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Question 196:

A company wants to deploy a web application across multiple AWS regions. Static content is stored in Amazon S3, and EC2 instances serve dynamic content. The architecture must ensure low latency, high availability, and secure access. Which solution is most appropriate?
A) Amazon CloudFront with S3 origin and multi-region EC2 failover
B) Public S3 bucket with HTTPS
C) Amazon SNS with cross-region replication
D) Amazon Global Accelerator with a single EC2 origin

Answer:

A) Amazon CloudFront with S3 origin and multi-region EC2 failover

Explanation:

Deploying a web application across multiple AWS regions introduces several challenges, including ensuring low latency for global users, maintaining high availability, and securing access to both static and dynamic content. Users expect fast response times regardless of their geographic location, and applications must remain resilient even if an entire region experiences an outage. To meet these requirements, an architecture must combine content distribution, redundancy, and intelligent routing.

Amazon CloudFront, when used with an S3 origin and multi-region EC2 failover, provides a solution that addresses these challenges. CloudFront is a content delivery network (CDN) that caches content at edge locations distributed around the world. By serving static content such as images, videos, or JavaScript files from edge locations closest to users, CloudFront significantly reduces latency. This ensures a faster and more responsive experience for users, regardless of their geographic location, while reducing load on the origin S3 buckets and backend servers.

For dynamic content served by EC2 instances, multi-region deployment ensures high availability and resilience. By hosting EC2 instances in multiple regions, the application can continue to operate even if one region becomes unavailable. Multi-region failover can be configured using Route 53 with health checks and routing policies such as latency-based routing or failover routing. This allows traffic to be directed to the healthiest and closest available region, ensuring both low latency and fault tolerance. Combined with CloudFront for static content, this architecture ensures that the application remains available, performant, and resilient to regional failures.

Security is another critical aspect of this architecture. CloudFront supports HTTPS for secure content delivery and can integrate with AWS Web Application Firewall (WAF) to protect against common web attacks such as SQL injection or cross-site scripting. Origin access identities (OAI) or signed URLs can restrict access to S3 buckets, ensuring that static content is served only through CloudFront and preventing direct public access. For dynamic content, EC2 instances can reside in private subnets behind load balancers, with traffic routed securely through CloudFront and Route 53. This combination of services ensures secure and controlled access to application content while maintaining global performance.

Option B, a public S3 bucket with HTTPS, provides secure access to static content but does not address dynamic content or global availability. Users far from the S3 region may experience high latency, and the architecture does not provide failover for EC2 instances. This makes it insufficient for applications requiring low-latency access and multi-region resiliency.

Option C, Amazon SNS with cross-region replication, is not suitable for serving web application content. SNS is designed for messaging and event notifications, not for delivering static or dynamic content to end users. While cross-region replication ensures data durability, it does not provide caching, global distribution, or low-latency content delivery.

Option D, Amazon Global Accelerator with a single EC2 origin, improves latency by routing user traffic to the nearest AWS edge location, but a single EC2 origin introduces a single point of failure. If the origin region fails, the application becomes unavailable. Additionally, Global Accelerator does not provide caching for static content, so performance improvements for frequently accessed static assets would be limited compared to CloudFront.

By combining CloudFront with S3 for static content and multi-region EC2 failover for dynamic content, the architecture achieves a balance of performance, availability, and security. CloudFront’s global edge network ensures fast access to static content, while multi-region EC2 deployment provides fault tolerance and low-latency access for dynamic content. Route 53 routing policies and health checks maintain application availability in case of regional failures, and security features such as HTTPS, WAF, and origin access controls protect content from unauthorized access and attacks.

In Amazon CloudFront with an S3 origin and multi-region EC2 failover is the most appropriate solution for deploying a globally distributed web application. It delivers low latency for users worldwide, ensures high availability through regional redundancy, and provides secure access to both static and dynamic content. This architecture supports scalability, resilience, and performance, meeting the essential requirements for modern multi-region web applications.

Question 197:

A company wants to process millions of real-time IoT telemetry events per second. Multiple applications must read the same data concurrently with durability and low latency. Which service is most suitable?
A) Amazon Kinesis Data Streams
B) Amazon SQS Standard Queue
C) Amazon SNS
D) Amazon MQ

Answer:

A) Amazon Kinesis Data Streams

Explanation:

Amazon Kinesis Data Streams is a fully managed service designed for high-throughput, low-latency, real-time streaming data. Data is divided into shards, enabling multiple consumers to read concurrently without affecting throughput. Enhanced fan-out ensures dedicated throughput per consumer, minimizing latency even under heavy loads.

