Amazon AWS Certified Solutions Architect — Professional SAP-C02 Exam Dumps and Practice Test Questions Set 7 Q91-105
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Question 91
A company wants to implement a messaging system that ensures ordered delivery and exactly-once processing for critical microservices. Which 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 provides at-least-once delivery but does not guarantee the order of messages, which can lead to inconsistent workflows for microservices requiring sequential processing. SNS is a pub/sub system that delivers messages to multiple subscribers but does not guarantee ordering or deduplication. Kinesis Data Streams provides ordered data per shard and is suitable for streaming analytics but adds complexity for simple messaging requirements. SQS FIFO Queue guarantees exactly-once processing and preserves the sequence of messages, ensuring that critical microservices receive events in the correct order. It also supports deduplication, which prevents duplicate processing of messages. This ensures reliable, predictable, and consistent communication between services, making it ideal for transaction-sensitive workflows and fault-tolerant architectures.
Question 92
A company wants to analyze large, semi-structured datasets stored in S3 using SQL without provisioning infrastructure. Which service is most suitable?
A) Athena
B) Redshift
C) RDS MySQL
D) DynamoDB
Answer: A) Athena
Explanation:
Redshift is optimized for structured data and requires predefined schemas, making it less flexible for semi-structured datasets. RDS MySQL requires schema-on-write and cannot efficiently handle large datasets for analytics. DynamoDB is a NoSQL key-value store and does not support SQL-based queries directly for analytics. Athena is a serverless query service that allows SQL queries on S3 data using schema-on-read, supporting CSV, JSON, Parquet, ORC, and other formats. It scales automatically, and charges are based only on the data scanned. Integration with AWS Glue enables cataloging and metadata management, allowing efficient and flexible analytics. Athena is ideal for exploratory queries, ad-hoc reporting, and analyzing large semi-structured datasets without managing servers, providing cost efficiency, scalability, and quick insights.
Question 93
A company wants to implement a highly available, multi-AZ relational database with automatic failover for production workloads. Which service is most appropriate?
A) RDS Multi-AZ Deployment
B) RDS Single-AZ Deployment
C) DynamoDB
D) S3
Answer: A) RDS Multi-AZ Deployment
Explanation:
When designing a highly available relational database architecture in AWS, the limitations of basic deployment options must be carefully considered. A standard RDS Single-AZ deployment, for example, operates within a single availability zone. While this configuration provides a functional relational database, it lacks redundancy across multiple zones. Consequently, if the underlying infrastructure in that single availability zone fails due to hardware issues, network outages, or maintenance events, the database becomes unavailable. This introduces downtime for dependent applications, which can be especially problematic for production workloads that require continuous availability and reliability. Additionally, Single-AZ deployments do not include automatic failover capabilities, leaving the responsibility for disaster recovery largely on the operations team or manual intervention, which can extend downtime and impact service-level agreements.
Other storage and database options, while useful for specific scenarios, are insufficient for high-availability relational workloads. DynamoDB, for instance, is a highly scalable NoSQL database designed for low-latency key-value and document storage. Although it excels at horizontal scaling and serverless management, it does not natively support relational features such as complex joins, multi-statement transactions, or referential integrity. Applications that require these relational capabilities cannot rely solely on DynamoDB for critical transactional workloads. Similarly, Amazon S3 provides highly durable object storage but is not a database and cannot process structured relational queries, enforce transactional consistency, or maintain row-level integrity. While S3 is excellent for storing large volumes of unstructured or semi-structured data, it cannot replace a relational database for applications that require ACID (atomicity, consistency, isolation, durability) compliance.
RDS Multi-AZ deployments address these limitations by providing synchronous replication to a standby instance in a separate availability zone. This architecture ensures that the primary database has a fully synchronized replica ready to take over if the primary instance becomes unavailable. Automatic failover occurs without manual intervention, which significantly reduces downtime and ensures that applications remain available even during hardware failures, AZ outages, or maintenance events. In addition to high availability, Multi-AZ deployments handle operational tasks such as patching, minor version upgrades, and automated backups transparently, allowing organizations to focus on application functionality rather than database maintenance.
