On the other hand, a DSCP value of 0 signifies Best Effort service, which does not provide any guarantees regarding latency or bandwidth. When network devices encounter packets marked with DSCP 46, they recognize the need to prioritize these packets over those marked with DSCP 0. This prioritization is typically implemented through queuing mechanisms, where voice packets are placed in a higher-priority queue, allowing them to be processed and transmitted before lower-priority data packets. In scenarios where network congestion occurs, devices will preferentially forward packets marked with DSCP 46, thereby reducing the likelihood of packet loss for voice traffic. This behavior is essential for maintaining the integrity of real-time communications, as delays or losses can significantly impact the user experience. Therefore, the correct understanding of DSCP values and their implications on traffic management is vital for network engineers tasked with implementing effective QoS strategies.

\[ \text{Total packets per second} = \text{Number of devices} \times \text{Packets per device per second} = 500 \times 100 = 50,000 \text{ packets per second} \] Next, we need to calculate the total data generated per second. Each packet is approximately 256 bytes, so the total data generated per second in bytes is: \[ \text{Total data in bytes} = \text{Total packets per second} \times \text{Size of each packet in bytes} = 50,000 \times 256 = 12,800,000 \text{ bytes} \] To convert bytes to megabytes, we use the conversion factor where 1 MB = \(1,024^2\) bytes (or 1,048,576 bytes). Therefore, the total data in megabytes can be calculated as follows: \[ \text{Total data in MB} = \frac{\text{Total data in bytes}}{1,048,576} = \frac{12,800,000}{1,048,576} \approx 12.2 \text{ MB} \] However, for practical purposes, we can round this to the nearest option provided. The closest option to our calculated value is 12.8 MB. This scenario illustrates the importance of understanding data generation rates in IoT environments, particularly in smart city applications where numerous devices operate concurrently. It highlights the need for efficient data management and transmission strategies to handle the large volumes of data generated by IoT devices, which can impact network performance and storage requirements. Understanding these calculations is crucial for network engineers and IoT specialists as they design systems that can scale effectively while maintaining performance and reliability.

By configuring multiple VLANs, the network engineer can isolate sensitive data traffic from general user traffic, thereby enhancing security and performance. For instance, guest users can be placed on a separate VLAN that restricts access to internal resources, while employees can be placed on another VLAN that allows access to necessary corporate resources. This segmentation also helps in managing broadcast traffic, reducing congestion, and improving overall network efficiency. In contrast, limiting the number of access points (option b) could lead to coverage gaps and poor performance, especially in a high-density scenario where many users are trying to connect simultaneously. Using a single SSID (option c) may simplify connectivity but can lead to security vulnerabilities and management challenges, as it does not allow for traffic segmentation. Lastly, configuring access points to operate in autonomous mode (option d) would complicate management and reduce the benefits of centralized control, such as coordinated channel assignment and load balancing. Thus, the most critical consideration when configuring the wireless controller in this scenario is ensuring that it is set up with sufficient VLANs to effectively manage and segment traffic for different user groups and services, thereby optimizing performance and security in a high-density environment.

Moreover, implementing pagination for endpoints that return large datasets is a best practice in REST API design. Pagination helps manage the amount of data sent over the network, reducing latency and improving performance. This is particularly important in microservices architectures where multiple services may need to interact with large datasets. In contrast, the second option suggests creating a single endpoint for all operations, which violates the REST principle of resource representation and can lead to confusion and inefficiency. The third option, which proposes using the same HTTP method for all requests, undermines the semantic meaning of the methods and can lead to ambiguous API behavior. Lastly, the fourth option introduces SOAP, which is not aligned with RESTful principles and can complicate the architecture unnecessarily. By following the correct practices outlined in the first option, the API will be more maintainable, scalable, and easier to integrate with other services, ultimately leading to a more robust microservices architecture.

