AVL trees require rotations to maintain balance after insertions and deletions to ensure that the balance factor remains within the allowed range.

The height of a binary tree with n nodes can be at most n in the case of a skewed tree, hence Height <= n.

The height of a binary tree is defined as the number of edges in the longest path from the root to a leaf node.

The height of a Red-Black tree can be at most twice the height of an AVL tree, making it less balanced.

Inorder traversal of a binary search tree visits nodes in ascending order.

Level-order traversal visits nodes level by level, typically implemented using a queue.

AVL trees provide faster search operations due to their stricter balancing, but they can be slower for insertions and deletions because they may require more rotations.

All of the mentioned methods (Elbow method, Silhouette analysis, and Gap statistic) can be used to determine the optimal number of clusters in K-means.

Both the Silhouette score and the Davies-Bouldin index are methods used to evaluate the quality of clusters formed by K-means.

Mean Squared Error (MSE) is a common metric used to evaluate the performance of regression models by measuring the average squared difference between predicted and actual values.

Accuracy is a common metric for evaluating the performance of classification Decision Trees.

Mean Squared Error is not typically used to evaluate clustering performance; it is more relevant for regression tasks.

To find the first occurrence, continue searching to the left even after finding the target value.

All of the operations (Enqueue, Dequeue, and Peek) can be performed in constant time, O(1), in a properly implemented queue.

Accessing an element in an array by its index is done in constant time, O(1).

Deletion from the beginning of a linked list can be done in O(1) time as it only requires updating the head pointer.

All operations (push, pop, and peek) can be performed in O(1) time on a stack.

Both insertion and deletion can cause the AVL tree to become unbalanced, requiring rebalancing operations.

Both insertion and deletion operations can cause an imbalance in an AVL tree, requiring rebalancing.

All operations (Push, Pop, and Peek) in a stack are performed in constant time, O(1).

Searching for a value in a linked list requires traversing the list, which takes O(n) time in the worst case.

Accessing an element by index in a singly linked list requires traversing the list, which takes O(n) time.

All basic operations (Push, Pop, Peek) in a stack have a time complexity of O(1). Therefore, the correct answer is 'None of the above'.

Accessing an element in a singly linked list requires traversal from the head to the desired node, which takes O(n) time in the worst case.

All operations (insertion, deletion, and searching) in an AVL tree are guaranteed to be O(log n) due to the tree's balanced nature.

All of the operations (insertion, deletion, and searching) are guaranteed to be O(log n) in both AVL and Red-Black trees.

Deletion is generally more efficient in Red-Black trees due to fewer rotations required compared to AVL trees.

Searching is generally more efficient in AVL trees due to their stricter balancing, leading to a more consistent height.

Search operations are generally more efficient in AVL trees due to their stricter balancing.

In a Red-Black tree, a node cannot have two red children, as it violates the properties of the tree.