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Requirement Analysis: The company needs highly available, scalable storage for a document management application without modifying the application during migration.

EFS Overview: Amazon EFS provides scalable file storage that can be mounted concurrently on multiple EC2 instances across different Availability Zones.

EFS Standard-IA: Using the Standard-IA storage class helps reduce costs for infrequently accessed data while maintaining high availability and scalability.

Implementation:

Create an EFS file system.

Configure mount targets in multiple Availability Zones to ensure high availability.

Mount the EFS file system on EC2 instances in the Auto Scaling group.

Conclusion: This solution meets the high availability, scalability, and cost-effectiveness requirements without needing application modifications.

References

Amazon EFS:Amazon EFS Documentation

EFS Storage Classes:Amazon EFS Storage Classes

Understanding the Requirement: The company needs to copy files generated by an on-premises application to AWS without modifying the application code. The files are stored on an SMB file share and require a low-latency connection to the application servers.

Analysis of Options:

Amazon Elastic File System (EFS): EFS is designed for Linux-based workloads and does not natively support SMB file shares.

Amazon FSx for Windows File Server: FSx supports SMB file shares but would require changes to the application or additional infrastructure to connect on-premises systems.

AWS Snowball: Suitable for large data transfers but not for continuous, low-latency file copying.

AWS Storage Gateway: Provides a hybrid cloud storage solution, supporting SMB file shares and enabling seamless copying of files to AWS without requiring changes to the application.

Best Solution:

AWS Storage Gateway: This service meets the requirement for a low-latency, seamless file transfer solution from on-premises to AWS without modifying the application code.

Understanding the Requirement: The development team needs to prevent the launch of large EC2 instances across multiple AWS accounts used for development, staging, and production environments.

Analysis of Options:

IAM Policies: Would need to be applied individually to each user in every account, leading to significant operational overhead.

AWS Resource Access Manager: Used for sharing resources, not for enforcing restrictions on resource creation.

IAM Role in Each Account: Requires creating and managing roles in each account, leading to higher operational overhead compared to using a centralized approach.

Service Control Policy (SCP) with AWS Organizations: Provides a centralized way to enforce policies across multiple AWS accounts, ensuring that large EC2 instances cannot be launched in any account.

Best Solution:

Service Control Policy (SCP) with AWS Organizations: This solution offers the least operational overhead by allowing centralized management and enforcement of policies across all accounts, effectively preventing the launch of large EC2 instances.

Understanding the Requirement: The company wants to migrate an application to AWS, increase its availability, and use AWS WAF in the architecture.

Analysis of Options:

Auto Scaling group with ALB and WAF: This option provides high availability by distributing instances across multiple Availability Zones. The ALB ensures even traffic distribution, and AWS WAF provides security at the application layer.

Cluster placement group with ALB and WAF: Cluster placement groups are for low-latency networking within a single AZ, which does not provide the high availability across AZs.

Two EC2 instances with ALB and WAF: This setup provides some availability but does not scale automatically, missing the benefits of an Auto Scaling group.

Auto Scaling group with WAF directly: AWS WAF cannot be directly connected to an Auto Scaling group; it needs to be attached to an ALB, CloudFront distribution, or API Gateway.

Best Solution:

Auto Scaling group with ALB and WAF: This configuration ensures high availability, scalability, and security, meeting all the requirements effectively.

Understanding the Requirement: The company needs to design a scalable and serverless solution for a near-real-time streaming application that experiences high latency due to large amounts of incoming data. The job processing takes about 30 minutes.

Analysis of Options:

Amazon Kinesis Data Firehose: Provides a fully managed service for real-time data streaming and ingestion, allowing for seamless data delivery to destinations such as Amazon S3, Redshift, and Elasticsearch.

AWS Lambda with AWS Step Functions: While suitable for orchestration and lightweight processing, Lambda might not handle long-running jobs (max 15 minutes execution limit) efficiently.

AWS DMS: Primarily used for database migration, not for real-time data ingestion in this context.

Amazon EC2 in Auto Scaling Group: Provides scalability but involves managing servers, which is not serverless and adds operational overhead.

