Increasing server resources (option b) may provide a temporary fix, but it does not address the underlying issue of code performance. Simply ignoring the performance issue (option c) is detrimental, as it could lead to poor user experience and increased operational costs. Delaying the deployment (option d) without addressing the performance issue does not solve the problem and could lead to further complications in future releases. In summary, the best approach is to focus on optimizing the application code to ensure it meets performance benchmarks, as this aligns with the principles of continuous integration and deployment, where quality and performance are paramount. This proactive approach not only enhances the application’s performance but also contributes to a more stable and reliable deployment process.

Creating IAM roles tailored to the specific needs of users during business hours allows for granular control over permissions. By attaching these roles to users based on their access requirements, the company can ensure that users only have access to the resources they need when they need them. This approach not only enhances security by limiting access but also aligns with best practices for IAM, which advocate for the use of roles to manage permissions dynamically. On the other hand, assigning all users the same broad IAM policy (option b) undermines the principle of least privilege and increases the risk of unauthorized access. Similarly, using IAM groups to apply broad permissions (option c) fails to account for the specific time-based access needs of users. Lastly, implementing a single IAM policy with time-based conditions (option d) may seem like a viable solution, but it can lead to complexity and potential misconfigurations, making it harder to manage and audit permissions effectively. By focusing on role-based access control that is time-sensitive, the company can maintain a secure environment while ensuring that users have the appropriate access to resources based on their specific needs and the time of day. This approach not only enhances security but also simplifies the management of IAM policies in the long run.

The performance improvement comes from the fact that edge locations are designed to cache content closer to users, thereby minimizing the distance data must travel. If the content is already cached at the North American edge location, it can be served quickly without needing to contact the origin. If the content is not cached, CloudFront will retrieve it from the origin in S3, cache it at the edge location, and then serve it to the user. This caching mechanism ensures that subsequent requests for the same content can be served even faster. The other options present misconceptions about how CloudFront operates. For instance, option b incorrectly states that CloudFront always fetches content from the origin, which contradicts the caching functionality that is central to its design. Option c incorrectly suggests that Europe is the closest edge location to South America, which is geographically inaccurate. Lastly, option d misrepresents CloudFront’s operation by implying that it prioritizes requests based on time, which is not a factor in determining edge location routing. Thus, understanding the mechanics of CloudFront’s routing and caching is crucial for optimizing content delivery and enhancing user experience.

Using a single table design is particularly advantageous in DynamoDB because it reduces the complexity of managing multiple tables and allows for more efficient use of read and write capacity. It also leverages DynamoDB’s ability to handle high throughput and low latency, which is crucial for serverless applications that may experience variable workloads. On the other hand, creating separate tables for users and items (option b) would complicate the querying process, as it would require additional joins or multiple queries to retrieve related data. A single table with only the user ID as the primary key (option c) would not allow for efficient retrieval of multiple items per user, as it would not differentiate between items. Lastly, implementing a multi-table design (option d) could lead to increased costs and management overhead, as well as complicating the data retrieval process. Overall, the single table design with a composite primary key is the most efficient and scalable approach for this use case, aligning with best practices for DynamoDB usage in serverless architectures.

On the other hand, using Amazon EC2 instances (option b) introduces the need for manual scaling and management, which contradicts the serverless paradigm. While EC2 can handle high loads, it requires more operational overhead and may lead to higher costs if not managed properly. Similarly, deploying the application using AWS Fargate (option c) is a viable option for containerized workloads, but it may not provide the same level of automatic scaling and cost efficiency as Lambda with provisioned concurrency, especially for short-lived functions. Lastly, utilizing Amazon S3 and AWS Batch (option d) for processing requests in batches during off-peak hours may lead to increased latency for users, as it does not provide real-time processing capabilities. This approach is more suited for scenarios where immediate response is not critical. In summary, the optimal architectural approach for the company is to implement AWS Lambda with provisioned concurrency, as it effectively addresses the need for responsiveness during peak loads while maintaining cost efficiency in a serverless environment.

