Amazon AWS Certified Solutions Architect — Professional SAP-C02 Exam Dumps and Practice Test Questions Set 14 Q196-210
Visit here for our full Amazon AWS Certified Solutions Architect — Professional SAP-C02 exam dumps and practice test questions.
Question 196
A company wants to store infrequently accessed backup data cost-effectively but with fast retrieval when needed. Which AWS service is most suitable?
A) S3 Glacier Instant Retrieval
B) S3 Standard
C) EBS gp3
D) EFS Standard
Answer: A) S3 Glacier Instant Retrieval
Explanation:
S3 Standard is optimized for frequently accessed data and is more expensive for infrequently accessed backups. EBS gp3 provides block storage for EC2 instances and is not intended for cost-effective archival. EFS Standard is a managed file system designed for active workloads, making it expensive for infrequent access. S3 Glacier Instant Retrieval provides low-cost archival storage with millisecond retrieval capability. It integrates with lifecycle policies for automatic transition from S3 Standard or Intelligent-Tiering. Glacier Instant Retrieval offers 11 nines of durability, encryption at rest using SSE-S3 or SSE-KMS, and audit logging via CloudTrail. This service is ideal for backups, compliance archives, and disaster recovery datasets that require occasional fast retrieval while keeping storage costs minimal.
Question 197
A company wants to migrate an on-premises Oracle database to AWS with minimal downtime. Which approach is most suitable?
A) RDS Oracle with AWS DMS continuous replication
B) EC2 Oracle with manual backup/restore
C) Aurora PostgreSQL
D) DynamoDB
Answer: A) RDS Oracle with AWS DMS continuous replication
Explanation:
EC2 Oracle with manual backup/restore requires extended downtime and is operationally intensive. Aurora PostgreSQL is not Oracle-compatible and would require significant schema and application changes. DynamoDB is a NoSQL service and cannot host Oracle workloads. RDS Oracle with AWS DMS enables near real-time replication from the source database while keeping it operational. DMS ensures data integrity and minimal downtime. RDS provides automated backups, Multi-AZ deployment, patching, and maintenance. This approach ensures a smooth, low-downtime migration for production Oracle workloads while reducing operational complexity and ensuring high availability and reliability.
Question 198
A company wants to automatically stop EC2 instances in non-production environments outside business hours to reduce costs. Which AWS service is most suitable?
A) Systems Manager Automation with a cron schedule
B) Auto Scaling scheduled actions only
C) Manual stopping of instances
D) Spot Instances only
Answer: A) Systems Manager Automation with a cron schedule
Explanation:
Auto Scaling scheduled actions are primarily for production workloads and do not address stopping non-production instances. Manual stopping is prone to human error and requires constant attention. Spot Instances reduce costs for interruptible workloads but do not allow scheduled automation. Systems Manager Automation allows creation of automated runbooks to start or stop EC2 instances on a defined cron schedule. It ensures consistent execution, reduces operational overhead, and provides auditing for compliance. This solution is efficient for multiple non-production environments, optimizing costs without affecting availability during working hours, and provides full automation for cost management.
Question 199
A company wants to implement a global web application with low latency for static and dynamic content. Which architecture is most suitable?
A) CloudFront with S3 for static content and ALB for dynamic content
B) S3 only
C) EC2 with ALB only
D) Route 53 only
Answer: A) CloudFront with S3 for static content and ALB for dynamic content
Explanation:
S3 alone can serve static content but cannot cache or deliver dynamic content efficiently. EC2 with ALB provides high availability in a single region, increasing latency for global users. Route 53 handles DNS but does not deliver or cache content. CloudFront is a global CDN that caches static content at edge locations, reducing latency for worldwide users. Combining CloudFront with S3 for static content and ALB for dynamic content ensures fast, secure, and highly available delivery. CloudFront integrates with WAF, SSL/TLS, and origin failover, providing security, resilience, and performance optimization for global applications.
Question 200
A company wants to implement serverless, event-driven processing for files uploaded to S3 and messages from SQS. Which AWS service is most suitable?
A) Lambda triggered by S3 and SQS
B) EC2 polling S3 and SQS
C) ECS on EC2 launch type
D) EKS with EC2 nodes
Answer: A) Lambda triggered by S3 and SQS
Explanation:
When designing a modern, scalable, and cost-efficient architecture for event-driven workloads, choosing the right compute service is critical to minimizing operational overhead while maximizing performance and reliability. Traditional EC2 instances offer raw compute power and flexibility, but they come with significant management responsibilities. Developers or administrators must provision and configure instances, handle patching and updates, monitor health and performance, and scale the infrastructure to match demand. These operational tasks require ongoing attention and can become complex and time-consuming, particularly for workloads that experience variable or unpredictable traffic patterns. Manual scaling may result in underutilized resources during low-demand periods or performance bottlenecks during spikes, increasing costs or reducing service quality.
