS Lambda, Amazon Athena, Amazon QuickSight
D) Amazon EFS, Amazon EC2, AWS Config, AWS CloudTrail

Answer:

A) Amazon S3, AWS Lake Formation, AWS Glue Data Catalog, AWS IAM

Explanation:

Managing sensitive healthcare data requires a secure, compliant, and highly scalable data lake architecture. Amazon S3 provides durable and scalable storage suitable for structured and unstructured healthcare data, including electronic health records, lab results, and medical imaging. S3 supports encryption at rest using KMS and encryption in transit via TLS, ensuring compliance with HIPAA and other regulatory standards.

AWS Lake Formation allows centralized management of access control policies, providing fine-grained permissions at database, table, column, or row levels. This ensures that only authorized personnel can access specific datasets. Lake Formation also supports auditing, governance, and operational oversight, which are critical for healthcare compliance.

AWS Glue Data Catalog maintains a metadata repository, managing schemas, partitions, and data lineage. This enables data discovery, efficient querying, and preparation for analytics or machine learning workflows without moving the data. Glue ETL jobs can transform, enrich, and process the data for downstream analytics while maintaining compliance and operational oversight.

IAM integrates with Lake Formation and S3 to enforce authentication and authorization policies. Roles, policies, and groups ensure least-privilege access. CloudTrail logs all access and administrative actions, providing comprehensive audit trails for compliance reporting. CloudWatch monitors S3, Lake Formation, and Glue operations, enabling proactive operational management.

Option B, RDS and Redshift, is primarily suitable for structured data and lacks unified governance for unstructured data. Option C, DynamoDB with Athena, provides querying capabilities but lacks centralized access control and governance required for sensitive patient data. Option D, EFS with EC2, introduces operational complexity, lacks scalability, and does not provide built-in governance or compliance features.

By combining S3, Lake Formation, Glue, and IAM, the healthcare provider can implement a secure, scalable, and compliant data lake. This architecture supports analytics and machine learning, ensures regulatory compliance, maintains data durability, and reduces operational overhead. Integration with security, monitoring, and governance tools ensures operational reliability, auditability, and efficient data management for healthcare workloads, enabling advanced insights while protecting patient privacy.

Question 33:

A media streaming company wants to deliver video content globally with low latency, secure access, and regional content compliance. The solution must support caching at edge locations, dynamic request processing, and protection against web attacks. Which AWS architecture is most suitable?

A) Amazon CloudFront, Amazon S3, AWS Lambda@Edge, AWS WAF
B) Amazon EC2 in multiple regions with Route 53 failover
C) Amazon S3 with public access and pre-signed URLs
D) Amazon CloudFront with S3 origin without edge processing or security

Answer:

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

Explanation:

Global media content delivery requires minimizing latency, ensuring secure access, and enforcing regional compliance. Amazon CloudFront, a CDN, caches content at edge locations worldwide to reduce latency and improve availability. Edge caching reduces load on origin servers, enabling scalability during high-demand periods such as live streaming or viral content events.

Amazon S3 serves as the origin, providing durable, scalable storage with encryption at rest via KMS and access control through IAM and bucket policies. S3 lifecycle policies optimize storage costs by moving less accessed content to lower-cost tiers.

AWS Lambda@Edge enables execution of custom logic at CloudFront edge locations. It supports dynamic URL rewriting, authentication, authorization, and enforcement of regional content restrictions. This ensures that compliance policies and access rules are applied close to users, reducing latency and enhancing user experience.

AWS WAF protects against web attacks, including SQL injection, cross-site scripting, and DDoS attacks. Integrated with CloudFront, WAF evaluates traffic at the edge, preventing malicious requests from reaching origin servers. CloudWatch and CloudTrail provide logging, monitoring, and auditing for operational visibility and compliance.

