Amazon AWS Certified Solutions Architect — Professional SAP-C02 Exam Dumps and Practice Test Questions Set 3 Q31-45
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Question 31
A solutions architect wants to monitor API activity for compliance purposes and detect anomalies in AWS accounts. Which service combination is ideal?
A) CloudTrail and GuardDuty
B) CloudWatch and Lambda
C) Config and SNS
D) S3 and Athena
Answer: A) CloudTrail and GuardDuty
Explanation:
Monitoring and maintaining security within AWS environments requires more than just tracking resource performance or configuration changes. While CloudWatch and Lambda provide critical capabilities for monitoring metrics and automating responses to resource events, they are not designed to capture API activity for auditing or compliance purposes. They excel at alerting and remediation, but they do not offer visibility into who performed what action across the AWS account.
Similarly, AWS Config combined with Simple Notification Service (SNS) can track configuration changes and notify administrators when specific resource modifications occur. This provides a mechanism to maintain configuration compliance, but it lacks the intelligence to detect suspicious activity or anomalous behavior that might indicate a security incident. Config is useful for ensuring that resources remain in their intended state, but it does not offer proactive threat detection.
On the other hand, services like S3 and Athena are primarily designed for storage and querying large datasets. While they are invaluable for storing logs or performing ad hoc analysis, they do not provide real-time monitoring or automated detection of security threats. Using them alone does not provide comprehensive security visibility or compliance auditing.
CloudTrail fills this gap by logging all API activity across AWS accounts, including actions taken through the AWS Management Console, SDKs, and CLI. This extensive logging provides a full audit trail, which is essential for compliance reporting, forensic analysis, and accountability. With CloudTrail, administrators can understand exactly what actions were performed, by whom, and when, creating a strong foundation for governance and compliance.
To add proactive security detection, Amazon GuardDuty analyzes the CloudTrail logs and other data sources to identify unusual activity, such as unauthorized API calls, reconnaissance, or privilege escalation attempts. GuardDuty leverages machine learning, threat intelligence feeds, and anomaly detection to surface potential security threats in real time. By continuously analyzing account activity and network behavior, it enables administrators to respond swiftly to incidents before they escalate.
Combining CloudTrail and GuardDuty provides a comprehensive solution for both compliance and security monitoring. CloudTrail ensures that all actions are auditable and accountable, supporting regulatory requirements, while GuardDuty actively detects suspicious activity, providing actionable insights to protect the environment. Together, they allow organizations to maintain secure, compliant AWS environments without relying solely on manual monitoring or static rules, ensuring both visibility and proactive threat detection.
Question 32
A company wants to reduce data transfer costs for a globally distributed application serving static content. Which AWS architecture should be used?
A) S3 only
B) CloudFront with S3 origin
C) EC2 with ALB
D) Route 53 latency-based routing only
Answer: B) CloudFront with S3 origin
Explanation:
When organizations aim to deliver content to users around the globe, simply relying on Amazon S3 as a storage solution does not fully address performance and cost considerations associated with wide-scale distribution. While S3 provides highly durable, scalable, and reliable object storage, it is designed to serve content from a centralized AWS region. This approach can introduce higher latency for users located far from the bucket’s region, as each request must traverse long distances over the internet. Additionally, repeated requests from geographically distant locations result in increased data transfer from the origin, which can lead to elevated bandwidth costs. For applications serving a global audience, these factors can degrade the user experience and increase operational expenses, making S3 alone insufficient for efficient content delivery.
Another approach sometimes considered is deploying EC2 instances behind an Application Load Balancer (ALB). This setup allows dynamic content delivery and distributes incoming requests across multiple compute instances, improving availability and scaling horizontally to meet demand. While this configuration can enhance reliability and throughput for web applications, it does not inherently solve the problem of global latency. Users far from the deployment region still experience slower response times, as the content originates from a limited number of regional endpoints. Additionally, managing EC2 instances introduces operational complexity, including provisioning, patching, monitoring, and scaling, which adds cost and overhead without fundamentally addressing the challenge of delivering content efficiently on a global scale.
