Amazon AWS Certified Solutions Architect — Professional SAP-C02 Exam Dumps and Practice Test Questions Set 10 Q136-150
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Question 136
A company wants to migrate an on-premises SQL Server database to AWS with minimal downtime and continuous replication. Which solution is most suitable?
A) RDS SQL Server with AWS DMS
B) RDS SQL Server only
C) Aurora PostgreSQL
D) EC2 SQL Server with manual backup/restore
Answer: A) RDS SQL Server with AWS DMS
Explanation:
RDS SQL Server alone requires downtime to export and import data. Aurora PostgreSQL is not SQL Server-compatible, requiring extensive application and schema changes. EC2 SQL Server with manual backup/restore is operationally intensive and requires extended downtime. RDS SQL Server with AWS DMS supports near real-time replication from the source database, allowing it to remain operational during migration. DMS ensures data integrity and minimal downtime, and RDS provides automated backups, Multi-AZ deployment, and maintenance. This approach ensures reliable, low-downtime migration, reduces operational complexity, and is suitble for mission-critical SQL Server workloads.
Question 137
A company wants to analyze petabyte-scale structured datasets stored in S3 using SQL queries. 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 but is not optimized for continuous analysis of petabyte-scale datasets. RDS MySQL is designed for transactional workloads and cannot efficiently handle massive structured datasets. DynamoDB is NoSQL and does not natively support SQL-based analytics. Redshift is a fully managed, columnar data warehouse designed for large-scale structured analytics. It supports parallel query execution, compression, and integration with S3 for data ingestion. Redshift also supports high concurrency for multiple users or applications. This service is ideal for enterprises requiring fast, reliable analytics on massive structured datasets with cost efficiency and high performance.
Question 138
A company wants to reduce costs by automatically stopping EC2 instances in non-production environments outside business hours. Which AWS service is most suitable?
A) Systems Manager Automation with a cron schedule
B) Auto Scaling scheduled actions only
C) Manual stopping of instances
D) Spot Instances only
Answer: A) Systems Manager Automation with a cron schedule
Explanation:
Auto Scaling scheduled actions are primarily designed for production workloads and do not stop instances for cost-saving in non-production environments. Manual stopping is error-prone and requires continuous human intervention. Spot Instances reduce costs for interruptible workloads but do not provide scheduling capabilities. Systems Manager Automation allows automated runbooks to start or stop EC2 instances based on cron schedules. It reduces operational overhead, ensures consistent execution, and provides auditing for compliance. This approach is ideal for multiple non-production environments, saving costs while maintaining operational control.
Question 139
A company wants to implement a messaging system that ensures exactly-once processing and preserves message order. Which AWS service is most suitable?
A) SQS FIFO Queue
B) SQS Standard Queue
C) SNS
D) Kinesis Data Streams
Answer: A) SQS FIFO Queue
Explanation:
When designing communication between microservices, choosing the right messaging service is critical to ensure reliability, consistency, and predictability. Amazon SQS Standard Queue is a fully managed message queuing service that offers at-least-once delivery. While this ensures that messages are eventually delivered, it does not guarantee the order in which messages are received. For many applications, this lack of ordering is acceptable, but for transaction-sensitive workflows or systems where the sequence of operations is critical, it can introduce inconsistencies. Messages may arrive out of order or be delivered more than once, which requires additional logic in the application to handle deduplication and ordering, increasing the complexity of the solution.
Amazon SNS, or Simple Notification Service, is another messaging option, but it operates under a publish-subscribe paradigm. SNS allows multiple subscribers to receive messages from a single topic simultaneously, which is ideal for fan-out scenarios where notifications need to reach several endpoints. However, like SQS Standard, SNS does not provide message ordering guarantees, nor does it prevent duplicate messages. This means that applications relying solely on SNS for sequential processing or exactly-once execution must implement additional mechanisms to maintain data consistency, adding operational overhead and potential points of failure.
