Amazon AWS Certified Solutions Architect — Professional SAP-C02 Exam Dumps and Practice Test Questions Set 8 Q106-120

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Question 106

A company wants to implement a highly available relational database for production workloads with automated failover across availability zones. 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:

RDS Single-AZ Deployment operates in only one availability zone, which makes it vulnerable to downtime during failures. DynamoDB is a NoSQL database and does not support relational features such as transactions, joins, or complex queries. S3 is object storage and cannot serve as a relational database. RDS Multi-AZ Deployment automatically creates a synchronous standby replica in a separate availability zone. If the primary instance fails, the standby is promoted automatically, ensuring minimal downtime and high availability. Multi-AZ also manages routine maintenance, patching, and automated backups without impacting availability. This setup is ideal for production workloads requiring relational database features, transactional integrity, and fault tolerance.

Question 107

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:

Running containerized applications in the cloud can be approached in multiple ways, each offering different trade-offs in terms of operational overhead, scalability, and resource management. Traditional approaches, such as using ECS with the EC2 launch type or deploying Kubernetes clusters via EKS on EC2 nodes, require teams to handle the management of underlying EC2 instances. This includes tasks such as provisioning virtual machines, monitoring health, patching operating systems, scaling resources to meet demand, and ensuring high availability. While these approaches offer flexibility and control over the infrastructure, they also increase operational complexity and demand continuous attention from DevOps teams, diverting focus from application development and deployment.

Using EC2 instances alone provides only raw compute capacity. Without additional orchestration, developers and operations teams must implement mechanisms for scheduling containers, managing networking and load balancing, handling fault tolerance, and scaling workloads. These responsibilities can be time-consuming and error-prone, especially in environments with dynamic workloads or rapidly evolving applications. Furthermore, maintaining consistency across multiple instances and ensuring efficient resource utilization adds another layer of challenge.

To address these challenges, ECS with Fargate provides a fully managed, serverless approach to running containerized applications. Fargate eliminates the need to provision or manage servers, as it automatically allocates the compute resources required for each container. Developers simply define their container specifications, including CPU and memory requirements, and Fargate takes care of launching and scaling containers according to demand. This serverless model abstracts the underlying infrastructure, allowing teams to focus on application logic and service deployment rather than system administration.

Fargate integrates seamlessly with other AWS services, including networking, security, and monitoring tools. For instance, containers launched on Fargate can leverage VPC networking for secure communication, IAM roles for fine-grained access control, and CloudWatch for logging, metrics, and event-driven alerts. This integration ensures that applications are not only highly available but also secure and observable, which is critical for production workloads.

One of the key advantages of using Fargate is its ability to automatically scale resources based on workload demands. Whether the application experiences sudden spikes in traffic or fluctuating loads throughout the day, Fargate dynamically adjusts compute capacity to meet requirements. This eliminates the need for manual intervention or over-provisioning, reducing both operational overhead and costs. Moreover, billing is based solely on the resources consumed by the containers during execution, providing a cost-efficient model that aligns expenditure with actual usage.

By removing the burden of infrastructure management, ECS with Fargate empowers development teams to deploy microservices quickly and reliably. It supports high availability and fault tolerance out of the box, without requiring extensive setup or ongoing operational effort. Organizations can therefore focus on building scalable, resilient applications while minimizing administrative overhead, ensuring a streamlined path from development to production. Fargate’s serverless container model represents a significant evolution in cloud-native application deployment, combining the flexibility of containers with the simplicity and efficiency of managed services.

Question 108

A company needs 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 amounts of data to the cloud presents significant challenges, especially when dealing with datasets that span multiple terabytes. Using Amazon S3 alone for migration might seem straightforward, but it often proves impractical for extremely large datasets due to network bandwidth limitations. Moving terabytes of information over the internet can be prohibitively slow, highly susceptible to interruptions, and can incur substantial costs. For organizations attempting to migrate massive volumes of data to S3 using standard network transfers, the process can take weeks or even months, depending on connection speed and reliability. This makes alternative solutions essential for large-scale migrations.

