Amazon AWS Certified Solutions Architect — Professional SAP-C02 Exam Dumps and Practice Test Questions Set 11 Q151-165
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Question 151
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:
DynamoDB offers low-latency storage but may not consistently achieve sub-millisecond response times under heavy load. RDS MySQL introduces latency due to disk I/O and connection management, making it unsuitable for session management. S3 is object storage and cannot handle frequent small reads/writes efficiently. ElastiCache Redis is an in-memory key-value store optimized for extremely low latency and high throughput. It supports replication, clustering, and optional persistence. Redis is ideal for storing session data across multiple web servers, providing fast, reliable access, scalability, and high availability. This ensures smooth user experiences for high-traffic applications while minimizing operational complexity.
Question 152
A company wants to implement a highly available, multi-region web application that automatically routes users to the nearest healthy region. Which AWS service combination is most suitable?
A) Route 53 with health checks, S3 Cross-Region Replication, Multi-Region Auto Scaling groups
B) CloudFront only
C) Single-region ALB with Auto Scaling
D) RDS Single-AZ
Answer: A) Route 53 with health checks, S3 Cross-Region Replication, Multi-Region Auto Scaling groups
Explanation:
Building a globally distributed, highly available web application requires careful orchestration of compute, storage, and networking resources to ensure minimal latency, fault tolerance, and seamless user experience. While Amazon CloudFront provides a robust solution for caching content at edge locations worldwide, it has limitations when considered alone. CloudFront accelerates content delivery by storing copies of static and dynamic assets close to end users, reducing latency and improving load times. However, CloudFront does not natively handle multi-region failover or automatically route users to the nearest healthy region in case of an outage. To achieve a truly resilient architecture, additional services must be integrated to handle regional failures and ensure traffic is routed intelligently.
A single-region Application Load Balancer (ALB) with Auto Scaling can provide availability and load distribution within one region. It allows traffic to be distributed across multiple EC2 instances, which can scale in response to changing demand. While this setup ensures redundancy at the instance level within a single region, it does not protect against regional outages. If an entire region becomes unavailable due to natural disasters, network failures, or data center maintenance, users relying on that single region will experience downtime. Similarly, RDS instances deployed in a Single-AZ configuration provide database redundancy within a single availability zone, but they lack multi-region routing capabilities. This makes them insufficient for applications requiring global fault tolerance.
To overcome these limitations, Amazon Route 53 can be leveraged for global DNS management and intelligent traffic routing. By configuring health checks, Route 53 can continuously monitor endpoints across multiple regions and direct user requests to the nearest healthy region. This approach reduces latency by ensuring that users connect to geographically closer resources while simultaneously providing failover capabilities to maintain high availability during regional disruptions. Route 53 forms a critical component of a multi-region, resilient architecture.
For static content, Amazon S3 Cross-Region Replication (CRR) ensures that objects stored in one S3 bucket are automatically replicated to buckets in other regions. This replication keeps static assets synchronized across multiple locations, allowing users worldwide to access content with low latency. In addition, CRR provides a robust disaster recovery solution, as data remains available even if one region experiences a failure.
Multi-Region Auto Scaling groups further enhance availability and scalability by replicating EC2 instances across multiple regions. This ensures that compute resources remain operational and responsive, even if a regional failure occurs. By combining these groups with CloudFront caching, users experience rapid content delivery while the backend infrastructure remains resilient and scalable.
Together, this combination of CloudFront, Route 53, S3 Cross-Region Replication, and Multi-Region Auto Scaling provides a globally distributed web architecture that is both highly available and fault-tolerant. Users benefit from optimal performance and low latency, while organizations gain operational resilience, automated failover, and simplified management. This approach ensures that web applications can serve global audiences reliably, maintaining continuity and performance regardless of regional disruptions.
Question 153
A company wants to run containerized microservices without managing EC2 instances. Which AWS service is most suitable?
A) ECS with Fargate
B) ECS with EC2 launch type
C) EKS with EC2 nodes
D) EC2 only
Answer: A) ECS with Fargate
Explanation:
ECS with EC2 launch type and EKS with EC2 nodes require managing the underlying EC2 instances, scaling, patching, and monitoring. EC2 alone provides raw compute without container orchestration capabilities. ECS with Fargate is a serverless container service that automatically provisions and scales compute resources to run containers. It integrates with networking, security, and monitoring services, allowing developers to focus solely on deploying microservices. Fargate provides automatic scaling, high availability, and cost efficiency by charging only for consumed resources. This makes it ideal for fully managed, serverless deployment of containerized applications.