Amazon Route 53 provides intelligent DNS-based routing, allowing requests to be directed to regional endpoints closest to users. This service helps improve availability and can reduce latency by selecting optimal routes based on geographic proximity. However, Route 53 does not provide caching capabilities, and requests still reach the origin endpoints, whether they are S3 buckets or EC2 instances. As a result, although DNS routing reduces some latency and improves redundancy, it does not decrease the amount of data transferred from the origin or accelerate access to frequently requested content. In scenarios where millions of users worldwide are accessing the same content repeatedly, reliance on DNS routing alone does not significantly enhance performance or reduce operational costs.
The optimal solution for delivering content efficiently and at scale is Amazon CloudFront, a globally distributed content delivery network (CDN). CloudFront caches both static and dynamic content at edge locations spread across the world, bringing data closer to users. By serving content from these edge nodes, CloudFront dramatically reduces the distance that data must travel, improving response times and lowering latency for end users regardless of their location. This approach ensures faster load times for websites, streaming media, or web applications, directly enhancing the user experience.
Beyond performance improvements, CloudFront reduces bandwidth costs by offloading requests from the origin. When content is cached at edge locations, repeated access does not require fetching data from the origin S3 bucket or EC2 instances. This reduces data transfer from the source, which can translate into significant cost savings for applications with large volumes of global traffic. Additionally, CloudFront supports flexible caching policies, enabling organizations to define how long content should be stored at edge locations and when it should be refreshed. Features like HTTPS support, request-based throttling, and geographic restrictions provide both security and performance enhancements, making CloudFront a comprehensive solution for global content delivery.
Integrating CloudFront with S3 as the origin combines the strengths of both services. S3 ensures highly durable and scalable storage for static assets such as images, videos, or application resources, while CloudFront ensures these assets are delivered quickly and efficiently to users worldwide. This integration allows organizations to maintain a simple, low-maintenance architecture without deploying and managing additional compute resources in multiple regions. Moreover, dynamic content generated by web applications or APIs can also be accelerated through CloudFront, further extending its benefits beyond purely static files.
Delivering content globally requires more than durable storage or regional compute instances. While S3, EC2 with ALB, and Route 53 each contribute to availability and scalability, none fully address the challenges of latency, bandwidth costs, and efficient distribution on a worldwide scale. CloudFront, especially when paired with S3, provides a robust solution, caching content at edge locations, reducing latency, lowering origin data transfer costs, and offering security and performance features that support enterprise-grade applications. By leveraging CloudFront with S3, organizations achieve a globally optimized content delivery solution that balances speed, cost-efficiency, and operational simplicity, ensuring an excellent user experience while minimizing infrastructure overhead.
Question 33
A company requires a scalable, fully managed relational database compatible with MySQL that can handle variable workloads automatically. Which service is most appropriate?
A) Aurora MySQL
B) RDS MySQL
C) DynamoDB
D) Redshift
Answer: A) Aurora MySQL
Explanation:
When choosing a relational database solution for applications with fluctuating workloads, it is essential to consider scalability, compatibility, and operational efficiency. RDS MySQL is a fully managed relational database service that provides automated backups, patching, and high availability. However, it has limitations when it comes to dynamically scaling storage or efficiently handling read-heavy workloads. While read replicas can be created, managing them to meet unpredictable demand often requires manual configuration and monitoring, which increases operational complexity. For applications experiencing variable workloads, this can result in performance bottlenecks or the need for frequent manual adjustments to maintain responsiveness.
On the other hand, DynamoDB is a highly scalable NoSQL database that excels at handling massive amounts of unstructured data with low-latency access. While it offers automatic scaling and high throughput, it does not support relational schemas or SQL-based applications. Applications built on MySQL or other relational databases cannot directly leverage DynamoDB without significant changes to the data model and application logic. This makes it unsuitable for workloads that rely on transactional consistency and relational query patterns inherent to MySQL.
Redshift, while a powerful database service, is designed for analytical workloads rather than transactional operations. It excels at running complex queries over large datasets for business intelligence and data warehousing purposes. However, its architecture is not optimized for high-frequency transactional operations or workloads requiring immediate consistency across multiple tables, which limits its applicability for traditional MySQL-based applications.