This deployment model is particularly well-suited for production workloads that require transactional integrity, fault tolerance, and uninterrupted access. By maintaining a standby in a separate availability zone, the risk associated with single points of failure is mitigated. Applications can continue to serve users reliably even under adverse conditions, and administrators gain peace of mind knowing that database failover and maintenance are managed automatically. Overall, RDS Multi-AZ provides a robust, reliable, and fully managed relational database solution that meets the stringent requirements of enterprise production environments, delivering both resilience and operational simplicity.
Question 94
A company wants a fully managed, serverless solution 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:
Managing containerized applications on AWS can take multiple forms, but not all approaches minimize operational overhead or maximize efficiency. Using ECS with the EC2 launch type or EKS with EC2 nodes involves deploying containers on EC2 instances, which requires developers and operations teams to handle tasks such as instance provisioning, scaling, patching, and monitoring. This adds complexity and consumes significant operational resources, as each compute node must be managed individually. Additionally, scaling these environments requires careful planning to ensure that sufficient compute resources are available for workloads while avoiding over-provisioning that drives up costs. EC2 alone provides raw compute power, but it lacks built-in container orchestration, requiring teams to implement orchestration, load balancing, and failover strategies themselves.
ECS with Fargate offers a serverless alternative that eliminates the need to manage the underlying infrastructure. With Fargate, developers define the containerized application requirements, such as CPU and memory, and AWS automatically provisions the necessary compute resources to run the containers. The platform handles scaling transparently, adjusting resources to meet workload demand without requiring manual intervention. This serverless approach removes the complexity of instance management and allows teams to focus exclusively on building and deploying application logic rather than worrying about infrastructure maintenance. Fargate integrates seamlessly with AWS networking, security, and monitoring services, providing features such as VPC isolation, IAM-based access control, CloudWatch logging, and metrics collection.
High availability is inherently supported in Fargate, as containers can be distributed across multiple availability zones, reducing the risk of downtime due to localized failures. Auto-scaling ensures that applications can handle spikes in demand without delay, and resources are billed only for what is consumed, offering significant cost efficiency compared to running always-on EC2 instances. Because Fargate abstracts away the management of servers, it also simplifies compliance, patching, and security updates, which would otherwise require dedicated operational effort in EC2-based deployments.
This architecture is particularly well-suited for microservices, batch processing, and other containerized workloads where flexibility, scalability, and operational simplicity are critical. Developers can deploy independent services without worrying about provisioning or maintaining the underlying compute resources, while operations teams benefit from reduced overhead and predictable costs. Fargate’s fully managed model allows organizations to deploy containerized applications in a resilient, secure, and cost-efficient manner while minimizing the operational complexity that comes with traditional EC2-based container orchestration.
ECS with the EC2 launch type and EKS with EC2 nodes require significant operational management for compute, scaling, and maintenance, whereas ECS with Fargate provides a fully managed, serverless container environment. By automatically provisioning and scaling resources, integrating with security and monitoring services, and providing high availability with cost-efficient billing, Fargate enables teams to focus on application development rather than infrastructure management. This makes ECS with Fargate the optimal choice for modern containerized workloads that require scalability, reliability, and operational simplicity.
Question 95
A company wants to automatically stop EC2 instances in non-production environments outside business hours to save costs. Which 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 costs in cloud environments requires more than just provisioning resources; it demands intelligent automation, particularly for non-production workloads that don’t need to run 24/7. While Auto Scaling scheduled actions are excellent for adjusting production workloads according to traffic patterns, they are not designed to handle stopping or starting instances in development, testing, or staging environments. Auto Scaling focuses on scaling out or in based on demand metrics rather than enforcing strict start/stop schedules for non-critical instances. Relying solely on manual intervention to shut down unused non-production instances is inherently error-prone. Engineers may forget to stop instances, or inconsistencies may arise across different environments, leading to unnecessary operational costs and wasted compute resources.
