Amazon AWS Certified Solutions Architect — Professional SAP-C02 Exam Dumps and Practice Test Questions Set 9 Q121-135

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Question 121

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:

EC2 Oracle with manual backup and restore requires downtime and extensive operational effort. Aurora PostgreSQL is not Oracle-compatible, requiring schema and application changes. DynamoDB is NoSQL and cannot host Oracle workloads. RDS Oracle with AWS DMS enables near real-time replication from the source database to a managed RDS instance. The source database remains operational during migration, minimizing downtime. DMS can handle homogeneous migrations with minimal intervention. RDS provides automated backups, Multi-AZ deployment, and maintenance, ensuring high availability and reliability. This approach is ideal for mission-critical Oracle workloads needing smooth, low-downtime migration while minimizing operational complexity.

Question 122

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 scaling, patching, and monitoring, adding operational overhead. ECS and EKS with EC2 nodes require infrastructure management. Lambda is serverless and can be triggered directly by S3 events or SQS messages. It automatically scales based on workload and charges only for execution duration. Integration with CloudWatch enables logging, monitoring, and error handling. Lambda provides a fully managed, event-driven solution that eliminates server management, ensures high availability, scalability, and cost efficiency, making it ideal for workloads like order processing or data transformation triggered by S3 or SQS events.

Question 123

A company wants to store session data for a high-traffic web application with extremely low latency. Which AWS service is most suitable?

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

Answer: A) ElastiCache Redis

Explanation:

Managing session data in modern, high-traffic web applications requires a solution that balances low latency, high throughput, scalability, and operational simplicity. While several database and storage options exist within the AWS ecosystem, not all are suitable for the rapid, frequent read and write patterns that session management demands. Each option has limitations that can impact application performance and user experience if chosen incorrectly.

DynamoDB is a fully managed NoSQL database that provides high availability and low-latency access for key-value and document workloads. It can scale horizontally to handle millions of requests per second, making it appealing for many use cases. However, under conditions of extreme traffic, DynamoDB may not consistently achieve sub-millisecond response times, especially when complex access patterns, conditional updates, or burst traffic are involved. For session management, where microsecond-level response times are often critical, these fluctuations can translate into noticeable latency for end users.

RDS MySQL, as a traditional relational database, offers robust features such as ACID transactions, foreign key constraints, and complex query capabilities. However, it introduces inherent latency due to disk I/O, network connections, and connection pooling overhead. Every read and write operation involves a round trip to the database, which can become a bottleneck when thousands of users are actively creating, updating, or reading session data simultaneously. This makes MySQL less suitable for applications where rapid session state access is essential, as any delay can affect responsiveness and overall user satisfaction.

S3, on the other hand, excels at durable object storage, making it perfect for storing files, backups, and large datasets. Yet, S3 is not designed for frequent, small-scale read and write operations. Its object storage model and eventual consistency characteristics mean that attempting to use S3 for session data would result in significant performance issues, as each access involves HTTP calls, network latency, and consistency considerations.

ElastiCache Redis emerges as the ideal solution for session management in this context. Redis is an in-memory key-value store engineered for ultra-low latency and high throughput. Because it operates entirely in memory, read and write operations can occur in microseconds, ensuring that session information is instantly available across multiple web servers. Redis supports advanced features like replication and clustering, which allow it to scale horizontally and provide high availability. Optional persistence can be enabled to back data to disk for durability without compromising performance. These capabilities make Redis particularly well-suited for applications where high concurrency, rapid state changes, and fault tolerance are essential.

In addition to speed and scalability, Redis reduces operational complexity. Unlike managing traditional relational databases or EC2-hosted data stores, Redis can be deployed in fully managed configurations through AWS ElastiCache. AWS handles patching, monitoring, failover, and backups, enabling developers to focus on application logic rather than infrastructure management.

By leveraging Redis for session storage, applications achieve a combination of high performance, reliability, and scalability. Session data is available in real-time, user experiences remain seamless even under heavy load, and operational overhead is minimized. This architecture ensures that high-traffic applications can maintain responsiveness, consistency, and resilience, making Redis the optimal choice for session management in modern cloud-native environments.