Amazon Elastic File System (EFS) is another option, providing a fully managed, scalable file storage solution designed for shared access across multiple EC2 instances. While EFS excels in providing persistent, high-performance file storage, it is not intended for bulk data migrations. Its architecture is optimized for live, operational workloads rather than one-time mass transfers. Attempting to migrate multi-terabyte datasets via EFS can result in slow performance and increased costs, making it inefficient for large-scale data ingestion projects.

AWS Database Migration Service (DMS) is specifically built to facilitate database migrations. It enables organizations to replicate databases between on-premises environments and AWS with minimal downtime. However, DMS is purpose-built for structured data within relational or NoSQL databases and does not support general-purpose file transfers. Therefore, it cannot be leveraged for bulk migration of large unstructured datasets, such as media files, logs, or archival records.

To address these challenges, AWS offers Snowball, a physical data transfer appliance designed to simplify the migration of large-scale datasets securely and efficiently. Snowball enables customers to move terabytes or even petabytes of data to AWS without relying on potentially slow and unreliable internet connections. The process begins by shipping the Snowball appliance to the customer’s location. Users then load their data locally onto the device, benefiting from the high transfer speeds provided by the appliance’s local storage interface. Once the data is loaded, the appliance is returned to AWS, where the contents are automatically uploaded to Amazon S3. This approach significantly reduces the time required for transferring massive datasets while avoiding strain on the organization’s network bandwidth.

Security is a critical consideration for large-scale data migrations, and Snowball addresses this with robust encryption. Data stored on the appliance is encrypted at rest, and end-to-end encryption ensures that the information remains protected during transit. This guarantees that sensitive information is not exposed at any point during the migration process, which is essential for compliance and regulatory requirements.

Overall, AWS Snowball provides a cost-effective, secure, and highly reliable method for moving vast amounts of data to the cloud. It is particularly suited for scenarios where time, bandwidth, and security are significant concerns. By leveraging Snowball, organizations can complete large-scale migrations efficiently, avoiding the delays, complexity, and costs associated with network-based transfers, and ensuring a smooth path to cloud adoption.

Question 109

A company wants to implement serverless, event-driven processing for orders uploaded to S3 and messages from SQS. Which architecture 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:

Managing compute resources in the cloud can take many forms, each with its own trade-offs in terms of operational overhead, scalability, and efficiency. Traditional approaches, such as running EC2 instances, require a significant amount of manual management. Teams are responsible for provisioning virtual machines, applying security patches, monitoring system performance, scaling resources to meet demand, and ensuring high availability. This manual effort not only increases operational complexity but also consumes valuable time and resources that could otherwise be dedicated to developing and improving application functionality. Even when using container orchestration platforms like ECS with EC2 launch type or EKS with EC2 nodes, much of the same responsibility persists. Clusters must be managed, instances monitored, scaling policies implemented, and updates applied consistently across the infrastructure, creating a substantial administrative burden.

In contrast, serverless architectures, such as AWS Lambda, dramatically simplify operational responsibilities by removing the need to manage underlying infrastructure. Lambda allows developers to focus entirely on application logic rather than the mechanics of servers or virtual machines. Functions can be triggered directly by a variety of event sources, including S3 bucket operations or SQS message queues, enabling a highly responsive, event-driven architecture. This approach ensures that applications react to incoming data or tasks automatically, without requiring the constant intervention of system administrators.

One of the core benefits of Lambda is its ability to automatically scale in response to workload demands. Whether a system experiences a sudden surge in traffic or a gradual increase in processing requirements, Lambda dynamically allocates resources to meet the demand. This automatic scaling eliminates the need to pre-provision instances or overestimate capacity, reducing both operational complexity and infrastructure costs. Billing is based solely on the actual execution time and resources consumed by functions, making it a cost-efficient solution for workloads that experience variable or unpredictable usage patterns.