Question 154
A company wants to migrate terabytes of on-premises data to AWS efficiently, avoiding excessive network usage. Which solution is most suitable?
A) AWS Snowball
B) S3 only
C) EFS
D) AWS DMS
Answer: A) AWS Snowball
Explanation:
Transferring large datasets to the cloud can be challenging when relying solely on traditional network uploads, especially when the datasets are multi-terabyte in size. Using Amazon S3 as the destination for these datasets might seem straightforward; however, uploading directly over the network can be slow, expensive, and prone to interruptions due to bandwidth limitations or network instability. For organizations with very large volumes of data, this approach often results in extended migration times and increased operational complexity, making it inefficient for timely project execution.
Amazon EFS, while providing scalable and shared file storage, is primarily designed for active file storage and workloads that require low-latency access. It is not optimized for bulk migrations, particularly for datasets that are not actively used during the migration process. The service’s strengths lie in supporting applications that need concurrent file access, rather than in efficiently transferring massive datasets to AWS. Similarly, AWS Database Migration Service (DMS) is specialized for migrating relational and non-relational databases, offering tools to replicate database changes and manage schema transformations. While extremely useful for database-specific migrations, DMS cannot effectively handle general-purpose file transfers or bulk datasets, limiting its applicability for large-scale file ingestion scenarios.
AWS Snowball addresses these limitations by providing a secure, physical appliance designed specifically for large-scale offline data transfer. The Snowball device is shipped directly to the customer, allowing them to load terabytes or even petabytes of data locally. This eliminates reliance on potentially slow or expensive network connections and significantly reduces transfer times compared to online uploads. Snowball appliances are engineered for high durability, secure storage, and ease of use. They incorporate strong encryption both at rest and in transit, ensuring that sensitive data remains protected throughout the shipping and ingestion process.
Once the data is loaded onto the Snowball appliance, it is returned to AWS, where the contents are automatically ingested into S3. This process minimizes operational overhead, as the organization does not need to provision temporary high-bandwidth infrastructure or manage large-scale network transfers. The integration with S3 ensures that the data becomes immediately accessible for processing, analytics, or archival purposes once uploaded. Additionally, Snowball supports incremental transfers, making it suitable for scenarios where data accumulates over time or where multiple appliances are required for extremely large datasets.
AWS Snowball provides an ideal solution for organizations seeking to migrate large amounts of data securely, efficiently, and cost-effectively. It overcomes the limitations of network-dependent transfers and non-specialized storage or migration services, offering a practical approach for multi-terabyte or petabyte-scale datasets. By leveraging Snowball, companies can ensure that their data arrives at AWS quickly and securely, ready for immediate use in cloud applications, analytics workflows, and long-term storage solutions. Overall, Snowball reduces migration complexity, avoids the bottlenecks of network uploads, and enables organizations to move massive datasets to the cloud with confidence, reliability, and minimal operational effort.
Question 155
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:
In cloud computing, selecting the appropriate compute model significantly affects operational complexity, scalability, and cost efficiency. Traditional virtual servers, such as Amazon EC2 instances, offer complete control over the operating system and software environment. However, this flexibility comes with a high operational burden. EC2 instances require manual patching to address security vulnerabilities, continuous monitoring to maintain performance and availability, and careful capacity planning to handle variable workloads. Scaling these instances to accommodate traffic spikes or reduce costs during periods of low demand also requires active management, adding to the operational overhead. These responsibilities make EC2 more suitable for workloads where full control is necessary but less ideal for applications that benefit from automatic scaling and reduced maintenance.
Containerized solutions, including Amazon ECS and Amazon EKS, provide a more modern approach by allowing applications to run within isolated containers. These services simplify deployment and orchestration compared to managing raw EC2 instances. However, they do not completely remove infrastructure responsibilities. Users must still provision and manage the EC2 instances underlying the clusters, configure networking, monitor both the containers and host instances, and plan for scaling. While ECS and EKS reduce some operational complexity relative to traditional virtual machines, they still require ongoing management to ensure high availability, fault tolerance, and optimized performance.
Serverless computing, exemplified by AWS Lambda, offers a fundamentally different model. Lambda allows developers to focus entirely on code rather than server management. Functions can be triggered directly by events from services such as S3, where file uploads initiate processing workflows, or SQS, which supports asynchronous message-driven processing. Once deployed, Lambda automatically scales to match the workload, handling sudden spikes in requests without manual intervention. Billing is based exclusively on execution time and the resources consumed during function execution, which provides cost efficiency for workloads with variable or intermittent traffic.
