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Question 166:

A company wants to deploy a web application across multiple Availability Zones for high availability. The application must automatically scale based on incoming traffic. Which architecture is most suitable?
A) Auto Scaling group across multiple Availability Zones behind an Application Load Balancer
B) Single EC2 instance in one Availability Zone with manual scaling
C) EC2 instances in one Availability Zone behind a Network Load Balancer
D) Amazon Lightsail instance with periodic snapshots

Answer:

A) Auto Scaling group across multiple Availability Zones behind an Application Load Balancer

Explanation:

In this scenario, a company needs to deploy a web application across multiple Availability Zones to ensure high availability and resilience against failures, while also providing the ability to automatically scale in response to changes in incoming traffic. The most suitable architecture for these requirements is an Auto Scaling group deployed across multiple Availability Zones behind an Application Load Balancer (ALB). This configuration delivers a highly available, fault-tolerant, and scalable solution, which is essential for modern web applications serving variable traffic patterns.

Auto Scaling groups allow organizations to dynamically adjust the number of EC2 instances based on predefined policies or real-time metrics, such as CPU utilization or request count. During periods of high demand, the Auto Scaling group can launch additional instances to ensure that the application remains responsive and performant. Conversely, when traffic decreases, unnecessary instances can be terminated automatically, helping to optimize costs. This elasticity ensures that resources are allocated efficiently and that the application can handle unpredictable traffic spikes without manual intervention, reducing operational overhead.

Deploying instances across multiple Availability Zones provides fault tolerance and high availability. If one Availability Zone becomes unavailable due to hardware failures, network issues, or maintenance events, traffic can continue to be served from instances in the remaining healthy Availability Zones. This design ensures minimal downtime and maintains application continuity, which is critical for business operations that rely on the web application. Multi-AZ deployment also allows for seamless maintenance, such as instance patching or OS upgrades, without impacting overall service availability.

The Application Load Balancer (ALB) is a critical component in this architecture, as it evenly distributes incoming traffic across all healthy instances in multiple Availability Zones. The ALB supports layer 7 routing, enabling path-based or host-based routing, SSL termination, and sticky sessions, which helps efficiently route requests to the appropriate instances. ALB health checks continuously monitor the status of registered instances and automatically stop sending traffic to unhealthy instances, ensuring that users always interact with functioning resources. This contributes to a seamless user experience and enhances the overall reliability of the application.

Option B, a single EC2 instance in one Availability Zone with manual scaling, does not provide the required high availability. A single instance represents a single point of failure, and if it or its Availability Zone fails, the application would experience downtime. Manual scaling is also slower and prone to errors, which can lead to performance degradation during traffic spikes.

Option C, EC2 instances in one Availability Zone behind a Network Load Balancer (NLB), offers high throughput and low-latency TCP traffic handling but does not inherently provide multi-AZ redundancy. A failure in the single Availability Zone hosting the instances would result in downtime. NLBs are optimized for network-level load balancing and do not offer advanced application-layer features, which may be required for modern web applications.

Option D, Amazon Lightsail instance with periodic snapshots, is suitable for simple or small-scale applications. Lightsail does not provide multi-AZ deployment, automatic scaling, or advanced load balancing features. Using Lightsail for this scenario would fail to meet high availability and scalability requirements, making it unsuitable for production workloads.

By implementing an Auto Scaling group across multiple Availability Zones behind an ALB, the company ensures that the web application can handle variable traffic efficiently, remain available during failures, and provide a consistent user experience. The combination of automatic scaling, multi-AZ redundancy, and intelligent traffic distribution reduces operational complexity, enhances reliability, and ensures cost-effective resource utilization.

Question 167:

A company wants to process millions of IoT telemetry events per second. Multiple applications need concurrent access to the same stream with durability and low latency. Which service is most appropriate?
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 designed for high-throughput, real-time streaming workloads. Data is partitioned into shards, allowing multiple applications to consume the same stream concurrently. Enhanced fan-out provides dedicated throughput for each consumer, ensuring low latency even under heavy traffic conditions.