Container orchestration platforms such as ECS and EKS with EC2 nodes provide improvements by enabling containerized workloads and automating some aspects of deployment. However, they still rely on EC2 infrastructure, meaning administrators must manage the underlying instances, handle cluster scaling, update operating systems, and maintain network and storage configurations. While ECS and EKS simplify container orchestration and workload management, the responsibility for the EC2 nodes themselves continues to introduce operational overhead. Scaling these clusters to accommodate sudden increases in traffic or large batch jobs requires careful planning and configuration, and mismanagement can lead to performance degradation or downtime.
In contrast, AWS Lambda offers a fully serverless approach, eliminating the need for infrastructure management entirely. Lambda functions are event-driven and can be triggered by a wide variety of sources, including S3 uploads, SQS messages, DynamoDB streams, or API Gateway calls. When an event occurs, Lambda automatically provisions compute resources to execute the function, scales elastically to handle any number of concurrent events, and then deallocates resources when execution completes. This automatic scaling ensures that workloads of any size, from a single request to thousands of concurrent events, are handled efficiently without manual intervention.
Cost efficiency is another significant advantage of Lambda. Unlike EC2 or managed container services, which incur charges based on instance uptime or allocated resources regardless of utilization, Lambda bills only for actual execution time and resource consumption. Organizations can achieve significant cost savings for workloads that are intermittent, unpredictable, or event-driven. Additionally, Lambda integrates seamlessly with other AWS services such as CloudWatch for logging, monitoring, and alerting, providing visibility into function execution, error rates, and performance metrics. Automated error handling, retry policies, and dead-letter queues further enhance reliability and ensure that events are processed accurately even in the case of transient failures.
This serverless, event-driven architecture is ideal for workloads such as ETL jobs, image or video processing, real-time data transformations, order processing triggered by S3 uploads or SQS messages, and automated notification systems. By removing the burden of infrastructure management, developers can focus entirely on business logic while the platform handles scaling, availability, and fault tolerance. Lambda’s integration with other AWS services, combined with its automated scaling and cost model, provides a robust, highly available, and fully managed solution for modern application workloads.
In summary, choosing Lambda over EC2 or container-based EC2 solutions dramatically reduces operational overhead, improves scalability, increases cost efficiency, and simplifies architecture management. By leveraging a fully serverless, event-driven design, organizations can handle variable workloads seamlessly, achieve high availability, and respond rapidly to incoming events without manual intervention. This approach represents a modern, resilient, and efficient solution for processing events and workloads at scale.
Question 201
A company wants to migrate a multi-terabyte on-premises SQL Server database to AWS with minimal downtime. Which solution is most suitable?
A) RDS SQL Server with AWS DMS
B) RDS SQL Server only
C) Aurora PostgreSQL
D) EC2 SQL Server with manual backup/restore
Answer: A) RDS SQL Server with AWS DMS
Explanation:
RDS SQL Server alone requires downtime for exporting and importing data, which is unsuitable for production workloads. Aurora PostgreSQL is not SQL Server-compatible and would require extensive schema and application modifications. EC2 SQL Server with manual backup/restore requires operational effort and extended downtime, increasing risk. RDS SQL Server with AWS DMS enables near real-time replication from the source database, keeping it operational during migration. DMS ensures data integrity and minimal downtime, while RDS provides automated backups, Multi-AZ deployment, patching, and maintenance. This approach reduces operational complexity, ensures high availability, and is ideal for mission-critical SQL Server migrations with minimal service interruption.
Question 202
A company wants to implement a global web application with low latency for both static and dynamic content. Which architecture is most suitable?
A) CloudFront with S3 for static content and ALB for dynamic content
B) S3 only
C) EC2 with ALB only
D) Route 53 only
Answer: A) CloudFront with S3 for static content and ALB for dynamic content
Explanation:
When designing a globally distributed web application, delivering content quickly, securely, and reliably to users across diverse geographic locations is critical for ensuring optimal user experience and maintaining service performance. Relying on a single service or region for content delivery often introduces latency, reduces resilience, and may fail to meet global performance expectations. Understanding the strengths and limitations of services like S3, EC2, ALB, Route 53, and CloudFront is essential for building a highly performant and fault-tolerant architecture.
