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Internships in Automobile Engineering

Bangalore Internships - Automobile Engineering

Sanfoundry located at Bangalore offers internships to deserving B.Tech/M.Tech Students in Automobile Engineering Branch. In Automobile Engineering internships offered by Sanfoundry, shortlisted interns will be working towards the creation of useful artifacts like questions and answers, tutorials, articles, real-world problems and solutions on Automobile Engineering. Moreover, every intern working on Automobile Engineering internship will focus on one specific subject under the Automobile branch. This will help the intern to develop an in-depth understanding of that particular subject in their branch.

Here’s the list of topics for Internship in “Automobile Engineering”.

Applied Thermodynamics

Two Phase flow and Heat Transfer

Air Conditioning and Ventilation

Air Pollution Control and Design of Equipment

Automotive Chassis, Suspension and Components Design

Automotive chassis & Suspension

Hybrid Vehicle Technology

Advance Strength of Materials

Auxiliary Systems of Automotive Engines

Automotive Fuels, Lubricants and Combustion

Automotive Pollution and Control

Automotive Transmission

Vehicle Maintenance

Statistics and Numerical Methods

Chemical Engineering Thermodynamics

CAD/CAM/CAE

Computer Aided Machine Drawing

Control Engineering

Design of Machine Elements

Kinematics and Dynamics of Machines

Applied Electronics and Microprocessors

Engine and Vehicle Management System

Design and Metallurgy of Welded Joints

Advance Engineering Thermodynamics

Mechanics of Materials

Modeling and Finite Element Analysis

Fluid Mechanics

Fluid Mechanics and Machinery

Rail Vehicle Dynamics

Convective Heat and Mass Transfer

Nontradtional Machining

Machine Shop and Tool Design

Manufacturing Processes

Advance Manufacturing Process Simulation and Management

Computer Science and Operation Research

Mechanical Measurements and Metallurgy

Flight Vehicle Design

Advance Theory of IC Engines

Automobile Engineering

Alternative Fuels and Energy Systems

Artificial Intelligence

Automotive Electrical and Electronics Systems

Automotive Aerodynamics

Refrigeration and Air conditioning

Automotive Safety

Design of Composite Materials

Computer Graphics

Computer Integrated Manufacturing and Automation

Programming Language DBMS

Automotive Engine Components Design and Auxiliary Systems

Design of Experiments and Sample Survey Methods

Design of Jigs, Fixtures and Press Tools

Disaster Resistant Building and Management

Earth Moving Equipments and Tractors

Engine Auxiliary Systems

Estimation, Costing and Specifications

Engineering System Design

Entrepreneurship Development, Management and Apparel Entrepreneurship

Advance Foundry Technology

Advance Gas Dynamics

Applied Tribology

Air Transport Management

Hydraulics and Pneumatics

Metrology and Instrumentation

Fundamentals and Applications of Nanotechnology

Vibration Analysis

off Road Vehicles

Industrial Engineering and Operations Research

Robotics

Smart Materials and Structures

Theory of Plasticity and Fracture Mechanics

New Total Quality Management

Basic Electrical Engineering

Basic Electronics Engineering

Partial Differential Equations and Transform Theorems

Computer Aided Engineering Drawing

Differential and Difference Equations

Elements of Civil Engineering and Engineering Mechanics

Elements of Mechanical Engineering

Engineering Chemistry

Engineering Mathematics

Advance Engineering Mathematics

Engineering Physics

Environmental Science and Engineering

Multivariable Calculus and Differential Equations

Probability and Statistics

Algorithms and Data Structures

Applications of Differential and Difference Equations

Sanfoundry is looking for Interns who are passionate about their field of study and like core subjects in Automobile Engineering. Every intern contributes to Sanfoundry’s Global learning project during their internship and is Mentored and Guided by our Founder and CTO. If you are interested to contribute and apply, here’s full detail of Sanfoundry’s Internship Program.

Sanfoundry Global Education & Learning Series – Automobile Engineering Internships!

