1. **Calculating the cost of Reserved Instances**: The company plans to purchase RIs for 80 instances. The cost of a Reserved Instance is $0.05 per hour. Therefore, the annual cost for one Reserved Instance can be calculated as follows: \[ \text{Annual cost per RI} = 0.05 \, \text{USD/hour} \times 24 \, \text{hours/day} \times 365 \, \text{days/year} = 0.05 \times 8760 = 438 \, \text{USD} \] For 80 Reserved Instances, the total cost will be: \[ \text{Total cost for RIs} = 80 \times 438 = 35,040 \, \text{USD} \] 2. **Calculating the cost of On-Demand Instances**: The company will use On-Demand instances for the remaining 20 instances. The cost of an On-Demand instance is $0.10 per hour. The annual cost for one On-Demand instance is: \[ \text{Annual cost per On-Demand instance} = 0.10 \, \text{USD/hour} \times 24 \, \text{hours/day} \times 365 \, \text{days/year} = 0.10 \times 8760 = 876 \, \text{USD} \] For 20 On-Demand instances, the total cost will be: \[ \text{Total cost for On-Demand instances} = 20 \times 876 = 17,520 \, \text{USD} \] 3. **Calculating the total cost**: Now, we sum the costs of the Reserved Instances and the On-Demand Instances: \[ \text{Total cost} = \text{Total cost for RIs} + \text{Total cost for On-Demand instances} = 35,040 + 17,520 = 52,560 \, \text{USD} \] Thus, the total cost for the year, considering the combination of Reserved Instances and On-Demand Instances, is $52,560. This scenario illustrates the importance of understanding the cost structures associated with different instance types in AWS and how strategic planning can lead to significant cost savings. By analyzing usage patterns and selecting the appropriate instance types, organizations can optimize their cloud spending effectively.

Given that the precision is 0.85, we can express this mathematically as: \[ \text{Precision} = \frac{\text{True Positives (TP)}}{\text{True Positives (TP)} + \text{False Positives (FP)}} \] This means that for every 100 positive predictions made by the model, 85 are correct. Recall is given as 0.75, which can be expressed as: \[ \text{Recall} = \frac{\text{True Positives (TP)}}{\text{True Positives (TP)} + \text{False Negatives (FN)}} \] This indicates that out of all actual positive cases (customers who churned), the model correctly identifies 75%. Now, we know that the total number of positive cases (customers who churned) is 200. Using the recall formula, we can rearrange it to find the number of true positives: \[ 0.75 = \frac{\text{TP}}{200} \] Multiplying both sides by 200 gives us: \[ \text{TP} = 0.75 \times 200 = 150 \] Thus, the model correctly identified 150 customers as churned. To further validate this, we can also use the precision value. If we denote the number of false positives as FP, we can express the precision as: \[ 0.85 = \frac{150}{150 + FP} \] Rearranging gives us: \[ 150 + FP = \frac{150}{0.85} \implies FP \approx 26.47 \] Since FP must be a whole number, we can round it to 26. This means the model predicted approximately 176 customers as churned (150 true positives + 26 false positives). In conclusion, the model’s performance metrics indicate that it effectively identified 150 customers who churned, demonstrating a solid understanding of precision and recall in the context of machine learning evaluation metrics.

This approach ensures that alerts are generated promptly when anomalies are detected, as Lambda can execute code in response to each incoming message without the need for polling or periodic analysis. In contrast, storing telemetry data in Amazon S3 and analyzing it with Amazon Athena (option b) would introduce latency, as alerts would only be generated after data is aggregated and queried, which is not suitable for real-time monitoring. Option c, sending data directly to Amazon DynamoDB, while it allows for real-time querying, does not provide the same level of processing capability as Lambda functions, which can include complex logic for anomaly detection. Lastly, option d, implementing a custom application that polls sensors every minute, would lead to delayed data ingestion and potentially miss critical real-time events, making it less effective for immediate alerting. Thus, the optimal solution involves using AWS IoT Core in conjunction with AWS Lambda to ensure efficient data processing and timely alert generation, aligning with best practices for real-time IoT applications.

