WeInvest Interview Questions and Answers

Round duration - 70 minutes
Round difficulty - Medium

It consisted of three coding questions varying from easy to medium.

Q1. 
Distributing Coins in a Binary Tree
You are provided with the root of a binary tree that consists of 'N' nodes. Each node in this tree contains coins, and the total number of coins across all nodes is equ...read more
Ans. 
Determine the minimum number of moves required to distribute coins in a binary tree so that every node has exactly one coin.
• Traverse the binary tree in a bottom-up manner to distribute coins efficiently.

• Keep track of the excess or deficit of coins at each node to calculate the minimum moves required.

• Transfer coins from nodes with excess coins to nodes with deficits to balance the distribution.

• Example: For the input ROO...read more
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Q2. 
Distinct Subsets Count
Given an array arr of N integers that may include duplicates, determine the number of subsets of this array containing only distinct elements.
The result should be returned modulo ...read more
Ans. 
Count the number of distinct-element subsets in an array modulo 10^9+7.
• Iterate through the array and keep track of distinct elements using a set.

• Calculate the number of subsets using the formula 2^distinct_count - 1.

• Return the result modulo 10^9+7 for each test case.
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Q3. 
0-1 Knapsack Problem Statement
A thief is robbing a store and can carry a maximal weight 'W' in his knapsack. There are 'N' items, where the i-th item has a weight 'wi' and value 'vi'. Consider the maximu...read more
Ans. 
The 0-1 Knapsack Problem involves maximizing the value of items a thief can steal within a weight limit.
• Use dynamic programming to solve the problem efficiently.

• Create a 2D array to store the maximum value that can be achieved at each weight limit.

• Iterate through the items and weights to fill the array with optimal values.

• Return the maximum value achievable at the given weight limit.
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Round duration - 60 minutes
Round difficulty - Medium

The interview was focused on data structures as well as computer fundamentals along with puzzles.

Q1. 
Sort an Array in Wave Form
You are given an unsorted array ARR. Your task is to sort it so that it forms a wave-like array.
Input:
The first line contains an integer 'T', the number of test cases.
For ea...read more
Ans. 
Sort an array in wave form where each element is greater than or equal to its adjacent elements.
• Iterate through the array and swap adjacent elements to form a wave pattern.

• Ensure that the first element is greater than or equal to the second element.

• There can be multiple valid wave arrays, so any valid wave array is acceptable.
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Q2. 
Reverse a Linked List Problem Statement
You are given a Singly Linked List of integers. Your task is to reverse the Linked List by changing the links between nodes.
Input:
The first line of input contai...read more
Ans. 
Reverse a given singly linked list by changing the links between nodes.
• Iterate through the linked list and reverse the links between nodes

• Use three pointers to keep track of the current, previous, and next nodes

• Update the links between nodes to reverse the list

• Return the head of the reversed linked list
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Round duration - 90 minutes
Round difficulty - Easy

The Round was mainly focused on resume+ dsa + system design +computer fundamentals

Q1. 
Running Median Problem
Given a stream of integers, calculate and print the median after each new integer is added to the stream.
Output only the integer part of the median.
Example:
Input:
N = 5  
Stre...read more
Ans. 
Calculate and print the median after each new integer is added to the stream.
• Use a min heap to store the larger half of the numbers and a max heap to store the smaller half.

• Keep the two heaps balanced by ensuring the size difference is at most 1.

• If the total number of elements is odd, the median is the top of the larger heap. If even, average the tops of both heaps.
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Q2. Can you explain the implementation of an LRU (Least Recently Used) Cache in a database management system?
Ans. 
LRU cache in a database management system stores most recently used data and removes least recently used data when full.
• LRU cache is implemented using a doubly linked list and a hash map.

• Each node in the linked list represents a key-value pair.

• When a key is accessed, it is moved to the front of the linked list.

• If the cache is full, the least recently used node at the end of the list is removed.

• Example: If cache size is...read more
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Q3. Design a stack that supports the getMin() operation in O(1) time and O(1) extra space.
Ans. 
Use two stacks - one to store actual values and one to store minimum values.
• Use two stacks - one to store actual values and one to store minimum values

• When pushing a new value, check if it is smaller than the current minimum and push it to the min stack if so

• When popping a value, check if it is the current minimum and pop from min stack if so

• getMin() operation can be done by peeking at the top of the min stack
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Q1. Tell me about yourself
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Q2. Why you want to join us
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