Manish Bhojasia, a technology veteran with 20+ years @ Cisco & Wipro, is Founder and CTO at Sanfoundry. He is Linux Kernel Developer & SAN Architect and is passionate about competency developments in these areas. He lives in Bangalore and delivers focused training sessions to IT professionals in Linux Kernel, Linux Debugging, Linux Device Drivers, Linux Networking, Linux Storage, Advanced C Programming, SAN Storage Technologies, SCSI Internals & Storage Protocols such as iSCSI & Fiber Channel. Stay connected with him @
LinkedIn | Facebook | Twitter

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Count pairs of non-overlapping palindromic sub-strings of the given string

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#include <bits/stdc++.h>

using namespace std;

#define N 100

void pre_process(bool dp[N][N], string s)

{

int n = s.size();

for (int i = 0; i < n; i++) {

for (int j = 0; j < n; j++)

dp[i][j] = false;

}

for (int j = 1; j <= n; j++) {

for (int i = 0; i <= n - j; i++) {

if (j <= 2) {

if (s[i] == s[i + j - 1])

dp[i][i + j - 1] = true;

}

else if (s[i] == s[i + j - 1])

dp[i][i + j - 1] = dp[i + 1][i + j - 2];

}

int countPairs(string s)

{

bool dp[N][N];

pre_process(dp, s);

int n = s.length();

int left[n];

memset(left, 0, sizeof left);

int right[n];

memset(right, 0, sizeof right);

left[0] = 1;

for (int i = 1; i < n; i++) {

for (int j = 0; j <= i; j++) {

if (dp[j][i] == 1)

left[i]++;

}

right[n - 1] = 1;

for (int i = n - 2; i >= 0; i--) {

right[i] = right[i + 1];

for (int j = n - 1; j >= i; j--) {

if (dp[i][j] == 1)

right[i]++;

}

int ans = 0;

for (int i = 0; i < n - 1; i++)

ans += left[i] * right[i + 1];

return ans;

}

int main()

{

string s = "abacaba";

cout << countPairs(s);

return 0;

}

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Co-ordinate Compression Multiple Choice Questions and Answers (MCQs)

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This set of Data Structures & Algorithms Multiple Choice Questions & Answers (MCQs) focuses on “Co-ordinate Compression”.

1. What is co-ordinate compression?
a) process of reassigning co-ordinates to remove gaps
b) inserting gaps in a co-ordinate system
c) removing redundant co-ordinates
d) adding extra gaps
View Answer

Answer: a
Explanation: Co-ordinate compression is the process of reassigning co-ordinates in order to remove gaps. This helps in improving efficiency of a solution.

2. What is the need for co-ordinate compression?
a) for improving time complexity
b) for improving space complexity
c) for improving both time and space complexity
d) for making code simpler
View Answer

Answer: c
Explanation:Co-ordinate compression is the process of reassigning co-ordinates in order to remove gaps. This helps in improving both time and space complexity of a solution.

3. What is the output for the following code?

#include <bits/stdc++.h> 
using namespace std;  
void convert(int a[], int n) 
{ 	
	vector <pair<int, int> > vec;	
	for (int i = 0; i < n; i++) 
		vec.push_back(make_pair(a[i], i)); 
	sort(vec.begin(), vec.end()); 	
	for (int i=0; i<n; i++) 
		a[vec[i].second] = i; 
} 
void printArr(int a[], int n) 
{ 
	for (int i=0; i<n; i++) 
		cout << a[i] << " "; 
} 
int main() 
{ 
	int arr[] = {10,8,2,5,7}; 
	int n = sizeof(arr)/sizeof(arr[0]);  
	convert(arr , n); 
   	printArr(arr, n); 
	return 0; 
}

a) 4 3 0 1 2
b) 1 2 3 4 5
c) 5 4 1 2 3
d) 0 1 2 3 4
View Answer

Answer: a
Explanation: The given code converts the elements of the input array. They are replaced with their respective position number in the sorted array.

4. What will be the time complexity of given code?

#include <bits/stdc++.h> 
using namespace std;  
void convert(int a[], int n) 
{ 	
	vector <pair<int, int> > vec; 	
	for (int i = 0; i < n; i++) 
		vec.push_back(make_pair(a[i], i)); 	
	sort(vec.begin(), vec.end()); 	
	for (int i=0; i<n; i++) 
		a[vec[i].second] = i; 
} 
void printArr(int a[], int n) 
{ 
	for (int i=0; i<n; i++) 
		cout << a[i] << " "; 
} 
int main() 
{ 
	int arr[] = {10,8,2,5,7}; 
	int n = sizeof(arr)/sizeof(arr[0]); 	
	convert(arr , n); 
   	printArr(arr, n); 
	return 0; 
}

a) O(n)
b) O(n log n)
c) O(n²)
d) O(log n)
View Answer

Answer: b
Explanation: The time complexity of the given code will be governed by the time complexity of the sorting algorithm used. As this code uses in built sorting of C++ so it will take O(n log n) time.