AWS Fargate with ECS: Offers a serverless compute engine for containers, allowing easy scaling and management without managing the underlying infrastructure.

Best Solution:

Amazon Kinesis Data Firehose: For ingesting the streaming data efficiently.

AWS Fargate with ECS: For processing the data in a scalable and serverless manner.

Requirement Analysis: The application was overwhelmed with unexpected data volume, leading to data loss and the need for a replay mechanism.

Amazon SQS Overview: SQS is a fully managed message queuing service that decouples and scales microservices, distributed systems, and serverless applications.

Data Decoupling: By using an SQS queue, the application can store incoming tracker data reliably and process it asynchronously, preventing data loss.

Implementation:

Create an SQS queue.

Modify the web application to send incoming data to the SQS queue.

Configure the application instances to poll the SQS queue and process the messages.

Conclusion: This solution meets the requirements with minimal operational overhead, ensuring data is not lost and can be processed at the application’s own pace.

References

Amazon SQS:Amazon SQS Documentation

Current Backup Settings: By default, Amazon RDS creates automated backups with a retention period of 7 days.

Regulatory Requirements: The requirement is to retain daily backups for 30 days.

Adjusting Retention Period: You can modify the RDS instance settings to increase the automated backup retention period to 30 days.

Operational Overhead: This solution is the simplest as it leverages existing automated backups and requires minimal intervention.

Implementation: The change can be made via the AWS Management Console, AWS CLI, or AWS SDKs.

References

Amazon RDS Backups:Amazon RDS Documentation

Understanding the Requirement: The application is stateful and requires at least two EC2 instances to be running at all times, with a highly available and fault-tolerant architecture.

Analysis of Options:

Minimum capacity of two with instances in separate AZs: Ensures high availability by distributing instances across multiple AZs, fulfilling the requirement of always having two instances running.

Minimum capacity of four: Provides redundancy but is more than what is required and increases cost without additional benefit.

Spot Instances: Not suitable for a stateful application requiring guaranteed availability, as Spot Instances can be terminated at any time.

Combination of On-Demand and Spot Instances: Mixing instance types might provide cost savings but does not ensure the required availability for a stateful application.

Best Solution:

Minimum capacity of two with instances in separate AZs: This setup ensures high availability and meets the requirement with the least cost and complexity.

Understanding the Requirement: The company wants to optimize costs and reduce operational overhead for an RDS for PostgreSQL database that only needs to be active during business hours on weekdays.

Analysis of Options:

Instance Scheduler on AWS: Allows for automated start and stop schedules based on specified times, ideal for resources only needed during certain hours. This directly optimizes costs by running the database only when needed.

Turn off automatic backups and create weekly snapshots: Does not address the requirement of reducing operational overhead and optimizing runtime costs.

Custom Lambda function: This could work but adds unnecessary complexity compared to using the Instance Scheduler.

All Upfront Reserved DB Instances: While this reduces costs, it does not optimize for usage patterns that require the database only during specific hours.

Best Solution:

Instance Scheduler on AWS: This option effectively manages the database runtime based on the specified schedule, reducing costs and operational overhead.

Understanding the Requirement: The company wants to improve caching to enhance website performance while ensuring that stale content is not served for more than a few minutes after a deployment.

Analysis of Options:

Set CloudFront TTL: Setting a short TTL (e.g., 2 minutes) ensures that cached content is refreshed frequently, reducing the risk of serving stale content.

S3 Bucket TTL: This would not control the cache duration for the CloudFront distribution.

Cache-Control Private: This directive is for controlling caching by private caches (e.g., browsers) and is not applicable for CloudFront.

Lambda@Edge: While this can add headers dynamically, it adds complexity and operational overhead.

Cache-Control max-age and CloudFront Invalidation: Setting a longer max-age for objects ensures they are cached longer, reducing load on the origin. Invalidation ensures that updated content is refreshed immediately after deployment.

Best Combination of Caching Methods:

Set the CloudFront default TTL to 2 minutes: This balances caching and freshness of content.

Add a Cache-Control max-age directive of 24 hours and use CloudFront invalidation: This ensures efficient caching while providing a mechanism to clear outdated content immediately after a deployment.

