30+ Tetra Pak Interview Questions and Answers

Given a list of process IDs and their corresponding parent process IDs, print the IDs of all processes that are children of a specific process ID, and recursively kill all their children.

• Iterate through the list of process IDs and parent process IDs

• Check if the current process ID is the one to be killed

• If yes, recursively find and print all its children

• If a child has further children, recursively kill them as well

N queen problem is to place N queens on an NxN chessboard without any two queens threatening each other.

• The problem can be solved using backtracking algorithm

• Start with placing a queen in the first row and move to the next row

• If no safe position is found, backtrack to the previous row and try a different position

• Repeat until all queens are placed or no safe position is found

• Code can be written in any programming language

• Example: https://www.geeksforgeeks.org/n-queen-problem-b...read more

Check if there exists any sub array with given sum in the array with O(1K) space complexity.

• Use two pointers to traverse the array and maintain a current sum.

• If the current sum is greater than the given sum, move the left pointer.

• If the current sum is less than the given sum, move the right pointer.

• If the current sum is equal to the given sum, return true.

References and pointers are both used to access memory locations, but references cannot be null and cannot be reassigned.

• Pointers can be null and can be reassigned to point to different memory locations.

• References are automatically dereferenced, while pointers need to be explicitly dereferenced.

• Pointers can perform arithmetic operations, while references cannot.

• Example: int x = 5; int *ptr = &x; int &ref = x;

• Example: int *ptr = nullptr; int &ref = x; // not allowed

A reference variable is a variable that holds the memory address of an object, while an actual reference is the object itself.

• A reference variable is declared with a specific type and can only refer to objects of that type.

• An actual reference is the object itself, which can be accessed and manipulated using the reference variable.

• Changing the value of a reference variable does not affect the original object, but changing the value of an actual reference does.

• Reference variabl...read more

When we type an URL, the browser sends a request to the server hosting the website and retrieves the corresponding webpage.

• The browser parses the URL to extract the protocol, domain, and path.

• It resolves the domain name to an IP address using DNS.

• The browser establishes a TCP connection with the server.

• It sends an HTTP request to the server.

• The server processes the request and sends back an HTTP response.

• The browser receives the response and renders the webpage.

Indexing in DBMS is a technique to improve query performance by creating a data structure that allows faster data retrieval.

• Indexing is used to speed up data retrieval operations in a database.

• It involves creating a separate data structure that maps the values of a specific column to their corresponding records.

• This data structure is called an index.

• Indexes are typically created on columns that are frequently used in search conditions or join operations.

• Maintaining an index i...read more

The height of the tree can be found by counting the number of edges from the root to the farthest leaf node.

• Count the number of edges from the root to the farthest leaf node

• This will give the height of the tree

• Example: If the farthest leaf node is 4 edges away from the root, the height of the tree is 4

Caching is the process of storing frequently accessed data in a temporary storage to improve performance.

• Caching improves performance by reducing the need to fetch data from the original source.

• It involves storing data in a temporary storage, such as memory or disk, closer to the user or application.

• Caching can be done at various levels, including browser caching, server-side caching, and database caching.

• Examples of caching include caching web pages, caching database query r...read more

AVL trees are self-balancing binary search trees. They maintain a balance factor to ensure height balance.

• AVL trees are named after their inventors, Adelson-Velsky and Landis.

• They are height-balanced, meaning the difference in height between left and right subtrees is at most 1.

• Insertion and deletion operations may cause imbalance, which is corrected by rotations.

• AVL trees have a worst-case time complexity of O(log n) for search, insertion, and deletion.

• Example: Inserting 5, ...read more

Implementation of LRU cache using a doubly linked list and a hash map.

• LRU (Least Recently Used) cache is a data structure that stores a fixed number of items and evicts the least recently used item when the cache is full.

• To implement LRU cache, we can use a doubly linked list to maintain the order of items based on their usage frequency.

• We can also use a hash map to store the key-value pairs for quick access and retrieval.

