Disabling all existing rules and starting from scratch (option b) is not advisable as it would leave the application vulnerable during the reconfiguration process. Increasing the sensitivity of existing rules (option c) could exacerbate the issue of blocking legitimate traffic, leading to a poor user experience. Logging all traffic without filtering (option d) may help identify patterns but does not provide immediate protection against threats, leaving the application exposed during the analysis phase. By adopting a whitelisting approach, the company can create a more nuanced security posture that allows legitimate users to access the application while still leveraging the WAF’s capabilities to filter out potentially harmful traffic. This balance is crucial in maintaining both security and usability, which is essential for applications handling sensitive data. Additionally, ongoing monitoring and adjustment of the WAF rules based on traffic patterns and emerging threats will further enhance the security framework.

For dynamic content, which is user-specific and cannot be cached in the same way as static assets, the best approach is to set up an origin failover. This means that while CloudFront serves cached static content from its edge locations, it can also retrieve dynamic content from the RDS instance when needed. This setup allows for efficient handling of dynamic requests without unnecessarily routing all traffic through CloudFront, which could lead to increased costs and latency. Option b suggests caching both static and dynamic content, which is not ideal since dynamic content is often unique to each user and should not be cached in the same manner as static content. This could lead to stale data being served to users. Option c proposes using Lambda@Edge to modify requests and responses, which adds complexity and may not be necessary for simply retrieving dynamic content from an RDS instance. Lastly, option d suggests setting up a direct connection from CloudFront to the RDS instance, which is not feasible as CloudFront is a CDN and does not connect directly to database instances. Thus, the optimal strategy is to leverage CloudFront for caching static assets while using an origin failover to retrieve dynamic content from the RDS instance, ensuring both performance and cost-effectiveness.

\[ \text{Total Instances} = \text{Front-end Instances} + \text{Back-end Instances} + \text{Database Instances} = 3 + 2 + 1 = 6 \] Next, to calculate the total hourly cost of running all instances, we multiply the total number of instances by the cost per instance per hour. Given that each instance costs $50 per hour, the total cost can be calculated as: \[ \text{Total Cost} = \text{Total Instances} \times \text{Cost per Instance} = 6 \times 50 = 300 \] Thus, the total number of instances needed for the entire application deployment is 6, and the total hourly cost for running all instances is $300. This scenario illustrates the importance of understanding how stacks and layers interact in a cloud architecture, as well as the financial implications of scaling each layer independently. By deploying each layer separately, you can achieve greater flexibility and efficiency in managing resources, which is a key principle in cloud architecture design.

Choosing the largest instance types available (option b) may seem like a way to ensure performance, but it can lead to unnecessary costs, especially if the application does not consistently require that level of resource. This approach does not align with the cost optimization principle, which encourages using the right-sized resources for the workload. Utilizing a single instance type for all workloads (option c) simplifies management but can lead to inefficiencies. Different workloads may have different performance requirements, and a one-size-fits-all approach can result in over-provisioning or under-provisioning resources. Relying solely on on-demand instances (option d) may provide flexibility, but it can also lead to higher costs compared to using a mix of on-demand, reserved, and spot instances. The Well-Architected Framework encourages leveraging various pricing models to optimize costs while maintaining performance. In summary, the best strategy is to implement a monitoring solution that allows for dynamic adjustments based on real-time performance metrics, thereby aligning with both the Performance Efficiency and Cost Optimization pillars of the AWS Well-Architected Framework. This approach not only enhances the application’s ability to handle variable workloads but also ensures that resources are utilized efficiently, minimizing unnecessary expenditures.

To address this issue, the company should first verify that the State Manager document is correctly associated with all intended instances. This can be done through the AWS Management Console or AWS CLI by checking the association status of the document. If any instances are found to be unassociated, the company should create or update the association to include those instances. Additionally, it is important to ensure that the State Manager document is configured correctly and that the instances are in a state that allows for the application of the document. While other options present plausible scenarios, they do not directly address the core issue of association, which is fundamental to the operation of State Manager. For instance, if instances were terminated and recreated, they would need to be reassociated with the State Manager document, but this is a secondary concern to ensuring that the initial association was comprehensive. Similarly, while IAM roles and document syntax are important considerations, they would not directly lead to non-compliance if the document was properly associated with the instances. Thus, ensuring that all instances are correctly targeted by the State Manager document is the primary step to achieving compliance.

