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

Q1. Technical Project Related Questions
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Q1. Technical Project Related Questions
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Q1. Technical Project Related and Basics of Python and Data Science
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Technical and Code Snippet MCQs

Q1. Technical depending on which profile you are interviewing for. Some case studies as well.
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Q1. Situation based question, some technical as well.
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Q1. Behavioral, HR based questions.
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It was campus placement. We were givien the topic "future of AI and ML in automobile industry". The time given was around 15mins. The explained the process in great detail and very clearly.

Q1. CV and basic of ML
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Q2. What if questions. For eg, if you want to launch a new EV in market then what will be your approach What if you are the CEO.. ETC
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Q1. Ethical questions
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Q2. Besic HR questions like why this company
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Q1. Introduction your self
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Q2. What are the language you learned
Ans. 
I have learned multiple programming languages including Python, R, SQL, and Java.
• Python

• R

• SQL

• Java
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Q1. Tell me about yourself
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Q2. Questions from projects which was mentioned on resume
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Q1. Fitment based questions
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Q2. Explain a project
Ans. 
Developed a machine learning model to predict customer churn for a telecom company.
• Collected and cleaned customer data including usage patterns and demographics

• Used classification algorithms like Random Forest and Logistic Regression to build the model

• Evaluated model performance using metrics like accuracy, precision, and recall
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Math, English, reasoning

Q1. Asked me about expected salary
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Q1. What is difference between lstm and rnn
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LSTM is a type of RNN that addresses the vanishing gradient problem by using memory cells.
• RNN stands for Recurrent Neural Network, a type of neural network that processes sequential data.

• LSTM stands for Long Short-Term Memory, a type of RNN that includes memory cells to retain information over long sequences.

• LSTM is designed to overcome the vanishing gradient problem, which occurs when training RNNs on long sequences.

• L...read more
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Q1. What are different evaluation matrix ?
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Evaluation matrices are used to assess the performance of models in data science.
• Confusion matrix: used to evaluate the performance of classification models.

• Precision, recall, and F1 score: measures for binary classification models.

• Mean squared error (MSE): evaluates the performance of regression models.

• R-squared: assesses the goodness of fit for regression models.

• Area under the ROC curve (AUC-ROC): evaluates the perfo...read more
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Q1. Questions related to your life and society to check your presence of mind and awareness of the world.
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Q2. Question from ic engines,about their working and processes involved.
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