Here are the top 16 OOP interview questions๐
1. What is the difference between a class and an object?
2. What is the difference between a static and non-static method?
3. What is the purpose of an interface in OOP?
4. Explain the 4 pillars of OOP.
5. What is the difference between a public and private constructor?
6. What is the difference between an abstract class and an interface?
7. What is the difference between a shallow copy and a deep copy?
8. What is the role of the "this" keyword in OOP?
9. What is a virtual function, and how is it implemented in OOP?
10. What is the difference between overloading and overriding a method? 11. What is an Abstract class?
12. Explain different types of constructors.
13. What is Coupling in OOP and why is it helpful?
14. What is a destructor in OOP?
15. What is a static keyword in cpp?
16. What is the difference between encapsulation and data abstraction?
Best DSA RESOURCES: https://topmate.io/coding/886874
All the best ๐๐
1. What is the difference between a class and an object?
2. What is the difference between a static and non-static method?
3. What is the purpose of an interface in OOP?
4. Explain the 4 pillars of OOP.
5. What is the difference between a public and private constructor?
6. What is the difference between an abstract class and an interface?
7. What is the difference between a shallow copy and a deep copy?
8. What is the role of the "this" keyword in OOP?
9. What is a virtual function, and how is it implemented in OOP?
10. What is the difference between overloading and overriding a method? 11. What is an Abstract class?
12. Explain different types of constructors.
13. What is Coupling in OOP and why is it helpful?
14. What is a destructor in OOP?
15. What is a static keyword in cpp?
16. What is the difference between encapsulation and data abstraction?
Best DSA RESOURCES: https://topmate.io/coding/886874
All the best ๐๐
๐6โค5
Beginnerโs Roadmap to Learn Data Structures & Algorithms
1. Foundations: Start with the basics of programming and mathematical concepts to build a strong foundation.
2. Data Structure: Dive into essential data structures like arrays, linked lists, stacks, and queues to organise and store data efficiently.
3. Searching & Sorting: Learn various search and sort techniques to optimise data retrieval and organisation.
4. Trees & Graphs: Understand the concepts of binary trees and graph representation to tackle complex hierarchical data.
5. Recursion: Grasp the principles of recursion and how to implement recursive algorithms for problem-solving.
6. Advanced Data Structures: Explore advanced structures like hashing, heaps, and hash maps to enhance data manipulation.
7. Algorithms: Master algorithms such as greedy, divide and conquer, and dynamic programming to solve intricate problems.
8. Advanced Topics: Delve into backtracking, string algorithms, and bit manipulation for a deeper understanding.
9. Problem Solving: Practice on coding platforms like LeetCode to sharpen your skills and solve real-world algorithmic challenges.
10. Projects & Portfolio: Build real-world projects and showcase your skills on GitHub to create an impressive portfolio.
Best DSA RESOURCES: https://topmate.io/coding/886874
All the best ๐๐
1. Foundations: Start with the basics of programming and mathematical concepts to build a strong foundation.
2. Data Structure: Dive into essential data structures like arrays, linked lists, stacks, and queues to organise and store data efficiently.
3. Searching & Sorting: Learn various search and sort techniques to optimise data retrieval and organisation.
4. Trees & Graphs: Understand the concepts of binary trees and graph representation to tackle complex hierarchical data.
5. Recursion: Grasp the principles of recursion and how to implement recursive algorithms for problem-solving.
6. Advanced Data Structures: Explore advanced structures like hashing, heaps, and hash maps to enhance data manipulation.
7. Algorithms: Master algorithms such as greedy, divide and conquer, and dynamic programming to solve intricate problems.
8. Advanced Topics: Delve into backtracking, string algorithms, and bit manipulation for a deeper understanding.
9. Problem Solving: Practice on coding platforms like LeetCode to sharpen your skills and solve real-world algorithmic challenges.
10. Projects & Portfolio: Build real-world projects and showcase your skills on GitHub to create an impressive portfolio.
