Synechron 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?

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
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. About Resume and basic ML
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Leet code practice solve medium difficulty questions

Q1. Linear regression logistic regression deep learning nlp
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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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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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