Question 124

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 content to a global audience requires careful consideration of performance, availability, and security. While S3 is an excellent solution for hosting static content such as images, CSS, JavaScript files, or downloadable assets, it has inherent limitations when it comes to delivering content efficiently to users distributed across the globe. S3 provides reliable storage and high durability, but it does not include built-in caching or content distribution features. As a result, requests from distant locations may experience higher latency, and dynamic content cannot be served effectively from S3 alone.

Using EC2 instances behind an Application Load Balancer (ALB) allows applications to process dynamic requests and ensures high availability within a particular region. The ALB distributes incoming traffic across multiple instances, improving fault tolerance and scalability. However, deploying EC2 with ALB is inherently regional, meaning that users located far from the deployment region may face noticeable latency. Additionally, managing EC2 instances adds operational complexity, including patching, scaling, monitoring, and capacity planning. While this approach is suitable for regional applications, it does not provide the low-latency, globally distributed performance necessary for worldwide user bases.

DNS routing through Amazon Route 53 enables intelligent traffic management, directing users to the nearest available endpoints, performing health checks, and supporting failover. However, Route 53 is strictly a DNS service and does not serve or cache application content. While it can route requests efficiently, it cannot reduce the time required for content to travel across networks or accelerate static content delivery.

Amazon CloudFront, a global content delivery network (CDN), addresses these challenges by caching content at strategically located edge locations around the world. By serving static content such as images, videos, or scripts from edge caches closer to end users, CloudFront dramatically reduces latency, improving the overall responsiveness of the application. When combined with S3 for static content, CloudFront ensures that frequently requested objects are delivered quickly, minimizing the load on the origin S3 bucket and reducing operational costs. For dynamic content, requests can still be routed through an ALB to EC2 instances, allowing the system to handle complex processing and business logic while benefiting from the CDN for caching static components.

This hybrid architecture not only improves performance but also enhances security and reliability. CloudFront integrates seamlessly with SSL/TLS encryption, ensuring secure communication between users and edge locations. Additionally, integration with AWS Web Application Firewall (WAF) provides protection against common web threats such as SQL injection, cross-site scripting, and DDoS attacks. Origin failover capabilities allow the system to automatically route traffic to alternate origins if one becomes unavailable, ensuring uninterrupted service.

By leveraging CloudFront for global caching, S3 for static storage, ALB and EC2 for dynamic processing, and Route 53 for intelligent routing, this architecture provides a highly available, low-latency, and secure solution for delivering content worldwide. Users experience rapid load times regardless of location, while application owners benefit from reduced operational complexity, optimized resource usage, and a robust security posture. This approach ensures that global applications can scale efficiently, remain resilient, and provide a seamless user experience across continents.

Question 125

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:

Efficient and cost-effective storage is a cornerstone of modern cloud architecture, particularly when balancing performance, durability, and budget constraints. Different AWS storage solutions are tailored for distinct use cases, and selecting the right option is essential for managing data effectively while minimizing operational and financial overhead. For frequently accessed data, S3 Standard provides high durability and availability, ensuring rapid access to objects whenever needed. This storage class is ideal for workloads with consistent read and write activity, such as active application content or user-generated files. However, the cost of S3 Standard can become significant when storing large volumes of data that are rarely accessed, making it less optimal for long-term retention or archival purposes.

EBS gp3 volumes, which offer block storage attached to EC2 instances, deliver high-performance storage for running applications, databases, and other workloads requiring low-latency access. Despite its performance benefits, EBS gp3 is not designed to serve as an economical long-term storage solution. Using EBS for archival or infrequently accessed data would result in high costs, as billing is based on provisioned volume size rather than usage, and it lacks the integration and lifecycle management features available in S3.