Amazon Kinesis Data Streams provides a different approach to messaging, focusing on real-time streaming of large volumes of data. Kinesis maintains ordering within each shard and can achieve very high throughput, making it suitable for analytics, log processing, and event-driven applications that require continuous streams. However, for simple microservice-to-microservice communication, Kinesis introduces complexity that may be unnecessary. Managing shards, monitoring throughput, handling scaling, and implementing checkpointing mechanisms are all responsibilities that increase the operational burden for teams that only require straightforward, reliable messaging between services.
In contrast, Amazon SQS FIFO (First-In-First-Out) Queue addresses the limitations of standard queues by ensuring exactly-once processing and preserving the order of messages. FIFO queues guarantee that messages are delivered in the precise sequence in which they were sent, preventing out-of-order execution that can compromise workflows. Additionally, FIFO queues support deduplication, meaning that duplicate messages generated accidentally or through retries are automatically handled, reducing the need for custom logic in the application layer. This combination of ordering, deduplication, and exactly-once delivery makes FIFO queues particularly well suited for workloads where transactional integrity and data consistency are paramount.
By using SQS FIFO Queues, microservices can communicate in a predictable and reliable manner. Applications that process financial transactions, order fulfillment, inventory updates, or other sensitive operations benefit from this approach because the risk of data inconsistencies is minimized. Developers can focus on business logic without worrying about implementing complex mechanisms to preserve message order or handle duplicates. FIFO queues also integrate seamlessly with other AWS services, offering high availability, fault tolerance, and scalability while simplifying the overall architecture.
For scenarios where workflow correctness, transaction accuracy, and fault-tolerant communication are critical, SQS FIFO Queue provides the ideal solution. It ensures reliable delivery, maintains message order, and supports deduplication, enabling microservices to interact safely and predictably. This makes it a superior choice over standard queues, SNS, or complex streaming services like Kinesis when the primary concern is consistent, ordered, and dependable messaging between components of a distributed system.
Question 140
A company wants to implement a global web application with low latency for both static and dynamic content. Which architecture is most suitable?
A) CloudFront with S3 for static content and ALB for dynamic content
B) S3 only
C) EC2 with ALB only
D) Route 53 only
Answer: A) CloudFront with S3 for static content and ALB for dynamic content
Explanation:
When delivering web applications and content to users around the world, the architecture of the underlying infrastructure plays a critical role in performance, reliability, and security. Amazon S3 is a popular service for hosting static content such as images, CSS, JavaScript files, and other assets. While S3 provides durable storage and high availability within a region, it does not offer built-in global caching or acceleration. Consequently, users located far from the S3 bucket’s region may experience higher latency, and serving dynamic content directly from S3 is not feasible. On its own, S3 is optimized for storage and retrieval but does not address performance challenges associated with delivering content to a globally distributed audience.
Using EC2 instances behind an Application Load Balancer (ALB) enables the delivery of dynamic content with high availability within a single region. ALB distributes incoming traffic across multiple EC2 instances, preventing individual servers from becoming bottlenecks and ensuring consistent performance under varying loads. However, relying solely on EC2 and ALB for global content delivery can result in higher latency for users located in regions distant from the deployment, as requests must travel across the network to reach the regional instances. This configuration also increases operational overhead due to the need for scaling, patching, and monitoring the underlying instances to maintain optimal performance.
Amazon Route 53 provides DNS-based traffic routing, allowing organizations to direct users to specific endpoints based on geographic location, latency, or health checks. While Route 53 improves availability and resiliency at the DNS level, it does not cache content or accelerate delivery, meaning that all requests still require traversal to the origin servers, which can result in slower response times for global users.
