Amazon AWS Certified Solutions Architect — Professional SAP-C02 Exam Dumps and Practice Test Questions Set 15 Q211-225

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Question 211

A company wants to store infrequently accessed backup data cost-effectively but with fast retrieval when needed. Which AWS service is most suitable?

A) S3 Glacier Instant Retrieval
B) S3 Standard
C) EBS gp3
D) EFS Standard

Answer: A) S3 Glacier Instant Retrieval

Explanation:

Managing data storage in the cloud requires careful consideration of access patterns, cost efficiency, and durability requirements. Different AWS storage services and classes are optimized for specific use cases, and selecting the right option can dramatically impact both operational costs and system performance. For data that is frequently accessed, Amazon S3 Standard is a suitable choice. It provides high availability, low latency, and strong durability, making it ideal for active datasets that need to be readily accessible. However, this convenience comes at a cost. For data that is accessed infrequently, S3 Standard can be expensive, especially when storing backups or archival information that rarely needs to be retrieved.

Amazon Elastic Block Store (EBS), particularly the gp3 volume type, offers block-level storage for Amazon EC2 instances. EBS gp3 is highly performant, with configurable IOPS and throughput, making it ideal for transactional databases, boot volumes, and applications requiring consistent and fast access to storage. Despite its advantages for active workloads, EBS is not cost-efficient for long-term archival storage. Keeping large volumes of infrequently accessed data on EBS results in unnecessary expenses because it is designed for performance rather than cost optimization.

Amazon Elastic File System (EFS) Standard provides scalable file storage for use cases requiring frequent access and shared access across multiple EC2 instances. While it supports high availability and provides a managed network file system, EFS Standard is not designed for infrequent access. Using it for backup or archival purposes can lead to high costs without significant benefits, since the storage is priced for active usage rather than long-term retention.

For infrequently accessed data that still requires rapid retrieval, Amazon S3 Glacier Instant Retrieval is an optimal solution. This storage class provides low-cost archival storage while enabling millisecond retrieval times. It is particularly useful for datasets such as backups, compliance archives, or disaster recovery files that are not regularly accessed but must be available immediately when needed. Glacier Instant Retrieval integrates seamlessly with lifecycle policies, allowing organizations to automate the transition of objects from S3 Standard or Intelligent-Tiering to Glacier Instant Retrieval based on predefined rules. This automation reduces manual management overhead and ensures that storage costs remain optimized over time.

In addition to performance and cost efficiency, Glacier Instant Retrieval offers enterprise-grade durability and security. The service guarantees eleven nines of durability, ensuring that data is highly resilient against loss. Encryption at rest is supported through both SSE-S3 and SSE-KMS, allowing organizations to meet security and regulatory requirements. Audit logging is enabled through integration with AWS CloudTrail, providing visibility into access and changes to stored data, which is crucial for compliance and governance purposes.

By leveraging Glacier Instant Retrieval, organizations can maintain infrequently accessed datasets at a fraction of the cost of more active storage options, without sacrificing immediate availability. This makes it an ideal solution for managing backups, long-term archives, and disaster recovery data. It balances affordability with performance, durability, and security, ensuring that critical data remains accessible, protected, and compliant while keeping overall storage expenses under control. Implementing this storage strategy allows organizations to optimize costs while maintaining readiness for operational or regulatory demands.

Question 212

A company wants to migrate an on-premises Oracle database to AWS with minimal downtime. Which approach is most suitable?

A) RDS Oracle with AWS DMS continuous replication
B) EC2 Oracle with manual backup and restore
C) Aurora PostgreSQL
D) DynamoDB

Answer: A) RDS Oracle with AWS DMS continuous replication

Explanation:

Migrating a production Oracle database to the cloud is a critical task that demands careful planning, minimal downtime, and operational efficiency. Traditional approaches, such as deploying Oracle on an Amazon EC2 instance and performing manual backup and restore operations, often require extended periods of downtime, creating significant disruption for production workloads. These manual processes are labor-intensive and prone to errors, as database administrators must manage backup scheduling, storage, restoration, and validation. The operational overhead of maintaining EC2 instances—including patching, scaling, and monitoring—adds further complexity and increases the risk of downtime during migration.