Lambda integrates seamlessly with AWS CloudWatch, offering built-in logging, monitoring, and error tracking. This integration allows developers and operations teams to observe function performance, detect anomalies, and debug issues without needing access to underlying servers. CloudWatch’s monitoring capabilities also support automated alerts, enabling proactive response to potential issues and enhancing the operational reliability of applications.
This serverless, event-driven architecture is particularly well-suited for workloads that respond to specific triggers. Tasks such as transforming files uploaded to S3, performing extract-transform-load (ETL) operations on incoming datasets, or processing orders in response to messages in SQS can be implemented efficiently using Lambda. By leveraging managed services alongside Lambda, applications achieve automatic scaling, high availability, and reduced operational overhead, all while maintaining cost efficiency.
while EC2 and container-based services offer flexibility and control, they require significant operational effort for patching, scaling, and monitoring. Lambda removes the need for server management, automatically scales based on demand, integrates with monitoring tools, and charges only for execution time. For event-driven workloads that require responsiveness, reliability, and cost-effective scalability, Lambda provides a fully managed solution that enables organizations to focus on developing business logic rather than maintaining infrastructure, delivering a highly available, scalable, and efficient cloud architecture.
Question 156
A company is running a multi-tier web application in AWS with a requirement for high availability and disaster recovery. The application stores session state in Amazon RDS and uses Amazon S3 for static content. Which architecture change should be implemented to ensure minimal downtime during a regional failure?
A) Enable RDS Multi-AZ for the database and replicate S3 content to another region using Cross-Region Replication
B) Use RDS Read Replicas in the same region and configure S3 lifecycle policies
C) Deploy a backup solution using AWS Backup for RDS and S3
D) Migrate the database to Amazon DynamoDB and store content in Amazon EFS
Answer: A) Enable RDS Multi-AZ for the database and replicate S3 content to another region using Cross-Region Replication
Explanation:
Enabling RDS Multi-AZ ensures that a synchronous standby is automatically maintained in a different Availability Zone within the same region. This setup provides high availability during AZ-level failures and allows automatic failover to the standby. Replicating S3 content to another region ensures that even in a full regional outage, static content remains accessible from the secondary region. Using RDS Read Replicas in the same region provides scalability for read-heavy workloads but does not provide high availability or disaster recovery in the case of a regional outage. Lifecycle policies on S3 primarily manage storage costs by transitioning objects to different storage classes and do not address disaster recovery or high availability. AWS Backup enables centralized backups, but restoring databases or content from backup during a regional outage will involve significant downtime. Migrating the database to DynamoDB could provide high availability, but DynamoDB is a different database paradigm, may require significant application changes, and does not automatically provide a full cross-region disaster recovery strategy. Similarly, storing content in EFS does not natively support cross-region replication without additional setup. The selected approach ensures automatic failover and content availability across regions, meeting high availability and disaster recovery requirements without major application changes.
Question 157
An organization wants to migrate a legacy on-premises application to AWS while minimizing changes to its existing architecture. The application is monolithic and heavily reliant on session state stored in local memory. Which AWS service combination should be used to achieve this goal with minimal refactoring?
A) Amazon EC2 with Elastic Load Balancing and Amazon ElastiCache for session state
B) Amazon ECS with Fargate and DynamoDB for session state
C) Amazon Lambda with S3 for session storage
D) Amazon EKS with RDS for session state
Answer: A) Amazon EC2 with Elastic Load Balancing and Amazon ElastiCache for session state
Explanation:
Deploying the application on EC2 allows running the legacy application with minimal changes because it supports traditional operating systems and application environments. Elastic Load Balancing distributes incoming traffic across multiple instances to improve availability and scalability. Since the application relies on session state stored in memory, Amazon ElastiCache provides an in-memory caching solution that allows session data to persist independently of individual EC2 instances, enabling seamless scaling and high availability. ECS with Fargate is container-based and would require refactoring the application into containers. Using DynamoDB for session state would require modifying the application to interact with a NoSQL database instead of its current memory-based session mechanism. Lambda is serverless and event-driven, which would require rewriting the application to fit the stateless function execution model. Using EKS would involve containerizing the application and managing Kubernetes clusters, which also necessitates significant refactoring. The selected approach minimizes application changes while providing necessary high availability and session persistence.