Spot Instances, while highly cost-effective, are intended for workloads that can tolerate interruptions. They reduce compute costs substantially for batch jobs or other interruptible processes but do not provide the scheduling capability needed to enforce start and stop times on non-production servers. Spot Instances alone cannot guarantee that development or QA environments are only active during business hours, meaning cost savings remain limited without additional management.
AWS Systems Manager Automation addresses these challenges by offering the ability to create fully automated runbooks that manage EC2 instance lifecycles across multiple environments. By configuring Systems Manager Automation to execute cron-like schedules, teams can automatically start or stop instances at predefined times. This ensures that non-production servers are only running when needed, for example, starting instances at the beginning of a workday and shutting them down after hours or during weekends. Such automation reduces the need for human intervention, minimizing the risk of errors or missed shutdowns, while enabling predictable and repeatable processes across all environments.
Furthermore, Systems Manager Automation provides logging and auditing capabilities, which are essential for compliance and operational governance. Each action taken by a runbook is recorded, offering transparency and accountability for changes made to the environment. This ensures that organizations can track which instances were stopped or started and when, aiding both internal audits and regulatory compliance requirements. Teams can manage multiple non-production environments—development, testing, QA, and staging—consistently, without relying on manual coordination.
By leveraging Systems Manager Automation, organizations gain the dual benefits of cost optimization and operational efficiency. Non-production EC2 instances no longer consume resources unnecessarily, which reduces cloud expenditure while preserving the agility needed for developers and testers. At the same time, automated management ensures environments are reliably available when required, eliminating downtime surprises and enhancing developer productivity. This approach establishes a controlled, repeatable, and compliant method to manage compute resources, ensuring that organizations achieve both cost savings and operational reliability in their AWS environments.
This solution represents a best practice for managing non-production workloads, combining automation, auditing, and efficiency in a way that traditional manual approaches or other AWS services cannot match.
Question 96
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:
Delivering content to users across the globe presents unique challenges, and relying solely on Amazon S3 is insufficient for optimizing performance. While S3 is excellent for durable and scalable object storage, it primarily serves content from a single region. This can result in higher latency for users located far from the S3 bucket’s region and increased data transfer costs, as every request travels back to the origin. S3 alone also lacks capabilities for handling dynamic content or advanced caching, which are essential for responsive, global applications.
Using EC2 instances behind an Application Load Balancer (ALB) can provide high availability and distribute traffic among multiple servers within a single region. This setup is suitable for dynamic content processing but does not inherently solve the problem of global latency. Users accessing the application from regions far from the ALB may still experience slow load times, and scaling these resources to handle worldwide traffic introduces additional operational overhead. Managing EC2 instances requires careful planning for provisioning, patching, and monitoring, increasing complexity and operational cost.
Amazon Route 53 offers DNS-based routing, which helps direct users to the nearest regional endpoint. Although this improves availability and can slightly reduce latency, Route 53 does not provide caching or content delivery services. Requests still need to reach the origin server for each access, resulting in repeated data transfers and slower response times for distant users. Therefore, Route 53 alone is insufficient for globally optimized performance and efficient content distribution.
The optimal solution for worldwide content delivery is Amazon CloudFront, a global content delivery network (CDN). CloudFront caches static and dynamic content at edge locations distributed around the world, bringing data closer to end users. By storing frequently accessed content at these edge locations, CloudFront significantly reduces latency, improves page load times, and decreases the volume of requests reaching the origin servers, reducing bandwidth costs. CloudFront can serve both static assets from S3 and dynamic content routed through ALB, providing a comprehensive solution for a global audience.
Integrating CloudFront with S3 and ALB delivers numerous advantages. Static assets, such as images, CSS, and JavaScript files, are cached at edge locations, enabling rapid access for users anywhere. Dynamic content requests are routed efficiently to regional ALBs, which balance traffic across EC2 instances or containerized applications. CloudFront also supports features like SSL/TLS encryption for secure delivery, AWS Web Application Firewall (WAF) integration to protect against common attacks, and origin failover to maintain reliability if the primary origin is unavailable.
