Virtusa Consulting Services Data Scientist Interview Questions and Answers

Q1. Different ML Algorithms
Ans. 
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
Ans. 
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
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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Q1. How to do model inference?
Ans. 
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?
Ans. 
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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DS related Case study and discussion

Python code example test where they will ask basic python or sql questions.

Bulb and switch puzzle

Rope burning and length question

Q1. Why you want to join optum
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Quant, Reasoning and python based MCQs

Q1. Question on your past work experience. Can go deep down with respect to your prior work experience
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Q2. In what project have you been involved and what were your roles and responsibility
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Q3. Data Science project pipeline ,what components are involved , step by step process
Ans. 
Data science project pipeline involves multiple components and follows a step-by-step process.
• 1. Define the problem statement and objectives of the project.

• 2. Collect and preprocess the data needed for analysis.

• 3. Explore and visualize the data to gain insights.

• 4. Build and train machine learning models to solve the problem.

• 5. Evaluate the models using appropriate metrics.

• 6. Deploy the model into production and monitor...read more
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Q1. Your prior experience and salary expectations
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