Data is replicated across multiple Availability Zones to provide durability and fault tolerance. Kinesis integrates with AWS Lambda and other analytics services for serverless, event-driven processing. Horizontal scaling allows processing millions of events per second efficiently.

Option B, SQS, is a queueing service that does not efficiently support multiple consumers reading the same message concurrently. Option C, SNS, is a pub/sub service without replay capability and is not optimized for high-throughput streaming workloads. Option D, Amazon MQ, is a traditional message broker, which is less efficient for low-latency, high-volume streaming workloads.

This architecture aligns with SAA-C03 objectives for scalable, durable, and low-latency event-driven solutions, particularly for IoT or telemetry data processing.

Question 168:

A company runs a containerized application on ECS Fargate. Microservices require secure access to API keys and database credentials with encryption and automatic rotation. Which AWS service is recommended?
A) AWS Secrets Manager
B) Amazon RDS Parameter Groups
C) EC2 Instance Metadata
D) Amazon EFS

Answer:

A) AWS Secrets Manager

Explanation:

AWS Secrets Manager provides a centralized, secure solution for storing sensitive credentials such as API keys, passwords, and database credentials. Secrets are encrypted using AWS KMS and can be automatically rotated according to predefined schedules, which reduces operational overhead and improves compliance.

ECS Fargate tasks can programmatically retrieve secrets at runtime. Fine-grained IAM policies restrict each microservice to only access the secrets it requires. CloudTrail auditing tracks access and rotation events, providing complete visibility and governance for security and compliance purposes.

Option B, RDS Parameter Groups, manages database configuration settings but cannot store general application secrets. Option C, EC2 Instance Metadata, is unavailable for Fargate containers. Option D, Amazon EFS, is a shared filesystem and lacks encryption, automated rotation, or fine-grained access control for secrets.

This design follows AWS best practices for secure containerized applications and aligns with SAA-C03 objectives for automated secret management, security, and compliance in serverless containerized workloads.

Question 169:

A company wants to analyze large volumes of log data stored in S3 without building ETL pipelines. Which service is most suitable?
A) Amazon Athena
B) Amazon EMR
C) Amazon Redshift
D) AWS Glue

Answer:

A) Amazon Athena

Explanation:

Amazon Athena is a serverless, interactive query service that enables organizations to analyze data stored in Amazon S3 using standard SQL. It is specifically designed for scenarios where companies need to perform ad-hoc analysis on large datasets without the overhead of provisioning infrastructure or building ETL pipelines. In the context of log data stored in S3, Athena is highly suitable because it allows direct querying of raw or semi-structured data without requiring pre-processing or transformation, significantly simplifying the analytics workflow.

Athena supports multiple data formats, including CSV, JSON, Parquet, and ORC, which are commonly used for storing log data. This flexibility ensures that data collected from applications, servers, or IoT devices can be queried directly, regardless of its format. By eliminating the need to move or transform data before analysis, Athena saves both time and operational effort, allowing organizations to focus on generating insights from the data rather than managing complex pipelines.

One of the key advantages of Athena is its serverless architecture. Users do not need to manage servers, clusters, or scaling configurations. Athena automatically handles the computational resources required to execute queries, dynamically scaling to meet workload demands. This makes it ideal for analyzing log data, which often arrives in unpredictable volumes. Users pay only for the amount of data scanned during queries, providing a cost-efficient solution that is particularly valuable when dealing with massive datasets.

In contrast, Amazon EMR is a managed platform for big data processing using distributed frameworks such as Apache Spark, Hadoop, or Hive. EMR is powerful for transforming and processing very large datasets, but it requires provisioning clusters, writing processing scripts, and managing nodes. For scenarios where the goal is ad-hoc querying of log data without building ETL pipelines, EMR introduces unnecessary operational complexity. Setting up EMR clusters for analysis involves additional time, configuration, and maintenance, which makes it less efficient than Athena for quick log analysis.

Amazon Redshift is a data warehouse optimized for structured, relational data. While Redshift excels at complex queries, aggregations, and joining large datasets, it requires data to be loaded and transformed into the warehouse before analysis. Using Redshift for log data would necessitate building ETL pipelines to extract data from S3, convert it to a suitable format, and load it into Redshift. This extra step contradicts the requirement to avoid ETL pipelines, making Redshift less suitable for direct log data analysis.