Option B, EC2 multi-region with Route 53, lacks edge caching, dynamic request processing, and integrated security at scale. Option C, S3 public access with pre-signed URLs, provides limited access control without caching, dynamic processing, or security. Option D, CloudFront without Lambda@Edge or WAF, improves latency but does not enforce security, compliance, or dynamic content processing.

This architecture ensures globally distributed, low-latency video delivery with secure, compliant access. CloudFront edge caching reduces origin load, Lambda@Edge enables dynamic request processing and regulatory compliance, and WAF provides robust security. Together, these services create a resilient, scalable, and secure media streaming solution suitable for global operations.

Question 34:

A global e-commerce company wants to implement a highly available, fault-tolerant, and scalable architecture for processing real-time transactions from its online stores. The system should allow multiple services to consume the same transaction data concurrently, guarantee no message loss, and scale automatically during traffic spikes. Which AWS architecture is most appropriate?

Answer:

B) Amazon Kinesis Data Streams with multiple Lambda consumers and Amazon DynamoDB for storage

Explanation:

In high-volume e-commerce environments, real-time transaction processing requires an architecture that ensures fault tolerance, scalability, and reliability. Amazon Kinesis Data Streams provides a fully managed solution for real-time streaming data, dividing the stream into shards that allow multiple consumers to process data concurrently while preserving event ordering within each shard. Shards also provide horizontal scaling to handle variable and unpredictable traffic spikes, which is critical during peak shopping seasons or flash sales.

AWS Lambda acts as serverless consumers for Kinesis streams, enabling parallel processing of transactions such as validation, enrichment, routing, and analytics. Lambda automatically scales with the stream’s incoming volume, eliminating the need for server provisioning and reducing operational overhead. Lambda functions can process the same data stream concurrently, supporting multiple downstream services without duplicating logic.

Amazon DynamoDB offers a highly available and low-latency storage layer for processed transactions. Its on-demand capacity mode scales dynamically with workload, ensuring consistent performance under high load. DynamoDB also provides built-in encryption using AWS KMS, fine-grained access control via IAM, and point-in-time recovery to ensure durability and compliance with financial and operational regulations.

Option A, SQS with EC2 consumers, provides decoupling but does not efficiently allow multiple consumers to process the same messages simultaneously, which can introduce latency and limit scalability. Option C, SNS with a single SQS subscription, restricts parallel consumption and limits fan-out capabilities. Option D, Amazon MQ with a single EC2 consumer, introduces a single point of failure and adds operational complexity, making it unsuitable for high-volume real-time transaction processing.

Operational monitoring is achieved through CloudWatch metrics for Kinesis shard throughput, Lambda execution, and DynamoDB performance. CloudTrail logs API activity for auditing and compliance. Combining Kinesis, Lambda, and DynamoDB ensures a resilient, scalable, and fault-tolerant architecture capable of processing millions of transactions in real-time while maintaining high availability, durability, and operational efficiency. This architecture ensures the e-commerce company can reliably process transactions during traffic spikes while supporting multiple services and analytics workloads simultaneously.

Question 35:

A healthcare provider wants to build a secure and scalable data lake for patient data, including electronic health records, medical imaging, and unstructured notes. The solution must enforce fine-grained access control, auditing, and compliance with HIPAA while supporting analytics and machine learning workloads. Which AWS services combination is most suitable?

Answer:

A) Amazon S3, AWS Lake Formation, AWS Glue Data Catalog, AWS IAM

Explanation:

Healthcare organizations managing sensitive patient data require a secure, scalable, and compliant data lake architecture. Amazon S3 provides durable and scalable storage capable of holding structured and unstructured healthcare data, including electronic health records, lab results, and medical imaging. Data stored in S3 can be encrypted at rest using AWS KMS and in transit via TLS, ensuring HIPAA compliance and data security.

AWS Lake Formation simplifies the creation and management of a secure data lake by providing fine-grained access control. Administrators can enforce permissions at the database, table, column, or row level, ensuring that only authorized personnel access specific datasets. Lake Formation also supports auditing, governance, and operational control, which are essential for compliance and regulatory requirements in healthcare environments.