To address these limitations, Amazon CloudFront serves as a global content delivery network that caches content at edge locations around the world. By caching static content closer to end users, CloudFront dramatically reduces latency and improves the performance of web applications. Additionally, CloudFront supports dynamic content delivery by forwarding requests to origins such as S3 buckets for static assets and ALB-backed EC2 instances for dynamic content. This combination enables a single architecture to efficiently handle both static and dynamic workloads, providing a seamless experience for end users regardless of their geographic location.
CloudFront also enhances security and reliability by integrating with AWS Web Application Firewall (WAF), supporting SSL/TLS encryption for secure communications, and providing origin failover capabilities. In case an origin becomes unavailable, CloudFront can automatically route requests to a backup origin, ensuring uninterrupted access to content. These features make CloudFront an essential component in delivering globally optimized, fault-tolerant web applications.
By combining S3 for static assets, ALB for dynamic application endpoints, and CloudFront for global caching and acceleration, organizations can build a web architecture that is fast, secure, and highly available. This design minimizes latency for users worldwide, improves fault tolerance, and reduces operational complexity. Users benefit from faster page load times and a more responsive experience, while developers and administrators gain a resilient, scalable, and globally distributed delivery system that meets modern performance and security requirements.
Question 141
A company wants to implement a highly available relational database for production workloads with automatic failover across multiple 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:
A Single-AZ deployment in Amazon RDS places the entire database instance within a single availability zone. While this setup can be cost-effective and suitable for non-critical environments, it introduces a significant risk when it comes to reliability. Because the database exists only in one zone, any outage affecting that zone—whether caused by hardware failure, networking issues, or an unexpected service disruption—can immediately lead to downtime. During such events, applications that rely on the database may become unavailable, making this approach less suitable for workloads that require continuous uptime or strong resilience.
DynamoDB, although highly scalable and fully managed, serves a different purpose and cannot function as a replacement for a relational database when complex querying or full transactional integrity is required. DynamoDB is a NoSQL service that excels in use cases involving flexible schemas, rapid scaling, and extremely low latency for key-value or document-based interactions. However, organizations that depend on structured relationships, joins, or advanced SQL operations will find DynamoDB insufficient for those needs. Its architecture is optimized for performance and scalability rather than strict relational behavior.
Amazon S3 is another core AWS service, but it operates as an object storage platform. S3 is ideal for storing backups, media assets, logs, and large datasets, but it does not offer the functionality of a relational database. It does not support SQL querying, ACID transactions, indexing, or any of the capabilities needed for applications that rely on structured data models. While S3 is valuable for durable storage and archival purposes, it cannot be used to handle transactional workloads or real-time relational queries.
In contrast, an RDS Multi-AZ deployment is specifically designed for environments where availability and reliability are top priorities. When Multi-AZ is enabled, Amazon RDS automatically provisions a fully synchronized standby replica in a separate availability zone. This replication is synchronous, meaning updates to the primary database are committed to both the primary and the standby simultaneously, ensuring data durability and consistency. If the primary instance becomes unavailable due to maintenance, failure, or an unexpected outage, RDS performs an automatic failover to the standby instance. This process significantly reduces downtime and allows applications to continue operating with minimal interruption.
Multi-AZ configurations also benefit operational management. Routine tasks such as system maintenance, patches, and automated backups are handled in a way that does not disrupt the primary database’s availability. Backups, for example, are taken from the standby rather than the primary, preventing performance degradation during critical application usage.
Because of these capabilities, RDS Multi-AZ is strongly recommended for production systems where consistent performance, resilience, and data integrity are essential. Businesses that rely on continuous operations, high fault tolerance, and the advanced features of relational databases will find Multi-AZ deployments to be the most appropriate and reliable solution within the RDS ecosystem.
Question 142
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:
When organizations run containers using ECS with the EC2 launch type or EKS with EC2 worker nodes, they inherit the responsibility of managing the underlying infrastructure. This means they must create, configure, and maintain EC2 instances that form the foundation of their container environments. Tasks such as scaling capacity during peak demand, applying security patches, monitoring node health, and handling OS-level maintenance all fall on the operations team. Over time, this continuous management becomes a substantial operational burden, especially as application architectures grow more complex or require strict uptime guarantees. Although these deployment models allow deep control over the environment, they inevitably increase administrative overhead, making them less suited for teams seeking a hands-off operational approach.
