Moreover, split-tunneling maintains a high level of security by ensuring that all traffic to the corporate network is encrypted, which is essential for protecting sensitive data. This approach also alleviates potential bandwidth issues that could arise from routing all traffic through the VPN, especially when dealing with a maximum of 500 concurrent connections. On the other hand, configuring a static route for all traffic to the corporate network may lead to performance degradation, as all user traffic would be forced through the VPN, increasing latency and potentially overwhelming the VPN endpoint. Similarly, implementing a single security group that allows unrestricted access to all resources within the VPC poses significant security risks, as it could expose sensitive resources to unauthorized access. Lastly, using a single Client VPN endpoint with a default authentication method does not provide adequate security for sensitive data, as it lacks the necessary controls to ensure that only authorized users can access the corporate network. In summary, the optimal configuration for the AWS Client VPN in this scenario is to enable split-tunnel VPN, as it effectively balances security and performance, allowing remote employees to securely access corporate resources without compromising the overall user experience.

Option b is incorrect because directly referencing outputs without using `Fn::ImportValue` would lead to a failure in stack creation, as CloudFormation does not allow direct cross-stack references without exporting and importing values. Option c, while it suggests creating a new stack to aggregate outputs, adds unnecessary complexity and does not leverage the built-in capabilities of CloudFormation for managing outputs and imports. This could lead to additional overhead in managing multiple stacks and could complicate the architecture. Option d introduces unnecessary complexity by using AWS Lambda functions, which would not only complicate the stack creation process but also introduce potential latency and failure points that could be avoided by using intrinsic functions. In summary, using `Fn::ImportValue` is the most efficient and effective way to manage outputs between nested stacks in AWS CloudFormation, ensuring a clean architecture while maintaining the necessary dependencies between the different tiers of the application. This approach adheres to best practices in cloud architecture by promoting modularity and reusability of stack components.

In this scenario, while reviewing CloudTrail logs can provide insights into unauthorized access attempts, it does not directly address the connectivity issue at hand. Similarly, analyzing application logs may reveal errors, but if the underlying network configuration is flawed, the application may not even be reachable to log those errors. Lastly, inspecting Route 53 DNS settings is important for domain resolution, but if the security groups are blocking traffic, the application will still be unreachable regardless of DNS settings. Thus, the most logical and effective first step is to verify the security group rules associated with the application instances. This approach aligns with best practices for troubleshooting network-related issues in AWS, ensuring that the fundamental aspects of connectivity are addressed before delving into more complex areas. By systematically ruling out potential causes, the team can efficiently identify and resolve the connectivity issues affecting their critical application.

Increasing the instance size of the application server (option b) may provide a temporary solution, but it does not address the need for scalability under varying loads. A larger instance may handle more requests, but it is still limited by the maximum capacity of a single instance. Additionally, using a load balancer (option c) to distribute requests across a fixed number of instances would not solve the underlying issue of insufficient capacity; if the number of instances is not increased, the load balancer would still lead to performance bottlenecks. Optimizing database queries (option d) could improve performance but does not directly address the application server’s capacity to process incoming requests. While it is essential to ensure that the database can handle transactions efficiently, the primary challenge in this scenario is the application server’s ability to scale with the incoming request load. Therefore, the most effective architectural approach is to implement an Auto Scaling group, which provides the necessary flexibility to adapt to fluctuating demand while ensuring optimal performance and reliability.

Using a static number of instances (option b) does not adapt to changing traffic patterns, leading to either over-provisioning during low traffic (increased costs) or under-provisioning during high traffic (potential performance issues). Relying on memory utilization (option c) instead of CPU utilization may not accurately reflect the application’s performance needs, as CPU utilization is often a more direct indicator of the processing load on web applications. Lastly, a manual scaling approach (option d) is inefficient and reactive, as it relies on human observation rather than automated responses to real-time metrics. This can lead to delays in scaling during critical traffic spikes, negatively impacting user experience. By leveraging CloudWatch alarms in conjunction with Auto Scaling and scheduled scaling policies, the company can ensure that their application remains responsive to traffic demands while minimizing costs during off-peak times. This strategy aligns with best practices for cloud resource management, emphasizing the importance of automation and proactive resource allocation in dynamic environments.

