Random Forest uses bagging (Bootstrap Aggregating) to create diverse trees by training each tree on a random subset of the data.

Pre-order traversal is often used to copy a binary tree because it processes the root node before its children, allowing for a straightforward reconstruction.

Post-order traversal is used to delete a binary tree because it ensures that a node is deleted only after its children have been deleted.

In-order traversal of a binary search tree (BST) visits nodes in sorted order, making it the correct choice for this requirement.

HTTP uses URL addressing to identify resources on the web.

IPv4 uses logical addressing, which allows devices to communicate over a network using IP addresses.

The Data Link layer uses MAC Addressing to identify devices on the same local network.

The Network Layer uses IP addressing to identify devices on a network.

The Network Layer uses logical addresses (such as IP addresses) to identify devices on a network.

The Network Layer uses logical addressing, such as IP addresses, to identify devices on a network.

The Data Link layer uses MAC Addressing to identify devices on the same local network.

IP addressing is used at the Network Layer (Layer 3) of the OSI model for routing packets.

Fiber Optic Cable is commonly used for high-speed data transmission due to its high bandwidth and low attenuation.

Fiber Optic Cable is commonly used for high-speed data transmission due to its high bandwidth and low attenuation.

DBSCAN is a density-based clustering algorithm that groups together points that are closely packed.

Clustering algorithms are designed to work with unlabeled data to find inherent groupings.

Clustering is an unsupervised learning technique, making it best suited for unlabeled data.

Decision Trees can handle both categorical and numerical data effectively.

Hierarchical clustering is best suited for data where relationships between clusters are important.

LSTM networks are best suited for sequential data, such as time series or natural language.

Clustering is most effective with unlabeled data, as it seeks to find patterns without predefined categories.

Hierarchical clustering is particularly useful for data where understanding the relationships between clusters is important, as it provides a visual representation of these relationships.

K-means clustering is best suited for numerical data, as it relies on calculating distances between data points.

Supervised learning requires labeled data, where each input is associated with a corresponding output.

Clustering algorithms operate on unlabeled data, as they seek to find inherent groupings.

Binary search is typically implemented on arrays, as they allow direct access to elements.

TCP uses an acknowledgment mechanism to ensure that packets are received correctly and to request retransmission if necessary.

Dijkstra's algorithm can be applied to both directed and undirected graphs as long as the edge weights are non-negative.

Dijkstra's algorithm is typically applied to weighted graphs, where edges have different costs associated with them.

An adjacency list is generally more efficient for Dijkstra's algorithm, especially for sparse graphs, as it allows for quicker access to neighboring nodes.