EFS Standard provides persistent, shared file storage suitable for applications requiring concurrent access from multiple instances or servers. While it delivers scalability and flexibility for active workloads, the cost structure makes it less suitable for storing data that is rarely accessed. EFS incurs ongoing charges for storage usage and throughput capacity, so infrequently accessed datasets, such as older backups or compliance archives, can become cost-prohibitive over time.

For scenarios where long-term retention and cost efficiency are priorities, S3 Glacier Instant Retrieval offers a compelling solution. This storage class is optimized for data that is infrequently accessed but occasionally required on demand. It allows millisecond-level retrieval of objects, combining the benefits of archival storage with near-instant accessibility. By integrating with S3 lifecycle policies, organizations can automate the movement of objects from S3 Standard or Intelligent-Tiering to Glacier Instant Retrieval based on age, usage patterns, or compliance requirements. This automation reduces administrative overhead while ensuring that storage costs are minimized without compromising access to critical data.

Glacier Instant Retrieval also maintains AWS’s industry-leading durability, providing 11 nines of protection against data loss. Objects are encrypted at rest using either SSE-S3 or customer-managed keys via SSE-KMS, ensuring compliance with stringent security and regulatory standards. Additionally, full auditing and monitoring are available through CloudTrail, enabling organizations to track access and changes for governance and compliance purposes. These capabilities make Glacier Instant Retrieval suitable for storing sensitive backups, regulatory records, or disaster recovery datasets while keeping operational and financial overhead low.

By leveraging Glacier Instant Retrieval in conjunction with automated lifecycle policies, organizations achieve a balanced storage strategy that optimizes costs, durability, and accessibility. Frequently accessed data can remain in S3 Standard, ensuring low-latency availability, while older or less critical datasets can seamlessly transition to Glacier for archival purposes. This approach supports both operational efficiency and compliance requirements, providing a secure, cost-effective, and highly resilient storage solution for modern enterprises.

Question 126

A company wants to implement a highly available, multi-region web application that automatically routes users to the nearest healthy region. Which AWS service combination is most suitable?

A) Route 53 with health checks, S3 Cross-Region Replication, Multi-Region Auto Scaling groups
B) CloudFront only
C) Single-region ALB with Auto Scaling
D) RDS Single-AZ

Answer: A) Route 53 with health checks, S3 Cross-Region Replication, Multi-Region Auto Scaling groups

Explanation:

Designing a web application for global users requires careful consideration of performance, availability, and fault tolerance. Relying on a single component or a single region can leave applications vulnerable to outages and latency issues. Each AWS service offers distinct capabilities, but combining them intelligently allows the creation of a resilient, high-performing, multi-region architecture.

CloudFront, Amazon’s global content delivery network, is highly effective at caching static content at edge locations around the world. This reduces latency by bringing content closer to end users and helps offload traffic from origin servers. However, while CloudFront ensures low-latency access, it does not provide multi-region failover on its own. In the event of a regional outage affecting the origin server, CloudFront cannot automatically route requests to a healthy origin in a different region, potentially impacting availability.

An Application Load Balancer deployed in a single region with Auto Scaling groups ensures that compute resources within that region remain available and can handle changes in traffic load. While this setup is suitable for scaling compute capacity within a region, it does not address regional failures. If an entire region becomes unavailable due to a natural disaster, hardware failure, or network disruption, users in that region would lose access to the application. Similarly, deploying RDS in a single availability zone restricts database availability to a single zone. Any outage at that zone level could interrupt application functionality, leaving critical data temporarily inaccessible.

To address these limitations, Route 53 can be employed with health checks to enable intelligent DNS-based routing. Route 53 continuously monitors the health of endpoints and can route users to the nearest healthy region, minimizing latency while ensuring continuous availability. This capability is essential for multi-region web applications where uptime and performance are critical.

For static content, Amazon S3 with Cross-Region Replication ensures that objects are synchronized across multiple regions. This replication increases data durability and provides a fallback in case of regional outages. Users can continue to access static content without interruption, and any updates to content are automatically propagated to replicated buckets.