Using a single KMS key for all applications (as suggested in option b) poses a significant risk, as it creates a single point of failure and complicates access management. If the key is compromised, all applications would be affected. Additionally, manual rotation is prone to oversight and may not occur consistently. Creating multiple KMS keys for different environments without enabling automatic rotation (option c) does not fulfill the requirement for regular key rotation and could lead to security vulnerabilities if keys are not rotated as planned. Lastly, implementing a third-party key management solution (option d) may introduce unnecessary complexity and potential integration issues, especially if the solution does not align with AWS best practices. It is crucial to leverage AWS-native services like KMS for key management to maintain compliance and security. In summary, the correct approach involves creating separate KMS keys for each application, enabling automatic rotation, and implementing strict IAM policies to control access, thereby ensuring a robust key management strategy that adheres to security best practices.

Enabling health checks for each target group is crucial for maintaining high availability. Health checks allow the ALB to monitor the status of the EC2 instances in each target group. If an instance becomes unhealthy, the ALB will automatically stop routing traffic to it, ensuring that users are only directed to healthy instances. This minimizes downtime and improves the overall user experience. In contrast, setting up a single target group for all microservices and using round-robin routing would not leverage the benefits of path-based routing, leading to inefficient traffic management. IP-based routing is not suitable for microservices as it does not consider the content of the requests, and using a Network Load Balancer (NLB) would not provide the same level of application-layer routing capabilities that an ALB offers. NLBs are better suited for TCP traffic and do not support advanced routing features like path-based or host-based routing, which are essential for modern web applications. Therefore, the optimal configuration involves leveraging the advanced features of the ALB to ensure efficient request routing and high availability.

Similarly, NACLs provide an additional layer of security at the subnet level. They are stateless, meaning that rules must be defined for both inbound and outbound traffic. Misconfigured NACLs can inadvertently block traffic, leading to connectivity issues. Therefore, checking for any conflicting rules or misconfigurations in both security groups and NACLs is vital. Restarting the EC2 instance may temporarily resolve some issues, but it does not address the underlying configuration problems that could be causing the connectivity issues. Increasing the instance size might seem like a solution, but it is not a guaranteed fix, especially if the root cause is related to network settings rather than resource limitations. Changing the public IP address is also unlikely to resolve the issue unless there is a specific problem with the current IP, which is rare. In summary, the most effective approach is to systematically review and validate the security group and NACL configurations to ensure that they permit the necessary traffic for the application, thereby addressing the connectivity issues at their source.

The performance improvement occurs because the index allows the database to use a more efficient search algorithm, such as a B-tree structure, which reduces the time complexity of lookups. For example, if a query filters on both `customer_id` and `order_date`, the database can utilize the composite index to quickly narrow down the search space, leading to faster execution times. However, it is important to note that while the composite index improves query performance, it also incurs additional storage overhead. Each index requires space to store the indexed values along with pointers to the actual records in the table. Therefore, the overall storage requirements will increase as the index data is added to the database. In contrast, the other options present misconceptions about how composite indexes function. For instance, a composite index does not solely benefit queries filtering by one of the indexed columns; it is specifically designed to optimize queries that involve both columns. Additionally, the notion that a composite index reduces storage requirements is incorrect, as it actually adds to the storage needs. Lastly, the claim that indexes have no impact on query performance is fundamentally flawed, as indexes are a primary mechanism for enhancing data retrieval efficiency in relational databases. In summary, the implementation of a composite index on `customer_id` and `order_date` will improve query performance for relevant queries while also increasing storage requirements due to the additional index data. Understanding these dynamics is crucial for database administrators when optimizing database performance and managing storage effectively.

In this case, the average CPU utilization spikes to 80% for 10 minutes. Since this exceeds the 70% threshold, the Auto Scaling group will add 1 instance, bringing the total to 3 instances. Next, the average CPU utilization drops to 25% for 10 minutes. This is below the 30% threshold, which triggers the removal policy. According to the scaling policy, 1 instance will be removed. Therefore, the Auto Scaling group will reduce the number of instances from 3 to 2. However, since the minimum size of the Auto Scaling group is set to 2 instances, it cannot scale down below this limit. Thus, even though the policy would suggest removing an instance, the Auto Scaling group will maintain the minimum of 2 instances. In summary, after the spike in CPU utilization and the subsequent drop, the Auto Scaling group will stabilize at 2 instances, as it cannot go below the defined minimum. This illustrates the importance of understanding both the scaling policies and the minimum and maximum instance limits when configuring Auto Scaling in AWS.

