Amazon AWS Certified Solutions Architect – Professional SAP-C02 Exam Dumps and Practice Test Questions Set 5 Q61-75

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Question 61:

A global e-commerce company wants to implement a highly available, event-driven payment processing system. The system must process payment events in real-time, allow multiple services to consume the same events for analytics, fraud detection, and accounting, and scale automatically during peak shopping 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
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:

For a payment processing system, real-time ingestion, order preservation, high availability, and scalability are critical. Amazon Kinesis Data Streams provides a managed streaming service that can capture and process millions of events per second. Its shard-based architecture enables parallel processing while preserving the sequence of events, which is vital to ensure transaction accuracy, prevent double billing, and maintain financial integrity.

Multiple AWS Lambda functions can process the same Kinesis stream concurrently. For example, one Lambda can update the payment ledger, another can perform fraud detection, while a third triggers notifications to customers. Lambda scales automatically with the event volume, ensuring seamless handling during peak shopping events like Black Friday or holiday sales, eliminating the need for manual server management.

Amazon DynamoDB provides a secure, low-latency, and highly available storage solution for processed payments. On-demand scaling handles variable workloads efficiently, while encryption at rest using AWS KMS secures sensitive payment data. Fine-grained IAM policies allow access control, ensuring only authorized services and personnel can access transaction records. Point-in-time recovery and cross-AZ replication enhance durability and fault tolerance.

Option A, SQS with EC2 consumers, does not support multiple consumers efficiently and can introduce delays or ordering issues. Option C, SNS with a single SQS subscription, restricts concurrent consumer processing and is insufficient for multi-service ingestion. Option D, Amazon MQ with a single EC2 consumer, introduces a single point of failure and requires operational overhead, making it unsuitable for high-volume real-time payment processing.

Monitoring and observability are essential. CloudWatch provides metrics for Kinesis shard throughput, Lambda execution duration, and DynamoDB performance. CloudTrail records all API activity for auditing and compliance. This architecture ensures a fault-tolerant, secure, real-time, and scalable payment processing system capable of supporting multiple services and high transaction volumes.

Question 62:

A financial services firm wants to implement a real-time risk analysis system for trades. The system must process trade events as they occur, maintain order, allow multiple services to analyze the same events, and automatically scale during market volatility. Which AWS architecture is appropriate?

Answer:

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

Explanation:

Financial risk analysis systems require real-time event processing, order preservation, and multi-consumer support. Amazon Kinesis Data Streams provides a scalable streaming service with shard-based partitioning, which preserves event order within each shard. This ensures trades are analyzed in sequence, critical for accurate risk assessment and regulatory compliance.

Multiple Lambda functions can simultaneously consume the same Kinesis stream, enabling separate workflows for risk scoring, compliance monitoring, anomaly detection, and reporting. Lambda automatically scales with the volume of trade events, ensuring uninterrupted processing during market spikes or periods of high trading activity, without manual intervention.

Amazon DynamoDB acts as a low-latency, highly available storage backend for processed trade data. DynamoDB supports on-demand scaling, cross-AZ replication, and point-in-time recovery, providing durability and resilience. Encryption at rest via AWS KMS protects sensitive financial data, and IAM policies enforce secure access control. These features ensure compliance with financial regulations and internal security policies.

Option A, SQS with EC2 consumers, does not guarantee ordered delivery for multiple consumers and may introduce latency. Option C, SNS with a single SQS subscription, restricts concurrent processing and fan-out, making it unsuitable for multiple analytical services. Option D, Amazon MQ with a single EC2 consumer, introduces operational complexity and a single point of failure, reducing reliability and scalability.

Observability is key for financial systems. CloudWatch metrics monitor Kinesis throughput, Lambda execution, and DynamoDB performance, while CloudTrail provides audit logs. This architecture provides real-time, scalable, secure, and fault-tolerant trade processing, enabling accurate and compliant risk analysis across multiple analytical services.

Question 63:

A media company wants to deliver video streaming content globally with low latency, secure access, dynamic content personalization at the edge, and protection against attacks. The solution must comply with regional restrictions and support large concurrent viewers. Which AWS architecture is optimal?

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 video streaming requires low latency, secure access, and the ability to personalize content dynamically. Amazon CloudFront is a global CDN that caches content at edge locations, reducing latency and improving the viewer experience. It also helps offload traffic from the origin, ensuring scalability during peak usage periods.