Aurora MySQL addresses these challenges by combining the benefits of a fully managed relational database with the performance, availability, and scalability necessary for modern applications. It is fully compatible with MySQL, allowing existing applications to migrate with minimal code changes. Aurora automatically scales storage up to hundreds of terabytes as needed, eliminating the need for manual provisioning. It also supports multiple read replicas, enabling applications to distribute read workloads efficiently across instances and handle sudden spikes in traffic without performance degradation. High availability is built in, with fault-tolerant, self-healing storage that automatically replicates data across multiple availability zones.
This combination of automatic scaling, MySQL compatibility, read replica support, and high availability makes Aurora MySQL an ideal solution for applications that experience variable workloads. It delivers operational simplicity by reducing the administrative overhead associated with database management while ensuring consistent performance and reliability. Organizations can focus on application development and business outcomes rather than database operations, making Aurora MySQL the preferred choice for scalable, relational workloads requiring both flexibility and robustness.
Question 34
A company wants to deploy a serverless application with automatic scaling for backend processing. Which AWS service combination is ideal?
A) Lambda with API Gateway
B) EC2 with Auto Scaling
C) ECS with EC2 launch type
D) S3 with EC2
Answer: A) Lambda with API Gateway
Explanation:
Building a scalable backend architecture in the cloud often starts with evaluating compute options. Using EC2 instances with Auto Scaling provides the ability to adjust the number of servers based on demand. While this approach allows horizontal scaling, it still requires managing the underlying infrastructure, including patching, monitoring, and capacity planning. Organizations must maintain and optimize the EC2 instances, which adds operational overhead and complexity, especially as traffic patterns fluctuate.
Similarly, deploying containerized workloads using Amazon ECS with the EC2 launch type also requires direct management of the underlying EC2 instances. While ECS simplifies container orchestration, administrators are still responsible for instance provisioning, scaling, and maintenance. This introduces operational tasks that can slow development cycles and increase the potential for configuration errors or performance bottlenecks.
Some teams may consider combining EC2 with S3 for storage and compute needs. While S3 offers highly durable and scalable object storage and EC2 provides flexible compute, this combination does not inherently provide serverless capabilities or automatic scaling. Applications still need to handle infrastructure management, and scaling must be planned and implemented manually to meet variable workloads.
In contrast, AWS Lambda provides a truly serverless compute environment. Lambda automatically scales in response to incoming requests, ensuring that applications can handle sudden spikes in traffic without the need for manual intervention. Developers do not need to provision or manage servers, apply updates, or monitor capacity, which significantly reduces operational burden and allows teams to focus on business logic rather than infrastructure.
To expose Lambda functions as APIs, Amazon API Gateway serves as a fully managed API front-end. API Gateway handles essential features such as authentication, request routing, throttling, and caching, ensuring secure and reliable access to backend functions. It can integrate seamlessly with Lambda, allowing developers to build robust, scalable APIs without deploying or managing additional infrastructure.
Combining Lambda with API Gateway results in a fully serverless architecture that scales automatically based on demand. This setup eliminates the need to manage servers, reduces operational complexity, and allows rapid deployment of backend services. Developers can build APIs that respond to high volumes of traffic reliably while paying only for the compute and requests actually used.
This serverless approach enables organizations to create highly scalable and secure backend systems efficiently. By leveraging Lambda for computation and API Gateway for exposure and security, teams can focus entirely on application functionality and business logic while enjoying the benefits of automatic scaling, reduced operational overhead, and cost optimization.
Question 35
A solutions architect needs to create a high-performance data store for session management across multiple web servers. Which AWS service is most suitable?
A) ElastiCache Redis
B) DynamoDB
C) RDS PostgreSQL
D) S3
Answer: A) ElastiCache Redis
Explanation:
When designing a system to handle session management for web applications, selecting the right data store is critical to ensure high performance, low latency, and consistency. While several AWS services provide storage and scalability, not all are suitable for rapid session reads and writes, which are essential for maintaining an optimal user experience.
DynamoDB is a fully managed NoSQL database that can scale seamlessly to handle large workloads. It is highly reliable and provides high throughput for many types of applications. However, while DynamoDB is fast, it may not consistently deliver the microsecond-level latency required for session management, especially in scenarios where sessions are updated or read frequently. For applications that rely on sub-millisecond access times to maintain user interactions, this limitation can introduce noticeable delays.