Lambda integrates seamlessly with AWS monitoring and management tools such as CloudWatch, providing comprehensive logging, metrics, and automated error handling. Developers can track function performance, detect anomalies, and respond to failures without manually inspecting individual servers. This integration ensures that applications remain observable, maintainable, and resilient, even in complex distributed environments. By leveraging CloudWatch, teams gain real-time visibility into operational health while minimizing the overhead associated with traditional monitoring approaches.

For use cases like order processing, event-driven architecture enabled by Lambda provides significant advantages. Incoming orders can trigger functions to handle validation, inventory updates, payment processing, and notification delivery, all without requiring pre-provisioned servers or clusters. The architecture ensures reliability and high availability, as Lambda functions are executed across multiple availability zones, providing fault tolerance by default. Additionally, the serverless model supports microservice-oriented designs, enabling modular, independently deployable components that can evolve and scale without impacting the overall system.

Overall, Lambda offers a fully managed, scalable, and cost-effective solution for event-driven workloads. It eliminates the operational overhead associated with managing EC2 instances or container clusters, supports automatic scaling based on demand, and integrates with monitoring and logging tools for observability and reliability. This makes it an ideal choice for processing tasks such as order management, data transformation, and real-time notifications, allowing organizations to build responsive, high-performance systems without the complexity of traditional infrastructure management.

Question 110

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 appropriate?

A) ElastiCache Redis
B) DynamoDB
C) RDS MySQL
D) S3

Answer: A) ElastiCache Redis

Explanation:

DynamoDB is low-latency but may not consistently achieve sub-millisecond performance under heavy load. RDS MySQL introduces latency due to disk I/O and connections, making it less suitable for session data. 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 and scalability for high-traffic applications. This ensures smooth user experiences while minimizing operational complexity.

Question 111

A company wants to reduce latency for a globally distributed web application serving 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 does not optimize delivery for global users or provide caching at edge locations. EC2 with ALB offers high availability within a region but increases latency for users in other regions. Route 53 provides DNS-based routing but does not serve or cache content. CloudFront is a global content delivery network that caches static content at edge locations close to users, reducing latency. Combining CloudFront with S3 for static content and ALB for dynamic content ensures fast, secure, and highly available delivery worldwide. CloudFront integrates with AWS WAF for security, SSL/TLS for encrypted connections, and origin failover for high availability. This architecture improves user experience, reduces load on origin servers, and ensures a globally optimized, fault-tolerant web application.

Question 112

A company wants to store infrequently accessed backup data cost-effectively but with fast retrieval when needed. Which 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 selecting a storage solution for data backup, archival, or disaster recovery, it is essential to consider both cost and performance characteristics. Different AWS storage services are optimized for specific use cases, and understanding these differences is crucial to ensuring efficient and cost-effective data management.

Amazon S3 Standard is one of the most commonly used storage classes, designed for workloads that require frequent access to data. While it offers low latency and high throughput, it comes at a higher cost compared with other storage options. This makes S3 Standard ideal for active datasets and applications that require constant access to files but less suitable for storing large volumes of infrequently accessed data due to the associated expense.

Amazon Elastic Block Store (EBS) gp3 volumes provide block-level storage that can be attached to EC2 instances. EBS is excellent for scenarios requiring high-performance storage, such as transactional databases or applications with intensive I/O operations. However, using EBS for backups is not cost-effective because it is primarily designed for active, attached storage rather than long-term data retention. Regular snapshots can mitigate this somewhat, but at scale, the cost can quickly become prohibitive.

Amazon Elastic File System (EFS) Standard is a managed network file system optimized for active file storage with multiple EC2 instances accessing the same data simultaneously. While EFS Standard offers flexibility and scalability, it is priced higher than archival options, particularly for data that is accessed infrequently. As a result, EFS is best suited for shared workloads where files need to be read and written continuously, rather than serving as a long-term, low-cost backup solution.

