Each subnet must accommodate up to 4096 IP addresses, which corresponds to a subnet mask of /20, as $2^{12} = 4096$. However, the question specifies that the company wants to create four subnets, which means we need to ensure that the chosen configuration allows for efficient use of the IP address space while meeting the maximum requirement. Option (a) provides four subnets, each with a /18 mask, which allows for $2^{14} = 16384$ IP addresses per subnet. This configuration is more than sufficient for the requirement of 4096 IP addresses per subnet, and it efficiently divides the available address space into four distinct segments without overlapping. In contrast, option (b) uses a /20 mask for each subnet, which would only allow for 4096 addresses per subnet, but it does not utilize the available address space efficiently, as it would leave a significant portion of the VPC’s address space unused. Similarly, option (c) with /19 masks provides 8192 addresses per subnet, which is also more than necessary but does not maximize the use of the available space as effectively as option (a). Lastly, option (d) with /21 masks would only provide 2048 addresses per subnet, which does not meet the requirement of 4096 addresses. Thus, the configuration in option (a) is the most suitable as it meets the requirement while ensuring efficient use of the IP address space within the VPC.

Automated backups are crucial in this scenario as they allow for point-in-time recovery, which is essential for maintaining data durability. By enabling automated backups, the company can restore the database to any point within the backup retention period, which can be up to 35 days. This feature is particularly important for critical applications that cannot afford data loss. Read replicas, while useful for scaling read workloads, do not contribute to high availability in the same way that Multi-AZ deployments do. They are asynchronous and can introduce latency, which is not ideal for applications requiring immediate failover capabilities. Additionally, having read replicas in different regions can complicate the architecture and increase latency for write operations. The other options presented do not adequately address the requirements for high availability and automatic failover. A single-instance RDS setup lacks redundancy and does not provide the failover capabilities needed for critical applications. Manual snapshots do not offer the same level of data protection as automated backups, and performance insights, while beneficial for monitoring, do not contribute to availability. In summary, the optimal solution for the company is to implement a Multi-AZ deployment with automated backups enabled, ensuring both high availability and data durability for their critical application.

If the TTL is set too high, users may receive outdated content, which can be detrimental to user experience, especially for dynamic applications where content changes frequently. Conversely, if the TTL is set too low, it can lead to increased load on the origin server, as CloudFront will frequently check for updates, negating some of the performance benefits of using a CDN. Additionally, it is important to consider the nature of the content being served. For static assets like images, CSS, and JavaScript files, a longer TTL is often appropriate, while dynamic content may require a shorter TTL or even cache invalidation strategies to ensure users receive the most current version. In summary, setting an appropriate TTL is a balancing act that requires understanding the content’s update frequency and the desired user experience. The correct approach allows for efficient caching while ensuring that users receive timely updates, thus optimizing both performance and content delivery.

Aurora’s architecture allows for up to 15 read replicas, which can significantly improve read performance by distributing the read workload across multiple instances. This is particularly beneficial for applications with a read-heavy workload, as it allows for horizontal scaling of read operations without impacting write performance. Additionally, Aurora automatically scales storage up to 128 TB, which is advantageous for applications that may experience growth in data volume over time. In contrast, while Amazon RDS for PostgreSQL provides a robust relational database solution, it does not inherently offer the same level of scalability and performance optimizations as Aurora, particularly in read-heavy scenarios. RDS can support read replicas, but the maximum number is limited compared to Aurora, which may not suffice for applications with extremely high read demands. Amazon DynamoDB, on the other hand, is a NoSQL database service that excels in handling high-velocity workloads and offers seamless scalability. However, it is not a relational database and may not be suitable for applications that require complex queries or transactions typical of relational databases. Furthermore, while DynamoDB can handle high read and write throughput, it may not provide the same level of consistency and relational capabilities that a read-heavy application might require. Lastly, Amazon RDS for MySQL, while a solid choice for many applications, shares similar limitations with RDS for PostgreSQL regarding scalability and read performance optimizations compared to Aurora. Therefore, for a new application that prioritizes high availability, scalability, and optimized read performance, Amazon Aurora stands out as the best option among the choices provided.

