L&T Technology Services Lead Data Scientist Interview Questions and Answers

There were verbal, non verbal, reasoning , English and maths questions

Q1. Tell me about your project.
Ans. 
I worked on a project analyzing customer behavior using machine learning algorithms.
• Used Python for data preprocessing and analysis

• Implemented machine learning models such as decision trees and logistic regression

• Performed feature engineering to improve model performance
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Q2. What programming knowledge you have ?
Ans. 
Proficient in Python, R, and SQL with experience in data manipulation, visualization, and machine learning algorithms.
• Proficient in Python for data analysis and machine learning tasks

• Experience with R for statistical analysis and visualization

• Knowledge of SQL for querying databases and extracting data

• Familiarity with libraries such as Pandas, NumPy, Matplotlib, and Scikit-learn
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Q1. Where do you stay ?
Ans. 
I currently stay in an apartment in downtown area.
• I stay in an apartment in downtown area

• My current residence is in a city

• I live close to my workplace
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Q2. Tell me about you
Ans. 
I am a data science enthusiast with a strong background in statistics and machine learning.
• Background in statistics and machine learning

• Passionate about data science

• Experience with data analysis tools like Python and R
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NER training using deep learning

Q1. Describe the approach taken for assignment
Ans. 
I approach assignments by breaking them down into smaller tasks, setting deadlines, and regularly checking progress.
• Break down the assignment into smaller tasks to make it more manageable

• Set deadlines for each task to stay on track

• Regularly check progress to ensure everything is on schedule

• Seek feedback from colleagues or supervisors to improve the quality of work
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Q2. Scenario based questions
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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. What is LLMOps? Explain in details
Ans. 
LLMOps stands for Low Latency Model Operations, a process of deploying and managing machine learning models with minimal delay.
• LLMOps focuses on reducing the latency in deploying and managing machine learning models.

• It involves optimizing the infrastructure and processes to ensure quick and efficient model operations.

• Examples include real-time prediction systems, automated model monitoring, and rapid model updates.

• LLMO...read more
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Q2. How to perform Tunnig in LLMs?
Ans. 
Tuning in LLMs involves adjusting hyperparameters to optimize model performance.
• Perform grid search or random search to find the best hyperparameters

• Use cross-validation to evaluate different hyperparameter combinations

• Consider using automated hyperparameter tuning tools like Optuna or Hyperopt
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On python , SQL , Using pandas

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. Machine learning related questions and the theory of its operation
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Q1. Basic questions in python
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Q2. Questions in Ml
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