Amazon RDS offers managed relational databases, including MySQL, PostgreSQL, and others. It provides strong consistency and supports complex queries, making it ideal for structured data storage. However, for session management, RDS can introduce higher latency due to disk-based storage and transaction overhead, particularly when session updates are frequent and must be processed in real-time. Constant reads and writes to an RDS database for session data may result in slower response times and reduced application performance under heavy traffic.
S3, while highly durable and scalable for object storage, is fundamentally designed for storing large files and documents rather than rapid, transactional data. Accessing S3 for session information is inefficient because each read or write operation incurs network and protocol overhead, which makes it unsuitable for workloads requiring low-latency, frequent access patterns. S3 excels in archival, backups, and large static content delivery but does not meet the performance needs of real-time session management.
ElastiCache Redis, on the other hand, is an in-memory data store designed for speed and high availability. Because data is stored in memory rather than on disk, Redis provides microsecond-level latency for reads and writes, making it ideal for session storage. It supports replication and clustering, which ensures that session state can be consistently maintained across multiple web servers while providing fault tolerance. Redis also allows features such as automatic expiration of session data, high throughput, and support for concurrent access, all of which are critical for handling session management at scale.
By using ElastiCache Redis for session state, applications benefit from extremely low latency, high performance, and consistent session handling. It allows web servers to quickly read and write session information, supporting fast user interactions and a responsive experience. In comparison, other storage options like DynamoDB, RDS, or S3 either lack the required speed or introduce unnecessary complexity, making Redis the optimal choice for session management.
Question 36
A company needs to migrate large amounts of on-premises data to AWS for analytics while minimizing downtime. Which service combination is recommended?
A) AWS DMS with S3
B) AWS Snowball
C) S3 only
D) RDS snapshot
Answer: B) AWS Snowball
Explanation:
When organizations need to transfer large amounts of data to the cloud, selecting the right service is critical to minimize downtime, reduce costs, and ensure data integrity. Traditional migration methods often face limitations when handling massive datasets, which can span terabytes or even petabytes. Using inappropriate tools can result in long transfer times, network congestion, or operational bottlenecks.
AWS Database Migration Service (DMS) is primarily designed to migrate databases from on-premises or other cloud environments into AWS. It excels at continuously replicating relational databases, minimizing downtime for database workloads. However, DMS is not intended for moving bulk files, unstructured data, or large datasets that are not structured as databases. Attempting to use DMS for general file transfers would be inefficient and likely impractical.
Using S3 alone for large-scale file transfers requires uploading data over the network. While S3 can store virtually unlimited amounts of data, uploading terabytes or petabytes through the internet demands high-bandwidth connections. This approach is often time-consuming, susceptible to network interruptions, and can result in extended migration windows. For enterprises with limited bandwidth or strict migration timelines, relying solely on online transfers is rarely feasible.
RDS snapshots are another AWS option, but they are specific to relational database backups. While snapshots are effective for replicating or restoring databases within AWS, they are not designed to transfer large files or datasets that reside outside of relational databases. Using snapshots for general-purpose data migration is therefore not viable.
AWS Snowball addresses these challenges by offering a secure, offline solution for transferring large volumes of data to AWS. Snowball devices are physical appliances that can handle terabytes to petabytes of data, enabling organizations to bypass bandwidth limitations and transfer data efficiently. Users load their data onto the Snowball device on-premises, ship it to AWS, and the data is automatically uploaded to the specified S3 buckets. This approach reduces dependency on network speed, mitigates the risks of long online uploads, and accelerates migration timelines.
Once the data reaches S3 via Snowball, it becomes immediately accessible for analytics, machine learning, or other cloud-based processing. Snowball also includes built-in encryption, ensuring that data is securely transported and meets compliance requirements. By leveraging Snowball, organizations can efficiently move massive datasets without relying solely on network bandwidth, reducing both operational complexity and migration time compared to traditional transfer methods.
AWS Snowball provides the ideal solution for large-scale data migration, offering speed, security, and integration with S3, whereas DMS, S3 alone, and RDS snapshots are either unsuitable or limited in scope for handling massive unstructured datasets.
Question 37
A company wants to implement a data lake with diverse analytics workloads, including SQL queries, ML, and streaming analytics. Which combination of AWS services is best?