Best DSA RESOURCES: https://topmate.io/coding/886874
All the best ๐๐
๐8โค2
25+ essential tools specifically for tech and developer students ๐
https://t.me/AI_Best_Tools/301
https://t.me/AI_Best_Tools/301
Telegram
Best AI Tools | ChatGPT | Bard | Perplexity
25+ essential tools specifically for tech and developer students ๐
1. Canva โ Graphics
2. Notion โ Organize
3. Webflow โ Website
4. Beehiiv โ Newsletter
5. Senja โ Testimonials
6. CopyAI โ Copywriting
7.ChatGPT โ Knowledge
8. Tweetlify โ Tweet schedulingโฆ
1. Canva โ Graphics
2. Notion โ Organize
3. Webflow โ Website
4. Beehiiv โ Newsletter
5. Senja โ Testimonials
6. CopyAI โ Copywriting
7.ChatGPT โ Knowledge
8. Tweetlify โ Tweet schedulingโฆ
How to guess the solution for DSA problems?
Yes, it is possible.
You can predict the solution for a problem by analyzing the constraints.
Curious if you need a greedy approach or a backtracking solution? Trying to decide between an O(n^3) or an O(n log n) approach? Just scroll down the LeetCode question and look at the constraints of the main element.
Wondering if you should use dynamic programming or plain recursion? Should your solution be O(n^2) or O(n)? Simply examine the constraints of the main variable.
Here's a quick guide based on the value of (n):
- If n <= 12 Time complexity can be O(n!).
- If n <= 25 Time complexity can be O(2^n).
- If n <= 100 Time complexity can be O(n^4).
- If n <= 500 Time complexity can be O(n^3).
- If n <= 10 ^ 4 Time complexity can be O(n^2).
- If n <= 10 ^ 6 Time complexity can be O(n log n).
- If n <= 10 ^ 8 Time complexity can be O(n).
- If n > 10 ^ 8 Time complexity can be O(log n) or 0(1).
- If n <= 10 ^ 9 Time complexity can be O(sqrt{n}).
- If n > 10 ^ 9 Time complexity can be O(log n) or 0(1).
Understanding these constraints can help you choose the right algorithm and improve your problem-solving efficiency.
Best DSA RESOURCES: https://topmate.io/coding/886874
All the best ๐๐
Yes, it is possible.
You can predict the solution for a problem by analyzing the constraints.
Curious if you need a greedy approach or a backtracking solution? Trying to decide between an O(n^3) or an O(n log n) approach? Just scroll down the LeetCode question and look at the constraints of the main element.
Wondering if you should use dynamic programming or plain recursion? Should your solution be O(n^2) or O(n)? Simply examine the constraints of the main variable.
Here's a quick guide based on the value of (n):
- If n <= 12 Time complexity can be O(n!).
- If n <= 25 Time complexity can be O(2^n).
- If n <= 100 Time complexity can be O(n^4).
- If n <= 500 Time complexity can be O(n^3).
- If n <= 10 ^ 4 Time complexity can be O(n^2).
- If n <= 10 ^ 6 Time complexity can be O(n log n).
- If n <= 10 ^ 8 Time complexity can be O(n).
- If n > 10 ^ 8 Time complexity can be O(log n) or 0(1).
- If n <= 10 ^ 9 Time complexity can be O(sqrt{n}).
- If n > 10 ^ 9 Time complexity can be O(log n) or 0(1).
Understanding these constraints can help you choose the right algorithm and improve your problem-solving efficiency.
Best DSA RESOURCES: https://topmate.io/coding/886874
All the best ๐๐
๐19
Three-Tier Architecture
__ _
Three-tier architecture is a software design pattern that separates an application into three layers: presentation, business logic, and data. This separation promotes scalability, maintainability, and flexibility.
1๏ธโฃPresentation Layer (Client Tier)
- Role: Manages the user interface.
- Components: Web browsers, mobile apps.
- Technologies: HTML, CSS, JavaScript.
