Amazon AWS Certified Solutions Architect — Professional SAP-C02 Exam Dumps and Practice Test Questions Set 2 Q16-30
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Question 16
A company wants to distribute its web application globally with low latency and high availability. Which AWS service combination should a solutions architect use?
A) CloudFront with S3 origin
B) CloudFront with EC2 origin
C) Route 53 with latency-based routing
D) CloudFront with S3 and Route 53 latency-based routing
Answer: D) CloudFront with S3 and Route 53 latency-based routing
Explanation:
CloudFront with S3 or EC2 alone improves latency but does not provide intelligent global routing. Route 53 with latency-based routing helps direct users to the closest region but does not include caching for static content. Combining CloudFront with S3 for static content delivery and Route 53 latency-based routing ensures that dynamic requests are routed to the optimal region while static content is cached globally, reducing latency and improving user experience. This architecture provides both high availability and low latency for a global audience.
Question 17
A company requires secure, scalable, and temporary storage for large data processing tasks that can be shared across multiple EC2 instances. Which service should be recommended?
A) S3
B) EBS
C) EFS
D) FSx for Lustre
Answer: D) FSx for Lustre
Explanation:
When evaluating storage solutions for large-scale data processing tasks, it is critical to match the storage type with the performance, sharing capabilities, and workload requirements. Different AWS storage services are designed for specific use cases, and selecting the right one ensures both operational efficiency and cost-effectiveness.
Amazon S3 is an object storage service that excels at storing large amounts of data with high durability and availability. It is ideal for long-term storage, backup, and archival purposes, where access patterns are infrequent or predictable. However, S3 is not designed for scenarios that demand high-performance shared storage or low-latency access for compute-intensive workloads. While it integrates well with analytics frameworks and provides massive scalability, the latency and object-based access model make it unsuitable for temporary storage required by high-performance applications.
Amazon EBS, in contrast, offers block-level storage that is attached directly to individual EC2 instances. It provides high performance, low-latency access suitable for database workloads or transactional applications running on a single instance. Although EBS volumes are reliable and performant for single-instance workloads, they cannot be easily shared across multiple EC2 instances. This limitation makes EBS unsuitable for distributed compute tasks where multiple instances need concurrent access to the same dataset, such as large-scale machine learning model training or analytics pipelines.
Amazon EFS provides a managed, scalable, shared file system that can be accessed concurrently by multiple EC2 instances. EFS is ideal for general-purpose workloads requiring shared storage, such as content management systems, development environments, and web applications. However, while EFS provides flexibility and convenience, its performance tiers may not meet the requirements of compute-intensive workloads that require very high throughput and low latency. For large-scale data processing tasks, the general-purpose performance of EFS may become a bottleneck, especially when dealing with massive datasets or temporary storage requirements that change rapidly.
Amazon FSx for Lustre, on the other hand, is purpose-built for high-performance, shared temporary storage. Lustre is a parallel file system widely used in high-performance computing environments, offering extremely low-latency access and high throughput for large-scale workloads. FSx for Lustre integrates seamlessly with S3, allowing users to link data stored in S3 to the Lustre file system. This integration enables fast, temporary access to large datasets without the need to move data manually, making it ideal for analytics, machine learning, and other compute-intensive tasks. FSx for Lustre is designed to support shared access across multiple EC2 instances, enabling parallel processing and significantly reducing compute time for large datasets.
For workloads that require fast, temporary storage shared across multiple compute instances, FSx for Lustre provides an optimal solution. Its low-latency access, scalability, and tight integration with S3 make it far more suitable than S3, EBS, or EFS for high-performance data processing. By leveraging FSx for Lustre, organizations can efficiently process large datasets, accelerate analytics, and optimize compute resources without being limited by storage performance or access constraints, ensuring both speed and scalability for demanding tasks.
Question 18
A solutions architect must design a cost-optimized storage solution for a backup workload where data is rarely accessed but must be retained for several years. Which service should be used?
A) S3 Standard
B) S3 Intelligent-Tiering
C) S3 Glacier Flexible Retrieval
D) EBS gp3
Answer: C) S3 Glacier Flexible Retrieval
Explanation:
When designing a storage strategy for long-term data retention, it is important to evaluate the specific characteristics, costs, and use cases of different AWS storage options. Not all storage types are appropriate for archival purposes, and selecting the right service can significantly impact both cost efficiency and operational management.
