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Question 1:

A company plans to migrate its on-premises data center workloads to AWS while ensuring high availability, fault tolerance, and minimal downtime. The workloads include multiple web servers, application servers, and relational databases. Which architectural approach best meets these requirements?

A) Launch EC2 instances in a single Availability Zone, configure Auto Scaling, and use a single RDS instance.
B) Deploy EC2 instances across multiple Availability Zones with Elastic Load Balancer, Auto Scaling, and RDS Multi-AZ.
C) Deploy EC2 instances in a single region with Route 53 failover routing and an S3-backed database.
D) Use Amazon Lightsail instances and attach them to a single RDS instance in one Availability Zone.

Answer:

B) Deploy EC2 instances across multiple Availability Zones with Elastic Load Balancer, Auto Scaling, and RDS Multi-AZ

Explanation:

Deploying workloads across multiple Availability Zones ensures fault tolerance and high availability. EC2 instances in multiple AZs prevent single points of failure. An Elastic Load Balancer (ELB) distributes incoming traffic evenly across instances, providing resilience against server failures and traffic spikes. Auto Scaling automatically adjusts the number of EC2 instances based on demand, optimizing cost while maintaining performance. RDS Multi-AZ provides synchronous replication to a standby database in a different Availability Zone, ensuring database availability and automatic failover in case of instance failure. This combination addresses compute, database, and network resiliency.

Using a single Availability Zone or a single RDS instance, as in Options A and D, introduces a risk of downtime if the AZ fails. Option C, while utilizing Route 53 failover, does not provide true multi-AZ database redundancy and relies on S3, which is not a relational database replacement. For enterprise-grade migrations, the multi-AZ, multi-instance approach is the industry standard, providing both operational reliability and scalability. This setup also supports future growth as traffic patterns change and workloads scale.

Additionally, by distributing EC2 instances across AZs, the architecture benefits from AWS’s backbone network, reducing latency and ensuring session stickiness if required. Combining ELB and Auto Scaling allows dynamic traffic management during traffic spikes, reducing potential bottlenecks. RDS Multi-AZ automatically handles failover without manual intervention, reducing operational overhead and the risk of human error. The approach also supports integration with AWS CloudWatch for monitoring and alarms, further enhancing operational visibility. Security measures such as IAM roles, security groups, and network ACLs can be applied consistently across AZs. In summary, Option B provides a resilient, scalable, and operationally efficient architecture suitable for enterprise migrations to AWS.

Question 2:

A company needs to implement a secure, serverless data processing pipeline that ingests sensitive files uploaded by clients, processes them, and stores the output for downstream analytics. The pipeline must ensure that only authorized users can access the data and that the data is encrypted at rest and in transit. Which combination of AWS services should be used?

A) Amazon S3, AWS Lambda, AWS Key Management Service (KMS), AWS Identity and Access Management (IAM)
B) Amazon S3, Amazon EC2, AWS Secrets Manager, Amazon RDS
C) AWS Glue, Amazon Redshift, AWS IAM, Amazon CloudTrail
D) Amazon EFS, Amazon EC2, AWS Certificate Manager

Answer:

A) Amazon S3, AWS Lambda, AWS Key Management Service (KMS), AWS Identity and Access Management (IAM)

Explanation:

This solution leverages AWS serverless capabilities for secure, scalable, and automated data processing. Amazon S3 provides highly durable object storage with built-in encryption support, enabling secure storage of client files. By applying IAM policies, access to S3 buckets is restricted only to authorized users or services. AWS KMS manages encryption keys, allowing automatic rotation and fine-grained control over data encryption at rest. TLS ensures encryption in transit, protecting data from interception while moving between clients and AWS.

AWS Lambda processes files as they arrive in S3. Lambda functions can be triggered automatically by S3 events, removing the need for server management and allowing scalable, pay-per-use processing. IAM roles assigned to Lambda functions ensure the functions have appropriate permissions without embedding credentials, following AWS best practices for least privilege. KMS integration allows Lambda to encrypt or decrypt data securely, meeting regulatory compliance requirements.

Option B introduces EC2, which adds management overhead and complexity compared to a fully serverless architecture. Using Secrets Manager is ideal for storing credentials, but it is not necessary for general file processing if IAM roles are properly configured. Option C combines ETL with analytics (Glue and Redshift) but does not address real-time file ingestion and serverless processing. Option D (EFS + EC2 + ACM) introduces a traditional server approach, which lacks the scalability, automation, and cost efficiency of Lambda.

Using S3, Lambda, KMS, and IAM together creates a secure, scalable, and automated processing pipeline. The architecture is resilient, with Lambda automatically scaling with file ingestion rates. Data governance is enhanced through KMS auditing and S3 bucket policies, ensuring compliance with organizational or regulatory requirements. CloudTrail can log Lambda and S3 access, providing end-to-end auditability. This approach balances security, cost efficiency, and operational simplicity, making it ideal for sensitive serverless data processing workloads.

Question 3:

A company runs an e-commerce website with unpredictable traffic patterns. They want to reduce operational complexity while ensuring that the application can scale automatically during peak periods without over-provisioning infrastructure. Which AWS architecture best supports these requirements?