Question 158
A company wants to run containerized microservices without managing underlying servers. Which AWS service is most suitable?
A) ECS with Fargate
B) ECS with EC2 launch type
C) EKS with EC2 nodes
D) EC2 only
Answer: A) ECS with Fargate
Explanation:
Running containerized applications in the cloud requires a careful balance between operational simplicity, scalability, and cost efficiency. Traditional approaches, such as using ECS with EC2 launch type or EKS with EC2 nodes, provide full control over the underlying compute infrastructure. In these setups, organizations are responsible for provisioning EC2 instances, managing scaling policies, applying security patches, monitoring system health, and ensuring high availability. While this provides flexibility and control, it introduces significant operational overhead, as teams must maintain the infrastructure in addition to managing containerized workloads. Any mismanagement of EC2 instances can result in performance bottlenecks, downtime, or inefficient resource utilization.
Using plain EC2 instances alone does not solve these challenges either. EC2 provides raw compute capacity but lacks built-in container orchestration capabilities, leaving developers and operations teams to manually configure, schedule, and maintain containers on the virtual machines. This approach can be complex for microservices architectures where multiple services must interact reliably, and scaling must respond to variable workloads in real time. It also increases the risk of human error, as operational tasks such as instance scaling, load balancing, and failover must be handled manually or via custom scripts.
In contrast, ECS with Fargate provides a fully serverless approach to running containers. With Fargate, developers define the container images, tasks, and configurations without worrying about the underlying infrastructure. Fargate automatically provisions the required compute resources, schedules containers, and handles scaling based on demand. This removes the need for manual instance management, patching, and capacity planning, which significantly reduces operational complexity and allows teams to focus on application development rather than infrastructure maintenance.
Fargate also integrates seamlessly with other AWS services to provide networking, security, and monitoring capabilities. Tasks can be deployed within a VPC, enabling fine-grained network control, and IAM roles can be applied at the task level to ensure secure access to AWS resources. CloudWatch integration provides visibility into container performance and logs, helping teams monitor applications, detect anomalies, and troubleshoot issues without managing individual EC2 hosts. Additionally, Fargate’s serverless billing model charges only for the CPU and memory resources consumed by running tasks, optimizing cost efficiency, especially for applications with variable workloads.
This combination of serverless container orchestration, integrated security and monitoring, automated scaling, and cost optimization makes ECS with Fargate an ideal choice for running containerized microservices in production. Organizations can deploy highly available, resilient applications without the operational burden of managing EC2 instances, while maintaining flexibility, performance, and security. By leveraging Fargate, development teams can accelerate deployment cycles, improve operational reliability, and focus on building business logic and features rather than managing infrastructure.
This approach not only simplifies container management but also aligns with modern cloud-native practices, enabling scalable, secure, and cost-effective containerized environments suitable for production workloads.
Question 159
A company wants to migrate terabytes of on-premises data to AWS efficiently without overloading the network. Which solution is most suitable?
A) AWS Snowball
B) S3 only
C) EFS
D) AWS DMS
Answer: A) AWS Snowball
Explanation:
Transferring large volumes of data to the cloud can present significant challenges, particularly when dealing with multi-terabyte datasets. Using Amazon S3 alone for such migrations often proves inefficient. Uploading massive amounts of data over the internet can be extremely time-consuming and costly, especially if network bandwidth is limited or the connection is unreliable. While S3 is highly durable and scalable for storing data once it is in the cloud, it is not optimized for high-speed ingestion of very large datasets from on-premises environments, making direct network uploads impractical for enterprise-scale migrations.
Amazon EFS, although a fully managed file system that supports scalable, shared file storage for active workloads, is also not suited for bulk data migration. Its architecture is optimized for applications that require frequent access to files across multiple compute instances, rather than for transferring large quantities of data in a single operation. Using EFS for large-scale ingestion would be inefficient, both in terms of time and cost, because it is designed for low-latency access rather than high-throughput, one-time transfers.
AWS Database Migration Service (DMS) is another specialized service, but its focus is on migrating relational and non-relational databases. While DMS can efficiently move tables, schemas, and transactional data from one database to another, it does not handle general-purpose file transfers effectively. Large datasets consisting of files such as media, logs, or backups cannot be easily migrated using DMS, making it unsuitable for bulk file migration scenarios.