Amazon S3 serves as a secure, durable origin for media content. Encryption at rest via AWS KMS protects content, while fine-grained IAM policies control access. S3 lifecycle policies optimize storage costs by managing infrequently accessed content and archival media.

AWS Lambda@Edge enables execution of custom code at CloudFront edge locations. This allows dynamic content personalization, authentication, regional compliance enforcement, and A/B testing at the edge. Lambda@Edge processing reduces latency because logic executes close to end-users, enhancing the experience for global audiences.

AWS WAF integrates with CloudFront to protect against web-based attacks, including SQL injection, cross-site scripting, and DDoS. WAF rules inspect requests at the edge before reaching the origin, providing security while minimizing latency. CloudWatch monitors CloudFront, Lambda@Edge, and WAF metrics, while CloudTrail captures API activity for auditing and compliance.

Option B, EC2 in multiple regions with Route 53 failover, lacks edge caching and dynamic content processing and adds operational complexity. Option C, S3 with public access and pre-signed URLs, does not support edge caching, dynamic personalization, or WAF security. Option D, CloudFront without Lambda@Edge or WAF, reduces latency but does not provide content customization or robust security.

This architecture ensures low-latency delivery, secure global access, edge personalization, regional compliance, and protection against attacks. CloudFront, Lambda@Edge, and WAF together create a scalable, resilient, and secure video streaming solution capable of serving large audiences worldwide.

Question 64:

A global e-commerce company is designing an event-driven order processing system. The system must process high volumes of order events in real-time, maintain order per customer, allow multiple services such as billing, inventory, and notifications to consume the same events concurrently, and scale automatically during peak shopping seasons. Which AWS architecture best meets these requirements?

Answer:

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

Explanation:

An event-driven order processing system requires real-time event ingestion, order preservation, multiple concurrent consumers, and scalable infrastructure. Amazon Kinesis Data Streams is a fully managed streaming service that supports real-time data ingestion, partitioned into shards that allow parallel processing while maintaining order within each shard. This ensures that all events from a specific customer or order are processed sequentially, which is critical for financial integrity and accurate inventory management.

Multiple AWS Lambda functions can consume the same Kinesis stream, enabling multiple services to process the same event concurrently. For example, one Lambda can update inventory in DynamoDB, another can process billing transactions, and a third can send notifications to the customer. Lambda’s serverless nature ensures automatic scaling with event volume, allowing the system to handle peak loads such as Black Friday sales without manual provisioning of servers.

Amazon DynamoDB provides low-latency, highly available, and durable storage for processed events. It supports on-demand scaling, which allows the system to handle sudden spikes in order volume. DynamoDB’s encryption at rest using AWS KMS protects sensitive customer data, and IAM policies enforce fine-grained access control for multiple services. Point-in-time recovery and cross-AZ replication ensure resilience against failures and data loss.

Option A, SQS with EC2 consumers, does not efficiently support multiple concurrent consumers for the same message and may result in processing delays or duplicated work. Option C, SNS with a single SQS subscription, limits fan-out and cannot support multiple concurrent processing efficiently. Option D, Amazon MQ with a single EC2 consumer, introduces a single point of failure and operational overhead, making it unsuitable for high-volume, globally distributed order processing.

Monitoring and observability are essential for this architecture. Amazon CloudWatch provides metrics for Kinesis shard throughput, Lambda execution duration, and DynamoDB performance. AWS CloudTrail logs API activity for auditing and compliance. This architecture provides a highly available, fault-tolerant, secure, and scalable solution for real-time order processing with multiple concurrent consumers, meeting the business requirement for global e-commerce operations.

Question 65:

A financial trading firm needs to implement a real-time trade risk analysis system. The system must maintain the order of trades, allow multiple services to perform independent analyses such as risk scoring, compliance checks, and anomaly detection, and scale automatically during market spikes. Which AWS architecture fulfills these requirements?

Answer:

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

Explanation:

Real-time trade risk analysis requires low-latency event processing, preservation of order, concurrent multi-consumer processing, and scalability. Amazon Kinesis Data Streams allows high-throughput ingestion of trade events, partitioned into shards to preserve order for trades from the same source or trading account. Maintaining order is critical to ensure accurate risk scoring and compliance reporting, as processing trades out of sequence could result in financial inconsistencies or regulatory violations.