The primary benefit of this composite index is the reduction in disk I/O operations. When a query is executed, the database engine can utilize the index to directly access the relevant data pages instead of performing a full table scan. This is crucial, especially with a large dataset of 1 million records, where a full scan could be significantly slower due to the increased number of disk reads required. The index effectively narrows down the search space, allowing the database to retrieve the necessary records much faster. Moreover, while it is true that creating an index may increase the overall size of the database due to the additional data structure that needs to be maintained, the performance gains during read operations typically outweigh the costs associated with increased storage and potential overhead during write operations. It is also important to note that the composite index will improve performance for queries that filter on both `CustomerID` and `OrderDate`, not just one of them. However, it is essential to consider that while read operations will benefit from the index, write operations (inserts, updates, and deletes) may incur additional overhead because the index must also be updated whenever the underlying data changes. This trade-off is a common consideration in database design, where the goal is to balance read and write performance based on the application’s specific needs. Thus, the creation of a composite index is a powerful tool for enhancing query performance, particularly in scenarios involving large datasets and frequent queries on specific columns.

The database server, on the other hand, should be placed in a private subnet. This setup prevents direct access from the internet, enhancing security. To allow the database server to access the internet for necessary updates or external API calls, a NAT Gateway is utilized. The NAT Gateway, which is also located in the public subnet, enables instances in the private subnet to initiate outbound traffic to the internet while preventing unsolicited inbound traffic from reaching those instances. This configuration adheres to AWS best practices for security and network architecture. It effectively isolates the database server while still allowing it to perform necessary functions that require internet access. The other options present various flaws: option b incorrectly places the database server in a public subnet, exposing it to the internet; option c allows both servers unrestricted access, compromising security; and option d introduces unnecessary complexity without addressing the requirement for internet access for the web server. Thus, the outlined configuration is the most effective and secure approach for the given scenario.

On the other hand, while AWS Config rules can monitor compliance, they do not prevent the creation of non-compliant resources; they only flag them after the fact, which does not align with the company’s goal of minimizing manual oversight. Similarly, using AWS Lambda functions to periodically check for compliance introduces a delay in identifying non-compliant resources, which could lead to potential governance issues. Lastly, AWS Service Catalog is designed to manage and provision approved products, but it does not inherently enforce tagging policies during the provisioning process unless explicitly configured to do so. In summary, the most effective approach to ensure compliance with tagging policies during deployment, while minimizing manual intervention, is to leverage AWS CloudFormation with a custom resource that checks for compliance at the time of resource creation and updates. This proactive approach aligns with best practices for governance and compliance in cloud environments, ensuring that all resources are consistently tagged according to the company’s standards from the outset.

The presentation layer is responsible for handling user requests and serving static content. Amazon S3 is an ideal choice for this layer as it provides highly durable and scalable storage for static files such as HTML, CSS, and JavaScript. Additionally, S3 can be integrated with Amazon CloudFront, a content delivery network (CDN), to enhance performance by caching content closer to users. For the application layer, AWS Lambda is a serverless compute service that allows you to run code in response to events without provisioning or managing servers. This service is particularly beneficial for processing business logic as it can scale automatically based on the number of incoming requests, ensuring that the application can handle varying loads efficiently. The data layer requires a robust solution for managing database transactions. Amazon RDS (Relational Database Service) is a managed database service that supports multiple database engines and automates tasks such as backups, patching, and scaling. It provides the necessary reliability and performance for handling transactional workloads. In contrast, the other options present combinations that do not align as effectively with the requirements. For instance, using Amazon EC2 for static content introduces unnecessary complexity and cost, as EC2 instances require management and scaling. Similarly, while DynamoDB is a powerful NoSQL database, it may not be the best fit for applications requiring complex transactions typically handled by relational databases. Thus, the combination of Amazon S3, AWS Lambda, and Amazon RDS provides a well-architected solution that meets the needs of each layer while ensuring optimal performance and cost management.