AWS Glue Data Catalog acts as a centralized metadata repository, storing schema, partitioning, and lineage information. This enables efficient discovery, querying, and preparation of datasets for analytics and machine learning without moving the data. Glue ETL jobs can transform, enrich, and process data for downstream analytics while maintaining governance and compliance.

IAM integrates with Lake Formation and S3 to enforce strict authentication and authorization policies. CloudTrail logs all access and administrative actions, providing a complete audit trail for regulatory compliance. CloudWatch monitors S3, Lake Formation, and Glue jobs, enabling proactive operational oversight and issue detection.

Option B, RDS and Redshift, is limited to structured data and lacks unified governance and security for unstructured healthcare data. Option C, DynamoDB with Athena, allows querying but does not provide centralized fine-grained access control or data governance. Option D, EFS with EC2, introduces high operational complexity and lacks built-in scalability, security, and governance features required for HIPAA compliance.

By leveraging S3, Lake Formation, Glue, and IAM, the healthcare provider can implement a secure, scalable, and compliant data lake architecture. This solution supports analytics and machine learning workloads while ensuring durability, security, and regulatory compliance. It reduces operational overhead, enables fine-grained access control, maintains auditability, and provides operational visibility for large-scale healthcare datasets.

Question 36:

A media streaming company wants to deliver video content globally with low latency, secure access, and regional content compliance. The solution must support edge caching, dynamic request processing, and protection against web attacks. Which AWS services combination is most appropriate?

Answer:

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

Explanation:

Global media content delivery requires low latency, secure access, and compliance with regional content regulations. Amazon CloudFront, a global content delivery network (CDN), caches content at edge locations worldwide, reducing latency and improving availability for users regardless of geographic location. Edge caching also decreases the load on origin servers, allowing the architecture to scale efficiently during traffic spikes or live events.

Amazon S3 serves as the origin storage, providing durable, scalable storage for video content with encryption at rest using AWS KMS and secure access control through IAM and bucket policies. Lifecycle policies can be implemented to manage storage costs by transitioning less frequently accessed content to lower-cost tiers.

AWS Lambda@Edge allows execution of custom logic at CloudFront edge locations. It enables dynamic request processing, authentication, authorization, and enforcement of regional compliance rules. Lambda@Edge ensures that content delivery adheres to licensing requirements and access policies at the edge, reducing latency and improving user experience.

AWS WAF protects against web-based attacks such as SQL injection, cross-site scripting, and DDoS. Integrated with CloudFront, WAF evaluates traffic at the edge before it reaches the origin, reducing the risk of downtime or data compromise. CloudWatch and CloudTrail provide monitoring, operational visibility, and audit logging for compliance and security tracking.

Option B, EC2 multi-region with Route 53 failover, lacks edge caching and dynamic request processing capabilities, limiting performance and security at a global scale. Option C, S3 with public access and pre-signed URLs, provides limited access control and lacks edge caching, dynamic processing, and security features. Option D, CloudFront without Lambda@Edge or WAF, reduces latency but does not enforce security, compliance, or dynamic content processing.

This architecture ensures globally distributed, low-latency video delivery with secure, compliant access. CloudFront edge caching reduces origin load, Lambda@Edge provides dynamic request processing and compliance enforcement, and WAF protects against malicious traffic. Together, these services deliver a resilient, scalable, secure, and compliant solution suitable for global media streaming operations.

Question 37:

A large online retailer wants to build a real-time recommendation system that can process millions of events per second, such as user clicks, purchases, and product views. The system must provide personalized recommendations with low latency and scale automatically during high traffic periods. Which AWS services architecture is most appropriate?