AWS Glue is a fully managed ETL service that catalogs, cleans, and transforms data for analytics. While Glue can be used to prepare data for analysis, it does not provide a query engine by itself. Using Glue alone would still require building ETL workflows to process raw log data before analysis. Athena, however, can leverage Glue Data Catalog to read table metadata, allowing it to query partitioned data efficiently without additional data preparation.

Integration with AWS Glue Data Catalog further enhances Athena’s capabilities. The Data Catalog stores metadata about datasets, including table structures and partitions, which helps optimize queries and reduce the amount of data scanned. Partitioning log data by attributes such as timestamps, log types, or sources enables selective queries that improve performance and reduce costs. This approach is particularly effective for large-scale log analysis where only specific segments of the dataset are relevant to each query.

Another benefit of Athena is its support for standard SQL syntax, which makes it accessible to analysts and engineers without requiring specialized knowledge of big data frameworks. Users can perform filtering, aggregation, joins, and sorting directly on log data stored in S3. Additionally, Athena integrates with visualization tools like Amazon QuickSight, allowing dashboards and reports to be created directly from query results. This enables real-time insights into operational, security, or application logs without the need for additional ETL processes.

Athena’s pay-per-query pricing model and serverless design also ensure that organizations do not pay for idle resources, making it highly cost-efficient for analyzing variable volumes of log data. Queries are executed immediately on the S3 data, allowing organizations to gain insights quickly without waiting for data transformations or warehouse loading processes.

Amazon Athena is the most suitable service for analyzing large volumes of log data stored in S3 without building ETL pipelines. Its serverless architecture, ability to query raw and semi-structured data directly, integration with Glue Data Catalog, cost efficiency, scalability, and SQL accessibility make it ideal for rapid, ad-hoc log analysis. By eliminating the need for ETL workflows, Athena enables organizations to focus on generating insights from their data efficiently, securely, and reliably.

Question 170:

A company wants to deploy a multi-tier web application with a highly available database and caching layer. Automatic failover must occur 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:

Amazon RDS Multi-AZ deployments provide synchronous replication to a standby instance in a different Availability Zone. This configuration ensures automatic failover in the event of primary database failure, maintaining high availability and minimizing downtime.

ElastiCache acts as an in-memory caching layer that reduces load on the database, accelerates application response times, and improves scalability. Together, RDS Multi-AZ and ElastiCache create a highly resilient, performant multi-tier architecture.

Option B, a single RDS instance with snapshots, requires manual recovery, increasing downtime and operational risk. Option C, read replicas, provide read scalability but cannot automatically replace a failed primary. Option D, self-managed EC2 replication, increases operational complexity and the risk of misconfiguration.

This architecture follows AWS best practices for multi-tier applications with high availability, fault tolerance, disaster recovery, and performance optimization, fully aligned with SAA-C03 objectives.

Question 171:

A company wants to implement a serverless architecture for its web application. It needs to run code without provisioning or managing servers and scale automatically based on user traffic. Which AWS service should be used?
A) AWS Lambda
B) Amazon EC2
C) AWS Elastic Beanstalk
D) Amazon Lightsail

Answer:

A) AWS Lambda

Explanation:

AWS Lambda is a serverless compute service that allows running code in response to events without provisioning or managing servers. Lambda automatically scales the application by running code in parallel for each incoming request. Users are billed only for the compute time consumed, making it cost-efficient for variable workloads.

In a serverless architecture, Lambda integrates with multiple AWS services, such as API Gateway, S3, DynamoDB, and CloudWatch. API Gateway can expose Lambda functions as RESTful endpoints, while CloudWatch monitors performance and logs execution metrics. Lambda also supports environment variables for configuration management and integrates with Secrets Manager to access sensitive credentials securely.

Option B, Amazon EC2, requires provisioning, configuring, and maintaining servers, which is operationally intensive and not serverless. Option C, Elastic Beanstalk, simplifies deployment but still relies on underlying EC2 instances and does not provide the full serverless benefits. Option D, Lightsail, is a simplified virtual server solution and does not offer serverless compute or automatic scaling per request.