\[ 0.80 \times 10,000 = 8,000 \] This means that the company wants to be alerted when their spending reaches $8,000. Next, we need to consider the current spending of $6,500. To find out how much more they can spend before reaching the alert threshold, we subtract the current spending from the alert threshold: \[ 8,000 – 6,500 = 1,500 \] This indicates that the company can spend an additional $1,500 before they hit the 80% threshold. Therefore, the alert should be set to notify them when their total spending reaches $8,000. Setting the alert at this threshold ensures that the company is notified before they exceed their budget, allowing them to take corrective actions if necessary. If they set the alert at any value higher than $8,000, they risk exceeding their budget without being notified in time. In summary, the correct threshold for the AWS Budget alert is $8,000, as it aligns with the company’s goal of being alerted when they are approaching 80% of their budget, thus enabling better financial management and control over AWS expenditures.

In this scenario, the Lambda function would be designed to check for the specific key-value pair in the tags of EC2 instances. If an instance is found to be non-compliant, the function can use the AWS SDK to apply the required tags. This method ensures that the remediation is immediate and does not require manual intervention, thus maintaining compliance in real-time. On the other hand, using AWS Systems Manager to manually tag instances (option b) is not an automated solution and would require human effort, which defeats the purpose of automation. Similarly, logging non-compliant instances with AWS CloudTrail (option c) does not provide a mechanism for remediation; it merely records events without taking action. Lastly, implementing an AWS CloudFormation stack (option d) would not address existing non-compliant instances, as CloudFormation is primarily used for provisioning resources rather than managing compliance post-deployment. Therefore, leveraging AWS Lambda in conjunction with AWS Config rules provides a robust solution for automating compliance and ensuring that all EC2 instances are tagged appropriately, thereby aligning with best practices for resource management and compliance in AWS environments.

To meet the requirement of retrieving the most recent version of an object within 24 hours, the company can adjust their lifecycle policies to transition objects to S3 Standard-IA (Infrequent Access) instead of Glacier. S3 Standard-IA allows for lower storage costs while still providing immediate access to the data, which is essential for their machine learning applications. If the company were to rely solely on S3 Glacier for storage, they would face challenges in meeting their retrieval time requirement, as standard retrieval from Glacier can take several hours. Therefore, managing retrieval costs effectively while ensuring compliance with the retrieval time requirement involves a strategic approach to lifecycle management and understanding the implications of versioning in S3. By keeping the deleted object versions and adjusting the lifecycle policies, the company can optimize both cost and access speed, ensuring they can retrieve necessary data promptly without incurring excessive costs.

Enabling automatic approvals for critical and security updates is essential for maintaining compliance with security policies, as it ensures that the most important updates are applied promptly without requiring manual intervention. This approach not only enhances security posture but also reduces the administrative overhead associated with manual approvals. In contrast, the other options present various pitfalls. Scheduling patching during peak hours can lead to significant downtime and user dissatisfaction. Using a single patch baseline for all instances may not account for the specific needs of different operating systems, potentially leading to compatibility issues. Disabling automatic approvals entirely could result in delays in applying critical updates, leaving the instances vulnerable to security threats. Lastly, approving all updates automatically without review could lead to unintended consequences, such as application failures or system instability due to incompatible patches. Thus, the optimal configuration involves a well-structured approach that leverages AWS Systems Manager’s capabilities to ensure timely and effective patch management while adhering to best practices for operational efficiency and security compliance.