Multi-region Auto Scaling groups further enhance resilience by replicating EC2 instances across different geographic regions. These groups allow compute capacity to scale independently in each region based on demand, ensuring that the application can handle spikes in traffic anywhere in the world while maintaining redundancy. Combined with CloudFront, this setup ensures that users experience low-latency access and that both dynamic and static content remain available even during regional disruptions.

Integrating these components—CloudFront for edge caching, Route 53 for intelligent routing, S3 Cross-Region Replication for durable static content, RDS Multi-AZ or Multi-Region for database reliability, and multi-region Auto Scaling for compute redundancy—creates a robust architecture. This design ensures that a web application is globally distributed, fault-tolerant, and highly available, providing seamless performance to users worldwide.

By combining caching, replication, intelligent routing, and multi-region scalability, the architecture balances performance, cost, and operational simplicity while mitigating risks associated with regional failures. Users benefit from consistent, low-latency access, and the application can withstand a variety of infrastructure disruptions without service interruption.

Question 127

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

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

Answer: A) Lambda triggered by S3 and SQS

Explanation:

Building scalable, cost-efficient, and highly available applications in the cloud requires careful selection of compute services based on operational overhead, workload patterns, and integration needs. Traditional EC2 instances offer flexible compute resources but come with significant operational responsibilities. Each EC2 instance must be provisioned, patched, monitored, and scaled manually. Ensuring reliability often involves setting up Auto Scaling groups, configuring monitoring through CloudWatch, and handling backup, failover, and recovery strategies. While this provides granular control, it increases the burden on operations teams and diverts focus from core application development.

Container orchestration platforms like ECS and EKS, when paired with EC2 nodes, offer a higher-level abstraction for deploying microservices. ECS simplifies container management within AWS, and EKS provides Kubernetes-based orchestration. However, both still require managing the underlying EC2 infrastructure. Teams must maintain the cluster, monitor node health, apply security patches, and handle scaling policies. Although these solutions streamline container deployment, the operational overhead of managing compute resources remains significant, particularly for dynamic or unpredictable workloads.

AWS Lambda provides a fundamentally different approach by offering serverless compute. With Lambda, developers can execute code in response to events without worrying about the underlying servers. Lambda functions can be triggered by a wide range of event sources, including S3 object uploads and SQS message queues. This event-driven model allows applications to respond automatically to changes in data or user interactions, such as processing uploaded files, executing background workflows, or orchestrating microservices.

One of the key benefits of Lambda is its automatic scaling. Functions scale seamlessly with workload demand, from a few invocations per day to thousands per second, without manual intervention. Billing is based solely on execution duration and memory consumption, eliminating the cost of idle infrastructure. Lambda also integrates tightly with CloudWatch, providing comprehensive logging, monitoring, and error handling. This ensures that developers and operators can gain real-time visibility into execution patterns, identify failures, and implement retries or alerts efficiently.

By leveraging Lambda for event-driven workloads, organizations can achieve a fully managed processing architecture that is highly resilient and scalable. Common use cases include order processing pipelines where each order triggers a function to update inventory, notify users, or generate invoices; image or video processing tasks where uploaded media is automatically resized, transcoded, or analyzed; and ETL operations where data ingested into S3 or streamed through SQS is transformed and loaded into downstream storage systems.

This serverless architecture significantly reduces operational complexity, as there is no need to manage servers, clusters, or scaling policies manually. The integration of event-driven triggers with automated scaling, monitoring, and logging ensures consistent performance and reliability. Organizations can focus on building business logic and delivering features rather than maintaining infrastructure.

while EC2 and container-based approaches provide control, they require extensive operational management. Lambda offers a serverless alternative that abstracts infrastructure concerns, automatically scales with workload demand, and provides cost-effective execution. Combined with S3 and SQS as event sources, Lambda enables fully managed, event-driven applications that are highly available, scalable, and efficient, making it ideal for modern cloud-native workloads such as automated order processing, media handling, and ETL pipelines.

Question 128

A company wants to store session data for a high-traffic web application with extremely low latency and high throughput. Which AWS service is most suitable?