Using Lambda enables event-driven, cost-effective, and fully managed serverless applications. This architecture aligns with SAA-C03 exam objectives for building scalable, reliable, and operationally efficient serverless solutions.

Question 172:

A company wants to analyze petabytes of structured and semi-structured data with a fully managed data warehouse. Queries must be fast, and data must be compressed for storage efficiency. 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, petabyte-scale data warehouse that provides fast query performance by using columnar storage, data compression, and massively parallel processing (MPP). It is optimized for analytics workloads involving large volumes of structured and semi-structured data, making it ideal for complex queries, business intelligence, and reporting.

Redshift supports various compression techniques that reduce storage costs and improve query performance by minimizing I/O. It also integrates with Amazon S3 through Redshift Spectrum, allowing queries on external data without data movement. Security is provided through encryption at rest with AWS KMS, SSL for in-transit data, and fine-grained access control via IAM and Redshift roles.

Option B, Athena, is serverless and great for ad hoc querying of S3 data but may not match Redshift’s performance for large, complex analytical workloads. Option C, EMR, is primarily for large-scale data processing using Hadoop or Spark, which is operationally more complex. Option D, Glue, is mainly an ETL service and not optimized for high-performance querying or analytics.

Redshift aligns with SAA-C03 exam objectives for implementing scalable, high-performance data warehouses with security, compression, and analytics capabilities.

Question 173:

A company wants to implement a highly available relational database with automatic failover. The database must support read scalability for analytical workloads. Which configuration is most appropriate?
A) Amazon RDS Multi-AZ deployment with read replicas
B) Single RDS instance with snapshots
C) Self-managed EC2 database with manual replication
D) Amazon DynamoDB

Answer:

A) Amazon RDS Multi-AZ deployment with read replicas

Explanation:

When designing a highly available relational database that supports both automatic failover and read scalability, Amazon RDS Multi-AZ deployments combined with read replicas provide the most suitable solution. This configuration addresses the critical requirements of high availability, durability, and the ability to handle analytical workloads without impacting the performance of primary transactional operations.

Amazon RDS Multi-AZ deployments provide built-in high availability for relational databases. In a Multi-AZ configuration, Amazon RDS automatically provisions a synchronous standby instance in a different Availability Zone. This standby instance acts as a failover target in case the primary database becomes unavailable due to hardware failure, network issues, or maintenance events. When a failure occurs, RDS automatically switches the application to the standby instance with minimal downtime, ensuring continuous availability for the application. This automated failover capability is essential for mission-critical applications that require minimal disruption and cannot tolerate prolonged outages.

To address the need for read scalability, RDS read replicas are used. Read replicas allow one or more asynchronous copies of the primary database to be created and used for read-only queries. This separation of read and write workloads ensures that analytical queries or reporting operations do not degrade the performance of transactional operations on the primary database. Read replicas can scale horizontally, allowing multiple analytical workloads to run concurrently without impacting the primary database’s performance. This makes the system well-suited for applications that require both high availability and read-intensive analytical processing.

The combination of Multi-AZ deployments with read replicas ensures that the database is highly resilient and scalable. While Multi-AZ addresses availability and durability, read replicas address performance and scalability for read-heavy operations. This design enables organizations to maintain robust operational performance for transactional workloads while supporting analytical queries on replicas, providing both reliability and efficiency.

Alternative options are less suitable. A single RDS instance with snapshots (option B) can provide basic data backup and recovery, but it lacks automatic failover. In the event of a primary database failure, recovery would require manual intervention, leading to potential downtime. This option does not meet the requirement for high availability and automatic failover.

A self-managed EC2 database with manual replication (option C) could theoretically achieve similar functionality, but it introduces significant operational overhead. The company would need to manage replication, failover, backups, patching, and monitoring manually. Configuring automatic failover across Availability Zones is complex and error-prone. In comparison, using managed services like Amazon RDS greatly reduces operational complexity while providing built-in failover, monitoring, and backups.

