Capgemini Engineering Data Scientist Interview Questions and Answers

They gave a span of 3 days to build an AI-powered webapp

Q1. How would you go about learning a new skill
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Q2. Experience in cloud technologies
Ans. 
I have experience working with cloud technologies such as AWS, Azure, and Google Cloud Platform.
• Experience in setting up and managing virtual machines, storage, and networking in cloud environments

• Knowledge of cloud services like EC2, S3, RDS, and Lambda

• Experience with cloud-based data processing and analytics tools like AWS Glue and Google BigQuery
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Q1. Tell me about yourself
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Q2. Project details and challenges faced in the project
Ans. 
Developed a predictive model for customer churn in a telecom company
• Collected and cleaned customer data from various sources

• Performed exploratory data analysis to identify key factors influencing churn

• Built and fine-tuned machine learning models to predict customer churn

• Challenges included imbalanced data, feature engineering, and model interpretability
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Q1. Tell me about your self?
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Q2. What is maths and stats
Ans. 
Maths and stats refer to the study of mathematical concepts and statistical methods for analyzing data.
• Maths involves the study of numbers, quantities, shapes, and patterns.

• Stats involves collecting, analyzing, interpreting, and presenting data.

• Maths is used to solve equations, calculate probabilities, and model real-world phenomena.

• Stats is used to make informed decisions, draw conclusions, and test hypotheses.

• Both ma...read more
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Confusion matrix what are your job rolls explain me Gradient boosting algorithm?

Asked the question about ml and basic python questions

Q1. 1. Basics of Python 2. Basics of ML like supervised and unsupervised learning
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Bulb and switch puzzle

Rope burning and length question

Q1. Why you want to join optum
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Basic DP, Array Questions

Q1. Resume Walkthrough and Discussion, Medium level coding questions
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Q1. Discussion with Manager
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Q1. Normal HR round
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Q1. Different ML Algorithms
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There are various ML algorithms such as linear regression, decision trees, random forests, SVM, KNN, neural networks, etc.
• Linear regression is used for predicting continuous values

• Decision trees and random forests are used for classification and regression

• SVM is used for classification and regression

• KNN is used for classification and regression

• Neural networks are used for complex problems such as image recognition and
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Q2. Python Libraries
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Q1. About Resume and basic ML
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