$$ 2,000 + 2,500 + 3,000 = 7,500 $$ To find the average, divide the total by the number of months (3): $$ \text{Average Cost} = \frac{7,500}{3} = 2,500 $$ This average of $2,500 provides a baseline for the company to understand its spending patterns. By analyzing the average cost, the company can set a more informed budget for the next quarter. Additionally, they can use AWS Cost Explorer to visualize trends and identify specific services or resources that contribute to cost fluctuations. For instance, if they notice that certain services are driving up costs during peak usage times, they can consider optimizing those services or implementing cost-saving measures, such as using Reserved Instances or adjusting their resource allocation based on demand. Furthermore, understanding the average cost allows the company to forecast future expenses more accurately. If they anticipate similar usage patterns, they can expect their costs to hover around this average, but they should also account for potential increases due to scaling or new projects. By leveraging AWS Cost Explorer’s forecasting capabilities, they can create more precise budgets and financial plans, ensuring they remain within their desired spending limits while effectively managing their AWS resources.

Health checks are essential in this configuration as they ensure that the NLB only routes traffic to healthy instances, thereby enhancing the reliability of the application. If an instance fails the health check, the NLB will automatically stop sending traffic to it, ensuring that users do not experience downtime or degraded performance. In contrast, using UDP listeners would not be appropriate since the application requires TCP traffic handling, which is not supported by UDP. Disabling cross-zone load balancing could lead to uneven traffic distribution, resulting in some instances being overwhelmed while others remain underutilized. Choosing an Application Load Balancer (ALB) instead of an NLB would not meet the requirement for TCP traffic, as ALBs are primarily designed for HTTP/HTTPS traffic and do not support TCP connections natively. Lastly, while sticky sessions can be beneficial for maintaining user sessions, they can also lead to uneven load distribution and are not suitable for applications requiring high availability and performance under fluctuating loads. Therefore, the configuration that includes TCP listeners, cross-zone load balancing, and health checks is the most effective solution for the given requirements.

In the context of IT infrastructure changes, this approach is crucial as it minimizes service downtime and reduces the risk of data loss. For instance, if a company were to switch its entire infrastructure to a new cloud-based application all at once, the potential for widespread disruption is high. However, by rolling out the changes in phases, the organization can ensure that critical systems remain operational while new components are tested and validated. Moreover, a phased rollout can help address user resistance by allowing employees to adapt to changes gradually. Training can be tailored to each phase, ensuring that users are comfortable with new processes before additional changes are introduced. This contrasts with a simultaneous rollout, which may overwhelm users and lead to higher resistance. While it is important to note that a phased approach does not guarantee the elimination of all risks, it does provide a structured way to manage and mitigate them effectively. Additionally, while simplifying the change management process by reducing stakeholders may seem beneficial, it can lead to a lack of diverse input and oversight, which is critical in complex IT environments. Therefore, the primary advantage of a phased rollout is its capacity to facilitate gradual adjustment and feedback, ultimately leading to a smoother transition and better overall outcomes.

On the other hand, the private subnets, which contain the application and database servers, should not be directly accessible from the internet for security reasons. Instead, they should have routes to a NAT (Network Address Translation) gateway. This configuration allows instances in the private subnets to initiate outbound traffic to the internet (for software updates, for example) while preventing unsolicited inbound traffic from reaching them. The CIDR block of the VPC (10.0.0.0/16) allows for a significant number of IP addresses, and the chosen subnets (10.0.1.0/24 for public, 10.0.2.0/24 and 10.0.3.0/24 for private) are appropriately sized for the intended use. The public subnet’s route table must include a route directing traffic destined for 0.0.0.0/0 (all internet traffic) to the internet gateway, while the private subnets’ route tables should include a route directing traffic destined for 0.0.0.0/0 to the NAT gateway. This setup ensures that the web servers can communicate with the application servers in the private subnet, while the database servers remain secure and inaccessible from the internet. The NAT gateway acts as a bridge for the private subnets to access the internet without exposing them directly, thus maintaining a secure architecture.

