Sharding in NoSQL databases involves horizontally partitioning data across multiple servers. This distributes the workload and allows parallel processing, enhancing scalability. It reduces the burden on individual servers, preventing bottlenecks and improving overall system performance. Understanding sharding is crucial for designing scalable NoSQL database architectures.

Symmetric encryption uses the same key for both encryption and decryption, making it faster but requiring secure key distribution. Asymmetric encryption, on the other hand, uses different keys for encryption and decryption, providing enhanced security but slower processing. The choice between them depends on the specific security and performance requirements of the system.

To diagnose and optimize memory management in a system experiencing high memory utilization, using memory profiling tools is crucial. These tools help analyze memory usage patterns, identify memory-intensive processes, and pinpoint areas of inefficiency. By understanding which processes are consuming excessive memory, developers can implement targeted optimizations, such as optimizing data structures, reducing memory leaks, or revising memory allocation strategies. Additionally, implementing a garbage collection mechanism can further improve memory efficiency by reclaiming unused memory, reducing fragmentation, and enhancing overall system performance. These approaches collectively help address memory-related issues, optimize resource utilization, and enhance system stability and responsiveness.

VLANs separate broadcast domains, but for inter-VLAN communication, a Layer 3 switch is crucial to route traffic between VLANs. This maintains security by keeping traffic within intended VLANs. ACLs further enhance security by controlling which traffic is allowed between VLANs. DHCP assigns IPs but doesn't directly address VLAN communication. VLAN trunking with tagging is important for VLAN identification but doesn't ensure inter-VLAN communication or security.

The Observer design pattern is used when there is a need for a one-to-many dependency between objects, where one object's state change should notify and update multiple other objects. This pattern is commonly used in event handling systems and UI frameworks.

Spectrum sharing is the method used by wireless access points to allocate available bandwidth among connected devices. This technique ensures efficient use of the available frequency spectrum by dynamically assigning frequencies to different devices based on their needs and the current network conditions. Channel allocation refers to the process of assigning specific frequency channels to devices for communication, which is related but not as dynamic as spectrum sharing. Frequency hopping is a technique where a device switches frequencies during transmission to avoid interference, but it's not the same as overall bandwidth allocation. Beamforming is a technology that focuses wireless signals towards specific devices, improving signal strength and quality but is not directly related to bandwidth allocation.

The "D" in ACID stands for Durability. This property ensures that once a transaction is committed, it remains permanently stored in the system, even in the event of power failures or system crashes, guaranteeing data persistence and reliability.

The correct option is "TCP." TCP (Transmission Control Protocol) is a connection-oriented protocol in the TCP/IP suite. It provides reliable and ordered delivery of data between two hosts by establishing a connection, acknowledging data receipt, and handling retransmissions if packets are lost. UDP (Option 1), on the other hand, is a connectionless protocol that is faster but less reliable. HTTP (Option 2) is a protocol used for web communication, operating at the Application layer. IP (Option 4) is a network layer protocol responsible for addressing and routing.

A file system hierarchy is commonly represented using a tree data structure. In this structure, directories (folders) are nodes, and files are the leaves or nodes without children. Each directory node can have multiple child nodes, reflecting the nested organization of files and directories within the system. This hierarchical representation facilitates efficient file navigation and management operations, such as creating, moving, and deleting files and directories.

Dynamic programming can optimize matrix multiplication by breaking down the problem into smaller subproblems and storing their solutions in a table (memoization). This approach reduces redundant computations and improves efficiency compared to naive methods. Techniques like Strassen's algorithm and divide and conquer can also be used, but dynamic programming specifically focuses on efficiently solving overlapping subproblems in matrix multiplication.