Multiple Lambda functions can subscribe to the same Kinesis stream, enabling concurrent workflows for risk scoring, compliance verification, anomaly detection, and reporting. Lambda’s serverless architecture allows automatic scaling, ensuring consistent performance even during periods of market volatility, such as sudden spikes in trading activity. This eliminates the need for manual server management and ensures high availability.

Amazon DynamoDB serves as a low-latency, durable storage backend for processed trade data. On-demand scaling ensures the system can handle surges in trade volume, while cross-AZ replication and point-in-time recovery provide resilience against failures. Encryption at rest via AWS KMS secures sensitive financial data, and IAM policies allow fine-grained access for different analysis services, ensuring compliance with internal and external regulations.

Option A, SQS with EC2 consumers, cannot guarantee message order for multiple consumers and may introduce latency or inconsistent processing. Option C, SNS with a single SQS subscription, restricts concurrency and fan-out capabilities, making it unsuitable for multiple analytical workflows. Option D, Amazon MQ with a single EC2 consumer, adds operational complexity and a single point of failure, reducing reliability in high-volume, low-latency trading environments.

Observability is critical in financial environments. CloudWatch monitors Kinesis shard throughput, Lambda execution metrics, and DynamoDB performance, while CloudTrail logs all API activity for auditing and compliance. This architecture delivers a highly scalable, secure, real-time, and fault-tolerant solution for trade risk analysis, enabling multiple independent analytical services to operate on the same event stream while maintaining data integrity and regulatory compliance.

Question 66:

A media company wants to provide global video streaming with low latency, secure access, dynamic personalization at the edge, and protection against web-based attacks. The solution must comply with regional content restrictions and handle high concurrent viewer loads. Which AWS architecture is optimal?

Answer:

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

Explanation:

Delivering video globally with low latency requires edge caching and dynamic content processing close to end-users. Amazon CloudFront is a global CDN that caches video content at edge locations, reducing latency and improving user experience while offloading traffic from the origin. CloudFront also automatically scales to handle high concurrent viewers without additional operational overhead.

Amazon S3 provides secure, highly durable storage for media content. Encryption at rest using AWS KMS and fine-grained IAM policies protect content from unauthorized access. S3 lifecycle management allows cost optimization by archiving or deleting infrequently accessed content.

AWS Lambda@Edge enables dynamic request processing at CloudFront edge locations. This allows custom authentication, content personalization, A/B testing, and enforcement of regional compliance rules directly at the edge, minimizing latency and improving user experience for global audiences. Lambda@Edge ensures that users in different geographic regions receive content optimized for their location while enforcing content restrictions as needed.

AWS WAF integrates with CloudFront to provide protection against web-based attacks such as SQL injection, cross-site scripting, and DDoS. Rules are applied at edge locations before requests reach the origin, providing security without impacting performance. CloudWatch metrics track CloudFront cache hit ratios, Lambda@Edge execution times, and WAF rule evaluations, while CloudTrail logs all API activity for auditing and compliance.

Option B, EC2 in multiple regions with Route 53 failover, lacks edge caching, dynamic processing, and integrated security, adding operational complexity. Option C, S3 with public access and pre-signed URLs, does not provide caching, edge personalization, or WAF security. Option D, CloudFront without Lambda@Edge or WAF, reduces latency but lacks dynamic personalization, compliance enforcement, and protection against attacks.

This architecture ensures low-latency delivery, secure access, dynamic personalization, compliance with regional regulations, and protection against cyber threats. CloudFront edge caching, Lambda@Edge dynamic processing, and WAF protection create a scalable, resilient, and secure video streaming solution capable of serving millions of users worldwide.

Question 67:

A multinational retail company wants to migrate its legacy on-premises relational database workloads to AWS. The workloads include high-volume transactional systems and reporting systems. The solution must minimize downtime, ensure high availability across multiple regions, and support both read-heavy and write-heavy operations. Which AWS service combination best meets these requirements?