5. What is the auxiliary space complexity of the given code?

#include <bits/stdc++.h> 
using namespace std;  
void convert(int a[], int n) 
{ 	
	vector <pair<int, int> > vec; 	
	for (int i = 0; i < n; i++) 
		vec.push_back(make_pair(a[i], i)); 	
	sort(vec.begin(), vec.end()); 
	for (int i=0; i<n; i++) 
		a[vec[i].second] = i; 
} 
void printArr(int a[], int n) 
{ 
	for (int i=0; i<n; i++) 
		cout << a[i] << " "; 
} 
int main() 
{ 
	int arr[] = {10,8,2,5,7}; 
	int n = sizeof(arr)/sizeof(arr[0]);  
	convert(arr , n); 
   	printArr(arr, n); 
	return 0; 
}

a) O(1)
b) O(n)
c) O(log n)
d) O(n log n)
View Answer

Answer: b
Explanation: The given code uses an auxiliary space of O(n). It is used by a vector which pairs each element of the array with their respective index number of the original array.

6. What will be the output of the following code?

#include <bits/stdc++.h> 
using namespace std; 
void convert(int arr[], int n) 
{ 
	int temp[n]; 
	memcpy(temp, arr, n*sizeof(int)); 
	sort(temp, temp + n); 	
        unordered_map<int, int> map; 	
	int sort_index = 0; 
	for (int i = 0; i < n; i++) 
		map[temp[i]] = sort_index++; 	
	for (int i = 0; i < n; i++) 
		arr[i] = map[arr[i]]; 
} 
void printArr(int arr[], int n) 
{ 
	for (int i=0; i<n; i++) 
		cout << arr[i] << " "; 
} 
int main() 
{ 
	int arr[] = {3,5,2,4}; 
	int n = sizeof(arr)/sizeof(arr[0]); 
	convert(arr , n); 	
	printArr(arr, n); 
	return 0; 
}

a) 0 2 3 4
b) 1 3 0 2
c) 2 4 1 3
d) 1 2 3 4
View Answer

Answer: b
Explanation: The given code converts the elements of input array. They are replaced with their respective position number in the sorted array.

7. What is the time complexity of the following code?

#include <bits/stdc++.h> 
using namespace std; 
void convert(int arr[], int n) 
{ 	
	int temp[n]; 
	memcpy(temp, arr, n*sizeof(int)); 
	sort(temp, temp + n); 	
        unordered_map<int, int> map; 	
	int sort_index = 0; 
	for (int i = 0; i < n; i++) 
		map[temp[i]] = sort_index++; 	
	for (int i = 0; i < n; i++) 
		arr[i] = map[arr[i]]; 
} 
void printArr(int arr[], int n) 
{ 
	for (int i=0; i<n; i++) 
		cout << arr[i] << " "; 
} 
int main() 
{ 
	int arr[] = {10, 20, 15, 12, 11, 50}; 
	int n = sizeof(arr)/sizeof(arr[0]); 
	convert(arr , n); 	
	printArr(arr, n); 
	return 0; 
}

a) O(n)
b) O(1)
c) O(n log n)
d) O(n²)
View Answer

Answer: c
Explanation: The time complexity of the given code will be governed by the time complexity of the sorting algorithm used. As this code uses inbuilt sorting of C++ so it will take O(n log n) time.

8. What will be the auxiliary space complexity of the following code?
a) O(n)
b) O(1)
c) O(log n)
d) O(n log n)
View Answer

Answer: a
Explanation: The given code uses an auxiliary space of O(n). It is used by a vector which pairs each element of the array with their respective index number of the original array.

9. Co-ordinate compression reduces the number of squares in a graph.
a) true
b) false
View Answer

Answer: a
Explanation: The idea behind co-ordinate compression is to reduce the number of squares in a graph by converting them into rectangles of viable size. This reduces the time complexity of traversal.

10. Co-ordinate compression can only be applied in a co-ordinate system problem
a) true
b) false
View Answer

Answer: b
Explanation: Co-ordinate compression technique can be applied where such optimization is suitable. It does not require to co-ordinate system problem only.

Sanfoundry Global Education & Learning Series – Data Structures & Algorithms.