Additionally, AWS CloudTrail is essential for logging API calls, which provides a detailed history of actions taken on resources. This logging capability is vital for audits, as it allows the company to demonstrate who accessed what data and when, thereby fulfilling GDPR’s accountability requirements. In contrast, relying on AWS Lambda for manual checks introduces human error and delays, making it less effective for real-time compliance monitoring. AWS Trusted Advisor, while useful for best practices, does not specifically address compliance with regulations like GDPR and may not provide the depth of monitoring required. Lastly, consolidating resources into a single AWS account may simplify management but can complicate compliance efforts, as it may lead to a lack of visibility and control over data residency across different jurisdictions. Thus, the combination of AWS Config and AWS CloudTrail provides a comprehensive solution for ongoing compliance checking, ensuring that the company can effectively manage its cloud resources while adhering to GDPR requirements.

Increasing the bandwidth of the internet connection may seem like a viable solution, but it is often a temporary fix that does not address the underlying issue of malicious traffic. Attackers can easily scale their attacks to match or exceed the increased bandwidth, leading to continued service disruption. Deploying a CDN can help distribute traffic and absorb some of the load, but without additional security measures, it may not provide adequate protection against sophisticated DDoS attacks. CDNs are primarily designed for content delivery rather than security, and relying on them alone can leave vulnerabilities. Lastly, relying solely on the existing firewall is insufficient for DDoS protection. Traditional firewalls are not designed to handle the volume and complexity of DDoS attacks, which can bypass standard filtering mechanisms. Therefore, a comprehensive strategy that includes a WAF with rate limiting and IP reputation filtering is crucial for effectively mitigating DDoS attacks while ensuring that legitimate traffic is not adversely affected. This approach aligns with best practices in cybersecurity, emphasizing the importance of layered defenses to protect against evolving threats.

Using BGP facilitates dynamic routing, which is essential for maintaining optimal paths for traffic between the on-premises data center and the AWS VPC. This is particularly important in scenarios where network changes may occur, as BGP can quickly adapt to these changes, providing a more resilient connection. In contrast, setting up a single VPN connection with a static route (option b) does not provide redundancy; if that connection fails, the entire communication path is lost. Implementing a third-party VPN appliance (option c) may introduce additional complexity and potential points of failure, and while it could provide failover capabilities, it may not be as seamless as using AWS’s built-in features. Lastly, while AWS Direct Connect (option d) offers a reliable and high-throughput connection, it does not provide the encryption that a Site-to-Site VPN inherently offers, making it unsuitable for scenarios where data security during transit is a priority. Thus, the optimal solution involves leveraging AWS’s native capabilities to ensure both high availability and secure communication between the on-premises data center and the AWS VPC.

For dynamic content, which is user-specific and changes frequently, cache-busting techniques should be employed. This can involve appending a version number or a hash of the content to the URL, ensuring that users always receive the latest version when it changes. By using this method, the company can effectively cache static content while ensuring that dynamic content remains fresh and relevant to each user. The other options present various pitfalls. Option b suggests caching both static and dynamic content with a TTL of 1 hour, which may not be optimal for static content that can be cached longer. Option c, which disables caching entirely, would negate the performance benefits of using a CDN and lead to slower load times. Option d proposes a shorter TTL for static content and relies on server-side caching for dynamic content, which may not be as efficient as using a CDN for static assets. Thus, the combination of a longer TTL for static content and cache-busting for dynamic content is the most effective strategy for this scenario.

Amazon RDS for PostgreSQL is a managed service that simplifies the setup, operation, and scaling of PostgreSQL databases. While it provides high availability through Multi-AZ deployments, it may not scale as seamlessly as Aurora under heavy read loads, especially when the application experiences spikes in traffic. Amazon DynamoDB, on the other hand, is a fully managed NoSQL database service that excels in handling high-velocity workloads with low-latency responses. It is designed for applications that require consistent, single-digit millisecond response times at any scale. However, it is not a relational database and may not be suitable if the application requires complex queries or transactions typical of relational databases. Amazon Redshift is a data warehousing service optimized for online analytical processing (OLAP) and is not designed for transactional workloads. It is more suited for complex queries on large datasets rather than handling high volumes of concurrent read and write operations typical of OLTP applications. Given these considerations, Amazon Aurora stands out as the most suitable choice for the application due to its ability to automatically scale, handle high read loads efficiently, and provide high availability through its architecture. This makes it an ideal fit for applications that require both performance and reliability in a cloud environment.