Data replication across multiple Availability Zones ensures durability and fault tolerance. Integration with AWS Lambda, Kinesis Firehose, and analytics services supports serverless, event-driven processing without managing infrastructure. Horizontal scaling allows the system to handle millions of events per second, which is critical for IoT telemetry.

Option B, SQS, does not efficiently support multiple consumers reading the same message simultaneously. Option C, SNS, lacks replay capability and high throughput optimization for streaming. Option D, Amazon MQ, introduces higher operational overhead and cannot match Kinesis for low-latency, high-volume streaming workloads.

This approach satisfies SAA-C03 objectives for building scalable, reliable, and low-latency event-driven architectures for IoT and telemetry applications.

Question 198:

A company needs a highly available relational database for production workloads, with automatic failover, automated backups, and support for read scalability. Which configuration is most suitable?
A) Amazon RDS Multi-AZ deployment with read replicas
B) Single RDS instance with snapshots
C) Self-managed EC2 database with replication
D) Amazon DynamoDB

Answer:

A) Amazon RDS Multi-AZ deployment with read replicas

Explanation:

For production workloads that rely on a relational database, ensuring high availability, fault tolerance, and scalability is essential. Production applications often require continuous operation with minimal downtime, reliable backups, and the ability to handle increasing read traffic without impacting performance. A relational database solution that provides automatic failover, automated backups, and read scalability ensures that applications can operate smoothly even under high load or in the event of failures.

Amazon RDS Multi-AZ deployment with read replicas meets all these requirements and is a fully managed solution that simplifies operations. In a Multi-AZ setup, Amazon RDS automatically provisions and maintains a standby replica of the primary database in a different Availability Zone. This configuration provides synchronous replication, ensuring that data changes on the primary instance are immediately replicated to the standby. In case of a failure on the primary instance, RDS automatically promotes the standby instance to become the new primary, providing minimal downtime and maintaining data integrity. Automatic failover eliminates the need for manual intervention, which is critical for production systems where continuous availability is essential.

Automated backups are another important feature of RDS Multi-AZ deployments. Amazon RDS performs daily backups and transaction log backups, allowing point-in-time recovery within the retention period, typically up to 35 days. These backups occur without affecting the performance of the primary database, ensuring that operational activities are not disrupted. Automated backups provide an additional layer of data protection, safeguarding against accidental deletion, corruption, or other operational risks.

Read replicas complement Multi-AZ deployments by providing horizontal scaling for read-heavy workloads. A read replica is an asynchronous copy of the primary database that can serve read queries, allowing the primary instance to focus on write operations. This improves overall performance by offloading read requests, which is especially beneficial for applications with high read-to-write ratios. Multiple read replicas can be deployed across different regions, further enhancing scalability and resilience. Additionally, read replicas can be promoted to primary in case of a failure, providing flexibility for disaster recovery scenarios.

Other options are less suitable for production workloads requiring high availability and scalability. A single RDS instance with snapshots, as suggested in option B, provides basic backup capabilities but does not offer automatic failover. In case of an instance failure, recovery from a snapshot would require manual intervention and could lead to significant downtime. This configuration is inadequate for mission-critical applications where continuous availability is required.

Option C, a self-managed EC2 database with replication, requires extensive operational effort. Administrators must configure replication, monitor health, handle failover, and manage backups manually. This increases the complexity and risk of errors, making it less reliable compared to the fully managed Multi-AZ solution. Maintaining a self-managed database also incurs additional operational overhead and may not provide the same level of fault tolerance and automation as Amazon RDS.

Option D, Amazon DynamoDB, is a NoSQL database designed for high performance and scalability but does not provide traditional relational database capabilities such as SQL queries, joins, or transactional consistency across multiple tables. While DynamoDB is highly available and managed, it is unsuitable for workloads that require a relational data model and structured transactional operations.

Amazon RDS Multi-AZ deployment with read replicas is the most appropriate configuration for a production relational database that requires high availability, automatic failover, automated backups, and read scalability. It ensures continuous operation even during failures, provides data protection through automated backups, and supports increased read traffic with replicas. This solution minimizes operational complexity while delivering reliability, performance, and resilience, making it ideal for production workloads that depend on a robust relational database infrastructure.

