Navikenz India Data Scientist Interview Questions and Answers

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
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
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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?
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
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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
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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?
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
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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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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
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Q1. Basic HR questions 1. How early you will be able to join? 2. Are you willing to relocate?
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Q1. Python collections numpy pandas library Sql windows functions Spark
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Q1. 1. difference between list & tuple 2. describe your day-to-day work 3. describe your favourite project
Ans. 
List is mutable, tuple is immutable. Day-to-day work involves data analysis and modeling. Favorite project involved developing a predictive analytics model.
• List can be modified after creation, tuple cannot

• List uses square brackets [], tuple uses parentheses ()

• Day-to-day work includes data cleaning, exploratory data analysis, model building, and communication of results

• Favorite project involved collecting and analyzing ...read more
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Q2. How does your day look like
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Q3. Your favourite project
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Q4. Difference between regression & classification based algorithms
Ans. 
Regression predicts continuous values, while classification predicts discrete values.
• Regression algorithms predict continuous values, such as predicting house prices based on features like size and location.

• Classification algorithms predict discrete values, such as classifying emails as spam or not spam based on content.

• Regression algorithms include linear regression, polynomial regression, and support vector regressio...read more
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Q5. Difference between RNN & CNN
Ans. 
RNN is used for sequential data like time series, while CNN is used for spatial data like images.
• RNN processes sequential data by maintaining memory of past inputs, suitable for time series forecasting.

• CNN is designed for spatial data like images, using filters to extract features and patterns.

• RNN is good for text data analysis, language translation, and speech recognition.

• CNN is commonly used in image recognition, obj
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Q1. Spark optimizations
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Q2. Pyspark quetions
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Check whether a string is a valid parenthesis or not.

Q1. Remove duplicates from array
Ans. 
Remove duplicates from array of strings
• Use a Set data structure to store unique elements

• Convert the array to a Set to remove duplicates

• Convert the Set back to an array if needed
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Q2. Window function
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