A) Amazon EC2 instances with Auto Scaling and Elastic Load Balancer
B) Amazon S3 static website with Amazon CloudFront
C) AWS Lambda functions triggered by Amazon API Gateway with Amazon DynamoDB
D) Amazon EC2 instances in a single Availability Zone with a fixed RDS database

Answer:

C) AWS Lambda functions triggered by Amazon API Gateway with Amazon DynamoDB

Explanation:

The combination of AWS Lambda, API Gateway, and DynamoDB provides a fully serverless architecture that automatically scales based on demand. Lambda functions execute code without requiring server management, and API Gateway routes incoming requests to Lambda with minimal latency. DynamoDB provides a managed NoSQL database that scales automatically to handle varying workloads, ensuring consistent performance even under unpredictable traffic patterns. This combination reduces operational overhead, as there are no servers to manage, patch, or provision manually.

Option A, while scalable, relies on EC2 instances and ELB, which introduce operational complexity and may result in over-provisioning during traffic spikes. Option B supports static content delivery but cannot handle dynamic e-commerce operations such as user sessions, transactions, or inventory management. Option D introduces a single point of failure and does not support automatic scaling, making it unsuitable for unpredictable traffic.

Serverless architectures like Lambda + API Gateway + DynamoDB allow for event-driven processing and microservices patterns. Each incoming request is handled independently, eliminating the need for pre-provisioned infrastructure. DynamoDB’s on-demand capacity mode adjusts automatically based on traffic, reducing costs during low-traffic periods. Security can be enforced using IAM roles for Lambda and fine-grained access policies for DynamoDB. Monitoring and logging can be implemented with CloudWatch and X-Ray, providing end-to-end visibility.

This architecture also enables faster deployment cycles, as new functions or features can be updated independently without affecting the rest of the system. Integration with other AWS services such as S3 for static content, SNS for notifications, or Kinesis for real-time analytics further enhances scalability and functionality. By combining these serverless components, the architecture achieves high availability, automatic scaling, operational efficiency, and cost optimization, which aligns with the company’s requirements for an e-commerce website experiencing variable traffic patterns.

Question 4:

A financial services company wants to implement a secure, highly available database solution for their transactional application. The application requires automatic failover, encryption at rest and in transit, and minimal downtime during maintenance. Which AWS architecture best meets these requirements?

A) Amazon RDS Single-AZ with periodic snapshots and manual failover
B) Amazon Aurora Multi-AZ with read replicas and encryption enabled with KMS
C) Amazon DynamoDB with on-demand capacity and no encryption
D) Amazon Redshift with single node cluster and periodic backups

Answer:

B) Amazon Aurora Multi-AZ with read replicas and encryption enabled with KMS

Explanation:

Amazon Aurora provides a fully managed relational database solution compatible with MySQL and PostgreSQL, offering high availability and fault tolerance. Deploying Aurora in Multi-AZ configuration ensures that the primary instance is replicated synchronously to a standby instance in a different Availability Zone. This replication allows automatic failover in the event of primary instance failure, reducing downtime to a few seconds and ensuring business continuity.

Aurora also provides read replicas that can be deployed in the same region or across regions. Read replicas enhance scalability by offloading read-heavy workloads from the primary database, ensuring that the transactional application maintains performance even under high traffic conditions. Encryption at rest is managed through AWS Key Management Service (KMS), providing automated key rotation and compliance with financial and regulatory standards. Encryption in transit is enabled using SSL/TLS, securing connections between the application and the database to protect sensitive financial data.

Option A, Single-AZ RDS with manual failover, does not provide true high availability, as downtime will occur during failover events. Relying solely on periodic snapshots does not meet strict uptime requirements. Option C, DynamoDB, is a NoSQL solution that is highly scalable but may not support complex transactional operations or relational queries required for financial applications. Additionally, the lack of encryption in the option makes it non-compliant for sensitive financial data. Option D, Redshift, is designed for analytics and data warehousing, not transactional workloads, and a single-node cluster introduces a single point of failure.

Aurora’s architecture separates storage and compute layers, with six copies of data automatically replicated across three Availability Zones, ensuring durability and minimizing the risk of data loss. Automated backups are continuous and do not impact performance, allowing point-in-time recovery. Security integration with IAM policies, security groups, and VPC configurations ensures that only authorized applications and users access the database. This approach significantly reduces operational overhead while providing a highly resilient and secure database platform.

Additionally, Aurora supports fast failover mechanisms without requiring manual intervention. It continuously monitors instance health and triggers failover automatically, which is crucial for financial services where downtime can result in financial loss or regulatory penalties. The architecture supports automated maintenance, patching, and monitoring using CloudWatch metrics and enhanced logging features. By combining Aurora Multi-AZ deployment, read replicas, and encryption managed by KMS, the solution ensures compliance, high availability, performance, and security for critical financial applications, making it the best choice for this scenario.

Question 5:

A media company wants to build a content delivery architecture for a global video streaming application. The solution should minimize latency for users, protect content from unauthorized access, and scale automatically based on user demand. Which combination of AWS services best meets these requirements?