$$ \text{Total Weight} = \text{Weight of A} + \text{Weight of B} = 70 + 30 = 100 $$ To find the probability that a DNS query will be routed to Resource A, we use the formula: $$ \text{Probability of A} = \frac{\text{Weight of A}}{\text{Total Weight}} = \frac{70}{100} = 0.7 $$ This means that 70% of the DNS queries will be routed to Resource A, while the remaining 30% will go to Resource B. This approach allows the company to effectively manage traffic distribution based on their specific needs, ensuring that Resource A, which presumably has more capacity or is more critical, receives a larger share of the traffic. Using weighted routing policies is particularly beneficial in scenarios where different resources have varying capabilities or when testing new resources before fully transitioning traffic to them. It is essential to monitor the performance of both resources to ensure that the routing behaves as expected and to adjust the weights as necessary based on real-time traffic patterns and resource performance. This method not only enhances availability but also optimizes resource utilization, which is crucial for maintaining a robust and responsive web application in a cloud environment.

\[ \text{Average} = \frac{\text{Sum of response times}}{\text{Number of observations}} = \frac{250 + 320 + 290 + 310 + 330}{5} \] Calculating the sum: \[ 250 + 320 + 290 + 310 + 330 = 1500 \text{ ms} \] Now, dividing by the number of observations (5): \[ \text{Average} = \frac{1500}{5} = 300 \text{ ms} \] The alarm is set to trigger when the average response time exceeds 300 ms over the specified period. In this case, the calculated average response time is exactly 300 ms. According to AWS CloudWatch alarm behavior, the alarm will not trigger if the average is equal to or below the threshold. Therefore, since the average response time does not exceed the threshold of 300 ms, the alarm will not trigger. This scenario illustrates the importance of understanding how CloudWatch metrics and alarms function, particularly the distinction between exceeding a threshold and meeting it. It also emphasizes the need for careful analysis of metrics over time, as transient spikes in response time do not necessarily indicate a persistent issue unless they consistently exceed the defined thresholds.

1. **Calculate the cost of on-demand instances**: The company expects to use an average of 10 instances per month, with each instance costing $100. Therefore, the monthly cost for on-demand instances is: \[ \text{Monthly Cost} = 10 \text{ instances} \times 100 \text{ USD/instance} = 1,000 \text{ USD} \] Over three years (which is 36 months), the total on-demand cost would be: \[ \text{Total On-Demand Cost} = 1,000 \text{ USD/month} \times 36 \text{ months} = 36,000 \text{ USD} \] 2. **Calculate the cost of the Reserved Instance**: The cost of the 3-year Standard Reserved Instance is $1,200 per year. Therefore, over three years, the total cost for the Reserved Instance is: \[ \text{Total RI Cost} = 1,200 \text{ USD/year} \times 3 \text{ years} = 3,600 \text{ USD} \] 3. **Calculate the cost of the Savings Plan**: The cost of the Compute Savings Plan is $1,000 per year. Thus, over three years, the total cost for the Savings Plan is: \[ \text{Total Savings Plan Cost} = 1,000 \text{ USD/year} \times 3 \text{ years} = 3,000 \text{ USD} \] 4. **Determine the cost savings**: To find the cost savings when choosing the Savings Plan over the Reserved Instance, we subtract the total cost of the Savings Plan from the total cost of the Reserved Instance: \[ \text{Cost Savings} = \text{Total RI Cost} – \text{Total Savings Plan Cost} = 3,600 \text{ USD} – 3,000 \text{ USD} = 600 \text{ USD} \] However, since the question asks for the total cost savings over three years if they choose the Savings Plan instead of the Reserved Instance, we need to consider the difference in costs between the on-demand usage and the Savings Plan. The Savings Plan provides a discount compared to on-demand pricing, which is not directly calculated here. If we consider the total cost of using on-demand instances for three years ($36,000) and compare it to the Savings Plan ($3,000), the savings would be: \[ \text{Total Savings} = 36,000 \text{ USD} – 3,000 \text{ USD} = 33,000 \text{ USD} \] Thus, the Savings Plan provides significant savings compared to the on-demand pricing, but the question specifically compares the Savings Plan to the Reserved Instance. The correct interpretation of the question leads to the conclusion that the Savings Plan is more cost-effective than the Reserved Instance, resulting in a total savings of $600 when comparing the two options directly. In summary, the Savings Plan offers a more flexible and potentially lower-cost option for the company, especially given their anticipated increase in compute usage. The analysis highlights the importance of understanding the cost implications of different purchasing options in AWS, particularly in the context of long-term cloud strategies.

