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LinkedIn Business Analyst Interview Questions | Day 5

LinkedIn Business Analyst Interview Questions

Name of company – LinkedIn
Designation – Business Analyst
Salary – 12 to 15 LPA (including stocks)
Number of rounds – 4

Complete explanation of

LinkedIn Business Analyst Interview Questions

LinkedIn Business Analyst Interview Questions
LinkedIn Business Analyst Interview Questions
There were in total 4 rounds in LinkedIn for the post of Business Analyst post. The focus was mostly on SQL and case studies.
LinkedIn Business Analyst Interview Questions

There were in total 4 rounds
Round 1 – SQL
Round 2 – Case Study and Python
Round 3 – Statistics and Big data
Round 4 – Hiring Manager Round
Below are some questions that were asked in these technical round

SQL Questions 

For SQL, the basic concepts were checked in the initial rounds, there were a few output based questions asked and a lot of questions were asked based on the current project that you have written in your resume.

Some of the important concepts to check are:-
– Window functions
– Date functions
– JSON functions
– Regular Expression in Presto

  1. Types of join and explain any one of the join?

Ans :- Types of join are Inner join, full outer join, right outer join and left outer join. Inner Join retrieves records that have matching values in both tables involved in the join. This is the widely used join for queries.

FROM Table_A
JOIN Table_B;
FROM Table_A

  1. What is meant by aggregator function? List the Aggregators

Ans :-  An aggregate function performs a calculation on a set of values, and returns a single value. Except for COUNT(*) , aggregate functions ignore null values.

Aggregators are :- MAX, MIN, COUNT, AVG and SUM

SELECT Max(Salary) FROM Emp;  #O/P => Maximum Salary

SELECT DISTINCT(Count(Salary)) FROM Emp; #O/P => Count of all unique values from the Salary column except the NULL entries.

  1. How to use conditional statements in SQL?

Ans :- The result for the matching value is returned. If no match is found, the result from the ELSE clause is returned if it exists, otherwise null is returned. Example:

WHEN 1 THEN ‘one’
WHEN 2 THEN ‘two’
ELSE ‘many’

  1. Date and Time in SQL?

Ans:-  date ‘2012-08-08’ + interval ‘2’ day

Result – ‘2012-08-10’

time ‘01:00’ + interval ‘3’ hour

Result – ‘04:00:00:000’

  1. What is Regular Expression in SQL?

Ans :- A Regular Expression is popularly known as RegEx, is a generalized expression that is used to match patterns with various sequences of characters. A RegEx can be a combination of different data types such as integer, special characters, Strings, images, etc.

regexp_extract_all(string, pattern)

Returns the substring(s) matched by the regular expression pattern in string:

SELECT regexp_extract_all(‘1a 2b 14m’, ‘(\d+)); — [‘1’, ‘5’, ‘8’]

Case Study 

How will you increase the revenue of OLA or Uber or any cab service?

There could be multiple reasons for the low conversion of freemium to premium customers:-

1. High prices of premium service

2. Ineffective Marketing

3. Lack of important and useful features

4. Data security issues

5. Was there a data breach or some security issues in the past. If yes, do a pre and post issue analysis to find out the impact of the same.

6. Do a competitor analysis on both cost and features provided

7. Measure the impact of promotional or marketing strategies. Do it for both online and offline mode of promotion.

8. Narrow down on a break-even point to arrive at a price at which the company can compete with other players in the market as well as to keep the cost-feature optimum.


  1. Create a DataFrame in pandas using Dictionary:

# import pandas library
import pandas as pd

# dictionary with list object in values

details = {

    ‘Name’ : [‘Ankit’, ‘Aishwarya’, ‘Shaurya’, ‘Shivangi’],

    ‘Age’ : [23, 21, 22, 21],

    ‘University’ : [‘BHU’, ‘JNU’, ‘DU’, ‘BHU’],


 # creating a Dataframe object 

df = pd.DataFrame(details)


      2.  List the steps to create a 1D array and 2D array:

A one-dimensional array is created as follows:
num = np.array(num)
print(“1d array : “,num)
A two-dimensional array is created as follows:
num2 = np.array(num2)
print(“\n2d array : “,num2)

A 1-D array stands for a one-dimensional array that creates the array in one dimension. Whereas the 2D arrays have a collection of rows and columns.

Big Data 

Why is Hadoop used for Big Data Analytics?

Since data analysis has become one of the key parameters of business, hence, enterprises are dealing with a massive amount of structured, unstructured and semi-structured data. Analyzing unstructured data is quite difficult where Hadoop takes major part with its capabilities of  

  • Storage
  • Processing
  • Data collection

Moreover, Hadoop is open source and runs on commodity hardware. Hence it is a cost-benefit solution for businesses.


How is missing data handled in statistics?

There are many ways to handle missing data in Statistics:

  • Prediction of the missing values
  • Assignment of individual (unique) values
  • Deletion of rows, which have the missing data
  • Mean imputation or median imputation
  • Using random forests, which support the missing values

The Data Monk Interview Books – Don’t Miss

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10 e-book bundle with 1400 interview questions spread across SQL, Python, Statistics, Case Studies, and Machine Learning AlgorithmsIdeal for 0-3 years experienced candidates

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Individual 50+ e-books on separate topics

Important Resources to crack interviews (Mostly Free)

There are a few things that might be very useful for your preparation

The Data Monk Youtube channel – Here you will get only those videos that are asked in interviews with Data Analysts, Data Scientists, Machine Learning Engineers, Business Intelligence Engineers, Analytics managers, etc.

Go through the watchlist which makes you uncomfortable:-

All the list of 200 videos

Complete Python Playlist for Data Science

Company-wise Data Science Interview Questions – Must Watch

All important Machine Learning Algorithm with code in Python

Complete Python Numpy Playlist

Complete Python Pandas Playlist

SQL Complete Playlist

Case Study and Guesstimates Complete Playlist

Complete Playlist of Statistics

Keep Learning !!


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