A) S3, Glue, Athena, SageMaker, and Kinesis
B) S3 only
C) RDS, EC2, and Lambda
D) DynamoDB and Lambda
Answer: A) S3, Glue, Athena, SageMaker, and Kinesis
Explanation:
Building a modern, scalable data platform in the cloud requires more than just storage; it demands the ability to process, analyze, and derive insights from data efficiently across multiple workloads. While Amazon S3 is a robust object storage solution capable of storing vast amounts of structured and unstructured data, it alone does not provide the analytics or machine learning capabilities necessary for a comprehensive data platform. Without additional services, S3 cannot perform complex queries, catalog datasets, or enable real-time streaming analytics, limiting its usefulness for enterprises that need actionable insights from their data.
Relational databases such as RDS combined with EC2 or Lambda functions can handle data processing tasks, but this approach is constrained in scalability and lacks an integrated data lake architecture. Using EC2 and Lambda to manage ETL jobs or run queries introduces operational overhead, as these services require provisioning, scaling, and management. Furthermore, RDS is primarily relational and does not naturally support serverless analytics or native integration with machine learning services, making it less flexible for workloads that include batch processing, ad-hoc querying, and predictive analytics.
Similarly, DynamoDB paired with Lambda offers high-performance key-value storage and serverless compute, but it is limited to key-value or single-table access patterns. While this is useful for specific use cases such as caching or real-time lookups, it is not suitable for building a full-fledged data lake that needs to handle varied data formats, complex joins, or large-scale analytical queries.
A more robust and scalable approach involves integrating S3 with a suite of AWS services designed for serverless data lake architectures. S3 acts as the centralized storage layer, providing durability and virtually unlimited capacity for structured, semi-structured, or unstructured datasets. AWS Glue serves as the ETL and cataloging engine, automatically discovering data schemas, transforming datasets, and preparing them for analysis. Athena enables ad-hoc SQL queries directly on data stored in S3 without requiring any infrastructure management, allowing quick insights and operational analytics.
For machine learning workloads, Amazon SageMaker provides a fully managed environment for building, training, and deploying models directly on the data stored in S3, seamlessly integrating predictive analytics into the data lake. Real-time data streams can be ingested using Amazon Kinesis, allowing for immediate processing and analytics on streaming data.
This combination delivers a complete serverless data lake solution that supports batch, real-time, and machine learning workloads efficiently. It eliminates infrastructure management overhead, scales automatically with data volume, and provides a unified architecture for analytics, processing, and machine learning, ensuring enterprises can derive maximum value from their data while maintaining flexibility, scalability, and cost efficiency.
Question 38
A company wants to reduce operational overhead for containerized applications while running them in AWS. Which solution 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 in AWS can be approached in several ways, each with distinct operational implications. Using ECS with the EC2 launch type or EKS with EC2 worker nodes demands significant infrastructure management. In these models, teams are responsible for provisioning and maintaining the underlying EC2 instances, including tasks such as scaling capacity up or down, applying OS patches, monitoring performance, and planning for peak workloads. This operational overhead can divert focus away from application development and slow down deployment cycles. Similarly, using plain EC2 instances to host containers or applications means full responsibility for server management, including installing dependencies, handling updates, and ensuring availability during high demand.
ECS with Fargate offers a more streamlined alternative by providing serverless container orchestration. With Fargate, there is no need to manage the underlying compute infrastructure. It automatically provisions the right amount of compute capacity for container workloads and scales resources dynamically based on demand. This eliminates the complexities of capacity planning and reduces the operational burden of patching, monitoring, and scaling individual EC2 instances. Developers can define the container specifications, and Fargate handles execution in a secure and managed environment.
The integration of Fargate with AWS networking, logging, and security services further enhances operational efficiency. Containers can seamlessly connect with VPCs, subnets, and security groups without manual configuration. Logging can be automatically captured using CloudWatch, and IAM roles can be assigned directly to containers to enforce fine-grained security policies. This ensures that containers operate in a secure, compliant, and observable environment while reducing manual setup.
By removing the need for infrastructure management, ECS with Fargate allows development teams to focus entirely on application logic, deployment, and business functionality. Teams no longer need to dedicate resources to monitoring server health, managing auto scaling groups, or performing routine maintenance tasks. The serverless nature of Fargate also ensures cost efficiency, as organizations pay only for the compute and storage resources their containers consume, without over-provisioning.