For instance, the back-end layer may require scaling based on the number of requests it receives from the front-end layer, while the database layer may need to scale based on the number of active connections or query performance metrics. By setting up CloudWatch alarms for these specific metrics, the company can ensure that each layer scales appropriately without over-provisioning resources or causing bottlenecks. In contrast, deploying the back-end and database layers as part of the same stack (option b) could lead to challenges in managing their scaling behaviors, as they may not have the same load characteristics. Using AWS Lambda (option c) could simplify the architecture but may not be suitable for all back-end processing tasks, especially if they require persistent state or complex transactions. Lastly, a manual scaling approach (option d) is not ideal in a cloud environment where dynamic scaling can be automated, leading to inefficiencies and potential downtime during peak loads. Thus, the most effective strategy is to utilize AWS OpsWorks to create separate stacks for the back-end and database layers, allowing for tailored scaling configurations that respect the dependencies and performance requirements of the entire application. This ensures that the application can respond dynamically to varying loads while maintaining optimal performance across all layers.

The second path pattern, `/dynamic/*`, has a cache TTL of 0 seconds, meaning that requests for dynamic content will always be forwarded to the origin server (in this case, the EC2 instance). This is essential for ensuring that users receive the most current data, as dynamic content often changes frequently and must reflect real-time updates. The third path pattern, `/*`, with a cache TTL of 3600 seconds (1 hour), serves as a catch-all for any other requests that do not match the previous patterns. This setting allows for a reasonable caching duration for content that may not be as dynamic as the content served under the `/dynamic/*` path but still requires some level of freshness. By implementing this configuration, the company effectively balances performance and content freshness. Static assets benefit from long caching times, while dynamic content is always retrieved fresh from the origin. This approach minimizes the load on the origin servers and enhances the overall user experience by reducing latency for static content while ensuring that dynamic content is always up-to-date. The other options present configurations that either do not optimize caching effectively or risk serving stale content, which could lead to a poor user experience.

The use of BGP allows for the automatic exchange of routing information between the on-premises network and the AWS VPC, which is crucial for maintaining an efficient and reliable connection. This setup can handle the required bandwidth of up to 1 Gbps, as AWS VPN connections can support this level of throughput when configured correctly. In contrast, establishing a single Site-to-Site VPN connection with one virtual private gateway and static routing (option b) does not provide the redundancy needed for high availability, making it less suitable for critical applications. While AWS Direct Connect (option c) offers higher bandwidth and lower latency, it is not a VPN solution and does not meet the requirement for a secure encrypted connection over the internet. Lastly, using AWS Client VPN (option d) is designed for individual user access rather than site-to-site connectivity, making it inappropriate for connecting an entire data center to a VPC. Thus, the optimal configuration involves leveraging the capabilities of AWS VPN with multiple virtual private gateways and dynamic routing to ensure a secure, high-bandwidth, and resilient connection.

Firstly, using Amazon EC2 for the web front-end allows for flexibility in configuring the server environment, which is essential for handling varying traffic loads. EC2 instances can be scaled up or down based on demand, and they can be placed behind an Elastic Load Balancer to distribute incoming traffic effectively. Secondly, Amazon ECS is a powerful service for running containerized applications. It allows the backend API to be deployed in a microservices architecture, which enhances scalability and maintainability. ECS can automatically scale the number of containers based on the load, ensuring that the application remains responsive even during peak usage. Lastly, Amazon RDS with Multi-AZ deployment provides a managed relational database solution that automatically replicates data across multiple Availability Zones. This setup not only enhances availability but also ensures data durability and failover capabilities. In the event of an outage in one Availability Zone, RDS can automatically switch to a standby instance in another zone, minimizing downtime. In contrast, the other options present various drawbacks. For instance, using AWS Lambda for the web front-end may not be suitable due to the need for persistent connections and state management, which are typically required for web applications. Similarly, while Amazon DynamoDB is a great NoSQL database, it may not be the best fit for applications requiring complex queries and transactions that relational databases handle more effectively. Overall, the selected combination of services aligns with AWS best practices for building scalable, resilient, and cost-effective applications in the cloud, making it the most suitable choice for the company’s migration strategy.