When EC2 is used as the base compute layer without an orchestration service, it offers only raw virtual machines. While this provides flexibility and full control over the operating system and runtime, it lacks essential container management capabilities. Features such as scheduling containers onto available nodes, automatically restarting failed tasks, and distributing workloads efficiently across instances must be handled by an external orchestration tool. Without such orchestration, teams must manually install and manage container runtimes, networking configurations, and scaling mechanisms, leading to more complexity than most modern application teams require.
In contrast, ECS with Fargate introduces a serverless method of running containers that eliminates the need to provision or manage servers. With Fargate, the platform automatically allocates compute power based on the requirements of each task, ensuring that containers have the exact resources they need without wasting capacity. This removes the need to estimate cluster size or manage underlying instances. As workloads grow or shrink, the compute layer seamlessly adjusts, allowing developers to deploy services without thinking about instance limits, cluster scaling policies, or infrastructure engineering.
Fargate also integrates deeply with AWS networking and security services, making it easier to apply consistent policies across environments. Features such as VPC networking, IAM roles for tasks, and integration with CloudWatch for logging and monitoring are built into the service. Because much of the operational complexity is abstracted away, developers can concentrate on improving microservices, shipping features faster, and avoiding time-consuming infrastructure tasks. Teams no longer need to patch Linux kernels, replace failed EC2 nodes, or troubleshoot cluster-level issues.
High availability is built directly into the Fargate platform. Containers run across multiple infrastructure layers that AWS manages behind the scenes, reducing the risk of outages caused by hardware or instance failure. Additionally, because Fargate pricing is based on actual CPU and memory usage rather than instance uptime, it enables cost-efficient operation, especially for applications with variable or unpredictable workloads. This model prevents waste and aligns compute spending directly with application demand.
For organizations seeking a simplified, fully managed approach to deploying containerized applications, ECS with Fargate stands out as an ideal solution. It provides a streamlined, serverless platform that removes infrastructure responsibilities, supports microservices architectures, and enables developers to focus purely on application logic rather than operational overhead.
Question 143
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 extremely large volumes of data to the cloud presents unique challenges, particularly when datasets span multiple terabytes. Traditional network-based transfers to S3, while feasible for smaller workloads, become inefficient and impractical at this scale due to bandwidth limitations, prolonged transfer times, and potential network interruptions. Attempting to migrate large datasets directly over the internet can introduce significant operational delays, increase costs, and amplify the risk of incomplete or failed transfers, making it an unsuitable approach for time-sensitive or large-scale migration projects.
Amazon Elastic File System (EFS) provides a managed file storage solution designed for active workloads, enabling multiple instances to access shared file systems. While EFS excels in delivering scalable and highly available storage for live applications, it is not optimized for bulk data migration. Its design is intended for active use cases where frequent read and write operations occur, rather than for moving massive, pre-existing datasets from on-premises environments into the cloud. As a result, relying on EFS for large-scale migration can be slow and inefficient, further complicating the process.
AWS Database Migration Service (DMS) is another popular tool, primarily built for migrating databases with minimal downtime. While DMS is highly effective for relational and non-relational databases, it is not intended for transferring general-purpose files. Its functionality is focused on structured data, schema conversion, and replication, making it unsuitable for scenarios where multi-terabyte unstructured datasets, such as media files, log archives, or large application data stores, need to be migrated.
AWS Snowball addresses these limitations by providing a secure, offline data transfer solution for large datasets. Snowball is a physical appliance designed to handle terabytes to petabytes of data efficiently and reliably. Customers can load their data onto the device locally, without being constrained by network bandwidth. Once the data is transferred to the appliance, it is physically shipped back to AWS, where it is securely ingested into S3. This method circumvents the network bottlenecks and allows large-scale migrations to proceed much faster and with fewer interruptions.
