Additionally, using HTTPS for secure communication is essential to protect data in transit. HTTPS encrypts the data exchanged between the client and the server, preventing eavesdropping and man-in-the-middle attacks. This is especially crucial when sensitive information, such as user credentials or personal data, is being transmitted. On the other hand, relying solely on API keys for authentication is insufficient because API keys can be easily compromised if not managed properly. They do not provide a robust mechanism for user authorization and can lead to security vulnerabilities if exposed. Allowing unrestricted access to the API for internal applications poses a significant risk, as it can lead to potential data leaks or misuse of sensitive information. Lastly, using basic authentication over HTTP is highly discouraged because it transmits credentials in an easily decodable format, making it vulnerable to interception. In summary, prioritizing OAuth 2.0 for authorization and HTTPS for secure communication establishes a strong foundation for API security, effectively mitigating risks associated with unauthorized access and data breaches.

This method ensures that meetings are scheduled only when all necessary participants are available, thus preventing conflicts and enhancing productivity. In contrast, directly embedding meeting links without checking availability (option b) could lead to scheduling conflicts, as users may not be available at the proposed times. Using a third-party scheduling tool that does not integrate with Webex (option c) would create additional friction in the workflow, as it would require manual updates and could lead to discrepancies between the CRM and actual meeting times. Lastly, implementing a polling mechanism (option d) could introduce unnecessary complexity and delay in the scheduling process, as it relies on user confirmation rather than automating the availability check. In summary, the integration should prioritize user experience and efficiency by utilizing the Webex Meetings API to automate the scheduling process, ensuring that meetings are set up based on real-time availability data from the CRM. This approach not only streamlines the workflow but also enhances collaboration among team members.

The function begins by checking if the input \( n \) is less than 0. If it is, a `ValueError` is raised with a message indicating that the input must be a non-negative integer. This is crucial for ensuring that the function adheres to the mathematical definition of factorial, which is only defined for non-negative integers. If \( n \) is 0, the function correctly returns 1, as \( 0! = 1 \). For any positive integer \( n \), the function recursively calls itself with \( n – 1 \), multiplying the result by \( n \). This recursive approach effectively breaks down the problem into smaller subproblems until it reaches the base case. The incorrect options present common misconceptions. Option b suggests that the function returns 0 for negative inputs, which is false since the function raises an error instead. Option c implies that the function would enter an infinite recursion for negative inputs, which is also incorrect due to the error handling in place. Lastly, option d states that the function would return 1 for negative inputs, which is misleading as the function does not return a value but raises an error instead. Thus, the implementation is robust, correctly handling both valid and invalid inputs, and adheres to the principles of recursion and error handling in Python.

For instance, using Ansible’s `when` conditionals, the engineer can specify tasks that should only run for certain IOS versions. Additionally, by utilizing variables, the playbook can dynamically adjust parameters based on the router’s current configuration. This method not only reduces redundancy by avoiding the need for multiple playbooks but also enhances maintainability, as updates can be made in a single location rather than across numerous scripts. In contrast, creating separate playbooks for each IOS version (option b) leads to increased complexity and maintenance overhead. A static playbook (option c) fails to account for the variations in configurations and could result in misconfigurations or failures. Lastly, implementing a manual process (option d) undermines the purpose of automation and introduces the potential for human error, which automation seeks to eliminate. By utilizing Ansible’s features effectively, the engineer can create a robust automation framework that accommodates the diverse requirements of the network, ensuring efficient and error-free configuration management across all devices. This approach aligns with best practices in network automation, emphasizing flexibility, scalability, and maintainability.

Additionally, utilizing status codes from the API responses is essential. Most REST APIs return HTTP status codes that indicate the result of the request, such as 200 for success, 404 for not found, or 500 for server errors. By checking these status codes, the engineer can determine whether the API call was successful or if further action is needed, such as retrying the request or logging an error message. Logging is another critical aspect of this process. By logging error messages, the engineer can gain insights into what went wrong during the execution of the script, which is invaluable for troubleshooting and improving the automation process. This comprehensive error handling strategy not only enhances the robustness of the automation script but also contributes to better maintainability and operational efficiency in the network environment. In contrast, using a single try block for the entire script without specific error logging would make it difficult to identify the source of errors, while relying solely on the API’s built-in logging features would limit the engineer’s control over error handling. Ignoring failures by only logging successful calls would lead to a lack of visibility into potential issues, which could result in undetected problems in the network configuration. Therefore, a combination of structured error handling and detailed logging is essential for effective network programmability.