Another alternative, Amazon Aurora PostgreSQL, offers managed relational database capabilities with high performance and scalability. However, it is not compatible with Oracle, meaning that moving an Oracle workload to Aurora would involve extensive schema modifications, application rewrites, and potential re-architecting of database logic. This level of migration effort is costly, time-consuming, and carries a high risk of introducing errors or inconsistencies. Similarly, Amazon DynamoDB, a fully managed NoSQL database, cannot host Oracle workloads, as it lacks relational features such as complex joins, transactions, and SQL-based querying, making it unsuitable for enterprise Oracle applications.

A more efficient and reliable approach involves using Amazon RDS for Oracle in conjunction with AWS Database Migration Service (DMS). This combination allows near real-time replication from the source Oracle database while the source system remains fully operational, significantly reducing downtime. AWS DMS continuously replicates data, ensuring consistency and integrity throughout the migration process. It minimizes the risk of data loss or corruption and allows organizations to test and validate the migration before cutting over to the cloud environment.

Amazon RDS further simplifies operational management by providing automated backups, built-in patching, and Multi-AZ deployments for high availability. Multi-AZ replication ensures that if a primary instance fails, a synchronous standby is automatically promoted with minimal downtime. This feature is particularly valuable for production workloads where uptime and reliability are critical. RDS also handles routine maintenance and monitoring, freeing database administrators from the operational burden associated with traditional EC2-based deployments.

Using RDS Oracle with AWS DMS for migration provides a streamlined, low-downtime pathway to the cloud. Organizations can move production Oracle workloads without the operational complexity of manual processes, avoiding the need for extensive application rewrites and minimizing disruption to end users. Data integrity is preserved, and high availability is maintained throughout the migration, allowing businesses to continue operating smoothly while transitioning to a cloud-native architecture.

Combining RDS Oracle with AWS DMS offers a powerful solution for migrating Oracle databases to the cloud. It addresses the challenges of downtime, operational effort, and application compatibility that accompany traditional migration approaches. By leveraging managed services, organizations can achieve a secure, reliable, and highly available environment, enabling a smooth transition to cloud infrastructure while maintaining the performance and stability required for critical production workloads. This method ensures efficiency, reduces risk, and simplifies the management of enterprise Oracle databases in the cloud.

Question 213

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:

Efficient management of non-production EC2 instances is an important consideration for organizations aiming to control cloud spending while maintaining operational consistency. Unlike production workloads, which often rely on Auto Scaling scheduled actions to handle dynamic capacity needs, non-production environments—such as development, testing, or staging—require different approaches to achieve cost optimization. Auto Scaling scheduled actions are designed primarily for scaling resources in response to predictable production traffic patterns. While useful for adjusting capacity, these actions do not inherently provide mechanisms to stop or start non-production instances on a fixed schedule, leaving a gap in cost management strategies for development workloads.

Manual management of non-production instances is an alternative but comes with significant drawbacks. Stopping and starting EC2 instances manually is error-prone and requires continuous human intervention. Team members must remember to perform these actions consistently, which can lead to mistakes, missed schedules, or unintended downtime. This approach is inefficient, particularly when managing multiple non-production environments across different teams, regions, or accounts. The operational burden increases significantly as the number of instances grows, reducing overall productivity and increasing the risk of wasted cloud spend.

Using Spot Instances can help reduce costs for workloads that are flexible and interruptible, but they are not designed for scheduled automation. Spot Instances allow organizations to take advantage of unused EC2 capacity at a lower price, yet they do not provide native capabilities to start or stop instances according to a pre-defined schedule. They are best suited for batch processing, testing, or fault-tolerant workloads rather than consistent cost control for non-production environments.

AWS Systems Manager Automation provides a more effective solution for managing non-production EC2 instances. Systems Manager allows the creation of automated runbooks that can start or stop instances based on a cron-style schedule. By defining the schedule in advance, organizations can ensure that instances are only running during working hours or other designated periods, significantly reducing costs without impacting availability during critical times. Automation ensures consistent execution and removes the need for manual intervention, reducing operational overhead and the likelihood of human error.