2๏ธโฃBusiness Logic Layer (Application Tier)
- Role: Processes business logic and rules.
- Components: Application servers.
- Technologies: Java, .NET, Python.
3๏ธโฃData Layer (Data Tier)
- Role: Manages data storage and retrieval.
- Components: Database servers.
- Technologies: SQL, NoSQL databases.
__ _
Three-tier architecture is a software design pattern that separates an application into three layers: presentation, business logic, and data. This separation promotes scalability, maintainability, and flexibility.
1๏ธโฃPresentation Layer (Client Tier)
- Role: Manages the user interface.
- Components: Web browsers, mobile apps.
- Technologies: HTML, CSS, JavaScript.
2๏ธโฃBusiness Logic Layer (Application Tier)
- Role: Processes business logic and rules.
- Components: Application servers.
- Technologies: Java, .NET, Python.
3๏ธโฃData Layer (Data Tier)
- Role: Manages data storage and retrieval.
- Components: Database servers.
- Technologies: SQL, NoSQL databases.
๐9โค2
DSA INTERVIEW QUESTIONS AND ANSWERS
1. What is the difference between file structure and storage structure?
The difference lies in the memory area accessed. Storage structure refers to the data structure in the memory of the computer system,
whereas file structure represents the storage structure in the auxiliary memory.
2. Are linked lists considered linear or non-linear Data Structures?
Linked lists are considered both linear and non-linear data structures depending upon the application they are used for. When used for
access strategies, it is considered as a linear data-structure. When used for data storage, it is considered a non-linear data structure.
3. How do you reference all of the elements in a one-dimension array?
All of the elements in a one-dimension array can be referenced using an indexed loop as the array subscript so that the counter runs
from 0 to the array size minus one.
4. What are dynamic Data Structures? Name a few.
They are collections of data in memory that expand and contract to grow or shrink in size as a program runs. This enables the programmer
to control exactly how much memory is to be utilized.Examples are the dynamic array, linked list, stack, queue, and heap.
5. What is a Dequeue?
It is a double-ended queue, or a data structure, where the elements can be inserted or deleted at both ends (FRONT and REAR).
6. What operations can be performed on queues?
enqueue() adds an element to the end of the queue
dequeue() removes an element from the front of the queue
init() is used for initializing the queue
isEmpty tests for whether or not the queue is empty
The front is used to get the value of the first data item but does not remove it
The rear is used to get the last item from a queue.
7. What is the merge sort? How does it work?
Merge sort is a divide-and-conquer algorithm for sorting the data. It works by merging and sorting adjacent data to create bigger sorted
lists, which are then merged recursively to form even bigger sorted lists until you have one single sorted list.
8.How does the Selection sort work?
Selection sort works by repeatedly picking the smallest number in ascending order from the list and placing it at the beginning. This process is repeated moving toward the end of the list or sorted subarray.
Scan all items and find the smallest. Switch over the position as the first item. Repeat the selection sort on the remaining N-1 items. We always iterate forward (i from 0 to N-1) and swap with the smallest element (always i).
Time complexity: best case O(n2); worst O(n2)
Space complexity: worst O(1)
9. What are the applications of graph Data Structure?
Transport grids where stations are represented as vertices and routes as the edges of the graph
Utility graphs of power or water, where vertices are connection points and edge the wires or pipes connecting them
Social network graphs to determine the flow of information and hotspots (edges and vertices)
Neural networks where vertices represent neurons and edge the synapses between them
10. What is an AVL tree?
An AVL (Adelson, Velskii, and Landi) tree is a height balancing binary search tree in which the difference of heights of the left
and right subtrees of any node is less than or equal to one. This controls the height of the binary search tree by not letting
it get skewed. This is used when working with a large data set, with continual pruning through insertion and deletion of data.