Amazon S3 Standard is optimized for frequently accessed data, delivering low latency and high throughput performance. While it is excellent for active workloads such as content hosting, transactional data, and frequently used datasets, it is not cost-effective for long-term archival storage. The storage costs for S3 Standard are relatively high compared to archival-oriented solutions, and using it to retain data that is rarely accessed can lead to unnecessary expenses over time. Additionally, its pricing model is designed around frequent read and write operations, which do not align with the nature of long-term backups.
S3 Intelligent-Tiering is another option that automatically moves data between access tiers based on observed access patterns. This service is suitable for datasets with unpredictable or changing access frequencies, as it attempts to optimize costs without requiring manual intervention. While Intelligent-Tiering can reduce storage costs compared to S3 Standard for data with variable usage, it may not provide the absolute lowest cost for data that is rarely accessed. Its automatic tiering mechanism is valuable for datasets that fluctuate between active and dormant states, but for strictly archival purposes, where access is infrequent and predictable, a more specialized service is often preferable.
Amazon EBS gp3 volumes are high-performance block storage solutions attached to EC2 instances. They provide predictable IOPS and low-latency storage for databases and transactional workloads. While gp3 volumes excel at high-performance operations, they are not designed for long-term data retention. Using EBS for archival purposes would be inefficient, as the cost structure is higher and the volumes are inherently tied to EC2 instances, making long-term management complex. Furthermore, EBS does not provide the native durability guarantees or lifecycle management features necessary for backup retention and compliance requirements.
For long-term archival needs, Amazon S3 Glacier Flexible Retrieval is purpose-built. It provides highly durable storage at minimal cost, ideal for workloads where data is seldom accessed but must be retained for compliance, regulatory, or business continuity purposes. Glacier Flexible Retrieval offers multiple retrieval options, enabling users to access archived data within minutes to hours depending on urgency. This makes it suitable for organizations that require cost-effective archival storage without compromising on durability or reliability. Additionally, Glacier supports lifecycle policies, allowing organizations to automatically transition data from more expensive storage classes to Glacier for cost optimization, further reducing management overhead.
By leveraging S3 Glacier Flexible Retrieval, businesses can maintain compliance and retain historical data efficiently while minimizing storage costs. Its combination of durability, cost-effectiveness, and flexible retrieval options makes it the optimal choice for long-term archival, ensuring that inactive data remains secure and accessible when needed without incurring the high costs associated with more frequently accessed storage classes.
Question 19
A company needs to decouple microservices while ensuring reliable message delivery in the correct order. Which AWS service should be used?
A) SNS
B) SQS Standard Queue
C) SQS FIFO Queue
D) Kinesis Data Firehose
Answer: C) SQS FIFO Queue
Explanation:
SNS is a pub/sub messaging service that cannot guarantee message order. SQS Standard Queue provides reliable message delivery but does not guarantee order, which may cause issues in workflows requiring sequential processing. Kinesis Data Firehose is for streaming data and real-time analytics, not a queue for microservice decoupling. SQS FIFO Queue guarantees exactly-once processing and preserves message order, making it the ideal solution for microservices that require ordered, reliable messaging.
Question 20
A company wants to monitor CPU, memory, and disk utilization across EC2 instances and trigger automated remediation for underperforming instances. Which service combination is most appropriate?
A) CloudWatch Metrics and Auto Scaling
B) CloudWatch Metrics, CloudWatch Alarms, and Systems Manager Automation
C) CloudTrail and CloudWatch Logs
D) Config Rules and SNS
Answer: B) CloudWatch Metrics, CloudWatch Alarms, and Systems Manager Automation
Explanation:
Effective management of cloud infrastructure requires more than just reactive monitoring; it demands a proactive approach to tracking performance, detecting issues, and automating remediation. AWS offers a variety of services to support these goals, but understanding their specific capabilities and limitations is critical to designing a comprehensive, self-healing system.
CloudWatch Metrics is a fundamental tool for observing resource performance. It collects key data points such as CPU utilization, network throughput, and disk activity, which can be used to monitor the health and performance of EC2 instances, RDS databases, and other resources. Paired with Auto Scaling, CloudWatch Metrics enables systems to automatically adjust the number of instances based on predefined thresholds for metrics like CPU utilization. While this approach is highly effective for certain resource types, it has limitations. Notably, memory usage, disk I/O, and application-specific metrics are not collected by default, requiring either custom metrics or additional monitoring solutions. Without these additional metrics, scaling decisions might not fully reflect the actual performance requirements of workloads, leaving potential bottlenecks unaddressed.
