Renault Nissan Technology & Business Centre India Data Science Intern Interview Questions and Answers

Q1. Mechanical engineering questions
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Q2. Some questions related to power bi and Excel
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Aptitude, tech, tech

Q1. C progrsmming questions are importnanr
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Q2. C programming questions are importanr
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There were only theoretical and aptitude based mcq questions.

Q1. Mostly focus on problem solving and puzzles.
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Q1. About projects, and some technical as well.
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Q1. Standard HR round related questions.
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Q1. Motivation come to this company
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Q2. Would like to be a contributor in automotive industry
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Q3. What experience in school
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Q4. How you understand data-driven bussiness
Ans. 
Data-driven business involves using data to make informed decisions and drive strategies.
• Using data to analyze trends and patterns in customer behavior

• Utilizing data to optimize marketing campaigns and target specific demographics

• Implementing data-driven decision-making processes to improve operational efficiency

• Leveraging data to forecast sales and inventory needs

• Measuring key performance indicators (KPIs) to track bu
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Q5. What your strength and weakness
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Q1. 1. tell me about yourself 2. Why Tata Motors? 3. what are your expertise? 4. which field do you prefer in electrical?
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Speed distance, Age, percentage, logical and verbal

EV Growth and benefits

Q1. Self introduction
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Q2. Core, power electronics, circuits
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45 mins 15 min mcq data science and 30 mins 1 dsa problem

Q1. Python - All subsets of a list.
Ans. 
Generate all possible subsets of a given list in Python.
• Use itertools.combinations to generate all possible combinations of the list elements.

• Convert the combinations to lists and store them in a new list to get all subsets.
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Q2. Sql query - Customers who have ordered all products from all categories.
Ans. 
Use a SQL query to find customers who have ordered all products from all categories.
• Join the Customers, Orders, and Products tables

• Group by customer and count the distinct products ordered

• Filter for customers who have ordered the total number of products available in each category
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Q3. Importance of feature engineering.
Ans. 
Feature engineering is crucial in data science as it involves selecting, transforming, and creating new features to improve model performance.
• Feature engineering helps in improving model accuracy by providing relevant and meaningful input variables.

• It involves techniques like one-hot encoding, scaling, normalization, and creating interaction terms.

• Feature engineering can help in reducing overfitting and improving model...read more
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Q4. Questions on project.
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Q5. What is GAN.Have you worked with it.
Ans. 
GAN stands for Generative Adversarial Network, a type of neural network used for generating new data.
• Consists of two neural networks - generator and discriminator

• Generator creates new data samples while discriminator tries to distinguish between real and generated data

• Used in image generation, text generation, and other creative applications
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Q1. Discussion on project.Demo of project.
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Q2. Analysis sql query - whether class 11 marks or class 12 marks is greater., table given which contains student name,class,11th marks,12th marks.
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Q3. Similar table. Find students who scored more than avg marks of both 11th and 12th.
Ans. 
Find students who scored more than avg marks in both 11th and 12th grades.
• Calculate the average marks for each student in 11th and 12th grades.

• Compare each student's marks with the respective average marks to find those who scored higher in both grades.
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Q4. What is Cost function.
Ans. 
Cost function is a mathematical function that measures the error between predicted values and actual values in a machine learning model.
• Cost function helps in optimizing the parameters of a model to minimize the error.

• Common cost functions include Mean Squared Error (MSE) and Cross Entropy Loss.

• It is used in training machine learning models through techniques like gradient descent.

• The goal is to find the parameters tha
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Q5. What is entropy.
Ans. 
Entropy is a measure of disorder or randomness in a system.
• Entropy is used in information theory to quantify the amount of uncertainty involved in predicting the value of a random variable.

• It is often used in machine learning to measure the impurity or disorder in a dataset.

• In thermodynamics, entropy is a measure of the amount of energy in a physical system that is not available to do work.
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Q6. What is ginni coefficient.
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Gini coefficient is a measure of statistical dispersion intended to represent the income or wealth distribution of a nation's residents.
• Gini coefficient ranges from 0 to 1, where 0 represents perfect equality and 1 represents perfect inequality.

• A Gini coefficient of 0.4 is considered moderate inequality, while 0.6 or higher is considered high inequality.

• It is commonly used in economics to measure income inequality with...read more
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Q7. What will happen if linear regression is used for classification
Ans. 
Using linear regression for classification can lead to inaccurate predictions and unreliable results.
• Linear regression assumes a continuous output, making it unsuitable for discrete classification tasks.

• It may not handle outliers well, leading to incorrect classification boundaries.

• The predicted values may fall outside the 0-1 range for binary classification.

• Logistic regression is a more appropriate choice for classifi
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Q1. Pair sum problem in python.Discussion on space time complexity.
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Q2. Discussion on project.What algorithms used and why.
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It was a Normal Aptitude Test, They mainly focused on Arithmetic and Logical Reasoning

5 questions, 4 were SQL based (easy to advanced - like Trigger, Indexing, Functions etc), 1 was logical (You have 2 variables, swap the variable's data without using 3rd variable, and the last one was Logical Reasoning