A) Amazon SQS with multiple EC2 consumers and Amazon RDS
B) Amazon Kinesis Data Streams with AWS Lambda consumers and Amazon DynamoDB
C) Amazon SNS with a single SQS subscription and EC2 consumers
D) Amazon MQ with a single EC2 consumer

Answer:

B) Amazon Kinesis Data Streams with AWS Lambda consumers and Amazon DynamoDB

Explanation:

Real-time recommendation systems require processing vast amounts of event data with minimal latency while scaling automatically to accommodate unpredictable traffic patterns. Amazon Kinesis Data Streams is a fully managed service designed for real-time streaming data ingestion. It divides streams into shards, which allow multiple consumers to process data concurrently while maintaining ordering within shards. This ensures that events such as clicks, purchases, and product views are processed in the correct sequence, which is essential for accurate recommendation algorithms.

AWS Lambda acts as serverless consumers that process Kinesis events in parallel. Lambda functions can handle multiple shards simultaneously, enabling near real-time processing of user behavior data for personalized recommendations. Lambda scales automatically in response to incoming data volume, reducing operational overhead and allowing the system to handle spikes in traffic without manual intervention.

Amazon DynamoDB is used as a low-latency storage solution to maintain user profiles, recommendation results, and session data. DynamoDB on-demand capacity allows automatic scaling to handle the dynamic workload generated by millions of events. Additionally, DynamoDB’s fine-grained access control and encryption with AWS KMS ensure secure and compliant storage of sensitive user data.

Option A, SQS with EC2 consumers and RDS, is unsuitable due to limited parallel consumption and latency constraints. Option C, SNS with a single SQS subscription, restricts fan-out capabilities and parallel processing. Option D, Amazon MQ with a single EC2 consumer, introduces a single point of failure and operational overhead, making it unsuitable for high-volume, low-latency event processing.

Operational monitoring is implemented through CloudWatch metrics for Kinesis shards, Lambda function executions, and DynamoDB performance. CloudTrail logs all API actions for auditing and compliance purposes. By combining Kinesis, Lambda, and DynamoDB, the online retailer can implement a highly scalable, real-time recommendation system capable of handling millions of events per second with low latency, ensuring personalized recommendations are delivered promptly to users.

Question 38:

A financial services organization wants to implement a highly available, fault-tolerant, and scalable data processing system for trade transactions. The system must process trades in near real-time, allow multiple services to consume the same data, and maintain durability and compliance with regulatory standards. Which AWS architecture is most appropriate?

Answer:

B) Amazon Kinesis Data Streams with multiple Lambda consumers and Amazon DynamoDB for storage

Explanation:

Financial trading systems require architectures capable of handling real-time data ingestion, processing, and storage with high availability, fault tolerance, and compliance. Amazon Kinesis Data Streams is designed to ingest and process streaming data in real-time. Each stream is divided into shards that can scale horizontally to manage high-volume trade events. Shards maintain event ordering within the stream, which is crucial for financial transactions to ensure accurate trade execution and reporting.

Multiple Lambda functions can consume the Kinesis stream in parallel, processing trade data for validation, risk assessment, compliance checks, and downstream analytics. Lambda’s serverless architecture automatically scales to handle variations in trade volume, enabling near real-time processing without requiring server management. This is particularly important for financial markets where latency and throughput are critical for operational efficiency and regulatory compliance.

Amazon DynamoDB provides durable and low-latency storage for processed trade data. DynamoDB ensures transactional integrity, supports on-demand scaling, and encrypts data using AWS KMS to comply with regulatory requirements such as SEC and FINRA. Fine-grained IAM policies control access to trade data, ensuring only authorized personnel can read or write sensitive information.

Option A, SQS with EC2 consumers, cannot efficiently handle multiple consumers processing the same messages simultaneously, limiting throughput. Option C, SNS with a single SQS subscription, restricts parallel processing and scalability. Option D, Amazon MQ with a single EC2 consumer, introduces a single point of failure and high operational complexity, making it unsuitable for financial trade processing.