A) Amazon CloudFront, Amazon S3, AWS Lambda@Edge, AWS WAF
B) Amazon EC2 instances in multiple regions with Route 53 failover
C) Amazon S3 with public access and pre-signed URLs
D) Amazon CloudFront with S3 origin but no security or edge processing

Answer:

A) Amazon CloudFront, Amazon S3, AWS Lambda@Edge, AWS WAF

Explanation:

Amazon CloudFront is a globally distributed content delivery network (CDN) that caches content at edge locations to reduce latency for users worldwide. By integrating CloudFront with Amazon S3 as the origin, media files such as videos, images, and static content are served efficiently. S3 provides durable storage with lifecycle management, enabling cost-effective storage of large media libraries.

AWS Lambda@Edge allows execution of custom logic closer to end users, such as URL rewrites, content manipulation, authentication, and authorization, without modifying the origin servers. This reduces latency and ensures secure, dynamic content delivery. Lambda@Edge also supports per-request access control, caching policies, and personalized content handling.

AWS Web Application Firewall (WAF) protects the application from common web exploits such as SQL injection, cross-site scripting, and DDoS attacks. Combining WAF with CloudFront ensures that security policies are enforced globally, reducing the risk of attacks impacting the origin infrastructure.

Option B, using EC2 instances in multiple regions with Route 53 failover, introduces operational complexity, requires manual scaling, and does not provide built-in caching at edge locations. Latency will be higher for global users, and managing media content delivery at scale becomes operationally challenging. Option C, S3 with public access and pre-signed URLs, provides basic access control but does not offer caching, global distribution, or protection against application-layer attacks. Option D, CloudFront with S3 origin but no edge logic or WAF, improves latency but lacks advanced security and request-level customization, which are essential for media applications that need to protect premium content and ensure personalized access.

A fully managed CloudFront + S3 + Lambda@Edge + WAF solution reduces operational overhead, ensures content security, and provides automatic scalability in response to fluctuating traffic. CloudFront caching minimizes load on S3 origins, lowering costs and enhancing performance. Security is centralized at the edge, protecting both static and dynamic content. Lambda@Edge also allows real-time customization, such as geolocation-based content delivery, URL signing, and authentication mechanisms. Integration with AWS CloudWatch allows monitoring of cache hit ratios, request patterns, and WAF logs, enabling proactive optimization and incident response. This combination ensures a globally distributed, secure, and scalable architecture for delivering media content to a worldwide audience.

Question 6:

A logistics company wants to implement a real-time data analytics pipeline for tracking shipments. The pipeline should ingest large volumes of streaming data, process it in near real time, and store results for downstream analysis and reporting. Which AWS architecture best supports these requirements?

A) Amazon Kinesis Data Streams, AWS Lambda, Amazon DynamoDB, Amazon QuickSight
B) Amazon S3, AWS Glue, Amazon Athena, Amazon Redshift
C) Amazon RDS, Amazon EC2, Amazon SQS, Amazon EMR
D) Amazon Kinesis Firehose, Amazon S3, Amazon Redshift, AWS Glue

Answer:

A) Amazon Kinesis Data Streams, AWS Lambda, Amazon DynamoDB, Amazon QuickSight

Explanation:

Amazon Kinesis Data Streams provides a scalable platform for real-time ingestion of streaming data from IoT devices, applications, and sensors. Kinesis allows parallel processing of data in shards, ensuring that high-velocity data is captured efficiently and reliably without loss. It provides ordering guarantees within a shard and supports multiple consumers for processing the same data in parallel.

AWS Lambda enables serverless processing of the streaming data, allowing the company to apply transformations, aggregations, filtering, and enrichment in real time. Lambda functions scale automatically in response to data volume, reducing operational complexity and ensuring that the analytics pipeline can handle variable traffic patterns. By using DynamoDB as the storage backend, processed data is stored in a highly available, low-latency NoSQL database, enabling rapid querying for operational dashboards and reporting. DynamoDB’s on-demand capacity mode allows automatic scaling to accommodate unpredictable workloads, ensuring consistent performance without pre-provisioning resources.

Amazon QuickSight provides visualization and reporting capabilities for the processed data. QuickSight can connect to DynamoDB or other data sources to create dashboards, enabling business users to gain insights into shipments, identify delays, and monitor operational KPIs in real time. Integration with CloudWatch allows monitoring of data ingestion rates, Lambda execution metrics, and DynamoDB performance, enabling proactive operational management.

Option B represents a batch-oriented approach suitable for historical analysis but not real-time streaming. Glue and Athena perform ETL and SQL-based queries on static data in S3, which introduces latency incompatible with real-time analytics. Option C combines traditional database and batch processing services, which adds operational overhead and cannot support low-latency analytics. Option D, Kinesis Firehose with Redshift and Glue, is suitable for near real-time ETL and storage but introduces batching and buffering delays, which may not meet strict real-time processing requirements.

By leveraging Kinesis Data Streams, Lambda, DynamoDB, and QuickSight, the architecture supports fully managed, event-driven, and scalable real-time analytics. Security can be implemented using IAM roles for Lambda, encryption of data in transit using Kinesis TLS connections, and encryption at rest using DynamoDB-managed keys. The architecture ensures minimal operational complexity, high scalability, and rapid insights into shipping operations. Lambda’s ability to process streaming records in parallel ensures timely transformations, while DynamoDB enables fast retrieval for dashboards and downstream processing. Integration with monitoring and alerting systems ensures operational reliability and proactive incident response, making this architecture suitable for logistics companies requiring real-time visibility into shipments.