Amazon DynamoDB (option D) is a fully managed NoSQL database service optimized for key-value and document data models. While it offers high availability and scalability, it is not a relational database and does not support traditional SQL-based relational operations required by many applications. Analytical workloads designed for relational databases would be challenging to implement on DynamoDB without significant changes to application design.

Amazon RDS Multi-AZ with read replicas also integrates seamlessly with monitoring and management tools. Amazon CloudWatch provides metrics and alerts for performance and health monitoring, while automated backups and snapshots ensure that data can be restored in the event of accidental deletion or corruption. Multi-AZ deployments work with multiple database engines, including MySQL, PostgreSQL, MariaDB, Oracle, and SQL Server, offering flexibility to choose the database engine that best suits the application’s needs.

Question 174:

A company needs a secure, scalable messaging service for decoupling microservices. Messages must be retained 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:

When designing a system with microservices, one common architectural pattern is decoupling services using a messaging system. Decoupling allows microservices to communicate asynchronously, improving scalability, reliability, and maintainability. In this scenario, the company requires a secure, scalable messaging service that ensures message retention and guarantees at-least-once delivery. The most suitable service for this requirement is Amazon SQS Standard Queue.

Amazon Simple Queue Service (SQS) is a fully managed message queuing service that allows messages to be reliably stored until they are successfully processed by consuming applications. The Standard Queue type provides high throughput, at-least-once delivery, and best-effort ordering, which fits most microservices decoupling requirements. When a producer sends a message to SQS, it is stored durably across multiple Availability Zones, ensuring that messages are not lost even in the event of hardware failures or infrastructure issues.

A key feature of SQS is message retention, which allows messages to remain in the queue for up to 14 days. This capability ensures that if a consumer service is temporarily unavailable or experiences delays, messages are not lost and can be processed later. It also provides flexibility for batch processing and retry mechanisms, which is important for asynchronous communication between loosely coupled microservices.

SQS also guarantees at-least-once delivery. Each message is delivered at least once, and in some cases, it may be delivered more than once. Consumers must be designed to handle potential duplicate messages, typically through idempotent operations. This delivery guarantee ensures that messages are not accidentally dropped, which is critical for applications requiring reliable communication between services.

Security is another important aspect. SQS integrates with AWS Identity and Access Management (IAM) to define granular permissions for sending and receiving messages. It also supports encryption at rest using AWS KMS, ensuring that sensitive data contained in messages is securely stored. Additionally, messages are encrypted in transit using HTTPS, maintaining data confidentiality during communication between services.

In comparison, Amazon SNS (option B) is a pub/sub messaging service designed to deliver messages to multiple subscribers simultaneously. While SNS is useful for broadcasting notifications, it does not provide built-in message retention for asynchronous processing and does not guarantee at-least-once delivery in the same way SQS does. This makes SNS less suitable for decoupling microservices that require reliable message storage and processing.

Amazon Kinesis Data Streams (option C) is designed for high-throughput streaming data and real-time analytics. Although it supports multiple consumers and durability for streams, it is better suited for continuous data processing workloads rather than asynchronous decoupling of microservices with at-least-once message delivery guarantees.

Amazon MQ (option D) is a managed message broker for traditional messaging protocols such as JMS, AMQP, or MQTT. While it supports complex messaging patterns, setting up and managing a broker adds operational complexity compared to the fully managed SQS service. For most cloud-native microservices, SQS provides a simpler, scalable, and secure solution.

SQS is highly scalable and can handle virtually unlimited messages per second without requiring infrastructure provisioning or maintenance. It integrates easily with other AWS services like Lambda, ECS, and EC2, allowing microservices to automatically trigger processing in response to incoming messages. This enables event-driven architectures, further supporting scalability and flexibility.

Question 175:

A company wants to deploy a multi-tier application that must maintain session state across multiple web servers. Which solution is most appropriate?
A) Store session state in Amazon ElastiCache
B) Store session state in local EC2 instance memory
C) Use cookies only for session management
D) Store session state in S3 without caching

Answer:

A) Store session state in Amazon ElastiCache

Explanation:

In multi-tier applications, session state must be centralized if multiple web servers handle user requests. Amazon ElastiCache provides an in-memory key-value store (Redis or Memcached) that stores session data with extremely low latency, ensuring fast access across multiple web servers.