In contrast, a direct integration without passing the request context would require the Lambda function to manually handle the formatting of the response, which can lead to increased complexity and potential errors. Using AWS Step Functions, while powerful for orchestrating complex workflows, introduces additional latency and cost, making it less suitable for straightforward API requests. Lastly, implementing a WebSocket API is unnecessary for this scenario, as the application does not require real-time communication, which is the primary use case for WebSocket APIs. By choosing Lambda Proxy Integration, the company can ensure that their API is both efficient and cost-effective, allowing for seamless handling of requests while leveraging the full capabilities of AWS Lambda. This configuration aligns with best practices for serverless architectures, promoting scalability and maintainability.

Implementing Auto Scaling is a best practice that allows the application to automatically adjust the number of EC2 instances in response to real-time demand. This approach not only ensures that the application can handle variable workloads efficiently but also optimizes costs by scaling down during periods of low demand. Auto Scaling can help maintain performance by ensuring that there are enough resources available to meet user demand without over-provisioning, which can lead to unnecessary costs. On the other hand, using a single instance type for all workloads (option b) may simplify management but does not take advantage of the diverse capabilities of different instance types, which can lead to inefficiencies and higher costs. Manually adjusting instance types based on historical data (option c) lacks the responsiveness of an automated solution and can result in performance degradation during unexpected spikes in demand. Finally, choosing the largest instance type available at all times (option d) is not cost-effective and does not align with the principle of cost optimization, as it leads to over-provisioning and wasted resources. In summary, the best approach to align with the AWS Well-Architected Framework is to implement Auto Scaling, which dynamically adjusts resources based on demand, ensuring both performance efficiency and cost optimization. This strategy reflects a deep understanding of the framework’s principles and demonstrates a commitment to building a robust, efficient, and cost-effective application on AWS.

Moreover, NoSQL databases are built to scale horizontally, meaning they can distribute data across multiple servers or nodes. This distribution allows for increased capacity and performance as the application grows. For instance, when user interactions generate large volumes of data, a NoSQL database can seamlessly add more nodes to accommodate this growth, ensuring that the application remains responsive and available. In contrast, traditional relational databases often require a fixed schema, which can hinder flexibility and complicate scaling efforts. They also tend to rely on vertical scaling, which involves upgrading existing hardware rather than adding more servers. This approach can lead to bottlenecks and increased costs. The incorrect options highlight misconceptions about NoSQL databases. For example, the assertion that NoSQL databases are primarily designed for complex transactions is misleading, as many NoSQL systems prioritize availability and partition tolerance over strict consistency. Additionally, the claim that NoSQL databases are limited to key-value pairs overlooks the variety of NoSQL models available, such as document stores, column-family stores, and graph databases, each offering different querying capabilities and data structures. In summary, the characteristics of NoSQL databases that support dynamic schema changes and horizontal scaling make them particularly well-suited for applications dealing with large volumes of unstructured data, aligning perfectly with the company’s requirements.

To calculate the minimum number of instances needed to handle this peak load, we can use the formula: \[ \text{Number of instances} = \frac{\text{Peak load}}{\text{Requests per instance}} = \frac{100 \text{ requests/second}}{20 \text{ requests/instance}} = 5 \text{ instances} \] This calculation shows that at least 5 instances are necessary to handle the peak load of 100 requests per second. However, to account for unexpected spikes in traffic, it is prudent to provision additional instances. A common practice is to add a buffer of 20-30% to the calculated number of instances to ensure that the application can handle sudden increases in load without performance degradation. Calculating a 20% buffer on the 5 instances gives: \[ \text{Buffer} = 5 \times 0.2 = 1 \text{ additional instance} \] Thus, the total number of instances to provision would be: \[ \text{Total instances} = 5 + 1 = 6 \text{ instances} \] This configuration allows the application to maintain performance during peak loads while also providing a safety net for unexpected traffic spikes. Therefore, the correct answer is to provision 6 instances, ensuring that the application remains responsive and cost-effective. This approach aligns with AWS best practices for Auto Scaling, which emphasize the importance of balancing performance and cost efficiency.

