Genpact Assistant Data Science Manager Interview Questions and Answers

Q1. Design a dwmand planning system
Ans. 
Design a demand planning system for efficient forecasting and inventory management.
• Utilize historical sales data to identify trends and seasonality

• Incorporate external factors like market trends, promotions, and competitor activities

• Implement machine learning algorithms for accurate demand forecasting

• Integrate with inventory management systems for optimized stock levels

• Regularly review and adjust the system based on pe
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Q2. Pick one point from resume and explain
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Implemented machine learning model to predict customer churn for a telecom company
• Developed and trained a machine learning model using Python and scikit-learn

• Utilized historical customer data to identify patterns and factors leading to churn

• Evaluated model performance using metrics such as accuracy, precision, and recall

• Provided actionable insights to the telecom company based on the model's predictions
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Q1. M.s excel , red hat ,
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Q2. Introdution M.s word
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Q3. Linux , git , mavel , nexus.
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Q4. Sonar cube , aws , super putty.
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Q1. What are Large Language Models?
Ans. 
Large Language Models are advanced AI models that can generate human-like text based on input data.
• Large Language Models use deep learning techniques to understand and generate text.

• Examples include GPT-3 (Generative Pre-trained Transformer 3) and BERT (Bidirectional Encoder Representations from Transformers).

• They are trained on vast amounts of text data to improve their language generation capabilities.
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Q2. Do you know about RAGs?
Ans. 
RAGs stands for Red, Amber, Green. It is a project management tool used to visually indicate the status of tasks or projects.
• RAGs is commonly used in project management to quickly communicate the status of tasks or projects.

• Red typically indicates tasks or projects that are behind schedule or at risk.

• Amber signifies tasks or projects that are on track but may require attention.

• Green represents tasks or projects that ar...read more
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Q3. Which is the best clustering algorithm?
Ans. 
There is no one-size-fits-all answer as the best clustering algorithm depends on the specific dataset and goals.
• The best clustering algorithm depends on the dataset characteristics such as size, dimensionality, and noise level.

• K-means is popular for its simplicity and efficiency, but may not perform well on non-linear data.

• DBSCAN is good for clusters of varying shapes and sizes, but may struggle with high-dimensional d...read more
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Good execellnt and well done

Q1. Tell me your self
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Q2. Tell me why cogniznt
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Q1. Always prepare projects u have worked on in STAR format.
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Q2. Tell me about yourself .
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Q3. Explain any 4 projects in STAR format
Ans. 
Developed a recommendation system for an e-commerce website
• Used collaborative filtering to recommend products to users

• Implemented the system using Python and Apache Spark

• Evaluated the system's performance using precision and recall metrics

• Improved the system's performance by incorporating user feedback
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Q1. Difference between evar and prop
Ans. 
eVar is a conversion variable that captures values at the time of conversion, while prop is a traffic variable that captures values at the time of page view.
• eVar captures values at the time of conversion, while prop captures values at the time of page view.

• eVar is used to track conversion events, while prop is used to track traffic events.

• eVar is persistent across visits, while prop is not.

• Example: eVar can capture the...read more
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Q2. What is clustering and classification
Ans. 
Clustering is grouping similar data points together while classification is assigning labels to data points based on their features.
• Clustering is unsupervised learning while classification is supervised learning.

• Clustering algorithms include K-means, hierarchical clustering, and DBSCAN.

• Classification algorithms include decision trees, logistic regression, and support vector machines.

• Clustering is used for customer segm...read more
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Q1. Case study questions related to Data Science
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Q2. Why i want to join
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Q1. What is reason i am leaving previous company
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Q2. Team handling experience
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Q1. Details of interview person and family details and address and personal information and other study performance Ramya mother father brother Borigama inchoda
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Q1. How to do work yours
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Q2. Hiw to do spend time
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