Amazon AWS Certified Solutions Architect — Professional SAP-C02 Exam Dumps and Practice Test Questions Set 6 Q76-90
Visit here for our full Amazon AWS Certified Solutions Architect — Professional SAP-C02 exam dumps and practice test questions.
Question 76
A company wants to store data in S3 with strict encryption using customer-managed keys and audit key usage. Which approach is recommended?
A) Enable S3 default encryption with SSE-KMS
B) Use SSE-S3 only
C) Store objects unencrypted
D) Upload objects over HTTP
Answer: A) Enable S3 default encryption with SSE-KMS
Explanation:
When storing sensitive data in Amazon S3, relying solely on the default server-side encryption (SSE-S3) provides a baseline level of protection, but it has limitations in terms of control and auditing. SSE-S3 automatically encrypts data at rest using keys managed entirely by AWS, ensuring that stored objects are protected from unauthorized access. However, because AWS controls the key lifecycle, customers cannot directly manage key rotation or enforce granular access policies for auditing purposes. While this setup ensures data is encrypted, it does not meet stricter compliance requirements that demand visibility and control over key management.
Leaving S3 objects unencrypted exposes them to significant risk, as any unauthorized access to the storage bucket could compromise sensitive information. Similarly, transferring data over unsecured channels, such as HTTP, transmits information in plaintext, making it vulnerable to interception or man-in-the-middle attacks. Both unencrypted storage and insecure transport can violate regulatory requirements, making organizations liable for compliance breaches and potential data loss.
To address these challenges, enabling S3 default encryption using SSE-KMS is a more robust and secure approach. SSE-KMS uses keys stored in the AWS Key Management Service (KMS), allowing organizations to maintain full control over key creation, rotation, and usage policies. Each object uploaded to S3 is automatically encrypted with the specified KMS key, eliminating the risk of human error and ensuring that all data is consistently protected, even if an uploader does not explicitly request encryption.
One of the key advantages of SSE-KMS is its integration with CloudTrail, which provides detailed logging of every action performed on the encryption keys. This logging capability is critical for organizations that must demonstrate compliance with industry standards and regulatory frameworks, as it allows administrators to audit key usage, monitor access patterns, and detect potential misuse. By having full control over key lifecycle management, businesses can enforce policies such as automatic rotation and strict access permissions, significantly enhancing the overall security posture.
Furthermore, SSE-KMS supports compliance requirements for sensitive data by combining strong encryption with auditable controls. Organizations can securely store regulated information, such as personal data, financial records, or intellectual property, while maintaining the ability to demonstrate that proper security and governance practices are in place. The approach also reduces operational risk because encryption is handled automatically, freeing administrators from manually ensuring that every object is encrypted.
While SSE-S3 provides basic encryption, enabling S3 default encryption with SSE-KMS delivers a comprehensive, secure, and auditable solution for storing sensitive data in the cloud. It provides encryption at rest using customer-managed keys, integrates with CloudTrail for full auditability, enforces key lifecycle controls, and minimizes human error. By implementing SSE-KMS, organizations achieve a balance of robust security, compliance readiness, and operational efficiency, making it the preferred choice for sensitive or regulated workloads in Amazon S3.
Question 77
A company wants to process files uploaded to S3 automatically and trigger workflows without managing servers. Which architecture is most suitable?
A) Lambda triggered by S3 events
B) EC2 instances polling S3
C) ECS on EC2 launch type
D) EKS with EC2 nodes
Answer: A) Lambda triggered by S3 events
Explanation:
When building applications that respond to changes in S3, traditional approaches such as using EC2 instances to poll S3 for new objects come with significant operational challenges. Managing EC2 servers requires administrators to handle provisioning, scaling, patching, and monitoring. As workloads fluctuate, scaling these instances to meet demand often involves over-provisioning to prevent bottlenecks or under-provisioning that can lead to processing delays. This manual management increases operational complexity and can result in higher costs, as compute resources must be constantly available regardless of actual workload.
Similarly, container orchestration solutions such as ECS or EKS using EC2 nodes also require active infrastructure management. Deploying containers on EC2 nodes necessitates handling cluster scaling, patching the underlying instances, and ensuring high availability. While ECS and EKS provide container orchestration capabilities, they do not eliminate the need to manage the underlying compute resources. This makes them less suitable for fully automated, serverless, event-driven workflows, especially when the goal is to respond dynamically to S3 events without dedicating infrastructure continuously.