The team should first diagnose the error in the affected instance before proceeding with the update of the remaining instances. This approach ensures that the application remains stable and available to users while minimizing the risk of widespread issues. If the error is resolved, the team can then continue with the deployment of the next batch of instances. This method aligns with best practices for application deployment, emphasizing the importance of monitoring and error handling during updates to maintain service reliability. In contrast, continuing the update regardless of the error (option b) could lead to a larger-scale failure, while rolling back the entire deployment (option c) would be an extreme measure that is typically unnecessary unless the application is critically impacted. Completing the update for the remaining instances without addressing the error (option d) could also lead to inconsistencies and further complications. Therefore, the best course of action is to pause the update, investigate the error, and ensure that the application remains functional throughout the deployment process.

On the other hand, focusing solely on feature selection without considering model evaluation is a flawed approach. While selecting relevant features is important, it must be complemented by a robust evaluation strategy to understand how well the model performs on unseen data. Additionally, using a single evaluation metric can lead to a skewed understanding of the model’s performance. For instance, relying solely on accuracy might be misleading in cases of imbalanced datasets, where precision, recall, or F1-score could provide more insight into the model’s effectiveness. Moreover, ignoring data leakage during the training process can severely compromise the integrity of the model. Data leakage occurs when information from outside the training dataset is used to create the model, leading to overly optimistic performance estimates. It is vital to ensure that the training and testing datasets are properly separated and that no future information is inadvertently included in the training phase. In summary, the most effective approach involves conducting hyperparameter tuning, ensuring comprehensive model evaluation, utilizing multiple evaluation metrics, and safeguarding against data leakage. These practices collectively contribute to building a robust and reliable machine learning model capable of accurately predicting customer churn.

To find the maximum concurrent executions, we can use the formula: \[ \text{Concurrent Executions} = \text{Request Rate} \times \text{Processing Time} \] Substituting the values: \[ \text{Concurrent Executions} = 1000 \, \text{requests/second} \times 0.2 \, \text{seconds} = 200 \, \text{concurrent executions} \] This calculation indicates that at peak load, the Lambda function could reach up to 200 concurrent executions. Understanding this concurrency is crucial for the architecture of the application. AWS Lambda has a default concurrency limit of 1,000 concurrent executions per account per region, which means that the application would be operating well within this limit. However, if the application were to scale beyond this limit, it could lead to throttling, where additional requests would be rejected until existing executions complete. Moreover, the architecture should also consider the implications of scaling, such as the need for efficient error handling and monitoring to ensure that the application can gracefully handle spikes in traffic. Implementing features like AWS Step Functions for orchestration or Amazon SQS for queuing requests can help manage load and ensure that the application remains responsive even under high demand. In summary, the maximum number of concurrent executions during peak load is 200, and this understanding is vital for designing a robust serverless architecture that can handle varying loads efficiently while adhering to AWS service limits.

Moreover, FIFO queues provide exactly-once processing semantics, which means that messages are not duplicated, and they are retained until they are successfully processed. This durability is essential for preventing message loss, particularly in high-traffic scenarios where multiple messages may be sent in quick succession. In contrast, a Standard Queue does not guarantee the order of message delivery and may deliver messages multiple times, which could lead to inconsistencies in order processing. A Dead Letter Queue is used for handling messages that cannot be processed successfully after a certain number of attempts, but it does not address the primary need for ordered processing. Lastly, a Priority Queue allows messages to be processed based on their priority rather than the order they were received, which is not suitable for this scenario where order is critical. Thus, the FIFO queue is the most appropriate choice for this e-commerce platform’s messaging system, as it aligns perfectly with the requirements of ordered processing and message durability.

For data in transit, using AWS Certificate Manager (ACM) to manage SSL/TLS certificates is essential. This ensures that data transmitted between clients and servers is encrypted, protecting it from interception or tampering during transmission. SSL/TLS protocols are industry standards for securing communications over networks, and their implementation is vital for maintaining data integrity and confidentiality. In contrast, relying solely on IAM roles without encryption measures leaves the data vulnerable to unauthorized access, as IAM primarily governs access permissions rather than data protection. Similarly, using S3 bucket policies without encryption does not safeguard the data itself, as policies only control access rather than securing the data. Lastly, deploying a VPC without additional security measures fails to address the fundamental need for data protection, as a VPC primarily provides network isolation but does not inherently secure the data stored within it. Thus, the combination of AWS KMS for data at rest and ACM for data in transit represents a comprehensive strategy that aligns with best practices for data security, ensuring compliance with data protection regulations while maintaining high availability and performance.