CloudWatch metrics monitor shard throughput, Lambda execution performance, and DynamoDB operational metrics to ensure reliability and fault tolerance. CloudTrail captures all API calls and administrative actions for auditing and regulatory compliance. This architecture provides a robust, scalable, and compliant solution for real-time trade processing, enabling multiple downstream systems to process transactions concurrently while ensuring data durability, security, and operational visibility.

Question 39:

A global media company wants to deliver video content with low latency, secure access, and regional compliance. The solution must support caching at edge locations, dynamic content processing, and protection against web attacks. Which combination of AWS services is most appropriate?

Answer:

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

Explanation:

Delivering global media content requires a combination of low latency, secure access, and compliance with regional licensing and privacy regulations. Amazon CloudFront, a global content delivery network (CDN), caches content at edge locations worldwide. Edge caching reduces latency for end-users and decreases the load on the origin S3 bucket, allowing the architecture to scale efficiently during high-traffic events such as live streaming or new content releases.

Amazon S3 serves as the origin for video content. S3 provides durable, scalable storage with encryption at rest using AWS KMS and access control through IAM policies and bucket policies. Lifecycle management policies optimize storage costs by moving less-accessed content to lower-cost tiers, improving operational efficiency.

AWS Lambda@Edge enables execution of custom logic at CloudFront edge locations. Lambda@Edge allows dynamic content processing such as URL rewriting, authentication, authorization, and enforcement of regional content restrictions. This ensures compliance with licensing agreements and improves user experience by processing requests closer to users, reducing latency.

AWS WAF protects against web-based attacks such as SQL injection, cross-site scripting, and DDoS. WAF integrated with CloudFront evaluates incoming traffic at the edge, preventing malicious requests from reaching the origin. CloudWatch provides operational monitoring and CloudTrail provides auditing capabilities for security and compliance reporting.

Option B, EC2 multi-region with Route 53 failover, lacks edge caching, dynamic request processing, and integrated security at a global scale. Option C, S3 with public access and pre-signed URLs, provides minimal access control and does not support caching, edge processing, or security. Option D, CloudFront without Lambda@Edge or WAF, improves latency but does not provide dynamic content processing, security enforcement, or compliance measures.

This architecture ensures low-latency, secure, and compliant media delivery. CloudFront edge caching reduces load on origins, Lambda@Edge provides dynamic processing and compliance enforcement, and WAF protects against web attacks. Together, these services create a resilient, scalable, secure, and compliant solution for global media streaming operations.

Question 40:

A global e-commerce company wants to implement a scalable, highly available, and fault-tolerant architecture for processing customer orders in real-time. The system must allow multiple downstream services to consume order events concurrently and ensure no order is lost, even during traffic spikes. Which AWS architecture is most suitable?

Answer:

B) Amazon Kinesis Data Streams with multiple Lambda consumers and Amazon DynamoDB for storage

Explanation:

Real-time order processing at a global scale demands a system that is resilient, highly available, and capable of handling massive volumes of events. Amazon Kinesis Data Streams allows ingestion of real-time data from multiple sources, splitting the data into shards to support concurrent processing by multiple consumers. Shards maintain ordering for events, which is crucial in e-commerce to ensure transactional integrity, such as preventing duplicate processing of orders.

AWS Lambda functions consume Kinesis streams in parallel, enabling multiple services to react to events like payment validation, inventory updates, shipping coordination, and analytics. Lambda automatically scales with the volume of data, allowing the system to handle sudden spikes, such as flash sales or holiday traffic, without manual intervention. This serverless approach reduces operational complexity while providing high throughput and near real-time processing capabilities.

Amazon DynamoDB provides a highly available and low-latency storage layer to persist processed orders. DynamoDB’s on-demand capacity mode automatically scales with workload fluctuations and ensures durability and reliability. Fine-grained IAM permissions enforce strict access control, while encryption with AWS KMS ensures sensitive customer data is protected, which is critical for regulatory compliance, including PCI DSS standards.