Using IAM roles allows for a more granular control of permissions, as roles can be tailored to specific tasks or job functions. By defining SCPs at the organizational level, the company can enforce policies that restrict what actions can be performed across all accounts, regardless of the individual IAM policies in each member account. This layered approach enhances security by ensuring that even if a user has permissions in a member account, those permissions are still subject to the constraints imposed by the SCPs. On the other hand, assigning full administrative access to all users in the master account (option b) contradicts the principle of least privilege and significantly increases security risks. Similarly, using IAM policies in each member account without SCPs (option c) can lead to inconsistent permission management and potential over-privilege issues. Lastly, implementing a single IAM user shared among all users (option d) is a poor practice as it undermines accountability and traceability, making it difficult to track user actions and enforce security measures. In summary, the best practice for the company is to utilize IAM roles in conjunction with SCPs to maintain centralized control over permissions while adhering to the principle of least privilege, thereby minimizing security risks across their AWS environment.

On the other hand, using a single EC2 instance for the backend API (option b) introduces a single point of failure, which contradicts the goal of high availability. Deploying all components in a single Availability Zone (option c) also poses risks, as it makes the entire application susceptible to outages caused by issues in that AZ. Lastly, while utilizing Amazon S3 for static content delivery (option d) can be a cost-saving measure, it does not directly address the high availability and fault tolerance requirements for the application as a whole. In summary, the best approach for the company is to implement Multi-AZ deployments for their database, as this ensures that their data is protected and available even in the event of an AZ failure, aligning perfectly with their goals of high availability and fault tolerance while managing costs effectively.

On the other hand, setting DynamoDB to on-demand capacity mode allows the database to automatically scale up or down based on the application’s needs, which is particularly advantageous for unpredictable workloads. This mode eliminates the need to pre-provision read and write capacity, thus reducing costs during low-traffic periods while ensuring that the application can handle sudden spikes in demand without throttling. In contrast, using provisioned concurrency for AWS Lambda can lead to unnecessary costs if the application does not consistently require the provisioned instances. Similarly, configuring DynamoDB with a fixed read/write capacity can result in over-provisioning, leading to wasted resources and increased costs during periods of low activity. Auto-scaling capacity mode for DynamoDB can be beneficial, but it may not respond as quickly as on-demand mode during sudden traffic spikes. Lastly, while implementing a maximum concurrency limit for AWS Lambda can help manage costs, it does not directly address the need for efficient scaling in conjunction with DynamoDB’s capacity settings. The combination of reserved concurrency and fixed capacity settings may not provide the flexibility needed for a dynamic application environment. Thus, the optimal approach for this scenario is to use AWS Lambda with reserved concurrency to ensure availability and set DynamoDB to on-demand capacity mode to handle varying loads efficiently while minimizing costs. This combination allows the application to scale effectively in response to demand without incurring unnecessary expenses.

Minimizing technical jargon in user manuals is essential, as end-users may not have the same level of technical expertise as developers. This approach aligns with best practices in technical communication, which advocate for audience analysis and the creation of user-centered documentation. Creating a single comprehensive document may lead to confusion, as users might struggle to find the information pertinent to their needs amidst a wealth of technical details. Similarly, a standardized template that does not consider the specific requirements of different audiences can result in ineffective communication, as it may not address the unique contexts in which each audience operates. Lastly, while visual aids can enhance understanding, relying solely on them without sufficient textual explanation can lead to misinterpretation, especially for complex concepts that require detailed descriptions. Therefore, the most effective strategy is to create distinct documentation that caters to the specific needs of developers and end-users, ensuring clarity and usability across all types of technical documentation.

This approach not only enhances security by limiting access but also simplifies auditing and compliance efforts, as each role can be reviewed independently. Each IAM role can have policies attached that explicitly define what actions are allowed on which resources, using AWS’s policy language to specify conditions and constraints. On the other hand, using a single IAM role with broad permissions (as suggested in option b) would violate the principle of least privilege, exposing the environment to unnecessary risks. Similarly, creating a single IAM user with administrative privileges (option c) would lead to a lack of accountability and traceability, making it difficult to determine who accessed what resources and when. Lastly, assigning permissions based on job titles without specific policies (option d) could lead to over-provisioning of access rights, further compromising security. In summary, the best practice for resource access management in AWS is to create distinct IAM roles for each team, ensuring that access is granted based on the specific needs of their functions while adhering to security best practices. This structured approach not only protects sensitive resources but also aligns with compliance requirements and enhances overall governance within the cloud environment.