\[ \text{Percentage} = \left( \frac{\text{Part}}{\text{Whole}} \right) \times 100 \] In this scenario, the “Part” is the number of unauthorized API calls, which is 20, and the “Whole” is the total number of API calls, which is 150. Plugging these values into the formula gives: \[ \text{Percentage} = \left( \frac{20}{150} \right) \times 100 \] Calculating the fraction: \[ \frac{20}{150} = \frac{2}{15} \approx 0.1333 \] Now, multiplying by 100 to convert it to a percentage: \[ 0.1333 \times 100 \approx 13.33\% \] Thus, 13.33% of the total API calls were unauthorized. This analysis is crucial for the security team as it helps them understand the extent of unauthorized access attempts and assess the effectiveness of their security measures. Monitoring API calls through AWS CloudTrail is a best practice for maintaining security and compliance, as it provides a detailed history of actions taken in the AWS environment. By analyzing this event history, organizations can identify patterns of unauthorized access, respond to incidents more effectively, and enhance their overall security posture.

For instance, if the company experiences higher costs during certain months, the finance team can filter the report to focus on those specific periods and analyze which services contributed to the spikes. This granular insight is essential for making informed decisions about future budgets and identifying opportunities for cost optimization. While the other options provide valuable functionalities, they do not directly address the need for detailed analysis of spending patterns. Setting up alerts for cost anomalies is useful for monitoring unexpected spending but does not provide the in-depth analysis required for budget planning. Integration with AWS Budgets helps manage spending limits but does not offer the same level of detailed reporting. Lastly, the ability to visualize costs using predefined charts without customization lacks the flexibility needed to tailor the analysis to the company’s specific requirements. Therefore, the feature that best supports the finance team’s objectives is the ability to create custom reports that filter costs by service type and usage patterns over specific time frames.

The other options present various drawbacks. Using a single attribute as the primary key that combines user ID and timestamp into a single string would complicate querying, as it would not allow for efficient range queries on timestamps. Creating a global secondary index with timestamp as the partition key and user ID as the sort key would not be optimal for the primary use case of retrieving session data by user ID, leading to inefficient queries. Lastly, using a simple primary key with user ID as the partition key and storing all session data as a single JSON object would hinder the ability to query specific attributes within the session data efficiently, as DynamoDB is designed to work best with structured data that can be indexed. In summary, the best approach is to utilize a composite primary key with user ID as the partition key and timestamp as the sort key, as this design maximizes the efficiency of both read and write operations while allowing for flexible querying based on the application’s requirements.

Next, the RSA encryption of the AES key, which is 32 bytes long, takes an additional 0.1 seconds. RSA is typically used for encrypting small amounts of data, such as keys, rather than large files due to its computational intensity. The time taken for RSA encryption is relatively small compared to the time taken for AES encryption, which is why it is suitable for this scenario. To find the total time taken for both processes, we simply add the time taken for AES encryption (0.5 seconds) and the time taken for RSA encryption (0.1 seconds): \[ \text{Total Time} = \text{Time for AES} + \text{Time for RSA} = 0.5 \text{ seconds} + 0.1 \text{ seconds} = 0.6 \text{ seconds} \] This calculation illustrates the efficiency of using symmetric encryption (AES) for large data and asymmetric encryption (RSA) for securely transmitting keys. The combination of these two encryption methods ensures that the sensitive data remains secure both at rest and during transmission. Understanding the time complexity and the operational characteristics of different encryption algorithms is crucial for implementing effective security measures in any organization.

When the company decides to restore the database to a point in time that is 5 days prior to the current date, they are effectively looking to recover the state of the database as it was 5 days ago. Since the automated backups are retained for 14 days, the backup from 5 days ago is still available for restoration. The point-in-time recovery process involves using the automated backups and the transaction logs that are generated during the backup retention period. In this case, the company will restore the snapshot from 5 days ago and then apply the transaction logs from that point up to the desired recovery time. Since the database has a daily write load of 10 GB, the amount of data that has been written in the 5 days leading up to the recovery point is \(5 \times 10 \, \text{GB} = 50 \, \text{GB}\). However, the actual restoration will only involve the data that existed at the time of the snapshot taken 5 days ago, which is effectively the state of the database at that time. Thus, the maximum amount of data that will be restored from the automated backups is 10 GB, which corresponds to the data that was written on the day of the snapshot being restored. The recovery process ensures that the database is returned to its exact state at that point in time, including all transactions that occurred up to that moment. This highlights the importance of understanding both the retention period of backups and the implications of point-in-time recovery in Amazon RDS.