A) Amazon RDS Multi-AZ deployments with Read Replicas and Amazon S3 for reporting
B) Amazon Aurora Global Database with Aurora Replicas in multiple regions
C) Amazon DynamoDB with global tables and Lambda functions for reporting
D) Amazon Redshift with cross-region snapshots

Answer:

B) Amazon Aurora Global Database with Aurora Replicas in multiple regions

Explanation:

Migrating high-volume relational database workloads requires a solution that balances low-latency writes, high availability, read scalability, and minimal downtime. Amazon Aurora, a fully managed MySQL- and PostgreSQL-compatible relational database, is designed for high-performance transactional systems. Aurora Global Database extends this capability by providing a single database with up to five read-only secondary regions. This enables global low-latency reads and disaster recovery while maintaining a primary writable region.

Aurora’s architecture separates compute and storage. The storage layer is distributed across multiple Availability Zones, providing high durability and availability. Automated backups, point-in-time recovery, and continuous replication ensure minimal data loss and rapid recovery in case of failure. Aurora Global Database replicates data asynchronously to secondary regions, allowing regional read replicas to handle heavy read workloads without affecting the primary region’s write performance.

For reporting and analytical workloads, Aurora Replicas or Amazon Redshift can be used. Aurora Replicas can handle read-heavy operations and offload read traffic from the primary instance, enabling operational queries and reporting without impacting transactional performance. Aurora also integrates with AWS Lambda and Amazon S3 for ETL processes or complex reporting scenarios, providing flexibility for hybrid transactional-analytical workloads.

Option A, RDS Multi-AZ deployments with Read Replicas, supports high availability within a region but does not natively support low-latency global reads, which is critical for multinational operations. Option C, DynamoDB with global tables, is suitable for key-value workloads but lacks the relational database capabilities required for complex transactional operations and joins. Option D, Redshift, is optimized for analytics and not for transactional workloads, making it unsuitable for high-volume operational systems.

By implementing Aurora Global Database, the retail company gains high availability, low-latency global reads, automatic failover, and scalability for both transactional and reporting workloads. Aurora’s serverless options and integration with CloudWatch, IAM, and KMS ensure secure, cost-efficient, and observable operations, enabling smooth migration from on-premises systems with minimal disruption and risk.

Question 68:

A global travel company needs to build a recommendation system for its website. The system should personalize content based on user behavior, allow experimentation with different recommendation strategies, and scale automatically as the number of visitors increases. Which AWS services should be used to build this solution?

A) Amazon Personalize with Amazon S3, Lambda, and API Gateway
B) Amazon SageMaker for model training with EC2 instances for inference
C) Amazon Rekognition with S3 and CloudFront for serving recommendations
D) Amazon Comprehend with DynamoDB for storing user behavior

Answer:

A) Amazon Personalize with Amazon S3, Lambda, and API Gateway

Explanation:

Building a scalable, personalized recommendation system requires real-time behavior tracking, model training, and delivery of recommendations to end-users. Amazon Personalize is a fully managed service that enables developers to build individualized recommendations using machine learning without requiring deep expertise in ML algorithms. Personalize ingests historical and real-time user behavior data from sources such as Amazon S3 and updates models automatically to provide personalized content for each user.

Amazon S3 serves as a durable storage layer for historical interaction data, user profiles, and item metadata. Lambda functions can transform incoming real-time behavior events (e.g., clicks, views, purchases) and feed them into Amazon Personalize, ensuring that recommendation models remain up-to-date with the latest user activity. API Gateway provides secure, scalable endpoints for delivering recommendations to the website or mobile application.

Personalize supports multiple use cases such as personalized ranking, user segmentation, and recommendations based on contextual metadata (location, time, device). This allows experimentation with different recommendation strategies by creating multiple campaigns and comparing their performance through metrics like click-through rate and conversion rate. Lambda’s serverless execution ensures automatic scaling to handle traffic spikes during holiday seasons or promotional events.

Option B, SageMaker with EC2 inference, is suitable for custom ML model development but requires significant effort for feature engineering, model training, and deployment, making it less optimal for rapid experimentation and real-time personalization. Option C, Rekognition, is designed for image and video analysis and does not support behavioral recommendation use cases. Option D, Comprehend with DynamoDB, enables NLP tasks but does not provide recommendation engine capabilities.