Question 7:

A global retail company wants to implement a disaster recovery solution for their critical e-commerce application hosted in AWS. The company requires near-zero Recovery Time Objective (RTO) and a Recovery Point Objective (RPO) of less than five minutes. Which AWS architecture best meets these requirements?

A) Deploy EC2 instances and RDS databases in a single Availability Zone with snapshots stored in S3
B) Deploy EC2 instances and RDS databases across multiple Availability Zones with synchronous replication
C) Deploy EC2 instances in a single region and use Route 53 DNS failover with periodic backups
D) Deploy EC2 instances in multiple regions with asynchronous RDS replication and Route 53 weighted routing

Answer:

D) Deploy EC2 instances in multiple regions with asynchronous RDS replication and Route 53 weighted routing

Explanation:

Disaster recovery planning for critical applications requires careful consideration of RTO, RPO, and cost. Near-zero RTO requires that the application be quickly recoverable, while an RPO of less than five minutes ensures minimal data loss in case of failure. Deploying EC2 instances and RDS databases across multiple regions enables a fully resilient solution that can survive the failure of an entire region.

Asynchronous replication between RDS instances in separate regions ensures that transactional data is continuously copied from the primary region to the secondary region. This replication may introduce minimal latency, but it maintains high durability of data and supports RPO objectives. Weighted routing in Amazon Route 53 allows traffic to be directed to the healthy region automatically in case of failure, providing near-zero RTO. Route 53 health checks monitor endpoints and dynamically reroute traffic, ensuring seamless failover without manual intervention.

Option A, deploying in a single Availability Zone with snapshots, cannot meet the RTO and RPO requirements, as a complete AZ outage would result in extended downtime. Snapshots stored in S3 provide durability but require recovery time to restore, which exceeds near-zero RTO. Option B, multi-AZ deployment, provides high availability within a single region but does not protect against region-wide failures. Option C, single-region EC2 with Route 53 DNS failover, introduces delays during failover and risks higher RPO due to the reliance on periodic backups rather than continuous replication.

By implementing a multi-region architecture with asynchronous RDS replication, the company can maintain continuous availability and data durability. EC2 instances deployed in both primary and secondary regions can be pre-provisioned or launched using AWS CloudFormation templates and Auto Scaling groups. This ensures that application servers in the secondary region are ready to handle traffic immediately upon failover. Data replication is monitored, and CloudWatch alarms can be configured to track replication lag, instance health, and traffic routing.

Security and compliance considerations include encrypting data at rest using KMS-managed keys and in transit using SSL/TLS for database connections. IAM policies should restrict access to resources in both regions to authorized personnel and automated services. Additional features such as AWS Backup and cross-region replication for S3 objects complement the architecture, ensuring that static content and application assets are also resilient to disasters.

This approach provides a highly resilient, globally distributed, and automated disaster recovery solution that meets strict RTO and RPO requirements, minimizes operational overhead, and ensures that the e-commerce platform remains available to customers even in catastrophic failures of an entire region.

Question 8:

A healthcare company wants to implement a secure data lake on AWS to store and analyze sensitive patient data. The solution must support structured and unstructured data, fine-grained access controls, encryption, and audit logging. Which combination of AWS services best meets these requirements?

A) Amazon S3, AWS Glue Data Catalog, AWS Lake Formation, AWS IAM
B) Amazon RDS, Amazon Redshift, AWS Glue, AWS KMS
C) Amazon DynamoDB, AWS Lambda, Amazon Athena, Amazon QuickSight
D) Amazon EFS, Amazon EC2, AWS Config, AWS CloudTrail

Answer:

A) Amazon S3, AWS Glue Data Catalog, AWS Lake Formation, AWS IAM

Explanation:

Building a secure data lake for sensitive healthcare data requires a combination of scalable storage, metadata management, fine-grained access control, and auditing capabilities. Amazon S3 provides durable, highly available object storage capable of handling both structured and unstructured data. S3 also supports server-side encryption and integration with AWS Key Management Service (KMS) for managing encryption keys, ensuring that data at rest is protected in compliance with healthcare regulations such as HIPAA.

AWS Glue Data Catalog provides a centralized metadata repository, allowing administrators and analysts to discover, organize, and manage datasets across the data lake. Metadata includes information about schema, partitions, and data lineage, which is critical for governance, compliance, and efficient query execution. AWS Lake Formation extends S3 and Glue capabilities by providing fine-grained access controls. Lake Formation allows role-based access, column-level security, and row-level filtering, ensuring that users only access data they are authorized to see. This capability is essential in healthcare, where patient data privacy and compliance with regulations are non-negotiable.

IAM integrates with Lake Formation and S3 to enforce user and role permissions, enabling centralized identity and access management. This ensures that all access requests are authenticated, authorized, and logged. Audit trails are captured using AWS CloudTrail, which records API calls, administrative actions, and access events, providing full visibility for compliance audits.

Option B, using RDS and Redshift, is better suited for structured analytical workloads but does not efficiently handle unstructured data or provide a fully managed data lake with fine-grained access control. Option C combines serverless analytics components but does not provide centralized governance or secure storage at scale for sensitive data. Option D uses EFS and EC2, which are designed for traditional file systems and server-based workloads, and does not provide scalable object storage, fine-grained access, or integrated metadata management for a modern data lake.