Question 199:

A company wants to decouple microservices using a fully managed, scalable messaging service. Messages must be delivered at least once and retained until successfully processed. Which service is most appropriate?
A) Amazon SQS Standard Queue
B) Amazon SNS
C) Amazon Kinesis Data Streams
D) Amazon MQ

Answer:

A) Amazon SQS Standard Queue

Explanation:

Amazon SQS Standard Queue is a fully managed, highly available message queuing service. Messages are retained until successfully processed and delivered at least once, ensuring reliable communication between microservices.

SQS supports multiple consumers polling messages concurrently, allowing horizontal scaling. Messages are stored redundantly across multiple Availability Zones, providing durability. Server-side encryption (SSE) ensures confidentiality, and IAM policies allow fine-grained access control. Dead-letter queues allow failed messages to be isolated and retried.

Option B, SNS, is a pub/sub service that does not guarantee message retention for each subscriber. Option C, Kinesis Data Streams, is optimized for streaming workloads rather than discrete message delivery. Option D, Amazon MQ, provides traditional brokered messaging and introduces operational overhead compared to SQS.

SQS ensures reliable, scalable, and decoupled communication between microservices, adhering to SAA-C03 best practices for fault-tolerant architectures.

Question 200:

A company needs to maintain session state across multiple web servers for a scalable web application. Which solution is most suitable?
A) Store session state in Amazon ElastiCache
B) Store session state in local EC2 memory
C) Use client-side cookies only
D) Store session state in S3 without caching

Answer:

A) Store session state in Amazon ElastiCache

Explanation:

In multi-server web applications, session state must be centralized to maintain consistency across servers. Amazon ElastiCache provides an in-memory key-value store (Redis or Memcached) for fast, consistent access to session data.

Redis supports persistence, replication, and automatic failover, ensuring high availability and durability. Centralized session storage allows horizontal scaling of web servers without losing session continuity. ElastiCache integrates with IAM and VPC for secure access and handles high read/write throughput for real-time session management.

Option B, storing state in local memory, risks data loss if a server fails. Option C, using client-side cookies, cannot store complex session data securely. Option D, storing session state in S3 adds latency, unsuitable for fast session access.

This architecture ensures high performance, reliability, and scalability for session management, aligning with SAA-C03 best practices.

Question 201:

A company wants to run a serverless web application that scales automatically based on traffic. The application must integrate with API Gateway, DynamoDB, and Lambda functions. Which compute service should be used?
A) AWS Lambda
B) Amazon EC2
C) AWS Elastic Beanstalk
D) Amazon Lightsail

Answer:

A) AWS Lambda

Explanation:

Modern web applications increasingly require architectures that are both scalable and cost-efficient. A serverless approach allows applications to automatically scale based on traffic while minimizing operational overhead. In this context, a serverless web application relies on computing resources that are provisioned and managed by the cloud provider, enabling developers to focus on business logic rather than infrastructure management. The company’s requirements include integration with API Gateway, DynamoDB, and Lambda functions, which are common components in serverless architectures.

AWS Lambda is the most suitable compute service for this scenario. Lambda is a serverless compute service that runs code in response to events and automatically manages the underlying compute resources. Unlike traditional server-based architectures, Lambda functions are invoked only when needed and scale automatically with the number of incoming requests. This ensures that the application can handle sudden spikes in traffic without manual intervention or overprovisioning of servers. Since there are no servers to manage, the operational burden on the development and DevOps teams is significantly reduced.

Integration with API Gateway is a key advantage of using Lambda. API Gateway acts as a front-end interface that receives HTTP requests and routes them to Lambda functions for processing. This integration allows developers to build RESTful APIs without deploying and managing servers. Lambda functions can execute application logic, perform validations, and interact with other AWS services in response to incoming API requests. This event-driven model is highly efficient and cost-effective, as compute resources are only consumed when actual requests are processed.

DynamoDB, a fully managed NoSQL database, works seamlessly with Lambda. Lambda functions can read and write data to DynamoDB tables, enabling serverless applications to store and retrieve data efficiently. DynamoDB supports on-demand scaling and provides low-latency access, which complements Lambda’s event-driven model. This integration allows the application to maintain state and perform CRUD operations without requiring persistent server infrastructure. Together, Lambda, API Gateway, and DynamoDB form a tightly integrated, fully managed serverless stack capable of handling web traffic dynamically.