AWS Snowball addresses these limitations by providing a physical appliance specifically designed for secure, large-scale data transfer. Snowball allows customers to copy their data onto the device locally, eliminating the dependency on internet bandwidth for the initial transfer. Once the data is loaded, the appliance is physically shipped to AWS, where it is ingested directly into Amazon S3. This approach significantly accelerates the migration process for massive datasets, avoiding network congestion and reducing the overall transfer time.
Security is a key feature of Snowball. The device encrypts data at rest and ensures secure transmission, protecting sensitive information throughout the migration process. The appliance is tamper-resistant, and the data is automatically erased once the transfer is complete, providing an additional layer of security. Snowball also reduces operational complexity, as organizations do not need to manage extensive network resources or monitor lengthy data transfers over the internet.
This solution is particularly well-suited for enterprises and organizations undertaking large-scale migrations, such as moving multi-terabyte archives, media libraries, or scientific datasets to the cloud. By combining local data loading, physical transport, and direct ingestion into S3, AWS Snowball offers a cost-effective, reliable, and secure method to migrate vast amounts of data with minimal disruption. It ensures high-speed transfer while reducing the operational burden on IT teams and mitigating the risks associated with lengthy online transfers. Snowball therefore represents an ideal choice for large, complex, or bandwidth-intensive migration projects, providing a practical path to rapidly and securely move massive datasets into the AWS cloud.
Question 160
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 managing compute resources in the cloud, the choice of architecture significantly impacts operational complexity, scalability, and cost efficiency. Traditional approaches, such as deploying applications on EC2 instances, offer full control over the underlying servers, but this control comes with considerable responsibilities. EC2 instances require manual provisioning, ongoing patching, and capacity management to accommodate changes in workload. Administrators must continuously monitor performance metrics, configure load balancing, and scale resources up or down to maintain responsiveness. This operational overhead can be significant, particularly for applications with variable or unpredictable traffic patterns, and it requires dedicated effort to maintain availability and performance across multiple instances.
Container-based solutions, such as Amazon ECS and Amazon EKS, provide a more abstracted approach by enabling applications to run within containers orchestrated on EC2 nodes. While these services simplify aspects of deployment, they still require careful infrastructure management. Users must manage the underlying EC2 instances, configure clusters, handle networking, and monitor both the containers and the host instances. Scaling, patching, and ensuring high availability remain responsibilities of the operations team, meaning that while ECS and EKS reduce some complexity compared to raw EC2 management, they do not fully eliminate the need for operational oversight.
Serverless computing, represented by AWS Lambda, offers a fundamentally different paradigm. Lambda allows developers to focus solely on application code without worrying about the underlying servers. Functions can be triggered by events from services such as S3, where new file uploads initiate processing, or SQS, where messages in a queue trigger asynchronous workflows. Once configured, Lambda automatically scales in response to demand, handling thousands of concurrent executions without requiring manual provisioning or scaling decisions. Billing is based entirely on execution time and resources consumed, making it highly cost-efficient for workloads that have intermittent or bursty activity.
Integration with AWS CloudWatch further enhances the serverless model by providing centralized logging, monitoring, and error tracking. Developers and operators can observe execution metrics, set alarms, and troubleshoot issues without direct access to the underlying infrastructure. This integration ensures that applications remain observable and maintainable even in highly dynamic environments. Because the infrastructure is fully managed by AWS, Lambda functions are inherently highly available, eliminating single points of failure and reducing the operational burden associated with maintaining uptime.
Serverless architectures are particularly well-suited for event-driven workloads. Tasks such as processing files uploaded to S3, performing extract-transform-load (ETL) operations on incoming datasets, or handling order processing in e-commerce systems can be implemented efficiently with Lambda. By coupling Lambda with other managed services, applications can scale automatically, respond in real-time to events, and maintain operational simplicity. This approach ensures that developers can deliver reliable, scalable, and cost-effective solutions without the complexity of managing servers or clusters, enabling organizations to focus on delivering business value rather than infrastructure maintenance.
while EC2 and containerized solutions provide flexibility and control, they come with significant operational responsibilities. Lambda and serverless architectures remove the need for server management, automatically handle scaling and availability, and integrate seamlessly with monitoring tools. This makes serverless computing the ideal choice for event-driven applications that require agility, cost efficiency, and minimal operational overhead, providing a fully managed, high-performing, and resilient solution for modern workloads.
Question 161
A company wants to implement a highly available, multi-AZ relational database for production workloads with automatic failover. Which AWS service is most appropriate?