Furthermore, when integrating a Transit Gateway, it acts as a central hub for routing traffic between multiple VPCs and on-premises networks. Each VPC connected to the Transit Gateway must also have specific route table entries that point to the Transit Gateway for traffic destined for other connected VPCs. This setup allows for scalable and efficient communication as more VPCs are added. Regarding CIDR blocks, it is essential that the CIDR blocks of VPC-A and VPC-B do not overlap, as overlapping CIDR blocks would prevent successful routing between the two VPCs. However, the Transit Gateway does not support VPCs with overlapping CIDR blocks, which means that all connected VPCs must have unique CIDR ranges. In summary, the correct understanding of this setup involves recognizing the need for manual route table updates for both VPC Peering and Transit Gateway configurations, the requirement for disjoint CIDR blocks, and the role of the Transit Gateway in facilitating communication among multiple VPCs.

Conducting annual security audits, while beneficial, does not integrate security into the development lifecycle, which is essential for identifying vulnerabilities early and continuously throughout the application’s lifecycle. This approach can lead to significant risks if vulnerabilities are discovered only during annual reviews. Limiting access to customer data based solely on user roles without applying the principle of least privilege can lead to excessive access rights. The principle of least privilege dictates that users should only have access to the data necessary for their job functions, reducing the risk of data breaches. Storing all customer data in a single geographic location may simplify management but can pose risks related to data sovereignty and compliance with local regulations. GDPR requires that data be processed in a manner that ensures its security and integrity, which may necessitate a more distributed approach to data storage. In summary, the most effective measure to ensure compliance with GDPR while maintaining operational efficiency is to implement robust data encryption practices, as this directly addresses the need for data protection and aligns with regulatory requirements.

Creating a single SCP that applies universally to all accounts (as suggested in option b) would limit the flexibility necessary for different teams to operate effectively, as it would not account for the varying needs of different departments or projects. Similarly, relying solely on AWS Config rules (option c) for monitoring without enforcing policies at the OU level would lead to a lack of proactive governance, as it would only provide visibility into compliance rather than enforce it. Lastly, establishing a separate governance account (option d) would create a disconnect between governance and operational accounts, complicating policy management and potentially leading to inconsistencies in compliance enforcement. Thus, the most effective strategy for the governance team is to leverage SCPs at the OU level, which balances the need for compliance with the operational flexibility required by individual accounts. This method aligns with best practices for governance in cloud environments, ensuring that security and operational standards are met while allowing teams to function effectively within their own contexts.

\[ \text{New Read IOPS} = 3000 + (3000 \times 0.20) = 3000 + 600 = 3600 \] The current write IOPS is 1500, and with a 10% increase, the new write IOPS will be: \[ \text{New Write IOPS} = 1500 + (1500 \times 0.10) = 1500 + 150 = 1650 \] Now, the total IOPS requirement after migration will be: \[ \text{Total IOPS} = \text{New Read IOPS} + \text{New Write IOPS} = 3600 + 1650 = 5250 \] Next, we need to evaluate the IOPS capabilities of the available instance types. The db.m5.large instance type provides up to 3,000 IOPS, which is insufficient for the expected workload. The db.t3.medium instance type is designed for burstable workloads and has a baseline performance of 20% of the allocated CPU, which may not consistently meet the IOPS requirements. The db.r5.xlarge instance type offers up to 10,000 IOPS, making it suitable for the workload, but it may be more expensive than necessary. The db.m4.2xlarge instance type provides up to 7,500 IOPS, which comfortably meets the total IOPS requirement of 5250 while balancing performance and cost. Therefore, this instance type is the most appropriate choice for the company’s needs, as it provides sufficient IOPS capacity to handle the expected workload without incurring unnecessary costs associated with higher-tier instances. In summary, the analysis of the expected IOPS requirements and the capabilities of the various instance types leads to the conclusion that the db.m4.2xlarge instance type is the best fit for the anticipated workload after migration to Amazon RDS.