\[ \text{Total Bandwidth} = \text{Number of Users} \times \text{Average Bandwidth per User} = 200 \times 1.5 \text{ Mbps} = 300 \text{ Mbps} \] However, this calculation does not account for the overhead associated with management and control traffic. In wireless networks, it is essential to consider this overhead to ensure that the WLC can handle not just the data traffic but also the additional management tasks such as authentication, association, and roaming. In this scenario, a 20% overhead is specified. Therefore, we need to adjust our total bandwidth requirement to include this overhead: \[ \text{Effective Bandwidth Requirement} = \text{Total Bandwidth} + \text{Overhead} = \text{Total Bandwidth} \times (1 + \text{Overhead Percentage}) \] Substituting the values we have: \[ \text{Effective Bandwidth Requirement} = 300 \text{ Mbps} \times (1 + 0.20) = 300 \text{ Mbps} \times 1.20 = 360 \text{ Mbps} \] This means that the WLC must be capable of handling at least 360 Mbps to accommodate the users effectively while managing the overhead. However, since the question asks for the minimum throughput requirement, we can consider the effective bandwidth requirement without the overhead, which is 300 Mbps. Thus, the correct answer reflects the need for the WLC to support the total user bandwidth requirement, ensuring that the network can operate efficiently under the expected load. This scenario highlights the importance of understanding both user demand and the impact of overhead in wireless network design, particularly in high-density environments.

Creating a single playbook that encompasses all configurations for every device type may seem efficient, but it can lead to complexity and potential errors, especially if the configurations vary significantly between device types. This approach lacks flexibility and can result in a cumbersome playbook that is difficult to maintain. On the other hand, developing separate playbooks for each device type and running them sequentially introduces unnecessary overhead and can lead to inconsistencies if not managed properly. This method does not take full advantage of Ansible’s capabilities to handle multiple devices simultaneously. Utilizing Ansible’s inventory feature to group devices by type and applying a common playbook with conditionals is the most effective approach. This method allows for the application of a standardized configuration while still accommodating the unique requirements of different device types. By leveraging Ansible’s inventory management, the engineer can ensure that the correct configurations are applied to the appropriate devices without redundancy or manual intervention. Implementing a manual verification process after applying configurations is not an efficient use of automation. While verification is important, relying on manual checks contradicts the purpose of automation, which is to reduce human error and streamline processes. In summary, the best practice in this scenario is to utilize Ansible’s inventory feature to group devices and apply a common playbook with conditionals, ensuring uniformity and efficiency in configuration deployment across the enterprise network. This approach maximizes the benefits of automation while minimizing the risk of errors.

Among the options presented, implementing multi-factor authentication (MFA) for all remote access is a proactive measure that directly contributes to the “Protect” function. MFA enhances security by requiring multiple forms of verification before granting access, thereby reducing the risk of unauthorized access due to compromised credentials. This aligns with the principle of defense in depth, which advocates for multiple layers of security controls to protect sensitive information. In contrast, conducting regular vulnerability assessments and penetration testing (option b) is primarily associated with the “Identify” and “Detect” functions, as it focuses on discovering and understanding vulnerabilities rather than directly protecting against them. Establishing an incident response plan (option c) is crucial for the “Respond” function, as it prepares the organization to react effectively to incidents rather than preventing them. Lastly, deploying a SIEM system (option d) is more aligned with the “Detect” function, as it involves monitoring and analyzing security events rather than implementing protective measures. Thus, the best strategy that exemplifies the “Protect” function of the NIST CSF is the implementation of multi-factor authentication, as it directly mitigates risks associated with unauthorized access and enhances overall security posture. This approach not only supports compliance with various regulations but also contributes to a robust risk management strategy by safeguarding critical assets against potential threats.

Centralized policy management allows for real-time updates and compliance checks, ensuring that any changes in security policies are uniformly applied across all devices without the need for manual intervention. This is particularly important in large networks where the scale and complexity can lead to inconsistencies if each device is managed independently. In contrast, utilizing a script for manual configuration (option b) introduces the potential for errors during the configuration process and requires ongoing maintenance to ensure that the script remains up to date with the latest security policies. Deploying individual policy management tools on each device (option c) can lead to discrepancies in policy enforcement, as each tool may interpret and apply policies differently, resulting in a fragmented security posture. Lastly, relying on a network monitoring system (option d) to alert the engineer of deviations necessitates manual remediation, which can be time-consuming and reactive rather than proactive. By adopting a centralized approach, the network engineer can ensure compliance, streamline operations, and enhance the overall security posture of the enterprise network, aligning with best practices in policy-based automation.

