Zidio Development Data Science Intern Interview Questions and Answers

Q1. 1. Based on my resume, asked to explain ML projects and questions related to the algorithms used were asked. 2. SQL questions 3. Monty-Hall problem 4. Probability and Permutation & Combination questions 5....read more
Add your answer

Q1. Basic HR questions 1. How early you will be able to join? 2. Are you willing to relocate?
Add your answer

Q1. Give a brief about yourself.
Add your answer
Q2. Salary Expectation and earliest joining date
Add your answer

Q1. Brief about a project worked in the company.
Add your answer
Q2. What is Data Leakage?
Ans. 
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
Answered by AI
Add your answer
Q3. What is Encoder Decoder? What is a Transformer model and explain its architecture?
Ans. 
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
Answered by AI
Add your answer
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
Answered by AI
Add your answer
Q5. What is Regularization in machine learning?
Ans. 
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
Answered by AI
Add your answer
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
Answered by AI
Add your answer

Q1. Machine Learning
Add your answer

Q1. Machine Learning
Add your answer

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.
Add your answer
Q2. Questions around your resume
Add your answer

Q1. Intro about yourself
Add your answer
Q2. About the project
Add your answer

Q1. 1. Based on my resume, asked to explain ML projects and questions related to the algorithms used were asked. 2. SQL questions 3. Monty-Hall problem 4. Probability and Permutation & Combination questions 5....read more
Add your answer

Q1. Basic HR questions 1. How early you will be able to join? 2. Are you willing to relocate?
Add your answer

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
Answered by AI
Add your answer

Q1. How do u optimise a ML model How good are you in coding with Python. Rate yourself
Ans. 
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
Answered by AI
Add your answer