CloudTrail complements CloudWatch by capturing API activity across the AWS environment, providing a detailed audit trail for user actions and system events. While invaluable for security auditing, governance, and compliance reporting, CloudTrail does not monitor resource performance or take automated corrective actions. Similarly, CloudWatch Logs records application and system logs, enabling insights into operational behavior and error conditions, but it does not inherently trigger remediation or resource scaling. These services are crucial for visibility and investigation but must be integrated with additional mechanisms to maintain optimal system performance.
AWS Config Rules with SNS can monitor configuration compliance by evaluating resources against best practices or organizational policies. Alerts can be sent via SNS when a resource deviates from its desired configuration. While Config Rules are excellent for enforcing compliance and detecting misconfigurations, they do not automatically correct performance degradation or resource inefficiencies. They are reactive by design and cannot replace the need for dynamic, performance-based automation.
To build a fully automated performance management framework, CloudWatch Metrics, CloudWatch Alarms, and AWS Systems Manager Automation can be combined. CloudWatch Metrics provide continuous visibility into system health, while CloudWatch Alarms can be configured to trigger when specific thresholds are crossed, such as high memory usage or low disk availability. When an alarm is triggered, Systems Manager Automation can execute predefined remediation actions, such as restarting a service, resizing an instance, or performing cleanup operations. This integration ensures that performance issues are not only detected but also addressed automatically, reducing the need for manual intervention and minimizing potential downtime or degradation of services.
By leveraging this combination of monitoring, alerting, and automated remediation, organizations gain a robust system for maintaining application performance. Instances remain responsive, bottlenecks are alleviated quickly, and operational efficiency improves, all while providing a centralized view of metrics, logs, and automated actions. This approach balances proactive performance management with operational simplicity, enabling teams to focus on strategic initiatives rather than routine troubleshooting.
Question 21
A company is designing a disaster recovery plan for a critical application. They need a solution with the lowest Recovery Time Objective (RTO) and Recovery Point Objective (RPO). Which AWS architecture should be recommended?
A) Backup and restore from S3
B) Pilot Light using a secondary region
C) Warm standby in another region
D) Multi-region active-active deployment
Answer: D) Multi-region active-active deployment
Explanation:
Backup and restore from S3 provides a high RTO because data must be restored, and systems started manually. Pilot Light in a secondary region keeps minimal resources running, which lowers RPO but still requires time to scale up the infrastructure, so RTO is moderate. Warm standby in another region maintains scaled-down resources that can be activated quickly, improving RTO slightly. Multi-region active-active deployment runs fully functional applications in multiple regions simultaneously. This ensures the lowest RTO and RPO because traffic can immediately be routed to a healthy region during outages without downtime. It provides full resilience and high availability, making it the ideal solution for critical applications requiring minimal disruption.
Question 22
A company wants to provide its internal users access to AWS resources without creating IAM users for each employee. Which service provides this functionality securely?
A) IAM Users with console access
B) Cognito user pools
C) AWS Single Sign-On (SSO)
D) IAM Roles only
Answer: C) AWS Single Sign-On (SSO)
Explanation:
Managing user access in AWS requires a solution that balances security, scalability, and ease of administration. Creating individual IAM users for each employee may initially seem straightforward, but as an organization grows, this approach quickly becomes unmanageable. Each new employee requires the creation of a separate IAM account, assignment of permissions, and ongoing monitoring for changes in job roles or responsibilities. Over time, this leads to an administrative overhead that is both time-consuming and error-prone. Additionally, managing hundreds or thousands of IAM users increases the risk of inconsistencies, such as excessive privileges or outdated accounts, which can compromise security.
AWS Cognito user pools are primarily designed for managing authentication for external application users, such as customers of a web or mobile application. While Cognito provides robust authentication mechanisms, including multi-factor authentication and password management, it is not intended for managing internal enterprise employees who already have established corporate identities. Using Cognito for internal workforce authentication would require duplicating existing identity information, creating unnecessary complexity, and deviating from corporate identity governance standards.
IAM roles alone are also insufficient as a complete identity management solution. Roles are not standalone identities but are instead assumed by users, services, or applications to gain temporary permissions. While IAM roles are essential for enabling least-privilege access and cross-account permissions, they do not provide centralized user management or integration with corporate directories. Relying solely on IAM roles would necessitate creating separate IAM users or configuring complex trust relationships, which further increases administrative overhead and does not simplify the process of granting access to multiple AWS accounts.