Using ElastiCache allows horizontal scaling of web servers without losing session continuity. Redis supports features such as persistence, replication, and automatic failover, providing high availability and reliability for session management.

Option B, storing state in local EC2 memory, causes session loss if the instance fails and does not support load-balanced multi-server deployments. Option C, using cookies only, may not store complex session data securely and increases client-side dependency. Option D, storing session state in S3, introduces higher latency and is not designed for fast in-memory access.

ElastiCache ensures performance, scalability, and high availability for session management in distributed web applications, aligning with SAA-C03 objectives for multi-tier architectures and fault-tolerant designs.

Question 176:

A company wants to deploy a global web application with low latency. Static content is stored in Amazon S3, and dynamic content is served by EC2 instances in multiple regions. Which architecture ensures low latency, high availability, and secure access to S3?
A) Amazon CloudFront with S3 origin and regional EC2 origin 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 regional EC2 origin failover

Explanation:

When deploying a global web application that must provide low latency, high availability, and secure access to both static and dynamic content, using Amazon CloudFront with an S3 origin and regional EC2 origin failover is the most suitable architecture. CloudFront is a global content delivery network (CDN) that caches content at edge locations worldwide, reducing latency by serving content from locations close to users. It supports both static content stored in Amazon S3 and dynamic content from EC2 instances, providing a unified, performant, and secure delivery mechanism.

Static content, such as images, videos, JavaScript, and CSS files, can be stored in Amazon S3. Serving this content directly from S3 is possible, but doing so without a CDN may result in higher latency for users located far from the S3 bucket’s region. CloudFront integrates with S3 as an origin, caching static content at edge locations around the world. When a user requests a file, CloudFront serves it from the nearest edge location, dramatically improving load times and reducing latency for a global audience.

For dynamic content generated by EC2 instances, CloudFront can be configured with multiple regional origins and failover routing. This ensures that if one region becomes unavailable, traffic is automatically routed to another healthy origin, maintaining high availability and reliability. The combination of edge caching for static content and origin failover for dynamic content provides a seamless user experience with minimal latency and maximum uptime.

Security is another critical aspect addressed by this architecture. CloudFront provides secure access to S3 by using origin access identity (OAI), which ensures that content in S3 is not publicly accessible. Instead, only CloudFront can retrieve content from the S3 bucket, enforcing secure delivery. Additionally, CloudFront supports HTTPS to encrypt data in transit between the edge location and the end user, protecting sensitive information and maintaining compliance with security standards.

Alternative options are less suitable for this use case. A public S3 bucket with HTTPS (option B) can serve static content securely over HTTPS, but it lacks global edge caching and does not provide failover capabilities for dynamic content. Users located far from the S3 region may experience higher latency, and relying on a single S3 bucket does not address availability concerns for dynamic EC2-based content.

Amazon SNS with cross-region replication (option C) is primarily a messaging service used for notifications and pub/sub workflows. It is not designed for content delivery or serving static and dynamic web content. SNS cannot reduce latency for global users or provide caching, so it does not meet the requirements of this scenario.

Amazon Global Accelerator with a single EC2 origin (option D) improves global performance by routing traffic to the optimal AWS region using the AWS global network. While it can reduce latency for dynamic content, a single EC2 origin does not provide failover for regional outages and does not optimize the delivery of static S3 content. Without integration with CloudFront, caching for static assets is missing, leading to higher latency for frequently requested files.

CloudFront also provides additional features such as geographic restrictions, WAF integration, and logging. Geographic restrictions can prevent access from unauthorized regions, WAF (Web Application Firewall) integration helps protect against common web exploits, and detailed logs support monitoring and compliance reporting. Together, these capabilities enhance the security and operational insight of the application.

Moreover, CloudFront automatically compresses content where appropriate and can perform cache invalidation when content updates, ensuring users receive fresh data without manual intervention. This reduces load on the EC2 origins and S3 buckets, enhancing performance and cost efficiency.