Amazon S3 provides durable, scalable storage for static content, such as images, videos, stylesheets, or JavaScript files. While S3 is highly reliable for storing and serving static assets, it does not inherently provide global caching or content delivery optimization. Requests to S3 are served from the region in which the bucket resides, meaning that users located far from that region may experience increased latency and slower load times. For applications serving a global audience, relying solely on S3 for content delivery can negatively impact performance and user satisfaction, particularly in regions geographically distant from the S3 bucket location.
Amazon EC2, combined with an Application Load Balancer (ALB), enables hosting and load balancing of dynamic content and application logic. ALBs distribute traffic across multiple EC2 instances within a region, providing high availability and fault tolerance for dynamic workloads. However, while EC2 and ALB ensure regional redundancy and scaling for dynamic applications, they do not provide a global delivery network, meaning that users outside the hosting region can still experience higher latency. In addition, managing EC2 instances and scaling them efficiently can introduce operational overhead if not automated properly.
Route 53 provides advanced DNS routing capabilities, including latency-based routing, geolocation routing, and health checks. It ensures that user requests are directed to the most appropriate endpoints based on health and proximity, improving reliability. However, Route 53 does not cache or accelerate content delivery on its own. It only resolves domain names and directs traffic, which means it cannot reduce the latency associated with fetching content from a distant origin or improve performance for static assets.
Amazon CloudFront, a global content delivery network (CDN), addresses the limitations of S3, EC2, and Route 53 by caching content at edge locations worldwide. CloudFront delivers static content stored in S3 and dynamic content served by ALB with low latency to users regardless of their geographic location. By storing frequently accessed objects closer to users, CloudFront reduces the number of direct requests to the origin, improves response times, and minimizes the load on the underlying application infrastructure. CloudFront also integrates with AWS Web Application Firewall (WAF) for protection against common threats, supports SSL/TLS for secure content delivery, and provides origin failover capabilities to improve resilience in case of regional outages.
Combining S3 for static content, ALB for dynamic content, Route 53 for intelligent DNS routing, and CloudFront for global caching and acceleration results in a robust, highly available, and performant web architecture. Users around the world experience faster page loads, reduced latency, and improved reliability. CloudFront ensures content is delivered efficiently while providing integrated security and monitoring capabilities. This holistic approach optimizes both performance and resilience, enabling web applications to scale seamlessly and maintain a consistent user experience globally.
By leveraging these services together, organizations can achieve an architecture that is not only fast and reliable but also secure, scalable, and cost-effective, addressing both static and dynamic content delivery requirements across multiple regions worldwide.
Question 203
A company wants to store session data for a high-traffic web application with sub-millisecond latency and high throughput. Which AWS service is most suitable?
A) ElastiCache Redis
B) DynamoDB
C) RDS MySQL
D) S3
Answer: A) ElastiCache Redis
Explanation:
Amazon DynamoDB is a fully managed NoSQL database known for its ability to provide low-latency access to data at scale. It is particularly well-suited for workloads that require high availability and the ability to handle large volumes of requests. However, under extremely high traffic or complex queries, DynamoDB may not consistently maintain sub-millisecond response times. The performance can fluctuate depending on factors such as partitioning, read/write throughput, and the nature of the data access patterns. While DynamoDB is excellent for many real-time applications, systems with extremely stringent latency requirements might need additional strategies to achieve consistently ultra-fast response times.
Relational databases such as Amazon RDS with MySQL are widely used for applications requiring structured data, transactions, and complex queries. RDS MySQL offers managed services including automated backups, patching, and replication. Despite these advantages, relational databases inherently introduce some latency. Disk I/O operations, connection pooling, and query execution times can add delays, especially under heavy traffic or when the dataset grows large. While RDS is reliable and supports many use cases, it is generally not optimized for workloads that require microsecond-level response times or extremely high read/write throughput.
Amazon S3, on the other hand, is an object storage service designed for durability and scalability rather than frequent, small-scale data operations. S3 performs exceptionally well for storing large objects like media files, backups, and static assets. However, it is not ideal for applications that need rapid access to small pieces of data or require frequent updates. The service is optimized for throughput over latency, meaning that frequent read and write operations on small objects can result in delays and inefficiencies.
