FirstCry Data Scientist Interview Questions and Answers

Q1. Transformer models.
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Related to Mathematical intuition in Deep learning.

Q1. Basic Recommendation System Questions
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Q2. A B Testing Questions
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Q1. Tell me about your projects?
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Q2. How do you approach the project if you are using logistic regression model?
Ans. 
Approach involves data preprocessing, model training, evaluation, and interpretation.
• Perform data preprocessing such as handling missing values, encoding categorical variables, and scaling features.

• Split the data into training and testing sets.

• Train the logistic regression model on the training data.

• Evaluate the model using metrics like accuracy, precision, recall, and F1 score.

• Interpret the model coefficients to under...read more
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Q1. What are you future goals?
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Q2. What would you do if your interested field doesnt have any work in the company?
Ans. 
I would seek opportunities to apply my skills in related fields within the company.
• Explore other departments or teams within the company that may have projects related to my field of interest

• Offer to collaborate with colleagues in different departments to bring a new perspective to their projects

• Seek out professional development opportunities to expand my skills and knowledge in related areas
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Q1. Explain bagging vs boosting
Ans. 
Bagging and boosting are ensemble learning techniques used to improve the performance of machine learning models.
• Bagging (Bootstrap Aggregating) involves training multiple models independently on different subsets of the training data and then combining their predictions through averaging or voting.

• Boosting involves training multiple models sequentially, where each subsequent model corrects the errors made by the previ...read more
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How to judge if the comment is actually describing a product

Q1. Normal Data Science questions
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Q1. Salary discussion and HR related queries
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Business Related case study, signed NDA

Q1. Count number of Parameters in BERT
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Q2. 3 sum array problem
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Q1. Came for campus. Asked questions around basics of ML namely CV, L1, L2 loss & their assumptions, Boosting and Bagging
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Q1. Gave a case study on solving a NLP problem
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