AWS Single Sign-On (SSO) provides a more efficient and secure approach for managing access for internal employees. AWS SSO integrates directly with existing corporate identity providers, such as Microsoft Active Directory or other SAML-based identity services, allowing employees to log in using their existing organizational credentials. This eliminates the need to create separate IAM users for each employee, centralizes identity management, and maintains alignment with corporate security policies. By using SSO, organizations can enforce consistent authentication standards, including multi-factor authentication and password policies, across all AWS accounts and resources.
AWS SSO also simplifies the process of granting and revoking access. When an employee changes roles or leaves the organization, administrators can update or remove access centrally through the identity provider, and changes are automatically reflected in all connected AWS accounts. This reduces the likelihood of orphaned accounts or lingering permissions, which are common security risks in manual IAM management. Additionally, SSO provides role-based access controls, allowing employees to assume specific permissions based on their job functions without needing individual IAM configurations.
From a scalability perspective, AWS SSO enables organizations to manage access for hundreds or thousands of employees efficiently. Integration with corporate directories ensures that identity management remains consistent across the enterprise, reducing administrative workload and enhancing security posture. It also provides reporting and auditing capabilities to monitor access and compliance, ensuring that access policies meet organizational and regulatory requirements.
while IAM users, Cognito, and standalone roles each serve specific purposes, AWS SSO is the optimal solution for managing internal employee access. It centralizes authentication, integrates with corporate identity systems, reduces operational complexity, and ensures secure, scalable access to AWS resources. By leveraging SSO, organizations can maintain tight security controls while minimizing administrative burden and supporting enterprise growth.
Question 23
A company wants to encrypt data in transit and at rest for a distributed application deployed on EC2 instances. Which combination meets these requirements?
A) S3 SSE-S3 for at-rest, HTTP for in-transit
B) EBS encrypted volumes, HTTPS, and KMS for key management
C) Unencrypted EBS volumes, SSH for in-transit
D) S3 SSE-C and FTP
Answer: B) EBS encrypted volumes, HTTPS, and KMS for key management
Explanation:
Using SSE-S3 for at-rest encryption works for S3 but does not cover EC2 storage volumes. HTTP does not encrypt data in transit. Unencrypted EBS volumes with SSH only secure administrative access, not application traffic. S3 SSE-C requires manual key management and FTP is insecure for data transfer. Encrypting EBS volumes ensures data at rest is protected. HTTPS encrypts all traffic between clients and servers, providing in-transit encryption. Using KMS for key management centralizes and secures encryption keys with options for rotation and auditing, ensuring comprehensive protection for both at-rest and in-transit data.
Question 24
A company wants to run an application that requires high IOPS and low latency for database workloads. Which storage solution should be recommended?
A) EBS gp3 volumes
B) S3 Standard
C) EFS Standard
D) Glacier
Answer: A) EBS gp3 volumes
Explanation:
When selecting storage for database workloads in AWS, it is essential to consider performance characteristics such as input/output operations per second (IOPS), latency, and throughput, as these factors directly impact the responsiveness and reliability of database applications. AWS offers a variety of storage solutions, each tailored to different use cases, but not all are suitable for high-performance database environments.
Amazon S3 Standard is one of the most widely used storage options in AWS, offering durable object storage with high availability and scalability. It is well-suited for storing unstructured data, backups, media files, and static website content. However, S3 is an object storage service with relatively high access latency compared to block storage solutions. While it excels in durability and cost-effectiveness for large-scale storage needs, it is not designed for workloads requiring low-latency, high-frequency read and write operations, such as active transactional databases. Its throughput is excellent for sequential access and large objects but not optimized for random I/O patterns typical of database operations.
Amazon Elastic File System (EFS) Standard provides shared file storage accessible from multiple EC2 instances concurrently. It is a fully managed, scalable file system, supporting general-purpose workloads and enabling a file-based interface across multiple clients. While EFS offers better performance than S3 for active file access, its IOPS are more suitable for moderate workloads rather than intensive database operations. High I/O applications may experience inconsistent performance under peak loads, and latency can be higher compared to dedicated block storage, which is critical for database consistency and responsiveness.