Option A, SQS with EC2 consumers, lacks the ability to allow multiple consumers to process the same messages concurrently and can introduce delays. Option C, SNS with a single SQS subscription, limits the ability to fan out events to multiple services efficiently. Option D, Amazon MQ with a single EC2 consumer, introduces a single point of failure and operational overhead, which is unsuitable for large-scale real-time order processing.

CloudWatch metrics monitor shard throughput, Lambda execution performance, and DynamoDB operations, providing visibility and operational control. CloudTrail logs all API calls and changes, supporting auditing and compliance requirements. By combining Kinesis, Lambda, and DynamoDB, the company achieves a robust, fault-tolerant, and scalable architecture capable of processing real-time customer orders efficiently, reliably, and securely.

Question 41:

A financial services organization needs a real-time analytics platform to process stock trades, detect anomalies, and provide insights to multiple downstream systems. The solution must scale automatically during high market activity, preserve event ordering, and ensure durability of all trade events. Which AWS services architecture should be used?

A) Amazon SQS Standard queues with multiple EC2 consumers
B) Amazon Kinesis Data Streams with multiple Lambda consumers and Amazon DynamoDB
C) Amazon SNS with a single SQS subscription and EC2 consumers
D) Amazon MQ with a single EC2 consumer

Answer:

B) Amazon Kinesis Data Streams with multiple Lambda consumers and Amazon DynamoDB

Explanation:

Real-time analytics for stock trades requires high throughput, fault tolerance, and low latency. Amazon Kinesis Data Streams supports ingestion of millions of trade events per second. Sharding allows parallel processing, while maintaining ordering within shards, which is crucial for financial systems where the sequence of trades impacts analytics, risk assessment, and compliance reporting.

AWS Lambda functions consume Kinesis streams and process trade data in real-time. Multiple Lambda functions can operate on the same stream simultaneously, enabling anomaly detection, fraud monitoring, risk assessment, and feeding multiple downstream systems like dashboards or automated trading platforms. Lambda’s serverless nature allows automatic scaling in response to market activity spikes, reducing operational overhead and ensuring uninterrupted processing.

Amazon DynamoDB is used for durable, low-latency storage of processed trades and analytics results. DynamoDB ensures consistency and availability across regions and provides fine-grained access control using IAM. Encryption with AWS KMS protects sensitive financial data, while point-in-time recovery and backup features ensure durability and regulatory compliance.

Option A, SQS with EC2 consumers, cannot efficiently allow multiple consumers to process the same messages concurrently, limiting parallelism and increasing latency. Option C, SNS with a single SQS subscription, limits event fan-out, which is critical for multiple downstream analytics consumers. Option D, Amazon MQ with a single EC2 consumer, introduces a single point of failure and higher operational complexity, making it unsuitable for high-frequency financial analytics.

CloudWatch provides operational visibility into shard utilization, Lambda execution metrics, and DynamoDB performance, enabling proactive management and scaling decisions. CloudTrail ensures a detailed audit trail for all actions on streams, Lambda, and databases, which is essential for compliance with financial regulations. This architecture ensures real-time analytics, fault tolerance, automatic scalability, and secure handling of sensitive trade data, supporting multiple downstream consumers efficiently while preserving event ordering and regulatory compliance.

Question 42:

A global media company needs to deliver video content with low latency, secure access, and regional compliance. The solution must provide caching at edge locations, dynamic content processing, and protection against web attacks. Which AWS services architecture is most appropriate?

Answer:

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

Explanation:

Delivering global video content requires low latency, security, and compliance with regional content regulations. Amazon CloudFront, a global CDN, caches content at edge locations worldwide, reducing latency and improving user experience. Edge caching minimizes the load on origin servers, ensuring scalability during high-traffic events like live streaming or new releases.