To analyze the situation, we first note that the user has already exceeded the limit once in the first minute. If they continue to make requests at the same rate (120 requests in the first minute), they will exceed the limit again in the second minute, receiving another 429 response. This counts as the second violation. In the third minute, if they again make 120 requests, they will exceed the limit for the third time, triggering the throttling mechanism. At this point, the user will be throttled and their request rate will be reduced to 50 requests per minute for the remainder of the 10-minute window. Now, let’s calculate the total requests allowed after being throttled. The user has already made 120 requests in the first minute and 120 in the second minute, totaling 240 requests. After the third minute, they will be allowed to make only 50 requests per minute for the next 7 minutes (from minute 4 to minute 10). Calculating the requests during the throttled period: $$ 50 \text{ requests/minute} \times 7 \text{ minutes} = 350 \text{ requests} $$ Adding the requests made before throttling: $$ 240 \text{ requests} + 350 \text{ requests} = 590 \text{ requests} $$ However, the question specifically asks how many requests can be made in the next 10 minutes after the user has already made 120 requests in the first minute. Since they will be throttled after the third violation, they can only make 50 requests per minute for the next 7 minutes, which totals 350 requests. Therefore, the total number of requests they can make in the next 10 minutes, after being throttled, is 350 requests. Thus, the correct answer is that the user can make a total of 300 requests in the next 10 minutes before being throttled, considering the initial requests made and the subsequent throttling policy.

To address the issue of failing integration tests, the team should first analyze the unit tests to identify any gaps in coverage. This includes reviewing the scenarios tested and ensuring that edge cases—such as boundary conditions, invalid inputs, and unexpected states—are included. By enhancing the unit tests to cover these scenarios, the team can improve the reliability of the code and reduce the likelihood of integration issues. Additionally, while it is important to ensure that the integration tests are correctly implemented, the primary focus should be on strengthening the unit tests first. Misconfigurations in the development environment can also contribute to test failures, but these should be addressed after ensuring that the tests themselves are robust. Abandoning TDD is not a viable solution, as it promotes better design and code quality through continuous testing and feedback. Therefore, enhancing unit tests to cover more comprehensive scenarios is the most effective approach to improving overall code quality and ensuring that both unit and integration tests work harmoniously.

Logging error messages is essential for diagnosing issues later, and structured logging can provide context that is invaluable during troubleshooting. By returning a status code indicating success or failure, the script can inform the user of the outcome of the operation, allowing for immediate corrective actions if necessary. This method not only enhances user feedback but also improves the overall reliability of the automation process. In contrast, using print statements without structured logging (as suggested in option b) can lead to a cluttered output that is difficult to parse, especially in larger scripts. Relying solely on default error messages from the REST API (option c) lacks the customization needed for effective user communication and may not provide sufficient context for troubleshooting. Lastly, creating a separate error handling function that only logs errors to a file (option d) fails to notify the user in real-time, which can lead to confusion and delays in addressing issues. Therefore, the most effective approach combines structured error handling with user feedback, ensuring that the script is both robust and user-friendly. This comprehensive understanding of error handling in network programmability is essential for developing reliable automation solutions.

In contrast, monolithic application structures, while simpler to deploy, can create significant challenges when it comes to scaling. They often require the entire application to be redeployed for any changes, which can lead to downtime and inefficiencies. Similarly, traditional SOAP APIs, while still in use, are generally more rigid and require more extensive configuration, making them less suitable for modern agile development practices that prioritize speed and flexibility. Moreover, the mention of a single point of failure highlights a critical risk in application architecture. If an application is designed without redundancy and relies on a single component, it can lead to significant bottlenecks and downtime during peak usage, which is contrary to the goals of scalability and flexibility. Thus, the ability to leverage microservices architecture through RESTful APIs stands out as the primary advantage in this context, as it aligns with the modern principles of application development that emphasize agility, scalability, and resilience. This understanding is crucial for students preparing for the CISCO 350-901 exam, as it encapsulates the core benefits of Cisco’s platforms in real-world application scenarios.