For workloads that require cost-effective archival storage with occasional or predictable retrieval, Amazon S3 Glacier Instant Retrieval provides an ideal solution. This storage class combines the low cost of traditional archival storage with millisecond retrieval times, allowing rapid access to data whenever needed. It integrates seamlessly with S3 lifecycle policies, enabling automated transitions from S3 Standard or Intelligent-Tiering to Glacier Instant Retrieval. This reduces manual management while optimizing storage costs.

Glacier Instant Retrieval is built to provide extremely high durability, with 11 nines of reliability, ensuring that data is protected against loss over long periods. In addition, it offers strong encryption at rest using either SSE-S3 or SSE-KMS, maintaining compliance with organizational and regulatory security requirements. Integration with AWS CloudTrail enables auditing and tracking of access or modifications, which is especially important for regulatory or compliance-focused workloads.

The combination of low cost, high durability, encryption, and auditing capabilities makes Glacier Instant Retrieval an ideal choice for backups, disaster recovery, and compliance-related storage. Organizations can maintain large volumes of historical data without incurring the higher costs associated with frequently accessed storage while still ensuring that the data is available instantly when required. By leveraging Glacier Instant Retrieval, teams can balance cost-efficiency with operational accessibility, meeting both business and regulatory requirements while optimizing overall storage spend.

Overall, selecting the right storage class is about aligning cost, performance, and accessibility requirements with the intended use case. While S3 Standard, EBS, and EFS serve active workloads, Glacier Instant Retrieval stands out as a reliable, low-cost solution for archival and backup, providing rapid access to data, robust durability, and comprehensive security features.

Question 113

A company wants to migrate an on-premises SQL Server database to AWS with minimal downtime. Which approach is recommended?

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:

Migrating SQL Server databases to the cloud presents a range of challenges, particularly when minimizing downtime and ensuring data integrity are priorities. Using Amazon RDS for SQL Server without additional tools can be effective for hosting relational workloads, but performing a migration in this manner often involves manual export and import processes. These steps require the database to be offline during the transfer, which can result in extended downtime and disrupt business operations. For organizations running mission-critical workloads, this approach may be impractical, as the downtime window can be difficult to accommodate.

Another potential option is to migrate to Amazon Aurora PostgreSQL. While Aurora provides strong performance, scalability, and cloud-native features, it is not directly compatible with SQL Server. Moving to Aurora PostgreSQL typically involves significant application-level changes, including rewriting queries, modifying stored procedures, and adjusting schema structures. For many organizations, these modifications represent a substantial development effort and risk, making this path less attractive when the goal is a quick, low-disruption migration.

Some teams consider hosting SQL Server on EC2 instances and performing manual backup and restore operations. While this approach offers complete control over the database environment, it introduces several operational challenges. Manually managing backups and restores is time-intensive, prone to human error, and requires meticulous planning to avoid data loss. Additionally, the database must often be taken offline during migration, leading to longer downtime periods. This method also increases administrative overhead, as the team must handle patching, replication, and monitoring without the automated conveniences provided by managed services like RDS.

A more efficient and low-downtime approach is to use Amazon RDS for SQL Server in combination with AWS Database Migration Service (DMS). DMS enables continuous replication from the source database to the target RDS instance, allowing the source database to remain operational throughout the migration process. This near real-time replication ensures that changes occurring on the source system are synchronized with the target, drastically reducing downtime compared to traditional export-import methods.

In addition to data replication, AWS DMS supports schema conversion and transformation, which helps adapt the source database schema to the target environment with minimal manual intervention. This capability simplifies migration planning and ensures that data types, constraints, and relationships are preserved during the move. By leveraging DMS, organizations can migrate large, active databases with minimal impact on end users and business operations.

Amazon RDS itself provides additional benefits that enhance the reliability and manageability of the migration. Automated backups, Multi-AZ deployments, and routine maintenance management reduce operational complexity, ensuring that the database remains resilient and highly available. These features also support post-migration operations, providing a stable environment for ongoing production workloads.