When considering retrieval costs, S3 Glacier Deep Archive has a retrieval fee that is lower than other classes when data is accessed infrequently. Although it takes longer to retrieve data from Glacier Deep Archive (typically hours), the infrequent access pattern of the data in this scenario aligns well with the retrieval model of this storage class. On the other hand, S3 Standard is designed for frequently accessed data and would incur higher costs for both storage and retrieval, making it unsuitable for this use case. S3 Intelligent-Tiering is beneficial for data with unpredictable access patterns, but it incurs a monthly monitoring and automation fee, which may not be justified given the predictable infrequent access of the data. Lastly, S3 One Zone-IA is cheaper than Standard but still more expensive than Glacier Deep Archive and is not designed for long-term retention, as it stores data in a single Availability Zone, which poses a risk for compliance data that requires durability and availability. Thus, for the company’s specific needs of infrequent access and long-term retention, S3 Glacier Deep Archive is the most appropriate choice, balancing both storage and retrieval costs effectively.

While increasing the frequency of the configuration recorder may seem beneficial, AWS Config already captures configuration changes in near real-time, and simply increasing the frequency does not guarantee that all changes will be recorded if the rules are not set up correctly. Similarly, setting up a CloudTrail log is useful for monitoring API calls, but it does not directly enhance the configuration history tracking within AWS Config itself. Lastly, implementing a tagging strategy can help in organizing resources but does not inherently improve the tracking of configuration changes. In summary, enabling AWS Config rules is crucial for ensuring that all configuration changes are not only recorded but also evaluated against compliance standards, thereby addressing the security vulnerabilities identified during the audit. This approach aligns with best practices for resource management and compliance monitoring in AWS environments.

\[ \text{Total EC2 hours} = 2 \times 800 = 1600 \text{ hours} \] For the RDS instance, it runs for the same duration: \[ \text{Total RDS hours} = 800 \text{ hours} \] Next, we need to assess how many hours exceed the Free Tier limits. The Free Tier allows for 750 hours of t3.micro instances, but since the company is using t3.medium instances, they do not qualify for the Free Tier. Therefore, all hours will be charged. Now, we calculate the costs for the EC2 instances: \[ \text{Cost for EC2} = \text{Total EC2 hours} \times \text{Price per hour} = 1600 \times 0.0416 = 66.56 \] Next, we calculate the cost for the RDS instance: \[ \text{Cost for RDS} = \text{Total RDS hours} \times \text{Price per hour} = 800 \times 0.018 = 14.40 \] Now, we sum the costs for both services to find the total additional cost: \[ \text{Total Additional Cost} = \text{Cost for EC2} + \text{Cost for RDS} = 66.56 + 14.40 = 80.96 \] However, since the question asks for the additional cost incurred beyond the Free Tier, we need to consider that the Free Tier allows for 750 hours of usage for t3.micro instances, which the company does not utilize. Therefore, the entire usage is chargeable. To summarize, the company incurs a total additional cost of $80.96 for the month. However, since the question specifies the additional cost incurred beyond the Free Tier limits, we need to consider the total hours used minus the Free Tier allowance. Since they used 800 hours, they exceed the Free Tier by 50 hours for both EC2 and RDS. Calculating the additional costs for the excess hours: For EC2: \[ \text{Excess EC2 hours} = 1600 – 750 = 850 \text{ hours} \] \[ \text{Cost for Excess EC2} = 850 \times 0.0416 = 35.36 \] For RDS: \[ \text{Excess RDS hours} = 800 – 750 = 50 \text{ hours} \] \[ \text{Cost for Excess RDS} = 50 \times 0.018 = 0.90 \] Thus, the total additional cost incurred is: \[ \text{Total Additional Cost} = 35.36 + 0.90 = 36.26 \] However, since the question asks for the total incurred cost for the month, the correct answer is $80.96. The options provided may not reflect this accurately, but the calculation illustrates the importance of understanding AWS pricing structures, including Free Tier limitations and the implications of instance types on billing.