Using Amazon Personalize with S3, Lambda, and API Gateway provides a fully managed, secure, and scalable recommendation system. The architecture allows continuous learning from user interactions, supports multi-region deployment for low latency, and ensures compliance with data protection standards. Observability can be maintained with CloudWatch metrics for Lambda invocations, Personalize campaign metrics, and API Gateway request logs, ensuring performance monitoring, troubleshooting, and iterative improvement of recommendation strategies.

Question 69:

A healthcare organization is designing a data lake for storing structured and unstructured medical data from multiple hospitals. The solution must ensure secure access, compliance with HIPAA, and support analytics and machine learning workloads. Which AWS architecture meets these requirements?

A) Amazon S3 with AWS Lake Formation, AWS Glue, IAM, and Amazon Athena
B) Amazon RDS Multi-AZ for structured data and S3 for unstructured data
C) Amazon Redshift for all data storage and analytics
D) Amazon DynamoDB for structured data and S3 for unstructured files

Answer:

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

Explanation:

Building a secure, compliant, and scalable data lake for healthcare requires centralized storage, fine-grained access control, metadata management, and support for analytics and ML workloads. Amazon S3 provides durable, highly available storage for structured, semi-structured, and unstructured medical data, supporting encryption at rest with KMS and in transit via SSL/TLS, critical for HIPAA compliance.

AWS Lake Formation simplifies the creation and management of secure data lakes. It enables centralized control over data access with fine-grained policies, auditing, and integration with IAM. Lake Formation ensures that only authorized users or applications can access sensitive patient records, supporting compliance with healthcare regulations.

AWS Glue catalogs and prepares data for analytics and ML workloads. Glue Crawlers can automatically infer schema for structured and semi-structured data, while ETL jobs clean, normalize, and transform data for downstream processing. Amazon Athena provides serverless SQL querying directly on S3 data, enabling analysts to run ad-hoc queries efficiently without moving large datasets.

The architecture supports ML workloads by integrating with Amazon SageMaker. ML models can consume data from the data lake for predictive analytics, such as patient risk scoring, disease trend prediction, or operational optimization. Lake Formation ensures security and compliance throughout ML pipelines, tracking access and usage for audit purposes.

Option B, RDS for structured data and S3 for unstructured data, lacks centralized access control and metadata management for large-scale analytics. Option C, Redshift for all data, is not optimized for unstructured data and can be cost-prohibitive for massive datasets. Option D, DynamoDB for structured data, lacks relational and analytical query capabilities needed for complex healthcare analysis.

This solution provides a highly scalable, secure, and compliant data lake architecture. Fine-grained access control, encryption, and logging support HIPAA requirements. Serverless analytics with Athena, ETL with Glue, and ML integration with SageMaker enable powerful insights without compromising security or compliance, allowing the healthcare organization to leverage its data for operational, clinical, and research purposes efficiently.

Question 70:

A media streaming company wants to deliver high-definition video content globally. The solution must reduce latency, scale automatically during peak demand, and provide content security with geo-restriction capabilities. Which AWS architecture is most appropriate?

A) Amazon CloudFront with S3 Origin and Lambda@Edge for access control
B) Amazon S3 with cross-region replication and EC2 instances for distribution
C) Amazon API Gateway with S3 for storage and CloudWatch for monitoring
D) Amazon Elastic Load Balancer with EC2 Auto Scaling for streaming

Answer:

A) Amazon CloudFront with S3 Origin and Lambda@Edge for access control

Explanation:

Delivering high-definition media globally requires a content delivery network (CDN) to reduce latency and improve performance for end-users. Amazon CloudFront is a highly optimized CDN that caches content at edge locations worldwide, ensuring users receive content from the closest point, minimizing latency and enhancing streaming quality.

Using Amazon S3 as the origin storage provides a durable, highly available repository for video files. CloudFront automatically scales to accommodate spikes in traffic during peak streaming times, eliminating the need for manual intervention or over-provisioning infrastructure. The combination of CloudFront and S3 ensures seamless scalability while maintaining cost efficiency.

Security is critical for media content, particularly for licensing agreements and geo-restricted content. Lambda@Edge allows for dynamic access control, enabling authentication, token validation, or URL signing at edge locations, preventing unauthorized access and enforcing geo-restrictions. Additionally, CloudFront integrates with AWS WAF and Shield to protect against DDoS attacks and common web exploits, safeguarding media delivery without degrading performance.