Amazon S3 serves as the origin, providing durable storage for video files with encryption at rest using AWS KMS and access control through IAM and bucket policies. Lifecycle management optimizes storage costs by transitioning less frequently accessed content to lower-cost tiers.

AWS Lambda@Edge enables dynamic content processing at CloudFront edge locations. Lambda@Edge can perform URL rewrites, authentication and authorization, and enforce regional content restrictions to meet licensing and regulatory requirements. Processing at the edge reduces latency and enhances compliance while providing a better user experience.

AWS WAF protects against web attacks such as SQL injection, cross-site scripting, and DDoS. Integrated with CloudFront, WAF evaluates incoming requests at the edge, preventing malicious traffic from reaching origin servers. CloudWatch and CloudTrail provide monitoring and auditing, ensuring operational visibility and security compliance.

Option B, EC2 multi-region with Route 53 failover, lacks edge caching, dynamic content processing, and integrated security features. Option C, S3 with public access and pre-signed URLs, does not provide edge caching, dynamic processing, or security enforcement. Option D, CloudFront without Lambda@Edge or WAF, improves latency but does not enforce security, dynamic processing, or compliance.

This architecture delivers low-latency, secure, and compliant media streaming globally. CloudFront edge caching improves performance, Lambda@Edge enables dynamic processing and regulatory compliance enforcement, and WAF protects against web-based threats. Together, these services provide a resilient, scalable, secure, and compliant solution suitable for global media streaming operations.

Question 43:

A global online retailer wants to build a real-time order processing system that can handle millions of events per second. The system must allow multiple downstream services to consume order events concurrently, ensure event durability, and scale automatically during peak traffic periods. Which AWS architecture is most appropriate?

Answer:

B) Amazon Kinesis Data Streams with multiple Lambda consumers and Amazon DynamoDB for storage

Explanation:

High-volume e-commerce order processing requires a system that can handle real-time data ingestion, processing, and storage while maintaining high availability and fault tolerance. Amazon Kinesis Data Streams is designed for real-time streaming, dividing streams into shards that allow multiple consumers to process data concurrently while preserving order within each shard. Preserving event order ensures accurate processing of customer orders, avoiding duplication or loss of critical information.

AWS Lambda functions consume Kinesis streams in parallel, enabling multiple services to process order events concurrently. Lambda automatically scales with incoming traffic, ensuring uninterrupted processing even during flash sales or seasonal spikes. This serverless architecture reduces operational complexity, as there is no need to manage or scale servers manually. Lambda functions can perform tasks such as inventory updates, payment validation, fraud detection, shipping coordination, and analytics processing, all in near real-time.

Amazon DynamoDB provides a low-latency, highly available storage layer to persist processed orders. DynamoDB automatically scales with workload fluctuations, ensures durability across multiple Availability Zones, and supports fine-grained access control using IAM. Data encryption using AWS KMS guarantees compliance with regulatory and data protection requirements, including PCI DSS for financial transactions.

Option A, SQS with EC2 consumers, does not efficiently support multiple concurrent consumers processing the same messages, limiting scalability and throughput. Option C, SNS with a single SQS subscription, restricts event fan-out and parallel processing capabilities. Option D, Amazon MQ with a single EC2 consumer, introduces a single point of failure and high operational overhead, making it unsuitable for high-throughput order processing.

Monitoring is achieved using CloudWatch metrics for Kinesis shard throughput, Lambda execution times, and DynamoDB performance. CloudTrail logs all API activity for auditing, providing visibility into system operations and supporting compliance reporting. This architecture delivers a fault-tolerant, scalable, and highly available system for real-time order processing, enabling multiple downstream services to consume events concurrently while ensuring durability, security, and operational efficiency.

Question 44:

A financial services firm wants to build a real-time analytics platform for trade processing. The system must process trades in near real-time, detect anomalies, scale automatically during high market activity, preserve trade ordering, and ensure durability. Which AWS services architecture is most suitable?