This architecture ensures high availability, fast content delivery, and a secure, seamless user experience for a global audience. By combining S3, ALB, Route 53, and CloudFront, organizations can optimize performance, reduce operational overhead, and maintain security while efficiently handling both static and dynamic content. The result is a scalable, reliable, and globally responsive solution capable of meeting the demands of modern applications and delivering consistent, high-performance experiences to users worldwide.
Question 97
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 an existing Oracle database to the cloud while minimizing downtime and ensuring operational reliability requires careful selection of both database and migration tools. Traditional approaches, such as running Oracle on EC2 instances with manual backup and restore procedures, pose significant challenges. They require scheduling downtime for the database during the export and import processes, which can disrupt business operations. Additionally, this method demands manual intervention for backups, monitoring, and recovery, increasing the operational complexity and risk of errors. Such an approach is inefficient for organizations running mission-critical applications that require high availability and minimal disruption.
Considering alternatives, Aurora PostgreSQL is a fully managed relational database service with excellent performance and scalability. However, it is not compatible with Oracle workloads. Migrating an Oracle database to Aurora PostgreSQL would require extensive schema changes, application rewrites, and testing to ensure functionality, which introduces additional complexity and extended migration timelines. Similarly, DynamoDB, while a high-performance NoSQL database, is unsuitable for Oracle workloads due to its fundamental differences in data modeling, query capabilities, and transactional support. These options do not meet the requirements for maintaining Oracle-specific features, ensuring transactional consistency, and minimizing migration downtime.
The combination of Amazon RDS for Oracle and AWS Database Migration Service (DMS) provides a more effective and operationally efficient solution. RDS Oracle is a fully managed database service that supports features such as automated backups, software patching, and Multi-AZ deployments for high availability. By leveraging RDS, organizations can reduce administrative overhead and ensure that the database remains highly available and resilient during and after the migration process.
AWS DMS enables near real-time replication from the source Oracle database to the target RDS Oracle instance. Unlike manual migration approaches, DMS allows the source database to remain operational throughout the migration process. This continuous replication minimizes downtime, ensuring that users and applications can continue to access the database without interruption. DMS also handles schema conversion and data validation for homogeneous migrations, preserving data integrity and ensuring that the migrated database functions correctly in the target environment.
By using RDS Oracle with DMS, organizations gain a reliable, automated, and low-downtime migration pathway. The managed nature of RDS ensures that operational tasks such as backups, patching, and failover are handled automatically, reducing the operational burden on IT teams. Multi-AZ deployment further enhances reliability by providing synchronous replication across availability zones, allowing seamless failover in case of infrastructure issues.
This architecture delivers a highly predictable migration experience with minimal disruption to ongoing business operations. Organizations benefit from the performance, availability, and managed features of RDS Oracle while leveraging DMS for safe and efficient data migration. For mission-critical Oracle workloads, this combination ensures continuity, reliability, and operational simplicity, making it the preferred approach for cloud migration.
Question 98
A company wants to store backup data cost-effectively while allowing rapid retrieval when needed. Which service is most appropriate?
A) S3 Glacier Instant Retrieval
B) S3 Standard
C) EFS Standard
D) EBS gp3
Answer: A) S3 Glacier Instant Retrieval
Explanation:
For organizations looking to store data efficiently over the long term, selecting the right storage option is critical to balance cost, performance, and accessibility. While Amazon S3 Standard is highly suitable for frequently accessed data, it becomes an expensive choice for information that is rarely needed. Its design prioritizes low-latency access for active workloads, which is unnecessary for archival or compliance-related storage where access is sporadic. Using S3 Standard for such data can lead to inflated storage costs without providing significant performance benefits.
Similarly, Amazon EFS Standard is optimized for shared, actively accessed file storage, typically used by applications that require concurrent access from multiple compute instances. While it offers high availability and scalability, EFS Standard is not designed for long-term storage of infrequently accessed data. Its pricing model and performance characteristics make it cost-prohibitive for archival workloads, such as compliance records, backups, or historical datasets that are rarely queried but must be retained for regulatory purposes.