Security is a fundamental aspect of Snowball. Data is encrypted both at rest and in transit using industry-standard encryption protocols, ensuring that sensitive information is protected throughout the transfer process. The appliance also includes built-in tamper-resistant features to maintain data integrity during shipping. Operationally, Snowball reduces overhead by eliminating the need for continuous monitoring of large transfers over the network, providing a predictable, managed process that frees teams from logistical complexities and potential errors associated with manual data transfers.
Snowball’s ability to efficiently move massive datasets makes it ideal for enterprises undertaking large-scale cloud migration projects, whether for backup restoration, disaster recovery preparation, or consolidating data into S3 for analytics and application modernization. By combining offline transfer, encryption, and operational simplicity, Snowball ensures that organizations can migrate terabytes of data securely, cost-effectively, and without overburdening network resources, providing a practical, reliable solution to a challenging logistical problem.
This approach allows companies to achieve rapid migration of large datasets while maintaining high security and minimizing operational overhead, ultimately enabling efficient onboarding to AWS cloud environments.
Question 144
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:
Running applications on EC2 instances often requires teams to take on the full responsibility of managing server infrastructure. This means administrators must handle operating system patching, apply security updates, configure scaling policies, and monitor resource usage to ensure stable performance. As the number of instances grows, so does the operational burden, making it harder to maintain efficiency and reliability. Even when using container orchestration platforms such as ECS or EKS, relying on EC2 nodes still ties organizations to the ongoing management of virtual machines, cluster capacity, and node health. These responsibilities introduce complexity that many teams prefer to avoid, especially when focusing on rapid development and deployment.
ECS and EKS with EC2 nodes expand the capabilities of EC2 by adding container management features, yet they do not eliminate the need for underlying infrastructure oversight. Clusters require provisioning, scaling, and lifecycle management. Engineers must ensure nodes remain healthy, workloads are balanced, and compute capacity matches application demands. This constant attention to infrastructure can slow down innovation and increase operational costs, particularly for workloads that experience unpredictable traffic or require rapid scaling.
Lambda, on the other hand, removes the need to manage servers entirely. As a serverless compute service, it allows developers to run code in response to various AWS events without handling provisioning or maintenance of any compute environment. Lambda can be triggered automatically by S3 uploads, SQS messages, DynamoDB streams, API calls, or scheduled events. This event-driven design makes it an excellent fit for processes that react to incoming data or perform background tasks.
One of the key advantages of Lambda is its ability to scale instantly and transparently. As events arrive, Lambda launches additional executions automatically, without any configuration or manual scaling effort. When the workload decreases, Lambda simply stops running code and incurs no further cost. Its pay-per-execution pricing model ensures organizations only pay for compute time consumed, eliminating costs associated with idle infrastructure. This efficiency is particularly useful for workloads with sporadic or unpredictable traffic patterns.
Lambda also integrates deeply with CloudWatch, giving teams built-in access to logs, performance metrics, and error reports. This integration simplifies application monitoring and troubleshooting without the need to deploy additional tools. CloudWatch alarms can trigger notifications or automated remediation actions, enhancing reliability and observability. With these features, developers gain visibility into performance without spending time managing logging infrastructure.
By abstracting away server management, Lambda enables a fully managed architecture that inherently supports high availability. AWS handles redundancy, failover, and infrastructure maintenance behind the scenes, ensuring functions remain accessible without requiring any intervention. This level of automation reduces operational risk and frees development teams to focus on business logic rather than system administration.
Lambda is especially well suited for workloads such as order processing pipelines, ETL operations, real-time data transformations, and automation tasks triggered by events from services like S3 or SQS. Its flexibility, scalability, and cost-effective model make it an ideal choice for organizations seeking to build responsive, event-driven solutions without managing servers or complex cluster environments.