The document-based approach supports horizontal scaling, meaning that as the application grows, additional servers can be added to distribute the load without significant restructuring of the data. This is essential in a microservices environment where different services may require different data structures and formats. In contrast, a relational data model, while robust for structured data and complex queries, can become a bottleneck in a distributed system due to its reliance on fixed schemas and the need for joins across tables. This can hinder performance and flexibility, especially when dealing with diverse data types and rapid changes in requirements. The graph data model is excellent for representing relationships and interconnected data, but it may not be the best fit for applications that require a wide variety of data types and structures, such as user profiles and transactions. Similarly, while a key-value data model offers simplicity and speed for specific use cases, it lacks the ability to handle complex queries and relationships effectively. Therefore, the document-based data model stands out as the most appropriate choice for this microservices architecture, providing the necessary flexibility, scalability, and efficiency in data retrieval and storage. This model aligns well with the principles of microservices, where each service can manage its own data independently while still being able to interact with other services as needed.

One of the primary advantages of JSON is its compatibility with JavaScript, as it allows for direct manipulation of data structures without the need for complex parsing. This is particularly beneficial in modern web applications that utilize frameworks like React, Angular, or Vue.js, where JSON can be seamlessly integrated into the application’s state management and rendering processes. The simplicity of JSON’s syntax, which uses key-value pairs, makes it less verbose than XML, leading to reduced payload sizes during data transmission. This reduction in size translates to faster load times and improved performance, especially in scenarios where bandwidth is a concern. In contrast, XML, while powerful and flexible, introduces additional overhead due to its verbose nature. Each piece of data is wrapped in tags, which can significantly increase the size of the data being transmitted. Furthermore, XML requires more complex parsing logic, which can slow down the processing time in JavaScript environments. Although XML supports features like namespaces and attributes, these are often unnecessary for typical web applications focused on data interchange. CSV (Comma-Separated Values) and YAML (YAML Ain’t Markup Language) are also options, but they do not provide the same level of integration with JavaScript or the hierarchical data structure that JSON and XML offer. CSV is limited to flat data structures and lacks support for nested data, while YAML, while human-readable, is less commonly used in web applications compared to JSON. In summary, when evaluating the requirements of performance and ease of integration with JavaScript frameworks, JSON stands out as the optimal choice for data interchange in web applications. Its lightweight nature, ease of parsing, and direct compatibility with JavaScript make it the preferred format for modern web development.

The error rate of 5% indicates that a small portion of requests are failing, which can contribute to increased response times as the system may need to retry failed requests or handle exceptions. However, a 5% error rate is not excessively high and may not be the primary cause of the increased response time. Latency, measured at 300 milliseconds, is a critical metric that reflects the time taken to process requests. If latency increases, it directly impacts the response time experienced by users. In this scenario, the most plausible explanation for the increased response time is increased latency due to network congestion. Network congestion can occur when there is a bottleneck in the data transmission path, leading to delays in request processing. This situation can be exacerbated by factors such as increased user traffic or insufficient bandwidth. While insufficient server resources (option b) and inefficient database queries (option d) can also contribute to performance degradation, they are less likely to be the immediate cause of increased response time in this scenario, given the metrics provided. A sudden spike in user traffic (option c) could lead to overload, but without specific data indicating a significant increase in requests beyond the average throughput, it is less likely to be the primary factor. Therefore, analyzing the network conditions and potential congestion points would be a logical next step for the development team to address the performance issues effectively.

However, the reality of network environments often includes devices from multiple vendors, each potentially having unique features or capabilities. This is where YANG, the data modeling language used to define the structure of the configuration data, becomes crucial. By leveraging YANG for vendor-specific extensions, the engineer can accommodate unique requirements without sacrificing the benefits of a standardized approach. This dual strategy allows for flexibility and adaptability in managing configurations while maintaining compliance with overarching standards. On the other hand, relying solely on IETF models would limit the engineer’s ability to address vendor-specific features, which could lead to suboptimal configurations or missed opportunities for optimization. Implementing a proprietary model that does not align with OpenConfig or IETF standards would create silos in configuration management, making it difficult to achieve interoperability and complicating future integrations. Lastly, using a combination of OpenConfig and IETF models without considering device compatibility could lead to inconsistencies and potential failures in configuration deployment. Thus, the most effective approach is to utilize OpenConfig models as the primary configuration source while leveraging YANG for vendor-specific extensions, ensuring a balance between standardization and flexibility in network management. This strategy not only adheres to best practices in network automation but also prepares the network for future scalability and integration challenges.