EBS gp3 volumes provide block storage for Amazon EC2 instances and are excellent for low-latency, high-performance workloads, including database storage or transactional systems. However, EBS is not inherently a cost-effective solution for long-term archival. Although snapshots can be taken and stored in S3, relying solely on EBS for backup retention can be expensive due to the per-GB pricing and operational overhead associated with managing snapshots over time.
Amazon S3 Glacier Instant Retrieval provides an ideal alternative for long-term, infrequently accessed data that still requires rapid retrieval when needed. This service combines the cost-efficiency of archival storage with performance that allows data to be retrieved in milliseconds. Glacier Instant Retrieval enables organizations to store large volumes of data without incurring the high costs associated with standard storage tiers. Its integration with S3 lifecycle policies allows seamless automatic transitions of objects from S3 Standard or Intelligent-Tiering to Glacier, ensuring that data moves to the most cost-effective storage tier without manual intervention.
Security and compliance are also critical considerations in long-term storage. Glacier Instant Retrieval offers encryption at rest through either SSE-S3 or SSE-KMS, ensuring that sensitive information is protected according to organizational and regulatory requirements. Additionally, it provides durability of eleven nines, ensuring that data is highly resilient to loss or corruption over time. Organizations can rely on Glacier for backup, regulatory archives, and disaster recovery solutions, confident that the data will remain secure, highly available, and durable.
By combining cost efficiency, durability, and rapid access, Glacier Instant Retrieval provides a balanced solution for storing infrequently accessed information while maintaining operational efficiency. It enables companies to retain data for compliance, disaster recovery, or long-term archival purposes without the cost and complexity associated with more performance-oriented storage options. For organizations seeking to optimize storage expenses while ensuring fast access when needed, Glacier Instant Retrieval offers the ideal combination of affordability, reliability, and speed.
This approach ensures that organizations can meet regulatory requirements, maintain data integrity, and provide rapid recovery capabilities, all while minimizing storage costs and operational complexity.
Question 99
A company wants to implement a messaging system for microservices with exactly-once processing and ordered delivery. Which service is most suitable?
A) SQS FIFO Queue
B) SQS Standard Queue
C) SNS
D) Kinesis Data Streams
Answer: A) SQS FIFO Queue
Explanation:
In distributed microservice architectures, reliable communication between services is critical for maintaining data consistency and ensuring predictable system behavior. Selecting the right messaging service can have a significant impact on application performance, fault tolerance, and overall reliability. While AWS provides several messaging options, each has specific characteristics that determine its suitability for certain use cases, particularly when message order and exact delivery are important.
SQS Standard Queue is a widely used messaging service that guarantees at-least-once delivery. This means every message is delivered at least once, but in rare cases, duplicates may occur. Additionally, Standard Queues do not preserve the order of messages, which can lead to inconsistent processing when the sequence of operations matters. For example, in workflows where events must be processed in the exact order they were generated, using a Standard Queue could result in race conditions or logical errors, requiring additional orchestration or custom logic to manage sequencing and deduplication.
SNS, or Simple Notification Service, provides a pub/sub messaging model where messages are broadcast to multiple subscribers simultaneously. While SNS is excellent for sending notifications or triggering multiple downstream processes, it does not guarantee message ordering or prevent duplicates. Subscribers can receive messages in different sequences, making it unsuitable for workflows that depend on strict sequencing or exactly-once processing. Using SNS in scenarios where order matters would require additional handling at the consumer level, adding complexity to the system.
Kinesis Data Streams is designed for real-time streaming analytics and high-throughput data processing. It offers ordering guarantees at the shard level, which can maintain the sequence of events within a shard. While this makes it suitable for complex stream processing and analytics pipelines, Kinesis introduces unnecessary operational overhead for microservice messaging. Shard management, scaling, and consumer coordination add complexity that is often unnecessary for transactional message delivery between services. For simple message queues that require ordering and deduplication, Kinesis may be over-engineered.