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

Answer: A) ElastiCache Redis

Explanation:

Managing session data efficiently is a critical requirement for applications that serve high volumes of users and demand fast, reliable access. Choosing the right storage solution directly impacts performance, scalability, and overall user experience. Several AWS services offer different approaches to storing and managing data, but not all are optimized for session management under heavy workloads.

DynamoDB, for instance, is a fully managed NoSQL database known for its low-latency performance and ability to scale horizontally with ease. It handles high request rates and variable workloads effectively, making it suitable for many applications that require fast key-value access. However, DynamoDB may struggle to maintain consistent sub-millisecond latency under extreme traffic spikes, particularly in scenarios where session data requires frequent reads and writes. This limitation can result in variable response times, which is suboptimal for session management where consistent speed is crucial.

RDS MySQL, a relational database service, provides full SQL capabilities and strong data consistency. While it is excellent for transactional workloads, it introduces latency due to disk I/O, connection management, and schema overhead. These factors make it less suitable for scenarios requiring rapid updates and reads of session data. For applications handling thousands or millions of concurrent users, relying on RDS MySQL for session storage can become a performance bottleneck, impacting responsiveness and user satisfaction.

Amazon S3, while ideal for storing large objects and providing durable, scalable storage, is not designed to handle frequent, small read and write operations efficiently. Using S3 for session data would create significant latency because each request involves network round-trips to the storage service and object retrieval, which is too slow for the sub-millisecond access times typically required for session management.

ElastiCache Redis addresses these challenges by providing an in-memory key-value store designed specifically for high-throughput, low-latency workloads. Since Redis operates entirely in memory, it delivers extremely fast read and write operations, making it ideal for session management where response time is critical. Redis supports replication, clustering, and optional persistence, ensuring that session data is available across multiple application instances while maintaining high availability and durability. Its clustering capabilities allow the data store to scale horizontally, accommodating growing traffic without introducing latency.

By storing session data in Redis, applications can provide instant, consistent access to session information across multiple web servers. This ensures smooth user experiences even during periods of high concurrency, while eliminating the performance issues associated with traditional relational or object storage systems. Redis also reduces operational complexity because it eliminates the need to manage complex database connections, optimize disk I/O, or handle frequent reads and writes to slower storage layers.

In high-traffic applications, using Redis for session management provides a robust solution that balances performance, reliability, and scalability. It ensures rapid access to session data, supports distributed architectures, and maintains operational simplicity, allowing developers to focus on application logic rather than underlying infrastructure. With its ability to deliver sub-millisecond latency, handle high concurrency, and maintain high availability, Redis is the optimal choice for session storage in modern, large-scale web applications.

Question 129

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:

When it comes to analyzing large-scale structured datasets, choosing the right database service is critical for performance, scalability, and cost efficiency. While Athena is a serverless query service that excels at running ad-hoc queries directly on data stored in S3, it is not designed to sustain petabyte-scale workloads for extended periods. Athena allows users to query semi-structured and structured data on-demand without provisioning servers or managing infrastructure, but for continuous, large-scale analytics operations, it can become less efficient due to its per-query pricing model and lack of dedicated compute resources.

RDS MySQL, on the other hand, is optimized for transactional workloads, handling routine database operations with strong consistency and relational capabilities. However, it struggles with massive structured datasets, especially when complex queries, aggregations, or analytical workloads are involved. The performance of RDS MySQL is constrained by disk I/O, single-instance compute limits, and schema-on-write design, making it unsuitable for organizations that require rapid, large-scale analytics or high concurrency across multiple users and queries simultaneously.

DynamoDB is a highly performant NoSQL database capable of delivering low-latency access to key-value and document data. While it provides excellent horizontal scalability and supports high-volume transactional workloads, DynamoDB does not natively support SQL-based analytics on structured datasets. Analytical queries, aggregations, and joins are not its strong suit, and performing such operations often requires additional data processing layers or exporting data to separate analytics engines, adding complexity and operational overhead.