In contrast, deploying all services on a single container (option b) would create a bottleneck, as the entire application would be limited by the resources allocated to that single container. This approach negates the benefits of microservices, which are designed to be independently deployable and scalable. Using a monolithic architecture (option c) contradicts the principles of microservices, as it would centralize all services into one application instance, leading to increased complexity in scaling and potential performance issues due to inter-service communication latency. Configuring each service to run on a fixed number of replicas (option d) fails to adapt to varying traffic loads, which can result in either underutilization of resources during low traffic or insufficient capacity during high traffic, ultimately degrading performance. Thus, leveraging an orchestration tool like Kubernetes is the most effective strategy for managing microservices in a containerized environment, allowing for responsive scaling and efficient resource management.

On the other hand, using long-lived access tokens can significantly increase the risk of token theft, as an attacker would have a longer period to exploit a compromised token. Additionally, allowing all origins in CORS settings can expose the API to cross-origin attacks, making it easier for malicious actors to access sensitive data. Lastly, disabling HTTPS is a critical mistake; HTTPS is essential for encrypting data in transit, protecting against eavesdropping and man-in-the-middle attacks. Therefore, the combination of short-lived access tokens and secure handling of refresh tokens is the most effective way to enhance API security in this scenario.

The traditional relational model, while robust for transactions, does not lend itself well to distributed systems where services need to operate independently. It typically requires strong consistency, which can lead to bottlenecks and reduced availability in a microservices environment. Similarly, a document store with strong consistency may not provide the flexibility needed for high availability and partition tolerance, as it can lead to increased latency and reduced performance during network partitions. On the other hand, a graph database with immediate consistency is not suitable for a microservices architecture that prioritizes high availability and partition tolerance. Immediate consistency can lead to challenges in scaling and can create single points of failure, which contradicts the principles of microservices. Thus, Event Sourcing stands out as the most appropriate choice for managing data consistency in a distributed system, allowing for high availability and partition tolerance while adhering to the principles of eventual consistency. This model enables services to evolve independently and react to changes in a decoupled manner, which is essential in a microservices architecture.

Next, the function uses the `filter` method to create a new array containing only those users whose age is greater than the specified `ageThreshold`. This is a key aspect of functional programming in JavaScript, where immutability and pure functions are emphasized. After filtering, the `map` method is employed to transform the filtered user objects into an array of names, which is the desired output. The use of Promises allows the function to be asynchronous, which is essential in Node.js applications to avoid blocking the event loop. This is particularly important in scenarios where the application may need to handle multiple requests simultaneously. By resolving the Promise with the resulting array of names, the function provides a clear and manageable way to handle the asynchronous operation. In contrast, the other options present various misconceptions. Option b incorrectly states that the function does not handle non-array inputs, which is false since it explicitly checks for this condition. Option c suggests that the function uses synchronous processing, which is inaccurate as the Promise-based approach inherently supports asynchronous execution. Lastly, option d misrepresents the output of the function, as it correctly returns an array of names, not user objects. Thus, the implementation effectively meets the requirements of the task while adhering to best practices in JavaScript and Node.js development.

On the other hand, JWT serves as a compact, URL-safe means of representing claims to be transferred between two parties. The claims in a JWT are encoded as a JSON object that is used as the payload of a JSON Web Signature (JWS) structure or as the plaintext of a JSON Web Encryption (JWE) structure, allowing for secure transmission of user identity and claims. The stateless nature of JWT means that the server does not need to store session information, which aligns well with the microservices architecture where scalability and performance are critical. The first statement accurately captures the essence of using OAuth 2.0 with JWT: OAuth 2.0 facilitates the delegation of authorization, while JWT provides a secure and stateless method for transmitting user identity and claims. This combination allows for efficient and secure API access management, as each service can independently verify the JWT without needing to consult a central session store. In contrast, the second statement incorrectly asserts that OAuth 2.0 requires session-based authentication, which is not true; OAuth 2.0 can work with stateless tokens like JWT. The third statement misrepresents OAuth 2.0, as it does support token expiration, which is essential for maintaining security in access control. Lastly, the fourth statement is misleading because JWTs are designed to be self-contained and do not need to be stored in a database, as their validity can be verified through their signature, thus maintaining the stateless design principle of OAuth 2.0.