SQS FIFO Queue, on the other hand, is specifically designed to handle scenarios that demand exactly-once processing and strict message ordering. FIFO queues ensure that messages are delivered in the order they are sent and that each message is processed only once, eliminating the need for custom deduplication logic. This makes FIFO queues ideal for transaction-sensitive workflows, financial operations, or microservice interactions where consistent state and order are crucial. FIFO queues also support message batching and deduplication windows, further improving efficiency while maintaining reliability.
By leveraging SQS FIFO queues, developers can build microservice architectures that handle complex, order-sensitive workflows without sacrificing scalability or fault tolerance. The service guarantees predictable message delivery, reduces the risk of errors due to duplicate or out-of-order messages, and simplifies operational overhead, allowing teams to focus on application logic rather than managing message sequencing manually.
while Standard Queues, SNS, and Kinesis have their use cases, SQS FIFO Queue is the most suitable choice for microservice architectures that require strict message order and exactly-once processing. It combines reliability, consistency, and ease of use, making it the optimal solution for transactional and fault-tolerant messaging scenarios.
Question 100
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:
In modern web applications, managing session data efficiently is critical for delivering a seamless user experience, particularly under high traffic conditions. Several storage options exist within AWS, but not all are well-suited for the rapid, frequent read and write operations required for session management. Understanding the performance characteristics of each option is essential for choosing the right solution.
DynamoDB is a fully managed NoSQL database that provides low-latency storage, making it an attractive option for many high-performance workloads. It scales automatically to handle large volumes of requests and can accommodate unpredictable traffic patterns. However, under heavy load, DynamoDB may not consistently achieve sub-millisecond latency. While it performs admirably in many use cases, applications requiring extremely fast, consistent access to session data may experience minor delays, which can impact user experience when every millisecond counts.
RDS MySQL is a relational database that is commonly used for structured data and transactional workloads. Although it offers reliability and familiarity for developers, it is not optimized for the rapid, frequent read and write operations typical of session management. Disk I/O and connection overhead introduce latency, which makes RDS MySQL unsuitable for storing session data where high-speed access is necessary. Using MySQL in this scenario could lead to slower response times, particularly as traffic scales, negatively affecting the responsiveness of web applications.
S3, Amazon’s object storage service, provides highly durable and scalable storage for a wide range of data types. While S3 excels at storing large objects such as images, logs, or backups, it is not designed for frequent, small read and write operations. The latency involved in accessing individual objects, combined with the lack of native support for atomic updates, renders S3 inefficient for session storage. Frequent access patterns can increase costs and reduce performance, making S3 impractical for managing dynamic session data.
ElastiCache Redis, on the other hand, is an in-memory key-value store designed specifically for scenarios requiring extremely low latency and high throughput. Because data is stored in memory, read and write operations occur in microseconds, allowing near-instantaneous access. Redis supports advanced features such as replication, clustering, and optional persistence, ensuring both reliability and scalability across multiple web servers. This makes it ideal for managing session data, as it can handle high volumes of concurrent connections while maintaining consistent performance.
By using Redis for session storage, applications can deliver smooth, responsive experiences even during traffic spikes. Its architecture allows horizontal scaling, so as demand increases, Redis clusters can expand without impacting performance. Additionally, Redis eliminates much of the operational overhead associated with managing traditional databases, enabling teams to focus on application logic rather than infrastructure maintenance.
while DynamoDB, RDS MySQL, and S3 provide valuable storage capabilities, they do not meet the stringent latency and throughput requirements of session management in high-traffic web applications. ElastiCache Redis offers a specialized, in-memory solution that combines speed, scalability, and reliability, ensuring session data is accessible instantly across multiple servers. This approach supports consistent user experiences, reduces operational complexity, and enables applications to scale seamlessly in response to growing traffic demands.
Question 101
A company wants to implement a multi-region, highly available web application that automatically routes users to the nearest healthy region. Which AWS service combination is most suitable?