Question 177:

A company processes millions of IoT telemetry events per second. Multiple applications require concurrent access to the same stream 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 specifically designed for high-throughput, real-time streaming workloads. Data is divided into shards, allowing multiple applications to consume the same stream concurrently. Enhanced fan-out enables each consumer to have dedicated throughput, ensuring low latency even with heavy workloads.

Data replication across multiple Availability Zones ensures durability and fault tolerance. Kinesis integrates with AWS Lambda, Firehose, and analytics tools, enabling serverless event-driven processing. Horizontal scaling allows the system to efficiently handle millions of events per second, ensuring performance under extreme load.

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 data. Option D, Amazon MQ, is a traditional message broker and is less suitable for low-latency, high-volume streaming workloads.

This architecture meets SAA-C03 objectives for scalable, durable, and low-latency event-driven systems, especially for IoT and telemetry applications.

Question 178:

A company wants a highly available relational database with automated backups, patching, and automatic failover. Which solution is recommended?
A) Amazon RDS Multi-AZ deployment
B) Single RDS instance with manual snapshots
C) Self-managed database on EC2 instances
D) Amazon DynamoDB

Answer:

A) Amazon RDS Multi-AZ deployment

Explanation:

Amazon RDS Multi-AZ deployments replicate the primary database synchronously to a standby instance in a different Availability Zone. Automatic failover occurs in case of primary instance failure, ensuring high availability. RDS manages backups, software patching, and failover automatically, reducing operational overhead.

The Multi-AZ architecture maintains transaction consistency and minimizes downtime, critical for production workloads. Integration with CloudWatch allows monitoring metrics such as CPU utilization, free storage, and replica lag. Additionally, automated backups and snapshots enable point-in-time recovery, meeting compliance and disaster recovery requirements.

Option B, a single RDS instance with manual snapshots, lacks automatic failover and increases downtime risk. Option C, self-managed EC2 databases, require significant operational effort and carry higher failure risk. Option D, DynamoDB, is a NoSQL service and cannot fulfill relational database requirements such as ACID transactions and SQL queries.

This solution aligns with SAA-C03 best practices for deploying highly available, fault-tolerant relational databases with automated maintenance.

Question 179:

A company wants to decouple microservices using a scalable, fully managed messaging service. Messages must be delivered at least once and retained for processing. 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:

In this scenario, a company wants to decouple microservices in a way that allows them to communicate asynchronously while ensuring scalability, reliability, and message durability. The requirement specifies that messages must be delivered at least once and retained for processing, making Amazon SQS Standard Queue the most suitable service for this purpose.

Amazon SQS (Simple Queue Service) is a fully managed message queuing service that enables decoupling of components in distributed systems. By using a queue, microservices can send messages to SQS without needing to know the details of the consumers that process those messages. This loosely coupled architecture ensures that the failure or slowness of one microservice does not directly affect the others. SQS allows producers to send messages and consumers to retrieve and process them at their own pace, providing asynchronous communication between services.

The Standard Queue type in SQS guarantees at least once delivery, meaning that each message is delivered to a consumer at least once, though in rare cases it may be delivered multiple times. This behavior ensures reliability, as no messages are lost, which is critical for workflows where message processing is essential for business logic. Additionally, messages in the queue can be retained for up to 14 days, allowing consumers to process them even if there are temporary failures or delays. This retention capability ensures durability and prevents loss of important data, which is particularly useful in high-throughput, distributed environments.

SQS also supports scalability automatically. It can handle virtually unlimited numbers of messages and a high volume of transactions per second, allowing microservices to scale independently. Producers can continue sending messages regardless of how many consumers are processing them, and consumers can scale horizontally to process messages faster if the backlog grows. This flexibility allows the system to handle bursty workloads and maintain consistent performance without requiring manual intervention.

Option B, Amazon SNS (Simple Notification Service), is a pub/sub messaging service that delivers messages to multiple subscribers simultaneously. While SNS is highly effective for broadcasting messages, it does not inherently provide message retention for consumers that may be temporarily unavailable. SNS is designed for real-time notifications, and although it can be integrated with SQS for durability, SNS alone does not meet the requirement of retaining messages for processing with at-least-once delivery semantics.