A simple routing policy would not provide the necessary performance optimization, as it directs all traffic to a single endpoint, potentially leading to increased latency for users located far from that endpoint. Similarly, a weighted routing policy without health checks would distribute traffic evenly among endpoints regardless of their health status, which could result in users being directed to unhealthy or slow endpoints, negatively impacting the application’s performance. Lastly, while a geolocation routing policy could direct traffic based on the user’s location, it does not inherently provide the latency optimization that the company seeks. Additionally, not including health checks in any routing policy could lead to significant downtime or degraded performance if an endpoint becomes unhealthy. Therefore, the best approach is to use a latency-based routing policy combined with health checks to ensure both low latency and high availability for the web application. This configuration aligns with best practices for DNS management in a cloud environment, particularly when dealing with a global user base.

The option to immediately revoke all IAM permissions may seem proactive; however, it could lead to operational disruptions and may not address the root cause of the findings. Disabling Security Hub is counterproductive, as it would prevent the team from receiving ongoing visibility into their security posture, which is essential for continuous monitoring and improvement. Lastly, relying solely on findings from Amazon GuardDuty neglects the comprehensive view that Security Hub provides, as it aggregates findings from multiple sources, including Amazon Inspector and AWS Config, which may also highlight critical IAM issues. In summary, the most effective strategy for the security team is to utilize Security Hub’s insights to filter and prioritize findings based on severity, ensuring a structured and informed approach to remediation that minimizes risk while maintaining operational integrity. This method not only enhances the security posture but also aligns with AWS’s shared responsibility model, where customers are responsible for managing their security configurations and responding to alerts effectively.

To find out how many requests can be processed by a single target in one second, we can use the formula: \[ \text{Requests per target per second} = \frac{1 \text{ second}}{\text{Average processing time per request}} = \frac{1}{0.2} = 5,000 \text{ requests} \] This means that each target can handle 5,000 requests per second. Next, we need to ensure that the total number of requests (10,000 RPS) does not exceed the processing capacity of the targets. To find the minimum number of targets required, we can use the following formula: \[ \text{Number of targets required} = \frac{\text{Total requests per second}}{\text{Requests per target per second}} = \frac{10,000}{5,000} = 2 \] However, since we want to ensure that the application can handle the peak load without exceeding the maximum processing time of 300 milliseconds per request, we need to consider the maximum processing time. If each request takes 200 milliseconds, and we want to ensure that the processing time does not exceed 300 milliseconds, we can calculate the effective capacity of the targets under this constraint. If we assume that we want to maintain a buffer to handle spikes in traffic, we can double the number of targets calculated. Therefore, the minimum number of targets required to ensure that the application can handle the peak load effectively, while maintaining a buffer, would be: \[ \text{Minimum number of targets} = 2 \times 2 = 4 \] However, since the options provided do not include 4, we must consider the next higher number that can effectively handle the load while providing redundancy and fault tolerance. Thus, the correct answer is 5 targets, which allows for some additional capacity to handle unexpected spikes in traffic or potential failures of individual targets. This scenario illustrates the importance of understanding not only the basic calculations of load balancing but also the need for redundancy and fault tolerance in a high-availability architecture. The Network Load Balancer is designed to handle millions of requests per second while maintaining ultra-low latencies, making it a suitable choice for applications requiring high performance and reliability.

Implementing a centralized logging solution is the most effective way to address the compliance gap identified during the audit. This solution should be configured to automatically retain access logs for the mandated period, ensuring that the company can demonstrate compliance during future audits. Additionally, this approach aligns with best practices in security management, as it allows for continuous monitoring and analysis of access patterns, which can help in identifying potential security incidents. Increasing the encryption strength of data at rest, while important for data security, does not directly address the compliance issue related to access log retention. Similarly, conducting a one-time review of access controls may not be sufficient, as ongoing monitoring and adjustments are necessary to maintain compliance. Limiting access to sensitive data solely to the IT department could inadvertently create bottlenecks and hinder operational efficiency, while also not fulfilling the requirement for proper logging of access activities. Therefore, the implementation of a centralized logging solution that meets the regulatory retention requirements is the most appropriate and effective action for the company to take in response to the audit findings. This not only ensures compliance but also strengthens the overall security posture of the organization.