Lam Research Software Developer Interview Questions and Answers

Round duration - 30 minutes
Round difficulty - Easy

Q1. How do you search for a node in a linked list?
Ans. 
To search for a node in a linked list, iterate through the list and compare each node's value with the target value.
• Start at the head of the linked list

• Iterate through each node by following the 'next' pointer

• Compare the value of each node with the target value

• Return the node if found, otherwise return null
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Q2. How do you detect a loop in a linked list?
Ans. 
To detect a loop in a linked list, we can use Floyd's Cycle Detection Algorithm.
• Initialize two pointers, slow and fast, at the head of the linked list.

• Move slow pointer by one step and fast pointer by two steps.

• If there is a loop, the two pointers will eventually meet.

• Alternatively, we can use a hash set to store visited nodes and check for duplicates.
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Q3. Implement a stack using a singly linked list.
Ans. 
Implement a stack using a singly linked list
• Create a Node class with data and next pointer

• Create a Stack class with top pointer pointing to the top of the stack

• Implement push, pop, and peek operations by manipulating the linked list

• Example: Node class - Node { int data; Node next; }
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Round duration - 40 minutes
Round difficulty - Easy

Q1. What is the top view of a binary tree?
Ans. 
The top view of a binary tree shows the nodes visible from the top when looking down from the root node.
• The top view of a binary tree is the set of nodes visible from the top when looking down from the root node.

• Nodes at the same horizontal distance from the root are considered at the same level in the top view.

• If multiple nodes are at the same horizontal distance, only the topmost node at that level is included in the...read more
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Q2. Explain the process of deleting a node from a linked list, covering all possible cases.
Ans. 
Deleting a node from a linked list involves updating pointers to maintain the list's integrity.
• Identify the node to be deleted by traversing the list

• Update the previous node's next pointer to skip the node to be deleted

• Free the memory allocated to the node to be deleted
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Basic aptitude, c and data structures

Q1. C and data structures
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Q2. Operating system and puzzle
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Round duration - 75 minutes
Round difficulty - Medium

This was an online coding round where I had 2 questions to solve under 75 minutes. Both the coding questions were of Medium to Hard level of difficulty.

Q1. 
Smallest Window Problem Statement
Given two strings, S and X, your task is to find the smallest substring in S that contains all the characters present in X.
Example:
Input:
S = "abdd"
X = "bd"
Output:
...read more
Ans. 
Find the smallest substring in S that contains all characters in X.
• Use a sliding window approach to find the smallest substring in S that contains all characters in X.

• Keep track of the characters in X using a hashmap.

• Move the window by adjusting the start and end pointers until all characters in X are found.

• Return the smallest substring encountered.
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Q2. 
K - Sum Path In A Binary Tree
Given a binary tree where each node contains an integer value, and a value 'K', your task is to find all the paths in the binary tree such that the sum of the node values in ...read more
Ans. 
Find all paths in a binary tree where the sum of node values equals a given value 'K'.
• Traverse the binary tree using DFS and keep track of the current path and its sum.

• At each node, check if the current sum equals 'K' and add the path to the result.

• Continue traversal to the left and right child nodes recursively.

• Return the list of paths that sum up to 'K'.
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Round duration - 60 Minutes
Round difficulty - Medium

This round had 2 questions of DS/Algo to solve under 60 minutes and 2 questions related to Operating Systems.

Q1. 
Validate Partial Binary Search Tree (BST) Problem Statement
You are provided with a binary tree containing 'N' nodes. Your task is to determine if this tree is a Partial Binary Search Tree (BST). Return t...read more
Ans. 
Validate if a binary tree is a Partial Binary Search Tree (BST) by checking if each node's left subtree contains nodes with data less than or equal to the node's data, and each node's right subtree contains nodes with data greater than or equal to the node's data.
• Check if each node's left subtree follows BST property (data <= node's data) and right subtree follows BST property (data >= node's data)

• Recursively che...read more
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Q2. 
Longest Increasing Subsequence Problem Statement
Given an array of integers with 'N' elements, determine the length of the longest subsequence where each element is greater than the previous element. This...read more
Ans. 
Find the length of the longest strictly increasing subsequence in an array of integers.
• Use dynamic programming to solve this problem efficiently.

• Initialize an array to store the length of the longest increasing subsequence ending at each index.

• Iterate through the array and update the length of the longest increasing subsequence for each element.

• Return the maximum value in the array as the result.
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Q3. Can you define process and threads in operating systems?
Ans. 
Processes are instances of a program in execution, while threads are lightweight processes within a process.
• A process is a program in execution, with its own memory space and resources.

• Threads are lightweight processes within a process, sharing the same memory space and resources.

• Processes are independent of each other, while threads within the same process can communicate and share data.

• Example: A web browser running ...read more
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Q4. What are the different types of semaphores?
Ans. 
Different types of semaphores include binary semaphores, counting semaphores, and mutex semaphores.
• Binary semaphores: Can only have two states - 0 or 1. Used for mutual exclusion.

• Counting semaphores: Can have multiple states. Used for managing resources with limited capacity.

• Mutex semaphores: Similar to binary semaphores but with additional features like priority inheritance.

• Named semaphores: Can be shared between proc...read more
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Round duration - 60 Minutes
Round difficulty - Hard

In this round, I was asked 3 coding questions out of which I had to implement the first two and for the last question I was only asked the optimal approach. The main challenge in this round was to implement the first two questions in a production ready manner without any bugs and so I had to spent some time thinking about some Edge Cases which were important with respect to the question.

Q1. 
Next Greater Element Problem Statement
You are given an array arr of length N. For each element in the array, find the next greater element (NGE) that appears to the right. If there is no such greater ele...read more
Ans. 
The task is to find the next greater element for each element in an array to its right, if no greater element exists, return -1.
• Use a stack to keep track of elements for which the next greater element is not found yet.

• Iterate through the array from right to left and pop elements from the stack until a greater element is found.

• Store the next greater element for each element in a separate array.

• If the stack is empty afte...read more
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Q2. 
Power Set Generation
Given a sorted array of 'N' integers, your task is to generate the power set for this array. Each subset of this power set should be individually sorted.
A power set of a set 'ARR' i...read more
Ans. 
Generate power set of a sorted array of integers with individually sorted subsets.
• Use recursion to generate all possible subsets by including or excluding each element in the array.

• Sort each subset before adding it to the power set.

• Handle base case when all elements have been considered to add the subset to the power set.
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Q3. 
Counting Sort Problem Statement
Ninja is learning about sorting algorithms, specifically those that do not rely on comparisons. Can you help Ninja implement the counting sort algorithm?
Example:
Input:
...read more
Ans. 
Implement counting sort algorithm to sort an array of integers without comparisons.
• Count the frequency of each element in the input array.

• Create a prefix sum array to determine the position of each element in the sorted array.

• Iterate through the input array and place each element in its correct position based on the prefix sum array.

• Time complexity of counting sort is O(n+k), where n is the number of elements and k is
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