DRAINER CS : Data Structures Final Review

Quiz
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Computers
•
University
•
Easy
Ashtin Hall
Used 1+ times
FREE Resource
15 questions
Show all answers
1.
MULTIPLE CHOICE QUESTION
15 mins • 1 pt
Which of the following statements is true about Binary Search Trees (BSTs)?
BSTs are not suitable for storing data in a sorted order
The right subtree of a node in a BST contains values greater than the nodes value
BSTs can never be unbalanced
BSTs have a time complexity of O(n) for insertion and search operations
2.
MULTIPLE CHOICE QUESTION
15 mins • 1 pt
Which of the following operations can not be performed in O(log n) time in a perfect Binary Search Tree?
Insertion
Deletion
Searching for a given key
In-order traversal
3.
MULTIPLE CHOICE QUESTION
15 mins • 1 pt
What is the time complexity of finding the minimum value of a balanced Binary Search Tree
O(n)
O(1)
O( log n)
O(n log n)
4.
MULTIPLE CHOICE QUESTION
15 mins • 1 pt
Which of the following statements is true about the following code?
The function prints the height of the tree
The function prints the tree in post-order traversal
The function prints the tree in in-order traversal
The function prints the tree in pre-order traversal
5.
MULTIPLE CHOICE QUESTION
15 mins • 1 pt
Which of the following is not a property of Binary Search Trees
A node can have at most two children.
All nodes at the same level have the same value
All nodes in the left subtree of a node have values less thatn the nodes valu
All nodes in the right subtree of a node have values greater than the nodes value
6.
MULTIPLE CHOICE QUESTION
15 mins • 1 pt
Which of the following statements is true about the following python code?
The function deletes the node with the given key from the tree
The function inserts a new node with the given key in the tree
The function searches for the node with the given key in the tree
The function returns the minimum value in the tree
7.
MULTIPLE CHOICE QUESTION
15 mins • 1 pt
What could be the worst-case time complexity of inserting a node in a Binary Search Tree?
O(log n)
O(n log n)
O(n)
O(1)
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