A secure healthcare data lake built with S3, Glue, Lake Formation, and IAM supports various analytics and machine learning workloads. Data scientists can query the data using Amazon Athena or Redshift Spectrum, run ETL pipelines using AWS Glue jobs, and train models using Amazon SageMaker while maintaining compliance. Security is enhanced by applying bucket policies, encryption in transit using TLS, and logging all access events. Versioning and object locking in S3 protect against accidental deletions and ransomware threats.

By combining these AWS services, the company ensures that sensitive healthcare data is stored securely, accessed appropriately, and analyzed efficiently. This architecture balances scalability, security, and compliance requirements while enabling a modern analytics and AI-driven approach to healthcare data insights.

Question 9:

A logistics company wants to implement a highly available, scalable, and low-latency messaging system to track real-time shipment updates across multiple regions. The system must handle bursts of traffic and allow multiple consumers to process the same messages simultaneously. Which AWS architecture best supports this requirement?

A) Amazon SQS Standard Queues with multiple EC2 consumers
B) Amazon Kinesis Data Streams with multiple Lambda consumers and DynamoDB for storage
C) Amazon MQ with single EC2-based consumer
D) Amazon SNS with a single SQS subscription

Answer:

B) Amazon Kinesis Data Streams with multiple Lambda consumers and DynamoDB for storage

Explanation:

A highly available and scalable messaging system for real-time shipment tracking must support multiple concurrent consumers, low latency, and the ability to handle bursts in traffic. Amazon Kinesis Data Streams provides a fully managed, real-time streaming platform where data is ingested into shards. Shards allow parallel processing of incoming records, supporting horizontal scaling for high throughput workloads. Kinesis maintains ordering within a shard, which is crucial for tracking events such as shipment status updates that must be processed sequentially per shipment.

AWS Lambda functions can be configured as consumers for Kinesis streams, processing records in real time as they arrive. Lambda’s serverless nature enables automatic scaling, so the system can handle spikes in message volume without requiring manual provisioning or intervention. This ensures that the tracking system maintains low-latency updates even during peak periods, such as holiday shipping surges.

Processed data can be stored in Amazon DynamoDB, a highly available, fully managed NoSQL database that supports single-digit millisecond latency. DynamoDB’s on-demand capacity mode automatically adjusts throughput based on workload, eliminating the risk of throttling during bursts. The combination of Kinesis, Lambda, and DynamoDB allows multiple consumers to process messages concurrently, while ensuring consistent, durable storage of processed events for reporting and analytics.

Option A, SQS with multiple EC2 consumers, provides decoupled messaging but does not natively support multiple consumers reading the same message without duplication management. SQS also has higher latency compared to Kinesis for real-time streaming. Option C, Amazon MQ with a single EC2 consumer, introduces operational complexity and single points of failure. Option D, SNS with a single SQS subscription, cannot handle multiple consumers processing messages independently in real time, and may introduce latency for high-volume workloads.

By leveraging Kinesis, Lambda, and DynamoDB, the architecture supports multi-region streaming ingestion, automated scaling, and real-time processing with minimal operational overhead. Security is enforced using IAM roles for Lambda and Kinesis, encryption at rest with KMS, and encryption in transit using TLS. CloudWatch monitoring provides metrics for stream health, shard utilization, and Lambda execution, enabling proactive management of throughput and latency. The architecture can be extended with Kinesis Data Firehose for downstream storage and analytics in S3 or Redshift, enabling real-time operational dashboards and predictive analytics.

This solution provides a resilient, scalable, low-latency, and operationally efficient messaging system capable of supporting global logistics operations, ensuring accurate and timely shipment tracking across multiple regions, while minimizing data loss and operational complexity.

Question 10:

A company wants to migrate a legacy multi-tier web application to AWS. The application consists of web servers, application servers, and a relational database. The company wants minimal operational overhead, high availability, and the ability to scale automatically during traffic spikes. Which AWS architecture best meets these requirements?

A) Deploy EC2 instances in a single Availability Zone with a single RDS instance
B) Deploy EC2 instances in multiple Availability Zones with Elastic Load Balancer, Auto Scaling, and RDS Multi-AZ
C) Deploy EC2 instances behind a Route 53 weighted routing policy with a single RDS read replica
D) Deploy all application components on Amazon Lightsail in a single region

Answer:

B) Deploy EC2 instances in multiple Availability Zones with Elastic Load Balancer, Auto Scaling, and RDS Multi-AZ

Explanation:

Migrating a legacy multi-tier application to AWS requires an architecture that provides high availability, scalability, and reduced operational complexity. Deploying EC2 instances across multiple Availability Zones (AZs) ensures that the compute layer is resilient to failures within a single AZ. If one AZ becomes unavailable, traffic can continue to be served by instances in other AZs without impacting application availability.

The Elastic Load Balancer (ELB) distributes incoming traffic across multiple EC2 instances in different AZs. This not only balances load but also ensures that failed instances do not affect user experience. Auto Scaling dynamically adjusts the number of EC2 instances based on traffic demand, enabling cost optimization during low usage periods and performance maintenance during high traffic peaks. This combination reduces operational overhead as instances are managed automatically and scaling occurs without manual intervention.