In addition to efficiency, Systems Manager Automation offers auditing and compliance benefits. Each action executed by a runbook is logged, enabling teams to track when instances are started or stopped, and providing a verifiable record for internal audits or cost reporting. This transparency is particularly valuable in large organizations with multiple development teams and non-production environments, as it ensures that policies for resource usage and cost optimization are consistently enforced.

Implementing Systems Manager Automation for non-production EC2 instances provides a scalable, reliable, and cost-effective approach to managing cloud resources. By automating the start and stop of instances according to a defined schedule, organizations can optimize costs, reduce operational complexity, and maintain control over multiple environments without compromising productivity. This strategy balances efficiency, governance, and cost management, making it an ideal solution for organizations that operate extensive non-production workloads in AWS.

Question 214

A company wants to implement a global web application with low latency for static and dynamic content. Which architecture is most suitable?

A) CloudFront with S3 for static content and ALB for dynamic content
B) S3 only
C) EC2 with ALB only
D) Route 53 only

Answer: A) CloudFront with S3 for static content and ALB for dynamic content

Explanation:

Delivering web applications efficiently to users across the globe requires a carefully designed architecture that balances performance, availability, scalability, and security. Amazon S3 is a reliable and highly durable object storage service that excels at serving static content such as images, videos, style sheets, and JavaScript files. It provides a simple, scalable way to host static assets. However, S3 alone cannot efficiently handle dynamic content or provide caching for global users. Without caching mechanisms, requests for frequently accessed content must travel all the way to the origin bucket, increasing latency for users located far from the AWS region hosting the S3 bucket. This limitation can result in slower load times and a suboptimal user experience for a geographically distributed audience.

EC2 instances paired with an Application Load Balancer (ALB) can serve dynamic content and provide high availability within a single region. The ALB distributes incoming requests across multiple EC2 instances, ensuring that the workload is balanced and resilient against individual instance failures. While this setup improves availability and can handle dynamic processing, it does not inherently solve latency issues for users located far from the deployed region. Traffic must traverse long distances, increasing response times and potentially reducing the overall performance of the application for global users.

Route 53, AWS’s scalable Domain Name System (DNS) service, can direct user requests to the nearest healthy endpoint using latency-based or geolocation routing. While Route 53 ensures that users are directed to an optimal region, it does not provide caching or content delivery, so static content still needs to be retrieved from the origin, which can result in slower load times for global users.

Amazon CloudFront, a global Content Delivery Network (CDN), addresses these performance challenges by caching content at strategically placed edge locations around the world. Static content from S3 can be replicated to these edge caches, significantly reducing latency by serving requests from locations closer to the end users. CloudFront also supports dynamic content acceleration, enabling faster delivery of content generated by EC2 instances or other origins. The CDN integrates with AWS Web Application Firewall (WAF) for enhanced security, enforces SSL/TLS encryption for secure data transmission, and supports origin failover to ensure continued availability even if the primary origin experiences issues.

By combining CloudFront with S3 for static content and an ALB-backed EC2 deployment for dynamic content, organizations can deliver a globally optimized architecture. Static assets are served rapidly from edge locations, while dynamic requests are efficiently processed by scalable, highly available backend instances. CloudFront’s caching, along with S3’s durability and ALB’s load distribution, provides a seamless and fast user experience while maintaining security, resilience, and operational simplicity. This integrated approach ensures that web applications can meet the expectations of a worldwide audience, offering minimal latency, high reliability, and secure content delivery without compromising on scalability or performance.

Question 215

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:

In modern web applications, particularly those with high traffic and dynamic user interactions, the choice of a session storage and caching solution is critical to ensure performance, scalability, and reliability. Applications frequently need to store and retrieve session data, configuration states, and other rapidly changing information. Selecting a storage system that can handle frequent reads and writes while maintaining low latency directly impacts user experience and operational efficiency.

Amazon DynamoDB is a fully managed NoSQL database that provides low-latency access and scales automatically. It can handle large volumes of read and write requests and is highly available. However, while DynamoDB is fast, its performance under heavy traffic may not always maintain consistent sub-millisecond response times. During periods of intense load, latency can increase, which may affect applications that require immediate session retrieval. Additionally, DynamoDB is a key-value and document database, which limits its ability to perform relational operations, making it less ideal for workloads requiring complex joins or transactional consistency at extremely low latency.

