eClerx Data Scientist Interview Questions, Process, and Tips

Q1. Machine learning related questions and the theory of its operation
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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. I was asked to introduce myself . I was asked A/B testing
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Q2. ML use case implemented
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Implemented a machine learning model to predict customer churn in a telecom company.
• Collected and cleaned customer data including usage patterns and demographics

• Used classification algorithms like Random Forest and Logistic Regression

• Evaluated model performance using metrics like accuracy, precision, and recall
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Q3. Gen AI use case implemented
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NER training using deep learning

Q1. Describe the approach taken for assignment
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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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Q1. How to do model inference?
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Model inference is the process of using a trained machine learning model to make predictions on new data.
• Load the trained model

• Preprocess the new data in the same way as the training data

• Feed the preprocessed data into the model to make predictions

• Interpret the model's output to make decisions or take actions
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Q2. How to optimize spark query?
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Optimizing Spark queries involves tuning configurations, partitioning data, using appropriate data formats, and caching intermediate results.
• Tune Spark configurations for memory, cores, and parallelism

• Partition data to distribute workload evenly

• Use appropriate data formats like Parquet for efficient storage and retrieval

• Cache intermediate results to avoid recomputation
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Q1. Have you used GEN AI?
Ans. 
No, I have not used GEN AI in my work as a Data Scientist.
• I have not used GEN AI in any of my projects or analyses.

• I am not familiar with GEN AI and its capabilities.

• I have not had the opportunity to work with GEN AI in any capacity.
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Q2. How do you take your solution to production?
Ans. 
I take my solution to production by following a structured process involving testing, deployment, monitoring, and maintenance.
• Develop a robust testing strategy to ensure the solution performs as expected in a production environment

• Use continuous integration and continuous deployment (CI/CD) pipelines to automate the deployment process

• Implement monitoring tools to track the performance of the solution in real-time and a...read more
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