Question 145
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:
Efficient session management is a crucial component for modern web applications, especially those that experience high traffic and require consistent, low-latency access to user data. Selecting the right storage solution for session information can dramatically affect application performance, scalability, and overall user experience. While several AWS services offer storage capabilities, each comes with trade-offs that can impact suitability for session management.
DynamoDB is a managed NoSQL database that provides low-latency access and can scale automatically to handle variable workloads. It is a popular choice for many high-performance applications due to its ability to handle large volumes of structured data and provide predictable performance under normal loads. However, when the system experiences heavy traffic or sudden spikes, DynamoDB may not consistently achieve sub-millisecond latency. This limitation can result in slightly slower response times, which might affect the performance of session-heavy applications where near-instant access is critical.
RDS MySQL, another commonly used option, provides a relational database environment suitable for a wide range of applications. While it offers familiar SQL capabilities and strong transactional support, it relies on disk-based storage and network connections for each query. These factors introduce latency that makes RDS less optimal for session storage, especially in scenarios where rapid read and write operations are required. The cumulative latency can lead to slower response times, making it challenging to maintain smooth user experiences under high concurrent loads.
S3, Amazon’s object storage service, excels at durability and large-scale data storage, making it ideal for backups, logs, or archival purposes. However, its architecture is not suited for frequent, small read and write operations. Session data typically requires rapid, low-latency access with frequent updates, a workload pattern that S3 cannot efficiently support. Using S3 for session management would result in significant delays and could introduce operational complexity due to the mismatch between S3’s design and the access patterns required by session data.
In contrast, ElastiCache Redis provides a highly optimized solution for session management. As an in-memory key-value store, Redis delivers extremely low latency and high throughput, allowing applications to access session information in near real time. Redis supports replication, which ensures redundancy and high availability across multiple nodes, and clustering, which allows horizontal scaling to handle increasing loads. Additionally, optional persistence features enable storage of session data to disk, providing durability without sacrificing performance.
Redis is particularly effective for storing session data across multiple web servers in distributed environments. By centralizing session storage in a fast, in-memory system, Redis ensures that each application instance can access consistent session information quickly, enabling smooth user experiences even under heavy load. Its scalability allows it to handle spikes in traffic without degradation in performance, and its managed nature reduces operational overhead, allowing development teams to focus on application logic rather than infrastructure management.
Ultimately, ElastiCache Redis stands out as the optimal choice for session management in high-traffic web applications. It combines ultra-low latency, high throughput, and scalability, ensuring that session data remains highly available and responsive. By leveraging Redis, organizations can provide seamless user experiences, maintain operational efficiency, and achieve the performance and reliability required for modern web applications.
Question 146
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:
Running Oracle databases on EC2 instances using manual backup and restore procedures imposes significant operational challenges. Administrators must manage the full lifecycle of backups, plan for potential restore operations, and ensure that the database remains consistent and recoverable. This process can result in extended downtime, as backups often require the database to be quiesced or put into a state that limits availability. Furthermore, coordinating restores during failure scenarios can be labor-intensive and error-prone, creating risks for mission-critical applications. As the database size grows and workloads become more demanding, the operational overhead increases, making manual EC2-based management a less practical option for organizations seeking high availability and minimal disruptions.
Migrating to Aurora PostgreSQL might appear attractive due to its managed capabilities and scalability features, but it introduces its own complexities. Aurora PostgreSQL is not natively compatible with Oracle, which means that moving an Oracle database to Aurora requires extensive schema modifications, changes to stored procedures, and adjustments to application logic. This can be a lengthy and resource-intensive process, potentially involving rewriting SQL queries, altering indexing strategies, and testing application behavior to ensure correctness. For organizations that rely on Oracle-specific features such as PL/SQL, advanced partitioning, or materialized views, these changes can be particularly challenging, increasing project complexity and extending migration timelines.
DynamoDB, as a fully managed NoSQL database, is designed for key-value or document workloads and does not support relational database features. While it excels in scalability and low-latency access patterns, it cannot accommodate traditional Oracle workloads that depend on structured schemas, relational integrity, or transactional operations. Applications that require SQL querying, joins, and complex relationships would not function properly on DynamoDB without significant redesign, making it unsuitable as a migration target for Oracle environments.
A more suitable approach is to use Amazon RDS for Oracle in combination with AWS Database Migration Service (DMS). This strategy enables near real-time replication from the source Oracle database to a managed RDS Oracle instance. Importantly, the source database remains fully operational during the migration, minimizing downtime and reducing disruptions to business operations. DMS ensures that both schema and data integrity are preserved throughout the replication process, allowing for a smooth transition without data loss. Once the replication is in place, RDS provides a fully managed environment that handles automated backups, Multi-AZ deployments for high availability, patching, and ongoing maintenance.
This combination of RDS and DMS significantly reduces operational complexity while maintaining the capabilities and features of the original Oracle database. It is particularly well-suited for mission-critical workloads where downtime must be minimized and business continuity is essential. Organizations can migrate to a managed environment with confidence, benefiting from automated infrastructure management, built-in resiliency, and a robust migration path that supports low-downtime transitions. By leveraging RDS Oracle with DMS, businesses gain a streamlined, reliable solution for moving Oracle workloads to AWS without the operational overhead and risk associated with manual EC2 management.
Question 147
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 EC2 instance costs is a critical concern for organizations running multiple environments in AWS. While production workloads often require robust auto scaling capabilities to handle fluctuating demand, non-production environments such as development, testing, or staging frequently remain underutilized during off-hours. Auto Scaling scheduled actions are effective for adjusting the capacity of production instances based on predictable load patterns, but they do not provide a solution for non-production workloads that can be stopped entirely when not in use. Relying on manual intervention to stop and start these instances introduces the risk of human error and requires continuous monitoring, which can be both inefficient and costly over time.
Another approach often considered is the use of Spot Instances. Spot Instances are a cost-saving option for workloads that can tolerate interruptions, offering significantly reduced pricing compared to on-demand instances. However, while Spot Instances can be ideal for certain types of batch processing or non-critical tasks, they do not inherently solve the challenge of scheduling the start and stop of instances. Without automated scheduling, organizations still face operational overhead to ensure that resources are only running when needed.
Systems Manager Automation addresses this gap by providing a fully managed mechanism for automating the start and stop of EC2 instances in non-production environments. With Systems Manager, administrators can create automated runbooks that execute predefined sequences of actions based on cron schedules or event-driven triggers. These runbooks can be configured to stop all non-production instances during nights, weekends, or other periods of low activity, and then restart them before work hours commence. This approach ensures that instances are running only when required, optimizing cost savings without introducing delays or disruptions for users who rely on these environments during business hours.
In addition to reducing operational overhead, Systems Manager Automation improves consistency and reliability. Automated runbooks eliminate the risk of human error associated with manual instance management. Since the same schedule and procedures are applied every time, organizations gain confidence that instances will be stopped and started according to plan, without requiring ongoing attention from administrators. This is particularly valuable for environments that consist of a large number of instances, where manual management would be impractical and error-prone.
Furthermore, Systems Manager provides built-in auditing and compliance features. Each execution of an automated runbook is logged and can be tracked in AWS CloudTrail, giving organizations visibility into operational changes. This auditing capability is essential for maintaining compliance standards, providing assurance that cost optimization actions are applied in a controlled and accountable manner.
By leveraging Systems Manager Automation, organizations can implement a scalable, efficient, and reliable solution for managing non-production EC2 instances. It allows teams to optimize costs significantly by shutting down idle resources, reduces the operational burden of manual management, and ensures that instances are ready and available during required hours. This automated approach provides a balance between cost efficiency and operational control, enabling organizations to maintain productive non-production environments without unnecessary expenditure or administrative overhead.