For applications demanding extremely low latency and high throughput, Amazon ElastiCache using Redis provides a compelling solution. Redis is an in-memory key-value store that operates entirely in memory, eliminating disk I/O bottlenecks and enabling sub-millisecond response times for data retrieval. It is designed to handle very high request rates, making it ideal for real-time applications, caching, and session management. Redis supports advanced features such as replication, clustering, sharding, and optional persistence, allowing it to scale horizontally while maintaining high availability.
A key advantage of Redis is its ability to store session data centrally, enabling multiple web servers to access the same session information seamlessly. This allows for consistent user experiences in distributed environments and supports applications that require real-time updates or rapid user interactions. By offloading frequent reads and writes from primary databases, Redis reduces operational load, improves application responsiveness, and simplifies scaling strategies for high-traffic scenarios.
Integrating Redis into an application architecture ensures rapid, reliable access to critical data while maintaining scalability and high availability. It allows developers to focus on business logic rather than managing performance bottlenecks, and it provides a foundation for handling spikes in traffic without compromising user experience. For modern web applications and high-performance systems, Redis delivers the speed, reliability, and operational simplicity required to meet the demands of users at scale.
while DynamoDB, RDS MySQL, and S3 each serve valuable purposes depending on the workload, ElastiCache Redis excels in scenarios where ultra-low latency, high throughput, and distributed session management are critical. By combining in-memory data storage with advanced clustering and replication capabilities, Redis ensures that high-traffic applications remain responsive, reliable, and easy to manage.
Question 204
A company wants to implement a serverless, event-driven architecture to process uploaded files and messages in near real-time. Which AWS service is most suitable?
A) Lambda triggered by S3 and SQS
B) EC2 polling S3 and SQS
C) ECS on EC2 launch type
D) EKS with EC2 nodes
Answer: A) Lambda triggered by S3 and SQS
Explanation:
Amazon EC2 provides flexible virtual servers in the cloud, allowing organizations to run a wide range of applications. While EC2 instances offer control over the operating system, network configuration, and installed software, they come with significant operational responsibilities. Managing EC2 instances requires continuous monitoring, patching, scaling, and infrastructure maintenance. As workloads increase, manually scaling EC2 instances to handle traffic spikes can become complex and time-consuming. Additionally, maintaining high availability often involves configuring load balancers, redundancy, and failover mechanisms, further increasing operational overhead.
Container services such as Amazon ECS (Elastic Container Service) and Amazon EKS (Elastic Kubernetes Service) can run on EC2 instances, providing orchestration and management for containerized applications. While these services simplify aspects of deployment and container scheduling, they still rely on underlying EC2 nodes. This means organizations are responsible for managing the cluster infrastructure, updating EC2 nodes, and ensuring the environment scales efficiently to meet demand. For teams aiming to reduce operational burden, this hybrid approach still requires significant attention to infrastructure management.
For many modern applications, serverless computing offers a compelling alternative by removing the need to manage underlying servers. AWS Lambda is a fully managed, event-driven compute service that allows developers to run code without provisioning or maintaining servers. Lambda functions can be triggered by a variety of events, including file uploads to Amazon S3, messages arriving in Amazon SQS queues, database updates, or HTTP requests via API Gateway. The platform automatically handles scaling, allocating resources as needed to match incoming workloads. This means that whether a function is invoked once per day or thousands of times per second, Lambda adjusts seamlessly, eliminating the need for manual intervention.
A key advantage of Lambda is its cost model. Unlike traditional server-based environments where resources are provisioned continuously, Lambda charges only for the duration of code execution, measured in milliseconds. This provides significant cost efficiency for workloads with variable or intermittent traffic, such as ETL (Extract, Transform, Load) pipelines, image or video processing tasks, and order-processing systems triggered by file uploads or queued messages. Developers can focus entirely on business logic rather than managing servers, scaling policies, or failover configurations.
Lambda also integrates deeply with Amazon CloudWatch, enabling robust logging, metrics collection, and monitoring. CloudWatch allows teams to track function performance, detect errors, and implement automated alerts for operational issues. Combined with Lambda’s built-in retry mechanisms and the ability to connect with other AWS services, this architecture supports highly reliable, fault-tolerant processing.
By adopting a Lambda-based, event-driven approach, organizations can implement architectures that are fully managed, scalable, and cost-effective. It provides the reliability and availability required for mission-critical applications while reducing operational complexity. Tasks that traditionally required manual provisioning and scaling, such as batch processing, ETL workflows, or media transformations, can now be executed seamlessly with minimal human intervention.
while EC2 and container-based services offer control and flexibility, they carry operational responsibilities that can slow development and increase overhead. AWS Lambda delivers a serverless, event-driven solution that automatically scales with demand, integrates with monitoring and logging tools, and provides a pay-per-use pricing model. This makes it an ideal choice for high-traffic, event-triggered workloads where reliability, efficiency, and minimal operational management are essential.