By combining RDS SQL Server with AWS DMS, organizations can achieve a streamlined migration strategy that balances performance, reliability, and minimal downtime. This approach avoids the extensive downtime associated with manual export-import methods, reduces operational risk, and eliminates the need for significant application-level changes. For mission-critical SQL Server workloads, this methodology ensures a smooth, reliable transition to the cloud while preserving business continuity and minimizing administrative effort.

Question 114

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:

Managing traditional compute resources such as EC2 instances can introduce significant operational complexity. Each instance requires ongoing maintenance, including patching the operating system and any installed software, configuring scaling to handle variable workloads, and monitoring for performance and health issues. This manual management not only increases administrative overhead but also introduces the risk of human error, which can lead to downtime, security vulnerabilities, or performance bottlenecks. For teams managing large fleets of EC2 instances, these challenges can multiply, making it difficult to focus on application development and business priorities.

Containers, orchestrated via services like Amazon ECS or Amazon EKS running on EC2 nodes, provide some abstraction and scalability benefits. However, they still rely on underlying EC2 instances, meaning that clusters of nodes must be provisioned, patched, and monitored. Cluster management requires configuring auto-scaling groups, handling load balancing, and ensuring node health and capacity. While these container orchestration services improve deployment flexibility and resource utilization, they do not eliminate the need for significant operational oversight. Teams must continuously monitor cluster performance, update node AMIs, and manage scaling policies to ensure workloads run efficiently.

Serverless computing, particularly AWS Lambda, addresses many of these operational challenges by removing the need to manage servers entirely. Lambda allows functions to execute in response to events such as S3 object uploads, SQS messages, API Gateway requests, or DynamoDB streams. Functions scale automatically based on demand, providing virtually unlimited concurrency without requiring pre-provisioned infrastructure. Unlike EC2 or container-based approaches, organizations only pay for actual compute time, measured in milliseconds, which can lead to substantial cost savings, especially for workloads with intermittent or unpredictable traffic patterns.

Beyond scaling and cost benefits, Lambda integrates seamlessly with AWS CloudWatch for logging, metrics, and alerting. This provides real-time visibility into function executions, durations, and errors, enabling teams to monitor performance and troubleshoot issues efficiently. The combination of event-driven triggers and integrated monitoring allows developers to focus on writing business logic rather than managing the underlying infrastructure. Error handling, retries, and dead-letter queues are also natively supported, reducing operational risk and ensuring high reliability for critical processes.

This serverless architecture is particularly well-suited for workloads such as order processing, image or document processing, data transformations, and real-time notifications. Event-driven Lambda functions can respond immediately to incoming events without requiring the application to poll or manage servers, ensuring fast, predictable responses. With high availability built into the platform, functions are executed redundantly across multiple availability zones, reducing the likelihood of downtime.

while EC2 and container-based services provide control and flexibility, they introduce operational complexity and require ongoing maintenance. Lambda delivers a fully managed, event-driven computing model that automatically handles scaling, monitoring, and fault tolerance. By removing the burden of infrastructure management, it allows teams to focus on developing business logic, reduces operational overhead, and ensures reliable, cost-efficient execution. For workloads that benefit from event-driven processing and automatic scaling, Lambda represents a highly efficient, resilient, and economical solution.

Question 115

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:

Managing session data in high-traffic web applications requires careful consideration of latency, throughput, and reliability. While several AWS data storage services offer different performance characteristics, not all are well-suited for handling the demands of frequent, fast-changing session information. DynamoDB, for example, is a fully managed NoSQL database designed for low-latency access. It performs exceptionally well for many workloads, but under heavy traffic conditions, it may not consistently deliver sub-millisecond response times. This variability can become a concern when user sessions require rapid updates or near-instantaneous retrieval, particularly in applications that depend on real-time user interactions.

RDS MySQL, on the other hand, is a relational database offering strong consistency and robust transactional support. However, it introduces latency due to disk input/output operations and the overhead of managing database connections. While ideal for structured data and transactional applications, RDS MySQL is less suitable for session storage because session access patterns often involve small, frequent reads and writes that demand extremely low latency. The added delay from disk operations can degrade the user experience, especially under concurrent access from multiple web servers.