• When a new item is accessed, it is moved to the front ...read more

NP hardness refers to the difficulty of solving a problem in non-deterministic polynomial time.

• NP-hard problems are some of the most difficult problems in computer science.

• They cannot be solved in polynomial time by any known algorithm.

• Examples include the traveling salesman problem and the knapsack problem.

B trees and B+ trees are data structures used for efficient storage and retrieval of data in databases.

• B trees are balanced trees with a variable number of child nodes per parent node. They are commonly used in databases to store large amounts of data.

• B+ trees are a variant of B trees where all data is stored in the leaf nodes, and the internal nodes only contain keys. They are commonly used in databases for indexing.

• B+ trees are more efficient than B trees for range queries ...read more

Interview question on finding max sum contiguous subarray and median of array with sorting involved.

• For finding max sum contiguous subarray, use Kadane's algorithm which has O(n) time complexity.

• For finding median of array, sort the array and then find the middle element or average of middle two elements.

• If the array is too large to sort, use quickselect algorithm to find the kth smallest element in O(n) time.

Connect n ropes with minimum cost using priority queue

• Create a priority queue and insert all the ropes into it

• Pop the two smallest ropes from the queue and connect them

• Insert the new rope into the queue and repeat until only one rope remains

• The cost of connecting two ropes is the sum of their lengths

• Time complexity: O(nlogn)

• Example: Ropes of lengths 4, 3, 2, and 6 can be connected with a cost of 29

Find the first non-repeating character in a continuous character stream.

• Use a hash table to keep track of character frequency.

• Iterate through the stream and check if the current character has a frequency of 1.

• If yes, return the character as the first non-repeating character.

• If no non-repeating character is found, return null or a default value.

Dynamic programming problem involving arrays of strings.

• Use dynamic programming to efficiently solve problems by breaking them down into smaller subproblems.

• Consider using a 2D array to store intermediate results for optimal substructure.

• Examples: Longest Common Subsequence, Word Break, Minimum Path Sum.

Given a node in a family tree, print all nodes on the same level.

• Traverse the tree level by level using BFS

• Keep track of the level of each node while traversing

• Print all nodes with the same level as the given node

• Example: If the given node is 'John', print all his siblings and cousins

Find missing number in array without extra space

• Iterate through the array and XOR all the elements with their indices and the actual numbers

• The missing number will be the XOR result

• Example: ['1', '2', '4', '5'] -> XOR(0, 1) ^ XOR(1, 2) ^ XOR(2, 4) ^ XOR(3, 5) = 3

Random Forest is a machine learning algorithm that builds multiple decision trees and combines their outputs.

• Random Forest is an ensemble learning method.

• It builds multiple decision trees and combines their outputs to make a final prediction.

• It is used for both classification and regression tasks.

• It is less prone to overfitting compared to a single decision tree.

• It can handle missing values and outliers.

• Example: predicting whether a customer will buy a product based on their ...read more

String attachment algorithm joins two strings together.

• Create a new string variable to hold the result

• Loop through the first string and add each character to the new string

• Loop through the second string and add each character to the new string

• Return the new string

Sets in JavaScript are used to store unique values of any type.

• Create a new set using the Set constructor

• Add values to the set using the add() method

• Check if a value exists in the set using the has() method

• Remove a value from the set using the delete() method

• Iterate over the set using the forEach() method

POST requests are a type of HTTP request method used to submit data to a server.

• POST requests are used to create or update resources on a server.

• They are commonly used in web forms to submit user input data.

• POST requests have a request body that contains the data being submitted.

• They are different from GET requests, which are used to retrieve data from a server.

• POST requests are more secure than GET requests because the data is not visible in the URL.

2 variable variation of LIS

• The problem involves finding the longest increasing subsequence in two arrays

• Dynamic programming can be used to solve the problem

• The time complexity of the solution is O(n^2)

• Example: Given two arrays [1, 3, 5, 4] and [2, 4, 3, 5], the longest increasing subsequence is [3, 5]

• Example: Given two arrays [10, 22, 9, 33, 21, 50, 41, 60] and [5, 24, 39, 60, 15, 28, 27, 40], the longest increasing subsequence is [10, 22, 33, 50, 60]

Search for an element in a rotated sorted array.