Amazon Redshift addresses the limitations of these services by providing a fully managed, columnar data warehouse built for large-scale structured analytics. Redshift is optimized for high-performance queries on massive datasets, offering features such as columnar storage, data compression, and parallel query execution to maximize throughput and reduce query latency. It integrates seamlessly with S3 for direct ingestion of large volumes of structured data, eliminating the need for intermediate processing and allowing organizations to build pipelines that scale efficiently. Redshift also supports complex SQL queries, joins, and aggregations, making it suitable for in-depth analytics and business intelligence applications.

High concurrency workloads are supported through workload management and automatic scaling features, ensuring that multiple users and queries can run simultaneously without performance degradation. With the ability to scale to petabyte-sized datasets and the integration of Redshift Spectrum for querying external S3 data, Redshift enables organizations to perform analytics across both local and external datasets efficiently. Its managed nature reduces operational overhead, handling patching, backups, and maintenance automatically, allowing teams to focus on deriving insights rather than managing infrastructure.

For enterprises that need reliable, fast, and cost-effective analytics on structured datasets at scale, Redshift provides an ideal solution. It combines high-performance query capabilities, massive scalability, and operational simplicity, making it the preferred choice for organizations looking to unlock insights from large datasets without compromising on performance or manageability. Redshift’s architecture ensures that businesses can process and analyze data efficiently, supporting strategic decision-making and enabling data-driven operations at scale.

Question 130

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 cloud infrastructure efficiently requires a thoughtful approach to balancing operational overhead, cost optimization, and reliability. In many organizations, non-production environments such as development, testing, and staging consume significant compute resources, yet they often remain underutilized outside business hours. While production environments demand consistent availability, non-production workloads typically do not require 24/7 uptime. Addressing this challenge with manual intervention or traditional automation tools can lead to inefficiencies and higher operational risk.

Auto Scaling in AWS is widely used to maintain application performance by dynamically adjusting the number of running EC2 instances based on demand. Scheduled actions within Auto Scaling can add or remove capacity at predefined times, ensuring production workloads meet performance requirements during peak periods while scaling down during off-peak hours. However, this feature is primarily intended for managing production workloads. It does not offer a flexible or safe solution for stopping non-production instances that do not require continuous operation. Relying on manual stopping of instances in non-production environments introduces human error, inconsistencies, and the potential for unnecessary costs when instances are left running. Manual processes also consume valuable operational time that could be spent on higher-value activities.

Spot Instances provide an alternative to reduce costs for workloads that can tolerate interruptions. They allow access to unused EC2 capacity at substantial discounts compared to on-demand pricing. However, Spot Instances are not inherently suitable for non-production scheduling because they lack a mechanism to automatically start or stop instances based on a predefined timetable. While they are excellent for batch jobs or fault-tolerant tasks, Spot Instances alone cannot fully address the need for controlled scheduling and operational consistency in non-production environments.

AWS Systems Manager Automation provides a robust solution for automating routine management tasks in the cloud. Using Systems Manager, administrators can create runbooks—predefined sequences of operations—that can start or stop EC2 instances according to a cron schedule or other triggers. This automation reduces manual effort, minimizes the risk of human error, and ensures that instances are only active when needed. By implementing automated scheduling for non-production environments, organizations can significantly reduce costs without compromising operational effectiveness.

Another key benefit of Systems Manager Automation is auditability. Each automated action is logged, providing a clear record of instance start and stop events. This capability is critical for compliance and governance, especially in environments where usage and resource allocation must be documented for auditing purposes. Automation runbooks can be reused across multiple environments, allowing consistent management practices for development, testing, and staging environments, thereby ensuring that all instances adhere to organizational policies.

