Artivatic Data Labs Data Scientist Interview Questions and Answers

Q1. Questions around the previous work
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Q2. Questions around how re-rank work, and image segmentation,
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Q3. Questions with podman and docker
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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
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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
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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. Asked questions related to stats , Senario based. Interviewer was very nice
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Q2. Asked questions on Python Programming and asked for Programs Like How can update List , Asked to Build a Tough Dict , Class etc.
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Q1. Why u want to join?
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Q2. What is your future goal?
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Asked the question about ml and basic python questions

Q1. Basic ML Question, Timesereis and RNN with some questions on Comp Vision
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Q1. Project driven. Some coding
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Q1. Salary Discussion
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Q1. 1. Basics of Python 2. Basics of ML like supervised and unsupervised learning
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Q1. Questions on Python datatypes, class, object, inheritance,
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Q2. Machine learning algorithms
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Machine learning algorithms are used to train models on data to make predictions or decisions.
• Supervised learning algorithms include linear regression, decision trees, and neural networks.

• Unsupervised learning algorithms include clustering and dimensionality reduction.

• Reinforcement learning algorithms involve learning through trial and error.

• Examples of machine learning applications include image recognition, natural l
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Q3. Sql queries and python coding questions
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Q1. Related to project
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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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