Aaum Research and Analytics Interview Questions and Answers for Freshers

The aptitude test was easy.just like usual aptitude questions like quants,verbal,reasoning etc....

Q1. Second round was all about one on one video call interview where they will ask to record a video and send it.(managerial questions). Like, why do you need to join mu sigma,why should we hire you....
Ans. Third round was online hr interview. Where there will ask questions based on your resumes.
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Q1. Basic ML questions based on your project, python basic questions
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Basic python questions on pandas,numpy, and basic SQL questions on windows function like lag

Python coding and ML resume based questions

Q1. Past Projects with the Director
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Q2. Behavioral Round questions Mostly
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Medium level of question are there in this section

Basic level of question are there in this section

Q1. Q. What is joints? Q. What is linear search? Q. What is your hobby?
Ans. 
Joints are connections between bones that allow movement and provide support to the body.
• Joints are found throughout the body, such as the knee, elbow, and shoulder.

• They are made up of bones, cartilage, ligaments, and synovial fluid.

• Joints enable various types of movements, including flexion, extension, rotation, and abduction.

• Different types of joints include hinge joints, ball-and-socket joints, and pivot joints.

• Join...read more
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Q2. What is joints and what is your hobby?
Ans. 
I'm not sure how joints relate to data science, but my hobby is playing guitar.
• Joints can refer to the connection between two bones in the body or the way two things are connected or joined together.

• Playing guitar is a hobby that helps me relax and unwind after a long day of working with data.

• While seemingly unrelated to data science, playing an instrument can actually improve cognitive function and creativity, which c
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There are 10 multiple-choice questions (MCQs) on Python, 20 MCQs on machine learning (ML), and 10 questions on deep learning (DL).

Q1. The technical round was divided in three phases - phase -1 : intro and professional projects They asked about the projects I have contributed in my full-time tenure. Then, asked me to pick any one of them...read more
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15 MCQ, 2 coding round

Q1. What is PCA explain eigen values
Ans. 
PCA is a dimensionality reduction technique that uses eigenvalues to find the principal components of a dataset.
• PCA is used to reduce the dimensionality of a dataset by transforming the data into a new coordinate system.

• Eigenvalues represent the amount of variance captured by each principal component.

• Higher eigenvalues indicate that the corresponding principal component explains more variance in the data.

• Eigenvalues ar...read more
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Q2. How chatbot works really
Ans. 
Chatbots use natural language processing and machine learning to interact with users and provide automated responses.
• Chatbots use natural language processing (NLP) to understand and interpret user input.

• They use machine learning algorithms to learn from past interactions and improve responses.

• Chatbots can be rule-based, where responses are pre-programmed, or AI-based, where they learn and adapt over time.

• Examples inclu...read more
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Q1. Questions on Transformer Architecture and
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Q2. System design tradeoffs and basic principles
Ans. 
System design tradeoffs involve balancing various factors to optimize performance and efficiency.
• Consider scalability, reliability, latency, and cost when designing systems

• Tradeoffs may involve sacrificing one aspect for the benefit of another

• Examples include choosing between consistency and availability in distributed systems
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Q1. Various questions on my projects
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Q2. NLP based questions and metrics calculation and case study
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Q1. Basic HR questions
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Q2. Why Fractal, etc
Ans. 
Fractals are used in data science for analyzing complex and self-similar patterns.
• Fractals are useful for analyzing data with repeating patterns at different scales.

• They are used in image compression, signal processing, and financial market analysis.

• Fractal analysis can help in understanding the underlying structure of data and making predictions.
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