In contrast, AWS Lambda provides a serverless solution that is optimized for event-driven workloads. Lambda functions can be triggered directly by S3 events, such as when a new object is uploaded or modified. This eliminates the need to continuously poll S3, reducing both operational overhead and latency. With Lambda, there is no need to provision or maintain servers; the service automatically scales to accommodate varying workloads, handling spikes in demand without manual intervention. Billing is based strictly on execution time, providing a cost-efficient alternative to always-on EC2 instances or managed container nodes.
Lambda integrates seamlessly with other AWS services to build fully automated and orchestrated workflows. For example, processed data can be routed to SNS topics for notifications, SQS queues for decoupled messaging, or DynamoDB tables for storage and indexing. CloudWatch enables logging, monitoring, and error tracking, ensuring visibility and control over the execution of workflows. This tight integration allows developers to create complex pipelines that respond to S3 events without building and maintaining the infrastructure traditionally required for such operations.
Adopting a Lambda-based approach for S3 event processing provides high availability, operational simplicity, and cost-effectiveness. Workflows automatically scale in response to incoming events, and the underlying infrastructure is fully managed by AWS, freeing teams from routine maintenance tasks. Additionally, serverless architectures reduce the risk of downtime due to misconfigured or unpatched servers and allow teams to focus on business logic and innovation rather than operational details.
Using EC2 instances or container-based solutions for S3 event processing adds significant management overhead, scaling challenges, and cost considerations. AWS Lambda offers a fully serverless, event-driven solution that executes automatically when objects are added to S3. By integrating with services like SNS, SQS, DynamoDB, and CloudWatch, Lambda enables highly scalable, reliable, and cost-efficient processing of S3 files, providing organizations with a modern approach to automated workflows without the complexities of server management.
Question 78
A company wants to migrate on-premises SQL Server databases to AWS with minimal downtime and continuous replication. Which approach is recommended?
A) RDS SQL Server with AWS DMS
B) RDS SQL Server only
C) Aurora PostgreSQL
D) EC2 SQL Server with manual backup/restore
Answer: A) RDS SQL Server with AWS DMS
Explanation:
Migrating on-premises SQL Server databases to the cloud can often be a complex and downtime-sensitive operation if approached without proper planning. Traditional methods such as exporting data from the source SQL Server, transferring it to a target environment, and performing a full import into RDS SQL Server are straightforward in concept but carry a significant drawback: extended downtime. During the period of data export, transfer, and import, the source database is typically unavailable for transactional operations, which can severely impact business continuity, especially for applications that require near-constant availability. This makes such an approach less suitable for production environments that cannot tolerate long interruptions.
Alternatively, Aurora PostgreSQL, while a robust relational database solution, is not compatible with SQL Server. Migrating from SQL Server to Aurora PostgreSQL would require extensive application changes, including rewriting queries, modifying stored procedures, and adapting business logic to match PostgreSQL behavior. This additional complexity increases both the risk and the duration of the migration, potentially introducing bugs and operational overhead that further delay the transition to the cloud.
Another common approach is deploying SQL Server on EC2 instances and performing manual backup and restore operations. While this method allows control over the database environment, it is highly time-consuming and prone to errors. Manual intervention at multiple stages of the migration increases the likelihood of mistakes, and restoring large datasets can take hours or even days, depending on database size and network throughput. Furthermore, managing EC2 instances for SQL Server requires ongoing patching, monitoring, and scaling, adding operational overhead that offsets the benefits of cloud migration.
A more efficient and reliable strategy is to use RDS SQL Server in combination with AWS Database Migration Service (DMS). This approach enables continuous replication from the source on-premises SQL Server to the managed RDS instance. With continuous replication, the source database remains fully operational throughout the migration, ensuring minimal disruption to business operations. DMS manages schema conversion, data type mapping, and near real-time synchronization, reducing manual effort and the risk of inconsistencies between source and target databases.
RDS SQL Server provides additional advantages that simplify migration and enhance reliability. Managed automated backups eliminate the need for manual snapshot management, and Multi-AZ deployment ensures high availability by maintaining a synchronous standby in a separate availability zone. Routine maintenance, including patching and minor version upgrades, is handled automatically with minimal impact on database availability. These features collectively reduce operational complexity, ensure data integrity, and provide a robust, low-downtime migration path.