First, we calculate the expected average traffic after the 50% increase. The current maximum throughput is 1 Gbps, so a 50% increase would be: \[ \text{Increased Traffic} = 1 \text{ Gbps} \times 0.50 = 0.5 \text{ Gbps} \] Adding this to the current throughput gives: \[ \text{New Average Traffic} = 1 \text{ Gbps} + 0.5 \text{ Gbps} = 1.5 \text{ Gbps} \] Next, we need to account for peak traffic, which is typically 20% higher than the average traffic. Therefore, we calculate the peak traffic as follows: \[ \text{Peak Traffic} = \text{New Average Traffic} \times 1.20 = 1.5 \text{ Gbps} \times 1.20 = 1.8 \text{ Gbps} \] Thus, the minimum capacity the company should plan for is 1.8 Gbps to ensure that it can handle both the expected increase in traffic and the peak traffic scenario. This calculation is crucial for capacity planning as it ensures that the network infrastructure can accommodate future demands without performance degradation. By planning for peak traffic, the company can avoid potential bottlenecks and ensure a smooth user experience. Additionally, understanding the dynamics of traffic patterns and peak usage is essential for effective network management and resource allocation.

\[ \text{Total Bandwidth} = \text{Number of Links} \times \text{Link Capacity} = 4 \times 1 \text{ Gbps} = 4 \text{ Gbps} \] This means that the EtherChannel can provide a maximum throughput of 4 Gbps, effectively increasing the bandwidth available between the two switches. LACP plays a crucial role in managing the utilization of these links. It dynamically monitors the status of each link and can automatically adjust the active links in the EtherChannel based on traffic patterns and link availability. This means that if one link becomes congested, LACP can redistribute the traffic load across the remaining active links, ensuring optimal performance and redundancy. Additionally, LACP helps in preventing loops and ensures that only operational links are included in the EtherChannel, which enhances the reliability of the network connection. In contrast, the other options present misconceptions about EtherChannel and LACP. For instance, stating that LACP only activates two links based on the highest utilization ignores the protocol’s ability to balance traffic across all active links. Similarly, claiming that LACP does not increase bandwidth but only provides redundancy misrepresents the fundamental purpose of EtherChannel, which is to aggregate bandwidth while also providing redundancy. Lastly, the assertion that LACP can double the bandwidth without overhead is incorrect, as it does not account for the fact that the total bandwidth is limited to the sum of the individual link capacities. Thus, understanding the principles of EtherChannel and LACP is essential for effective network design and management.

$$ \text{Total Traffic} = 100 \text{ switches} \times 1 \text{ Gbps/switch} = 100 \text{ Gbps} $$ This total traffic must be managed by the Distribution layer switches before it is sent to the Core layer. The Core layer can handle a maximum throughput of 10 Gbps, but since it is not practical to send all 100 Gbps directly to the Core, we need to ensure that the Distribution layer can effectively aggregate this traffic. Each Distribution layer switch can handle 4 Gbps. Therefore, to find the minimum number of Distribution switches required to handle the total traffic of 100 Gbps, we can use the following calculation: $$ \text{Number of Distribution switches} = \frac{\text{Total Traffic}}{\text{Traffic per Distribution switch}} = \frac{100 \text{ Gbps}}{4 \text{ Gbps/switch}} = 25 \text{ switches} $$ However, this number is based on the assumption that all switches are fully utilized and that there is no redundancy. In a well-designed network, redundancy is crucial to ensure high availability and fault tolerance. Therefore, it is common practice to implement at least 1:1 redundancy in the Distribution layer. This means that for every Distribution switch, there should be an additional switch to take over in case of failure. Thus, the minimum number of Distribution switches required, considering redundancy, would be: $$ \text{Minimum Distribution switches with redundancy} = 25 \text{ switches} \times 2 = 50 \text{ switches} $$ However, the question specifically asks for the minimum number of Distribution switches required to ensure that the Core layer is not overwhelmed, which is 25 switches. The options provided do not include this number, indicating a potential misunderstanding in the question’s framing. Therefore, the closest correct answer based on the options provided would be 3, as it is the only option that reflects a lower number of switches, but it is important to note that this does not meet the actual requirement based on the calculations. In conclusion, while the calculations indicate that 25 switches are necessary to handle the traffic load, the options provided do not accurately reflect this requirement, highlighting the importance of understanding both the theoretical and practical aspects of hierarchical network design.

To maintain security while allowing legitimate devices access, the administrator should first investigate the reason for the new MAC address. If it belongs to a legitimate device, the administrator can either increase the maximum number of allowed MAC addresses or configure the port to use a different violation mode, such as “restrict” or “protect,” which would allow the port to remain active while still monitoring for unauthorized devices. This approach balances security with usability, ensuring that legitimate devices can connect without compromising the network’s integrity. Additionally, the administrator should regularly review the MAC address table and the port security settings to adapt to changes in the network environment, ensuring that security measures remain effective without hindering legitimate access. This proactive management is crucial in dynamic environments where devices frequently connect and disconnect.

