GoComet Data Science Intern Interview Questions and Answers

Quant, Verbal, DI, Reasoning etc

Domain Based MCQ and 1 SQL Query + 1 Programming question

Q1. 1. Introduction 2. Project Explaination 3. DSA Questions: a) Strings b) Trees c) Graphs 4. What is Stream Processing?
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Q1. 1.Ask about yourself 2.From which city you come from? 3. SWOT Analysis
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Q1. Give a brief about yourself.
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Q2. Salary Expectation and earliest joining date
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Q1. Brief about a project worked in the company.
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Q2. What is Data Leakage?
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Data leakage occurs when information from outside the training dataset is used to create a model, leading to unrealistic performance.
• Occurs when information that would not be available in a real-world scenario is used in the model training process

• Can result in overly optimistic performance metrics for the model

• Examples include using future data, target leakage, and data preprocessing errors
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Q3. What is Encoder Decoder? What is a Transformer model and explain its architecture?
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Encoder Decoder is a neural network architecture used for sequence-to-sequence tasks. Transformer model is a type of neural network architecture that relies entirely on self-attention mechanisms.
• Encoder Decoder is commonly used in machine translation tasks where the input sequence is encoded into a fixed-length vector representation by the encoder and then decoded into the target sequence by the decoder.

• Transformer mod...read more
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Q4. Name some Deep learning models?
Ans. 
Deep learning models include CNN, RNN, LSTM, GAN, and Transformer.
• Convolutional Neural Networks (CNN) - used for image recognition tasks

• Recurrent Neural Networks (RNN) - used for sequential data like time series

• Long Short-Term Memory (LSTM) - a type of RNN with memory cells

• Generative Adversarial Networks (GAN) - used for generating new data samples

• Transformer - used for natural language processing tasks
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Q5. What is Regularization in machine learning?
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Regularization is a technique used in machine learning to prevent overfitting by adding a penalty term to the model's loss function.
• Regularization helps to reduce the complexity of the model by penalizing large coefficients.

• It adds a penalty term to the loss function, which discourages the model from fitting the training data too closely.

• Common types of regularization include L1 (Lasso) and L2 (Ridge) regularization.

• Re...read more
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Q6. What is Model Quantization?
Ans. 
Model quantization is the process of reducing the precision of the weights and activations of a neural network model to improve efficiency.
• Reduces memory usage and speeds up inference by using fewer bits to represent numbers

• Can be applied to both weights and activations in a neural network model

• Examples include converting 32-bit floating point numbers to 8-bit integers
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1. No one has joined the interview in the first time
2. One week later HR called and scheduled another interview, I communicated HR saying no panelist joined and no communication also. She simply said this time they well join.
Second time also no one joined.
3. Again after 10days HR called me and scheduled another interview. This time he joined.
4. Tell me about my self - I have introduced my self for 10 min
No introduction from his end and he didn’t turn on video
5. Directly he asked me to share screen entire window and asked me to fit a classification model by loading insurance day (mailed by them)

What I don’t understand is do we need to by-hart the entire code or what ? In this GPT age do we need to remember the complete syntax. I told him the steps what to do but he want me to code only.

Q1. Machine Learning
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Q1. Machine Learning
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Approach check for multiple case studies

Python, SQL questions were in the initial round of hiring process.

Q1. ML, DL & knowledge about Cloud were main focus of the interview.
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Q2. Questions around your resume
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Q1. Why do we hire you?
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Q2. What contributions should you make?
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Q1. Intro about yourself
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Q2. About the project
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Q1. How do you choose an ML algorithm basis the data given
Ans. 
ML algorithm selection is based on data characteristics, problem type, and desired outcomes.
• Understand the problem type (classification, regression, clustering, etc.)

• Consider the size and quality of the data

• Evaluate the complexity of the model and interpretability requirements

• Choose algorithms based on their strengths and weaknesses for the specific task

• Experiment with multiple algorithms and compare their performance

• F...read more
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Q1. How do u optimise a ML model How good are you in coding with Python. Rate yourself
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To optimize a ML model, one can tune hyperparameters, feature engineering, cross-validation, ensemble methods, and regularization techniques.
• Tune hyperparameters using techniques like grid search or random search

• Perform feature engineering to create new features or select relevant features

• Utilize cross-validation to evaluate model performance and prevent overfitting

• Explore ensemble methods like bagging and boosting to ...read more
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