Combining RDS SQL Server with AWS DMS offers a seamless, scalable solution for migrating SQL Server databases to the cloud. It minimizes downtime, ensures continuous availability, reduces administrative effort, and provides built-in high availability and automated maintenance. This strategy allows organizations to migrate critical workloads safely and efficiently while maintaining business continuity, making it the ideal choice for production environments.
Question 79
A company wants to implement a messaging system that decouples microservices and ensures exactly-once message processing in order. Which service is most suitable?
A) SQS FIFO Queue
B) SQS Standard Queue
C) SNS
D) Kinesis Data Streams
Answer: A) SQS FIFO Queue
Explanation:
When designing microservices architectures, selecting the right messaging system is critical to ensure reliable communication and maintain system consistency. Amazon SQS Standard Queues are widely used for decoupling microservices and buffering messages. They provide high throughput and guarantee at-least-once delivery, which ensures that messages are not lost in transit. However, Standard Queues do not preserve the order of messages, and duplicates can occasionally occur. In scenarios where the sequence of operations is important—such as processing financial transactions, user activity logs, or sequential tasks—this lack of ordering can lead to inconsistencies and unexpected application behavior. Developers must implement additional logic to handle out-of-order messages or detect duplicates, which increases operational complexity.
Amazon SNS is another popular messaging service, often used for pub/sub architectures where a single message must be delivered to multiple subscribers. SNS is highly scalable and can fan out messages efficiently to queues, Lambda functions, or HTTP endpoints. While SNS is excellent for broadcasting events across multiple systems, it does not guarantee message ordering or prevent duplicate deliveries. This makes it less suitable for scenarios where message sequence and deduplication are critical requirements. Applications that rely solely on SNS may need extra logic to ensure consistency, which adds overhead to the system design.
For use cases involving real-time analytics and high-volume streaming, Amazon Kinesis Data Streams is a powerful tool. Kinesis enables processing of large streams of data in near real time and supports ordered data consumption within individual shards. While Kinesis is optimized for analytics and streaming workloads, it introduces additional operational complexity for simple microservice-to-microservice communication. Managing shards, scaling streams, and handling data retention policies can be overkill for applications that require straightforward, reliable message delivery with strict ordering guarantees.
For microservices that need reliable, predictable communication while preserving order, Amazon SQS FIFO Queues offer a specialized solution. FIFO (First-In-First-Out) Queues ensure that messages are processed exactly once and in the precise order in which they were sent. This eliminates the risk of inconsistencies caused by duplicate or out-of-order messages, providing a predictable messaging pattern that simplifies application logic. FIFO Queues also support message deduplication, which ensures that even if the same message is sent multiple times due to network retries or application errors, it will only be processed once. This capability is critical in financial systems, order processing pipelines, and other domains where maintaining the integrity and sequence of operations is essential.
Using SQS FIFO Queues allows developers to build microservices architectures that are both fault-tolerant and scalable without the need for additional logic to handle duplicates or reordering. The queues integrate seamlessly with other AWS services, including Lambda, SNS, and Step Functions, enabling automated workflows and event-driven processing. By choosing FIFO Queues for scenarios requiring exact ordering and deduplication, organizations can ensure system consistency, reduce operational overhead, and support highly reliable, scalable distributed systems.
while SQS Standard, SNS, and Kinesis serve important roles in AWS messaging ecosystems, SQS FIFO Queues are the optimal choice for microservices requiring ordered, exactly-once message processing. Their combination of ordering, deduplication, and reliable delivery provides a robust foundation for building consistent, fault-tolerant, and scalable applications, allowing teams to focus on business logic rather than handling messaging anomalies.
Question 80
A company wants to analyze large, semi-structured datasets stored in S3 using SQL without provisioning infrastructure. Which service is most appropriate?
A) Athena
B) Redshift
C) RDS MySQL
D) DynamoDB
Answer: A) Athena
Explanation:
When handling large-scale data analytics, choosing the right service for querying and processing information is crucial, especially when datasets are semi-structured or vary in format. Amazon Redshift, for instance, is a highly optimized data warehouse designed to handle structured datasets efficiently. Its architecture excels at executing complex queries on predefined schemas, making it suitable for traditional relational data models. However, this reliance on schema-on-write can limit flexibility. Any structural change in the dataset requires schema modifications, which can slow down the analytics workflow. Moreover, Redshift is not inherently cost-effective for exploratory analysis or for datasets that are semi-structured, such as JSON, Parquet, or log files, because it demands upfront schema planning and storage provisioning.