Using HTTPS for all data transmissions is equally important, as it encrypts the data in transit, protecting it from eavesdropping and man-in-the-middle attacks. HTTPS ensures that the data sent between the client and server is secure, which is critical when dealing with sensitive information such as credit card details. In contrast, using plain HTTP for data transmission (as suggested in option b) exposes the data to potential interception, making it highly insecure. Storing sensitive information in a local database without proper encryption further increases the risk of data breaches. Relying solely on API keys for authentication (option c) is also inadequate, as API keys can be easily compromised if not managed properly. They should be used in conjunction with other security measures, such as OAuth and HTTPS. Lastly, utilizing a custom-built encryption algorithm (option d) is not advisable because it may not adhere to industry standards and could introduce vulnerabilities. Established encryption protocols, such as TLS, have been rigorously tested and are widely accepted for securing data transmission. In summary, the best approach for secure integration with third-party services involves using OAuth 2.0 for authorization and HTTPS for data transmission, ensuring compliance with industry standards for data protection and privacy.

Additionally, employing caching mechanisms for frequently accessed data significantly improves performance. Caching reduces the load on the server by storing copies of frequently requested resources, which can be served quickly to users without needing to fetch the data from the database each time. This is particularly important in a microservices architecture where multiple services may need to access the same data. On the other hand, using basic authentication (option b) is not recommended for production environments due to its vulnerability to interception and replay attacks. Limiting the number of API endpoints (also option b) does not inherently enhance security or performance; rather, it may restrict functionality and scalability. Relying solely on HTTPS (option c) is necessary for secure communication, but it is not sufficient on its own without robust authentication mechanisms like OAuth 2.0. Finally, creating a monolithic architecture (option d) contradicts the principles of microservices, which aim to enhance modularity and scalability. Monolithic systems can lead to bottlenecks and complicate deployment processes, which is counterproductive to the goals of a microservices architecture. In summary, the best approach combines robust authorization mechanisms with performance-enhancing strategies like caching, ensuring both security and efficiency in the API’s operation.

Additionally, securing all API endpoints with HTTPS is essential to protect data in transit from eavesdropping and man-in-the-middle attacks. HTTPS encrypts the data exchanged between the client and server, ensuring confidentiality and integrity. Using JSON Web Tokens (JWT) for session management and authorization further enhances security. JWTs allow the server to verify the authenticity of the token and the identity of the user without needing to store session information on the server, thus improving scalability. In contrast, the other options present significant security risks. Basic authentication, while simple, transmits credentials in an easily decodable format, making it vulnerable to interception. Allowing all HTTP methods without restrictions can lead to unintended actions being performed on the API, such as data deletion or modification. Developing an API without any authentication mechanism exposes it to unauthorized access, while using HTTP instead of HTTPS compromises data security. Finally, relying solely on API keys without implementing proper access controls can lead to abuse and unauthorized access, especially if the keys are exposed. Therefore, the best approach involves a combination of OAuth 2.0 for authentication, HTTPS for secure communication, and JWT for managing user sessions, ensuring that the API adheres to best practices in security and efficiency.

Moreover, RESTful APIs utilize standard HTTP methods (GET, POST, PUT, DELETE), which are widely understood and supported across various platforms and programming languages. This compatibility facilitates seamless integration with existing web technologies and services, making it easier for developers to build applications that interact with Cisco’s network devices. Performance is also enhanced through the use of RESTful APIs, as they can leverage caching mechanisms inherent in HTTP. Responses can be cached, reducing the need for repeated requests to the server for the same data, which can significantly improve response times and reduce server load. In contrast, the other options present misconceptions about RESTful APIs. For instance, the idea that RESTful APIs require maintaining session state contradicts their fundamental design principles. Additionally, the claim that RESTful APIs are limited to XML is inaccurate; they can support multiple data formats, including JSON, which is more commonly used in modern web applications due to its lightweight nature and ease of use. Overall, the advantages of RESTful APIs—statelessness, standardization, and performance optimization—make them an ideal choice for developing applications that require efficient interaction with network devices, particularly in a scalable and integrative manner.