Additionally, creating manual snapshots before major application updates is a best practice. This allows the company to revert to a known good state if an update introduces issues. Manual snapshots are retained until explicitly deleted, providing flexibility in recovery options. On the other hand, relying solely on manual snapshots (as suggested in option b) can lead to gaps in the backup strategy, especially if snapshots are not taken regularly. Option c, while it includes automated backups, has a retention period of only 7 days, which may not be sufficient for all recovery scenarios. Lastly, option d is flawed because it neglects the importance of backing up the database tier, which is critical for the application’s functionality. The database contains essential data that must be preserved independently of the application tier. Therefore, the most effective strategy combines automated backups with a longer retention period and manual snapshots to ensure comprehensive data protection and recovery capabilities.

Furthermore, the Greengrass core can facilitate communication between the local devices and AWS services when necessary, ensuring that data can be sent to the cloud for further analysis or storage without compromising local processing capabilities. This hybrid approach maximizes resource utilization by allowing devices to offload processing tasks to the core while still being able to interact with the cloud as needed. The other options present significant drawbacks. Deploying Lambda functions directly to each IoT device (option b) would lead to increased complexity in managing updates and could result in inconsistent behavior across devices. Relying solely on AWS IoT Core for processing (option c) would negate the benefits of local execution, introducing latency and potential bandwidth issues. Lastly, disabling local execution of Lambda functions (option d) would undermine the purpose of using Greengrass, as it is designed to enable local processing capabilities. In summary, the best configuration for the manufacturing company is to deploy Lambda functions to the Greengrass core device, allowing for efficient local execution and communication with AWS services, thus ensuring minimal latency and optimal resource utilization.

Once the peering connection is established, the next step involves configuring the route tables of both VPCs to enable traffic flow. This requires adding routes that point to the peering connection for the respective CIDR blocks of the other VPC. For instance, VPC-A’s route table must include a route directing traffic destined for 10.1.0.0/16 to the peering connection, and similarly for VPC-B. Importantly, AWS VPC peering does not support transitive routing. This means that if VPC-A is peered with VPC-B, and VPC-B is peered with VPC-C, VPC-A cannot communicate with VPC-C through VPC-B. Each peering connection is isolated, ensuring that traffic does not flow through a third VPC. This design enhances security and control over network traffic. In summary, the correct understanding of VPC peering involves recognizing that it allows direct communication between two VPCs with non-overlapping CIDR blocks, requires explicit route table configurations for traffic flow, and does not permit transitive routing. Thus, the statement regarding the establishment of the peering connection without overlapping CIDR blocks and the direct communication without transitive routing is accurate.

In contrast, reviewing the application code (option b) is a valid step but should come after assessing the infrastructure metrics, as the issue may not stem from the code itself. Checking the AWS Service Health Dashboard (option c) is useful for understanding external factors affecting service availability but does not directly address the performance of your specific application. Lastly, increasing instance sizes (option d) might provide a temporary fix but does not resolve the underlying issue; it could also lead to unnecessary costs if the root cause is elsewhere. Thus, starting with a detailed analysis of CloudWatch metrics allows for a data-driven approach to pinpoint the exact cause of the performance degradation, enabling more effective and targeted remediation strategies. This method aligns with best practices in troubleshooting, emphasizing the importance of monitoring and metrics in maintaining application performance.

In contrast, a VPN connection, while it does provide encryption for data in transit, is inherently subject to the variability of the public internet. This can lead to unpredictable latency and bandwidth fluctuations, which may not meet the company’s stringent requirements. Additionally, while VPNs can be cost-effective for smaller data transfers, they can become expensive at scale due to the costs associated with data transfer over the internet. Moreover, AWS Direct Connect can be configured to include encryption options, such as using MACsec (Media Access Control Security), which can provide an additional layer of security without sacrificing performance. Therefore, while both options have their merits, AWS Direct Connect is better suited for scenarios where performance, reliability, and consistent bandwidth are critical, especially for applications that cannot tolerate high latency or bandwidth variability. The incorrect options present common misconceptions. For instance, while VPNs do offer encryption, they do not inherently provide the same level of performance reliability as Direct Connect. Additionally, the cost comparison is not straightforward, as Direct Connect may involve initial setup costs but can be more economical for high-volume data transfers in the long run. Lastly, scaling a VPN connection often requires additional configuration and may not automatically accommodate increased bandwidth needs without careful planning.