Question 205
A company wants to implement a messaging system that guarantees exactly-once processing and preserves message order. Which AWS service is most suitable?
A) SQS FIFO Queue
B) SQS Standard Queue
C) SNS
D) Kinesis Data Streams
Answer: A) SQS FIFO QueueExplanation:
Amazon Simple Queue Service (SQS) provides a reliable way to decouple components in distributed systems by allowing microservices, applications, and backend processes to communicate asynchronously. The SQS Standard Queue is widely used for its scalability and high throughput. It guarantees that messages are delivered at least once, ensuring that no data is lost during transit. However, it does not guarantee the order in which messages are delivered. This means that in situations where the sequence of events is critical, using a Standard Queue can lead to inconsistent or out-of-order processing, which can complicate application logic and potentially introduce errors in workflows that depend on strict ordering.
Amazon Simple Notification Service (SNS) operates on a pub/sub model, allowing messages to be broadcast to multiple subscribers simultaneously. SNS is highly effective for notifications, alerts, or fan-out messaging patterns where multiple systems need to react to the same event. Similar to SQS Standard Queues, SNS does not guarantee message ordering, nor does it ensure exactly-once delivery. Messages may occasionally be delivered more than once, and subscribers may receive them in a different order than they were published. While SNS provides high flexibility and scalability, these characteristics make it less suitable for transaction-sensitive or sequential workloads where maintaining a strict order is necessary.
For applications that require ordered processing with high throughput, Amazon Kinesis Data Streams offers another option. Kinesis divides data into shards, preserving the order of records within each shard while allowing parallel processing across multiple shards. This ensures that ordered sequences can be maintained within each partition of the stream. However, implementing Kinesis can introduce operational complexity for applications that only require simple microservice-to-microservice messaging. It involves managing shard scaling, stream retention, and consumer applications, which may be overkill for straightforward queue-based communication.
For scenarios where both ordering and exactly-once processing are critical, Amazon SQS FIFO (First-In-First-Out) Queues provide an ideal solution. FIFO Queues guarantee that messages are delivered exactly once and in the order they were sent. They also support message deduplication, which prevents accidental processing of duplicate messages within a configurable deduplication interval. This combination of features ensures that microservices can communicate predictably and reliably, making FIFO Queues particularly valuable for applications such as financial transactions, order processing systems, inventory management, or any distributed system where sequence and consistency are paramount.
By using FIFO Queues, developers can simplify application logic, as they no longer need to implement complex mechanisms to handle out-of-order or duplicated messages. Messages are processed sequentially, reducing the potential for data inconsistencies and enabling deterministic behavior across distributed services. FIFO Queues integrate seamlessly with other AWS services, including Lambda, EC2, and ECS, allowing serverless or containerized applications to respond reliably to incoming messages without additional orchestration overhead.
while SQS Standard Queues, SNS, and Kinesis Data Streams each offer valuable messaging capabilities, FIFO Queues uniquely combine exactly-once processing, ordering guarantees, and deduplication. This ensures reliable, predictable communication between microservices, reduces the need for complex error handling, and provides a foundation for building transaction-sensitive and distributed systems that require sequential, consistent processing. For applications where order and reliability are critical, FIFO Queues offer the most straightforward and robust messaging solution.
Question 206
A company wants to migrate an on-premises Oracle database to AWS with minimal downtime. Which approach is most suitable?
A) RDS Oracle with AWS DMS continuous replication
B) EC2 Oracle with manual backup and restore
C) Aurora PostgreSQL
D) DynamoDB
Answer: A) RDS Oracle with AWS DMS continuous replication
Explanation:
Migrating Oracle databases to the cloud presents several challenges, particularly when balancing the need for high availability, minimal downtime, and operational simplicity. Traditional deployment on Amazon EC2 using Oracle requires manual backup and restore procedures. This approach is time-consuming and labor-intensive, often resulting in extended downtime during migration or recovery operations. Every backup, restore, or failover must be carefully orchestrated, which increases operational complexity and the risk of human error. Additionally, scaling EC2-based Oracle deployments involves provisioning additional instances manually and maintaining high availability through clustering or load-balancing solutions, further adding to administrative overhead.
