What is the below pseudo code trying to do, where pt is a no...

MCQ

What is the below pseudo code trying to do, where pt is a node pointer and root pointer.

redblack(Node root, Node pt) :
    if (root == NULL)
       return pt
 
    if (pt.data < root.data)
    {
        root.left  =   redblack(root.left, pt);
        root.left.parent = root
    }
    else if (pt.data > root.data)
    {
        root.right = redblackt(root.right, pt)
        root.right.parent = root
    }
   return root

insert a new node

delete a node

search a node

count the number of nodes

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MCQ

Select the code snippet which performs in-order traversal.

a)

public void inorder(Tree root)
{
 System.out.println(root.data);
 inorder(root.left);
 inorder(root.right);
}

b)

public void inorder(Tree root)
{
 inorder(root.left);
 System.out.println(root.data);
 inorder(root.right);
}

c)

public void inorder(Tree root)
{
 System.out.println(root.data);
 inorder(root.right);
 inorder(root.left);
}

d) None of the mentioned

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MCQ

Why Red-black trees are preferred over hash tables though hash tables have constant time complexity?

no they are not preferred

because of resizing issues of hash table and better ordering in redblack trees

because they can be implemented using trees

because they are balanced

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MCQ

Why to prefer red-black trees over AVL trees?

Because red-black is more rigidly balanced

AVL tree store balance factor in every node which costs space

AVL tree fails at scale

Red black is more efficient

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When it would be optimal to prefer Red-black trees over AVL trees?

when there are more insertions or deletions

when more search is needed

when tree must be balanced

when log(nodes) time complexity is needed

Discussion

MCQ

Which of the following is an application of Red-black trees and why?

used to store strings efficiently

used to store integers efficiently

can be used in process schedulers, maps, sets

for efficient sorting

Discussion

Banking

Engineering

General

Medical

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category More Data Structure Topics

Data Structure Basics

Abstract data types

Arrays

Lists

Stacks

Queues

Trees

Binary Trees

B Trees

Heaps

Hash based structures

Graphs

Mixed

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