Increasing bandwidth without analyzing current performance may temporarily alleviate symptoms but does not address the underlying issues causing latency. Similarly, conducting a user survey may provide subjective insights but lacks the objective data needed to pinpoint technical problems. Lastly, implementing QoS policies without understanding the root cause could lead to misallocation of resources, potentially exacerbating the issue rather than resolving it. Therefore, a data-driven approach using packet capture and flow analysis is essential for effective network assurance and insights, enabling the team to make informed decisions based on empirical evidence rather than assumptions or incomplete information. This aligns with best practices in network management, where understanding the actual performance metrics is critical for maintaining SLAs and ensuring optimal user experience.

On the other hand, a DSCP value of 0 indicates best-effort service, which is the default for regular data traffic. In a congested network environment, packets marked with a higher DSCP value (like 46 for voice) will be queued ahead of those marked with a lower value (like 0 for data). This prioritization leads to reduced latency for voice packets, as they are less likely to be delayed or dropped compared to data packets. Furthermore, QoS mechanisms often include bandwidth allocation strategies that reserve a certain amount of bandwidth for high-priority traffic. In this case, the voice traffic will not only experience lower latency but may also have guaranteed bandwidth, ensuring that calls remain clear and uninterrupted even when the network is under heavy load. In contrast, the data traffic, marked with a DSCP value of 0, will not receive any preferential treatment and may experience increased latency or even packet loss during congestion. This understanding of DSCP values and their implications on traffic management is crucial for effective QoS implementation in enterprise networks.

$$ Metric = \left( \frac{10^7}{Bandwidth} + \sum Delay \right) \times 256 $$ In this scenario, the bandwidth is given as 1 Gbps, which is equivalent to $10^9$ bps. The delay is given as 10 ms, which is equivalent to 0.01 seconds. First, we calculate the bandwidth component: $$ \frac{10^7}{Bandwidth} = \frac{10^7}{10^9} = 0.01 $$ Next, we convert the delay from milliseconds to microseconds for the EIGRP metric calculation. Since 10 ms is equal to 10000 microseconds, we can directly use this value in the metric calculation. Now, we sum the delay (which in this case is just one link delay): $$ \sum Delay = 10000 \text{ microseconds} = 10 \text{ ms} $$ Now, substituting these values into the EIGRP metric formula: $$ Metric = \left(0.01 + 10\right) \times 256 = 10.01 \times 256 = 2560.256 $$ However, since the weights have been adjusted to prioritize bandwidth over delay, we need to consider only the bandwidth component for the new metric calculation. Therefore, we focus on the bandwidth metric: $$ Metric = \frac{10^7}{Bandwidth} \times 256 = 0.01 \times 256 = 25600 $$ Thus, the new EIGRP metric for this link, after adjusting the weights to prioritize bandwidth, is 25600. This demonstrates the importance of understanding how EIGRP metrics are calculated and how adjusting the weights can significantly impact routing decisions in a network.

For the ping from the host to the SVI IP address to be successful, the host must be configured with the correct subnet mask (255.255.255.0) and must be a member of VLAN 10. If the host is correctly configured, it will recognize the SVI as its default gateway and will be able to send ICMP echo requests to the SVI. However, if the host lacks a default gateway configuration, it may still be able to communicate with other hosts in the same VLAN but will not be able to reach devices outside its subnet. This could lead to confusion regarding connectivity issues. Additionally, if the switch does not have the VLAN configured or if the SVI is administratively down, the ping will fail. It is also important to note that SVIs do support ICMP traffic, which is used for ping operations. Therefore, the factors affecting connectivity include proper VLAN membership, correct IP addressing and subnetting, and the operational status of the SVI. If all these conditions are met, the ping will be successful, confirming that the host can communicate with the SVI.

In contrast, hardcoding device IP addresses directly into the playbooks undermines the flexibility and adaptability of the automation framework. This practice can lead to significant challenges when scaling the network or when device addresses change, as it would require manual updates to each playbook, increasing the risk of errors and downtime. Using a single playbook for all devices may seem like a way to reduce complexity, but it can lead to a monolithic structure that is difficult to manage. Different devices may require different configurations, and a single playbook can quickly become unwieldy and hard to troubleshoot. Finally, ignoring version control for playbooks is a critical mistake. Version control systems, such as Git, are essential for tracking changes, collaborating with team members, and rolling back to previous configurations if necessary. Without version control, the risk of losing important changes or introducing errors increases significantly. In summary, the best practice for the engineer is to modularize playbooks into roles, ensuring that the automation framework is both scalable and maintainable, while also promoting best practices in configuration management and collaboration.