Question 148
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 order, which can lead to inconsistent workflows. SNS is a pub/sub service that lacks message ordering and deduplication. Kinesis Data Streams provides ordered delivery per shard and is designed for high-throughput analytics, but it adds unnecessary complexity for simple microservice messaging. SQS FIFO Queue guarantees exactly-once message processing, preserves the order of messages, and supports deduplication. This ensures reliable, predictable communication between microservices, making it ideal for transaction-sensitive applications and maintaining data consistency in distributed systems.
Question 149
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 deliver dynamic content efficiently or provide global caching. EC2 with ALB ensures 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, reducing latency and improving response times for users worldwide. Combining CloudFront with S3 for static content and ALB for dynamic content ensures fast, secure, and highly available delivery. CloudFront integrates with WAF, SSL/TLS, and origin failover, providing security, encryption, and high availability. This architecture delivers a globally optimized, fault-tolerant web application with improved user experience.
Question 150
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:
When it comes to storing data in the cloud, selecting the right storage solution is critical for balancing cost, accessibility, and durability. Amazon Web Services offers multiple storage options, each designed to meet specific use cases. S3 Standard is optimized for data that is accessed frequently. It provides high throughput and low latency, ensuring rapid retrieval of objects. However, this level of performance comes at a higher cost, making it less suitable for data that is rarely accessed or intended primarily for backup purposes. For organizations that primarily require archival storage, using S3 Standard can lead to unnecessary expenditure over time.
For workloads running on EC2 instances, EBS gp3 offers block storage with predictable performance for operating system drives or high-performance workloads. While it is excellent for active databases and transactional workloads, it is not designed for long-term, cost-effective backup or archival purposes. Maintaining large volumes of rarely accessed data on EBS can quickly become expensive, and managing snapshots for long-term retention adds operational complexity.
EFS Standard provides a managed file system designed for shared access among multiple EC2 instances. It offers high availability and scalability, making it well-suited for workloads requiring simultaneous file access. However, EFS incurs higher costs for infrequently accessed data, as its pricing model focuses on active file storage rather than archival or backup workloads. For organizations that need to store data primarily for compliance, disaster recovery, or historical reference, EFS may not be the most economical choice.
S3 Glacier Instant Retrieval addresses the challenges of cost and accessibility for archival storage. It offers a low-cost solution for storing data that is rarely accessed but must remain available when needed. One of the key advantages of Glacier Instant Retrieval is its ability to provide millisecond-level access to objects, making it suitable for use cases where occasional, rapid retrieval is required. This feature makes it possible to maintain an archival solution without sacrificing accessibility or performance when critical data must be retrieved quickly.
In addition to fast retrieval, Glacier Instant Retrieval integrates seamlessly with S3 lifecycle policies. Objects stored in S3 Standard or Intelligent-Tiering can be automatically transitioned to Glacier Instant Retrieval based on defined policies, ensuring that storage costs are optimized without manual intervention. The service guarantees eleven nines of durability, safeguarding against data loss over long periods. Encryption at rest is provided via SSE-S3 or SSE-KMS, meeting stringent security and compliance requirements. CloudTrail auditing enables detailed monitoring and tracking of access, helping organizations maintain accountability and transparency over their stored data.
Overall, Glacier Instant Retrieval is an excellent choice for backups, regulatory compliance archives, and disaster recovery. It provides a cost-effective way to store large volumes of data while retaining high durability, strong security, and fast retrieval capabilities. By combining lifecycle management with low-cost archival storage, organizations can strike a balance between operational efficiency, data accessibility, and financial prudence. This makes Glacier Instant Retrieval a versatile and reliable component of a modern cloud storage strategy, ensuring that important data remains protected, accessible, and cost-efficient without the operational overhead associated with more traditional storage solutions.