A) RDS Multi-AZ Deployment
B) RDS Single-AZ Deployment
C) DynamoDB
D) S3
Answer: A) RDS Multi-AZ Deployment
Explanation:
When designing a relational database architecture for production workloads, high availability and fault tolerance are critical considerations. A basic RDS Single-AZ deployment operates solely within a single availability zone. While this configuration is sufficient for non-critical workloads or development environments, it carries inherent risks for production systems. Because all database activity is concentrated in one location, any failure of the underlying infrastructure—such as hardware issues, network interruptions, or availability zone outages—can result in downtime. Additionally, planned maintenance events such as software patching or instance upgrades require the database to be temporarily unavailable, which can disrupt application operations and affect end users.
Alternative AWS storage and database options, while suitable for specific use cases, are not substitutes for a fully relational, highly available system. Amazon DynamoDB, for example, is a fully managed NoSQL database designed for key-value and document data models. It provides high scalability and low-latency access, but it does not offer the rich relational capabilities of RDS. Features such as complex queries, joins across tables, multi-step transactions, and referential integrity are not natively supported. For applications that require relational data structures and transactional consistency, DynamoDB cannot fully replace a relational database. Similarly, Amazon S3 is an object storage service optimized for storing and retrieving large amounts of unstructured data. While highly durable and scalable, S3 is not capable of providing relational database functionality, query processing, or transactional support, making it unsuitable for workloads that depend on structured data and transactional consistency.
RDS Multi-AZ deployment addresses the limitations of Single-AZ setups by automatically creating a synchronous standby replica in a separate availability zone. This standby instance acts as a failover target, ensuring that if the primary database encounters a failure, it can be promoted automatically with minimal downtime. This automatic failover process is seamless and does not require manual intervention, greatly improving reliability and continuity for mission-critical applications. Multi-AZ deployments also handle routine maintenance and patching more efficiently. Updates are applied to the standby first, and then the failover process ensures that the primary instance is updated without impacting availability. Automated backups are also integrated into this deployment, supporting point-in-time recovery while maintaining consistent uptime.
By using a Multi-AZ architecture, organizations gain both operational simplicity and resilience. Applications experience higher uptime, and developers can focus on business logic instead of managing complex disaster recovery procedures. This configuration provides built-in redundancy across multiple locations, ensuring that critical relational workloads remain available even during hardware failures, network issues, or scheduled maintenance. It is particularly well-suited for production environments where consistent performance, transactional integrity, and minimal service interruption are essential. Multi-AZ deployments combine the familiarity and power of RDS relational databases with robust fault tolerance and automated operational management, making them the ideal choice for enterprise-grade, mission-critical workloads that demand high availability, durability, and reliability.
Question 162
A company wants to implement a serverless, event-driven architecture to process 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:
In cloud computing, choosing the right compute model has a significant impact on operational efficiency, scalability, and cost management. Traditional compute services such as Amazon EC2 offer full control over virtual servers, allowing organizations to run applications exactly as needed. However, this level of control comes with substantial responsibilities. EC2 instances must be manually provisioned, patched, and monitored to maintain security and performance. Scaling requires careful planning to ensure that sufficient resources are available during peak demand while avoiding unnecessary costs during periods of low utilization. Additionally, administrators are responsible for monitoring metrics, configuring load balancing, and ensuring fault tolerance, all of which contribute to increased operational complexity.
Containerized solutions, including Amazon ECS and Amazon EKS, provide a more flexible approach by allowing applications to run within isolated containers managed on EC2 nodes. These services simplify deployment and orchestration compared to managing raw EC2 instances, but they do not eliminate the need for infrastructure management. Users must still provision and maintain EC2 instances, manage clusters, handle networking, and monitor both containers and the underlying nodes. Scaling and high availability require thoughtful configuration and ongoing attention, meaning that while ECS and EKS reduce some operational overhead, they do not remove it entirely.
Serverless computing, exemplified by AWS Lambda, represents a fundamental shift in how applications are deployed and executed. Lambda allows developers to focus entirely on writing business logic rather than managing servers or infrastructure. Functions can be triggered directly by events from other AWS services such as S3, where file uploads initiate processing tasks, or SQS, which enables asynchronous workflows. Once deployed, Lambda automatically adjusts compute resources in response to incoming workloads, scaling seamlessly to accommodate bursts in demand. Billing is based solely on the duration of function execution, which provides cost savings compared to maintaining idle EC2 instances.