S3, Amazon’s object storage service, excels at storing large amounts of unstructured data with high durability. It is highly cost-effective for archival storage or large file delivery, but it is not optimized for workloads involving frequent, small read/write operations. Using S3 to store session data would result in significant latency, as object storage is not designed for low-latency, high-frequency access patterns. Furthermore, S3 lacks the direct in-memory access that is often required for fast session retrieval.

ElastiCache Redis, in contrast, is specifically designed to address the challenges of session management in distributed web applications. As an in-memory key-value store, Redis provides extremely low latency and high throughput, ensuring that session data can be read and updated almost instantaneously. Redis supports replication and clustering, enabling horizontal scaling across multiple nodes while maintaining data availability. Optional persistence allows session data to be retained even in the event of node failures, providing durability without sacrificing performance.

By leveraging Redis for session storage, applications can maintain fast and reliable access to user sessions across multiple web servers. This eliminates bottlenecks associated with disk-based databases or object storage, allowing developers to achieve consistent sub-millisecond access times, even during periods of heavy traffic. Additionally, Redis reduces operational complexity by removing the need for constant tuning of database connections or I/O performance. High availability is supported through replication and automatic failover, which minimizes downtime and ensures that user sessions remain uninterrupted.

while DynamoDB, RDS MySQL, and S3 each have their strengths, they are not ideal for handling session data under high-concurrency conditions. Redis offers a specialized solution that combines ultra-low latency, high throughput, scalability, and reliability. Its design allows web applications to deliver seamless user experiences, reduce operational overhead, and maintain high availability, making it the preferred choice for session management in demanding, performance-sensitive environments.

Question 116

A company wants to implement a messaging system that ensures ordered delivery and exactly-once processing for critical microservices. 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 communication between microservices, selecting the right messaging service is critical to ensuring reliability, consistency, and fault tolerance. Amazon Simple Queue Service (SQS) offers different queue types, each with distinct behavior that impacts how messages are delivered and processed. The standard SQS queue provides at-least-once delivery, meaning every message is delivered at least once but may be delivered multiple times in some cases. While this ensures no messages are lost, it does not guarantee the order of delivery. For microservices that rely on sequential processing or maintain stateful workflows, this lack of ordering can introduce inconsistencies, resulting in unpredictable outcomes, duplicated work, or errors in transaction processing.

Amazon Simple Notification Service (SNS) takes a different approach by functioning as a pub/sub messaging system. SNS enables messages to be broadcast to multiple subscribers, supporting fan-out architectures where a single event triggers multiple workflows simultaneously. Although this is useful for event-driven systems requiring multiple actions from a single event, SNS does not provide ordering guarantees or automatic deduplication. As a result, systems that require strict sequencing or exactly-once processing cannot rely on SNS alone without implementing additional logic at the subscriber level, which increases complexity and operational overhead.

For scenarios where ordered, high-throughput streaming is essential, Amazon Kinesis Data Streams can be an appropriate choice. Kinesis delivers messages in order per shard and is optimized for large-scale, real-time data ingestion and analytics. It is ideal for use cases such as log aggregation, clickstream analysis, and streaming metrics processing. However, for microservice-to-microservice communication, Kinesis may be overly complex. Developers must manage shards, scaling, and consumer applications, which can introduce unnecessary operational overhead when the primary goal is reliable, ordered message delivery rather than analytics.

In contrast, SQS FIFO (First-In-First-Out) queues are specifically designed to address these challenges in distributed systems. FIFO queues ensure that messages are delivered exactly once and in the order they are sent, providing predictable sequencing that is essential for workflows that involve transactions, stateful operations, or dependency chains. Additionally, FIFO queues support deduplication, preventing the processing of duplicate messages that might otherwise occur due to network retries or client-side errors. By guaranteeing message order and eliminating duplication, FIFO queues simplify application logic, reduce the risk of inconsistent states, and enhance overall system reliability.