Similarly, Amazon RDS with MySQL provides managed relational database capabilities, offering transactional support and familiar SQL querying. While RDS MySQL is reliable for structured data and operational workloads, it also requires schema-on-write. This constraint, coupled with the cost of scaling storage and compute for large datasets, makes it less suitable for extensive analytics or exploratory querying on diverse data sources. The overhead of managing storage, ensuring performance, and handling schema modifications can add operational complexity and slow down insight generation.
DynamoDB, on the other hand, is a fully managed NoSQL database that delivers low-latency access for key-value and document-based workloads. While it provides excellent performance for transactional and high-throughput operations, it does not natively support SQL-based analytics. Running complex analytical queries on DynamoDB often requires additional processing layers, such as exporting data to other services, which can increase latency and cost. Its focus on operational efficiency makes it less optimal for ad-hoc querying and analytics over large datasets with varied formats.
Amazon Athena addresses these challenges by providing a serverless, schema-on-read query service that allows users to run standard SQL queries directly on data stored in S3. Athena eliminates the need to predefine schemas, making it highly adaptable for semi-structured and evolving datasets. It natively supports multiple file formats, including CSV, JSON, Parquet, and ORC, enabling users to analyze raw datasets without prior transformation. Athena automatically scales to handle varying query loads and charges based solely on the amount of data scanned, optimizing cost-efficiency. Integration with AWS Glue provides a centralized data catalog, simplifying schema management and enabling consistent metadata across analytics workflows. This setup allows for rapid exploration, ad-hoc querying, and efficient extraction of insights from diverse datasets without requiring extensive infrastructure provisioning or maintenance.
By leveraging Athena, organizations can efficiently analyze large, heterogeneous datasets stored in S3 while minimizing operational overhead and costs. Its serverless nature, flexible schema handling, and seamless integration with other AWS services make it ideal for modern analytics environments, providing agility, scalability, and cost-effective access to actionable insights. This approach is particularly suited for teams needing fast, reliable, and versatile analytics capabilities without the constraints of traditional, schema-dependent databases.
Question 81
A company wants to deploy a highly available, multi-AZ relational database for production workloads with automatic failover. Which service is most appropriate?
A) RDS Multi-AZ Deployment
B) RDS Single-AZ Deployment
C) DynamoDB
D) S3
Answer: A) RDS Multi-AZ Deployment
Explanation:
Deploying a relational database in a production environment requires careful consideration of availability, durability, and fault tolerance to ensure business continuity. Using a single-availability zone deployment, such as RDS Single-AZ, can be a significant risk for critical applications. In this configuration, the database runs entirely within one availability zone. While this setup may reduce costs and is suitable for development or non-critical workloads, it exposes the application to downtime if the underlying hardware or the availability zone experiences a failure. Any disruption, whether from infrastructure issues or maintenance activities, directly impacts database availability, leaving applications without access until the problem is resolved.
Some organizations may consider alternatives like DynamoDB or Amazon S3 for database needs. While DynamoDB offers high scalability and low-latency performance for NoSQL workloads, it lacks essential relational database features such as transactions, complex joins, and stored procedures, which are critical for many enterprise applications. Similarly, Amazon S3 is designed for durable object storage rather than serving as a transactional database. Relying on these services for workloads that require relational integrity and transactional consistency would introduce application complexity, as developers would need to implement mechanisms to enforce relational constraints and manage data consistency themselves.
RDS Multi-AZ deployments address these challenges by providing built-in high availability and automatic failover capabilities. In a Multi-AZ configuration, Amazon RDS automatically provisions a synchronous standby replica in a separate availability zone. This standby replica continuously replicates data from the primary database. If the primary instance encounters an outage due to hardware failure, network issues, or an availability zone disruption, RDS automatically promotes the standby to become the new primary. This failover process typically completes within minutes, ensuring minimal downtime for the application and preserving transactional integrity.