Using secure protocols like TLS (Transport Layer Security) for data in transit is essential to protect data from interception during transmission. This dual-layer approach—encrypting data at rest and in transit—provides a comprehensive security posture that mitigates risks associated with multi-cloud environments. On the other hand, relying solely on the cloud providers’ built-in encryption features can be risky, as it may not meet the specific security requirements of the organization or comply with regulatory standards. Additionally, using a single encryption key across multiple environments can create a single point of failure, making it easier for attackers to access all encrypted data if they compromise that key. Finally, storing sensitive data in only one cloud provider contradicts the multi-cloud strategy’s goal of resilience and increases the risk of data loss or exposure if that provider experiences a breach. Thus, the best strategy involves a proactive approach to encryption and secure data transmission, ensuring compliance and enhancing overall security in a multi-cloud architecture.

The first step involves redirecting users to the authorization server, where they can log in and grant permission to the application. Once the user authorizes the application, an authorization code is returned, which the application can exchange for an access token. This access token is then used to authenticate subsequent API requests, ensuring that only authenticated users can perform actions such as retrieving or updating device configurations. Moreover, securing API endpoints with HTTPS is essential to protect data in transit from eavesdropping and man-in-the-middle attacks. This means that all API interactions must be conducted over HTTPS to maintain confidentiality and integrity. In contrast, using basic authentication (option b) lacks the security benefits of token-based authentication and exposes user credentials in every request. Creating a self-signed certificate (option c) does not provide the same level of trust as certificates issued by a recognized Certificate Authority (CA) and does not address the need for user authentication. Lastly, the client credentials flow (option d) is inappropriate in this context, as it is designed for server-to-server communication without user interaction, and exposing API endpoints without security measures is a significant vulnerability. Thus, the correct approach involves implementing OAuth 2.0 authorization code flow, obtaining an access token, and ensuring that all API interactions are secured with HTTPS, thereby providing a robust framework for secure and efficient API interactions with Cisco devices.

\[ T_{\text{min}} = 50 – 10 = 40 \text{ Mbps} \] Conversely, the maximum value of \( T(t) \) occurs when \( \sin(0.2t) = 1\), leading to: \[ T_{\text{max}} = 50 + 10 = 60 \text{ Mbps} \] Thus, the maximum traffic load that can be expected on the network is 60 Mbps. In the context of the AI-driven optimization system, the threshold for triggering actions is set at 80% utilization of the network capacity. If we assume the network’s total capacity is \( C \) Mbps, then the threshold for triggering optimization actions can be expressed as: \[ 0.8C \] To find the total capacity \( C \) that corresponds to a maximum expected load of 60 Mbps, we can set up the equation: \[ 0.8C = 60 \implies C = \frac{60}{0.8} = 75 \text{ Mbps} \] This means that the optimization actions will be triggered when the traffic load approaches 60 Mbps, which is 80% of the total capacity of 75 Mbps. Understanding this relationship is crucial for network administrators as it allows them to set appropriate thresholds for traffic management and ensure optimal performance. The AI system’s ability to predict and respond to traffic patterns enhances the overall efficiency of the network, preventing potential bottlenecks before they impact performance.

\[ \text{Total Processing Rate} = \text{Packets per Sensor} \times \text{Number of Sensors} = 500 \, \text{packets/second} \times 20 = 10,000 \, \text{packets/second} \] Next, we need to evaluate the utilization of the edge computing node. The maximum capacity of the edge computing node is 10,000 packets per second. To find the percentage of capacity utilized, we use the formula: \[ \text{Utilization Percentage} = \left( \frac{\text{Total Processing Rate}}{\text{Maximum Capacity}} \right) \times 100 \] Substituting the values we have: \[ \text{Utilization Percentage} = \left( \frac{10,000}{10,000} \right) \times 100 = 100\% \] This means that the edge computing node will be fully utilized, processing exactly its maximum capacity. This scenario illustrates the importance of edge computing in managing real-time data efficiently, especially in environments like smart cities where timely decision-making is crucial. The ability to process data at the edge reduces latency and bandwidth usage, allowing for immediate responses to changing conditions, such as adjusting traffic signals based on real-time vehicle and pedestrian flow. Understanding these dynamics is essential for designing effective edge computing solutions that can scale with increasing data demands while maintaining performance.