Amazon RDS MySQL, on the other hand, is a relational database designed for transactional workloads and complex queries. While it provides durability, consistency, and structured query capabilities, it introduces latency due to disk I/O and connection overhead. High-volume, real-time session storage or rapidly changing data often suffers in performance when relying solely on RDS MySQL, as every read and write operation depends on the underlying storage subsystem and network latency.

Amazon S3 is an object storage service optimized for storing large files, backups, and archival data. Although S3 provides virtually unlimited scalability and high durability, it is not suitable for frequent small reads and writes, such as session data. Accessing S3 for high-volume, rapidly changing information can become inefficient due to network round-trip times and object-level consistency models, making it unsuitable for real-time session management.

ElastiCache Redis offers a solution specifically designed to meet the requirements of low-latency, high-throughput workloads. Redis is an in-memory key-value store that enables extremely fast data access. By keeping data in memory, Redis reduces response times to microseconds, supporting sub-millisecond latency even under significant load. Redis also supports replication for high availability, clustering for horizontal scaling, and optional persistence for durability. This makes it ideal for storing session data that must be shared across multiple web servers in distributed applications. By using Redis, applications can quickly retrieve user sessions, maintain consistent performance under high traffic, and scale seamlessly to accommodate growing workloads.

Implementing Redis minimizes operational complexity by offloading the need for manual caching strategies and complex database tuning. The combination of high availability, automatic failover, and clustering ensures that session data remains accessible even in case of node failures. This architecture provides an excellent user experience by delivering fast, reliable access to session information, reducing latency, and maintaining application responsiveness. For high-traffic applications where user experience and performance are paramount, Redis offers a scalable, efficient, and robust solution for session management and real-time data access.

Question 216

A company wants to implement a serverless, event-driven architecture for processing files uploaded to S3 and messages from SQS. Which AWS service is most suitable?

A) Lambda triggered by S3 and SQS
B) EC2 polling S3 and SQS
C) ECS on EC2 launch type
D) EKS with EC2 nodes

Answer: A) Lambda triggered by S3 and SQS

Explanation:

In modern cloud environments, the choice of compute architecture has a direct impact on operational efficiency, cost management, and application scalability. Traditional approaches, such as deploying workloads on Amazon EC2 instances, provide flexibility and full control over the underlying infrastructure. Organizations can select instance types, configure networking, and manage storage to suit their application requirements. However, this level of control comes with substantial operational overhead. EC2 instances demand continuous monitoring, regular patching, and careful capacity planning to maintain availability and performance. Scaling workloads to meet changing demand also requires manual intervention or the configuration of Auto Scaling policies, which can become complex as applications grow. These tasks consume valuable operational resources and increase the likelihood of errors or downtime if not managed carefully.

Containerized solutions such as Amazon ECS and Amazon EKS offer a more flexible approach to deploying applications by abstracting workloads into containers. While containers provide better resource utilization and portability, ECS and EKS with EC2 nodes still require management of the underlying infrastructure and clusters. This includes maintaining the operating system, patching nodes, managing cluster health, and ensuring networking and security configurations are consistently applied. While these platforms simplify application deployment and orchestration compared to raw EC2 instances, they still place a significant operational burden on engineering teams, particularly in environments with large-scale or rapidly changing workloads.

In contrast, AWS Lambda provides a fully serverless computing model that eliminates the need for managing servers or clusters. Lambda functions can be triggered directly by events from services such as Amazon S3 or Amazon SQS, enabling an event-driven architecture where workloads respond automatically to changes in data or incoming requests. One of Lambda’s most powerful features is its ability to scale automatically with the workload, handling spikes in demand without requiring manual provisioning. Customers are charged only for the compute time consumed during function execution, making it a cost-efficient option for variable workloads.