Once the affected systems are isolated, the team can then proceed with other important actions, such as notifying customers about the breach. However, immediate notification without containment could lead to further panic and potential exploitation of the situation. Conducting a full forensic analysis is also essential, but it should occur after the immediate threat has been contained. This analysis will help understand the breach’s scope and identify vulnerabilities, but it cannot be effectively performed if the systems are still at risk. Lastly, reviewing and updating the incident response plan is a critical step, but it is more of a long-term action that should take place after the incident has been managed. The incident response plan should be informed by the lessons learned from the current incident, but the priority must be to contain the breach first. In summary, the correct initial action in an incident response strategy is to isolate affected systems, as this step is fundamental to preventing further damage and allows for a more controlled and effective response to the incident.

In this scenario, the company is deploying its application in two different AWS Regions, and each region has three Availability Zones. Therefore, the total number of distinct Availability Zones can be calculated as follows: \[ \text{Total Availability Zones} = \text{Number of Regions} \times \text{Availability Zones per Region} = 2 \times 3 = 6 \] This means there are 6 distinct Availability Zones available for the application deployment. Next, to ensure that at least one instance of the application is running in each Availability Zone, the company must deploy a minimum of one instance per Availability Zone. Since there are 6 Availability Zones, the minimum number of instances required is: \[ \text{Minimum Instances} = \text{Total Availability Zones} = 6 \] Thus, the company must deploy at least 6 instances to achieve the desired high availability across all Availability Zones. The incorrect options can be analyzed as follows: – Option b (3 instances) would not provide coverage across all Availability Zones, as it would leave some zones without any instances. – Option c (9 instances) exceeds the minimum requirement but does not reflect the necessary distribution across the zones. – Option d (2 instances) is insufficient to ensure that each Availability Zone has at least one instance running. In conclusion, the correct answer reflects both the total number of Availability Zones and the minimum instances required to ensure high availability across all zones.

Using Amazon Route 53 for DNS failover further complements this strategy by directing traffic to healthy endpoints, ensuring that users can still access the application even if one part of the infrastructure fails. This combination of multi-AZ deployments and Route 53 enhances the overall resilience of the application, allowing it to withstand failures and maintain performance. In contrast, relying on a single EC2 instance with auto-scaling does not provide the same level of reliability, as it still presents a single point of failure. While auto-scaling can help manage traffic spikes, it does not address the risk of instance failure. Similarly, using Amazon S3 without a backup strategy neglects the need for data durability and availability, as data loss could occur if the application relies solely on one storage solution without redundancy. Lastly, deploying the application in a single AWS region increases the risk of downtime due to regional outages, which is contrary to the principles of building a reliable architecture. Therefore, the best strategy to enhance reliability in this scenario is to implement multi-AZ deployments along with DNS failover.

When AWS Config detects a non-compliance event, it can trigger the Lambda function, which will execute the logic to tag the instances accordingly. This method is efficient and ensures that the tagging is applied consistently and automatically, reducing the risk of human error and ensuring compliance with internal policies. In contrast, the other options present various limitations. For instance, setting up an AWS CloudFormation stack to enforce tagging policies does not provide a dynamic response to existing non-compliance; it is more suited for initial deployments rather than ongoing compliance management. Using AWS Systems Manager to run a manual script is not ideal for automation and requires manual intervention, which can lead to delays and inconsistencies. Lastly, while enabling AWS Config’s managed rules for tagging compliance may seem convenient, it does not provide the same level of customization and immediate remediation as a Lambda function would. Therefore, the most effective solution is to utilize AWS Lambda to automate the tagging process in response to compliance events detected by AWS Config.

\[ \text{Total Size} = 1,000 \text{ objects} \times 10 \text{ MB/object} = 10,000 \text{ MB} \] Next, we convert this size into gigabytes (GB) since the retrieval cost is given per GB. There are 1,024 MB in a GB, so: \[ \text{Total Size in GB} = \frac{10,000 \text{ MB}}{1,024 \text{ MB/GB}} \approx 9.765625 \text{ GB} \] Now, we need to calculate the retrieval cost. The cost to retrieve data from S3 Glacier is $0.01 per GB. Therefore, the total retrieval cost can be calculated as follows: \[ \text{Total Retrieval Cost} = 9.765625 \text{ GB} \times 0.01 \text{ USD/GB} \approx 0.09765625 \text{ USD} \] Rounding this to two decimal places gives us approximately $0.10. In this scenario, the company has effectively utilized S3’s lifecycle management features to transition data to a lower-cost storage class while still being able to retrieve it when needed. Understanding the cost implications of data retrieval from different storage classes is crucial for optimizing cloud storage expenses. The lifecycle policies help manage data efficiently, but it is essential to consider retrieval costs when planning data access strategies, especially for large datasets used in machine learning applications.