The combination of ordering, exactly-once delivery, and deduplication makes SQS FIFO queues particularly well-suited for microservices that require consistent, fault-tolerant communication. For example, in an e-commerce platform, order processing, payment handling, and inventory updates may all be orchestrated across multiple microservices. Using a FIFO queue ensures that each step occurs in the correct sequence and that no order is processed more than once, even under conditions of network retries or transient failures. This capability enables developers to build resilient systems that maintain data integrity across distributed components without implementing complex custom logic for handling duplicates or sequencing issues.

Ultimately, while SQS Standard, SNS, and Kinesis each serve important roles, SQS FIFO provides the most predictable and reliable solution for transactional, order-sensitive workflows in microservices. Its ability to combine ordered delivery, deduplication, and exactly-once processing reduces operational complexity, increases system reliability, and ensures consistent outcomes in distributed applications, making it a cornerstone for building fault-tolerant architectures.

Question 117

A company wants to analyze petabyte-scale structured datasets stored in S3 using SQL. Which AWS service is most appropriate?

A) Redshift
B) Athena
C) RDS MySQL
D) DynamoDB

Answer: A) Redshift

Explanation:

Athena is serverless and suitable for ad-hoc queries on S3 data but is not optimized for continuous querying of petabyte-scale structured datasets. RDS MySQL is designed for transactional workloads and cannot efficiently handle very large-scale analytics. DynamoDB is a NoSQL key-value store and does not natively support SQL-based analytics for structured datasets. Redshift is a fully managed, columnar data warehouse that provides high-performance SQL-based analytics on structured datasets at petabyte scale. It supports parallel query execution, compression, and columnar storage to optimize performance. Redshift integrates with S3 for direct data ingestion, allows high concurrency for multiple users or applications, and provides scalability for growing data volumes. This makes Redshift ideal for large-scale structured analytics with cost efficiency and reliability.

Question 118

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:

Managing compute resources efficiently is a critical aspect of running cloud environments, especially when dealing with multiple development, testing, or staging instances that are not required to run continuously. While production workloads require dynamic scaling to meet fluctuating traffic demands, non-production environments often remain idle for significant periods, leading to unnecessary costs if not managed properly. AWS offers several tools to optimize instance usage, but not all are suited for scheduling start and stop operations for non-production instances.

Auto Scaling scheduled actions are primarily designed to help production workloads by automatically adjusting the number of instances in a fleet based on anticipated traffic patterns. These actions are effective for handling predictable spikes and drops in user activity but are not intended to manage non-production environments. Attempting to use Auto Scaling for starting or stopping test instances can result in inconsistent behavior and is not aligned with the intended functionality of the service. As a result, relying solely on Auto Scaling for this purpose is not practical.

Another common approach is manual intervention, where administrators start or stop instances as needed. While straightforward, this method is prone to human error. Missing a scheduled stop, starting an instance late, or forgetting to shut down an environment can all result in unnecessary costs and inefficient resource utilization. Additionally, manual management requires continuous monitoring and effort from operations teams, which adds operational overhead and distracts from more strategic tasks.

Spot Instances are often employed to reduce costs, as they allow users to take advantage of unused capacity at a lower price. However, Spot Instances are not designed with scheduled start or stop capabilities in mind. They are better suited for workloads that can tolerate interruptions and variability in availability rather than environments requiring predictable start and stop schedules. Using Spot Instances alone will not address the need for automated, controlled scheduling of non-production instances.

AWS Systems Manager Automation provides a robust solution to these challenges. By creating automated runbooks, administrators can define cron-like schedules that start or stop EC2 instances at predefined times. This automation ensures that non-production resources are active only during working hours or other designated periods, thereby optimizing costs without requiring manual intervention. Systems Manager Automation also allows for centralized management across multiple environments, making it easier to maintain consistent schedules for all instances.