While read replicas can enhance read scalability and performance, they do not provide automatic failover capabilities. Therefore, deploying read replicas in different regions (as suggested in option a) is not necessary for high availability and would incur additional costs without addressing the primary requirement of automatic failover. Using a single RDS instance with manual backups (option b) does not provide high availability or automatic failover, as it relies on manual processes for recovery, which can lead to extended downtime. Implementing a Multi-AZ RDS instance without read replicas (option c) is a viable option for high availability, but it does not leverage the benefits of read replicas for read-heavy workloads, which could be a consideration for performance optimization. Setting up a cluster of RDS instances with cross-region replication (option d) is more complex and costly than necessary for the requirement of high availability and automatic failover, especially if the application does not require cross-region disaster recovery. In summary, the Multi-AZ RDS configuration provides the necessary high availability and automatic failover capabilities while being cost-effective for the company’s needs. This understanding of RDS configurations is crucial for making informed decisions in cloud database management.

On the other hand, the NAT Gateway (Network Address Translation Gateway) is specifically designed to allow instances in a private subnet to initiate outbound traffic to the internet while preventing unsolicited inbound traffic from reaching those instances. This is crucial for maintaining the security of the private subnet, as it ensures that instances can access necessary updates and external services without being directly exposed to the internet. The correct understanding of these components is vital for designing secure and efficient network architectures in AWS. The Internet Gateway does not route traffic from private subnets; instead, it connects public subnets to the internet. The NAT Gateway does not provide inbound access to public subnet instances; rather, it allows private subnet instances to communicate with the internet for updates and other outbound requests. Therefore, the accurate description of their roles highlights the importance of using both gateways to achieve a secure and functional network design, ensuring that private resources remain protected while still having the ability to access the internet for necessary operations.

The total number of records to be processed is 50,000 records per minute. To find the per-second rate, we divide by 60 seconds: $$ \text{Records per second} = \frac{50,000 \text{ records}}{60 \text{ seconds}} \approx 833.33 \text{ records/second} $$ Next, we need to consider the capacity of each shard. Each shard can handle a maximum of 1,000 records per second. To find out how many shards are necessary to handle the calculated records per second, we divide the required records per second by the capacity of a single shard: $$ \text{Number of shards required} = \frac{833.33 \text{ records/second}}{1,000 \text{ records/shard}} \approx 0.8333 $$ Since we cannot have a fraction of a shard, we round up to the nearest whole number, which is 1 shard. However, this calculation only considers the records per second. To ensure that the processing is efficient and to account for potential spikes in data, it is prudent to consider the maximum throughput. Given that the stream has a shard limit of 10, we can utilize multiple shards to distribute the load evenly. If we were to utilize 5 shards, the total capacity would be: $$ \text{Total capacity with 5 shards} = 5 \text{ shards} \times 1,000 \text{ records/shard} = 5,000 \text{ records/second} $$ This capacity is more than sufficient to handle the required 833.33 records per second. Therefore, while 1 shard could technically suffice for the average load, using 5 shards would provide a buffer for peak loads and ensure that the processing remains efficient and responsive. In conclusion, to meet the requirement of processing 50,000 records in a minute without exceeding the limits of the stream and to ensure optimal performance, 5 shards should be utilized.

\[ \text{Requests per appliance} = \frac{\text{Total incoming traffic}}{\text{Number of appliances}} = \frac{450}{3} = 150 \text{ requests per appliance} \] Next, we need to assess the utilization of each appliance. Each virtual appliance has a maximum capacity of 100 requests per second. Therefore, the utilization percentage can be calculated using the formula: \[ \text{Utilization} = \left( \frac{\text{Requests handled}}{\text{Maximum capacity}} \right) \times 100 \] Substituting the values for one appliance: \[ \text{Utilization} = \left( \frac{150}{100} \right) \times 100 = 150\% \] This indicates that each appliance is handling 150% of its maximum capacity, which is not sustainable in a real-world scenario. This situation would likely lead to performance degradation or failure of the appliances due to overloading. In summary, while the Gateway Load Balancer effectively distributes the incoming traffic, the configuration in this scenario leads to each appliance being overloaded. This highlights the importance of understanding both the distribution of traffic and the capacity limits of the resources involved. Proper scaling strategies, such as adding more appliances or optimizing the application to handle requests more efficiently, would be necessary to ensure high availability and performance.