Lambda’s integration with CloudWatch enhances observability, providing logging, monitoring, and error tracking in a centralized manner. This makes it easier to diagnose issues, set alerts, and maintain operational oversight without needing access to the underlying infrastructure. Because AWS manages the execution environment, Lambda inherently provides high availability and fault tolerance, reducing the risk of downtime. Developers and operations teams benefit from a fully managed environment that supports rapid deployment and reliable execution of event-driven workloads.
This serverless architecture is particularly well-suited for a variety of tasks that respond to specific events. Extract, transform, and load (ETL) jobs can process incoming datasets automatically. Order processing systems can handle transactions triggered by messages in queues. File or image processing workflows can respond immediately to new uploads in S3. By combining Lambda with other managed AWS services, applications can achieve real-time processing, automatic scaling, and cost-effective operation without the operational burden of server management.
Overall, Lambda provides a fully managed, event-driven solution that ensures scalability, high availability, and cost efficiency. Unlike EC2 or container-based approaches, it eliminates infrastructure maintenance, allowing teams to focus on application logic and business outcomes. For modern workloads that require agility, responsiveness, and minimal operational complexity, Lambda offers a robust, serverless computing platform that delivers performance, reliability, and flexibility in a cost-efficient manner.
Question 163
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:
When designing applications that require rapid access to session data, the choice of storage technology plays a critical role in overall performance and user experience. DynamoDB is often considered for such tasks due to its fully managed, serverless nature and generally low-latency data retrieval. While it performs well for many use cases, under conditions of very high traffic or heavy workloads, DynamoDB may not consistently achieve sub-millisecond response times. This variability can affect session-dependent applications, where even minor delays may lead to degraded responsiveness or user experience. Its performance is influenced by factors such as partitioning, access patterns, and request throughput, which may necessitate careful capacity planning and monitoring to maintain consistent speed.
Relational databases like MySQL running on RDS are another common choice for storing application state. RDS MySQL offers the benefits of strong consistency, transactional support, and familiar SQL querying. However, relational databases rely on disk-based storage and involve connection overhead, query parsing, and transactional operations, which can introduce latency. In high-speed, session-intensive scenarios, these delays can accumulate, making RDS less suitable for workloads that require extremely fast, frequent reads and writes. While RDS excels in structured data management and complex queries, its performance characteristics are not ideal for ephemeral session storage that demands immediate access.
Amazon S3, while highly durable and scalable, is fundamentally object storage designed for large, persistent files. Its architecture is optimized for storing and retrieving entire objects rather than handling numerous small, rapid read and write operations. Consequently, using S3 for session storage would lead to inefficient access patterns, increased latency, and higher operational complexity as each session update requires writing an entire object rather than modifying a small in-memory key. This makes S3 unsuitable for use cases that involve frequent updates and real-time state management.
For high-performance session storage, Amazon ElastiCache with Redis is widely recognized as a superior solution. Redis is an in-memory key-value store that provides extremely low-latency access and can handle a high volume of operations per second, making it ideal for session data, caching, and real-time analytics. Its memory-based architecture eliminates the disk I/O bottlenecks inherent in relational or object stores, delivering consistent sub-millisecond performance even under heavy load. Redis also supports features such as replication, clustering, and optional persistence, enabling both scalability and fault tolerance. This allows multiple web servers to access and update session data concurrently, ensuring a seamless experience for users while maintaining high availability.
By leveraging Redis, developers can simplify the operational aspects of session management. Its clustering and replication capabilities reduce the need for complex manual scaling, while persistence options provide data durability without sacrificing performance. Overall, Redis ensures fast, reliable, and scalable access to session data, making it the preferred choice for high-traffic web applications that prioritize responsiveness, consistency, and a smooth user experience across distributed environments.
Question 164
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:
S3 can host and deliver static files such as images, stylesheets, scripts, and HTML pages, making it a reliable option for storing web assets with high durability and availability. However, when used on its own, S3 does not provide the global acceleration or caching capabilities needed to serve users distributed across multiple regions with consistently low latency. Requests sent directly to an S3 bucket must travel to the bucket’s region, which increases response times for geographically distant users and results in a less optimized experience for large or globally accessed applications.
EC2 instances combined with an Application Load Balancer offer a resilient setup for dynamic content and backend processing. This configuration supports load distribution, health checks, and fault tolerance within a single region, helping maintain uptime and performance during fluctuating workloads. Still, because the compute resources and the load balancer reside in one region, users located far from that region may experience slower responses. This regional limitation makes it more suitable for applications with a localized user base rather than global audiences.