Beyond scalability and cost efficiency, Lambda integrates seamlessly with AWS CloudWatch, enabling centralized logging, monitoring, and error tracking. This integration simplifies operational management by providing insights into function performance and execution metrics, reducing the need for extensive manual monitoring. Developers can build fully managed, event-driven systems that handle high volumes of requests reliably, without worrying about underlying infrastructure management.

This architecture is particularly well-suited for workloads such as ETL (extract, transform, load) processes, image or video processing, and order management systems that respond to S3 uploads or messages in SQS queues. By leveraging Lambda, organizations achieve high availability, automatic scaling, and operational simplicity, while minimizing cost and complexity. Serverless processing ensures that applications remain resilient, responsive, and capable of supporting variable demand, freeing development teams to focus on business logic rather than infrastructure maintenance. In essence, Lambda enables a shift from managing servers to orchestrating functions, providing a reliable, cost-effective, and scalable solution for modern cloud workloads.

Question 217

A company wants to implement a messaging system that guarantees exactly-once processing and preserves message order. Which AWS service is most suitable?

A) SQS FIFO Queue
B) SQS Standard Queue
C) SNS
D) Kinesis Data Streams

Answer: A) SQS FIFO Queue

Explanation:

SQS Standard Queue delivers messages at least once but does not guarantee ordering, which may result in inconsistent processing. SNS is a pub/sub service that does not ensure message order or exactly-once delivery. Kinesis Data Streams preserves order per shard and supports high throughput but adds complexity for simple microservice messaging. SQS FIFO Queue guarantees exactly-once processing, preserves the order of messages, and supports deduplication. This ensures predictable and reliable communication between microservices, which is critical for transaction-sensitive workloads and distributed systems. FIFO queues simplify application logic by processing messages sequentially, maintaining consistency and reliability.

Question 218

A company wants to migrate terabytes of on-premises data to AWS efficiently without saturating network bandwidth. Which solution is most suitable?

A) AWS Snowball
B) S3 only
C) EFS
D) AWS DMS

Answer: A) AWS Snowball

Explanation:

In modern data analytics, organizations rely on multiple strategies to improve performance, manage large volumes of data, and ensure timely access to insights. Incremental refresh is one such approach, designed to optimize data ingestion by updating only new or modified records instead of reloading the entire dataset. This significantly reduces processing time and resource consumption, making it particularly valuable for large datasets where full refreshes can be time-consuming and costly. Incremental refresh ensures that analytics are up to date and reduces latency in delivering refreshed data to business users. However, while it streamlines data ingestion, incremental refresh does not provide any mechanism for controlling who can access the data. Users with access to a dataset can still see all the data contained within it, which creates a potential risk for sensitive information if additional security measures are not applied.

Partitioning is another common technique used to improve query performance. By dividing datasets into logical segments based on specific keys—such as date, region, or department—queries can scan only the relevant partitions, reducing execution time and improving responsiveness for end users. This is particularly important for interactive analytics and reporting scenarios, where slow query performance can negatively impact productivity and decision-making. However, partitioning focuses solely on performance optimization and does not provide access control. All users with access to a partitioned dataset can view every partition unless security rules are implemented elsewhere, meaning partitioning alone cannot protect sensitive data or meet compliance requirements.

Azure Synapse Pipelines addresses the challenge of automating and orchestrating ETL (Extract, Transform, Load) workflows. Pipelines allow organizations to schedule, monitor, and manage the movement and transformation of data from source systems into data warehouses or data lakes. This ensures consistency, repeatability, and reliability in data processing, which is critical for enterprise analytics environments. While Synapse Pipelines is excellent for orchestrating large-scale workflows, it does not include functionality for row-level access control. Data processed through pipelines may still be exposed to users without restriction, so additional measures are necessary to enforce security and compliance.

Power BI provides a robust solution for managing user access at a granular level through roles and DAX filters. Roles enable administrators to define rules that control which rows of data individual users or groups can view. Using DAX expressions, these rules can dynamically filter data based on attributes such as user identity, department, region, or job function. When a user interacts with a Power BI report or dataset, row-level security ensures that they can only access the subset of data for which they are authorized. This dynamic enforcement of access rules not only protects sensitive information but also supports regulatory compliance and data governance by maintaining visibility control, traceability, and accountability across all users.