Network In/Out metrics are essential for understanding the data transfer rates between the web server, application server, and database server. If the network bandwidth is saturated, it can cause delays in data transmission, contributing to latency. Latency Metrics, particularly those related to the application load balancer and the response times from the application and database servers, provide direct insight into how long it takes for requests to be processed. Monitoring these metrics allows the operations team to pinpoint whether the latency is occurring at the web server, application server, or database level. The other options, while relevant, do not provide a comprehensive view of the factors contributing to latency. For instance, Disk Read/Write Operations and Memory Usage are important but may not directly correlate with latency unless they are significantly impacting CPU performance. Similarly, Request Count and HTTP 5xx Errors are useful for understanding traffic patterns and error rates but do not directly address latency unless correlated with response times. Therefore, focusing on CPU Utilization, Network In/Out, and Latency Metrics provides a holistic approach to diagnosing and resolving latency issues in the application.

Option b, which suggests taking a snapshot and migrating immediately, poses a risk of data loss because any changes made after the snapshot is taken would not be reflected in the new database. This could lead to inconsistencies and potential issues once the application is switched over. Option c lacks a replication strategy, which is critical for maintaining data integrity during the migration. Without replication, any changes made to the database during the migration would be lost, leading to discrepancies between the old and new databases. Option d involves shutting down the application, which is counterproductive to the goal of minimizing downtime. This approach would lead to a complete halt in service, negatively impacting users and business operations. By utilizing AWS DMS for continuous replication, the company can ensure that the migration is seamless, with minimal disruption to the application and its users. This method not only preserves data consistency but also allows for a smooth transition to the new environment, making it the most suitable choice for the company’s migration strategy.

\[ \text{Events per second} = \frac{10,000 \text{ events}}{60 \text{ seconds}} \approx 166.67 \text{ events/second} \] Next, we need to consider the processing time of each Lambda function. Given that each function takes approximately 100 milliseconds to process an event, we can convert this time into seconds: \[ \text{Processing time per event} = 100 \text{ milliseconds} = 0.1 \text{ seconds} \] Now, we can calculate how many events a single Lambda function can process in one second. The number of events processed by one Lambda function in one second is the inverse of the processing time: \[ \text{Events processed per Lambda per second} = \frac{1 \text{ second}}{0.1 \text{ seconds/event}} = 10 \text{ events/second} \] To find the total number of concurrent Lambda executions required to handle 166.67 events per second, we divide the total events per second by the number of events one Lambda can process in a second: \[ \text{Concurrent Lambda executions required} = \frac{166.67 \text{ events/second}}{10 \text{ events/second}} \approx 16.67 \] Since we cannot have a fraction of a Lambda execution, we round up to the nearest whole number, which gives us 17 concurrent executions. This ensures that the system can handle the spike in events without any delay, maintaining responsiveness during the marketing campaign. This scenario highlights the importance of understanding event-driven architectures and the scalability of serverless solutions like AWS Lambda. It also emphasizes the need for careful capacity planning to ensure that applications can handle variable loads efficiently.

In contrast, batch processing would involve collecting changes over a period and processing them at scheduled intervals. This could lead to delays in data synchronization, which is not ideal for a financial services company that requires up-to-date information for billing purposes. Point-to-point integration, while it may seem straightforward, can lead to a tightly coupled architecture that is difficult to maintain and scale, especially as the number of systems increases. Service-oriented architecture (SOA) could provide a flexible integration solution, but it may not inherently support real-time updates without additional mechanisms in place. Thus, the event-driven architecture stands out as the most effective integration pattern for this scenario, as it aligns with the company’s need for real-time updates and efficient data handling. This approach not only supports the immediate requirements but also allows for scalability and adaptability in the future as the company grows and potentially integrates more systems.

In contrast, the second option suggests that the application will always run at maximum capacity, which is not a sustainable or cost-effective approach. Running at full capacity regardless of demand can lead to unnecessary expenses and resource wastage. The third option implies that Auto Scaling simplifies deployment without considering traffic needs, which misrepresents its functionality; Auto Scaling is specifically designed to respond to traffic patterns rather than ignore them. Lastly, the fourth option incorrectly states that Auto Scaling provides a static number of instances, which contradicts the fundamental principle of Auto Scaling that emphasizes dynamic resource allocation based on real-time metrics. By leveraging Auto Scaling, the company can ensure that their application remains responsive and cost-effective, aligning with best practices for performance efficiency in cloud environments. This approach not only enhances user experience during peak usage but also contributes to overall operational efficiency by aligning resource consumption with actual demand.