11. Differentiate NULL and VOID ?
Null is a value, whereas Void is a data type identifier
Null indicates an empty value for a variable, whereas void indicates pointers that have no initial size
Null means it never existed; Void means it existed but is not in effect
Credits: https://t.me/free4unow_backup
All the best ๐๐
1. What is the difference between file structure and storage structure?
The difference lies in the memory area accessed. Storage structure refers to the data structure in the memory of the computer system,
whereas file structure represents the storage structure in the auxiliary memory.
2. Are linked lists considered linear or non-linear Data Structures?
Linked lists are considered both linear and non-linear data structures depending upon the application they are used for. When used for
access strategies, it is considered as a linear data-structure. When used for data storage, it is considered a non-linear data structure.
3. How do you reference all of the elements in a one-dimension array?
All of the elements in a one-dimension array can be referenced using an indexed loop as the array subscript so that the counter runs
from 0 to the array size minus one.
4. What are dynamic Data Structures? Name a few.
They are collections of data in memory that expand and contract to grow or shrink in size as a program runs. This enables the programmer
to control exactly how much memory is to be utilized.Examples are the dynamic array, linked list, stack, queue, and heap.
5. What is a Dequeue?
It is a double-ended queue, or a data structure, where the elements can be inserted or deleted at both ends (FRONT and REAR).
6. What operations can be performed on queues?
enqueue() adds an element to the end of the queue
dequeue() removes an element from the front of the queue
init() is used for initializing the queue
isEmpty tests for whether or not the queue is empty
The front is used to get the value of the first data item but does not remove it
The rear is used to get the last item from a queue.
7. What is the merge sort? How does it work?
Merge sort is a divide-and-conquer algorithm for sorting the data. It works by merging and sorting adjacent data to create bigger sorted
lists, which are then merged recursively to form even bigger sorted lists until you have one single sorted list.
8.How does the Selection sort work?
Selection sort works by repeatedly picking the smallest number in ascending order from the list and placing it at the beginning. This process is repeated moving toward the end of the list or sorted subarray.
Scan all items and find the smallest. Switch over the position as the first item. Repeat the selection sort on the remaining N-1 items. We always iterate forward (i from 0 to N-1) and swap with the smallest element (always i).
Time complexity: best case O(n2); worst O(n2)
Space complexity: worst O(1)
9. What are the applications of graph Data Structure?
Transport grids where stations are represented as vertices and routes as the edges of the graph
Utility graphs of power or water, where vertices are connection points and edge the wires or pipes connecting them
Social network graphs to determine the flow of information and hotspots (edges and vertices)
Neural networks where vertices represent neurons and edge the synapses between them
10. What is an AVL tree?
An AVL (Adelson, Velskii, and Landi) tree is a height balancing binary search tree in which the difference of heights of the left
and right subtrees of any node is less than or equal to one. This controls the height of the binary search tree by not letting
it get skewed. This is used when working with a large data set, with continual pruning through insertion and deletion of data.
11. Differentiate NULL and VOID ?
Null is a value, whereas Void is a data type identifier
Null indicates an empty value for a variable, whereas void indicates pointers that have no initial size
Null means it never existed; Void means it existed but is not in effect
Credits: https://t.me/free4unow_backup
All the best ๐๐
โค6๐6
Preparing for a Java developer interview can be a bit overwhelming,
but breaking it down by difficulty and experience level can make it more manageable.
Whether you're a fresher or an experienced developer, here's a guide to help you focus your preparation and walk into your interview with confidence.
๐๐ผ๐ฟ ๐๐น๐น ๐๐ฒ๐๐ฒ๐น๐ (๐๐ป๐ฐ๐น๐๐ฑ๐ถ๐ป๐ด ๐๐ฟ๐ฒ๐๐ต๐ฒ๐ฟ๐)
โค Topic 1: Project Flow and Architecture (Medium)
- These questions are designed to gauge your understanding of project development, teamwork, and problem-solving. Be ready to discuss a project you've worked on, including the tech stack used, the challenges you faced, and how you overcame them.