RDS Multi-AZ deployment addresses the database layer’s high availability. Multi-AZ ensures synchronous replication to a standby instance in another AZ, enabling automatic failover in case of a primary instance failure. The setup also supports automatic backups and point-in-time recovery, further enhancing resiliency. Encryption at rest using AWS KMS and encryption in transit via SSL/TLS ensures that sensitive application data is protected against unauthorized access and meets compliance requirements.

Option A, deploying EC2 and RDS in a single AZ, introduces a single point of failure and cannot meet high availability requirements. Option C, while using Route 53 for weighted routing, does not provide true multi-AZ database redundancy and may cause downtime during failover events. Option D, deploying on Lightsail, reduces management overhead but lacks the advanced features for automatic scaling, multi-AZ deployment, and enterprise-grade availability.

A properly designed multi-AZ architecture also integrates monitoring and operational insights using Amazon CloudWatch. Metrics such as CPU utilization, request latency, and database performance allow proactive scaling and troubleshooting. AWS CloudTrail and VPC Flow Logs provide auditing and security tracking, ensuring compliance with corporate governance policies. By combining these services, the architecture delivers a highly available, scalable, secure, and operationally efficient solution for migrating multi-tier applications to AWS.

In addition, security considerations are enhanced through IAM roles, security groups, and network ACLs, controlling access to compute and database resources. This approach aligns with enterprise best practices for cloud migration, ensuring minimal downtime, high performance, and cost-efficient operations during peak and off-peak periods. The combination of ELB, Auto Scaling, and RDS Multi-AZ also simplifies disaster recovery planning, as failover mechanisms are automated and integrated across compute and database layers.

Question 11:

A company wants to implement a cost-effective and scalable solution to analyze large volumes of log data generated by their web applications. The solution must support near real-time analytics, query flexibility, and minimal operational overhead. Which AWS services should be used?

A) Amazon S3, Amazon Athena, AWS Glue, Amazon QuickSight
B) Amazon RDS, Amazon Redshift, EC2-based log processors
C) Amazon Kinesis Firehose, Amazon Redshift, AWS Lambda
D) Amazon EC2 with custom analytics scripts

Answer:

A) Amazon S3, Amazon Athena, AWS Glue, Amazon QuickSight

Explanation:

Analyzing large volumes of web application log data requires scalable storage, flexible querying, and minimal operational overhead. Amazon S3 provides durable, cost-effective object storage for raw log files. S3 supports versioning, lifecycle policies, and encryption at rest using KMS-managed keys, ensuring data durability, cost management, and compliance. Logs can be ingested directly into S3 from applications, web servers, or streaming services, maintaining a centralized repository for analytics.

Amazon Athena allows serverless, ad-hoc querying of log data stored in S3 using standard SQL syntax. Athena’s serverless architecture eliminates the need to provision or manage servers, automatically scaling to query large datasets efficiently. Athena integrates with the AWS Glue Data Catalog, which provides a centralized metadata repository for schema discovery, table definitions, and data partitions. Glue ensures that Athena can query structured, semi-structured, and unstructured log data efficiently while maintaining accurate metadata for governance.

Amazon QuickSight provides visualization and reporting capabilities on top of Athena queries. Dashboards and reports can be created to monitor user behavior, application performance, error rates, and other operational metrics. QuickSight supports dynamic filters, interactive dashboards, and scheduled reporting, enabling near real-time operational insights without additional infrastructure management.

Option B, using RDS, Redshift, and EC2-based log processors, introduces significant operational overhead and cost for provisioning, managing, and scaling clusters, which is unnecessary for serverless log analytics. Option C, Kinesis Firehose with Redshift and Lambda, is better suited for streaming data pipelines but may not provide cost-effective ad-hoc querying of historical log data in S3. Option D, EC2 with custom scripts, requires manual scaling, patching, and operational monitoring, leading to higher operational complexity and potential downtime.

The S3 + Athena + Glue + QuickSight combination provides a fully serverless, highly scalable, and cost-efficient solution for log analytics. Athena supports partitioning strategies to optimize query performance and reduce costs by scanning only relevant subsets of data. Glue ETL jobs can transform and enrich raw logs for analytics, while QuickSight can connect to multiple data sources for holistic reporting. Security is ensured using IAM policies, S3 bucket policies, encryption in transit via SSL/TLS, and CloudTrail for auditing query and access activity.

Furthermore, this architecture enables integration with machine learning workflows. Logs stored in S3 can be processed and used for predictive analytics or anomaly detection using SageMaker. CloudWatch metrics from applications can also be ingested into S3 for correlation and deeper insights. By providing a fully serverless, automated, and scalable architecture, the company achieves real-time analytics capabilities, reduces operational costs, and ensures compliance and security while maintaining flexibility for future data growth and analysis requirements.

Question 12:

A media streaming company wants to implement a global, low-latency, and secure video delivery system to users. The solution must support caching at edge locations, content protection, and custom routing logic for regional compliance. Which AWS services and architecture best fulfill these requirements?