Amazon Glacier, or Amazon S3 Glacier, is designed for archival purposes and long-term storage of infrequently accessed data. It offers extremely low cost and high durability, making it ideal for backups and compliance storage. However, Glacier is optimized for retrievals over hours or minutes and cannot support the millisecond-level latency required by active database workloads. Attempting to run a database on Glacier storage would result in severe performance issues due to its high access latency and limited IOPS.
Amazon Elastic Block Store (EBS), particularly the gp3 volume type, provides high-performance block storage designed specifically for workloads requiring consistent, low-latency storage. EBS gp3 offers predictable IOPS and throughput that can be scaled independently of the allocated storage capacity, giving database administrators fine-grained control over performance tuning. This makes gp3 volumes ideal for transactional and relational databases, as they require fast, random I/O access with minimal latency. Additionally, EBS integrates seamlessly with Amazon EC2 instances, providing persistent storage that supports features like snapshots, encryption, and high availability within an availability zone.
Unlike object or file storage solutions, EBS gp3 delivers consistent performance that aligns with the demands of high-throughput and high-transaction applications. It ensures that read and write operations are executed rapidly, providing predictable latency and IOPS for database workloads. Organizations migrating active databases to the cloud benefit from the combination of reliability, speed, and scalability that EBS gp3 offers, enabling critical applications to maintain high performance under variable loads without compromising data integrity.
while S3, EFS, and Glacier each have their strengths for archival, shared, or unstructured storage, EBS gp3 stands out as the optimal solution for database workloads requiring low latency, high IOPS, and consistent throughput. Its design ensures that databases can perform efficiently and reliably, supporting demanding enterprise applications and transactional systems with minimal performance bottlenecks.
Question 25
A company wants to reduce latency for a global user base accessing its static website. Which AWS service combination provides the most effective solution?
A) CloudFront with S3
B) S3 only
C) EC2 with EBS only
D) Route 53 only
Answer: A) CloudFront with S3
Explanation:
S3 alone stores the website, but users far from the S3 region experience higher latency. EC2 with EBS provides compute and storage but does not provide global caching for static content. Route 53 handles DNS routing but does not cache content. CloudFront is a global Content Delivery Network (CDN) that caches static content in edge locations near users. Using CloudFront with S3 as the origin stores the website and distributes it globally, significantly reducing latency for a global audience while providing high availability and scalability.
Question 26
A solutions architect is designing a system that must handle a large volume of concurrent API requests while providing authentication and throttling. Which service combination is best?
A) API Gateway with Lambda and AWS WAF
B) API Gateway with S3 only
C) Lambda only
D) EC2 instances behind an ALB
Answer: A) API Gateway with Lambda and AWS WAF
Explanation:
Designing a highly scalable, secure, and manageable API infrastructure in AWS requires a combination of services that work together to address the different aspects of API delivery. While it may seem straightforward to rely on individual services such as S3, Lambda, or EC2, none of these services alone can provide the comprehensive functionality required for modern API deployments.
Amazon S3 is a robust storage service, well-suited for hosting static assets like HTML, CSS, JavaScript, images, and other files. When paired with CloudFront, it can deliver content globally with low latency. However, S3 is inherently designed for static content and does not provide the mechanisms necessary to manage dynamic APIs. It cannot handle request authentication, rate limiting, or input validation for API calls, making it unsuitable for scenarios where backend logic and security are required.
AWS Lambda, on the other hand, provides serverless compute capabilities, allowing developers to run backend code without managing servers. Lambda is ideal for executing business logic in response to events, including API calls. However, when used alone, Lambda does not offer API management features. Developers would need to implement custom solutions to handle request throttling, authentication, request validation, and other operational concerns, which can quickly become complex and error-prone at scale.
Deploying APIs on EC2 instances behind an Application Load Balancer (ALB) provides another option. This setup allows for dynamic scaling of compute resources and supports web application deployments with HTTP/S traffic. While this configuration is flexible and can support custom authentication and security measures, it requires significant manual management. Developers and operations teams must handle scaling policies, server maintenance, patching, and security updates. Additionally, implementing API-level throttling and protection against common web threats requires additional services or complex custom code, adding operational overhead.
The most effective solution for building scalable and secure APIs is a combination of API Gateway, Lambda, and AWS WAF. API Gateway acts as the entry point for all API requests, providing a managed interface that supports request throttling, authentication, and fine-grained access control. It can enforce usage plans, manage API keys, and integrate directly with identity providers such as Cognito or third-party OAuth providers, ensuring that only authorized users can access the APIs. API Gateway also supports caching and request transformation, enhancing performance and flexibility.