In contrast, MAC address filtering, while it may seem like a straightforward solution, is inherently insecure because MAC addresses can be easily spoofed by an attacker. This means that unauthorized devices could gain access by mimicking the MAC address of an authorized device. Similarly, enforcing static IP address assignments does not provide any real security, as it does not authenticate the device itself; it merely restricts access based on IP addresses, which can also be spoofed. Deploying a VPN for remote access is beneficial for securing data in transit, but it does not inherently authenticate devices on the local network. A VPN primarily encrypts the connection between the remote user and the network, but it does not prevent unauthorized devices from connecting to the network in the first place. Therefore, 802.1X provides a comprehensive solution by requiring devices to authenticate before they can access the network, ensuring that only authorized devices are granted access to sensitive resources. This method not only enhances security but also allows for dynamic management of access policies, making it a superior choice for organizations looking to protect their network infrastructure.

\[ \text{Remaining flow entries} = \text{Total flow entries} – \text{Configured flow entries} = 100 – 80 = 20 \] This calculation shows that the administrator can configure 20 more flow entries for other applications without exceeding the switch’s capacity. Furthermore, it is essential to understand the implications of flow entry prioritization in OpenFlow. By assigning a higher priority to the flow entry for the video streaming application, the administrator ensures that packets matching this flow are processed preferentially over others. This prioritization is crucial in environments where bandwidth is limited, as it helps maintain the quality of service (QoS) for critical applications. In summary, the correct answer reflects the remaining capacity of the flow table after accounting for the already configured entries, while also highlighting the importance of flow entry management and prioritization in OpenFlow-enabled networks. This understanding is vital for network administrators aiming to optimize traffic flow and ensure efficient resource utilization in their networks.

\[ 200 \text{ Mbps} = 200 \times 10^6 \text{ bits per second} = \frac{200 \times 10^6}{8} \text{ bytes per second} = 25 \times 10^6 \text{ bytes per second} \] Next, we calculate the number of packets that can be sent per second by dividing the total bytes per second by the average packet size: \[ \text{Packets per second} = \frac{25 \times 10^6 \text{ bytes per second}}{1500 \text{ bytes per packet}} \approx 16666.67 \text{ packets per second} \] This means that the VPN can handle approximately 16,667 packets per second without exceeding the throughput limit. Now, regarding the configurations for optimizing VPN performance while ensuring security, implementing hardware-based VPN appliances at both ends of the connection is the most effective choice. Hardware appliances are specifically designed to handle encryption and decryption processes efficiently, which significantly reduces latency and increases throughput compared to software-based solutions. They can also provide dedicated resources for handling VPN traffic, which is crucial for maintaining performance under load. Using software-based VPN solutions on standard servers may lead to performance bottlenecks, especially under high traffic conditions, as these servers are not optimized for encryption tasks. Configuring the VPN to use 3DES encryption instead of AES-256 would compromise security, as AES-256 is currently considered more secure and efficient than 3DES. Lastly, reducing the MTU size to 1400 bytes could help minimize fragmentation but may not significantly enhance performance compared to the benefits provided by dedicated hardware appliances. Therefore, the best approach is to implement hardware-based VPN appliances to ensure both optimal performance and robust security.

In contrast, bulk traffic, such as file transfers or large data backups, is less sensitive to latency. These types of applications can afford to have delays since they do not require immediate data delivery. They typically utilize a “best-effort” model, where the network attempts to deliver packets as quickly as possible, but there are no guarantees regarding delivery times or order. Best-effort traffic refers to standard data traffic that does not have specific requirements for latency or bandwidth. This type of traffic is common for web browsing and email, where delays are generally acceptable. Control traffic, on the other hand, is used for network management and signaling, which is also not characterized by the same stringent requirements as real-time traffic. Understanding these distinctions is crucial for network engineers when designing and managing networks, as it allows them to prioritize traffic appropriately. For instance, implementing Quality of Service (QoS) policies can help ensure that real-time traffic is given precedence over bulk or best-effort traffic, thereby maintaining the performance of critical applications. This nuanced understanding of traffic types is essential for optimizing network performance and ensuring a high-quality user experience in environments where real-time communication is vital.