A) Route 53 with health checks, S3 Cross-Region Replication, Multi-Region Auto Scaling groups
B) CloudFront only
C) Single-region ALB with Auto Scaling
D) RDS Single-AZ
Answer: A) Route 53 with health checks, S3 Cross-Region Replication, Multi-Region Auto Scaling groups
Explanation:
CloudFront alone caches content at edge locations but does not provide multi-region failover or routing based on health. Single-region ALB with Auto Scaling ensures availability only within one region, leaving users exposed to regional outages. RDS Single-AZ provides database availability only in a single availability zone and does not address multi-region web application resilience. Using Route 53 with health checks allows intelligent routing of traffic to the nearest healthy region, ensuring low latency and high availability. S3 Cross-Region Replication keeps static content synchronized across regions for fault tolerance. Multi-Region Auto Scaling groups replicate EC2 instances across regions, providing scalable compute redundancy. This combination ensures a highly available, fault-tolerant, and globally distributed application that maintains performance and minimizes downtime for users worldwide.
Question 102
A company wants to store large amounts of data that is infrequently accessed but must be quickly retrievable. Which AWS storage service is most appropriate?
A) S3 Glacier Instant Retrieval
B) S3 Standard
C) EFS Standard
D) EBS gp3
Answer: A) S3 Glacier Instant RetrievalExplanation:
When considering long-term data storage and archival in AWS, it is essential to balance cost, performance, durability, and security. While multiple storage options exist, not all are suitable for infrequently accessed data that must remain durable and secure. S3 Standard, for example, is optimized for frequently accessed data. It delivers high availability and low latency for active workloads but comes at a higher cost, making it less suitable for long-term archival storage where access is rare. Using S3 Standard for archival data can lead to unnecessary expense without delivering significant benefits, since frequent access performance is not required.
EFS Standard, a fully managed elastic file system, is designed for actively used shared file storage. It is ideal for workloads requiring concurrent access from multiple instances, such as content management or shared project directories. However, storing infrequently accessed backup or archival data in EFS can become cost-prohibitive over time. Its pricing model is geared toward high availability and performance for active workloads rather than cost-optimized, long-term storage.
EBS gp3, the block storage option for EC2, is intended for low-latency storage directly attached to compute instances. While EBS provides high performance for applications such as databases and transactional workloads, it is not optimized for long-term retention of backups. Using EBS for archival purposes is inefficient because it is more expensive per gigabyte than object storage solutions and does not provide the same ease of automated lifecycle management.
S3 Glacier Instant Retrieval is purpose-built for infrequently accessed data that still requires fast retrieval when needed. This storage class combines low cost with millisecond access, offering a practical solution for backups, compliance records, or archival datasets that occasionally need to be retrieved quickly. One of its key advantages is integration with S3 lifecycle policies, which allow automated transitions from S3 Standard or Intelligent-Tiering to Glacier Instant Retrieval. This reduces operational overhead and ensures that data moves seamlessly to the most cost-effective storage tier as access patterns change.
Durability is a core feature of S3 Glacier Instant Retrieval, offering eleven nines (99.999999999%) of durability. This ensures that data is safely retained over extended periods. Additionally, encryption at rest using either SSE-S3 or SSE-KMS provides strong protection for sensitive information, helping organizations meet regulatory requirements and internal security standards. CloudTrail integration further enhances compliance by enabling detailed logging and auditing of all access to stored objects.
Overall, S3 Glacier Instant Retrieval delivers a balanced solution for organizations seeking cost-effective archival storage without sacrificing durability, accessibility, or security. It addresses the limitations of higher-cost options like S3 Standard, EFS Standard, and EBS gp3 by providing a storage tier optimized for infrequent access, automatic lifecycle management, and rapid retrieval when required. This makes it an ideal choice for long-term backups, regulatory compliance data, or archival datasets, ensuring organizations can store critical information efficiently while maintaining operational flexibility and security.