AWS Lambda serves as the backend compute layer in this architecture, executing the core business logic of the API. It scales automatically in response to demand, handling high volumes of concurrent requests without requiring manual provisioning of servers. The serverless nature of Lambda reduces operational overhead while providing a cost-efficient solution that only charges for actual execution time.
AWS WAF adds an additional layer of security by protecting APIs from common web-based attacks such as SQL injection, cross-site scripting, and other vulnerabilities. When combined with API Gateway and Lambda, it ensures that APIs remain secure while supporting large-scale, high-traffic workloads.
leveraging API Gateway, Lambda, and AWS WAF together provides a fully managed, scalable, and secure API solution. This architecture handles authentication, request throttling, backend execution, and protection against attacks efficiently, eliminating the operational complexity of managing individual services while enabling high-performance API delivery across global workloads.
Question 27
A company wants to migrate on-premises Oracle databases to AWS with minimal downtime. Which service combination is ideal?
A) RDS Oracle with DMS
B) EC2 Oracle with manual SQL dump
C) DynamoDB only
D) Aurora MySQL
Answer: A) RDS Oracle with DMS
Explanation:
Migrating a database from an on-premises Oracle environment to the cloud can be a complex and challenging process, particularly when minimizing downtime is critical for business operations. One approach, such as using Amazon EC2 instances to host Oracle and performing a manual SQL dump, can be effective in moving data but typically involves extended periods of downtime. This method requires exporting the entire database, transferring it to the target environment, and then importing it, which can take hours or even days depending on the database size. During this migration window, the source database is usually unavailable for updates, which can disrupt business processes and create operational challenges.
Another potential option is migrating to a NoSQL database like DynamoDB. While DynamoDB offers benefits such as automatic scaling, high availability, and low-latency access, it is not designed to be compatible with Oracle workloads. Applications built to interact with relational databases would require significant redesign and rewriting to work with a NoSQL schema. This not only increases the complexity of migration but also introduces potential risks in terms of data integrity, application logic, and long-term maintainability.
Aurora MySQL represents a relational database service that is managed by AWS and provides high performance, automated backups, and scalability. However, while it is relational, it is not fully compatible with Oracle databases. Migrating an Oracle workload to Aurora MySQL would require considerable modifications to database schema, stored procedures, triggers, and application code to accommodate differences in SQL syntax and feature support. For organizations looking to minimize disruption and avoid extensive application rewrites, this can be a major obstacle and increase both cost and time.
In contrast, Amazon RDS for Oracle provides a managed database environment that is fully compatible with existing Oracle workloads. By using RDS Oracle, organizations can benefit from automatic backups, software patching, high availability through Multi-AZ deployments, and monitoring, all without managing the underlying infrastructure. This simplifies operational management and ensures that the environment meets enterprise reliability and compliance requirements.
When paired with AWS Database Migration Service (DMS), RDS Oracle allows for a near-zero downtime migration strategy. DMS enables continuous replication from the source database to the target RDS Oracle instance while the source remains fully operational. This approach ensures that the production system remains available to users, while data is incrementally synchronized to the new environment. Once the data replication is complete and verified, a brief cutover can be performed, reducing downtime to a minimal window. This combination of RDS Oracle and DMS allows organizations to migrate with confidence, maintaining data consistency and application functionality throughout the process.
Overall, leveraging Amazon RDS for Oracle along with AWS DMS streamlines the migration process, reduces operational complexity, and minimizes business disruption. It allows enterprises to maintain compatibility with existing Oracle workloads, avoid extensive application rewrites, and benefit from the reliability and manageability of a fully managed database service. By eliminating long downtime periods and enabling continuous replication, this approach ensures that critical business operations can continue uninterrupted while achieving a smooth and efficient cloud migration.
Question 28
A company needs to process streaming clickstream data in real-time and store it for analytics. Which AWS service combination is most appropriate?
A) Kinesis Data Streams and S3
B) SQS and S3
C) SNS and RDS
D) S3 only
Answer: A) Kinesis Data Streams and S3
Explanation:
Amazon Web Services provides a variety of messaging, storage, and streaming tools, but each service is designed for specific use cases. When an organization needs to ingest, process, and analyze high-volume clickstream data in real time, it is important to choose services built to handle continuous, high-throughput, low-latency data streams. Clickstream data often arrives at extremely high frequencies and must be processed with minimal delays to provide immediate insights into user behavior, application performance, and engagement trends. As a result, selecting the right combination of services directly influences system performance, scalability, and operational efficiency.