Route 53 plays an important role in directing user requests through DNS routing, health checks, and traffic management. While it helps guide users to the most appropriate endpoint based on policies such as latency routing or geolocation routing, Route 53 does not handle the actual content delivery or implement caching. Its function is strictly related to DNS resolution, meaning it cannot reduce the distance between the end user and the requested resource or improve delivery speeds in isolation.
CloudFront bridges these gaps by providing a globally distributed content delivery network (CDN) designed to bring content closer to users. Through its network of edge locations around the world, CloudFront caches frequently accessed static files, dramatically reducing the time it takes for users to receive content. This edge-level caching minimizes latency, improves overall application responsiveness, and reduces load on origin resources such as S3 or ALB. For static resources stored in S3, CloudFront acts as an effective performance layer that accelerates distribution and handles requests at scale without requiring additional infrastructure.
When CloudFront is paired with an Application Load Balancer, dynamic content also benefits from improved performance because CloudFront can keep persistent connections and optimize communication with the ALB. Even if dynamic responses cannot be cached, CloudFront still provides routing efficiency, connection reuse, and enhanced throughput. This combination supports modern architectures where static resources are served from S3 while dynamic features and APIs operate behind an ALB.
CloudFront also strengthens security and reliability. It integrates with AWS WAF to protect against common web threats, supports SSL/TLS for encrypted communication, and offers origin failover to maintain continuity during regional or application-level disruptions. These features combine to form a highly secure and resilient content delivery framework.
By leveraging CloudFront with S3 for static assets and ALB for dynamic workloads, organizations can deliver content with speed, consistency, and protection across the globe. This multi-layered approach ensures optimized performance and availability for users regardless of location.
Question 165
A company wants to store infrequently accessed backup data cost-effectively 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:
When considering storage options for data that is accessed infrequently but still requires occasional fast retrieval, understanding the cost-performance trade-offs of different AWS services is essential. S3 Standard, for instance, is designed to provide high durability and low latency for frequently accessed objects. It offers excellent performance for active workloads, but the pricing structure reflects that frequent access. Using S3 Standard for data that is rarely read, such as long-term backups or archival content, can quickly become cost-prohibitive, as storage costs are higher than services optimized for infrequent access.
Similarly, Amazon EBS gp3 volumes are block storage devices designed to attach to EC2 instances, providing low-latency access for operating systems and applications that require high input/output operations per second (IOPS). While EBS is suitable for active workloads, it is not intended for large-scale, cost-efficient archival. Data stored on EBS incurs ongoing costs regardless of access frequency, making it unsuitable for backups or compliance archives where reads are rare and cost optimization is a priority.
Amazon EFS Standard is another option for file-based storage in AWS. It provides scalable, managed network file storage that is accessible across multiple EC2 instances and is suitable for applications that need shared, persistent file systems. EFS Standard, however, is also optimized for active workloads and frequent file access. Storing large amounts of rarely accessed data in EFS Standard can result in unnecessarily high expenses, particularly for backup and archival use cases, where retrievals are occasional and predictable performance for frequent operations is not required.
For scenarios where cost-effective storage is critical but occasional fast access is still necessary, S3 Glacier Instant Retrieval is a highly suitable solution. This storage class is specifically designed for archival data that does not require constant access but still needs to be retrieved quickly when requested. Glacier Instant Retrieval provides millisecond retrieval, enabling immediate access to stored objects without the delays associated with traditional archival solutions. It integrates seamlessly with lifecycle policies in Amazon S3, allowing organizations to automatically transition objects from S3 Standard or S3 Intelligent-Tiering to Glacier Instant Retrieval as they age, ensuring cost optimization without manual intervention.
Beyond its speed and cost benefits, Glacier Instant Retrieval also offers high durability, with eleven nines of data reliability, ensuring that critical backups or compliance archives are protected against loss. Data is encrypted at rest using either SSE-S3 or SSE-KMS, and access and usage can be monitored through AWS CloudTrail audit logging, supporting compliance and governance requirements. This makes it an ideal solution for organizations that need to maintain long-term backup datasets, disaster recovery archives, or regulatory-compliant storage while keeping operational costs low.
By leveraging S3 Glacier Instant Retrieval, businesses can strike a balance between affordability and accessibility, ensuring that their infrequently accessed data remains secure, durable, and immediately available when required. It allows IT teams to implement automated storage lifecycle policies, reduce overall storage expenses, and maintain reliable, rapid access to archival data, making it a strategic choice for cost-conscious and compliance-focused environments.