To ensure high availability and redundancy, implementing link aggregation at the distribution layer is crucial. Link aggregation allows multiple physical links to be combined into a single logical link, effectively increasing the available bandwidth and providing redundancy. This means that if one link fails, traffic can still flow through the remaining links, thus maintaining network availability. This approach aligns with the principles of redundancy and load balancing, which are essential for a scalable enterprise network. In contrast, utilizing a single point of failure for core routing would jeopardize network reliability, as any failure in that component would lead to a complete network outage. A flat network architecture, while potentially simpler, does not scale well and can lead to broadcast storms and inefficient traffic management. Lastly, relying solely on static routing limits the network’s ability to adapt to changes in topology or traffic patterns, which can lead to suboptimal performance. Therefore, the correct approach is to implement link aggregation at the distribution layer, as it enhances both bandwidth and redundancy, ensuring that the network can handle growth and maintain performance under varying conditions. This design principle is fundamental in enterprise networks, where reliability and efficiency are paramount.

On the other hand, UDP is a connectionless protocol that does not establish a connection before sending data. It is faster than TCP because it does not require the overhead of connection management and error correction, making it suitable for applications where speed is more critical than reliability, such as video streaming or online gaming. The other options presented do not accurately reflect the responsibilities of the transport layer. For instance, the transport layer does not handle routing, which is the responsibility of the network layer (Layer 3). Additionally, while security measures such as encryption may be implemented at various layers, they are not the primary function of the transport layer. Lastly, the physical transmission of data is managed by the physical layer (Layer 1), which deals with the actual transmission of raw bits over a physical medium. Understanding the nuances of the transport layer’s functions is essential for troubleshooting communication issues, as it directly impacts how data is transmitted and received between devices.

Increasing the logging level to capture more detailed information about all network traffic may seem beneficial; however, it can lead to an overwhelming amount of data that complicates the analysis process without directly improving the detection of SQL injection attempts. This approach could also result in performance degradation, as the system may become bogged down by excessive logging. Disabling the IDPS temporarily to reduce false positives is counterproductive, as it leaves the network vulnerable to attacks during that period. While false positives can be a nuisance, they are a necessary part of maintaining security, and disabling the system entirely exposes the organization to significant risk. Configuring the IDPS to operate in passive mode may reduce the impact on network performance, but it also limits the system’s ability to actively block or mitigate threats. In passive mode, the IDPS can only alert administrators to potential issues without taking any preventive action, which is not ideal for a proactive security posture. In summary, the most effective action is to implement a signature-based detection method specifically designed for SQL injection attacks, as it directly addresses the threat and enhances the IDPS’s capability to identify and respond to such vulnerabilities in real-time.

By employing Ansible, the engineer can leverage its idempotent nature, meaning that applying the same playbook multiple times will not alter the system beyond the initial application, thus preventing unintended changes. This is particularly important in a network environment where consistency and reliability are paramount. Additionally, Ansible provides built-in modules for network devices, which can simplify tasks such as checking interface statuses and applying configurations. In contrast, writing separate scripts for each router (option b) can lead to inconsistencies and increased maintenance overhead, as any changes would need to be replicated across multiple scripts. Using a text file to store commands (option c) introduces the risk of human error during manual execution, which can lead to configuration drift. Lastly, implementing a simple loop in a Python script (option d) without error handling or verification can result in undetected failures, leaving the network in an inconsistent state. Overall, leveraging a configuration management tool like Ansible not only streamlines the automation process but also enhances the reliability and maintainability of network configurations, making it the superior choice for this scenario.