Other options are less suitable for this use case. Amazon EC2, option B, provides virtual servers that can run applications, but it is not serverless. Using EC2 would require provisioning, scaling, patching, and monitoring instances, increasing operational complexity. While EC2 can integrate with API Gateway and DynamoDB, the company would lose the automatic scaling and event-driven benefits of Lambda. Manual or auto-scaling groups can help, but they do not achieve the same level of seamless scalability and cost efficiency.

Option C, AWS Elastic Beanstalk, is a managed platform that simplifies application deployment on EC2 instances. While it reduces some operational tasks, it is still server-based and does not provide the pay-per-request model that Lambda offers. Scaling is automatic but slower compared to the event-driven invocation of Lambda functions, making it less efficient for highly variable traffic patterns.

Option D, Amazon Lightsail, is designed for simple applications, small-scale deployments, or development environments. Lightsail instances are server-based and do not integrate natively with API Gateway and DynamoDB in a serverless manner. Using Lightsail would require managing instances and scaling manually, which is contrary to the requirement for automatic, serverless scaling.

AWS Lambda is the most appropriate compute service for a serverless web application that integrates with API Gateway and DynamoDB. It provides automatic scaling, reduces operational overhead, and enables an event-driven architecture where resources are consumed only when needed. By leveraging Lambda, the company can build a fully serverless application that handles variable traffic efficiently, remains cost-effective, and integrates seamlessly with other managed AWS services, ensuring a scalable, resilient, and maintainable architecture.

Question 202:

A company needs to analyze petabytes of structured and semi-structured data stored in S3. Queries must be fast, and storage must be optimized with compression. Which service is most suitable?
A) Amazon Redshift
B) Amazon Athena
C) Amazon EMR
D) AWS Glue

Answer:

A) Amazon Redshift

Explanation:

Amazon Redshift is a fully managed data warehouse optimized for large-scale analytics. It uses columnar storage, compression, and massively parallel processing (MPP) to execute queries efficiently, even on petabytes of structured or semi-structured data.

Redshift Spectrum allows querying data directly in S3 without moving it, combining the benefits of a data warehouse with external storage. Compression reduces storage costs while improving query performance. Security is provided through encryption at rest using KMS and SSL in transit. IAM policies enable granular access control for users and services.

Option B, Athena, is serverless and ideal for ad hoc querying of S3 data but may not perform as well as Redshift for large-scale, complex queries. Option C, EMR, is suited for large-scale data processing using Hadoop or Spark but requires cluster management. Option D, Glue, is an ETL service for transforming data rather than a high-performance query engine.

Redshift aligns with SAA-C03 objectives for scalable, fast, and cost-efficient analytics solutions with minimal infrastructure management.

Question 203:

A company wants to deploy a multi-tier web application with a highly available relational database and caching layer. Automatic failover is required in case the primary database fails. Which configuration is most suitable?
A) Amazon RDS Multi-AZ deployment with Amazon ElastiCache
B) Single RDS instance with snapshots and caching
C) RDS read replicas only
D) Self-managed EC2 database with replication

Answer:

A) Amazon RDS Multi-AZ deployment with Amazon ElastiCache

Explanation:

Multi-tier web applications typically consist of a web front-end, an application layer, a relational database, and optionally, a caching layer to improve performance. For production workloads, it is critical to ensure high availability, fault tolerance, and low latency for both static and dynamic content. Automatic failover for the database layer is particularly important because database downtime can halt critical application functionality and negatively affect the user experience. Combining a highly available relational database with a caching layer ensures that the application remains resilient, responsive, and capable of handling high traffic volumes.

Amazon RDS Multi-AZ deployment with Amazon ElastiCache is the most suitable configuration to meet these requirements. RDS Multi-AZ deployments provide synchronous replication of the primary database to a standby instance in a different Availability Zone. This ensures that if the primary database fails due to hardware issues, network disruptions, or maintenance events, the standby instance is automatically promoted to become the new primary database. The automatic failover process minimizes downtime and ensures that application operations continue without manual intervention, providing robust high availability for mission-critical workloads.

In addition to high availability, RDS Multi-AZ deployments include automated backups and point-in-time recovery capabilities. These backups occur without impacting the primary database performance, and they allow recovery to any second within the retention period, typically up to 35 days. This ensures that data integrity is maintained and enables the application to recover quickly in the event of accidental data deletion, corruption, or other operational issues. Automated backups and failover together provide a resilient database architecture capable of sustaining production workloads with minimal operational overhead.