๐๐ผ๐ฟ ๐๐ฒ๐๐ฒ๐น๐ผ๐ฝ๐ฒ๐ฟ๐ ๐๐ถ๐๐ต ๐๐ผ๐ฟ๐ฒ ๐๐ฎ๐๐ฎ ๐ฆ๐ธ๐ถ๐น๐น๐ (๐ญ-๐ฏ ๐ฌ๐ฒ๐ฎ๐ฟ๐ ๐๐ ๐ฝ๐ฒ๐ฟ๐ถ๐ฒ๐ป๐ฐ๐ฒ)
โค Topic 2: Core Java (Medium to Hard)
- Fundamental Java concepts. You'll likely face questions on strings, object-oriented programming (OOP), collections, exception handling, and multithreading.
๐๐ผ๐ฟ ๐๐ ๐ฝ๐ฒ๐ฟ๐ถ๐ฒ๐ป๐ฐ๐ฒ๐ฑ ๐๐ฒ๐๐ฒ๐น๐ผ๐ฝ๐ฒ๐ฟ๐ (๐ฏ+ ๐ฌ๐ฒ๐ฎ๐ฟ๐ ๐๐ ๐ฝ๐ฒ๐ฟ๐ถ๐ฒ๐ป๐ฐ๐ฒ)
โค Topic 3: Java 8/11/17 Features (Hard)
- This is where the interview gets more challenging. You'll asked advanced features introduced in recent Java versions, such as lambda expressions, functional interfaces, the Stream API, and modules.
โค Topic 4: Spring Framework, Spring Boot, Microservices, and REST API (Hard)
- Expect questions on popular frameworks and backend development architectures. Be prepared to explain concepts like dependency injection, Spring MVC, and microservices.
๐๐ผ๐ฟ ๐๐ฒ๐๐ฒ๐น๐ผ๐ฝ๐ฒ๐ฟ๐ ๐๐ถ๐๐ต ๐๐ฎ๐๐ฎ๐ฏ๐ฎ๐๐ฒ ๐๐ ๐ฝ๐ฒ๐ฟ๐ถ๐ฒ๐ป๐ฐ๐ฒ
โค Topic 5: Hibernate/Spring Data JPA/Database (Hard)
- This section focuses on data persistence with JPA and working with relational (SQL) or NoSQL databases. Be ready to discuss JPA repositories, entity relationships, and complex querying techniques.
๐๐ผ๐ฟ ๐๐ฒ๐๐ฒ๐น๐ผ๐ฝ๐ฒ๐ฟ๐ ๐๐ถ๐๐ต ๐๐ฑ๐ฑ๐ถ๐๐ถ๐ผ๐ป๐ฎ๐น ๐ฆ๐ธ๐ถ๐น๐น๐
โค Topic 6: Coding (Medium to Hard)
- You'll likely encounter coding challenges related to data structures and algorithms (DSA), as well as using the Java Stream API.
โค Topic 7: DevOps Questions on Deployment Tools (Advanced)
- These questions are often posed by managers or leads, especially if you're applying for a role that involves DevOps. Be prepared to discuss deployment tools like Jenkins, Kubernetes, and cloud platforms.
โค Topic 8: Best Practices (Medium)
- Interviewers may ask about design patterns like Singletons, Factories, or Observers to see how well you write clean, reusable code.
All the best ๐๐
but breaking it down by difficulty and experience level can make it more manageable.
Whether you're a fresher or an experienced developer, here's a guide to help you focus your preparation and walk into your interview with confidence.
๐๐ผ๐ฟ ๐๐น๐น ๐๐ฒ๐๐ฒ๐น๐ (๐๐ป๐ฐ๐น๐๐ฑ๐ถ๐ป๐ด ๐๐ฟ๐ฒ๐๐ต๐ฒ๐ฟ๐)
โค Topic 1: Project Flow and Architecture (Medium)
- These questions are designed to gauge your understanding of project development, teamwork, and problem-solving. Be ready to discuss a project you've worked on, including the tech stack used, the challenges you faced, and how you overcame them.