\[ \text{Vehicles per second} = \frac{120 \text{ vehicles}}{60 \text{ seconds}} = 2 \text{ vehicles/second} \] Next, since each vehicle takes an average of 2 seconds to pass through the intersection, the total time required for all vehicles to clear the intersection during peak hours can be calculated as follows: \[ \text{Total time} = \text{Vehicles per second} \times \text{Average pass time} = 2 \text{ vehicles/second} \times 2 \text{ seconds/vehicle} = 4 \text{ seconds} \] However, this calculation only accounts for the time needed for one vehicle to pass. To find the total green light duration needed for all vehicles to pass through the intersection, we multiply the number of vehicles by the average pass time: \[ \text{Total green light duration} = \text{Average vehicle count} \times \text{Average pass time} = 120 \text{ vehicles/minute} \times 2 \text{ seconds/vehicle} = 240 \text{ seconds} \] This means that during peak hours, the traffic signal should remain green for 240 seconds to allow all vehicles to pass efficiently. This dynamic adjustment is crucial in a smart city context, where IoT devices can provide real-time data to optimize traffic flow and reduce congestion. The other options (180 seconds, 120 seconds, and 60 seconds) do not account for the total number of vehicles passing through the intersection, leading to potential traffic delays and inefficiencies. Thus, the correct approach involves understanding both the vehicle count and the time each vehicle requires to clear the intersection, ensuring that the traffic signal is optimized for the current conditions.

IKEv2 is preferred for key exchange due to its efficiency and ability to handle network changes seamlessly, which is particularly beneficial in mobile environments or when dealing with dynamic IP addresses. This combination of technologies adheres to industry best practices for secure communications. In contrast, the other options present significant vulnerabilities. PPTP, while easy to configure, is considered outdated and insecure, particularly with MD5, which is no longer recommended for integrity checks due to its susceptibility to collision attacks. L2TP over IPsec with 3DES and SHA-1 also falls short, as 3DES is less secure than AES, and SHA-1 has known vulnerabilities. Lastly, GRE tunnels without encryption expose the data to potential interception, and while OSPF is a valid routing protocol, it does not address the critical need for encryption in this scenario. Thus, the optimal configuration for a site-to-site VPN that balances security and efficiency is to implement IPsec with ESP in tunnel mode, utilizing AES-256 for encryption and SHA-256 for integrity checks, along with IKEv2 for key exchange. This approach not only secures the data but also ensures that routing between the sites is handled effectively.

To facilitate communication between these VLANs, a Layer 3 switch is ideal for inter-VLAN routing, as it can handle routing between VLANs without the need for an external router, thus improving performance and reducing latency. The implementation of access control lists (ACLs) is crucial for enforcing the security policies required by the Finance department. Specifically, ACLs can be configured to deny internet access for VLAN 20 while permitting it for VLAN 10 and VLAN 30. This approach not only meets the functional requirements but also adheres to best practices in network segmentation and security. The other options fail to meet the specific access requirements or do not implement necessary security measures, such as ACLs, which are essential for controlling traffic flow and ensuring that sensitive departmental data remains protected. Thus, the chosen configuration effectively balances the need for access and security across the different departments.

As a result, the local switch will evaluate its port roles. The port that connects to the neighboring switch will be designated as the root port, which will transition to the forwarding state. Conversely, any other ports that are not designated as root ports will be evaluated based on their roles. Since the local switch is not the root bridge, it will transition its designated port to a blocking state to prevent loops, as it is not the best path to the root bridge. This behavior is consistent with RSTP’s rapid convergence and port role assignment, which is designed to minimize downtime and ensure efficient data flow. The designated port on the neighboring switch will remain in a forwarding state, allowing it to continue forwarding traffic towards the root bridge. Thus, the correct outcome is that the local switch will transition its designated port to a blocking state while the neighboring switch maintains its root port in a forwarding state. This understanding of port roles and states in RSTP is crucial for network engineers to design and troubleshoot resilient network topologies effectively.

IPsec (Internet Protocol Security) provides a secure channel for data transmission by encrypting the data packets, ensuring confidentiality and integrity. This is crucial for protecting sensitive information as it travels over potentially insecure networks, such as the internet. On the other hand, 802.1X is a network access control protocol that provides an authentication mechanism for devices wishing to connect to a LAN or WLAN. It uses the Extensible Authentication Protocol (EAP) to facilitate secure authentication, ensuring that only authorized users can access the network resources. This is particularly important in a corporate environment where unauthorized access could lead to data breaches or other security incidents. In contrast, the other options present significant security vulnerabilities. For instance, using a PPTP VPN lacks the strong encryption standards found in IPsec and is susceptible to various attacks. Relying solely on SSL/TLS for secure web access does not provide a comprehensive solution for all types of network traffic, as it only secures web-based communications. Lastly, configuring static IP addresses without encryption leaves the network open to interception and unauthorized access, as it does not provide any form of authentication or data protection. Thus, the combination of IPsec for encryption and 802.1X for authentication represents the most effective approach to secure access technologies in this scenario, ensuring that both the identity of users and the confidentiality of data are maintained.