To practice all areas of Data Structures & Algorithms, here is complete set of 1000+ Multiple Choice Questions and Answers.

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18. Class bindings

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Welcome to yet another binding discussion. Today, I will be schooling you on class bindings. In other words, we will be playing around with HTML element’s class attribute by binding data to it using Vue. Why? Because we might be required to often manipulate the class list of an element. Just like any other data binding, we do the same song and dance here as well, i.e. the mighty “v-bind” or its shortcut “: “can be used to handle this scenario. Expressions can be evaluated to strings, objects or arrays.

Sounds confusing? No worries! Let’s take one step at a time with the backing of some examples. You will see yourself sprinting in no time!

Initial code:

index.html

<!DOCTYPE html>
<html>
  <head>
    <title>Hello Vue!</title>
    <!-- including Vue with development version CDN -->
    <script src="https://cdn.jsdelivr.net/npm/vue/dist/vue.js"></script>
  </head>
  <body>
    <div id="app">
      <h2>Welcome</h2>
      <div> 
        <!-- OUR CODE GOES HERE -->
      </div>
    </div>
    <!-- including index.js file -->
    <script src="index.js"></script>
    <link rel="stylesheet" href="style.css"></link>
  </body>
</html>

index.js

new Vue({
  el: "#app",
  data: {}
});

style.css

<!-- LET US WRITE SOME CODE AS WE GO -->

Object Syntax

Let’s say we have a class named “success” and its styles are defined in style.css stylesheet as follows,

.success {
  color: green;
  font-weight: bold;
  font-size: 70px;
}

With Vue, it is very easy to dynamically toggle this class by passing it as an object to v-bind:class.

index.html (snippet)

<div id="app">
  <h2>Welcome</h2>
  <div> 
  <!-- class bindings: Objects -->
  <div :class="{ success : successFlag }">
    Hello!!
  </div>
    <p>The value of successFlag is {{ successFlag }}</p>
  </div>
</div>

The shortcut : is used for the directive v-bind here.

index.js

new Vue({
  el: "#app",
  data: {
    successFlag : true
  }
});

Very simple! We have a div tag which displays, “Hello!!”. Since the data property, successFlag value is true, the class success is included in the div tag and its styles are applied to the text within the tag.

class binding with objects

The result can be clearly seen in the browser. Chrome DevTools pane shows how the ‘success’ class is present in the HTML code.

In case we set the value of successFlag to false, then this is how the output will look.

index.js (snippet)

data: {
  successFlag : false
}

Toggle class

Works like a charm!

WHAT IF we have the plain class attribute and on top of it, we would like to toggle the presence of another class with the help of v-bind:class? In other words, can we have class and v-bind:class at the same time on an element? Is it possible?

YESSSS!! With Vue, almost everything is a yes!

The best part is, we can achieve this in two ways.

Method 1: Inline object

index.html (snippet)

<div> 
  <!-- class bindings: Objects -->
  <div class="underline" :class="{ success : successFlag, bcg : bcgFlag }">
    Hello!!
  </div>
  <p>The value of successFlag is {{ successFlag }}</p>
</div >

index.js (snippet)

data: {
  successFlag: false,
  bcgFlag: true
}

style.css (snippet)

.underline {
  text-decoration-color: red;
  text-decoration-line: underline;
}
.bcg {
  background-color: aqua;
  padding: 5px;
  width: fit-content;
}

The plain class attribute has the class, underline which will always be rendered.

It is also possible to have more fields in the object passed to v-bind:class. To understand this, let us toggle two classes based on the truthiness of its values. In the above code, two classes success and bcg are passed as an object to v-bind:class. successFlag and bcgFlag are set to false and true respectively. So, the class list that is rendered becomes, “underline bcg” and only those styles are applied.

Method 2: Bound object not inline

index.html (snippet)

<div class="underline" :class="classObject">
  Hello!!
</div>

index.js (snippet)

data: {
  classObject: { 
    success : false, 
    bcg : true 
  }
}

The object to be bound to v-bind:class is specified in vue instance’s data.

binding multiple objects

Array Syntax

Instead of using objects, classes can be passed in the form of an array to v-bind:class like so,

index.html (snippet)

<!-- class bindings: Arrays -->
<div class="underline" :class="[successClass, bcgClass]">
  Array Bindings.
</div>

index.js (snippet)

data: {
  successFlag: false,
  bcgFlag: true,
  successClass: 'success',
  bcgClass: 'bcg'
}

For simplicity, I am using the same classes for this example too.