A) Amazon CloudFront, Amazon S3, AWS Lambda@Edge, AWS WAF
B) Amazon EC2 with multi-region deployment and Route 53 failover
C) Amazon S3 with public access and pre-signed URLs
D) Amazon CloudFront with S3 origin without security or edge processing

Answer:

A) Amazon CloudFront, Amazon S3, AWS Lambda@Edge, AWS WAF

Explanation:

Delivering media content globally with low latency requires a combination of caching, content protection, and edge processing. Amazon CloudFront is a content delivery network (CDN) that caches media content at edge locations around the world, reducing latency for end users and improving performance during peak traffic periods. CloudFront’s edge locations enable fast content delivery regardless of the user’s geographical location.

Amazon S3 serves as the origin for media content, providing durable, highly available, and scalable storage for video files. S3 supports server-side encryption with KMS-managed keys for data at rest, lifecycle management to control storage costs, and versioning to prevent accidental data loss. Integration with CloudFront ensures efficient global distribution while maintaining secure storage of original media content.

AWS Lambda@Edge allows execution of custom logic at CloudFront edge locations. This enables URL rewrites, authentication, authorization, regional content compliance enforcement, and request-based routing. By handling custom logic at the edge, the system reduces latency and avoids unnecessary round trips to origin servers. Lambda@Edge can also generate signed URLs or cookies, providing secure, temporary access to premium content while preventing unauthorized downloads.

AWS WAF protects the application from web attacks such as SQL injection, cross-site scripting, and DDoS attacks. WAF can be configured with CloudFront to enforce security policies globally, ensuring that only legitimate traffic reaches the origin and edge functions. Integration with CloudWatch and CloudTrail provides logging and monitoring for security and operational analytics.

Option B, EC2 multi-region deployment with Route 53 failover, lacks the global edge caching and fine-grained request processing capabilities of CloudFront and Lambda@Edge. Option C, S3 with public access and pre-signed URLs, does not provide edge caching or advanced security features. Option D, CloudFront without Lambda@Edge or WAF, improves latency but does not enforce security, regional compliance, or request-based logic.

By combining CloudFront, S3, Lambda@Edge, and WAF, the media company achieves global low-latency content delivery, secure access control, and flexible routing for regional compliance. The system can handle high-volume streaming requests, scale automatically, and maintain operational efficiency without managing servers. CloudFront caching reduces origin load and improves user experience, while Lambda@Edge ensures dynamic content delivery policies. Security and monitoring are enforced end-to-end, creating a robust, resilient, and scalable media streaming architecture suitable for global audiences.

Question 13:

A global e-commerce company wants to implement a highly available, fault-tolerant, and scalable solution for processing orders. The system should handle unpredictable traffic spikes, maintain low latency, and ensure that order data is never lost. Which AWS architecture best supports these requirements?

A) Amazon SQS Standard queues with multiple EC2 consumers
B) Amazon Kinesis Data Streams with multiple Lambda consumers and Amazon DynamoDB for storage
C) Amazon SNS with a single SQS subscription and EC2 consumers
D) Amazon MQ with a single EC2-based consumer

Answer:

B) Amazon Kinesis Data Streams with multiple Lambda consumers and Amazon DynamoDB for storage

Explanation:

Processing real-time orders in a highly available and scalable manner requires an architecture capable of handling high-volume, low-latency data streams while ensuring durability and fault tolerance. Amazon Kinesis Data Streams provides a fully managed platform for ingesting, processing, and analyzing streaming data. Shards in Kinesis allow parallel processing, ensuring that multiple consumers can handle different parts of the data simultaneously without bottlenecks.

AWS Lambda functions serve as consumers for the Kinesis streams. Lambda automatically scales with the volume of incoming data, processing records in real time without manual provisioning of servers. Each Lambda function can process messages independently, enabling multiple consumers to handle different streams concurrently. This ensures that order processing can scale elastically with traffic spikes, such as Black Friday or holiday promotions, while maintaining low latency.

Amazon DynamoDB stores processed order data. DynamoDB’s on-demand capacity mode allows automatic scaling to accommodate variable workloads without pre-provisioning, ensuring consistent read and write performance. The combination of Kinesis, Lambda, and DynamoDB guarantees durability, as Kinesis ensures data retention for a configurable period, and DynamoDB provides highly available, persistent storage.

Option A, SQS Standard queues with EC2 consumers, provides decoupled messaging but does not natively support multiple consumers processing the same message in real time. Option C, SNS with a single SQS subscription, cannot support multiple independent consumers for high-volume workloads efficiently. Option D, Amazon MQ with a single EC2 consumer, introduces operational complexity and single points of failure.

Security and compliance are enforced through IAM roles for Lambda and Kinesis, encryption at rest using KMS for both Kinesis and DynamoDB, and encryption in transit using TLS. Monitoring and observability are achieved using CloudWatch metrics for Kinesis shard throughput, Lambda execution metrics, and DynamoDB performance indicators. This architecture ensures that order processing remains resilient, scalable, and cost-efficient while maintaining the durability of order data and supporting operational visibility for business-critical e-commerce workflows.

Question 14:

A healthcare provider wants to implement a HIPAA-compliant solution for storing and analyzing patient records in AWS. The solution must support structured and unstructured data, enforce fine-grained access control, and provide auditing capabilities for all access and modifications. Which combination of AWS services is most appropriate?