Understanding the Requirement: The company needs a container solution that can scale across on-premises, hybrid, and cloud environments, with a load balancer for HTTP traffic.

Analysis of Options:

Fargate Launch Type and ECS Anywhere: Using Fargate for cloud-based containers and ECS Anywhere for on-premises containers provides a unified management experience across environments without needing to manage infrastructure.

Application Load Balancer: Suitable for HTTP traffic and can distribute requests to the ECS services, ensuring scalability and performance.

Network Load Balancer: Typically used for TCP/UDP traffic, not specifically optimized for HTTP traffic.

EC2 Launch Type for ECS and ECS Anywhere with Fargate: Involves managing infrastructure for EC2 instances, increasing operational overhead.

Best Combination of Solutions:

ECS with Fargate Launch Type and ECS Anywhere: This provides flexibility and scalability across hybrid environments with minimal operational overhead.

Application Load Balancer: Optimized for HTTP traffic, ensuring efficient load distribution and scaling for the ECS services.

Understanding the Requirement: The company needs to process images as they are uploaded to S3 in a cost-effective manner, currently using an EC2 Spot Fleet for nightly processing.

Analysis of Options:

S3 Event Notifications to SQS and Lambda: This setup allows for event-driven processing with Lambda, which scales automatically based on the number of messages in the queue. It is cost-effective as Lambda charges are based on the compute time used.

S3 Event Notifications to SQS and EC2 Reserved Instance: Involves managing EC2 instances, which adds operational overhead and is less cost-effective.

S3 Event Notifications to SNS and ECS: More complex and potentially less cost-effective compared to using Lambda for simple processing tasks.

S3 Event Notifications to SNS: Requires additional configuration and management to process messages.

Best Solution:

S3 Event Notifications to SQS and Lambda: This option is the most cost-effective and scalable, leveraging AWS managed services with minimal operational overhead.

Understanding the Requirement: The company needs to migrate applications to AWS, maintaining isolated networks while allowing communication with a shared services VPC and among the applications.

Analysis of Options:

Software VPN Tunnels: This approach involves high administrative overhead and complexity in managing multiple VPN connections.

VPC Peering: While suitable for smaller numbers of VPCs, it becomes complex and hard to manage at scale with over 100 applications.

Direct Connect: Primarily used for high-bandwidth, low-latency connections to on-premises networks, not inter-VPC communication.

Transit Gateway: Simplifies network management by acting as a central hub, allowing easy routing and scalability as more applications are migrated.

Best Solution:

Transit Gateway: This provides a scalable, efficient solution with minimal administrative overhead for managing network connections between multiple VPCs and the shared services VPC.

Understanding the Requirement: The company needs to migrate sensitive and critical data from on-premises SQL Server databases to AWS, aiming to increase security and reduce operational overhead.

Analysis of Options:

EC2 Instances with KMS: Running SQL Server on EC2 provides control but requires significant operational overhead for management, backups, patching, and high availability.

Multi-AZ Amazon RDS for SQL Server with KMS: Amazon RDS for SQL Server offers managed database services, reducing operational overhead. Multi-AZ deployment provides high availability, and KMS encryption ensures data security.

Amazon S3 and Macie: S3 is not a suitable replacement for relational databases, and Macie is used for data security and compliance but not for database operations.

Amazon DynamoDB and CloudWatch Logs: DynamoDB is a NoSQL database and does not support SQL Server workloads directly. CloudWatch Logs are used for monitoring, not for ensuring database security.

Best Solution:

Multi-AZ Amazon RDS for SQL Server with KMS: This solution meets the requirements for security, high availability, and reduced operational overhead by using a managed database service with encryption.

Understanding the Requirement: Developers in the Development account need to push code changes to the Production account, with phased access control for different stages of the project.

Analysis of Options:

Policy Documents in Each Account: This approach increases complexity and is harder to manage compared to role-based access.

IAM Role in Development Account: Roles in the Development account cannot directly control access to resources in the Production account.

IAM Role in Production Account: Creating a role in the Production account with a trust policy that allows the Development account to assume it provides controlled, secure access.

IAM Group in Production Account: This approach does not provide the required cross-account access control.