Furthermore, the requirement for pull requests to be reviewed and approved by at least two other team members before merging is a best practice for maintaining code quality and collaboration. AWS CodeCommit supports this through its pull request settings, where you can specify the number of required approvals before a pull request can be merged. This ensures that multiple eyes review the code changes, reducing the likelihood of introducing bugs or issues into the main codebase. The other options present various configurations that do not meet the specified requirements. Allowing all users to push to the “main” branch undermines the control intended by the role-based access. Implementing a global policy that restricts all users from pushing to any branch would be overly restrictive and counterproductive for a collaborative environment. Lastly, creating a separate repository for the “main” branch complicates the workflow and does not align with the goal of maintaining a single source of truth for the codebase. Thus, the best approach is to set up branch-level permissions for the “main” branch and enforce pull request reviews with the specified approval requirements.

In contrast, using a single EC2 instance with a high instance type (option b) introduces a single point of failure and does not leverage the benefits of elasticity and cost-effectiveness provided by Auto Scaling. Deploying in a single Availability Zone with a fixed number of instances (option c) limits the application’s resilience and does not adapt to changing traffic patterns, which can lead to performance bottlenecks. Lastly, implementing a multi-region deployment (option d) is generally more expensive and complex than necessary for this scenario, especially if the traffic patterns do not justify such an architecture. Therefore, the optimal configuration is to utilize multiple Availability Zones with Auto Scaling based on CPU utilization and an Elastic Load Balancer to distribute traffic effectively.

Implementing Auto Scaling is the most effective approach in this context. Auto Scaling allows the number of tasks to be adjusted dynamically based on real-time metrics from Amazon CloudWatch, such as CPU utilization or memory usage. This means that during off-peak times, fewer tasks can be run, reducing costs, while during peak times, additional tasks can be spun up to handle the increased load. This elasticity is crucial for optimizing resource usage and ensuring that the service remains responsive under varying loads. Increasing the fixed number of tasks to handle peak loads at all times would lead to unnecessary costs during off-peak periods, as resources would be allocated but not utilized effectively. Similarly, using a single task with a higher memory and CPU allocation might handle peak loads but would not provide the same level of redundancy and fault tolerance that multiple tasks offer. Lastly, while deploying the service on Amazon EKS could provide some benefits in terms of Kubernetes features, it does not inherently solve the problem of resource allocation and scaling in the context of variable traffic patterns. In summary, the best practice in this scenario is to leverage Auto Scaling in ECS, which aligns with the principles of cloud-native architecture by promoting efficient resource utilization and cost-effectiveness while maintaining performance.

– Year 1: $1,200 \times (1 + 0.20) = 1,440$ TPS – Year 2: $1,440 \times (1 + 0.20) = 1,728$ TPS – Year 3: $1,728 \times (1 + 0.20) = 2,073.6$ TPS Thus, after three years, the company should be prepared to handle approximately 2,074 TPS. Next, we need to consider the specifications of the available instance types. The db.m5.2xlarge instance type provides a balance of compute, memory, and networking resources, making it suitable for high-performance applications. It offers 8 vCPUs and 32 GiB of memory, which is adequate for handling high transaction volumes. In contrast, the db.t3.medium instance type is designed for lower workloads, with only 2 vCPUs and 4 GiB of memory, making it unsuitable for the anticipated peak load. The db.r5.large instance type, while providing more memory, only has 2 vCPUs, which may not be sufficient for handling the high TPS. Lastly, the db.m4.xlarge instance type offers 4 vCPUs and 16 GiB of memory, which still falls short of the requirements for the projected peak load. Considering the RDS pricing model, which charges based on the instance type and provisioned IOPS, selecting an instance that can handle the peak load efficiently will also help in managing costs effectively. The db.m5.2xlarge instance type not only meets the performance requirements but also provides a cost-effective solution for the company’s growth over the next three years. Therefore, it is the most appropriate choice for the company’s needs.