Amazon ElastiCache complements the RDS Multi-AZ database by providing an in-memory caching layer. By storing frequently accessed data in memory, ElastiCache significantly reduces the number of read requests hitting the primary database. This improves application performance, reduces latency for end users, and allows the database to handle write-intensive workloads more efficiently. ElastiCache supports engines such as Redis and Memcached, offering features like data replication, persistence, and automatic failover for caching nodes. This ensures that even if a caching node fails, the application can continue operating without interruption.

Other options are less suitable for highly available, production-grade deployments. A single RDS instance with snapshots and caching, as suggested in option B, provides basic backup capabilities but does not support automatic failover. If the primary instance fails, manual intervention is required to restore from a snapshot, resulting in significant downtime. This configuration is insufficient for applications that demand continuous availability and high reliability.

Option C, RDS read replicas only, is also inadequate. While read replicas allow horizontal scaling for read-heavy workloads, they do not provide automatic failover for write operations. The primary database remains a single point of failure, and promoting a read replica to primary requires manual action, which increases recovery time and operational complexity.

Option D, a self-managed EC2 database with replication, requires extensive operational effort. Administrators must manually configure replication, backups, and failover, and ensure that monitoring and recovery procedures are in place. This increases complexity and risk while requiring ongoing maintenance, making it less reliable and more resource-intensive than using RDS Multi-AZ with ElastiCache.

Deploying an Amazon RDS Multi-AZ instance with Amazon ElastiCache provides a robust, highly available, and scalable architecture for multi-tier web applications. Multi-AZ ensures automatic failover and data durability, while ElastiCache improves performance and reduces database load. This combination delivers resilience, low-latency access, and operational simplicity, making it the most appropriate solution for production workloads that require high availability, fault tolerance, and efficient resource utilization.

Question 204:

A company wants to decouple microservices using a scalable messaging solution. Messages must be retained until processed and delivered at least once. Which service is most suitable?
A) Amazon SQS Standard Queue
B) Amazon SNS
C) Amazon Kinesis Data Streams
D) Amazon MQ

Answer:

A) Amazon SQS Standard Queue

Explanation:

Amazon SQS Standard Queue provides reliable message delivery with at least once semantics. Messages are retained until successfully processed, ensuring microservices communication is reliable.

SQS supports concurrent consumers for high throughput, allowing horizontal scaling. Messages are redundantly stored across multiple Availability Zones. Server-side encryption ensures confidentiality, and IAM policies enable fine-grained access control. Dead-letter queues allow error handling and retries for failed messages.

Option B, SNS, is a pub/sub service but does not guarantee message retention for individual subscribers. Option C, Kinesis Data Streams, is designed for real-time streaming rather than discrete messages. Option D, Amazon MQ, is a managed broker, introducing higher operational overhead compared to SQS.

SQS ensures fault-tolerant, scalable, and decoupled communication between microservices, meeting SAA-C03 best practices.

Question 205:

A company needs to maintain session state across multiple web servers for a scalable web application. Which solution provides high performance and reliability?
A) Store session state in Amazon ElastiCache
B) Store session state in local EC2 memory
C) Use client-side cookies only
D) Store session state in S3 without caching

Answer:

A) Store session state in Amazon ElastiCache

Explanation:

Multi-server web applications require centralized session management to maintain consistency. Amazon ElastiCache provides an in-memory key-value store (Redis or Memcached) for fast session access.

Redis supports replication, persistence, and automatic failover, ensuring high availability and durability. Centralized storage allows web servers to scale horizontally without losing session continuity. ElastiCache integrates with IAM and VPC for security and handles high throughput, supporting real-time session access.

Option B, local memory, risks session loss if a server fails. Option C, client-side cookies, cannot store complex session data securely. Option D, S3, introduces latency unsuitable for real-time session management.

This architecture provides performance, reliability, and scalability, aligning with SAA-C03 best practices for multi-tier applications.

Question 206:

A company wants to deploy a multi-tier web application with a relational database backend. The database must be highly available and support automatic failover. Which architecture is most suitable?
A) Amazon RDS Multi-AZ deployment with read replicas
B) Single RDS instance with snapshots
C) Self-managed EC2 database with replication
D) Amazon DynamoDB

Answer:

A) Amazon RDS Multi-AZ deployment with read replicas

Explanation:

Amazon RDS Multi-AZ deployments provide high availability by synchronously replicating the primary database to a standby in another Availability Zone. Automatic failover ensures minimal downtime, supporting production-grade workloads.