\[ \text{Total Data Rate} = 10 \, \text{MB/min} + 5 \, \text{MB/min} = 15 \, \text{MB/min} \] Next, we calculate the total data generated over 2 hours (which is 120 minutes): \[ \text{Total Data} = \text{Total Data Rate} \times \text{Time} = 15 \, \text{MB/min} \times 120 \, \text{min} = 1800 \, \text{MB} \] Now, to find out how long the edge computing node can sustain processing without exceeding its capacity, we need to consider the maximum data handling capacity of the edge node, which is 100 MB per minute. To find the maximum duration (in minutes) that the edge node can process the data generated, we can use the following formula: \[ \text{Maximum Duration} = \frac{\text{Edge Node Capacity}}{\text{Total Data Rate}} = \frac{100 \, \text{MB/min}}{15 \, \text{MB/min}} \approx 6.67 \, \text{minutes} \] However, since the question asks for the maximum duration in minutes that the edge node can sustain processing without exceeding its capacity, we need to consider the total data generated over the 2-hour period. The edge node can handle 100 MB per minute, so over 2 hours (120 minutes), it can handle: \[ \text{Total Capacity} = 100 \, \text{MB/min} \times 120 \, \text{min} = 12000 \, \text{MB} \] Since the total data generated (1800 MB) is well within the edge node’s capacity, the edge node can sustain processing for the entire 120 minutes without exceeding its capacity. Thus, the correct answer is 120 minutes. This scenario illustrates the importance of understanding both data generation rates and processing capacities in edge computing environments, especially in applications like smart cities where real-time data processing is critical.

On the other hand, relying solely on user authentication does not address the vulnerabilities associated with SQL injection, as attackers can exploit these vulnerabilities regardless of authentication measures. Similarly, while encrypting data at rest is important, neglecting data in transit exposes the application to interception and manipulation during transmission. Lastly, conducting security audits and penetration testing only after deployment is insufficient; security should be integrated throughout the development lifecycle, including regular assessments during the development phase to identify and remediate vulnerabilities proactively. In summary, while the initial measures are crucial, complementing them with a WAF provides a robust defense against SQL injection and other web application threats, aligning with best practices in application security as recommended by OWASP. This holistic approach ensures that the application is resilient against a wide range of attacks, thereby safeguarding sensitive user data effectively.

While the average packet loss rate is also an important metric, it primarily indicates issues with packet delivery rather than processing delays. High packet loss can contribute to latency but is not the primary cause. Similarly, total bandwidth utilization provides insight into how much of the available bandwidth is being used, which can help identify congestion but does not directly correlate with latency unless the bandwidth is saturated. The number of active network sessions, while informative about the load on the network, does not provide direct insight into latency issues. It is possible to have many active sessions without significant latency if the network is adequately provisioned. Therefore, focusing on CPU utilization allows the engineer to identify whether the devices are capable of handling the current load and processing packets in a timely manner, making it the most critical metric to examine in this scenario. Understanding these relationships is vital for effective network management and optimization, especially in environments where performance and security are paramount.

\[ 2 \, \text{KB/min} \times 60 \, \text{min} = 120 \, \text{KB/hour} \] In one day (24 hours), the data generated by one sensor is: \[ 120 \, \text{KB/hour} \times 24 \, \text{hours} = 2880 \, \text{KB/day} \] For 500 sensors, the total data generated in one day is: \[ 2880 \, \text{KB/day} \times 500 \, \text{sensors} = 1,440,000 \, \text{KB/day} = 1,440 \, \text{MB/day} = 1.44 \, \text{GB/day} \] Given this substantial data influx, effective management and analysis are crucial. A cloud-based data processing solution with edge computing capabilities is optimal. Edge computing allows for preliminary data processing at the sensor level, filtering out irrelevant data and reducing the volume sent to the cloud. This approach minimizes latency, enabling real-time insights and actions based on the data collected. In contrast, storing all data on local servers without processing (option b) would lead to overwhelming data storage needs and slow response times. A centralized database without preprocessing (option c) would also struggle with the volume of data, leading to delays in analysis. Limiting the number of sensors (option d) compromises the project’s goals of comprehensive monitoring and data collection. Therefore, leveraging cloud solutions with edge computing is the most effective strategy for managing the data generated by the smart city IoT deployment.