๐๐ผ๐ฟ ๐๐ฒ๐๐ฒ๐น๐ผ๐ฝ๐ฒ๐ฟ๐ ๐๐ถ๐๐ต ๐๐ผ๐ฟ๐ฒ ๐๐ฎ๐๐ฎ ๐ฆ๐ธ๐ถ๐น๐น๐ (๐ญ-๐ฏ ๐ฌ๐ฒ๐ฎ๐ฟ๐ ๐๐ ๐ฝ๐ฒ๐ฟ๐ถ๐ฒ๐ป๐ฐ๐ฒ)
โค Topic 2: Core Java (Medium to Hard)
- Fundamental Java concepts. You'll likely face questions on strings, object-oriented programming (OOP), collections, exception handling, and multithreading.
๐๐ผ๐ฟ ๐๐ ๐ฝ๐ฒ๐ฟ๐ถ๐ฒ๐ป๐ฐ๐ฒ๐ฑ ๐๐ฒ๐๐ฒ๐น๐ผ๐ฝ๐ฒ๐ฟ๐ (๐ฏ+ ๐ฌ๐ฒ๐ฎ๐ฟ๐ ๐๐ ๐ฝ๐ฒ๐ฟ๐ถ๐ฒ๐ป๐ฐ๐ฒ)
โค Topic 3: Java 8/11/17 Features (Hard)
- This is where the interview gets more challenging. You'll asked advanced features introduced in recent Java versions, such as lambda expressions, functional interfaces, the Stream API, and modules.
โค Topic 4: Spring Framework, Spring Boot, Microservices, and REST API (Hard)
- Expect questions on popular frameworks and backend development architectures. Be prepared to explain concepts like dependency injection, Spring MVC, and microservices.
๐๐ผ๐ฟ ๐๐ฒ๐๐ฒ๐น๐ผ๐ฝ๐ฒ๐ฟ๐ ๐๐ถ๐๐ต ๐๐ฎ๐๐ฎ๐ฏ๐ฎ๐๐ฒ ๐๐ ๐ฝ๐ฒ๐ฟ๐ถ๐ฒ๐ป๐ฐ๐ฒ
โค Topic 5: Hibernate/Spring Data JPA/Database (Hard)
- This section focuses on data persistence with JPA and working with relational (SQL) or NoSQL databases. Be ready to discuss JPA repositories, entity relationships, and complex querying techniques.
๐๐ผ๐ฟ ๐๐ฒ๐๐ฒ๐น๐ผ๐ฝ๐ฒ๐ฟ๐ ๐๐ถ๐๐ต ๐๐ฑ๐ฑ๐ถ๐๐ถ๐ผ๐ป๐ฎ๐น ๐ฆ๐ธ๐ถ๐น๐น๐
โค Topic 6: Coding (Medium to Hard)
- You'll likely encounter coding challenges related to data structures and algorithms (DSA), as well as using the Java Stream API.
โค Topic 7: DevOps Questions on Deployment Tools (Advanced)
- These questions are often posed by managers or leads, especially if you're applying for a role that involves DevOps. Be prepared to discuss deployment tools like Jenkins, Kubernetes, and cloud platforms.
โค Topic 8: Best Practices (Medium)
- Interviewers may ask about design patterns like Singletons, Factories, or Observers to see how well you write clean, reusable code.
All the best ๐๐
โค8๐2
5 Sites to Prepare for Tech Interviews:
1. Leetcode
๐ leetcode.com
2. Interviewing .io
๐ interviewing.io
3. Coding Interview University
๐ https://github.com/jwasham/coding-interview-university
4. JavaScript Algorithms
๐ https://github.com/trekhleb/javascript-algorithms
5. JavaScript Questions
๐ https://github.com/lydiahallie/javascript-questions
โก๏ธ Give Reactions ๐ค
1. Leetcode
๐ leetcode.com
2. Interviewing .io
๐ interviewing.io
3. Coding Interview University
๐ https://github.com/jwasham/coding-interview-university
4. JavaScript Algorithms
๐ https://github.com/trekhleb/javascript-algorithms
5. JavaScript Questions
๐ https://github.com/lydiahallie/javascript-questions
โก๏ธ Give Reactions ๐ค
๐11โค8๐1
International students are having a tough time securing a full time job in Tech.