Best Solution:

IAM Role in the Production Account: This method allows precise control over who can access the Production account from the Development account, with the ability to manage permissions and access levels effectively.

Understanding the Requirements: The company needs to optimize costs and has the flexibility to change EC2 instance types and families frequently (every 2-3 months).

Savings Plans Overview: Savings Plans offer significant savings over On-Demand pricing, with the flexibility to use any instance type and family within a region.

No Upfront Compute Savings Plan: This plan allows for cost optimization without any upfront payment, offering flexibility to change instance types and families.

Term Selection: A 1-year term is appropriate for balancing cost savings and flexibility given the frequent changes in instance types.

Conclusion: A No Upfront Compute Savings Plan for a 1-year term provides the needed flexibility and cost savings without the commitment and inflexibility of Reserved Instances.

References

AWS Savings Plans:AWS Savings Plans

AWS Cost Management Documentation:AWS Cost Management

Understanding the Requirement: The application running on EC2 instances in private subnets needs to securely connect to an Amazon SQS queue without exposing traffic to the public internet.

Analysis of Options:

Interface VPC Endpoint in Private Subnets: Allows private, secure connectivity to SQS without using the public internet.Configuring security groups ensures controlled access from EC2 instances.

Interface VPC Endpoint in Public Subnets: Not necessary for private EC2 instances and exposes additional security risks.

Gateway Endpoint: Gateway endpoints are not supported for SQS; they are used for services like S3 and DynamoDB.

NAT Gateway with IAM Role: Increases costs and complexity compared to using an interface VPC endpoint directly.

Best Solution:

Interface VPC Endpoint in Private Subnets: This option ensures secure, private connectivity to SQS, meeting the requirement with minimal complexity and optimal security.

Understanding the Requirement: The application experiences slow performance at the start of peak hours, but normalizes after a few hours. The goal is to ensure proper performance at the beginning of peak hours.

Analysis of Options:

Application Load Balancer: Ensures proper traffic distribution but does not address the need to have sufficient instances running at the start of peak hours.

Dynamic Scaling Policy Based on Memory or CPU Utilization: While dynamic scaling reacts to usage metrics, it may not preemptively scale in anticipation of peak hours, leading to delays as new instances are launched and become available.

Scheduled Scaling Policy: This allows the Auto Scaling group to launch instances ahead of time, ensuring that enough instances are available and ready to handle the increased load right at the start of peak hours.

Best Solution:

Scheduled Scaling Policy: This approach ensures that new instances are launched and ready before peak hours begin, addressing the slow performance issue at the start of peak periods.

Understanding the Requirement: The application running in a private subnet needs to store and retrieve data from S3 without data traveling over the public internet.

Analysis of Options:

NAT Gateway: Allows private subnets to access the internet but incurs additional costs and still routes traffic through the public internet.

AWS Storage Gateway: Provides hybrid cloud storage solutions but is not the most cost-effective for direct S3 access from within the VPC.

S3 Interface Endpoint: Provides private access to S3 but is generally used for specific use cases where more granular control is required, which might be overkill and more expensive.

S3 Gateway Endpoint: Provides private, cost-effective access to S3 from within the VPC without routing traffic through the public internet.

Best Solution:

S3 Gateway Endpoint: This option meets the requirements for secure, private access to S3 from a private subnet most cost-effectively.

Understanding the Requirement: The company needs to mitigate DDoS attacks on its website, which uses AWS Global Accelerator to route traffic to an Application Load Balancer (ALB).

Analysis of Options:

AWS WAF on Global Accelerator: Allows for centralized protection and can block traffic based on rate-based rules, effectively mitigating DDoS attacks with minimal implementation effort.

Lambda Function and VPC Network ACL: Requires custom implementation and ongoing management, increasing complexity and effort.

AWS WAF on ALB: Provides protection but involves additional configuration and management at the ALB level.

CloudFront Distribution in front of Global Accelerator: Adds unnecessary complexity and changes the current traffic flow setup.

Best Solution:

AWS WAF on Global Accelerator: This provides the required protection with the least implementation effort, ensuring effective DDoS mitigation and maintaining the existing architecture.