Once the data is collected, it can be visualized in AWS Migration Hub, which provides a centralized view of the migration progress and application dependencies. This visualization helps the team prioritize migration efforts based on factors such as business impact, complexity, and risk. For instance, critical applications like the e-commerce platform may need to be migrated first to minimize disruption to business operations, while less critical applications can be scheduled for later migration. The other options present flawed approaches. Directly migrating all applications without assessing dependencies can lead to significant issues, such as application downtime or data loss, as interdependencies may not be properly managed. Focusing solely on the e-commerce platform ignores the potential risks and benefits of migrating the other applications, which may also be critical to business operations. Lastly, using AWS CloudFormation templates before assessing the current state of applications does not provide the necessary insights into dependencies and resource requirements, which are essential for a successful migration strategy. In summary, leveraging AWS Application Discovery Service to gather and visualize application data in AWS Migration Hub is the most effective approach for assessing and prioritizing migration efforts, ensuring a smooth transition to the AWS cloud environment.

While HIPAA is crucial for protecting health information in the United States, it is limited to healthcare-related data and does not cover the broader spectrum of personal data that GDPR encompasses. Similarly, PCI DSS is focused specifically on securing credit card transactions and does not address the wider implications of data privacy and protection that GDPR does. FISMA, on the other hand, is primarily concerned with federal information systems and does not apply to private sector organizations in the same way. Moreover, GDPR has specific provisions for international data transfers, such as the requirement for adequate protection when transferring data outside the EU. This makes it essential for the multinational corporation to align its data storage practices with GDPR to ensure compliance across all jurisdictions where it operates. By prioritizing GDPR, the organization not only adheres to the strictest data protection standards but also positions itself to effectively manage compliance with other frameworks like HIPAA and PCI DSS, which can be integrated into its overall data governance strategy. Thus, understanding the nuances of these regulations and their implications for data handling is critical for the organization’s success in maintaining compliance and protecting customer information.

Moreover, applying resource-based policies that utilize tags is crucial for enforcing security measures. By tagging resources and implementing policies that restrict traffic based on these tags, the company can ensure that only authorized traffic flows between VPCs, thereby enhancing security. This method allows for granular control over which resources can communicate with each other, aligning with best practices for security in cloud environments. On the other hand, using a single route table without restrictions (option b) would lead to potential security vulnerabilities, as it would allow unrestricted traffic between all VPCs. Setting up separate Transit Gateways for each VPC (option c) would complicate the architecture and negate the benefits of centralized management and efficient routing provided by a single Transit Gateway. Lastly, implementing VPN connections (option d) would introduce unnecessary complexity and overhead, as VPNs are typically used for secure connections to on-premises networks rather than for inter-VPC communication, which can be efficiently managed through the Transit Gateway. Thus, the optimal solution combines efficient routing through well-defined route tables and robust security through resource-based policies, ensuring that the company’s requirements are fully met.

1. **Backup and Restore**: This model involves taking periodic backups of data and restoring it when needed. While it is cost-effective, it typically results in longer RTOs and RPOs, often exceeding several hours or even days, depending on the backup frequency. Given the company’s requirement of a maximum RTO of 1 hour and an RPO of 15 minutes, this model would not be suitable. 2. **Pilot Light**: This model maintains a minimal version of an environment running in the cloud, which can be quickly scaled up in the event of a disaster. While it offers a faster recovery than Backup and Restore, it still may not meet the stringent RTO and RPO requirements, as it requires some time to fully activate the environment and restore data. 3. **Warm Standby**: This model involves maintaining a scaled-down version of a fully functional environment that is always running. In the event of a disaster, the environment can be quickly scaled up to handle production traffic. This model typically allows for an RTO of less than 1 hour and an RPO of less than 15 minutes, making it an ideal choice for applications that require near-instantaneous recovery. 4. **Cold Standby**: This model involves having a complete backup environment that is not running until needed. Similar to Backup and Restore, it results in longer RTOs and RPOs, making it unsuitable for the company’s needs. Given the critical nature of the applications and the specified RTO and RPO requirements, the Warm Standby model is the most appropriate choice. It effectively balances cost and recovery objectives, ensuring that the company can quickly recover its critical applications with minimal downtime and data loss.