Option B, S3 with cross-region replication and EC2 distribution, does not offer low-latency global delivery inherently and requires custom mechanisms to manage edge caching, which increases operational complexity. Option C, API Gateway with S3, is designed for API-driven workloads rather than media streaming and cannot handle large-scale video delivery efficiently. Option D, ELB with EC2 Auto Scaling, is suitable for dynamic web applications but cannot optimize global latency or provide the advanced caching capabilities of a CDN.

Implementing CloudFront with S3 and Lambda@Edge ensures a fully managed, scalable, secure, and high-performance solution. It allows global viewers to access high-definition content quickly, supports dynamic authentication workflows, and automatically scales with demand. Observability is facilitated with CloudWatch metrics, enabling tracking of cache hit ratios, latency, and request patterns, providing insights into user behavior and streaming performance, which can inform operational improvements and cost optimizations.

Question 71:

A financial institution needs a secure environment to store sensitive transaction data and ensure compliance with PCI DSS. The institution wants encryption at rest, audit logging, and fine-grained access control. Which AWS services and features should be combined to meet these requirements?

A) Amazon S3 with Server-Side Encryption, AWS CloudTrail, and IAM policies
B) Amazon RDS Multi-AZ with IAM authentication and CloudWatch alarms
C) Amazon Redshift with VPC endpoints and KMS-managed keys
D) Amazon DynamoDB with SSE, CloudTrail, and Cognito authentication

Answer:

A) Amazon S3 with Server-Side Encryption, AWS CloudTrail, and IAM policies

Explanation:

Storing sensitive transaction data for PCI DSS compliance requires encryption, auditability, and controlled access. Amazon S3 provides a highly durable, scalable storage solution with built-in options for server-side encryption (SSE) using AWS Key Management Service (KMS) keys or S3-managed keys, ensuring that all data at rest is encrypted to meet regulatory requirements. SSE-KMS offers additional controls for key rotation, access policies, and auditing of key usage, supporting strict compliance standards.

Fine-grained access control is essential to limit data exposure only to authorized users and services. IAM policies allow defining least-privilege access, specifying which users or roles can read, write, or manage objects in S3 buckets. This granular control ensures that sensitive data is only accessible by authorized personnel, reducing the risk of data breaches.

Audit logging is another critical requirement for compliance. AWS CloudTrail captures all API activity within the AWS account, including S3 object-level actions if enabled. This enables monitoring and auditing of all access and modification events, providing a detailed history for internal audits or regulatory review. CloudTrail logs can be stored in a secure, immutable S3 bucket and integrated with Amazon Athena for querying or automated alerting through Amazon CloudWatch.

Option B, RDS Multi-AZ with IAM authentication, provides strong security for relational databases but may not cover unstructured data or provide object-level audit logging natively without additional configuration. Option C, Redshift, is optimized for analytics but requires careful configuration for PCI compliance and may not natively support object-level logging across all data types. Option D, DynamoDB with SSE, covers encryption but lacks the detailed auditing capabilities and broad integration with compliance monitoring tools offered by S3 with CloudTrail.

By combining S3, SSE, IAM policies, and CloudTrail, the financial institution can meet PCI DSS requirements for encryption, access control, and audit logging. This architecture also supports scalability, durability, and operational efficiency. Security features such as bucket policies, MFA Delete, and logging ensure that sensitive transaction data remains protected, and organizations can demonstrate regulatory compliance with minimal operational overhead.

Question 72:

A global e-commerce company wants to implement a high-availability, multi-region architecture for its application to minimize downtime and improve latency for users worldwide. The architecture must support automatic failover and seamless data replication for transactional workloads. Which AWS solution is most suitable?

A) Amazon Route 53 with health checks, Amazon Aurora Global Database, and multi-region VPC
B) Amazon ELB with EC2 Auto Scaling across multiple Availability Zones
C) Amazon CloudFront with S3 static content and Lambda@Edge for dynamic requests
D) Amazon RDS Multi-AZ with Read Replicas in a single region

Answer:

A) Amazon Route 53 with health checks, Amazon Aurora Global Database, and multi-region VPC

Explanation:

For a global e-commerce application, achieving low latency, high availability, and disaster recovery is critical. Amazon Aurora Global Database is designed for globally distributed, transactional workloads. It allows one writable primary region and up to five read-only secondary regions, providing low-latency reads worldwide while supporting rapid recovery from regional failures. Aurora separates compute and storage, ensuring continuous availability with minimal downtime, and provides automated backups, point-in-time recovery, and replication for disaster recovery.

