Compared to DFS, BFS typically requires more memory. This is because BFS stores all nodes at the current level in memory, leading to higher space complexity compared to DFS, which explores as far as possible along each branch before backtracking.

No, DFS cannot guarantee the shortest path in a weighted graph because it may explore longer paths first. DFS is more suitable for unweighted graphs, and algorithms like Dijkstra's or Bellman-Ford are preferred for finding the shortest path in weighted graphs.

The Bellman-Ford Algorithm is the appropriate choice for scenarios with graphs containing negative edge weights. Unlike Dijkstra's Algorithm, Bellman-Ford can handle negative weights, making it suitable for finding the shortest paths from a single source vertex in such scenarios.

Linear search may not be the most appropriate approach for searching a specific word in a large document due to its time complexity. Binary search, hashing, or indexing could be more suitable alternatives. Binary search is efficient for sorted data, hashing provides constant time complexity on average, and indexing can expedite search operations by creating a mapping between words and their locations.

An AVL tree is a self-balancing binary search tree where the height factor (also known as the balance factor) of each node is at most 1. The balance factor is the difference between the height of the left and right subtrees.

The sorting algorithm similar to selection sort, in terms of repeatedly finding the minimum element from the unsorted portion and placing it at the beginning, is Insertion Sort. Both algorithms involve building the sorted portion of the array incrementally.

Common algorithms for string compression include Run-Length Encoding, Huffman Coding, Burrows-Wheeler Transform, and Arithmetic Coding. These algorithms efficiently represent repeated patterns or characters in a compressed form, reducing the overall size of the string.

Elements in an array are accessed by using the array's index within square brackets. The index indicates the position of the element in the array, starting from 0 for the first element.

In a memory-critical scenario, a Red-Black Tree is more suitable. While AVL Trees provide faster search operations, they have a higher memory overhead due to stricter balancing requirements. Red-Black Trees offer a better compromise in terms of both time and space complexities, making them more efficient for large datasets with limited memory.

DFS can be optimized by ordering the vertices in a particular way before traversal. The choice of vertex order can impact the algorithm's performance, and certain orders may result in a more efficient exploration of the graph.