Infocepts Technologies Data Analyst Intern Interview Questions and Answers

Q1. Optimization techniques in spark
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Q1. Tell me about your project
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Q1. After bragging about their company for 25 minutes, they proceed to give you a 15% hike. All the negative reviews about this company is true
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Q1. Basic ML questions based on your project, python basic questions
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Basic python questions on pandas,numpy, and basic SQL questions on windows function like lag

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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1.Describe your project.
2.Explain machine learning concept.

Presentation based question on case study

Q1. Tell something which you haven't put in your resume
Ans. 
I have experience in organizing and leading community service projects
• Organized a charity event to raise funds for a local animal shelter

• Led a team of volunteers in cleaning up a park in the neighborhood

• Coordinated a food drive for a homeless shelter
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Aptitude, coding on python NLP

Python Data Frames, String list manipulation

Q1. Common discussion
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Q2. Salary location etc
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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
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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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Test+2 interviews(technical and statistics)

Basic guestimates and case studies