Overall, compared to ECS or EKS on EC2, or running workloads on standalone EC2 instances, ECS with Fargate provides a fully managed, serverless container platform. It combines the benefits of automated scaling, simplified operations, integration with AWS security and monitoring services, and cost-efficient resource usage. This allows organizations to deploy and operate containerized applications with minimal infrastructure overhead while maintaining high availability, security, and scalability, making it an ideal solution for modern cloud-native workloads.
Question 39
A company needs to implement a cost-efficient storage solution for frequently accessed and infrequently accessed data with automatic tiering. Which service should be used?
A) S3 Standard and S3 Intelligent-Tiering
B) EBS gp3 only
C) S3 Glacier only
D) EFS Standard only
Answer: A) S3 Standard and S3 Intelligent-Tiering
Explanation:
When designing a storage strategy for workloads with varying access patterns, choosing the right combination of AWS storage services is crucial for balancing performance and cost. EBS gp3 provides high-performance block storage for EC2 instances and delivers predictable IOPS, low latency, and high throughput. While it is excellent for workloads requiring consistent performance, it may not be the most cost-effective solution for datasets with mixed or unpredictable access patterns, as it incurs ongoing charges regardless of usage frequency. Similarly, S3 Glacier is ideal for long-term archival storage, offering extremely low storage costs but high retrieval latency. It is not suitable for objects that need frequent or near-real-time access, as retrieving data can take minutes to hours.
EFS Standard, another storage option, provides shared file storage that can be accessed concurrently by multiple EC2 instances. It supports general-purpose file workloads and ensures high durability. However, it does not automatically manage the movement of data between storage tiers, meaning organizations must manually monitor and adjust storage allocations to control costs for less frequently accessed files. This can increase operational overhead and reduce cost efficiency over time.
A more effective approach is to combine S3 Standard and S3 Intelligent-Tiering. S3 Standard is optimized for frequently accessed data, delivering low latency and high throughput. It is ideal for objects that need rapid retrieval and constant availability, such as active application data, user-generated content, or operational files. S3 Intelligent-Tiering complements this by automatically moving objects between access tiers based on usage patterns. Objects that are not accessed frequently are shifted to lower-cost tiers without any performance impact for the remaining active data. This automated tiering reduces storage costs while ensuring that frequently accessed objects remain readily available.
This combination of S3 Standard for hot, active data and Intelligent-Tiering for objects with varying access frequency provides a flexible, cost-efficient solution. It ensures that performance-critical data is delivered quickly while optimizing expenditures for data that does not need to be retrieved constantly. By leveraging these services, organizations can maintain low latency for high-demand workloads, reduce operational complexity, and achieve significant cost savings compared to using static storage tiers or manually managed alternatives.
Overall, using S3 Standard and S3 Intelligent-Tiering together creates a storage architecture that is both high-performing and cost-conscious, enabling businesses to respond to dynamic access patterns without sacrificing efficiency or reliability. This approach ensures that frequently used data is always accessible while controlling costs for infrequently accessed objects.
Question 40
A company wants to provide globally distributed users with low-latency access to dynamic and static web content. Which combination is ideal?
A) CloudFront with S3 and ALB as origin
B) S3 only
C) EC2 only
D) Route 53 only
Answer: A) CloudFront with S3 and ALB as origin
Explanation:
When building a globally accessible web application, simply using S3, EC2, or Route 53 independently does not provide the performance and scalability needed for both static and dynamic content. S3 is excellent for storing static assets like images, CSS, and JavaScript, but it cannot process dynamic requests or provide low-latency access for users distributed across multiple regions. EC2 instances, while capable of handling dynamic application logic, do not inherently reduce latency for users located far from the instance’s region, and scaling EC2 globally requires careful planning and additional management. Route 53, on the other hand, is a DNS routing service that can direct traffic based on geography or latency, but it does not cache content or reduce load times on its own.
CloudFront addresses these limitations by acting as a global content delivery network, caching static content in edge locations worldwide. By storing frequently accessed static resources closer to end users, CloudFront significantly reduces latency and improves page load times, delivering a smoother user experience. Beyond static content, CloudFront can also handle dynamic requests by forwarding them to Application Load Balancer (ALB) origins. This ensures that dynamic content, such as API responses or database-driven pages, is processed efficiently while still benefiting from CloudFront’s optimized network paths, minimizing the time it takes for data to travel between the user and the backend servers.