Reasons
1. Market has limited opportunities for entry level junior data/software engineers.
2. People having 0-3 years of workex are competing against job seekers having 8+ years of seasoned experience.
3. Lot of companies have their operations in off-shore, as companies grow the headcount of offshore>>onshore.
Solution :
Build up relevant skillsets, latch on to the newer technologies that will bring you at par with experienced candidates.
Build apps/ai models/data pipelines/dashboards that tracks live data.
People who have secured job has everything to lose and their routine is restricted and have limitied time to be dedicated to learning. You on the other hand have nothing to lose and everything to gain.
Request for that coffee chat.
Take that certification exam.
Attend that quarterly event.
Success is sweeter when its delayed.
Reasons
1. Market has limited opportunities for entry level junior data/software engineers.
2. People having 0-3 years of workex are competing against job seekers having 8+ years of seasoned experience.
3. Lot of companies have their operations in off-shore, as companies grow the headcount of offshore>>onshore.
Solution :
Build up relevant skillsets, latch on to the newer technologies that will bring you at par with experienced candidates.
Build apps/ai models/data pipelines/dashboards that tracks live data.
People who have secured job has everything to lose and their routine is restricted and have limitied time to be dedicated to learning. You on the other hand have nothing to lose and everything to gain.
Request for that coffee chat.
Take that certification exam.
Attend that quarterly event.
Success is sweeter when its delayed.
๐17๐ฅฐ2
Leetcode is a tool to learn
Neetcode is a tool to learn
CodeChef is a tool to learn
Codeforces is a tool to learn
HackerRank is a tool to learn
GeeksForGeeks is a tool to learn
It doesn't matter:
- which platform you are using
- or how many problems you solve
All that matters is how strong you grasp concepts and adapt to problems.
Don't chase the status of 500 or 1000 problems solved.
Chase proper learning & training your mind for problem-solving.
Best DSA RESOURCES: https://topmate.io/coding/886874
All the best ๐๐
Neetcode is a tool to learn
CodeChef is a tool to learn
Codeforces is a tool to learn
HackerRank is a tool to learn
GeeksForGeeks is a tool to learn
It doesn't matter:
- which platform you are using
- or how many problems you solve
All that matters is how strong you grasp concepts and adapt to problems.
Don't chase the status of 500 or 1000 problems solved.
Chase proper learning & training your mind for problem-solving.
Best DSA RESOURCES: https://topmate.io/coding/886874
All the best ๐๐
๐27
โญ TECH MAHINDRA Interview Exp โญ
1) Self Intro.
2) Project.
3) Qns on Project.
4) Which Programming Language
you r Familiar with It.
5) What is Primarykey, Foreignkey.
6) Views in SQL.
7) Syntax of View.
8) Rank.
9) External Table.
10) Method Overloading.
11) Method Overriding.
12) Inheritance.
13) Explain Types of Inheritance
14) Why Multiple Inheritance is not
Supported.
15) Is Multiple Inheritance.
Supported injava and wha is it.
16) Relocation.
17) Night Shifts.
18) Why you Choose TechM.
19) About TechM.
1) Self Intro.
2) Project.
3) Qns on Project.
4) Which Programming Language
you r Familiar with It.
5) What is Primarykey, Foreignkey.
6) Views in SQL.
7) Syntax of View.
8) Rank.
9) External Table.
10) Method Overloading.
11) Method Overriding.
12) Inheritance.
13) Explain Types of Inheritance
14) Why Multiple Inheritance is not
Supported.
15) Is Multiple Inheritance.
Supported injava and wha is it.
16) Relocation.
17) Night Shifts.
18) Why you Choose TechM.
19) About TechM.
โค10๐5