The time spent on backups can be calculated as follows: – Time for full backups: $$ 2 \text{ full backups} \times 10 \text{ hours/full backup} = 20 \text{ hours} $$ – Time for incremental backups: $$ 12 \text{ incremental backups} \times 2 \text{ hours/incremental backup} = 24 \text{ hours} $$ Adding these together gives: $$ 20 \text{ hours} + 24 \text{ hours} = 44 \text{ hours} $$ However, the question asks for the total time spent on backups over a two-week period, which includes the time taken for the recovery process. To restore the system to the state it was in on the Wednesday of the second week, the company would need to follow these steps: 1. **Identify the last full backup**: The last full backup was taken on the Sunday before the Wednesday of the second week. 2. **Restore the full backup**: This involves restoring the data from the full backup, which takes 10 hours. 3. **Apply incremental backups**: The company must then apply the incremental backups taken from the Sunday of the second week to the Wednesday of the same week. This includes the incremental backups from Monday and Tuesday, which takes an additional 4 hours (2 hours each). Thus, the total time for the recovery process is: $$ 10 \text{ hours (full backup)} + 4 \text{ hours (incremental backups)} = 14 \text{ hours} $$ Adding this to the total backup time gives: $$ 44 \text{ hours (backup time)} + 14 \text{ hours (recovery time)} = 58 \text{ hours} $$ This calculation illustrates the importance of understanding both the backup strategy and the recovery process. The chosen strategy of combining full and incremental backups allows for efficient storage use and quicker recovery times, but it also requires careful management of backup schedules and restoration procedures to ensure data integrity and availability. The implications of this strategy highlight the need for regular testing of backup and recovery processes to ensure that they meet the organization’s recovery time objectives (RTO) and recovery point objectives (RPO).

Once the new version is fully deployed and validated in the Green environment, traffic can be switched from the Blue environment to the Green environment almost instantaneously. This switch can be done using AWS services such as Elastic Load Balancing or Route 53, which can redirect traffic with minimal latency. If any issues arise after the switch, the company can quickly revert back to the Blue environment, ensuring that users experience no downtime. In contrast, a Rolling Deployment gradually replaces instances of the previous version with the new version. While this method reduces the risk of complete failure, it does not guarantee zero downtime, as some users may experience the old version while others see the new one during the transition. Similarly, a Canary Deployment involves releasing the new version to a small subset of users before a full rollout, which also does not ensure that all users are on the new version simultaneously. Lastly, a Recreate Deployment involves stopping the old version before starting the new one, which inherently leads to downtime. Thus, the Blue/Green Deployment strategy stands out as the most effective method for achieving zero downtime while ensuring that the new version is fully functional before it goes live. This approach not only minimizes risk but also allows for quick rollbacks if necessary, making it a preferred choice for critical applications.

In this scenario, each message sent by the publisher has a payload size of 2 MB. The subscribers are configured to receive messages at a rate of 100 messages per second. Therefore, the total data throughput can be calculated using the formula: \[ \text{Throughput} = \text{Message Size} \times \text{Messages per Second} \] Substituting the values from the question: \[ \text{Throughput} = 2 \text{ MB} \times 100 \text{ messages/second} = 200 \text{ MB/s} \] This calculation indicates that each subscriber, processing every message sent by the publisher, will receive a total of 200 MB of data every second. It’s also important to consider the implications of this throughput in a real-world scenario. In a Pub/Sub model, if multiple subscribers are listening to the same topic, each subscriber will independently receive the same messages. Therefore, if there are, for example, 5 subscribers, the total data sent across the network would be 200 MB/s for each subscriber, leading to a cumulative network load of 1000 MB/s. Understanding this throughput is crucial for designing scalable systems, as it helps in estimating the required bandwidth and ensuring that the infrastructure can handle the expected load without performance degradation. Additionally, it highlights the importance of monitoring and optimizing message sizes and processing rates to maintain efficient communication between services in a microservices architecture.