\[ \text{Low Severity Findings} = \text{Total Findings} – (\text{High Severity Findings} + \text{Medium Severity Findings}) \] Substituting the values: \[ \text{Low Severity Findings} = 120 – (30 + 50) = 120 – 80 = 40 \] Next, to find the percentage of low severity findings, we use the formula: \[ \text{Percentage of Low Severity Findings} = \left( \frac{\text{Low Severity Findings}}{\text{Total Findings}} \right) \times 100 \] Substituting the values: \[ \text{Percentage of Low Severity Findings} = \left( \frac{40}{120} \right) \times 100 = \frac{1}{3} \times 100 \approx 33.33\% \] Thus, 33.33% of the total findings are classified as low severity. This question not only tests the candidate’s ability to perform basic arithmetic but also requires an understanding of how AWS Security Hub categorizes findings and the importance of prioritizing security incidents based on severity. In a real-world scenario, security teams must be adept at interpreting findings from AWS Security Hub and responding appropriately, which is critical for maintaining a robust security posture. Understanding the implications of severity levels can help teams allocate resources effectively and mitigate risks in their cloud environments.

Automated backups in Amazon RDS include both the daily snapshots and transaction logs, which allow for point-in-time recovery. Therefore, the company can successfully restore the database to its state from 5 days ago without any issues, as the backup from that day is still retained. The incorrect options present various misconceptions about the backup retention policy. For instance, the second option incorrectly states that backups are not retained beyond the last 7 days, which contradicts the defined retention period of 14 days. The third option suggests that manual intervention is required to locate the backup file, which is misleading because the automated backup process allows for straightforward restoration through the AWS Management Console or CLI without needing to manually search for backup files. Lastly, the fourth option incorrectly claims that the company can only restore to 3 days ago, which misrepresents the retention policy and the availability of backups. Understanding the nuances of Amazon RDS backup strategies, including retention periods and the implications for restoration, is crucial for effective database management and disaster recovery planning.

Implementing multi-AZ (Availability Zone) deployments for the database is a critical strategy. This approach ensures that the database is replicated across multiple physical locations within a region, providing redundancy in case one AZ experiences an outage. Coupled with Auto Scaling for application servers, this setup allows the application to automatically adjust its capacity based on demand, ensuring that it can handle varying loads while maintaining performance and availability. In contrast, relying on a single EC2 instance with a high-performance SSD for the database introduces a single point of failure, which is contrary to the principles of reliability. If that instance fails, the entire application could become unavailable. Similarly, deploying the application in a single AWS Region may simplify management but increases the risk of downtime due to regional outages. Lastly, while AWS CloudTrail is a valuable tool for auditing and monitoring API calls, it does not provide real-time performance monitoring or reliability metrics necessary for maintaining application uptime. Thus, the most effective approach to enhance reliability involves leveraging multi-AZ deployments and Auto Scaling, aligning with the best practices outlined in the AWS Well-Architected Framework. This ensures that the application remains resilient and can recover quickly from potential disruptions, thereby supporting the overall business continuity strategy.