Integrating CloudFront with S3 and ALB creates a comprehensive architecture for a high-performance web application. S3 serves as the origin for static assets, while ALB handles dynamic content and routes requests to appropriate EC2 instances or backend services. CloudFront acts as a unified front, caching content where possible, routing dynamic requests intelligently, and reducing the overall workload on origin servers. This combination also helps lower global data transfer costs by minimizing repeated requests to the origin, as frequently requested assets are served directly from edge locations.
By leveraging this architecture, developers can ensure their web application scales globally without compromising performance. Users from different continents experience faster page loads for both static and dynamic content. Additionally, the infrastructure is more resilient, as edge caching reduces dependency on a single origin, and ALB can distribute traffic efficiently across multiple backend servers. Overall, pairing CloudFront with S3 for static content and ALB for dynamic requests delivers a globally optimized, low-latency, and highly scalable web application suitable for modern, performance-conscious users.
Question 41
A company wants to migrate its on-premises SQL Server databases to AWS with minimal downtime and continuous replication. Which service should be used?
A) RDS SQL Server with AWS DMS
B) RDS SQL Server only
C) Aurora PostgreSQL
D) EC2 SQL Server with manual backup and restore
Answer: A) RDS SQL Server with AWS DMS
Explanation:
RDS SQL Server alone provides a managed database environment, handling patching, backups, and high availability, but if migration from on-premises is required, using only RDS would involve downtime because data must first be exported, transferred, and imported, resulting in service disruption. Aurora PostgreSQL is fully managed and scalable, but it is not compatible with SQL Server applications; migrating to it would require significant application changes, increasing complexity and downtime. EC2 SQL Server with manual backup and restore allows for full control over the database environment, but migration is time-consuming, requires manual scripting, and may cause extended downtime because the database must be stopped or in a consistent backup state before data transfer. AWS Database Migration Service (DMS) paired with RDS SQL Server enables continuous replication from on-premises SQL Server databases. This means data changes on the source can be replicated in near real-time to the target RDS database, ensuring minimal downtime during the migration process. DMS handles schema conversion for homogeneous migrations and reduces operational complexity. By using RDS with DMS, the company benefits from a fully managed target database with high availability, automated backups, and maintenance, while DMS ensures the source remains operational during the migration. This combination is ideal for organizations that need a reliable, low-downtime migration strategy. It ensures data consistency, reduces risk, and allows applications to continue functioning almost uninterrupted during the transition to AWS.
Question 42
A company wants to ensure that all objects uploaded to S3 are encrypted with customer-managed keys. Which approach should be implemented?
A) Enable S3 default encryption with SSE-KMS
B) Use SSE-S3 only
C) Upload objects over HTTP
D) Store objects unencrypted
Answer: A) Enable S3 default encryption with SSE-KMS
Explanation:
SSE-S3 encrypts objects at rest using keys managed entirely by AWS. While it protects data, it does not allow customers to control key rotation or auditing, which may be required for compliance or regulatory purposes. Uploading objects over HTTP transmits data in plaintext over the network, which is insecure and violates best practices. Storing objects unencrypted is entirely insecure and non-compliant with any standard regulatory framework, exposing sensitive data to risk. Enabling S3 default encryption with SSE-KMS ensures that every object uploaded to S3 is automatically encrypted using a key managed by AWS Key Management Service (KMS). SSE-KMS gives customers complete control over encryption keys, including creation, rotation, and access policies. AWS KMS integrates with CloudTrail, providing full audit logs of key usage. Using default encryption also reduces the chance of human error by ensuring that encryption is applied automatically, even if the upload command does not specify encryption explicitly. This approach meets enterprise-level security and compliance requirements, guarantees end-to-end protection, and simplifies operational management of encrypted objects in S3.
Question 43
A company wants to store session data for a high-traffic web application with low latency and high throughput. Which AWS service is most suitable?