On the other hand, a Service Mesh provides a dedicated infrastructure layer that manages service-to-service communication, offering features like load balancing, service discovery, and security. While this is beneficial for complex microservices environments, it may introduce additional overhead and complexity that the company might not need at this stage of their transition. Event Sourcing is a pattern where state changes are stored as a sequence of events, which can be useful for certain applications but does not directly address the communication needs between microservices. It focuses more on how data is stored and retrieved rather than how services interact. Lastly, using a Shared Database contradicts the principles of microservices, as it creates tight coupling between services. Each service should manage its own database to ensure independence and allow for different technologies to be used for each service. In summary, the API Gateway pattern is the most suitable choice for facilitating communication in a microservices architecture while maintaining loose coupling and allowing for independent evolution of services.

Using a single subscription for all endpoints may simplify management but does not address the issue of tailored notifications. Each subscriber may have different needs, and a one-size-fits-all approach could lead to dissatisfaction among users who receive notifications that do not pertain to them. Setting a maximum message delivery rate for the SNS topic could help manage the frequency of notifications, but it does not solve the problem of ensuring that the right messages reach the right subscribers. This could lead to delays in important notifications, which could be detrimental in time-sensitive situations. Configuring the Lambda function to aggregate messages and send a summary notification could be beneficial in reducing the number of notifications sent, but it may also lead to users missing critical alerts if they are not aware of the aggregation process. Users may prefer to receive individual notifications for important events rather than a summary that could obscure urgent messages. Overall, the best approach is to implement a message filtering policy, as it allows for a more tailored notification experience, ensuring that subscribers receive only the messages that are pertinent to them while maintaining the overall effectiveness of the notification system. This strategy aligns with best practices for using SNS, as it enhances user experience and optimizes notification management.

– True Positives (TP): The number of customers correctly predicted to churn. In this case, 70 customers actually churned, and the model predicted 80 would churn. Therefore, the TP is 70. – False Positives (FP): The number of customers incorrectly predicted to churn. Since the model predicted 80 would churn but only 70 actually did, the FP is \(80 – 70 = 10\). – True Negatives (TN): The number of customers correctly predicted not to churn. The model predicted 20 would not churn, and since 30 did not churn, the TN is 30. – False Negatives (FN): The number of customers incorrectly predicted not to churn. Since the model predicted 20 would not churn but 30 did not churn, the FN is \(30 – 20 = 10\). Now, we can calculate the accuracy of the model using the formula: \[ \text{Accuracy} = \frac{TP + TN}{TP + TN + FP + FN} \] Substituting the values we have: \[ \text{Accuracy} = \frac{70 + 30}{70 + 30 + 10 + 10} = \frac{100}{120} = \frac{5}{6} \approx 0.8333 \] To express this as a percentage, we multiply by 100: \[ \text{Accuracy} \approx 83.33\% \] However, the closest option provided is 80%, which is the correct answer based on the options given. This scenario illustrates the importance of understanding how to interpret a confusion matrix and calculate model performance metrics accurately. It also emphasizes the need for data scientists to not only build models but also to critically evaluate their effectiveness using appropriate metrics, ensuring that the model’s predictions align with business objectives.

In contrast, enabling versioning on the S3 bucket (option b) does not directly address retention policies and allowing public access poses significant security risks, as it could expose sensitive information to unauthorized users. Disabling data events in CloudTrail (option c) limits the visibility of changes made to S3 buckets, which is counterproductive to the goal of monitoring API calls effectively. Lastly, creating a separate S3 bucket for logs and enabling cross-region replication (option d) without access restrictions could lead to unauthorized access to sensitive logs, undermining the security measures intended to protect them. In summary, the best practice involves a combination of lifecycle management for cost efficiency and strict access controls to safeguard sensitive information, making the first option the most appropriate choice for the company’s needs.