Read replicas allow horizontal scaling for read-heavy workloads, reducing load on the primary database. They can also be promoted for failover scenarios, enhancing availability. Automated backups and snapshots provide point-in-time recovery and support compliance requirements.

Option B, a single RDS instance, lacks automatic failover and read scaling. Option C, self-managed EC2 replication, increases operational complexity and the risk of misconfiguration. Option D, DynamoDB, is a NoSQL solution and does not meet relational database requirements.

This design aligns with SAA-C03 best practices for high availability, fault tolerance, and scalability in relational database architectures.

Question 207:

A company wants to decouple microservices with a scalable, fully managed messaging solution. Messages must be retained until successfully processed and delivered at least once. Which service should be used?
A) Amazon SQS Standard Queue
B) Amazon SNS
C) Amazon Kinesis Data Streams
D) Amazon MQ

Answer:

A) Amazon SQS Standard Queue

Explanation:

In modern application architectures, decoupling microservices is critical for achieving scalability, resilience, and maintainability. Microservices often need to communicate asynchronously to ensure that each service can operate independently without being tightly coupled to others. A messaging solution enables services to exchange information reliably, process requests asynchronously, and scale independently. For production systems, it is important that messages are delivered at least once and retained until they are successfully processed to prevent data loss and maintain consistency across services.

Amazon SQS Standard Queue is the most suitable solution for this scenario. SQS is a fully managed message queuing service that allows asynchronous communication between distributed services. It supports at-least-once message delivery, ensuring that every message is processed even in the presence of temporary failures. Messages are stored durably within the queue until successfully consumed by a processing application. This guarantees that no messages are lost and allows consumers to retry processing if a temporary failure occurs. SQS also scales automatically to handle any volume of messages, which is essential for dynamic, high-throughput applications.

The architecture of SQS Standard Queue allows multiple consumers to process messages concurrently, providing horizontal scalability. Each consumer can retrieve messages independently, which ensures that workloads can be distributed efficiently across multiple instances or services. SQS also provides configurable visibility timeouts, which prevent multiple consumers from processing the same message simultaneously while allowing retries if a message is not successfully acknowledged. Dead-letter queues can capture messages that fail processing repeatedly, enabling developers to investigate and resolve issues without losing data. These features make SQS ideal for microservices requiring reliable message handling, fault tolerance, and automatic scaling.

Option B, Amazon SNS, is a publish-subscribe messaging service that broadcasts messages to multiple subscribers. While it is useful for notifying multiple endpoints, it does not provide durable message storage or guaranteed message processing for individual consumers. Messages may be lost if a subscriber is unavailable, and SNS does not provide the same level of retention and at-least-once delivery semantics as SQS. Therefore, SNS is better suited for fan-out notifications rather than reliable service-to-service messaging in microservice architectures.

Option C, Amazon Kinesis Data Streams, is designed for real-time streaming of large volumes of data. It allows multiple applications to process the same data concurrently, but it is optimized for analytics and continuous data ingestion rather than decoupling transactional microservices. Kinesis requires managing shards and offsets, which adds complexity if the goal is simply reliable asynchronous messaging between services. While it supports retention, the operational overhead and design complexity make it less suitable for microservice decoupling compared to SQS.

Option D, Amazon MQ, is a managed message broker that supports traditional protocols such as AMQP, MQTT, and STOMP. While it can provide message durability and multiple consumers, it introduces operational overhead compared to SQS. MQ requires managing brokers and connections, making it more complex than a serverless, fully managed SQS queue. MQ is better suited for legacy systems that need protocol compatibility rather than modern serverless microservice architectures.

Using Amazon SQS Standard Queue ensures that microservices can communicate reliably, scale independently, and remain decoupled. Its fully managed nature eliminates the need for provisioning or maintaining servers, while its features like at-least-once delivery, message retention, visibility timeouts, and dead-letter queues provide resilience and fault tolerance. SQS allows services to process messages asynchronously, handle retries gracefully, and ensure that no messages are lost, which is essential for maintaining data consistency and operational reliability in distributed systems.

Amazon SQS Standard Queue is the ideal solution for decoupling microservices in a scalable and reliable manner. It guarantees message durability, supports at-least-once delivery, and enables multiple consumers to process messages concurrently. By using SQS, companies can build resilient, fault-tolerant microservice architectures that scale automatically, maintain message integrity, and reduce operational complexity, providing a robust foundation for distributed applications.