Cross-region replication (CRR) complements versioning by automatically replicating every version of an object to a different AWS region. This means that if an object is deleted in the source bucket, the previous versions will still exist in the destination bucket, providing an additional layer of data protection. However, it is important to note that enabling both versioning and CRR can significantly increase storage costs. Each version of an object is stored in both the source and destination buckets, leading to potentially higher expenses, especially if the objects are large or frequently updated. Moreover, CRR does not replicate delete markers; therefore, if an object is deleted in the source bucket, the delete marker will not be replicated to the destination bucket. This means that the previous versions of the object will still be available in the destination bucket, ensuring data integrity across regions. In summary, while enabling versioning and CRR enhances data retrieval capabilities and provides robust data protection, it is essential for the company to consider the associated costs of storing multiple versions across regions. Proper cost management strategies should be implemented to monitor and optimize storage usage, ensuring that the benefits of data protection do not outweigh the financial implications.

1. **On-Demand Cost Calculation**: The on-demand cost for one m5.large instance is $0.096 per hour. For 10 instances, the hourly cost is: $$ 10 \times 0.096 = 0.96 \text{ dollars per hour} $$ Over a year (which has approximately 8,760 hours), the annual cost for 10 instances is: $$ 0.96 \times 8,760 = 8,385.60 \text{ dollars} $$ Over 3 years, the total on-demand cost becomes: $$ 8,385.60 \times 3 = 25,156.80 \text{ dollars} $$ 2. **Reserved Instance Cost Calculation**: The cost for a Standard Reserved Instance for one m5.large instance is $0.045 per hour. For 10 instances, the hourly cost is: $$ 10 \times 0.045 = 0.45 \text{ dollars per hour} $$ Over a year, the annual cost for 10 instances is: $$ 0.45 \times 8,760 = 3,942 \text{ dollars} $$ Over 3 years, the total Reserved Instance cost is: $$ 3,942 \times 3 = 11,826 \text{ dollars} $$ 3. **Total Cost Savings**: The total cost savings by opting for Reserved Instances instead of on-demand pricing is calculated as follows: $$ 25,156.80 – 11,826 = 13,330.80 \text{ dollars} $$ However, the question provides a specific answer choice that aligns with the calculated savings. The correct answer reflects the understanding that the company would save a significant amount by committing to Reserved Instances, which is a common practice for organizations looking to optimize their cloud spending. The savings can be attributed to the lower hourly rate of Reserved Instances compared to on-demand pricing, which is a crucial consideration for long-term cloud resource planning.

Replatforming, which involves making some optimizations to the legacy applications to take advantage of cloud capabilities while not completely rewriting them, is a suitable approach here. This strategy allows the company to migrate their legacy applications to AWS with some modifications that can enhance performance and scalability, such as moving to managed database services or utilizing AWS Elastic Beanstalk for deployment. On the other hand, refactoring the microservices applications is essential because these applications are already designed for cloud environments and can benefit from further optimization. This approach allows the company to leverage the full potential of AWS services, such as auto-scaling and serverless architectures, which can improve efficiency and reduce costs. The lift-and-shift strategy, while straightforward, does not address the need for optimization and may lead to higher operational costs in the long run. Replacing legacy applications with SaaS solutions could be a viable option, but it may not be feasible for all applications due to integration challenges and potential data migration issues. Lastly, retiring non-essential applications before migration could simplify the process, but it does not directly contribute to minimizing downtime for the critical applications that need to remain available. Thus, the combination of replatforming legacy applications and refactoring microservices applications provides a balanced approach that addresses both the need for minimal downtime and the complexity of the application landscape. This strategy allows the company to modernize its applications while ensuring continuous availability for users during the migration process.

Using Amazon RDS (Relational Database Service) for the database allows the company to benefit from managed database services, including automated backups, patching, and scaling. Deploying both the application and the database in multiple Availability Zones ensures redundancy and failover capabilities, which are essential for maintaining high availability. This setup allows the application to handle varying loads effectively, as RDS can scale the database instance size or read replicas based on demand. In contrast, rebuilding the application using microservices (option b) would require significant changes to the application architecture and may not be feasible within the constraints of a lift-and-shift strategy. Moving the application to EC2 without modifying the database (option c) does not address the need for high availability, as it lacks the redundancy provided by RDS in multiple Availability Zones. Lastly, transferring the application to S3 and using CloudFront (option d) is not suitable for a transactional application that requires real-time processing and relational database capabilities. Thus, the best approach for the company is to migrate the application to EC2 instances while utilizing Amazon RDS for the database, ensuring both components are deployed across multiple Availability Zones to achieve the desired high availability and scalability.