Amazon Aurora PostgreSQL is a fully managed relational database offering high performance, scalability, and advanced features. However, Aurora is not compatible with Oracle. Migrating to Aurora PostgreSQL from an Oracle environment would require significant changes to the database schema, application queries, and stored procedures. This can introduce development delays, increase testing requirements, and carry the risk of application-level incompatibilities. For production workloads with strict deadlines or complex applications, this level of transformation can make Aurora PostgreSQL an impractical choice for Oracle migration.
Amazon DynamoDB, another fully managed AWS database, is a NoSQL key-value and document store. While DynamoDB excels at high-performance, scalable workloads requiring low-latency access, it is unsuitable for hosting traditional relational Oracle workloads. Applications that depend on ACID transactions, complex SQL queries, and relational schemas cannot be migrated to DynamoDB without complete architectural redesign, which often makes it an impractical solution for most Oracle migration scenarios.
The most effective approach for migrating Oracle workloads to AWS while minimizing downtime and operational complexity is to use Amazon RDS for Oracle in combination with AWS Database Migration Service (DMS). RDS Oracle provides a fully managed relational database platform that supports high availability through Multi-AZ deployments, automated backups, software patching, and maintenance tasks. By leveraging AWS DMS, organizations can replicate data from the on-premises or EC2-hosted Oracle source database to RDS in near real-time. This replication occurs while the source database remains operational, ensuring minimal disruption to ongoing business operations.
AWS DMS handles data migration reliably and efficiently. It validates data integrity during replication, handles schema changes where necessary, and allows for continuous data synchronization between the source and target databases. This enables organizations to perform live migrations with minimal downtime, avoiding long service interruptions that would otherwise impact business continuity. Additionally, RDS automation features reduce operational burden, allowing teams to focus on testing, validation, and cutover rather than managing infrastructure.
By combining RDS Oracle and AWS DMS, organizations can achieve a smooth, low-downtime migration strategy that maintains high availability, preserves data integrity, and simplifies management. This approach eliminates the need for extensive manual backup and restore processes, reduces operational risk, and allows teams to leverage the fully managed capabilities of RDS, including automatic patching, monitoring, and replication. It provides a reliable path for moving production Oracle workloads to the cloud while minimizing disruption to users and applications.
migrating Oracle databases to AWS using RDS Oracle with AWS DMS provides the most efficient and practical solution. It balances the need for high availability, minimal downtime, and operational simplicity, ensuring that critical production workloads are migrated smoothly and securely, while avoiding the complexities and downtime associated with manual EC2 deployments or incompatible database platforms.
Question 207
A company wants to implement a global web application with low latency for static and dynamic content. Which architecture is most suitable?
A) CloudFront with S3 for static content and ALB for dynamic content
B) S3 only
C) EC2 with ALB only
D) Route 53 only
Answer: A) CloudFront with S3 for static content and ALB for dynamic content
Explanation:
Amazon S3 is a highly durable and cost-effective storage solution that excels at hosting static content, such as images, videos, HTML files, and other media assets. While it is ideal for serving static resources, S3 alone is not optimized for dynamic content or frequent requests that require real-time processing. Additionally, S3 does not offer caching at a global scale, meaning that users located far from the S3 region may experience higher latency, which can impact overall performance and responsiveness.
Amazon EC2, when paired with an Application Load Balancer (ALB), allows applications to handle dynamic content by processing requests on server instances. EC2 provides full control over the operating system, software stack, and application environment, making it suitable for hosting complex web applications and APIs. The ALB distributes incoming traffic across multiple EC2 instances to improve availability and prevent single points of failure. However, this architecture is typically deployed within a single AWS region. For users located in other parts of the world, latency can increase, affecting load times and user experience. Scaling EC2 instances to meet global demand requires careful planning and management, adding operational overhead.
Amazon Route 53 complements these services by providing a highly available and scalable DNS management solution. It directs users to the nearest AWS region or endpoint using intelligent routing policies, health checks, and failover mechanisms. While Route 53 ensures that requests are routed efficiently and reliably, it does not cache or deliver actual content. Content delivery speed and latency reduction still depend on the underlying compute and storage infrastructure.
For globally distributed applications, Amazon CloudFront provides a solution that addresses the limitations of S3, EC2, and Route 53. CloudFront is a content delivery network (CDN) that caches content at edge locations across the globe. By serving cached content from locations closer to end users, CloudFront significantly reduces latency and improves response times. It supports both static and dynamic content, providing intelligent routing for dynamic requests back to the origin server while optimizing delivery of static assets from the cache. This hybrid approach enables applications to scale efficiently, handle high traffic volumes, and deliver a consistent experience to users worldwide.