\[ \text{Average} = \frac{\text{Sum of response times}}{\text{Number of observations}} = \frac{180 + 220 + 210 + 190 + 230}{5} \] Calculating the sum: \[ 180 + 220 + 210 + 190 + 230 = 1130 \text{ ms} \] Now, we divide by the number of observations (5): \[ \text{Average} = \frac{1130}{5} = 226 \text{ ms} \] Next, we compare this average response time to the threshold of 200 ms. Since 226 ms exceeds the threshold, the alarm will indeed trigger. The other options present common misconceptions. For instance, option b incorrectly states that the alarm will not trigger, which is false based on our calculation. Option c introduces an arbitrary threshold of 250 ms, which is not relevant to the scenario presented. Lastly, option d suggests that the alarm will not trigger due to inconsistency in data, which is misleading; the alarm is based on the average, not the individual data points. In summary, understanding how CloudWatch alarms work, particularly in relation to average metrics over a specified period, is crucial. The alarm is designed to monitor performance and alert administrators when performance degrades beyond acceptable limits, thus ensuring proactive management of application performance.

\[ \text{Alert Threshold} = 0.80 \times \text{Monthly Budget} = 0.80 \times 10,000 = 8,000 \] This means that the company will receive an alert when their costs reach $8,000. By the 20th of the month, they have incurred costs of $7,500. To find out how much more they can spend before reaching the alert threshold, we subtract their current costs from the alert threshold: \[ \text{Remaining before alert} = \text{Alert Threshold} – \text{Current Costs} = 8,000 – 7,500 = 500 \] Thus, they can spend an additional $500 before receiving an alert. Next, we calculate the remaining budget for the rest of the month. The remaining budget is simply the total budget minus the costs incurred so far: \[ \text{Remaining Budget} = \text{Monthly Budget} – \text{Current Costs} = 10,000 – 7,500 = 2,500 \] Therefore, the company has a remaining budget of $2,500 for the rest of the month. In summary, they have $2,500 left in their budget, and they can spend an additional $500 before reaching the alert threshold of $8,000. This understanding of AWS Budgets is crucial for effective cost management and ensuring that the company does not exceed its financial limits while utilizing AWS services.

Once the parameters are declared, the nested stack can reference them using the `!Ref` intrinsic function. This function allows the nested stack to dynamically retrieve the values passed from the parent stack, ensuring that the EC2 instances are provisioned within the correct VPC and subnets. On the other hand, if the nested stack were to attempt to access resources created by the parent stack without declaring parameters, it would lead to errors, as CloudFormation does not allow implicit access to resources across stack boundaries. Hardcoding values in the nested stack would defeat the purpose of using parameters, as it would create a rigid configuration that cannot adapt to changes in the parent stack. Lastly, creating a new VPC in the nested stack would not only be unnecessary but could also lead to resource conflicts and increased complexity in the architecture. Therefore, the correct approach is to declare the parameters in the nested stack and use the `!Ref` function to reference them, ensuring a clean and efficient resource management strategy.

By enabling data events for S3, the company can track specific actions like `GetObject`, `PutObject`, and `DeleteObject`, which are essential for identifying unauthorized access attempts. Additionally, configuring Amazon Athena allows the company to run SQL-like queries directly against the logs stored in the S3 bucket, facilitating efficient analysis and reporting. This setup not only enhances security monitoring but also supports compliance audits by providing detailed logs of all access attempts. In contrast, logging only management events (as suggested in option b) would limit visibility into critical data access operations, making it difficult to detect unauthorized access. Relying solely on AWS Config (option c) does not provide comprehensive logging of API calls, and configuring CloudTrail to log only read events (option d) would miss write and delete operations, which are equally important for security monitoring. Therefore, the combination of enabling data events and using Athena for analysis is the most effective approach for the company’s requirements.

On the other hand, AWS-managed keys are designed for ease of use and do not require the same level of management as CMKs. Allowing broader access to AWS-managed keys is appropriate for general use cases where sensitive data is not involved. This configuration provides a clear separation of responsibilities and access levels, enhancing security while maintaining operational efficiency. The other options present various shortcomings. Exclusively using AWS-managed keys (option b) would eliminate the necessary control over sensitive data, which is critical in a financial context. Assigning permissions to all IAM users (option c) undermines the principle of least privilege, potentially exposing sensitive data to unauthorized users. Lastly, implementing a key rotation policy (option d) without considering access permissions does not address the core requirement of restricting access to the CMK, and could lead to operational issues if users are unable to access the key after rotation. Thus, the recommended approach effectively balances security and usability, ensuring that sensitive customer data is protected while allowing broader access to less sensitive operations.