Amazon SQS and Amazon SNS are valuable messaging services but are not optimized for real-time streaming analytics. SQS is primarily designed for decoupling distributed systems and managing message queues, ensuring reliable message delivery and handling asynchronous tasks. However, it does not natively support real-time event partitioning, ordering guarantees required for analytics, or the throughput needed for large-scale clickstream ingestion. SNS, on the other hand, provides pub/sub messaging for broadcasting notifications to multiple subscribers, but it is not built for continuously processing large volumes of streaming data. Neither service can handle the parallel, shard-based processing model needed for real-time analytical workloads.
Amazon S3 provides highly durable and cost-effective object storage, making it ideal for long-term retention of clickstream logs, batch analytics, machine learning training data, or compliance-driven archiving. However, S3 is not designed to ingest or process data streams in real time. It does not provide event-by-event processing, partitioning, or sub-second ingestion required for streaming pipelines. While S3 can store the data after it has been processed, it cannot serve as a primary ingestion mechanism for real-time operational analytics.
Amazon Kinesis Data Streams fills this gap by offering a fully managed, scalable streaming service capable of collecting and processing massive amounts of data in real time. It supports high-throughput ingestion and enables parallel consumption through shard-based stream partitioning. Applications can process events within milliseconds, enabling immediate insights, such as detecting user behavior patterns, identifying anomalies, adjusting recommendation engines, and monitoring application performance dashboards. Kinesis integrates seamlessly with AWS analytics tools such as Kinesis Data Analytics, AWS Lambda, and Amazon EMR, allowing data to be transformed, enriched, or analyzed as it flows through the system.
Pairing Kinesis Data Streams with Amazon S3 creates a powerful architecture for both real-time and long-term analytics. Kinesis handles live ingestion and processing, making the data instantly available for dashboards, alerting, and fast decision-making. After processing, the data can be delivered to S3 for durable storage and future analysis. Analysts can then use services such as Amazon Athena, Amazon Redshift, Amazon Glue, or Amazon EMR to perform batch analytics, historical analysis, or machine learning model development using the archived clickstream data stored in S3.
This combination allows organizations to meet both operational and analytical requirements. Real-time insights improve the user experience and enable immediate responses to customer behavior, while S3 supports scalable, long-term data retention and deeper analytical capabilities. Because both services are managed by AWS, they scale automatically, reduce operational overhead, and ensure the reliability needed for mission-critical analytics workloads. Using Kinesis for ingestion and real-time processing along with S3 for durable storage is the most effective solution for a company seeking to perform real-time clickstream processing while maintaining historical visibility and analysis capabilities.
Question 29
A company wants to decouple microservices using a message queue that ensures messages are delivered at least once and can be processed asynchronously. Which service should be used?
A) SQS Standard Queue
B) SQS FIFO Queue
C) SNS
D) Kinesis Data Streams
Answer: A) SQS Standard Queue
Explanation:
SQS Standard Queue is often the most effective and flexible choice for asynchronous message processing in distributed or microservices-based architectures because it is designed to deliver high throughput, offer scalable message handling, and ensure at-least-once delivery without the added latency associated with ordered queues. In many real-world systems, strict ordering is not required for every workload, and performance is often a higher priority than maintaining exact sequence. Standard queues excel in this regard by supporting nearly unlimited transactions per second and enabling messages to be processed concurrently by multiple consumers, allowing an architecture to scale seamlessly as demand fluctuates.
In contrast, SQS FIFO queues are specialized for use cases where the strict order of operations must be maintained and where exactly-once processing is essential. While these features are valuable for financial transactions, inventory management systems, or other workflows where message order is critical, FIFO queues introduce additional overhead. They limit throughput compared to Standard queues and typically have slightly higher latency due to the mechanisms required to maintain correct sequencing. For applications that do not require this level of ordering, FIFO queues provide unnecessary complexity and reduced performance.
SNS, while often paired with SQS in event-driven designs, is not a queuing service and therefore does not deliver the durability, buffering, or worker coordination needed for asynchronous task handling. SNS is a pub/sub service designed to fan out notifications to multiple subscribers, such as email endpoints, Lambda functions, or HTTP endpoints. It is excellent for broadcasting real-time alerts or distributing events to many destinations simultaneously. However, it does not store messages for later processing, cannot manage consumer backpressure, and provides no guarantee that each subscriber will process messages asynchronously. Therefore, SNS alone does not replace the need for a queue in applications requiring reliable decoupling of services.