A) ElastiCache Redis
B) DynamoDB
C) RDS MySQL
D) S3
Answer: A) ElastiCache Redis
Explanation:
When designing a high-performance web application, managing session data efficiently is critical, especially for large-scale, high-traffic environments. While there are multiple database and storage options within AWS, not all of them are suited for the extreme performance and low-latency requirements of session management. For example, DynamoDB is a highly scalable NoSQL database that can handle significant workloads and provides low-latency access for many use cases. However, when session management demands microsecond-level response times or involves frequent read and write operations across multiple application servers, DynamoDB may not consistently meet the stringent latency and throughput requirements. Its underlying architecture, optimized for scalable key-value access and durability, introduces minimal but measurable delays that can accumulate in applications requiring real-time session updates.
Similarly, Amazon RDS MySQL provides a fully managed relational database experience with the familiar SQL interface and transactional consistency. While RDS excels for structured data and relational workloads, it is less suited for extremely fast, high-frequency session data operations. Each read or write operation in RDS involves disk I/O, connection overhead, and transactional processing, which can create latency bottlenecks when thousands of users are updating session state simultaneously. The relational model also requires managing schema design to optimize access patterns, adding operational complexity when trying to achieve millisecond or sub-millisecond responsiveness.
Amazon S3, while excellent for durable and scalable object storage, is not designed to handle frequent small reads and writes typical of session data. Each access involves network calls and object retrieval, which introduces too much latency for applications that need instantaneous session updates. S3’s strength lies in storing large objects for backup, archival, or static content distribution, not for maintaining live session state that requires rapid, frequent updates.
ElastiCache Redis offers an ideal solution for these requirements. Redis is an in-memory data store designed to deliver extremely low-latency access to data. Because session information is stored in memory rather than on disk, read and write operations are completed in microseconds, enabling real-time performance even under high concurrency. Redis supports clustering, allowing session data to be distributed across multiple nodes for scalability, and replication to enhance reliability and availability. If persistence is needed, Redis can also write snapshots to disk without impacting performance significantly. Its ability to allow multiple application instances to read and write session data consistently ensures that users experience seamless interactions regardless of which server handles their requests.
By integrating ElastiCache Redis for session management, organizations can ensure that session data is instantly available across all application servers, minimizing latency and preventing bottlenecks. This approach enables high-concurrency scenarios, supports large-scale applications, and provides reliable, fast response times for end users. Leveraging Redis allows developers to focus on application functionality while providing a robust, low-latency backend infrastructure that efficiently handles session state at scale.
Question 44
A company wants to decouple microservices and ensure messages are processed in order without duplicates. 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 delivers messages at least once but does not preserve order, which can be problematic for workflows requiring sequential processing. SNS is a pub/sub system that broadcasts messages to multiple subscribers but does not guarantee order or prevent duplicates. Kinesis Data Streams is designed for streaming data and real-time analytics, but it is more complex and not optimized for simple message queuing between microservices. SQS FIFO Queue guarantees exactly-once message processing and preserves message order, ensuring that each message is delivered and processed in sequence. FIFO queues also support deduplication, which prevents multiple processing of the same message. For microservice architectures where transaction order or sequential processing is critical, SQS FIFO provides the most reliable and operationally simple solution. It ensures decoupling between services while maintaining message integrity and order, allowing scalable, fault-tolerant system designs without complex orchestration.
Question 45
A company needs to provide low-latency access to static content for a global user base. Which AWS service combination is most appropriate?
A) CloudFront with S3
B) S3 only
C) EC2 with ALB
D) Route 53 only
Answer: A) CloudFront with S3
Explanation:
S3 alone stores static content but does not optimize for latency when serving users globally. EC2 with ALB provides compute and load balancing but lacks caching capabilities, which means all requests must travel to the origin, increasing latency. Route 53 only handles DNS resolution and cannot cache or deliver content. CloudFront is a global Content Delivery Network (CDN) that caches static content at edge locations near end users, reducing latency and improving load times. When paired with S3 as the origin, CloudFront automatically serves cached objects from the nearest edge location while retrieving uncached content from S3. This combination provides high availability, security with SSL/TLS, and performance optimization for users worldwide. CloudFront also integrates with AWS WAF for security and supports geo-restriction and access control. For globally distributed applications, this architecture ensures that users experience fast content delivery without additional infrastructure or complex configurations, making it the ideal solution for low-latency access to static assets.