• Use binary search to find the pivot point where the array is rotated.

• Compare the target element with the first element of the array to determine which half to search.

• Perform binary search on the selected half to find the target element.

• Time complexity: O(log n), Space complexity: O(1).

Finding the intersection point of two linked lists.

• Traverse both lists and find their lengths

• Move the head of the longer list by the difference in lengths

• Traverse both lists simultaneously until they meet at the intersection point

Matrix multiplication involves multiplying the elements of one matrix with another matrix.

• Create two matrices with compatible dimensions

• Multiply corresponding elements of each row in the first matrix with each column in the second matrix

• Sum the products to get the resulting matrix

To find the middle of a linked list, use two pointers - one moving twice as fast as the other. To add two numbers, traverse both lists simultaneously and add corresponding digits.

• Use two pointers to find the middle of the linked list - one moving twice as fast as the other

• To add two numbers represented by linked lists, traverse both lists simultaneously and add corresponding digits

• If the sum of two digits is greater than 9, carry over the 1 to the next digit

Calculate the sum of all paths between two nodes in a binary tree.

• Traverse the tree and keep track of the path and its sum from the root to the current node.

• When the target nodes are found, calculate the sum of all paths between them by adding the path sums of their common ancestor.

• Recursively traverse the left and right subtrees to find the target nodes.

• Use a hash table to store the path sums of each node for efficient lookup.

• Time complexity: O(n), Space complexity: O(n).

A linked list is a linear data structure where elements are stored in nodes with each node pointing to the next node in the sequence.

• Consists of nodes connected by pointers

• Does not have a fixed size like arrays

• Can easily insert or delete elements without shifting other elements

• Examples: Singly linked list, Doubly linked list, Circular linked list

Find the final element in a rotated array.

• Identify the pivot point where the array was rotated.

• Determine if the target element is in the first or second half of the array.

• Use binary search to find the target element.

Implementing Heap data structure in C++

• Use an array to represent the binary tree structure of the heap

• Implement functions for inserting elements, deleting elements, and heapifying the array

• Ensure that the heap property is maintained (parent node is always greater than or equal to its children)

Largest common ancestor is the most recent node that is a common ancestor of two or more nodes in a tree.

• It is commonly used in computer science and genealogy.

• In genealogy, it refers to the most recent common ancestor of two or more individuals.

• In computer science, it is used in algorithms for finding the lowest common ancestor of two nodes in a tree.

• It can be found using various algorithms such as Tarjan's off-line least common ancestors algorithm and the binary lifting algo...read more

Rain water trapping problem refers to the accumulation of rainwater in low-lying areas or on flat roofs.

• The problem can be solved by installing rainwater harvesting systems.

• Proper drainage systems can also prevent rainwater trapping.

• Green roofs and permeable pavements can help absorb rainwater.

• Rain gardens can be created to collect and filter rainwater.

• Regular maintenance of gutters and downspouts can prevent clogging and overflow.

• The problem can lead to water damage, mold gr...read more

A mountain array in an array refers to an array where the elements form a peak and then descend.

• The array should have a peak element and then the elements should descend on both sides.

• For example, [1, 3, 5, 4, 2] is a mountain array in an array.

• However, [1, 2, 3, 4, 5] is not a mountain array in an array.

Finding the median in a running stream of data.

• The median is the middle value in a sorted list of numbers.

• In a running stream, we cannot sort the data beforehand.

• We can use a min-heap and a max-heap to keep track of the median.

• As new data comes in, we add it to the appropriate heap and balance the heaps.

• The median is then the top element of the larger heap or the average of the top elements of both heaps.

Reorder a linked list in place.

• Use two pointers to find the middle of the list

• Reverse the second half of the list

• Merge the two halves of the list