In practice, this approach allows organizations to maintain multiple non-production environments efficiently while controlling costs. Instances are powered on during working hours or scheduled test periods and automatically shut down afterward. This eliminates unnecessary compute charges, enforces operational discipline, and frees administrators from repetitive tasks. The combination of scheduling, automation, and auditability ensures that cloud resources are used efficiently, costs are minimized, and environments remain predictable and compliant.

while Auto Scaling and Spot Instances offer valuable benefits, they are not designed for orchestrating scheduled control of non-production instances. Systems Manager Automation provides a flexible, reliable, and auditable approach, enabling organizations to automate the lifecycle of EC2 instances across multiple environments, reduce operational overhead, and achieve cost efficiency without sacrificing compliance or consistency.

Question 131

A company wants to implement a highly available, multi-AZ relational database with automatic failover for production workloads. Which AWS service is most appropriate?

A) RDS Multi-AZ Deployment
B) RDS Single-AZ Deployment
C) DynamoDB
D) S3

Answer: A) RDS Multi-AZ Deployment

Explanation:

When designing a highly available and resilient database architecture, understanding the limitations and strengths of different database services is critical. Traditional single-availability zone deployments, such as RDS Single-AZ, provide managed relational databases but operate entirely within a single availability zone. While this setup simplifies deployment and can be sufficient for development or low-traffic workloads, it exposes production systems to potential downtime if the underlying infrastructure encounters a failure. Hardware issues, network outages, or availability zone disruptions can all render the primary instance inaccessible, leading to interruptions in service and potential data loss if backups are not properly maintained. For organizations running mission-critical applications, relying solely on Single-AZ deployments can pose significant operational and business risks.

NoSQL databases such as DynamoDB offer highly scalable and low-latency storage, but they do not provide relational database capabilities. Features like multi-step transactions, complex joins, and sophisticated query support are not natively available in DynamoDB. While DynamoDB excels at handling large-scale, unstructured, or semi-structured data with predictable performance, it is not suitable for workloads that depend on relational integrity, transactional consistency, or complex query operations. Similarly, object storage solutions such as Amazon S3 are excellent for storing large volumes of unstructured data, but they are not designed to serve as a relational database. S3 cannot efficiently support queries, enforce relational constraints, or provide transactional guarantees, making it unsuitable for applications requiring structured, relational data management.

For production workloads that require reliability and continuity, RDS Multi-AZ Deployment addresses the limitations of single-zone setups. In a Multi-AZ configuration, the database service automatically provisions a synchronous standby replica in a separate availability zone. This standby instance acts as a failover target, ensuring that if the primary instance encounters a failure—whether due to hardware, network, or maintenance events—the system can automatically promote the standby to primary. This automatic failover minimizes downtime and ensures that applications maintain continuous access to the database without requiring manual intervention or complex disaster recovery processes.

Beyond failover, Multi-AZ deployments manage operational tasks such as patching, updates, and automated backups transparently. Routine maintenance is performed with minimal disruption, and backups are handled in a way that does not impact availability or performance. By offloading these operational responsibilities to the managed service, organizations reduce administrative overhead and can focus more on developing and optimizing application logic rather than maintaining infrastructure.

This combination of relational capabilities, fault tolerance, automated maintenance, and high availability makes RDS Multi-AZ an ideal choice for production-grade applications. Enterprises that depend on data integrity, consistent performance, and continuous availability benefit from this setup, as it provides peace of mind knowing that the database infrastructure is resilient and capable of handling unexpected failures. The Multi-AZ approach ensures business continuity, protects against data loss, and maintains operational efficiency while leveraging the fully managed features of AWS RDS.

while single-AZ deployments, NoSQL databases, and object storage each have their own advantages, Multi-AZ RDS provides a balanced, reliable solution for mission-critical relational workloads. Its automatic failover, high availability, and management of operational tasks make it the preferred choice for applications that cannot afford downtime or data inconsistency.

Question 132

A company wants to implement a 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:

ECS with EC2 launch type and EKS with EC2 nodes require managing EC2 instances, scaling, and patching, which adds operational complexity. EC2 only provides raw compute without container orchestration. ECS with Fargate is a serverless container solution that automatically provisions and scales compute resources needed to run containers. It integrates with networking, security, and monitoring services, allowing developers to focus solely on deploying microservices. Fargate provides high availability, automatic scaling, and cost efficiency by charging only for consumed resources. This makes it ideal for fully managed, serverless deployment of containerized applications.