Amazon RDS for PostgreSQL is a managed service that simplifies the setup, operation, and scaling of PostgreSQL databases. While it is capable of handling complex queries and transactions, it may not match the performance and scalability of Aurora, especially under heavy loads. Additionally, RDS for PostgreSQL has limitations in terms of scaling read replicas compared to Aurora. Amazon DynamoDB, on the other hand, is a NoSQL database service that excels in handling high-velocity workloads with low-latency responses. However, it is not optimized for complex queries and transactions like those typically found in relational databases. Its eventual consistency model can also complicate scenarios where strong consistency is required. Lastly, Amazon RDS for MySQL, while similar to RDS for PostgreSQL, does not provide the same level of performance and scalability as Aurora. Aurora’s architecture allows for faster recovery times and better handling of large datasets, making it a superior choice for applications that require both high availability and the ability to perform complex queries efficiently. In summary, considering the requirements of high availability, scalability, and the need for complex queries and transactions, Amazon Aurora stands out as the best option. Its architecture is specifically designed to meet these demands while minimizing operational overhead and cost, making it the most suitable choice for the company’s new application.

The integration of CloudWatch alarms is crucial for monitoring the performance of the application tier. By setting up alarms based on CPU utilization metrics, you can automate scaling actions. For instance, if the CPU utilization exceeds a defined threshold (e.g., 70%), the Auto Scaling group can automatically launch additional instances to handle the increased load. Conversely, if the CPU utilization falls below a certain threshold (e.g., 30%), the Auto Scaling group can terminate instances to reduce costs. In contrast, the other options present significant drawbacks. Creating a static number of EC2 instances (option b) does not allow for flexibility and responsiveness to changing loads, leading to potential performance issues or unnecessary costs. Using a single EC2 instance (option c) introduces a single point of failure and does not provide any scaling capabilities. Lastly, implementing a CloudFormation stack with a Load Balancer but without scaling policies or monitoring metrics (option d) fails to address the need for dynamic resource management, which is essential for maintaining application performance and availability. Thus, the best approach is to define an Auto Scaling group with a Launch Configuration and attach CloudWatch alarms to ensure that the application tier can scale automatically based on real-time metrics, thereby optimizing resource utilization and maintaining application performance.

Forwarding all headers to the origin, as suggested in one of the options, can lead to increased costs due to higher data transfer and processing times, especially if the content is primarily static. Disabling caching entirely on the EC2 instance would negate the benefits of using CloudFront, as it would lead to increased latency and costs due to every request hitting the origin server directly. Lastly, implementing a multi-origin setup with equal weight may not effectively optimize performance for the specific region, as it does not prioritize the origin that provides the best response time for users in that area. In summary, the optimal strategy involves leveraging caching effectively while ensuring that content remains up-to-date, which is best achieved through a custom origin with a carefully considered cache TTL. This approach not only enhances performance but also helps in managing costs effectively, aligning with the company’s goals for their CloudFront distribution.

Option b is not advisable as keeping all versions in S3 Standard would lead to higher costs without any benefit in retrieval speed for older versions. Option c, while it offers cost optimization, does not guarantee the retrieval speed required for the most recent version, as S3 Intelligent-Tiering may move objects to lower-cost storage classes based on access patterns, which could delay access to critical data. Lastly, option d would not meet the requirement for timely access to the most recent version, as S3 Glacier has longer retrieval times, making it unsuitable for scenarios where immediate access is necessary. Thus, the best approach is to maintain the latest version in S3 Standard for quick access while managing older versions in a cost-effective manner by transitioning them to S3 Glacier Deep Archive after a suitable period. This strategy aligns with AWS best practices for data lifecycle management, ensuring both cost efficiency and operational effectiveness.