Kinesis is purpose-built for real-time, high-throughput streaming data such as telemetry, logs, analytics, and clickstream events. It is often used in analytics pipelines where data must be captured continuously and processed in near real time. However, Kinesis is not intended to serve as a traditional asynchronous message queue and does not provide the simplicity or straightforward consumer model required for decoupling microservices. It is optimized for ordered data ingestion and stream processing rather than task distribution or background job execution.
SQS Standard Queue offers a robust solution for connecting distributed components, enabling asynchronous communication across microservices without creating tight coupling or introducing system fragility. Its ability to absorb fluctuations in workload ensures that services remain responsive even during traffic spikes, preventing cascading failures and improving overall system reliability. Since producers and consumers operate independently, the architecture becomes more resilient, easier to maintain, and more adaptable to future scaling needs.
By choosing SQS Standard Queue, organizations gain a highly scalable, fault-tolerant, and cost-effective messaging layer that supports diverse asynchronous workloads without imposing ordering constraints or throughput limitations. This makes it the best option for most microservices environments where reliability, speed, and scalability are the top priorities.
Question 30
A company wants to store archival data that is rarely accessed and cost-sensitive but must be retrievable within minutes. Which AWS storage solution should be used?
A) S3 Standard
B) S3 Glacier Instant Retrieval
C) S3 Intelligent-Tiering
D) EBS gp3
Answer: B) S3 Glacier Instant Retrieval
Explanation:
Amazon S3 provides multiple storage classes to accommodate different data access patterns, performance needs, and cost requirements. Selecting the appropriate storage class is critical for achieving both operational efficiency and cost optimization. When dealing with archival data that must remain durable, highly available, and quickly retrievable, it is important to evaluate the characteristics of each available storage option to ensure the best fit for the workload.
S3 Standard is designed for frequently accessed data and offers the highest level of performance and availability within Amazon S3. While it provides low latency and high throughput, it also comes at a higher cost compared to other storage tiers. Using S3 Standard for archival datasets is generally inefficient because these datasets typically do not require the frequent access patterns that justify the higher storage price. As a result, S3 Standard is better suited for active content, dynamic websites, or processing-intensive workloads where rapid and consistent access is essential.
S3 Intelligent-Tiering is intended for datasets with unpredictable or variable access patterns. It uses automatic monitoring and tiering to adjust data between several access tiers based on usage. Although it helps optimize costs for data where future access frequency is uncertain, it introduces monitoring fees and is not optimized for data that is known to be accessed infrequently. For data that is clearly archival in nature and rarely used, Intelligent-Tiering provides minimal advantage because its automation features are unnecessary. In such cases, choosing a purpose-built archival storage class provides better long-term cost savings.
Amazon EBS gp3 volumes are another option often considered by users, especially when they are familiar with EC2-based workloads. However, EBS gp3 is block storage designed for high-performance applications that require consistent IOPS and low-latency access to data. It is typically used for operating systems, databases, or transactional workloads. EBS is not designed for long-term or large-scale archival storage. It is also significantly more expensive than Amazon S3’s archival tiers and lacks the same level of cost efficiency, scalability, and durability for long-term data retention. Additionally, EBS volumes must be attached to EC2 instances to be usable, making them less operationally flexible than object storage.
For archival data that must be stored cost-effectively while remaining quickly accessible, S3 Glacier Instant Retrieval is the ideal solution. This storage class is specifically engineered for long-term data retention scenarios where access is infrequent but still time-sensitive. Glacier Instant Retrieval offers extremely low storage costs, making it suitable for large volumes of archival data, while also providing retrieval times ranging from milliseconds to minutes. This balances the low-cost advantages of deep archival storage with near-immediate access capabilities. It is especially beneficial for workloads such as medical records, compliance archives, research datasets, media content libraries, and long-term document repositories.
With high durability, low retrieval latency, and reduced storage cost, S3 Glacier Instant Retrieval allows organizations to maintain ready access to their archival data without incurring the high expenses associated with frequently accessed storage classes. It offers a dependable and financially efficient option for preserving data long term, ensuring that critical information remains secure, resilient, and available whenever it is needed.