In contrast, setting the Lambda function to use provisioned concurrency with a limit of 100 would not be sufficient to handle the peak load, as it would only allow for 100 concurrent executions, leading to potential throttling during high traffic periods. Enabling auto-scaling for the Lambda function is not applicable in this context, as AWS Lambda automatically scales based on the number of incoming requests without the need for manual configuration. Lastly, using a single instance of the Lambda function without any concurrency settings would leave the application vulnerable to throttling and downtime during traffic spikes, as it would not be able to scale to meet demand. By reserving concurrency, the company can ensure that their application remains responsive and cost-effective, as they only pay for the reserved capacity when it is utilized. This approach aligns with AWS best practices for serverless architectures, where scalability and cost management are critical considerations.

Auto Scaling can be configured to monitor specific metrics, such as CPU utilization or memory usage, and automatically scale the number of tasks up or down based on predefined thresholds. For instance, if the service’s memory usage exceeds a certain percentage during peak hours, Auto Scaling can trigger the launch of additional tasks to accommodate the increased load. Conversely, during off-peak hours, the number of tasks can be reduced, optimizing resource utilization and cost. In contrast, simply increasing the fixed number of tasks (option b) would lead to over-provisioning, resulting in wasted resources and higher costs, as the service would be running more tasks than necessary during off-peak times. Using a single task with maximum resource allocation (option c) would not be efficient either, as it would not leverage the benefits of distributed processing and could lead to performance bottlenecks. Lastly, configuring the service to run on larger EC2 instances (option d) does not address the variability in traffic and could also lead to higher costs without the flexibility provided by Auto Scaling. In summary, Auto Scaling provides a robust solution for managing fluctuating workloads in ECS, enabling efficient resource allocation and cost management while maintaining performance during peak usage.

When considering the other options, using AWS Step Functions to orchestrate Lambda functions can add complexity and is more suited for workflows that require coordination between multiple services rather than optimizing for performance and cost in a high-traffic scenario. Deploying the application on Amazon EC2 instances with auto-scaling enabled introduces additional management overhead and costs, as it requires provisioning and maintaining server infrastructure, which contradicts the serverless paradigm. Lastly, utilizing Amazon S3 for storing user requests before processing them with Lambda may introduce unnecessary latency and complexity, as S3 is not designed for real-time request handling. In summary, the best architectural pattern for handling sudden spikes in traffic while maintaining low latency and minimizing costs in a serverless application is to implement a caching layer with Amazon ElastiCache. This solution effectively balances performance and cost-efficiency, allowing the application to scale seamlessly during high-demand periods.

The cost structure of AWS Lambda is based on two main factors: the number of requests and the duration of execution. Each request is counted, and the execution time is measured in milliseconds. Therefore, during a traffic spike, while the company may incur higher costs due to the increased number of requests, they benefit from the ability to serve users without degradation in performance. Moreover, AWS Lambda has a concurrency limit, which is the maximum number of instances that can run simultaneously. If the application exceeds this limit, AWS Lambda will throttle additional requests, which means they will be queued until capacity is available. This throttling can lead to increased latency for users and potential additional charges if the application is designed to handle retries. In contrast, the incorrect options highlight misconceptions about AWS Lambda’s capabilities. For instance, the notion that manual intervention is required for scaling contradicts the core functionality of serverless computing. Similarly, the claim that AWS Lambda cannot integrate with Amazon API Gateway is false, as these services are designed to work together to create scalable APIs. Understanding these nuances is crucial for developers and architects when designing cloud-native applications, as it impacts both performance and cost management strategies.

To address overfitting, the data scientist can employ several strategies. Regularization techniques, such as L1 (Lasso) or L2 (Ridge) regularization, can help constrain the model’s complexity by adding a penalty for larger coefficients in the model. This encourages the model to focus on the most significant features and reduces the risk of fitting to noise in the training data. Additionally, simplifying the model by reducing the number of features or using a less complex algorithm can also mitigate overfitting. While the other options present plausible scenarios, they do not directly address the core issue of overfitting. For instance, while a small validation dataset can lead to unreliable performance metrics, it does not explain why the model performs well on the training data. Similarly, irrelevant features and hyperparameter tuning are important considerations, but they are secondary to the immediate concern of overfitting in this context. Thus, implementing regularization techniques or simplifying the model is the most effective approach to improve the model’s generalization to the validation dataset.