Combining CloudFront with S3 for static content and EC2 with an ALB for dynamic content creates a highly optimized web application architecture. Static resources are cached at edge locations for rapid access, while dynamic requests are processed by EC2 instances in a load-balanced environment. CloudFront integrates with additional security and reliability features, including AWS Web Application Firewall (WAF) for protection against malicious traffic, SSL/TLS encryption for secure communications, and origin failover to maintain availability in the event of an outage.
This architecture not only improves performance and security but also reduces operational complexity by leveraging fully managed services. It ensures high availability, fault tolerance, and a smooth user experience for globally distributed audiences. By addressing both static and dynamic content delivery challenges, this combination of services provides a comprehensive solution for building scalable, secure, and high-performance web applications.
using S3, EC2 with ALB, Route 53, and CloudFront together provides a robust framework for global content delivery. Static assets are delivered rapidly from edge locations, dynamic content is processed efficiently, and routing and security are handled seamlessly, making this architecture ideal for modern, globally distributed applications.
Question 208
A company wants to store session data for a high-traffic web application with extremely low latency and high throughput. Which AWS service is most suitable?
A) ElastiCache Redis
B) DynamoDB
C) RDS MySQL
D) S3
Answer: A) ElastiCache Redis
Explanation:
DynamoDB provides low-latency access but may not consistently achieve sub-millisecond response times under heavy load. RDS MySQL introduces latency due to disk I/O and connection management. S3 is object storage and cannot efficiently handle frequent small reads/writes. ElastiCache Redis is an in-memory key-value store optimized for extremely low latency and high throughput. Redis supports replication, clustering, and optional persistence. It allows session data to be shared across multiple web servers, ensuring fast, reliable access and scalability. This solution provides high availability, minimal operational complexity, and excellent user experience for high-traffic applications.
Amazon DynamoDB is a fully managed NoSQL database designed to provide low-latency access for a wide range of applications. It is highly scalable and can handle large volumes of requests with minimal operational effort, making it suitable for workloads that demand fast and reliable access to data. However, under extremely heavy traffic or complex query patterns, DynamoDB may not consistently achieve sub-millisecond response times. While it remains a strong choice for many real-time applications, workloads that require consistently ultra-low latency may need additional solutions to meet strict performance requirements.
Amazon RDS for MySQL is a relational database service that supports structured data and complex queries. RDS MySQL offers managed features such as automated backups, patching, and high availability configurations, which reduce administrative overhead. Despite these advantages, relational databases inherently introduce latency due to disk I/O operations, connection pooling, and query execution time. Under high traffic conditions or with growing datasets, response times may increase, impacting applications that require rapid data access for real-time interactions.
Amazon S3, in contrast, is an object storage service optimized for durability, scalability, and throughput rather than low-latency access to frequently changing data. It is ideal for storing large files such as media assets, backups, or logs. However, S3 is not designed for workloads that involve frequent small reads and writes. Attempting to use S3 for high-frequency transactional data can result in performance bottlenecks and inefficiencies, making it unsuitable for applications requiring fast, consistent access to small, rapidly changing datasets.
For applications where extremely low latency and high throughput are critical, Amazon ElastiCache with Redis offers a compelling solution. Redis is an in-memory key-value store that keeps data in RAM, eliminating the latency associated with disk access. It is optimized for rapid data retrieval and supports millions of read and write operations per second. Redis also includes advanced features such as replication for high availability, clustering for horizontal scalability, and optional persistence to disk, enabling data durability without sacrificing performance.
One of the key benefits of Redis is its ability to share session data across multiple web servers. This centralization allows distributed applications to maintain consistent state and user sessions, even in highly dynamic and scaled-out environments. By serving session data, caching frequent queries, or offloading read-heavy workloads from primary databases, Redis dramatically improves application responsiveness and reliability. Developers can focus on building business logic rather than managing latency or complex synchronization mechanisms, simplifying operational requirements.