This will render all three classes when inspected in chrome DevTools,

<div class="underline success bcg">

Class binding with arrays

All the code discussed above is available in the GitHub repo

Fire up your favorite IDE and code a few scenarios of your own! If you have any questions, feel free to shoot them in the comments section. Stay tuned as our next post is all about binding inline styles.

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Hike Interview Experience | Set 7(For SDE iOS) | 2+ Years Experience

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I have recently attend Hike Hiring Drive for iOS Developer in Delhi office. Candidates are 0-2, 2-4 and 4+ years of experience . I have got referral from recruiter on LinkedIn .

Round 1: Mobile App – (1:30 hour ) They have provided Flickr Api, task is build iOS App that fetch images using search bar input and display the images . Make sure UI should not blocked, mulitple request handling while user typing, code structure, pagination and readability and image caching is plus if you have implemented .

I have build the app in 50 minutes – Link to see https://github.com/Vasu05/Hike-Messenger-Hiring-iOS-Test

After the completion of app, My App is reveiwed by iOS manager .He was cool and friendly .He asked model structure I’ve followed, there is crash in my app, asked me debug and I did , told him the crash reason . He asked about GCD , asked for code optimization .He asked how to detect if user scrolled bottom of page . He inspect the app thoroughly, he was typing, cancelling, clearing data to see behaivour of app. He was impressed since I’ve build all the major features with code optimization . Then we have discussion on current projects I’m into, what I have built in past .This reveiw round was around for 1:20 hr . He asked me to have a lunch and wait for second round .

Round 2: DS Algo (30 min) –

Find two elements with sum equal to value x in Array.I did it using hashmap .
Is Tree is BST or Not.
LRU Implementation
Discussion on threads handling in iOS

Proper code was required, they have given enough time to think and asked for better approach . I code all three on board with explanation . Interviewer was impressed, asked me wait for another round .

Round 3: This round was supposed to taken by Vice President of company but he was busy with another candidates so another lead come and asked me for another round . He asked two questions

Detect loop in linkedlist and remove the loop.
get merging point of two linkedlist.

Meanwhile I was writing code for second questions on paper, he said he was done with interview and asked me to meet VP for further round .

Round 4 : This round taken by VP of company . He gaves introduction, what he did, what they are focusing on and talk about how they are different from another messaging apps in market . He asked me whether you prefer pen and paper or writing on screen, then he asked me come to board, it was samsung TV where i supposed to write code with pen . He asked –

Infinite stream of words you have to tell k most occuring words . I told him approach with heap and trie, he asked me use another data structure for searching, i gave him ternary tree apporoach still he was expecting better and gave me hint to use map then gave solution using heap and hashmap . He asked me to code .
check wether binary tree is min heap or not . He left the room and asked me think optimise solution . He comes after 30 minutes break in between i’ve written properties of binary tree and heap then write code for checking tree is min heap or not .He was satisfied .
He gave me design question – he asked me build sticker search option while user is typing . I told him about memory caching he asked me what all typing of caching we can used . I told him there is App cache maintain by app, disk memory and network memory . I have made diagram how search query will work step by step . Then we have discussion on multiple characters handling when user typing, I said we can use timer to process new request . Then he asked me main thread should not be blocked while user interacting with screen . I told him to use GCD’s and we have long discussion on this . He was satisfied, it was long round, he was helping me when I’m stuck . Then we discussed about company culture, whether he still code, how he started with Hike messenger . He asked me to wait for another round .
There are many another questions which I’m not remember .

This round goes for 1:40 hour .

After All this i was very exhaused since I have given all question correctly this continous evaluation was starts from 10:30 in morning .

It was around 5:30 when they scheduled another round of interview .

Round 5: Interviewer scanned my resume, aksed me for brief introduction.I explained him my current company projects and college projects. He was senior backened developer . He starts asking me Java questions . I told him I was not able in touch with since 1.5 years still he asked.

write code for 1 writer and n reader problem . I told him we can use locks (mutes and semaphore) and gave him solution .
He asked virtual pointers – I told him I don’t remember .
Stack vs Heap Memory . I gave him explanation with example .
Singleton class, why we are using this . He was not satisfied with explaination .
He asked basic java questions like what public, private, why there is single main function in Java .

