kipi.ai Interview Questions and Answers

Basic aptitude, cloud-related questions and some data warehousing questions

Q1. Identify result of given c++ code
Ans. 
The code prints the sum of elements in an array.
• The code initializes an array of integers with values 1, 2, 3, 4, 5.

• It then calculates the sum of all elements in the array and prints it.
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Q2. DSA questions and basic questions on data engineering domain
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Q1. What was discussed regarding your education and research experiences?
Ans. 
Discussed education and research experiences in detail.
• Discussed my academic background, including degrees obtained and relevant coursework.

• Talked about any research projects I have worked on, including methodologies used and results achieved.

• Highlighted any publications or presentations related to data science or relevant fields.

• Mentioned any internships or work experience in data analysis or research roles.
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Q2. What are the details of your research topics, including aspects such as scalability and the reasoning behind choosing specific models?
Ans. 
My research topics focus on developing scalable machine learning models for predictive analytics in finance.
• I have researched and implemented various machine learning algorithms such as random forests, gradient boosting, and neural networks.

• I have explored techniques for feature engineering and model optimization to improve scalability and performance.

• I have chosen specific models based on their ability to handle large...read more
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Q1. Can you discuss your education and research experience as reflected in your resume?
Ans. 
I have a strong educational background in data science and have conducted research in machine learning and predictive analytics.
• Completed a Master's degree in Data Science from XYZ University

• Conducted research on machine learning algorithms for predictive analytics during my internship at ABC Company

• Published a research paper on the application of deep learning in natural language processing
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Q2. What are your research experiences, and how would you approach the problem in specific use cases?
Ans. 
I have conducted research in machine learning and natural language processing, and I would approach problems by first understanding the data and then applying appropriate algorithms.
• Conducted research in machine learning and natural language processing

• Approach problems by understanding the data first

• Apply appropriate algorithms based on the problem

• Utilize data visualization techniques to gain insights
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Q1. Can you provide details about your education and research experience?
Ans. 
I have a Master's degree in Data Science and have conducted research on machine learning algorithms.
• Master's degree in Data Science

• Research experience in machine learning algorithms
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Q1. Tell me about yourself?
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Q2. Tell me about your recent Project in Detail?
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Q3. What is the Diff between Parameters and Hyper Parameters?
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Q4. Can we use Logistic Regression for Multi Class Classification?
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Q5. What will happen if we will increase the value of K in KNN?
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Q6. If you have 50 GB of training data and you want to train your Neural Network on you Local 2 GB RAM, what will you do?
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Q7. What is imbalanced Data?
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Q1. There were 2 technical round of interview in which I have asked about SQL related Query and concept of data warehousing.
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Q2. Difference between ETL and ELT, give pros and cons of it.
Ans. 
ETL involves extracting data, transforming it, and then loading it into a target system. ELT involves extracting data, loading it into a target system, and then transforming it.
• ETL: Extract, Transform, Load

• ELT: Extract, Load, Transform

• ETL is suitable for scenarios where data needs to be cleansed and transformed before loading into the target system.

• ELT is suitable for scenarios where raw data needs to be quickly loaded...read more
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Q1. Data warehousing questions like type of fact table , SCD, and type of SCD. Difference between Star and snowflake schema. Modern data tools and data migration concepts in cloud.
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