Cognizant GenC Next Interview Questions, Process, and Tips

Round duration - 180 minutes
This Round consisted of 30 MCQs from various topics like OOPS,JAVA,HTML,CSS,PL/SQL, Operating Systems, DBMS.
After MCQs, there were 4 coding questions and 2 SQL questions.
The difficulty of the first 2 questions was easy and that of 3rd and 4rth was medium.
These questions were from various programming topics such as greedy, dynamic programming, graphs etc.
The entire test duration was 3 hours.

Q1. One dynamic Programming question One Greedy algorithm question One based on graphs
Ans. 
Dynamic programming, greedy algorithm, and graph-based questions.
• Dynamic programming: Optimal substructure and overlapping subproblems.

• Greedy algorithm: Make locally optimal choices to achieve global optimal solution.

• Graph-based: BFS, DFS, shortest path, minimum spanning tree, etc.

• Examples: Knapsack problem, Dijkstra's algorithm, Kruskal's algorithm.
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They asked me coding questions and questions about my project. Since most of my projects were based on Machine Learning, they asked a few ML based question as well.

Q1. Write a program to perform Binary search on an array of N numbers.
Ans. 
Program to perform Binary search on an array of N numbers.
• Sort the array in ascending order

• Set low and high variables to the first and last index of the array respectively

• Calculate the mid index as (low+high)/2

• If the element at mid index is equal to the search element, return mid

• If the element at mid index is less than the search element, set low to mid+1

• If the element at mid index is greater than the search element, s...read more
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Q2. Write a program to perform Merge sort on an array of N numbers.
Ans. 
Program to perform Merge sort on an array of N numbers.
• Divide the array into two halves recursively

• Sort the two halves using merge sort

• Merge the two sorted halves

• Repeat until the entire array is sorted

• Time complexity: O(nlogn)
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Q3. Explain all the stages of creating a machine learning model on a given dataset
Ans. 
Creating a machine learning model involves data preparation, model selection, training, evaluation, and deployment.
• Data preparation involves cleaning, transforming, and splitting the data into training and testing sets.

• Model selection involves choosing the appropriate algorithm and hyperparameters for the problem.

• Training the model involves feeding the training data to the algorithm and adjusting the parameters to mini...read more
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Q4. What are the different regularization techniques?
Ans. 
Regularization techniques are used to prevent overfitting in machine learning models.
• L1 regularization (Lasso)

• L2 regularization (Ridge)

• Elastic Net regularization

• Dropout regularization

• Early stopping

• Data augmentation

• Batch normalization
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Q5. What is poison distribution?
Ans. 
Poison distribution refers to the spread of toxic substances through various means.
• Poison distribution can occur through intentional or accidental means.

• Examples include the distribution of poison through food or water, or the release of toxic chemicals into the environment.

• Poison distribution can have serious health consequences and may require immediate medical attention.

• Prevention measures such as proper storage and...read more
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Q6. What is the formula for gradient descent?
Ans. 
Gradient descent is an optimization algorithm used to minimize the cost function of a machine learning model.
• Start with an initial guess for the model parameters

• Calculate the gradient of the cost function with respect to each parameter

• Update the parameters in the opposite direction of the gradient

• Repeat until convergence or a maximum number of iterations is reached
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Q7. Explain any clustering algorithm.
Ans. 
Clustering algorithm groups similar data points together based on their characteristics.
• Clustering is an unsupervised learning technique.

• It can be used for customer segmentation, image segmentation, etc.

• K-means, hierarchical clustering, and DBSCAN are popular clustering algorithms.

• The choice of algorithm depends on the data and the problem at hand.
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Q8. What is PCA?
Ans. 
PCA stands for Principal Component Analysis. It is a statistical technique used for dimensionality reduction.
• PCA is used to identify patterns in data and reduce the number of variables in a dataset.

• It works by transforming the original variables into a new set of variables called principal components.

• These principal components are ordered by the amount of variance they explain in the data.

• PCA is commonly used in fields...read more
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Q9. What is virtual DOM and why ReactJS uses it?
Ans. 
Virtual DOM is a lightweight copy of the actual DOM used for efficient updates.
• Virtual DOM is a concept where a lightweight copy of the actual DOM is created.

• ReactJS uses it to efficiently update the UI without re-rendering the entire page.

• Virtual DOM compares the previous and current states and updates only the necessary changes.

• This results in faster rendering and better performance.

• Example: When a user types in a se...read more
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Q10. What are the bounding methods in React? Difference between Class Component and Functional Component in ReactJS?
Ans. 
Bounding methods in React are used to limit the scope of a component's updates.
• Bounding methods include shouldComponentUpdate, getSnapshotBeforeUpdate, and componentDidUpdate.

• shouldComponentUpdate allows a component to decide if it should update based on changes in props or state.

• getSnapshotBeforeUpdate allows a component to capture information before a change is made to the DOM.

• componentDidUpdate is called after a com...read more
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This was more of a document verification round.

Q1. Tell me about Yourself
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Q2. What are your hobbies?
Ans. 
I enjoy reading, hiking, and playing board games.
• Reading: I love getting lost in a good book, especially mysteries and thrillers.

• Hiking: I enjoy exploring new trails and being out in nature.

• Board games: I like playing strategy games with friends and family, like Settlers of Catan and Ticket to Ride.
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Q3. Tell me about your struggles to win your first hackathon (mentioned in my resume)
Ans. 
I struggled to win my first hackathon due to lack of experience and technical skills.
• I had limited knowledge of coding and programming languages.

• I struggled to come up with a unique and innovative idea.

• I had difficulty working in a team and communicating my ideas effectively.

• I lacked confidence in my abilities and felt intimidated by the competition.

• However, I learned from my mistakes and used the experience to improve...read more
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Q4. What is your favourite technology?
Ans. 
My favorite technology is artificial intelligence.
• AI has the potential to revolutionize various industries

• It can automate repetitive tasks and improve efficiency

• AI can also help in making better decisions by analyzing large amounts of data

• Examples include chatbots, image recognition, and predictive analytics
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