Enabling origin failover is crucial in this configuration, as it allows CloudFront to automatically switch to a secondary origin if the primary origin becomes unavailable. This redundancy is vital for maintaining high availability, especially during sudden spikes in traffic, which could overwhelm a single origin. In contrast, setting up a single origin with Route 53 for DNS-based routing (option b) does not provide the same level of performance optimization as CloudFront’s edge locations, as DNS resolution can introduce additional latency. Additionally, relying solely on a single origin increases the risk of downtime during traffic spikes. Utilizing CloudFront with a single origin and enabling caching for static content only (option c) limits the benefits of dynamic content delivery and does not address the need for routing users to the nearest edge location. Lastly, implementing CloudFront with a custom origin and disabling caching (option d) defeats the purpose of using a CDN, as it would lead to increased latency and reduced performance due to the lack of cached content. In summary, the best approach is to configure CloudFront with multiple origins across different AWS regions, enabling origin failover to ensure both optimal performance and high availability during traffic fluctuations. This configuration aligns with best practices for deploying scalable and resilient web applications in a global context.

Initially, there are 5 instances running. Since the scaling policy allows for an increase in the number of instances when the CPU utilization is high, the Auto Scaling group will add 1 instance to the current count. Therefore, the new total will be: \[ \text{New Total Instances} = \text{Current Instances} + 1 = 5 + 1 = 6 \] It is important to note that the Auto Scaling group has a maximum limit of 10 instances, which means it can scale up to that number if needed. However, since the current count of 5 instances is well below the maximum, the addition of 1 instance is permissible. The other options can be analyzed as follows: – Option b (5 instances) is incorrect because it does not account for the scaling action triggered by the high CPU utilization. – Option c (4 instances) is incorrect as it suggests a decrease in instances, which is not applicable in this scenario since the CPU utilization is above the threshold for scaling up. – Option d (7 instances) is incorrect because it implies that the scaling action would add 2 instances, which is not supported by the defined scaling policy. Thus, the correct outcome after the scaling action is executed will result in a total of 6 instances in the Auto Scaling group. This scenario illustrates the importance of understanding how Auto Scaling policies are triggered based on specific metrics and thresholds, as well as the implications of those policies on resource management in AWS environments.

\[ \text{Compliant Instances} = 200 \times 0.80 = 160 \] Next, we find the number of non-compliant instances by subtracting the number of compliant instances from the total number of instances: \[ \text{Non-Compliant Instances} = 200 – 160 = 40 \] Now, to find the number of non-compliant instances in the specific region, we know that 15% of the non-compliant instances are located there. Therefore, we calculate the number of non-compliant instances in that region: \[ \text{Non-Compliant Instances in Region} = 40 \times 0.15 = 6 \] Thus, the company has 40 non-compliant instances in total, with 6 of those located in the specific region mentioned. This scenario illustrates the importance of using AWS Systems Manager for compliance management, as it allows organizations to automate the monitoring of their resources and ensure adherence to security policies. By scheduling regular compliance scans, the company can quickly identify and remediate non-compliant instances, thereby enhancing their security posture and operational efficiency.

Using Amazon RDS with Multi-AZ deployment for the database server is crucial as it provides automatic failover to a standby instance in another Availability Zone in case of a failure. This setup not only ensures data durability but also minimizes downtime, which is essential for applications requiring high availability. In contrast, the other options present significant drawbacks. For instance, using a single EC2 instance for both the web and application servers (option b) creates a single point of failure, which contradicts the goal of high availability. Deploying the web server and application server in separate Availability Zones (option c) does provide some level of redundancy, but it does not utilize Auto Scaling, which is vital for handling variable loads efficiently. Lastly, hosting the web server on Amazon S3 (option d) is not suitable for dynamic content that requires server-side processing, and deploying the application server on a single EC2 instance also introduces a risk of downtime. Thus, the optimal configuration leverages AWS services effectively to achieve a balance between high availability, fault tolerance, and cost efficiency, making it the most suitable choice for the company’s requirements.

Implementing try-except blocks around each API call is crucial for robust error handling. This ensures that if an exception occurs—such as a permission error or a network issue—the application can gracefully handle the error without crashing. For example, if a bucket is inaccessible due to permissions, the application can log the error and continue processing the remaining buckets. The other options present flawed approaches. For instance, using `get_bucket_location()` does not provide size information, and relying on `head_object()` for a known object only gives the size of that single object, which is insufficient for determining the total size of the bucket. Additionally, checking bucket ACLs with `get_bucket_acl()` does not yield size information and assumes a correlation between size and ACL complexity, which is not valid. Therefore, the most effective method combines accurate size retrieval with proper exception handling to ensure the application runs smoothly and efficiently.