Beyond failover, RDS Multi-AZ deployments handle routine maintenance and patching with minimal operational impact. Updates can be applied to the standby instance first, allowing the primary database to continue serving requests uninterrupted. Backups are also managed automatically, with snapshots occurring without affecting database performance. This configuration provides strong durability guarantees for critical data while reducing the administrative burden on database administrators.
By leveraging Multi-AZ deployments, organizations gain the ability to maintain high availability and resilience without implementing complex, custom replication or disaster recovery strategies. This approach ensures that mission-critical applications continue to operate smoothly even during infrastructure failures or planned maintenance, supporting business continuity and minimizing operational risk. For production workloads where uptime, reliability, and automated failover are essential, RDS Multi-AZ represents the optimal solution, delivering both robust relational database capabilities and operational simplicity.
Question 82
A company wants a fully managed, serverless solution to run containerized microservices without managing EC2 instances. Which AWS service is most suitable?
A) ECS with Fargate
B) ECS with EC2 launch type
C) EKS with EC2 nodes
D) EC2 only
Answer: A) ECS with Fargate
Explanation:
Managing containerized applications on AWS using ECS with the EC2 launch type or EKS with EC2 nodes involves significant operational responsibilities. In these models, you must provision EC2 instances, ensure they are correctly sized, apply patches, configure security settings, and monitor health and performance. Scaling also requires careful planning, as you need to add or remove instances based on workload demand. Even though these services provide orchestration for containers, the underlying infrastructure management remains the user’s responsibility, adding operational complexity and overhead. EC2 by itself only offers raw compute capacity without any orchestration capabilities, leaving developers responsible for running, managing, and scaling applications manually.
AWS Fargate provides a compelling alternative by delivering a fully serverless container management solution. With Fargate, there is no need to provision, scale, or patch the underlying compute instances. Instead, you define the containers and their resource requirements, and Fargate automatically provisions the compute resources needed to run them. This approach eliminates the operational burden associated with managing the EC2 instances behind ECS or EKS, allowing teams to focus on building and deploying applications rather than maintaining infrastructure. Containers are launched in a managed environment with seamless integration to networking, logging, monitoring, and security services.
Fargate also provides automatic scaling based on workload. Whether you are running microservices, batch jobs, or long-running applications, it adjusts compute capacity dynamically to match demand. This ensures that applications remain highly available during spikes in traffic while avoiding over-provisioning during periods of low activity. Additionally, Fargate optimizes cost efficiency by billing only for the exact resources consumed by your containers, rather than for continuously running EC2 instances, which may remain underutilized during off-peak periods. This model reduces wasted spend and aligns costs with actual usage, making it a financially attractive option for scalable container workloads.
Beyond operational simplicity and cost efficiency, Fargate supports high availability and resilience. Containers can be deployed across multiple Availability Zones automatically, minimizing the risk of downtime due to localized infrastructure failures. Fargate integrates with AWS services such as CloudWatch for monitoring and logging, enabling visibility into container performance, metrics, and logs without the need to manage additional monitoring infrastructure. Security is also enhanced as Fargate handles the underlying host isolation, reducing exposure to vulnerabilities in the host operating system.
Overall, ECS with Fargate enables organizations to deploy microservices in a fully managed, serverless environment. It abstracts away infrastructure concerns, supports automatic scaling, ensures high availability, and provides a pay-for-use pricing model. Developers can focus entirely on application logic, microservice architecture, and continuous delivery pipelines while AWS manages the underlying compute, networking, and security layers. This approach is especially beneficial for modern cloud-native applications that require agility, operational simplicity, and predictable scalability, making Fargate an ideal choice for containerized workloads in the cloud.
Question 83
A company needs to migrate terabytes of on-premises data to AWS efficiently without consuming network bandwidth. Which service is most suitable?
A) AWS Snowball
B) S3 only
C) EFS
D) AWS DMS
Answer: A) AWS Snowball
Explanation:
S3 alone requires uploading data over the network, which can be slow and impractical for multi-terabyte datasets. EFS is a managed file system for shared access but is not designed for bulk migration. AWS DMS is for database migrations, not large file transfers. AWS Snowball is a physical appliance that allows secure offline transfer of large datasets to AWS. The appliance is shipped to the customer, data is loaded locally, and then it is returned to AWS, where data is uploaded to S3. Snowball provides strong encryption and integration with S3, minimizing network usage and migration time. It is a reliable solution for transferring large volumes of data securely and efficiently.