By combining incremental refresh, partitioning, ETL orchestration through Synapse Pipelines, and row-level security in Power BI, organizations can achieve an analytics environment that is both high-performing and secure. Incremental refresh and partitioning optimize data ingestion and query efficiency, Pipelines automate and streamline workflows, and Power BI ensures that users see only the data they are authorized to access. This comprehensive approach balances performance with security, enabling organizations to meet operational needs while maintaining compliance with regulatory and governance standards.

Question 219

A company wants to implement a global web application with low latency for static and dynamic content. Which architecture is most suitable?

A) CloudFront with S3 for static content and ALB for dynamic content
B) S3 only
C) EC2 with ALB only
D) Route 53 only

Answer: A) CloudFront with S3 for static content and ALB for dynamic content

Explanation:

Migrating extremely large datasets to the cloud presents significant challenges for organizations, particularly when dealing with multi-terabyte volumes of data. Relying solely on Amazon S3 for this process can be inefficient and costly. Uploading terabytes of data over the internet consumes substantial bandwidth and is prone to network interruptions, leading to prolonged migration timelines and higher operational costs. For enterprises needing to move large datasets quickly and securely, traditional network-based transfers often prove inadequate, creating bottlenecks and increasing complexity in project execution.

Amazon EFS, although optimized for active file storage and concurrent access, is not designed for bulk data migration. Its architecture focuses on low-latency file operations rather than large-scale, one-time data transfers. Using EFS for moving vast quantities of data can result in inefficient performance and unnecessary cost, making it unsuitable for large-scale migrations. Similarly, AWS Database Migration Service (DMS) is purpose-built for database replication and migration, allowing near real-time data transfer while minimizing downtime for database workloads. However, DMS is not suitable for general-purpose file transfers, as it cannot efficiently handle large volumes of unstructured or semi-structured data. Attempting to use DMS for such workloads can introduce operational complexity without providing the desired efficiency.

AWS Snowball addresses these challenges by offering a secure, reliable, and high-speed method for transferring large datasets to the cloud. Snowball is a physical data transfer appliance that allows organizations to move terabytes or even petabytes of data offline. Customers copy data directly onto the Snowball device in their local environment, which is then shipped to AWS for ingestion into S3. This offline approach eliminates dependency on network bandwidth, significantly reducing transfer time and avoiding congestion issues that can impact ongoing operations.

Security is a key consideration during large-scale migrations, and Snowball ensures that data is protected throughout the process. The appliance provides encryption both at rest and in transit, safeguarding sensitive information from unauthorized access. In addition, Snowball’s design supports tamper-evident enclosures and secure transport, giving organizations confidence that their data remains safe throughout the shipment and upload process.

Using Snowball simplifies operational management for large migrations. By offloading the data transfer process to a dedicated appliance, teams avoid the continuous monitoring and intervention typically required when uploading data over the network. The appliance can handle substantial datasets in a single shipment, reducing logistical complexity and streamlining the migration workflow. Snowball’s integration with Amazon S3 further ensures that the data is ingested directly into the cloud storage service upon arrival, making it immediately available for use or further processing.

Overall, AWS Snowball provides an ideal solution for large-scale migrations. It combines speed, security, and operational simplicity, enabling organizations to transfer multi-terabyte datasets efficiently while minimizing costs and reducing operational overhead. For enterprises facing the challenges of moving massive volumes of data to the cloud, Snowball ensures a reliable, scalable, and secure migration pathway that addresses both logistical and technical constraints, making it the preferred choice for large data migrations.

Question 220

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:

When designing high-performance applications, database and caching choices play a critical role in ensuring fast and reliable access to data. DynamoDB, Amazon’s managed NoSQL database, offers low-latency performance and is fully managed, which reduces operational overhead. Its ability to scale automatically allows it to handle large amounts of traffic without manual intervention. However, under extremely high workloads, DynamoDB may not always deliver sub-millisecond response times consistently. While it is excellent for many applications requiring rapid key-value lookups or simple queries, applications with extremely demanding latency requirements may encounter occasional delays during peak traffic periods.