Amazon Route 53, the AWS DNS service, enables intelligent routing and automatic failover between primary and secondary regions based on health checks. It monitors application endpoints and automatically reroutes traffic to healthy regions in case of failures, ensuring continuity of service without manual intervention. Using a multi-region VPC setup allows network-level isolation and routing control for resources deployed across multiple AWS regions.

Option B, ELB with Auto Scaling across multiple Availability Zones, ensures high availability within a single region but does not address multi-region failover or global latency optimization. Option C, CloudFront with S3 and Lambda@Edge, is optimal for static and dynamic content delivery but does not support transactional database workloads. Option D, RDS Multi-AZ with Read Replicas in a single region, provides high availability but is limited to one region and cannot achieve low-latency access for global users.

This architecture allows seamless failover, rapid scaling, and disaster recovery across regions. Aurora Global Database provides strong consistency and low-latency reads, while Route 53 ensures users are routed to the nearest healthy region. Integration with CloudWatch, CloudTrail, and KMS ensures observability, auditing, and encryption for secure operations. The approach balances performance, reliability, compliance, and operational efficiency, supporting the global e-commerce company in delivering a robust, highly available experience to users worldwide.

Question 73:

A company is running a large-scale analytics workload using Amazon EMR with Hadoop. They need to ensure that data is highly available, encrypted at rest and in transit, and that sensitive data can be masked for analytics purposes. Which AWS approach best satisfies these requirements?

A) Store raw data in S3 with SSE-KMS, enable EMR encryption in-transit, and use Apache Ranger for data masking
B) Store data in EBS volumes attached to EMR instances and enable EBS encryption
C) Use DynamoDB as a storage layer with SSE enabled and EMR for processing
D) Store data in S3 without encryption and rely on EMR to encrypt temporary files

Answer:

A) Store raw data in S3 with SSE-KMS, enable EMR encryption in-transit, and use Apache Ranger for data masking

Explanation:

Analytics workloads in large-scale environments like Hadoop running on Amazon EMR require a combination of availability, encryption, and compliance controls. Using Amazon S3 as the primary data lake ensures durability, high availability, and cost-effective storage. S3 provides 99.999999999% durability and integrates natively with EMR, making it the optimal choice for storing raw, processed, and intermediate data.

For sensitive data, encryption at rest is essential. Server-Side Encryption with AWS Key Management Service (SSE-KMS) ensures that all data stored in S3 is encrypted using customer-managed keys. KMS allows fine-grained key policies, rotation, and audit logging of key usage, supporting compliance frameworks such as HIPAA, PCI DSS, and GDPR. EMR supports encryption in-transit using TLS, protecting data while it moves between HDFS, S3, and other EMR components.

Data masking is necessary when sensitive information is analyzed without exposing personally identifiable information (PII) or confidential data. Apache Ranger can be used within EMR to define policies for row-level and column-level access control, masking sensitive data during analytics processing. This approach allows authorized analysts to perform queries while safeguarding sensitive fields.

Option B, using EBS for storage, is limited to instance-level storage and lacks the durability, cost efficiency, and global availability of S3. EBS encryption protects data at rest but does not provide cross-cluster accessibility or integration with S3 for long-term storage. Option C, DynamoDB with SSE, is suitable for structured key-value storage but does not support large-scale Hadoop analytics workloads efficiently. Option D, unencrypted S3 with EMR encrypting temporary files, risks compliance violations and potential exposure of sensitive data.

By combining S3 with SSE-KMS, EMR encryption in-transit, and Apache Ranger for masking, this architecture ensures high availability, security, and compliance. Additionally, CloudTrail can monitor access, CloudWatch can track performance, and AWS Glue can catalog data, supporting an end-to-end analytics pipeline that is secure, scalable, and compliant with regulatory requirements.

Question 74:

A healthcare organization wants to deploy a secure, highly available API backend for patient records. The system must enforce authentication, authorization, logging, and protect against DDoS attacks. Which architecture best meets these requirements?