Question 84
A company wants to implement a serverless event-driven architecture to process orders from S3 uploads and SQS messages. Which AWS services should be used?
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:
Managing a backend architecture using traditional EC2 instances requires significant manual intervention. Each instance must be provisioned, scaled to handle varying traffic, patched for security updates, and monitored to ensure availability. These tasks can be labor-intensive and prone to human error, especially when traffic patterns fluctuate unpredictably. Even when using container orchestration platforms like ECS or EKS with EC2 nodes, operational responsibilities remain substantial. Administrators still need to manage the underlying instances, perform scaling, handle cluster capacity planning, and ensure updates and security patches are applied promptly. This complexity can divert development teams’ focus from building application logic to managing infrastructure, increasing operational overhead and slowing down deployment cycles.
Serverless computing offers a transformative alternative, with AWS Lambda standing out as a highly flexible option for modern cloud-native applications. Lambda allows developers to run code without provisioning or managing servers. Functions can be triggered by a variety of events, including changes in S3 buckets, messages arriving in SQS queues, or API Gateway calls. This event-driven model ensures that the system reacts dynamically to real-time workloads. Lambda automatically handles scaling, running the necessary number of function instances in response to incoming requests, making it capable of supporting high-concurrency workloads without manual intervention. Users are billed solely for execution time, which often results in cost savings compared to provisioning EC2 instances that may remain underutilized.
Integrating Lambda with other AWS services further enhances its functionality. CloudWatch provides built-in monitoring and logging, allowing teams to track performance, identify errors, and set alarms for operational anomalies. This reduces the need for custom monitoring infrastructure and provides deep visibility into application behavior. Combined with AWS SQS or SNS, Lambda can efficiently process messages or events asynchronously, enabling decoupled and resilient architectures. This combination allows workloads such as order processing, file transformations, or background analytics to scale automatically without developer intervention, ensuring high availability and responsiveness.
Using Lambda eliminates many of the operational challenges associated with managing EC2-based infrastructure. There is no need to plan for peak capacity, no patching requirements, and no manual scaling. This fully managed environment allows development teams to focus on writing business logic and improving features rather than worrying about servers, clusters, or scaling strategies. Moreover, it supports modern development practices such as microservices, continuous deployment, and event-driven architectures, making applications more modular, flexible, and maintainable.
Replacing traditional EC2-based compute or even containerized EC2 setups with Lambda functions provides a cost-efficient, scalable, and fully managed backend. It automates scaling in response to demand, integrates with event sources and monitoring services, and eliminates much of the manual operational burden. This architecture enables organizations to handle variable workloads reliably, process events efficiently, and deploy applications faster while focusing on delivering value to users rather than managing infrastructure. Lambda’s serverless approach is especially suited for modern cloud-native applications that require agility, scalability, and reduced operational complexity.
Question 85
A company needs 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:
Managing session state in large-scale, high-traffic applications requires a storage solution that can handle rapid, frequent read and write operations with extremely low latency. While DynamoDB is a highly scalable NoSQL database capable of delivering generally low-latency responses, it may not consistently provide the microsecond-level access needed for session management during peak traffic periods. Its performance, while sufficient for many workloads, can experience variability under heavy, concurrent access, which may lead to delays in retrieving or updating session data.
RDS MySQL offers a relational database environment with managed capabilities, but its architecture introduces inherent latency due to disk I/O and the overhead of maintaining database connections. This makes it less suitable for workloads that require constant, rapid updates, such as storing session state, because each read or write operation can incur milliseconds of delay. Over time, these delays can accumulate, causing performance bottlenecks for applications that demand immediate responsiveness.
S3, while highly durable and scalable, is object storage and is designed for storing larger datasets rather than performing frequent, small read and write operations. Using S3 for session management would result in suboptimal performance, as each operation involves a network request to the storage service and cannot provide the rapid, consistent response times required for user session data.
ElastiCache Redis provides an ideal alternative for session state management in these scenarios. As an in-memory key-value store, Redis is specifically engineered for high throughput and sub-millisecond latency, allowing applications to access and update session data almost instantaneously. Its architecture supports replication and clustering, enabling horizontal scaling and fault tolerance, ensuring that session data is available and consistent across multiple nodes. Redis also offers optional persistence, allowing data to be stored on disk for recovery purposes without compromising performance during normal operations.