Question 103
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:
Using RDS SQL Server alone requires downtime to export and import data. Aurora PostgreSQL is not compatible with SQL Server, necessitating extensive schema and application changes. EC2 SQL Server with manual backup and restore is time-consuming, operationally complex, and requires extended downtime. RDS SQL Server with AWS DMS supports near real-time replication, allowing the source database to remain operational while syncing to AWS. DMS can handle schema conversion for homogeneous migrations, and RDS provides automated backups, Multi-AZ deployment, and maintenance. This approach ensures a smooth migration with minimal downtime, operational simplicity, and high reliability for mission-critical SQL Server workloads.
Question 104
A company wants to process event-driven workloads from S3 and SQS with minimal operational overhead. 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 patching, scaling, and monitoring, increasing operational overhead. ECS and EKS with EC2 nodes also demand infrastructure management. Lambda is a serverless compute service that can be triggered directly by S3 events or SQS messages, automatically scaling based on workload. It only charges for execution duration, providing cost efficiency. Integration with CloudWatch enables monitoring, logging, and error handling. Lambda provides a fully managed, scalable, and event-driven architecture for processing workloads, eliminating the need to manage servers and ensuring high availability and responsiveness.
Question 105
A company wants to store session data for a web application with extremely low 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:
In modern web applications, managing session data efficiently is crucial to ensuring responsive and reliable user experiences, particularly under high-traffic conditions. Choosing the right storage solution for session state requires careful consideration of latency, throughput, scalability, and operational complexity. While several AWS services offer storage and database capabilities, not all are suitable for the low-latency, high-throughput demands of session management.
DynamoDB is a managed NoSQL database that provides low-latency access to data and can scale horizontally to handle large workloads. However, under very high traffic scenarios, DynamoDB may not consistently deliver sub-millisecond response times, which is often a critical requirement for session storage where every millisecond of delay can impact user experience. While DynamoDB excels in durability and scalability, its access patterns and eventual consistency in some scenarios may introduce latency spikes that are unacceptable for session management.
RDS MySQL, as a relational database, offers transactional consistency and robust query capabilities. Yet, it is not optimized for workloads requiring rapid, frequent reads and writes of small objects, such as session data. Disk I/O and connection management overhead can introduce latency, particularly when multiple web servers are accessing the database concurrently. This makes RDS MySQL less suited for scenarios where maintaining real-time session state is essential, as each read and write operation may experience delays that impact responsiveness.
S3 is another storage option within AWS, providing highly durable object storage that is ideal for storing large amounts of data. However, it is not designed for rapid, fine-grained read and write operations. Frequent small updates to session information would result in performance bottlenecks and increased latency when using S3 for session state. While S3 excels for long-term storage and archival, it is not a practical solution for dynamic, high-traffic session management.
ElastiCache Redis addresses these challenges by providing an in-memory key-value store specifically designed for workloads that require extremely low latency and high throughput. Redis stores data directly in memory, allowing sub-millisecond access times even under heavy load. It supports advanced features such as replication, clustering, and optional persistence, which provide both high availability and durability. By deploying Redis as a centralized session store, multiple web servers can reliably read and write session data without introducing significant latency, ensuring a seamless user experience.
Redis also simplifies operational complexity. Its managed service in AWS, ElastiCache, handles tasks such as patching, failure recovery, and scaling, allowing developers to focus on application logic rather than infrastructure management. Clustering and replication allow session data to be distributed and highly available, while optional persistence ensures data is not lost in case of a failure. This combination of performance, reliability, and manageability makes Redis an ideal choice for session storage in high-traffic web applications.
while DynamoDB, RDS MySQL, and S3 each offer unique strengths, they fall short in addressing the specific performance requirements of session management. ElastiCache Redis, with its in-memory architecture, high throughput, low latency, and support for clustering and replication, provides the most effective solution. By leveraging Redis, organizations can ensure fast, reliable access to session data, maintain scalability as traffic grows, and deliver a smooth and responsive experience for users, all while minimizing operational overhead. This makes Redis the optimal choice for session storage in modern web environments.