Q. How does the balancing of an AVL tree differ from that of a Red-Black tree?
A.
AVL trees are more rigidly balanced than Red-Black trees
B.
Red-Black trees are always perfectly balanced
C.
AVL trees allow more flexibility in balancing
D.
There is no difference
Solution
AVL trees are more rigidly balanced than Red-Black trees, which allows AVL trees to provide faster lookups at the cost of more complex insertions and deletions.
Correct Answer:
A
— AVL trees are more rigidly balanced than Red-Black trees
Q. What is the main advantage of using an AVL tree over a regular binary search tree?
A.
AVL trees are easier to implement
B.
AVL trees are always balanced, ensuring O(log n) height
C.
AVL trees require less memory
D.
AVL trees can store duplicate values
Solution
AVL trees maintain a strict balance, ensuring that the height of the tree is always O(log n), which guarantees efficient search, insert, and delete operations.
Correct Answer:
B
— AVL trees are always balanced, ensuring O(log n) height
Q. Which of the following scenarios is best suited for using an AVL tree?
A.
When frequent insertions and deletions are expected
B.
When search operations are more frequent than insertions
C.
When memory usage is a critical concern
D.
When the data is mostly static
Solution
AVL trees are best suited for scenarios where search operations are frequent, as they maintain balance and ensure O(log n) time complexity for searches.
Correct Answer:
B
— When search operations are more frequent than insertions