Option C, Amazon Kinesis Data Streams, is optimized for real-time streaming data rather than decoupled message delivery. Kinesis provides ordered, durable, and high-throughput streaming, but it is more suitable for scenarios like real-time analytics or event processing, rather than standard asynchronous message queuing between microservices. Using Kinesis for simple message decoupling would introduce unnecessary complexity and cost.

Option D, Amazon MQ, is a managed message broker service that supports traditional protocols such as AMQP, MQTT, and JMS. While it provides at-least-once delivery and message durability, it is primarily designed for applications that require compatibility with existing message broker standards. For most modern microservice architectures, SQS offers a simpler, fully managed solution without the operational overhead of managing broker connections or configurations.

By using Amazon SQS Standard Queue, the company achieves reliable, scalable, and fully managed message queuing. It decouples microservices effectively, ensures messages are delivered at least once, retains messages for processing, and supports variable workloads. The service integrates seamlessly with other AWS services, including Lambda, ECS, and EC2, allowing for event-driven architectures and serverless processing pipelines.

Question 180:

A company wants to maintain session state across multiple web servers in a scalable web application. Which solution is most appropriate?
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 this scenario, a company wants to maintain session state across multiple web servers in a scalable web application. Web applications often require session state to keep track of user activity, such as login status, shopping cart contents, or user preferences. When deploying applications across multiple servers, relying on local memory for session state can lead to inconsistencies, as each server only knows about the sessions it directly handles. The most suitable solution for this requirement is to store session state in Amazon ElastiCache, a fully managed in-memory caching service that provides fast, scalable, and centralized session storage.

Amazon ElastiCache supports popular caching engines like Redis and Memcached. Both engines allow session data to be stored centrally, enabling all web servers to access the same session information regardless of which server handles a user request. This ensures consistency and reliability across multiple instances, which is crucial in a load-balanced environment. Using ElastiCache reduces latency for session retrieval compared to disk-based storage, as it operates entirely in memory, providing microsecond-level access times. This improves the overall user experience, especially for high-traffic web applications.

Centralized session storage also allows for scalability. As the application scales horizontally by adding more web servers, all servers can read and write to the same session store without modifying application logic or relying on sticky sessions at the load balancer. Sticky sessions, while sometimes used, can create dependency on specific servers and reduce fault tolerance. In contrast, using ElastiCache decouples session management from individual web servers, enabling seamless scaling and improved reliability.

ElastiCache also provides high availability options. For Redis, users can configure replication groups and automatic failover, ensuring that if one node fails, session data is still accessible from a standby node. This eliminates single points of failure and ensures uninterrupted access to session information, which is critical for maintaining user experience during failures or infrastructure issues. Additionally, Redis supports data persistence to disk, providing durability for session information if required.

Option B, storing session state in local EC2 memory, is unsuitable for scalable applications because sessions are tied to individual servers. If a server fails or a user request is routed to a different server, session data is lost or unavailable, leading to a poor user experience. This approach also complicates horizontal scaling, as new servers must be aware of existing sessions or rely on sticky sessions, which reduce flexibility and fault tolerance.

Option C, using client-side cookies only, can store session information on the client but comes with limitations. Sensitive data should not be stored in cookies due to security risks, and the size of cookies is limited. Relying solely on client-side cookies also requires encrypting and validating data, increasing application complexity. While cookies can complement server-side session storage, they are insufficient as a primary solution for managing session state across multiple servers.

Option D, storing session state in S3 without caching, provides durability but is not ideal for session management due to latency. S3 is optimized for durable object storage, not low-latency access. Reading and writing session data to S3 on every request would significantly slow down response times, impacting user experience and scalability, especially under high traffic conditions.

By storing session state in Amazon ElastiCache, the company achieves a fast, reliable, and scalable solution for managing session data in a distributed web application. It allows web servers to scale horizontally without losing session consistency, provides low-latency access to session information, and supports high availability and failover. This approach aligns with AWS best practices for session management in distributed and scalable applications.