First, we calculate the expected growth in terms of terabytes: \[ \text{Growth} = \text{Current Size} \times \text{Growth Rate} = 10 \, \text{TB} \times 0.20 = 2 \, \text{TB} \] Next, we add this growth to the current size to find the total size needed after one year: \[ \text{Total Size After One Year} = \text{Current Size} + \text{Growth} = 10 \, \text{TB} + 2 \, \text{TB} = 12 \, \text{TB} \] Thus, the company should provision at least 12 TB of storage in Amazon RDS to accommodate the expected growth without facing performance issues. It is important to note that while Amazon RDS allows for storage scaling, provisioning the correct amount initially is crucial to avoid any potential performance degradation during peak usage times. Additionally, the minimum storage requirement for Amazon RDS is 20 GB, which is well below the calculated requirement of 12 TB. In summary, the company should provision 12 TB to ensure they can handle the anticipated growth effectively while maintaining optimal performance levels. This approach aligns with best practices for database management and cloud resource provisioning, ensuring that the infrastructure can scale in line with business needs.

In the context of the given scenario, where the database is expected to handle a peak load of 10,000 TPS, maintaining high availability is essential. The Multi-AZ deployment not only provides failover capabilities but also enhances data durability by ensuring that data is replicated across different physical locations. This architecture is particularly beneficial for mission-critical applications where downtime can lead to significant financial losses or reputational damage. While read replicas (as mentioned in option b) can improve read performance, they do not provide the same level of availability and failover capabilities as Multi-AZ deployments. Horizontal scaling (option c) is not a feature of Multi-AZ deployments, as they focus on availability rather than scaling. Lastly, while Multi-AZ deployments do enhance data durability, they do not eliminate the need for backup and recovery strategies (option d), as backups are still essential for data protection and recovery in case of data corruption or accidental deletion. In summary, the use of Multi-AZ deployments in Amazon RDS for PostgreSQL significantly enhances the overall architecture by ensuring high availability, automatic failover, and data durability, making it a suitable choice for applications with stringent uptime requirements.

By creating two listeners—one for HTTP and another for HTTPS—the company can ensure secure communication over the internet. Path-based routing allows the ALB to direct traffic to specific target groups based on the request URL, which is essential for applications with multiple services or microservices architecture. Health checks are critical in maintaining application availability. They allow the ALB to monitor the health of the instances in each target group and only route traffic to those that are healthy. This ensures that users do not experience downtime or degraded performance due to unhealthy instances. The second option, which suggests using a Network Load Balancer (NLB), is not appropriate here because NLBs are optimized for TCP traffic and do not provide the advanced routing features that ALBs offer. The third option, which proposes using a single HTTPS listener without health checks, compromises both security and reliability, as it does not leverage the full capabilities of the ALB. Lastly, the fourth option dismisses the importance of health checks, which are vital for ensuring that the application remains responsive and available to users. In summary, the correct configuration involves setting up an ALB with both HTTP and HTTPS listeners, implementing path-based routing, and configuring health checks for optimal performance and reliability. This approach aligns with best practices for deploying scalable and resilient web applications in AWS.

In contrast, the Developer Support plan is more suited for developers experimenting with AWS services and does not provide the same level of architectural guidance or 24/7 support. Basic Support offers only access to customer service and documentation, lacking any technical support, which would not meet the company’s needs as they scale. The Enterprise Support plan, while comprehensive and offering the highest level of support, may be more than what the company requires at this stage, especially if they are not yet at a scale that justifies the cost. Thus, for a company looking for proactive support and architectural guidance while scaling its operations, the Business Support plan strikes the right balance between cost and the level of support needed. It ensures that the company can optimize its AWS usage effectively while having access to timely technical assistance, which is crucial during periods of growth.

In contrast, using a single-instance environment (option b) may simplify deployment but does not provide the necessary scalability or redundancy, making it vulnerable to downtime during traffic spikes. Disabling health checks (option c) can lead to routing traffic to instances that are not ready, resulting in errors and a poor user experience. Lastly, configuring a static IP address for the load balancer (option d) does not address the underlying issue of instance health and scaling; it merely directs traffic to a specific instance, which can still become a bottleneck during high load periods. Overall, the combination of rolling updates and health checks is essential for maintaining high availability and performance in a scalable application environment, particularly when using AWS Elastic Beanstalk with an RDS backend. This configuration allows the application to adapt to varying loads while minimizing the risk of downtime during scaling operations.