An additional benefit of using Automation is the inclusion of auditing and logging features. Every execution of a runbook can be tracked, providing a record of when instances were started or stopped. This capability enhances compliance and accountability, particularly in organizations that require strict operational oversight. By leveraging these logs, teams can analyze usage patterns, verify that schedules are being enforced, and maintain transparency for audit purposes.

Overall, implementing Systems Manager Automation for scheduled instance management reduces operational overhead, minimizes human error, and ensures predictable control over non-production resources. By running non-critical instances only when needed, organizations can achieve significant cost savings while maintaining the efficiency and reliability of their cloud environments. This approach streamlines management for multiple environments, providing both financial and operational benefits in a scalable, repeatable manner.

Question 119

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 alone can serve static content but lacks global caching and cannot deliver dynamic content efficiently. EC2 with ALB provides high availability in a single region but increases latency for global users. Route 53 manages DNS but cannot cache or serve content. CloudFront is a global CDN that caches static content at edge locations to reduce latency. Combining CloudFront with S3 for static content and ALB for dynamic content ensures fast, secure, and highly available delivery worldwide. CloudFront also integrates with WAF, SSL/TLS, and origin failover, providing security, encryption, and high availability. This architecture improves user experience, reduces load on origin servers, and provides a globally optimized, fault-tolerant solution.

Question 120

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 appropriate?

A) ElastiCache Redis
B) DynamoDB
C) RDS MySQL
D) S3

Answer: A) ElastiCache Redis

Explanation:

When designing a system to manage user sessions in web applications, choosing the right storage solution is critical for ensuring both performance and reliability. Various options exist in the AWS ecosystem, but not all are suited to handling the demands of session management, particularly in high-traffic environments where low latency and consistent responsiveness are essential.

Amazon DynamoDB is often considered for high-performance use cases because it provides low-latency access and can scale horizontally. However, while DynamoDB can achieve very fast response times under normal conditions, it does not always guarantee sub-millisecond performance, especially during periods of heavy load or traffic spikes. This inconsistency can introduce small but noticeable delays in applications where rapid session reads and writes are required. Although DynamoDB is highly available and resilient, the variable latency can make it less ideal for managing user sessions, which benefit from predictable, extremely fast access.

Relational databases such as Amazon RDS MySQL are also commonly used in web architectures. While RDS provides strong consistency and complex querying capabilities, it introduces additional overhead in the form of disk I/O operations and connection management. These factors can make RDS a bottleneck when handling frequent, small transactions like session reads and writes. Each session update involves a write to disk and the management of database connections, which can add latency and reduce throughput. In environments with many concurrent users, this can result in slower response times and may require additional scaling measures, increasing operational complexity.

Amazon S3, on the other hand, is optimized for object storage and excels at storing large files such as images, videos, and backups. However, S3 is not suitable for workloads that require frequent small read and write operations, such as session management. Its access patterns and eventual consistency model make it inefficient for the rapid, low-latency access needed to maintain active user sessions across multiple web servers.

ElastiCache for Redis stands out as an ideal solution for session management in high-performance applications. Redis is an in-memory key-value store that is designed specifically for extremely low latency and high throughput. By keeping data in memory rather than on disk, Redis can provide response times measured in microseconds, ensuring that session data is available almost instantaneously. Redis supports replication and clustering, which allows it to scale horizontally while maintaining high availability. It also offers optional persistence mechanisms, giving developers the ability to recover data in the event of failures without compromising performance.

Using Redis for session management provides several operational and user experience benefits. Because session reads and writes are extremely fast, users experience minimal latency during interactions with the application. Redis also reduces the operational overhead associated with maintaining session state across multiple web servers, as it centralizes session storage in a highly performant cache. Additionally, Redis’s ability to scale horizontally allows applications to handle sudden spikes in traffic without degradation in performance, ensuring reliability during peak usage periods. Overall, Redis delivers a combination of speed, reliability, and scalability that makes it the preferred choice for managing session data in modern, high-traffic web applications.