Integrating Redis into an application architecture ensures both high performance and operational efficiency. Applications benefit from predictable, sub-millisecond response times, even under heavy load, while leveraging Redis’ replication and clustering capabilities to handle growth seamlessly. This reduces the risk of downtime, improves user experience, and supports high-traffic scenarios with minimal manual intervention.
while DynamoDB, RDS MySQL, and S3 each serve important roles depending on the type of workload, ElastiCache Redis stands out for scenarios requiring ultra-fast, highly scalable access to frequently used data. By combining in-memory storage, advanced replication, and clustering, Redis provides a solution that ensures high availability, excellent performance, and minimal operational complexity for modern high-traffic applications.
Question 209
A company wants to implement a serverless, event-driven architecture for processing files uploaded to S3 and messages from SQS. Which AWS service is most suitable?
A) Lambda triggered by S3 and SQS
B) EC2 polling S3 and SQS
C) ECS on EC2 launch type
D) EKS with EC2 nodes
Answer: A) Lambda triggered by S3 and SQS
Explanation:
EC2 instances require manual scaling, patching, and monitoring, increasing operational overhead. ECS and EKS with EC2 nodes require cluster and infrastructure management. Lambda is serverless and can be triggered directly by S3 events or SQS messages. It automatically scales according to workload and charges only for execution duration. Lambda integrates with CloudWatch for logging, monitoring, and error handling. This fully managed, event-driven architecture provides high availability, scalability, and cost efficiency. It is ideal for ETL jobs, image processing, or order processing triggered by S3 or SQS, ensuring reliable serverless processing.
Question 210
A company wants to implement a messaging system that guarantees exactly-once processing and preserves message order. Which AWS service is most suitable?
A) SQS FIFO Queue
B) SQS Standard Queue
C) SNS
D) Kinesis Data Streams
Answer: A) SQS FIFO Queue
Explanation:
When designing messaging architectures for distributed systems and microservices, the choice of message queuing service has a significant impact on reliability, consistency, and overall application performance. Amazon Simple Queue Service (SQS) offers different queue types to address varying use cases, each with its own characteristics regarding message delivery, ordering, and throughput. Understanding these differences is critical for ensuring that workloads, particularly those that are transaction-sensitive, are processed correctly and efficiently.
SQS Standard Queues are the default type of queue in Amazon SQS. They provide at-least-once delivery, meaning that every message sent to the queue is delivered to one or more consumers at least once. This feature ensures that no message is lost, which is essential for reliability. However, Standard Queues do not guarantee the order in which messages are delivered. In high-throughput or concurrent environments, this can result in messages being processed out of sequence. For many applications, such as logging, notifications, or event processing where order is not critical, Standard Queues are highly suitable due to their scalability and simplicity. Nevertheless, for applications where the sequence of operations is important, such as financial transactions, inventory management, or order processing systems, out-of-order message delivery can introduce inconsistencies, requiring additional logic at the application level to reconcile messages and maintain consistency.
Amazon Simple Notification Service (SNS) is a pub/sub messaging system that allows messages to be published to multiple subscribers simultaneously. While SNS is effective for broadcasting notifications or updates to a wide range of recipients, it does not provide queuing guarantees. Messages delivered via SNS are not guaranteed to be processed in order, nor does SNS provide exactly-once delivery semantics. For scenarios where predictable processing order and message deduplication are necessary, relying solely on SNS may not meet the reliability requirements for critical transactional workflows.
Kinesis Data Streams offers high-throughput streaming capabilities and preserves ordering per shard. It is particularly suitable for real-time analytics and complex streaming workloads. While Kinesis ensures that events are processed in order within a shard, it introduces additional complexity in terms of shard management, scaling, and consumer coordination. For simple messaging between microservices, this complexity may be unnecessary and could increase operational overhead without providing commensurate benefits.
SQS FIFO Queues, on the other hand, are designed specifically to provide exactly-once processing and maintain strict message ordering. Each message in a FIFO queue is assigned a sequence, ensuring that messages are delivered and processed in the exact order in which they were sent. Additionally, FIFO queues support deduplication, preventing duplicate messages from being processed if they are sent multiple times within a defined window. These features simplify application logic by removing the need for developers to implement manual ordering or deduplication mechanisms, reducing the potential for errors and inconsistencies. FIFO queues are ideal for use cases where sequential processing is critical, such as financial transaction workflows, inventory updates, order fulfillment, and other systems where consistency and predictability are paramount.
By selecting SQS FIFO Queues for transaction-sensitive or sequence-dependent applications, organizations can ensure that microservices communicate reliably and maintain the integrity of operations. FIFO queues provide the necessary guarantees to preserve message order, prevent duplication, and simplify the design of distributed applications, making them the preferred choice when exact ordering and exactly-once processing are required.