Infogain Data Science Intern Interview Questions and Answers

There are 20 Aptitude Question (time to solve these 20 question is 30 minutes). Then 10 MCQ Questions on Computer Science Fundamentals (time 10 minutes).

There are 5 question based on DSA 3 question of 20 marks and 2 questions of 50 marks. You need to any two question of 20 marks questions and one of 50 marks question. Total time you get to solve these question is 60 minutes.

Q1. Get Second highest element from an array (duplicates elements are allowed). Required T.C-->O(N) Single traversal. S.C--->O(1)
Ans. 
Get second highest element from an array of strings with O(N) time complexity and O(1) space complexity.
• Initialize two variables to store the highest and second highest elements.

• Traverse the array and update the variables accordingly.

• Return the second highest element.

• Handle edge cases like empty array or array with only one element.
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Q2. Sort nearly sortes array.
Ans. 
Sort nearly sorted array using min heap
• Create a min heap of size k+1

• Insert first k+1 elements into min heap

• For remaining elements, extract min and insert new element

• Extract all remaining elements from min heap

• Time complexity: O(nlogk)

• Example: ['apple', 'banana', 'cherry', 'date', 'elderberry']
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Q3. Coffiecent of x^7 in equation ? y=(x^101-1)(x^100+1)(x^99-1)...........................................(X^0+1)
Ans. 
Coffiecent of x^7 in a given equation
• Use the binomial theorem to expand the equation

• Identify the term with x^7

• The coefficient of x^7 is the coefficient of that term
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Q4. Some more probabilty and statistic questions exactly didn't remember.
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Q1. Questions around the previous work
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Q2. Questions around how re-rank work, and image segmentation,
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Q3. Questions with podman and docker
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AWS and Python, basic Machine learning questions

Python, project description, AWS, in build package

Basic Machine learning data science

Q1. Questions were asked related to ML modelling process
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Q2. Basic python coding
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Q1. What is Transformers and how they work
Ans. 
Transformers are models used in natural language processing tasks, known for their ability to handle long-range dependencies.
• Transformers use self-attention mechanism to weigh the importance of different words in a sentence.

• They consist of encoder and decoder layers, with each layer containing multi-head attention and feed-forward neural network.

• Examples of transformer models include BERT, GPT-3, and TransformerXL.
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Q2. How to train a model with imbalance data
Ans. 
Use techniques like oversampling, undersampling, SMOTE, or ensemble methods to train a model with imbalanced data.
• Use oversampling to increase the number of minority class samples.

• Use undersampling to decrease the number of majority class samples.

• Use Synthetic Minority Over-sampling Technique (SMOTE) to generate synthetic samples for the minority class.

• Utilize ensemble methods like Random Forest or Gradient Boosting to
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Write a code for Factorial series

Q1. Deep learning related
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Q2. Maximum GenAI Based question
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Test+2 interviews(technical and statistics)

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