A) Amazon S3, AWS Lake Formation, AWS Glue Data Catalog, AWS IAM
B) Amazon RDS, Amazon Redshift, AWS Lambda, AWS KMS
C) Amazon DynamoDB, AWS Lambda, Amazon Athena, Amazon QuickSight
D) Amazon EFS, Amazon EC2, AWS Config, AWS CloudTrail

Answer:

A) Amazon S3, AWS Lake Formation, AWS Glue Data Catalog, AWS IAM

Explanation:

Creating a HIPAA-compliant data lake requires a combination of secure, scalable storage, fine-grained access controls, metadata management, and auditability. Amazon S3 provides highly durable object storage capable of handling both structured and unstructured healthcare data, including EHRs, imaging files, and lab results. S3 supports encryption at rest using KMS-managed keys and encryption in transit using TLS, ensuring data confidentiality and compliance.

AWS Lake Formation simplifies building and securing data lakes by providing fine-grained access control at the database, table, column, or row level. Lake Formation allows administrators to define policies that enforce strict access permissions, ensuring that only authorized personnel or applications can view or modify sensitive patient records. This is essential for HIPAA compliance, as patient data privacy is strictly regulated.

AWS Glue Data Catalog provides a centralized repository for metadata management. It allows administrators to define schemas, manage partitions, and maintain data lineage, enabling discovery and efficient querying of datasets while maintaining governance. Glue integration with Athena or Redshift Spectrum enables serverless, ad-hoc querying and analytics on structured and semi-structured data without moving it from S3, reducing operational overhead.

IAM manages authentication and authorization for all services. Roles, groups, and policies are used to enforce the principle of least privilege, ensuring that users and applications access only the data and operations they are permitted to use. CloudTrail records all API calls and actions performed in S3, Lake Formation, and Glue, providing a complete audit trail for compliance purposes.

Option B, using RDS and Redshift with Lambda, supports structured analytics but does not efficiently handle unstructured data or provide a fully managed, centralized data lake. Option C, using DynamoDB and Athena, lacks fine-grained access control and centralized governance for unstructured healthcare datasets. Option D, EFS with EC2 and Config, introduces operational complexity and does not provide scalable storage, metadata management, or fine-grained security required for HIPAA compliance.

This architecture allows healthcare providers to store, query, and analyze patient data securely while maintaining operational efficiency and regulatory compliance. The combination of S3, Lake Formation, Glue Data Catalog, and IAM ensures scalable storage, strict access policies, centralized metadata, audit logging, and operational observability. Security best practices such as encryption, secure key management, network isolation through VPC endpoints, and monitoring via CloudWatch enhance data protection. Additionally, integrating this data lake with AWS analytics and ML services, like Athena, SageMaker, and QuickSight, enables advanced analytics, predictive modeling, and reporting without compromising security or compliance.

Question 15:

A global media company wants to deliver video content to users with low latency, high availability, and content protection. The system must support edge caching, request-level authentication, and compliance with regional content restrictions. Which AWS services should be used to implement this architecture?

A) Amazon CloudFront, Amazon S3, AWS Lambda@Edge, AWS WAF
B) Amazon EC2 in multiple regions with Route 53 failover
C) Amazon S3 with public access and pre-signed URLs
D) Amazon CloudFront with S3 origin without edge processing or security

Answer:

A) Amazon CloudFront, Amazon S3, AWS Lambda@Edge, AWS WAF

Explanation:

Delivering global media content efficiently requires a combination of low-latency delivery, edge caching, security, and compliance enforcement. Amazon CloudFront is a content delivery network that caches content at edge locations globally, reducing latency and providing high availability for users. Caching at the edge decreases the load on the origin, improves performance during traffic spikes, and ensures fast content delivery worldwide.

Amazon S3 serves as the origin for CloudFront, providing durable, scalable storage for video content. S3 supports encryption at rest using KMS-managed keys and can enforce access control via bucket policies and IAM roles. By combining S3 with CloudFront, content is protected while still being delivered efficiently to end users.

AWS Lambda@Edge allows execution of custom logic at CloudFront edge locations. This enables URL rewrites, authentication, authorization, geographic-based content restriction, and custom header insertion without requiring changes at the origin. Lambda@Edge ensures that compliance policies, such as regional licensing restrictions, are enforced globally, while minimizing latency by processing requests at the closest edge location to the user.

AWS WAF protects the application from common web attacks such as SQL injection, cross-site scripting, and DDoS. WAF integration with CloudFront ensures that all requests are evaluated against security rules before reaching the origin or edge functions, providing an additional layer of protection. CloudWatch and CloudTrail monitor and log requests, enabling operational insights and compliance auditing.

Option B, EC2 multi-region deployment with Route 53 failover, lacks edge caching, introduces operational complexity, and does not provide request-level content protection. Option C, S3 with public access and pre-signed URLs, provides basic security but lacks global edge caching and advanced request processing. Option D, CloudFront without Lambda@Edge or WAF, improves latency but does not enforce security policies, compliance rules, or dynamic request handling.

By using CloudFront, S3, Lambda@Edge, and WAF, the media company achieves low-latency global delivery, edge caching, secure access control, request-level customization, and compliance enforcement. This architecture scales automatically during high demand periods, reduces origin load, and ensures that media content is delivered efficiently and securely. Security, monitoring, and operational management are integrated end-to-end, providing a resilient and compliant solution for global media streaming.