By centralizing session storage in Redis, multiple web servers can access the same session information reliably, avoiding inconsistencies that occur when session state is stored locally on individual servers. This ensures that user sessions remain seamless even in distributed or load-balanced environments. Additionally, Redis reduces operational complexity by handling the underlying scaling, replication, and failover processes automatically, allowing developers to focus on application logic rather than infrastructure management.
Overall, ElastiCache Redis provides a robust, high-performance solution for session state management, combining speed, scalability, and reliability. It enables applications to deliver consistent, low-latency experiences to users even under heavy traffic conditions, ensuring smooth interactions and enhanced responsiveness. By leveraging Redis, organizations can meet the demanding requirements of modern web applications while simplifying operational overhead and improving the overall user experience.
Question 86
A company wants to implement a cost-effective archival solution for infrequently accessed data with retrieval times of minutes. Which AWS service is most suitable?
A) S3 Glacier Flexible Retrieval
B) S3 Standard
C) EBS gp3
D) EFS Standard
Answer: A) S3 Glacier Flexible Retrieval
Explanation:
When considering storage solutions for different types of workloads in AWS, it is important to balance performance, cost, and access patterns. S3 Standard is designed for data that is frequently accessed, providing low latency and high throughput. However, for long-term storage or archival purposes, S3 Standard can become cost-prohibitive because its pricing is optimized for active, frequent access rather than infrequently used data. Using it for long-term retention would result in higher operational expenses without significant benefit.
EBS gp3 volumes provide block storage directly attached to EC2 instances, offering high performance for active workloads such as databases or applications requiring consistent IOPS. While it is excellent for transactional workloads, it is not designed for archival or infrequently accessed data. Using EBS for backups or long-term retention would lead to unnecessary costs, as it does not offer the tiered pricing or lifecycle management available in S3.
EFS Standard offers fully managed, persistent file storage that can be shared across multiple instances. This makes it ideal for active workloads that require concurrent access by multiple servers. However, EFS Standard is also priced for active usage and is not cost-effective for infrequently accessed data. Storing long-term backup data in EFS can result in high storage costs, especially when compared to archival-focused services.
For long-term storage and infrequent access, S3 Glacier Flexible Retrieval provides an optimal solution. This storage class is specifically designed to store data that does not need immediate access but must remain highly durable. It offers eleven nines durability and integrates seamlessly with S3 lifecycle policies, allowing automatic migration of objects from S3 Standard or Intelligent-Tiering into Glacier as access patterns change. This helps organizations reduce storage costs while ensuring data remains safe and available when needed. Retrieval times can range from a few minutes to several hours depending on the chosen retrieval tier, making it suitable for backups, compliance data, and disaster recovery scenarios.
Security and compliance are also critical considerations. S3 Glacier Flexible Retrieval supports encryption at rest using SSE-KMS or SSE-S3, ensuring that sensitive data remains protected. Additionally, all access and operations can be logged and monitored using AWS CloudTrail, supporting auditing and regulatory compliance requirements. By combining S3 Standard for active data with S3 Glacier Flexible Retrieval for archival data, organizations can implement a cost-efficient storage strategy that scales with business needs, maintains high durability, and ensures that data is accessible when required without incurring unnecessary expenses.
This approach allows organizations to optimize storage costs, meet compliance requirements, and maintain rapid data access for critical retrievals, making it a comprehensive solution for backup, archival, and disaster recovery workloads.
Question 87
A company wants to decouple microservices while maintaining message order and exactly-once processing guarantees. Which AWS service should be used?
A) SQS FIFO Queue
B) SQS Standard Queue
C) SNS
D) Kinesis Data Streams
Answer: A) SQS FIFO Queue
Explanation:
SQS Standard Queue ensures at-least-once delivery but does not maintain order, which can cause inconsistencies in microservices workflows. SNS is a pub/sub service that does not guarantee ordering or exactly-once delivery. Kinesis Data Streams provides high-throughput streaming with ordered delivery per shard but is more complex and primarily designed for analytics workloads. SQS FIFO Queue guarantees exactly-once processing and preserves the order of messages, which is essential for transactional or sequential workloads. It also supports deduplication, preventing duplicate processing of messages. This ensures predictable and reliable communication between microservices, enabling scalable, fault-tolerant, and consistent architectures.
Question 88\
A company needs to analyze petabyte-scale structured datasets stored in S3 using SQL queries. Which AWS service is most appropriate?