A) Amazon API Gateway with AWS Lambda, IAM roles for access, AWS WAF, and CloudWatch logging
B) Amazon EC2 instances behind an Application Load Balancer with Security Groups
C) Amazon S3 hosting static JSON APIs with CloudFront
D) Amazon RDS Multi-AZ deployment with public endpoints and VPC peering

Answer:

A) Amazon API Gateway with AWS Lambda, IAM roles for access, AWS WAF, and CloudWatch logging

Explanation:

For healthcare applications, security, compliance, and availability are critical. Amazon API Gateway provides a fully managed API endpoint that scales automatically, integrates natively with AWS Lambda for serverless backends, and supports multiple authorization mechanisms including IAM roles, Lambda authorizers, and Amazon Cognito. This enables fine-grained authentication and authorization, ensuring only authorized users can access sensitive patient data.

AWS WAF protects APIs against common web exploits and DDoS attacks. You can define rules to filter malicious traffic, block IP ranges, or throttle requests, ensuring availability during traffic spikes or potential attacks. API Gateway integrates with CloudWatch for logging all API requests, enabling auditing, monitoring, and operational troubleshooting. These logs are crucial for HIPAA compliance and internal auditing processes.

Option B, EC2 behind ALB, requires manual scaling, patching, and managing operating system security, which increases operational overhead. Option C, static JSON APIs in S3, cannot support dynamic patient data access or enforce complex authorization rules. Option D, RDS Multi-AZ with public endpoints, exposes sensitive data directly and lacks integrated API-level security, increasing compliance risk.

Combining API Gateway, Lambda, IAM, WAF, and CloudWatch enables a fully managed, secure, and scalable architecture. TLS encryption in transit ensures data confidentiality, CloudTrail provides API-level auditing, and serverless Lambda functions reduce the attack surface. This architecture ensures high availability, dynamic scaling, robust security, and operational visibility, all of which are crucial for managing sensitive healthcare data under stringent regulatory compliance frameworks.

Question 75:

A logistics company wants to process real-time IoT sensor data from vehicles and trigger alerts for maintenance anomalies. The solution should scale automatically, process data streams in real-time, and integrate with machine learning models for predictive maintenance. Which architecture is most suitable?

A) Amazon Kinesis Data Streams with AWS Lambda for real-time processing and Amazon SageMaker for ML inference
B) Amazon S3 batch uploads from vehicles with scheduled EMR jobs
C) Amazon MQ with EC2 consumers and manual scaling
D) Amazon DynamoDB Streams with Step Functions for alerting

Answer:

A) Amazon Kinesis Data Streams with AWS Lambda for real-time processing and Amazon SageMaker for ML inference

Explanation:

Real-time IoT data processing requires a streaming architecture that can handle continuous, high-throughput data ingestion and trigger immediate processing workflows. Amazon Kinesis Data Streams provides a fully managed, highly scalable data streaming platform capable of ingesting large volumes of sensor data from vehicles in real-time. It automatically scales to accommodate variable traffic and ensures data is durable and available for downstream processing.

AWS Lambda functions can be triggered by Kinesis Data Streams for real-time processing of each record. Lambda functions allow transformation, filtering, and routing of data without managing servers, reducing operational complexity. For predictive maintenance, processed data can be sent to Amazon SageMaker endpoints where trained machine learning models infer potential maintenance anomalies or failures. SageMaker provides real-time inference capabilities and can scale automatically to accommodate the volume of requests, ensuring alerts are delivered promptly.

Option B, S3 batch uploads with EMR, introduces latency and is unsuitable for real-time anomaly detection. Option C, Amazon MQ with EC2 consumers, requires manual scaling and does not provide the native serverless, real-time processing capabilities necessary for high-volume IoT workloads. Option D, DynamoDB Streams with Step Functions, is better suited for asynchronous workflows or transactional updates rather than high-throughput, low-latency sensor data streams.

This architecture enables a robust, end-to-end solution for predictive maintenance. Data is ingested, processed, and analyzed in real-time, ensuring immediate detection of anomalies. CloudWatch monitors performance and Lambda execution, CloudTrail logs activity for auditing, and IAM enforces secure access. The integration of Kinesis, Lambda, and SageMaker supports a cost-effective, scalable, and compliant system for mission-critical IoT operations.

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