Answer:

B) Amazon Kinesis Data Streams with multiple Lambda consumers and Amazon DynamoDB

Explanation:

Real-time financial trade analytics requires high throughput, low latency, fault tolerance, and regulatory compliance. Amazon Kinesis Data Streams supports ingestion of massive volumes of trade events, dividing streams into shards for parallel processing while preserving event order, which is critical in financial transactions to maintain accuracy in reporting, risk assessment, and anomaly detection.

AWS Lambda functions consume Kinesis streams in parallel, processing each trade event in near real-time. Multiple Lambda consumers allow concurrent processing for tasks such as anomaly detection, risk scoring, compliance validation, and real-time dashboard updates. Lambda’s serverless nature enables automatic scaling based on stream volume, handling market spikes without operational overhead.

Amazon DynamoDB acts as a durable storage layer for processed trade data. DynamoDB offers low-latency access, on-demand scaling, fine-grained IAM permissions, encryption at rest with AWS KMS, and point-in-time recovery. These features ensure compliance with financial regulations and provide durability and security for sensitive trade information.

Option A, SQS with EC2 consumers, does not allow multiple consumers to process the same message efficiently, limiting scalability and latency performance. Option C, SNS with a single SQS subscription, constrains fan-out to multiple consumers, reducing parallel processing capability. Option D, Amazon MQ with a single EC2 consumer, introduces a single point of failure and operational complexity, making it unsuitable for high-frequency trading systems.

Monitoring and observability are achieved using CloudWatch metrics for shard throughput, Lambda execution metrics, and DynamoDB performance. CloudTrail logs all API actions for auditing purposes, supporting compliance with financial regulations. This architecture ensures real-time processing, low-latency analytics, fault tolerance, scalability, and secure handling of financial trades, supporting multiple downstream consumers while maintaining event ordering and regulatory compliance.

Question 45:

A media company wants to deliver video content globally with low latency, secure access, and regional content compliance. The solution must support caching at edge locations, dynamic content processing, and protection against web attacks. Which AWS services architecture is most appropriate?

Answer:

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

Explanation:

Global video content delivery requires low latency, security, and compliance with regional licensing and privacy regulations. Amazon CloudFront is a global CDN that caches content at edge locations worldwide, reducing latency for end-users and minimizing origin server load. This ensures smooth playback and scalability during peak demand events such as live streaming or new content releases.

Amazon S3 serves as the origin storage for video content. S3 offers durable storage with encryption at rest using AWS KMS, fine-grained IAM access control, and lifecycle policies for cost optimization. It supports large-scale storage of video files, metadata, and associated assets, ensuring high availability and durability.

AWS Lambda@Edge allows execution of code at CloudFront edge locations, enabling dynamic request processing such as URL rewriting, authentication, authorization, and regional content compliance enforcement. Lambda@Edge reduces latency by processing requests close to the user, ensuring secure access and adherence to licensing or regional content restrictions.

AWS WAF protects against web attacks such as SQL injection, cross-site scripting, and DDoS. Integrated with CloudFront, WAF evaluates requests at the edge before reaching the origin, mitigating threats and ensuring reliable content delivery. CloudWatch provides operational metrics and monitoring for CloudFront, Lambda@Edge, and WAF, while CloudTrail records all API activity for auditing and compliance.

Option B, EC2 multi-region with Route 53 failover, lacks caching at edge locations, dynamic content processing, and integrated security. Option C, S3 with public access and pre-signed URLs, does not provide edge caching, dynamic request processing, or security enforcement. Option D, CloudFront without Lambda@Edge or WAF, improves latency but does not provide security, dynamic processing, or compliance enforcement.

This architecture ensures low-latency, secure, and compliant global media delivery. CloudFront edge caching improves performance, Lambda@Edge enables dynamic processing and regional compliance, and WAF protects against web threats. Together, these services deliver a resilient, scalable, secure, and globally compliant video streaming solution.

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