A) Redshift
B) Athena
C) RDS MySQL
D) DynamoDB
Answer: A) Redshift
Explanation:
Athena is serverless and ideal for ad-hoc queries on S3, but it is not optimized for sustained petabyte-scale structured analytics. RDS MySQL is suitable for transactional workloads but lacks the performance and scaling capabilities for massive analytics. DynamoDB is a NoSQL database and does not support SQL queries efficiently for large structured datasets. Redshift is a fully managed data warehouse that provides high-performance SQL-based analytics on structured data at scale. It uses columnar storage, compression, and parallel query execution to optimize performance. Redshift integrates with S3 for direct data ingestion and supports high concurrency for multiple users or applications. For enterprises requiring fast analytics on petabyte-scale datasets with structured schemas, Redshift provides the best combination of performance, scalability, and cost efficiency.
Question 89
A company wants to automatically stop EC2 instances in non-production environments outside business hours to reduce costs. Which service is most suitable?
A) Systems Manager Automation with a cron schedule
B) Auto Scaling scheduled actions only
C) Manual stopping of instances
D) Spot Instances only
Answer: A) Systems Manager Automation with a cron schedule
Explanation:
Auto Scaling scheduled actions are primarily used to scale production workloads up or down based on expected traffic, not for cost optimization in non-production environments. Manual stopping of instances is error-prone and requires human intervention. Spot Instances reduce costs for interruptible workloads but do not automate start/stop schedules. Systems Manager Automation enables creation of runbooks that automatically start or stop EC2 instances based on a schedule, triggered by cron expressions or EventBridge. This ensures consistent application of start/stop schedules, reduces operational overhead, lowers AWS costs, and provides logging and auditing for compliance. It is ideal for managing multiple non-production environments efficiently.
Question 90
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 web content efficiently on a global scale requires more than just storing data or running servers in a single region. While Amazon S3 is excellent for hosting static content such as images, videos, or HTML files, it does not inherently optimize content delivery for users distributed worldwide. Serving files directly from S3 can result in higher latency for users far from the bucket’s region, as each request must traverse the network from the origin to the client. This setup also does not address the delivery of dynamic content generated by backend services, leaving performance inconsistent for interactive applications.
Using Amazon EC2 instances behind an Application Load Balancer can provide scalable compute and distribute incoming traffic across multiple servers. This approach is effective for delivering dynamic content and improving availability within a specific region. However, while ALB can handle regional failover and balance requests between instances, it does not reduce the physical distance between users and the application. For a global user base, requests may still travel long distances to the origin servers, increasing response times and overall latency. Managing EC2 instances also requires operational effort, including scaling, patching, and monitoring, adding complexity and cost to the infrastructure.
Amazon Route 53 provides intelligent DNS routing, helping direct users to the nearest regional endpoint to improve availability and performance. While Route 53 can optimize request routing based on geographic proximity or latency, it does not cache content or accelerate delivery directly. Each request still needs to reach the origin infrastructure, which can lead to repeated data transfers from S3 or EC2 to end users and higher bandwidth costs.
The ideal solution to improve performance globally is Amazon CloudFront, a content delivery network that caches content at edge locations around the world. CloudFront stores copies of frequently accessed static files closer to end users, reducing latency and providing faster load times. It also allows dynamic content requests to be proxied to regional Application Load Balancers, ensuring that interactive applications remain responsive even under heavy global traffic. CloudFront provides additional benefits, including SSL/TLS encryption for secure communication, integration with AWS WAF to protect against common web exploits, and the ability to reduce load on origin servers by offloading repeated requests to the edge network.
By combining CloudFront with S3 for static assets and ALB for dynamic content, organizations can deliver a highly available, low-latency, and secure application to users anywhere in the world. Static content is quickly retrieved from edge locations, dynamic requests are efficiently routed and processed by regional backends, and security is enhanced through encryption and firewall policies. This architecture minimizes operational overhead, reduces data transfer costs from origin servers, and ensures a seamless user experience regardless of geographic location.
Overall, integrating CloudFront with S3 and ALB provides a comprehensive solution for global content delivery. It optimizes latency, scales efficiently to meet varying traffic demands, strengthens security, and delivers both static and dynamic content in a high-performance, reliable, and cost-effective manner for worldwide users.