Question 208:

A company wants to run a serverless application that scales automatically and integrates with multiple AWS services such as API Gateway and DynamoDB. Which compute service is most appropriate?
A) AWS Lambda
B) Amazon EC2
C) AWS Elastic Beanstalk
D) Amazon Lightsail

Answer:

A) AWS Lambda

Explanation:

Serverless architectures have become a preferred approach for modern applications because they simplify infrastructure management while providing automatic scaling, high availability, and cost efficiency. In a serverless model, developers focus on writing application logic, while the cloud provider handles provisioning, scaling, patching, and maintenance of the underlying infrastructure. This approach is particularly well-suited for applications that integrate with other AWS services such as API Gateway and DynamoDB, which are common building blocks in event-driven serverless applications.

AWS Lambda is the most appropriate compute service for this scenario. Lambda allows developers to run code without provisioning or managing servers. Functions are invoked in response to events, such as HTTP requests, database updates, or messages from a queue, and automatically scale based on the volume of incoming events. This ensures that applications can handle sudden spikes in traffic without manual intervention, providing elasticity and cost savings since billing is based on actual compute time consumed rather than idle capacity.

Lambda integrates seamlessly with API Gateway, which acts as a front-end interface for serverless applications. API Gateway routes HTTP requests to Lambda functions, enabling developers to build RESTful APIs without managing web servers. Lambda functions process incoming requests, perform business logic, and return responses dynamically. This combination allows applications to provide secure, scalable, and responsive endpoints for client applications, whether web, mobile, or IoT. The event-driven nature of Lambda ensures that compute resources are used efficiently and scale automatically with traffic.

Integration with DynamoDB further enhances Lambda’s capabilities for building serverless applications. Lambda functions can read and write data directly to DynamoDB tables, allowing applications to maintain state and perform CRUD operations without relying on traditional server-based databases. DynamoDB’s low-latency performance, on-demand scaling, and fully managed nature complement Lambda’s serverless model, creating a highly scalable and resilient backend for applications that require fast, reliable access to structured data. By combining Lambda with DynamoDB, applications can achieve both horizontal scalability and high availability while minimizing operational complexity.

Other compute options are less suitable for this scenario. Amazon EC2 provides virtual servers that can host application workloads, but it is not serverless. Using EC2 requires provisioning, monitoring, scaling, and patching instances manually or via auto-scaling groups. While EC2 can integrate with API Gateway and DynamoDB, it lacks the event-driven, pay-per-use efficiency of Lambda and requires more operational effort to achieve scalability and reliability.

AWS Elastic Beanstalk, while offering managed deployment of applications on EC2 instances, is also not fully serverless. Elastic Beanstalk simplifies infrastructure management and can handle scaling, but it still relies on underlying EC2 instances and does not provide the same granularity of event-driven compute scaling as Lambda. It is better suited for applications that require a managed platform but still rely on server-based compute.

Amazon Lightsail, on the other hand, is designed for small-scale applications, development environments, or simple web services. It does not provide native serverless compute capabilities or deep integration with API Gateway and DynamoDB. Scaling is limited, and infrastructure management remains largely manual compared to Lambda.

By using AWS Lambda, developers gain several advantages. Functions scale automatically in response to demand, integrate natively with other AWS services, and eliminate the need to manage servers. The serverless model enables cost efficiency because the company pays only for compute time consumed during function execution. Security is also enhanced since Lambda can run within a virtual private cloud (VPC) and leverage AWS Identity and Access Management (IAM) roles to control access to resources such as DynamoDB or S3. Additionally, Lambda supports multiple programming languages and can be deployed quickly, accelerating development and iteration cycles.

AWS Lambda is the most suitable compute service for running a serverless application that requires automatic scaling and integration with services like API Gateway and DynamoDB. Its fully managed, event-driven architecture ensures high performance, operational simplicity, and cost efficiency. By leveraging Lambda, companies can build scalable, resilient, and secure serverless applications that adapt to traffic patterns dynamically while minimizing infrastructure management overhead.

Question 209:

A company wants to analyze petabytes of structured and semi-structured data stored in S3. Queries must be fast, and storage costs minimized through compression. Which service is most suitable?
A) Amazon Redshift
B) Amazon Athena
C) Amazon EMR
D) AWS Glue

Answer:

A) Amazon Redshift

Explanation:

Amazon Redshift is a fully managed data warehouse optimized for analytical workloads. It uses columnar storage, compression, and massively parallel processing (MPP) to efficiently process large datasets.