RDS MySQL, a managed relational database service, provides familiar SQL-based relational functionality, transactional consistency, and durability. However, because it relies on disk I/O and manages database connections, read and write operations can experience higher latency compared to in-memory solutions. Applications that require extremely fast data retrieval or frequent access to small objects may face performance bottlenecks. Additionally, scaling RDS MySQL horizontally can be more complex, requiring replication and careful consideration of consistency across multiple instances.

Amazon S3, designed primarily for object storage, offers durability and scalability for storing large amounts of data. It is ideal for backups, media, or large binary objects but is not suitable for workloads that require frequent, rapid reads and writes of small items. Its eventual consistency model for certain operations and higher latency compared to in-memory or database solutions make it unsuitable for high-traffic session storage or caching scenarios.

ElastiCache Redis addresses these performance challenges by providing an in-memory key-value store optimized for ultra-low latency and high throughput. Being fully managed, Redis supports replication, clustering, and optional persistence, allowing applications to scale efficiently while maintaining high availability. Its in-memory architecture ensures sub-millisecond response times even under heavy workloads, making it ideal for session management, caching frequently accessed data, leaderboards, and real-time analytics.

One of Redis’s most significant advantages is its ability to share session data across multiple web servers. This allows stateless web applications to store session information centrally in Redis, providing fast, reliable access to user-specific data. Multiple application servers can retrieve or update session data without introducing delays, ensuring a consistent and responsive experience for end users. This design supports horizontal scalability while maintaining high performance across distributed systems.

By combining Redis for session storage or caching with other AWS services like DynamoDB or RDS for persistent storage, organizations can achieve a balanced architecture that maximizes performance, reliability, and operational simplicity. Redis minimizes latency and improves throughput, DynamoDB or RDS ensures durable storage and transactional consistency, and S3 provides cost-effective object storage for large datasets.

This approach allows developers to deliver applications capable of handling high traffic volumes with minimal operational complexity. High availability is ensured through Redis replication and clustering, while persistent databases provide durability. Overall, leveraging ElastiCache Redis for in-memory caching alongside other storage services delivers a fast, scalable, and resilient architecture that ensures excellent user experience, consistent performance, and simplified operational management for modern, high-traffic applications.

Question 221

A company wants to migrate a multi-terabyte on-premises SQL Server database to AWS with minimal downtime. Which solution is most suitable?

A) RDS SQL Server with AWS DMS
B) RDS SQL Server only
C) Aurora PostgreSQL
D) EC2 SQL Server with manual backup/restore

Answer: A) RDS SQL Server with AWS DMS

Explanation:

Migrating SQL Server databases in production environments is a complex task that requires balancing minimal downtime, operational efficiency, and data integrity. Using RDS SQL Server alone for database migration often involves exporting data from the source and importing it into the target. While this method works for small or non-critical databases, it demands significant downtime, making it unsuitable for production workloads where continuous availability is essential. Any interruptions during the migration process can impact business operations, cause revenue loss, and degrade user experience.

Aurora PostgreSQL, while a powerful managed relational database service, is not directly compatible with SQL Server. Migrating from SQL Server to Aurora PostgreSQL would require extensive schema conversions, changes in SQL queries, and application-level modifications. Such a migration introduces considerable complexity, increases the potential for errors, and often results in extended project timelines. For organizations that need to maintain application consistency and minimize disruption, this option may not be practical.

Similarly, running SQL Server on EC2 instances and performing manual backup and restore operations poses operational challenges. Administrators must handle all aspects of the database environment, including patching, scaling, monitoring, and ensuring consistent backups. The manual restore process is time-consuming, increases the risk of human error, and often leads to extended periods of downtime, which are unacceptable for critical production workloads.

A more efficient and reliable solution involves using RDS SQL Server in combination with AWS Database Migration Service (DMS). This approach allows near real-time replication from the source SQL Server database to the RDS target while keeping the source fully operational. DMS continuously migrates data, ensuring transactional consistency and minimizing downtime. By replicating changes in real-time, applications experience minimal disruption, and end-users maintain uninterrupted access to the system.

