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Big Data Interview Questions | Day 5

Welcome to the 5th Day of Big Data Interview Questions.
You can go through the question and answer for
Day 1
Day 2
Day 3
Day 4

Big Data Interview Questions

In this series, all you need to do is to go through the questions and try to attempt it with whatever knowledge you have.

Before or after you attempt the questions, do go through the complete road map about what you need to study to crack an analytics role. We have covered from 3rd year college student to 8 years of analytics or non-analytic experience. Make sure you understand where to emphasize more(after all by the end of the day you want to crack an interview)

Complete Roadmap for cracking an Analytics Role in the next 30 days

Now, coming back to our questions. Remember, these are the most asked questions in any interview, if you are shy to answer the questions in the comment section, then do make sure to Google the answer and jot it down in your notebook.

Big Data Interview Questions

Following are the topic wise questions


  1. Difference between 2NF and 3NF in Normalizaiton?
  2. There is this Student table with columns – Name, Roll No, Marks, Class.
    Calculate the class for which the average score was the highest?


  1. Import a csv using pandas and then remove all the columns with any null value
  2. Multiple methods or functions to get a gist of the type of data present in the table like number of rows, columns, mean, etc.

Case Study

  1. Flipkart has recently observed that people are falling out of it’s buy funnel i.e. a list of pages following which a customer makes a payment.
    The pages are – Listing page, Description Page, Delivery option page, Payment Page and Redirection page.

    How would you identify the problem faced by the customers

Machine Learning

  1. What is F1 score?
  2. What is ROC and AUC curver?

The Data Monk services

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  5. Mock Interviews/Career Guidance/Mentorship/Resume Making
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The Data Monk e-books

We know that each domain requires a different type of preparation, so we have divided our books in the same way:

1. 2200 Interview Questions to become Full Stack Analytics Professional – 2200 Most Asked Interview Questions
2.Data Scientist and Machine Learning Engineer -> 23 e-books covering all the ML Algorithms Interview Questions
3. 30 Days Analytics Course – Most Asked Interview Questions from 30 crucial topics

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Comments ( 2 )

  1. SQL
    2. with class_results as
    (select class, avg(marks) as avg_marks from student
    group by class order by avg(marks) desc)
    select class, avg_marks from class_results limit 1

    1. import pandas as pd
    file = pd.read_Csv(‘data.csv’)
    file_v2 = file.dropna(axis=1, how=’all’)
    2. describe (to know mean, std, etc. for all columns), info (to know the datatype of the columns), shape (to know number of rows and columns)

    Case Study
    The problem – people are falling out of Flipkart’s buy funnel (different pages mentioned, across the funnel)
    Clarification questions – Have there been any UI/UX or any changes made to any of the pages in recent times? Are customers of all profiles/locations falling out of the funnel, or is it specific to any? What is the horizon of this fall-out (timeframe since this has been happening)
    Approach – We have to calculate drop-off rate, bounce rate, click rates across page elements, error alerts sent to customers, avg. time spent across all these pages, for customers. This data would help us narrow down the page which is problematic. Based on the above tracked metrics, we can identify the problem too. If the error messages are high. then we need to fix some bug in the back-end. If the drop-off rate is high, we have to check our ‘items in the cart’ reminder alert system and customers’ response (one probable solution). If the click rates across elements have dropped, we have to check when the UI was updated and run some A/B tests to solve for the problem.

    Machine Learning
    1. F1 score is used as a metric in classification models. It is the harmonic mean of precision and recall. It ranges from 0 to 1. If it is 1, the model has perfect precision and recall.
    2. ROC is known as receiver-operator characteristic curve is the graph between TPR (True positive rate) and FPR (false positive rate) and AUC is the area under this curve, which indicates the model’s ability to differentiate between the classes.