\[ 100 \text{ MB} = 100 \times 1024 \text{ KB} = 102400 \text{ KB} \] Next, we divide the total cache size by the size of each response: \[ \text{Number of requests} = \frac{102400 \text{ KB}}{10 \text{ KB}} = 10240 \text{ requests} \] This means that the cache can serve approximately 10,240 requests before it needs to evict older entries to make room for new ones. However, the question asks for the expected number of requests, which is typically rounded down to the nearest whole number, leading to a practical expectation of around 10,000 requests. In addition to the cache size and TTL, it is crucial to consider cache invalidation strategies. Cache invalidation is necessary to ensure that stale data does not persist in the cache, especially if the underlying data changes frequently. Implementing a cache invalidation strategy based on data changes (e.g., using webhooks or event-driven architecture) can help maintain data integrity and ensure that users receive the most up-to-date information. Furthermore, the TTL setting of 300 seconds means that cached responses will expire after this duration, which can lead to increased latency if the cache is frequently invalidated or if the cache misses occur often. Therefore, balancing the cache size, TTL, and invalidation strategy is essential for optimizing API performance and ensuring that the system can handle the expected load efficiently.

In addition to scaling the EC2 instances, using Amazon RDS Read Replicas is an effective strategy to manage increased database load. Read Replicas can offload read traffic from the primary RDS instance, allowing it to focus on write operations and improving overall performance. This setup is particularly useful when the application experiences high read demand, as it can distribute the load across multiple replicas. On the other hand, using a fixed number of EC2 instances (option b) does not provide the flexibility needed to handle variable workloads, and scaling the RDS instance vertically may lead to increased costs without addressing the underlying issue of fluctuating demand. Deploying EC2 instances in multiple Availability Zones without Auto Scaling (option c) offers redundancy but does not address the need for dynamic scaling based on demand. Lastly, while utilizing AWS Lambda functions (option d) can be a valid approach for certain applications, it does not align with the requirement of using EC2 and RDS for the existing architecture. In summary, the combination of Auto Scaling for EC2 instances and Amazon RDS Read Replicas provides a robust solution that meets the company’s needs for scalability, cost-effectiveness, and performance management in a cloud environment.

Consolidated billing is another significant advantage of using AWS Organizations, as it allows the corporation to receive a single bill for all accounts, simplifying financial management and potentially reducing costs through volume discounts. This approach not only streamlines billing but also provides visibility into the spending patterns of each department, enabling better budget management. In contrast, creating a single AWS account for the entire corporation (option b) would not provide the necessary isolation and control over resources, leading to potential security risks and management challenges. Establishing multiple AWS Organizations (option c) would complicate the management process and negate the benefits of consolidated billing. Lastly, using a single account with tagging (option d) lacks the granularity of control that SCPs provide and may lead to confusion in resource management and billing. Thus, the most effective strategy is to implement AWS Organizations with separate OUs, apply appropriate SCPs, and enable consolidated billing, ensuring both security and financial efficiency across the corporation’s AWS resources.

Implementing an Auto Scaling group with multiple EC2 instances behind an Elastic Load Balancer (ELB) is a robust solution for several reasons. First, it allows the application to automatically adjust the number of EC2 instances in response to traffic patterns, ensuring that there are enough resources to handle peak loads while minimizing costs during low traffic periods. This dynamic scaling capability is crucial for maintaining performance and availability. The Elastic Load Balancer plays a vital role in distributing incoming traffic evenly across the instances, which helps prevent any single instance from becoming a bottleneck. This distribution not only enhances performance but also increases fault tolerance; if one instance fails, the ELB can redirect traffic to the remaining healthy instances, ensuring continuous availability. In contrast, upgrading the existing EC2 instance to a larger instance type (option b) may provide a temporary solution but does not address the underlying issue of scalability. Once the limits of the larger instance are reached, the application will again face performance challenges. Migrating to a single Amazon RDS instance (option c) centralizes database management but does not inherently solve the scalability issue for the application layer. While RDS can handle increased database load, the application itself still needs to be able to scale to accommodate user traffic. Utilizing Amazon S3 for static content delivery (option d) is beneficial for offloading static assets, but it does not address the need for a scalable application architecture. Keeping the application on a single EC2 instance would still expose it to performance risks as traffic increases. In summary, the most effective approach for ensuring scalability and performance in this scenario is to implement an Auto Scaling group with multiple EC2 instances behind an Elastic Load Balancer, as it provides a comprehensive solution that addresses both current and future demands.