In addition to replication, RDS provides built-in operational benefits such as automated backups, Multi-AZ deployment for high availability, patching, and maintenance. Automated backups and snapshots reduce the operational burden on administrators, while Multi-AZ deployment ensures that the database remains resilient to infrastructure failures. RDS also handles routine maintenance and patching tasks, eliminating the need for manual intervention and reducing operational complexity.

This combination of RDS SQL Server and AWS DMS delivers a robust, low-downtime migration strategy suitable for mission-critical workloads. It allows organizations to move production SQL Server databases with minimal service interruption while ensuring data integrity, high availability, and operational simplicity. By leveraging managed services, teams can focus on application performance and business outcomes rather than infrastructure management. This architecture is particularly effective for enterprises that require reliable, scalable, and secure database migrations with predictable execution and minimal risk, providing a seamless transition to the cloud for SQL Server workloads.

This solution balances efficiency, reliability, and operational simplicity, making it the ideal choice for organizations aiming to migrate production SQL Server databases with minimal disruption, while maintaining business continuity and reducing the complexity typically associated with large-scale database migrations.

Question 222

A company wants to implement a global web application with low latency for static and dynamic content. Which architecture is most suitable?

A) CloudFront with S3 for static content and ALB for dynamic content
B) S3 only
C) EC2 with ALB only
D) Route 53 only

Answer: A) CloudFront with S3 for static content and ALB for dynamic content

Explanation:

S3 alone serves static content but cannot provide caching or optimized delivery for dynamic content. EC2 with ALB ensures high availability in a single region but increases latency for global users. Route 53 handles DNS routing but cannot deliver or cache content. CloudFront is a global CDN that caches static content at edge locations, reducing latency and improving performance. Combining CloudFront with S3 for static content and ALB for dynamic content ensures fast, secure, and highly available delivery worldwide. CloudFront integrates with WAF, SSL/TLS, and origin failover, providing security, resilience, and optimal performance for global applications.

Question 223

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 access but may not consistently achieve sub-millisecond response under heavy load. RDS MySQL introduces latency due to disk I/O and connection management. S3 is object storage and cannot efficiently handle frequent small reads/writes. ElastiCache Redis is an in-memory key-value store optimized for extremely low latency and high throughput. Redis supports replication, clustering, and optional persistence. Session data can be shared across multiple web servers, ensuring fast, reliable access and scalability. This solution provides minimal operational complexity while maintaining high availability and consistent performance for high-traffic applications.

Question 224

A company wants to implement a serverless, event-driven architecture for processing files uploaded to S3 and messages from SQS. Which AWS service is most suitable?

A) Lambda triggered by S3 and SQS
B) EC2 polling S3 and SQS
C) ECS on EC2 launch type
D) EKS with EC2 nodes

Answer: A) Lambda triggered by S3 and SQS

Explanation:

EC2 instances require manual scaling, patching, and monitoring, which increases operational overhead. ECS and EKS with EC2 nodes require infrastructure management and cluster maintenance. Lambda is serverless and can be triggered directly by S3 events or SQS messages. It automatically scales with workload and incurs cost only for execution duration. Lambda integrates with CloudWatch for logging, monitoring, and error handling. This fully managed, event-driven architecture provides high availability, scalability, and cost efficiency. It is ideal for ETL jobs, image processing, or order processing triggered by S3 uploads or SQS messages, ensuring reliable serverless processing.

Question 225

A company wants to implement a messaging system that guarantees exactly-once processing and preserves message order. Which AWS service is most suitable?

A) SQS FIFO Queue
B) SQS Standard Queue
C) SNS
D) Kinesis Data Streams

Answer: A) SQS FIFO Queue

Explanation:

SQS Standard Queue delivers messages at least once but does not guarantee ordering, which can result in inconsistent processing. SNS is a pub/sub notification service that does not guarantee message order or exactly-once delivery. Kinesis Data Streams preserves order per shard and supports high throughput but adds unnecessary complexity for simple messaging. SQS FIFO Queue guarantees exactly-once processing, preserves message order, and supports deduplication. This ensures predictable and reliable communication between microservices, which is critical for transaction-sensitive workloads and distributed systems. FIFO queues simplify application logic by processing messages sequentially, maintaining consistency and reliability.