After all this recruiter asked me to leave and next day they said last round was not as per expectation . I’m not selected. Tips – prepare your interview like college placement, they can aks anything even if its not related to your hiring post .

Join Free Interview Preparation Course

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Square Root Decomposition Multiple Choice Questions and Answers (MCQs)

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This set of Data Structures & Algorithms Multiple Choice Questions & Answers (MCQs) focuses on “Square Root Decomposition”.

1. What is the purpose of using square root decomposition?
a) to reduce the time complexity of a code
b) to increase the space complexity of a code
c) to reduce the space complexity of a code
d) to reduce the space and time complexity of a code
View Answer

Answer: a
Explanation: Square decomposition is mainly used in competitive programming to optimize code. It reduces the time complexity by a factor of √n.

2. By what factor time complexity is reduced when we apply square root decomposition to a code?
a) n
b) √n
c) n²
d) n^-1/2
View Answer

Answer: b
Explanation: In square root decomposition a given array is decomposed into small parts each of size √n. This reduces the time complexity of the code by a factor of √n.

3. What will be the worst case time complexity of finding the sum of elements in a given range of (l,r) in an array of size n?
a) O(n)
b) O(l+r)
c) O(l-r)
d) O(r-l)
View Answer

Answer: a
Explanation: For a given array of size n we have to traverse all n elements in the worst case. In such a case l=0, r=n-1 so the time complexity will be O(n).

4. What will be the worst case time complexity of finding the sum of elements in a given range of (l,r) in an array of size n when we use square root optimization?
a) O(n)
b) O(l+r)
c) O(√n)
d) O(r-l)
View Answer

Answer: c
Explanation: When we use square root optimization we decompose the given array into √n chunks each of size √n. So after calculating the sum of each chunk individually, we require to iterate only 3*√n times to calculate the sum in the worst case.

5. Total how many iterations are required to find the sum of elements in a given range of (l,r) in an array of size n when we use square root optimization?
a) √n
b) 2*√n
c) 3*√n
d) n*√n
View Answer

Answer: c
Explanation: After calculating the sum of each chunk individually we require to iterate only 3*√n times to calculate the sum in the worst case. It is because two of the √n factors consider the worst case time complexity of summing elements in the first and last block. Whereas the third √n considers the factor of summing the √n chunks.

6. What will be the time complexity of update query operation in an array of size n when we use square root optimization?
a) O(√n)
b) O(n)
c) O(1)
d) O(n²)
View Answer

Answer:c
Explanation: The time complexity of query operation remains the same in both square root optimized code and non optimized code. We simply find the chunk in which the update requires to be performed and then add the new updated value at the desired index.

7. Square root decomposition technique is only applicable when the number of indices in an array is a perfect square.
a) true
b) false
View Answer

Answer: b
Explanation: Square root decomposition technique can be applied to an array with any number of indices. It does not require this number to be a perfect square.

8. What will be the worst case time complexity of code to find sum in given query range (l,r) in an array of size n with q number of such queries?
a) O(n)
b) O(q)
c) O(n*q)
d) O(n+q)
View Answer

Answer: c
Explanation: For finding the result of one query the worst case time complexity will be n. So for q queries the time complexity will be O(q*n). This can be reduced by using square root optimization.

9. What will be the worst case time complexity of code to find sum in given query range (l,r) in an array of size n with q number of such queries when we apply MO’s algorithm?
a) O(n*q)
b) O(n)
c) O((q+n)√n)
d) O(q*√n)
View Answer

Answer: c
Explanation: Mo’s algorithm requires O(q*√n) + O(n*√n) time for processing all the queries. It is better than the naive solution where O(n*q) time is required.

10. Mo’s algorithm can only be used for problems where the query can be calculated from the result of the previous query.
a) true
b) false
View Answer

Answer: a
Explanation: Mo’s algorithm uses the result of the previous query in order to compute the result of the given query. It cannot be implemented where such a scenario is not possible.

11. What will be the time complexity of the code to find a minimum element from an array of size n and uses square root decomposition(exclude pre processing time)?
a) O(√n)
b) O(n)
c) O(1)
d) O(n²)
View Answer

Answer: a
Explanation: For finding the minimum element in a given array of size n using square root decomposition we first divide the array into √n chunks and calculate the result for them individually. So for a given query, the result of middle blocks has to be calculated along with the extreme elements. This takes O(√n) time in the worst case.

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