Question 133

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:

S3 alone requires network transfer, which is slow and impractical for multi-terabyte datasets. EFS is a managed file system and not designed for bulk migration. AWS DMS is used for database migration and cannot transfer general-purpose files. AWS Snowball is a physical appliance that securely transfers large datasets offline. Customers load data locally onto Snowball, which is then shipped back to AWS for uploading to S3. Snowball ensures encryption at rest and in transit and provides fast, reliable, and cost-effective data transfer. This solution is ideal for large-scale migration projects, minimizing network consumption and operational overhead.

Question 134

A company wants to implement serverless, event-driven processing for orders 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, increasing operational overhead. ECS and EKS with EC2 nodes also demand infrastructure management. Lambda is serverless and can be triggered directly by S3 events or SQS messages. It automatically scales based on workload and charges only for execution duration. Integration with CloudWatch provides logging, monitoring, and error handling. Lambda offers a fully managed, event-driven architecture without server management, ensuring high availability, scalability, and cost efficiency for workloads like order processing or ETL tasks.

Question 135

A company wants to store session data for a high-traffic web application with sub-millisecond latency and high throughput. Which AWS service is most suitable?

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

Answer: A) ElastiCache Redis

Explanation:

When designing session management systems for high-traffic web applications, the choice of data storage and retrieval mechanisms is critical for ensuring performance, scalability, and reliability. Many traditional databases, while robust for certain workloads, are not optimized for the unique demands of session management, where data needs to be read and written frequently and with minimal latency.

Amazon DynamoDB is a fully managed NoSQL database that excels in providing low-latency access to key-value and document-based data. Its design allows for horizontal scaling, enabling it to handle substantial traffic while maintaining performance. However, under extremely high-concurrency workloads, DynamoDB may not consistently achieve sub-millisecond response times. This variability can become a bottleneck in session-heavy applications, where users expect instantaneous updates to session states, such as shopping carts, authentication tokens, or user preferences.

Relational databases like Amazon RDS with MySQL offer transactional integrity and structured storage, which are valuable for many applications. Yet, the disk I/O overhead, connection management, and schema enforcement inherent to RDS MySQL can introduce additional latency, making it less suitable for applications that require near-instantaneous session read and write operations. While MySQL can store session data, frequent small transactions may cause performance degradation, especially as traffic scales or spikes.

Object storage solutions such as Amazon S3 are optimized for durability, availability, and large-scale storage, rather than low-latency transactional access. While S3 is excellent for storing large, infrequently updated assets like images, videos, or backup data, it is ill-suited for the rapid, frequent access patterns required for session management. Attempting to use S3 for session data can result in slow response times and operational inefficiencies.

ElastiCache Redis addresses these limitations by offering an in-memory key-value store specifically designed for extremely low-latency, high-throughput operations. Since Redis operates entirely in memory, data retrieval and storage occur in microseconds, providing the speed required for real-time session management. Redis also supports clustering, which allows horizontal scaling across multiple nodes, and replication, which enhances reliability and ensures high availability across multiple availability zones. Optional persistence mechanisms allow session data to survive node failures or restarts, providing durability without compromising speed.

By centralizing session data in Redis, multiple web servers can consistently access and update session information in real time. This design eliminates bottlenecks caused by disk-based storage and reduces operational complexity, as Redis handles scaling, replication, and high availability automatically. For high-traffic applications, this ensures that user experiences remain smooth, with session updates reflecting instantly across all connected servers, minimizing latency and avoiding inconsistencies.

Additionally, integrating Redis with application frameworks often requires minimal changes, enabling developers to leverage its performance benefits without overhauling existing architectures. This combination of speed, reliability, scalability, and ease of integration makes ElastiCache Redis an ideal solution for session management in modern web applications, where responsiveness and uptime are critical to user satisfaction and business success.