Redshift Spectrum allows querying data directly in S3 without moving it, combining the benefits of a warehouse with external storage. Compression reduces storage costs and improves query performance. Security features include encryption at rest with KMS and in-transit SSL, with IAM for fine-grained access control.

Option B, Athena, is serverless and suitable for ad hoc queries, but Redshift performs better for complex analytics at scale. Option C, EMR, is suitable for large-scale data processing using Hadoop or Spark, but requires cluster management. Option D, Glue, is primarily for ETL and not optimized for analytical query performance.

Redshift aligns with SAA-C03 objectives for scalable, high-performance analytics solutions with minimal infrastructure overhead.

Question 210:

A company wants to deploy a multi-tier web application with a highly available relational database and caching layer. Automatic failover is required if the primary database fails. Which configuration is most suitable?
A) Amazon RDS Multi-AZ deployment with Amazon ElastiCache
B) Single RDS instance with snapshots and caching
C) RDS read replicas only
D) Self-managed EC2 database with replication

Answer:

A) Amazon RDS Multi-AZ deployment with Amazon ElastiCache

Explanation:

Multi-tier web applications are designed to separate concerns across different layers, typically including a web front-end, an application layer, a relational database, and a caching layer to enhance performance. For production workloads, it is critical to ensure high availability, automatic failover, data durability, and low latency for user interactions. An architecture that combines a highly available relational database with a caching layer addresses these requirements, ensuring that the application remains resilient and responsive even under high load or in the event of component failures.

Amazon RDS Multi-AZ deployment with Amazon ElastiCache is the optimal configuration to achieve high availability and performance. RDS Multi-AZ ensures that the primary database instance is automatically replicated to a standby instance in a different Availability Zone. This synchronous replication guarantees that any changes made to the primary database are immediately reflected in the standby. If the primary instance becomes unavailable due to hardware failure, network issues, or maintenance, RDS automatically promotes the standby instance to become the new primary. This automatic failover minimizes downtime, maintaining continuous operation for mission-critical applications without requiring manual intervention.

In addition to failover, RDS Multi-AZ deployments provide automated backups and point-in-time recovery, which are crucial for data protection. Backups are performed without impacting the performance of the primary database and are retained for a configurable period, typically up to 35 days. Point-in-time recovery allows administrators to restore the database to any moment within the retention window, protecting against accidental deletions, corruption, or operational errors. These features ensure that production data remains durable and recoverable while minimizing the operational burden on administrators.

Amazon ElastiCache complements RDS by providing a high-performance in-memory caching layer. Caching frequently accessed data reduces the number of read requests hitting the database, decreasing latency for end users and improving the scalability of the application. ElastiCache supports Redis and Memcached, both of which offer fast data retrieval, replication, and failover capabilities. Redis additionally provides persistence options, allowing cached data to survive node restarts. By offloading repetitive read queries to the cache, the database is free to handle write-intensive operations, improving overall system throughput and user experience.

Other options are less suitable for highly available, production-grade deployments. A single RDS instance with snapshots and caching, as mentioned in option B, provides backup capabilities but does not support automatic failover. In the event of a failure, manual intervention would be required to restore the database from a snapshot, resulting in significant downtime and operational disruption. This configuration is inadequate for applications where high availability and resilience are mandatory.

RDS read replicas only, option C, can offload read traffic and improve performance but do not provide automatic failover for the primary instance. If the primary database fails, a read replica cannot automatically take over as the new primary without manual promotion. This makes read replicas unsuitable as a standalone solution for ensuring high availability and continuous operation.

Option D, a self-managed EC2 database with replication, introduces operational complexity. Administrators must configure replication, monitoring, backups, and failover manually, which increases the risk of human error and prolongs recovery time in case of failure. Managing a database on EC2 requires ongoing maintenance, patching, and scaling efforts, making it less reliable and more labor-intensive compared to the fully managed RDS Multi-AZ solution.

Deploying an Amazon RDS Multi-AZ instance with Amazon ElastiCache provides a robust, highly available, and scalable architecture for multi-tier web applications. Multi-AZ deployment ensures automatic failover and data durability, while ElastiCache improves application performance and reduces database load. This combination offers resilience, low-latency access, and operational simplicity, making it the most suitable solution for production workloads that require high availability, fault tolerance, and efficient resource utilization. By using this architecture, organizations can deliver a responsive and reliable web application experience while minimizing operational overhead.