Tip 1 : Practice as many questions of Data Structures and Algorithms as you can. You can set a daily goal. Like 2 questions per day but be consistent. It will build an intution to problem solving and also you will remember the different concepts and approaches. You will start recognising the pattern of the logic in the questions. Solve questions from web platforms such as Leetcode, GeeksforGeeks, Hackerrank, etc. Also, study the theory from Youtube. If you are not able to solve a question after trying for say 30 mins, then look for the solution on Google or Youtube. But, dont just give up in 5 10 mins. At least try to solve the question on your own in 30 mins first.
Tip 2 : Build lots of projects. Understand the application of the concepts learned in real-life use cases. For example, if you have learnt web development use that knowledge to create a portfolio website or an application. If you have learnt machine learning, use that knowledge to predict some datasets. Basically create stuff and also, share it in public on your LinkedIn and other social professional platforms so that the recruiters can see your skills.
Tip 3 : Craft your resume according to the role which you are applying for. Don't just stick a generic resume when applying for different types of roles. Be honest and acccurate with the information.
Tip 4 : Know about the company for which you are applying. What are their products ? What is the work culture ? Which technologies the company is using ? Their case studies. Also, be prepared for HR interview questions. People prepare a lot for technical interview questions but they forget that HR round is equally important.
Tip 1 : Have projects on resume. Also, add clickable URL in the pdf so that the recruiter can directly see the project.
Tip 2 : Add your professional profiles such as GitHub, LinkedIn.
Tip 3 : Be honest and accurate with the information.
Tip 4 : Curate resume according to the role for which you are applying. Don't stick generic resume template everywhere.
It was an online take from the home test which was camera proctored. No negative marking was there.It consisted of 6 sections, namely : Verbal Ability, Reasoning Ability, Numerical Ability, Programming Logic, Hands On (Coding 1) and Hands On (Coding 2). The first three sections were easy. You can effortlessly clear it if you prepare from CAT study material as it's level is much lower than CAT exam. Programming Logic section was basically an MCQ on coding fundamentals. There were one word answer type questions on OOPS concepts and mostly theoretical questions were there with few code output based questions. After that there were two hands on coding questions in which there is a problem statement and we had to write a program for the same. The two rounds were of 15 and 30 mins respectively.



For the given binary tree [1, 2, 3, -1, -1, 4, 5, -1, -1, -1, -1]
1
/ \
2 3
/ \
4 5
Output: 1 3 2 4 5
Step 1 : An ideal approach to follow for ZigZag Level Order Traversal is the Breadth First Approach as followed in the Level Order Traversal. Add the root node, which is the first visited node, to a Queue whose each element takes a Tree Node data structure as the input.
Step 2 : Iterate until the contents of the queue is empty, resetting the ‘level’ list at each level to a new empty list. For each level, poll the visited element from the Queue. Maintain a boolean flag variable that toggles between false and true for left to right and right to left traversal. For left to right traversal, flag is false, in which case simply add the polled node’s value to the ‘level’ list, used for tracking the list of values in each particular level. For right to left traversal, flag is true, in which case add the polled node’s value at the 0th index of the ‘level’ list.
Step 3 : Once a visited node’s value has been added to level list, explore it’s neighbors, i.e. its left and right children, by adding them on to the queue, if they exist.
Step 4 : Once an entire level of nodes have been visited, add the level list to the final result list. Toggle the flag’s boolean value to achieve zig zag level order traversing. Continue to iterate the aforementioned steps until the queue is completely empty.



A Sudoku solution must satisfy all the following conditions-
1. Each of the digits 1-9 must occur exactly once in each row.
2. Each of the digits 1-9 must occur exactly once in each column.
3. Each of the digits 1-9 must occur exactly once in each of the 9, 3x3 sub-grids of the grid.
You can also assume that there will be only one sudoku solution for the given matrix.
I was able to write the logic for checking if digits 1-9 occur in each row and column. But, I was not able to think logic for the 3x3 sub-boxes of the grid. I could only write the code partially and it didn't submit successfully.
The video call interview was scheduled on Google meet. The panelists included the HR and two technical recruiters. The first question that they asked me was to tell a little about myself. I told them about my background and my interest in coding and Machine learning. Then the technical recruiter asked few questions related to the machine learning projects mentioned in the resume. Then, they went on to ask questions from OOPS, Data Structures and Algorithms. I answered them all. Afterwards, the HR asked me some questions.
I answered all the questions. As for the last question, I told the HR that given the opportunity, I would definitely switch to machine learning projects in TCS.
With this, the interview ended. Few months later, I received the offer letter with the location as Gurgaon(Delhi-NCR).
1. What is abstract class?
2. Explain Inheritance.
1. Tell me your 3 strengths and weaknesses.
2. Have you ever faced pressure/difficulties while working on a project with a deadline? How did you handle it? Explain.
3. Why do you want to join TCS? What do you know about TCS?
4. You have an interest in machine learning which seems evident from your projects. But the role we are hiring for is Software Development. Will you be willing to work in this domain? Also, do you have plans to switch into machine learning project opportunities within the company in future?



1. What is the difference between classification and regression problem ?
2. Explain neural networks in detail.
3. Give examples of supervised and unsupervised learning.
4. How do we minimise loss?
5. Explain convolutional neural networks in detail?
6. How do you decide which machine learning algorithm or model works the best?
7. What are performance metrics?

Here's your problem of the day
Solving this problem will increase your chance to get selected in this company
To make an AI less repetitive in a long paragraph, you should increase: