Zomato Business Analyst Interview Questions

Zomato Business Analyst Interview Questions
Name of company – Zomato
Designation – Business Analyst
Salary – 15 to 20 LPA (including stocks)
Number of rounds – 4
Zomato Business Analyst Interview Questions

Zomato Business Analyst Interview Questions
Zomato Business Analyst Interview Questions

There were in total 4 rounds in Zomato for the post of Business Analyst post. The focus was mostly on SQL and case studies.
In order to apply for Zomato, you need to have a referral and then you have to upload a minute of video on any topic of your choice. It could be something like ‘ Why do you like Sachin Tendulkar?’
‘Why Black is my favorite color?’, ‘Which is your favorite team?’
You will get a call if your profile is strong, but once you get a call, you need to work on your SQL logic and case studies.

Zomato Business Analyst Interview Questions

There were in total 4 rounds
Round 1 – SQL
Round 2 – The programming language of your choice, mostly Python and SQL
Round 3 – Case Study and SQL
Round 4 – Hiring Manager Round

Below are some questions that were asked in these technical round

SQL Questions

1. What is the use of offset command?
The OFFSET command can contain scalar values or subqueries, basically the OFFSET argument is used to identify the starting point to return rows from a result set. It excludes the first set of records
– Can only be used with ORDER BY clause
-Must be greater than or equal to zero, cannot be (-ve) else returns error

2. What is the order of execution of SQL query?
Order of execution for an SQL query
1) FROM, including JOINs
2) WHERE
3) GROUP BY
4) HAVING
5) WINDOW Functions
6) SELECT
7) DISTINCT
8) UNION
9) ORDER BY
10) LIMIT AND OFFSET

3. Can we use HAVING without any aggregate function?
No,
it’s not necessary for having to use aggregate functions and even without group by having can exist.
Eg: This query works well in PostgreSql
select 1 having 1 = 1;

4. Write a query to get all the student with name length 10, starting with K and ending with z
select name
from student
where length(name)=10 and lower(name) like ‘k%z’

5. We have the following values

10000
10000
20000
30000
30000
30000
What would be the result of row number, rank, and dense rank ?

A) Row_Number() assigns a sequential integer to each row within the partition of a result set.
Ans:
1000 1
1000 2
2000 3
3000 4
3000 5
3000 6

B) Rank() assigns a rank to each row within a partition of a result set.Rows in each partition receive the same ranks if they have the same values. But the ranks will be skipped here.
Ans:
1000 1
1000 1
2000 3
3000 4
3000 4
3000 4

C) Dense_Rank() differs from Rank() as it assigns consecutive ranks and ranks won’t be skipped.
Ans:
1000 1
1000 1
2000 2
3000 3
3000 3
3000 3

Statistics

1. What percentage of value lies between the Mean and one Standard deviation(both positive and negative)
~68%

2. Give the relation of Mean, median, and mode in a positively skewed distribution
Mean > Median > Mode

3. Negatively skewed distribution?
Mode > Median > Mean

4. What is the sum of squared deviation?
The Sum of Squared deviation is a measure of the total variability of a set of scores around a specific number

Case Study

If you have to recommend a product to a customer who has already filled his cart, then what data will you look for? Basically, how will you recommend a product to an e-commerce customer?

There could be multiple things that we can look for
a. If the customer has brought product A, then we should look in the data to find out the product that compliments that product. We can do this by looking into the purchase history of other customers who have brought the product A
b. We can get the cart information of the customer and look for those items which he has removed from the cart. At the check-out, you can again ask him if he wants to buy it
c. We can also look for a better product option for the customer. Suppose the customer is willing to pay $3 for soap, then we can show them ads or recommendations of a $5 soap with a better review
d. We can also look for the previous cart history of the customer to see if he is missing something which he used to buy regularly

Python

1.Why do we use the insert function in pandas ?
As we know whenever we want to add a column to the data frame , it is added to the last by default. But Pandas provides us the option that we can add a column at any position by using Insert Function.

We need to specify the position wherever we want to insert it. Let’s suppose we want to insert the column at 2nd Position.

new_column = np.random.randn(10)
#insert the new column at position 2
df.insert(2, ‘new_column’, new_column)
print(df)

2.How will you get the number of rows and columns of a Dataframe in pandas?
We can use the shape() method to finding the number of rows and columns in a data frame.

import pandas as pd
import numpy as np

raw_data = {‘name’: [‘Willard Morris’, ‘Al Jennings’, ‘Omar Mullins’, ‘Spencer McDaniel’],
‘age’: [20, 19, 22, 21],
‘favorite_color’: [‘blue’, ‘red’, ‘yellow’, “green”],

‘grade’: [88, 92, 95, 70]}
df = pd.DataFrame(raw_data, columns = [‘name’, ‘age’, ‘favorite_color’, ‘grade’])
df
# get the row and column count of the df
df.shape()

The Data Monk Interview Books – Don’t Miss

Now we are also available on our website where you can directly download the PDF of the topic you are interested in. On Amazon, each book costs ~299, on our website we have put it at a 60-80% discount. There are ~4000 solved interview questions prepared for you.

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

23 E-book with ~2000 interview questions spread across AWS, SQL, Python, 10+ ML algorithms, MS Excel, and Case Studies – Complete Package for someone between 0 to 8 years of experience (The above 10 e-book bundle has a completely different set of e-books)

12 E-books for 12 Machine Learning algorithms with 1000+ interview questions – For those candidates who want to include any Machine Learning Algorithm in their resume and to learn/revise the important concepts. These 12 e-books are a part of the 23 e-book package

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 !!

Thanks,

Chapter 4 – 100 Most Asked Numpy Interview Questions

100 Most Asked Numpy Interview Questions
Welcome to the 2200 questions series from The Data Monk, in this series we will cover all the topics in a Question-Answer mode that are required for anyone who wants to make a career in the following field:-

– Data Analysis
– Business Analysis
– Business Intelligence Engineering
– Machine Learning
– Data Science
– Product Analysis
– Data Engineering
– Risk Analysis

These 2200 questions are useful for anyone who is in their 2nd-3rd year of engineering to 8-10 years of experience in the IT industry( be it QA/Development/Support) and are willing to make a career in Analytics.
100 Most Asked Numpy Interview Questions

Why Analytics is a domain for you?

If you want to make a handsome switch with a good package then Analytics is for you because of the following reasons:-

– It is a high-paying job
– It is interesting as you will have a good impact on the growth of the organization
– It involves a lot of things like requirement gathering, building logic, making ETL, pipeline creation, reporting to the CXOs, and so on. So, it is a very impactful role
– It has a HUGE demand in the future as the data will keep on growing and so will your role

How much does an analytics role pay?

The CTC of the role will depend on multiple factors but just to give you a glimpse of it:-

“Anyone from a tier 2-3 college with good knowledge of the material that we are providing will have a fair chance to bag something like 15+ LPA for a fresher. The more you grind the better you get and the CTC grows with experience.”

Now coming back to why you should try The Data Monk for your Analytics journey.

Why The Data Monk?

We are a group of 30+ Analytics Engineers working in various product-based companies like Zomato, Ola, OYO, Google, Rapido, Uber, Ugam, BYJUs, etc. and we observed that people do not have a well-structured way to enhance their knowledge. There are multiple courses here and there, but no one has consolidated what needs to be learned to move to the analytics domain.

Further, there are courses from Large institutes where they charge you something like 2-5 lacks and try to teach you everything from Data structure to SQL to Power BI to ML. You do not have to spend so much on these topics.

We followed a very old-school way, take a topic and solve 100-200 questions on these topics. Learn them, understand them, and revise them. This should be enough for you to crack that domain.

For example, if I am a very beginner in SQL, then I will just try to solve 200 questions starting from the definition to advance level questions. After solving and revising these questions I should have a good amount of knowledge to answer 6 out of 10 questions asked in an interview and going by that calculation I can be a strong candidate in 5-7 out of 10 companies.

See, by the end, you need to convert a job first and then keep on learning in the organization.

Most of the books are on questions like ‘250 questions to crack SQL interview’ and this will cost you around 250 rupees, take the book, understand, and learn it. This small amount can bag you a 15 LPA job 🙂

You can trust us as we have guided more than 1000 people to make a career in Analytics

2200 Analytics Interview Questions

Coming back to the topic, below is the list of 250 SQL questions to Ace any Analytics Interview
Chapter 1 – SQL – 250 SQL questions to Ace any Analytics Interview
Chapter 2 – Python – 200 Most Asked Python Interview Questions
Chapter 3 – Pandas – 100 Most Asked Pandas Interview Questions with solution
Chapter 4 – Numpy – 100 Most Asked Numpy Interview Questions with solution

Most Asked Numpy Interview Questions
Most Asked Numpy Interview Questions

Most Asked Numpy Interview Questions

558. What is NumPy, and what are its main features?

559. How do you create a NumPy array from a Python list?

560. How do you perform element-wise arithmetic operations between two NumPy arrays?

561. How do you calculate the dot product of two NumPy arrays?

562. What is broadcasting in NumPy, and how does it work?

563. What are some common statistical functions available in NumPy, and how do you use them?

564. Define the mean function in numpy and give a simple example

565.  Define the median function in numpy and give a simple example

567.  Define the var function in numpy and give a simple example

568.  Define the min function in numpy and give a simple example

569.  Define the max function in numpy and give a simple example

570. How do you select elements from a NumPy array based on a conditional expression?

571. How do you reshape a NumPy array?

572. Convert a multidimensional array to 1D array

573. How do you perform matrix operations in NumPy, such as matrix multiplication and inversion?

574. What is Matrix Inversion?

575. What is Determinant calculation?

576. How do you save and load NumPy arrays from disk?

577. How do you concatenate two or more NumPy arrays horizontally?

578. How do you concatenate two or more NumPy arrays vertically?

579. How do you concatenate two or more NumPy arrays arbitarily?

580. How do you create a masked array in NumPy, and what is its purpose?

581. What is a shallow copy in a NumPy array?

582. What is a deep copy in NumPy?

583. How do you generate random numbers in NumPy, and what are some common distributions you can sample from?

584. How do you sort a NumPy array in ascending or descending order?

585. How do you perform element-wise logical operations between two NumPy arrays?
586. How do you compute the Fourier transform of a signal using NumPy?
587.How to use NumPy with SciPy?
588. How to use NumPy with matplotlib?

Coding Questions

589. Write a NumPy code snippet to create an array of zeros with shape (3, 4).

590. Write a NumPy code snippet to create an array of ones with shape (2, 5).

591. Write a NumPy code snippet to create an array of evenly spaced values between 0 and 10 with a step size of 2.

592. Write a NumPy code snippet to create a random array with shape (2, 3) and values between 0 and 1.

593. Write a NumPy code snippet to calculate the sum of all elements in a two-dimensional array.

594. Write a NumPy code snippet to calculate the mean of all elements in a one-dimensional array.

595. Write a NumPy code snippet to calculate the standard deviation of all elements in a one-dimensional array.

596. Write a NumPy code snippet to calculate the dot product of two one-dimensional arrays.

597. Write a NumPy code snippet to reshape a one-dimensional array into a two-dimensional array with 3 rows and 2 columns.

598. Write a NumPy code snippet to find the index of the maximum value in a one-dimensional array.

Data Cleaning using Numpy

599. How do you remove missing or null values from a NumPy array?

600. How can you identify and remove outliers in a NumPy array?


601. What are some common techniques for normalizing data in a NumPy array?

602. How do you sort a NumPy array, and what are some of the options for customizing the sort?

603. What are some functions in NumPy that are commonly used for data cleaning?

604. How can you handle duplicate values in a NumPy array?

605. What is the difference between slicing and indexing in NumPy, and how are they used for data cleaning?

606. How can you concatenate two NumPy arrays, and what are some of the considerations when doing so?

607. How can you reshape a NumPy array, and what are some common use cases for doing so in data cleaning?

608. How do you create a NumPy array with specific dimensions and data types?

609. How do you access specific elements in a NumPy array?

610. How can you perform basic arithmetic operations on NumPy arrays?

611. How can you create a mask for a NumPy array based on specific conditions?

612. How can you apply a function to specific elements in a NumPy array?

613. How can you combine two or more NumPy arrays to create a new array?

614. How can you save a NumPy array to a file, and how can you load a saved array back into Python?

615. How can you calculate dot products and matrix multiplication using NumPy?

616. How can you apply linear algebra operations (e.g., inverse, determinant) to a NumPy array?

617. What happens if you try to reshape a NumPy array with the wrong number of elements?

618. What is the difference between NumPy’s broadcasting rules and Python’s broadcasting rules?

619. How can you modify the data type of a NumPy array?

620. How can you perform element-wise comparison between two NumPy arrays with different shapes?

621. What is the difference between NumPy’s views and copies, and how can you determine which one you have?

622. How can you create a custom data type in NumPy, and what are some use cases for doing so?

623. What are some best practices for optimizing performance when working with large NumPy arrays?

624. How can you handle missing or invalid data in a NumPy array?

625. What are some ways to create a NumPy array, and when would you use each one?

626. How can you access and modify individual elements of a NumPy array?

627. What is the difference between slicing and indexing in NumPy, and how can you use them to extract subsets of an array?

628. How can you perform mathematical operations on a NumPy array, and what are some common functions for doing so?

629. What is broadcasting in NumPy, and how can you use it to perform element-wise operations on arrays with different shapes?

630. How can you reshape a NumPy array, and what are some common use cases for doing so?

631. What is a masked array in NumPy, and how can you use it to handle missing data?

632. How can you stack and concatenate NumPy arrays, and what are some use cases for doing so?

633. What are some best practices for optimizing performance when working with large NumPy arrays?

Numpy Advance Interview Questions

634. What are some of the performance benefits of using NumPy over pure Python when working with numerical data?

635. What is a view in NumPy, and how does it differ from a copy?

636. How can you use NumPy to perform linear algebra operations, such as matrix multiplication and solving systems of equations?

637. What are some of the built-in functions in NumPy for generating random numbers, and how can you use them to simulate data?

638. What is the difference between a structured array and a record array in NumPy, and what are some use cases for each?

639. How can you use NumPy to perform Fourier transforms, and what are some applications of Fourier analysis in signal processing and image processing?

640. How can you use NumPy to perform interpolation, and what are some use cases for doing so?

641. What are some of the limitations of NumPy, and how can you work around them?

642. What are some best practices for organizing and structuring code when working with NumPy, especially when dealing with large, complex arrays?

643. Write a NumPy code snippet to create an array of 100 random integers between 0 and 10.

644. Write a NumPy code snippet to calculate the element-wise sum of two arrays of the same shape.

645. Write a NumPy code snippet to compute the inner product of two one-dimensional arrays

646. Write a NumPy code snippet to find the indices of the maximum and minimum values in a two-dimensional array.

647. Write a NumPy code snippet to reshape a one-dimensional array into a two-dimensional array with 4 rows and 5 columns

648. Write a NumPy code snippet to calculate the correlation coefficient between two arrays of the same length.

649. Write a NumPy code snippet to create a diagonal matrix with the elements 1, 2, and 3 on the diagonal.
650. Write a NumPy code snippet to sort a one-dimensional array in ascending order.

651. Write a NumPy code snippet to calculate the element-wise product of two arrays of the same shape.
652. Write a NumPy code snippet to create a mask that selects all elements of an array that are greater than 5.

The Data Monk Product and Services

  1. Youtube Channel covering all the interview-related important topics in SQL, Python, MS Excel, Machine Learning Algorithm, Statistics, and Direct Interview Questions
    Link – The Data Monk Youtube Channel
  2. Website – ~2000 completed solved Interview questions in SQL, Python, ML, and Case Study
    Link – The Data Monk website
  3. E-book shop – We have 70+ e-books available on our website and 3 bundles covering 2000+ solved interview questions
    Link – The Data E-shop Page
  4. Mock Interviews
    Book a slot on Top Mate
  5. Career Guidance/Mentorship
    Book a slot on Top Mate
  6. Resume-making and review
    Book a slot on Top Mate 

The Data Monk e-book Bundle 

1.For Fresher to 7 Years of Experience

2000+ interview questions on 12 ML Algorithm,AWS, PCA, Data Preprocessing, Python, Numpy, Pandas, and 100s of case studies

2. For Fresher to 1-3 Years of Experience

Crack any analytics or data science interview with our 1400+ interview questions which focus on multiple domains i.e. SQL, R, Python, Machine Learning, Statistics, and Visualization
 

3.For 2-5 Years of Experience

1200+ Interview Questions on all the important Machine Learning algorithms (including complete Python code) Ada Boost, CNN, ANN, Forecasting (ARIMA, SARIMA, ARIMAX), Clustering, LSTM, SVM, Linear Regression, Logistic Regression, Sentiment Analysis, NLP, K-M

10 Most asked SQL Interview Questions – 1/5

10 Most asked SQL Interview Questions
What is the most important ingredient in a good Analyst?
The ability to crunch numbers and by far SQL has been the most important weapon of any Analytics Professional. it’s easy to use and understand. But, you can easily be surprised by the difficulty level and variety of questions asked in SQL interviews. I highly recommend you to go through the Daily Quiz Questions as well
10 Most asked SQL Interview Questions

10 Most asked SQL Interview Questions

You can answer the question or can upvote the already-answered questions

The Data Monk Interview Books – Don’t Miss

Now we are also available on our website where you can directly download the PDF of the topic you are interested in. On Amazon, each book costs ~299, on our website we have put it at a 60-80% discount. There are ~4000 solved interview questions prepared for you.

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

23 E-book with ~2000 interview questions spread across AWS, SQL, Python, 10+ ML algorithms, MS Excel, and Case StudiesComplete Package for someone between 0 to 8 years of experience (The above 10 e-book bundle has a completely different set of e-books)

12 E-books for 12 Machine Learning algorithms with 1000+ interview questionsFor those candidates who want to include any Machine Learning Algorithm in their resume and to learn/revise the important concepts. These 12 e-books are a part of the 23 e-book package

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 !!

Thanks,

Myntra Business Analyst Interview Questions – Day 3

Myntra Business Analyst Interview Questions

Company Name – Myntra
Designation – Business Analyst
Salary – 15 to 18 LPA (Depending on current CTC and graduation college)
Number of rounds – 4
Level of questions – Moderate to Hard

Myntra Business Analyst Interview Questions

Myntra Business Analyst Interview Questions
Myntra Business Analyst Interview Questions

Round 1 – 50 MCQs to be solved in 60 minutes
Round 2 – Technical Round with focus on SQL and Python (Pandas and Numpy)
Round 3 – Case Study Round
Round 4 – Hiring Manager with some SQL coding

Round 1 – MCQs

There were 50 questions that was supposed to be solved in one hour. There were 40 MCQs and 10 query writing questions. We can’t disclose the exact questions as it is still used for recruitment but below is the type of questions asked:-
1. Calculating date difference between two dates
2. Extracting string using Regular expression
3. Output of outer join
4. Caluclating cumulative sum on a data set
5. Difference between rank() and dense_rank()
6. Finding error in query (mostly on group by and aggregate functions)
7. Difference between key (primary and foreign key)
8. Output of simple but confusing commands like Select ‘1’+2 or Select Null+’2′

Round 2 – SQL and Python

Following is the link to the questions, most of the questions are solved by the fellow Analytics professionals. Do check it out

Find all the students who either are male or live in Mumbai ( have Mumbai as a part of their address).
Get all the distinct email id from a column in SQL
Join two tables without using any common column
Output based question
Output based question – 2

Python Questions

1. Difference between mutable and immutable data type
2. Missing data treatment using Pandas
3. Join and group by syntax
4. Write a nested lambda function to multiply two numbers.
5. Difference between parameter and argument

Round 3 – Case Study

There were a variety of case studies asked to different candidates, the one asked to me was

Can you choose a strategy for increasing the number of songs listened by the user on an online application? How will you decide the types of playlists to suggest him?

Approach

Mainly I will tackle this problem like any recommendation engine.

Machine learning algorithms in recommender systems are typically classified under two main categories :

1.  Content-based – strategy relies on analyzing factors and demographics that are directly associated with the user or product, such as the age, sex and demographic of the user or a song genre or playlist,

2. Collaborative filtering – Collaborative Filtering takes consumer behavior data and utilizes it to predict future behavior This consumer behavior leaves a trail of data, generated through implicit and explicit feedback, based on the user’s listening history, in tandem with songs enjoyed by users who seem to have a similar history

Round 4 – Hiring Manager Round

Hiring Manager round was mostly around work experience and past projects, but be prepared to have a few questions on SQL (related to your project) and a few basic questions on statistics.

First round is the most important as your score is passed to all the following rounds. A few questions might be asked from the 1st round question(that you might have messed up)

The Data Monk Interview Books – Don’t Miss

Now we are also available on our website where you can directly download the PDF of the topic you are interested in. On Amazon, each book costs ~299, on our website we have put it at a 60-80% discount. There are ~4000 solved interview questions prepared for you.

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

23 E-book with ~2000 interview questions spread across AWS, SQL, Python, 10+ ML algorithms, MS Excel, and Case Studies – Complete Package for someone between 0 to 8 years of experience (The above 10 e-book bundle has a completely different set of e-books)

12 E-books for 12 Machine Learning algorithms with 1000+ interview questions – For those candidates who want to include any Machine Learning Algorithm in their resume and to learn/revise the important concepts. These 12 e-books are a part of the 23 e-book package

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 !!

Thanks,

HDFS and YARN command cheat sheet

Topic – HDFS and YARN command cheat sheet
Let’s look into some of the top HDFS commands that you might require while working in a Big Data infrastructure.
HDFS and YARN command cheat sheetat sheet

HDFS and YARN command cheat sheet
HDFS and YARN command cheat sheet

HDFS and YARN command cheat sheet

  1. hadoop fs -ls
    It lists the files in hadoop home directory
  2. hadoop fs -ls -R/
    It lists the files in a recurrsive order
  3. hadoop fs -ls/
    It lists the files in hadoop root directory
  4. hadoop fs -ls -t -r
    It lists the files in reverse order, sorted by time
  5. hadoop fs -ls -s
    It lists the files in the descending order of size
  6. hadoop fs -ls /user |grep booking
    It searches the files with the name booking
  7. hadoop fs -tail /user/thedatamonk/restaurant.txt
    It will display the last 10 rows of the file restaurant.txt
  8. hadoop fs -rmdir /user/thedatamonk/direct
    It will remove directories
  9. hadoop fs -rm -R /user/thedatamonk/direct
    It will remove empty and non-empty directories as well
  10. hadoop fs -cp <complete file location 1> <complete directory location>
    It will move a file from one location to another directory.
    It copies a file from one location to another
  11. hadoop fs -mv <complete file location 1> <complete folder location>
    It cut and paste file from one location to another directory
  12. hadoop fs -copyFromLocal <Local folder location> <HDFS folder location>
    It copies file from local to HDFS location. Remember, the keyword copyFromLocal is case sensitive.
  13. hadoop fs -put <Local folder location> <HDFS folder location>
    Another command to copy files from local to HDFS location. All the words are keywords.
  14. hadoop fs -copyToLocal <HDFS data location> <Local folder location>
    It copies from HDFS to Local or desktop. Remember, the key word copyToLocal is case sensitive.
  15. hadoop fs -get <HDFS data location> <Local folder location>
    Another command to copy from HDFS to Local or desktop.
  16. hadoop fs -df -h <location>
    The above command gives the free space in the disk.
    -h converts the space into bytes
  17. hadoop fs -du -h <location>
    The above command gets the used space in the disk
  18. hadoop fs -touchz
    To create an empty file on the file system
  19. hadoop fs -cat
    It copies files to stdout

    YARN Commands
  20. yarn node -list
    list nodes in the yarn cluster
  21. yarn node -status <node id>
    It gives status of a node (memory used, free, number of containers, etc) for (first column from command above)
  22. yarn application -list
    It gives a list of Yarn applications and their state
  23. yarn logs -applicationId <app id>
    It dumps the logs for a particular application
  24. hdfs dfsadmin -safemode get
    It finds out if you’re in safe mode
  25. hdfs dfsadmin -report
    It finds out how much disk space is
    used, free, under-replicated, etc.

The Data Monk services

We are well known for our interview books and have 70+ e-book across Amazon and The Data Monk e-shop page . Following are best-seller combo packs and services that we are providing as of now

  1. YouTube channel covering all the interview-related important topics in SQL, Python, MS Excel, Machine Learning Algorithm, Statistics, and Direct Interview Questions
    Link – The Data Monk Youtube Channel
  2. Website – ~2000 completed solved Interview questions in SQL, Python, ML, and Case Study
    Link – The Data Monk website
  3. E-book shop – We have 70+ e-books available on our website and 3 bundles covering 2000+ solved interview questions. Do check it out
    Link – The Data E-shop Page
  4. Instagram Page – It covers only Most asked Questions and concepts (100+ posts). We have 100+ most asked interview topics explained in simple terms
    Link – The Data Monk Instagram page
  5. Mock Interviews/Career Guidance/Mentorship/Resume Making
    Book a slot on Top Mate

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

You can check out all the other e-books on our e-shop page – Do not miss it


For any information related to courses or e-books, please send an email to nitinkamal132@gmail.com

Nykaa Data Analyst Interview Questions | Day 9

Nykaa Data Analyst Interview Questions
Name – Nykaa
Designation – Senior Data Analyst
Location – Gurgaon
Salary – 22 LPA (including 10% variable)
Level of questions – 7/10
Nykaa Data Analyst Interview Questions

Nykaa Data Analyst Interview Questions
Nykaa Data Analyst Interview Questions

For the Senior Data Analyst position there were 4 rounds:

Round 1 – Technical Screening (SQL heavy)
Round 2 – Project, Case Study, and SQL
Round 3 – SQL, Python, and Guesstimate/Case Study
Round 4 – Cultural fit with the Hiring manager

Below are some of the questions and analogous concepts asked in the complete recruitment process, the candidate had some experience in the Natural Language Processing domain, so he was asked a few questions on that front:

  1. What is the use of the NVL function in Oracle?
    NVL function is the most important function to replace a null value with another value.
    Example:
    select NVL(null,’ Amit’) from dual;
    which will give you output as Amit.
  2. What is the result of the following query?
    Select
    case when null=null then ‘Amit’ else ‘Rahul’ end as Case_check
    from Table_Name;


    The null=null is always false. So the Answer to this query is Rahul.
  3. What is a parser?

    When SQL Statement has been written and generated the first step is parsing that SQL Statement. Parsing is nothing but checking the syntaxes of SQL queries. All the syntax of Query is correct or not is checked by SQL Parser.
    There are 2 functions of the parser:
    1. Syntax analysis
    2. Semantic analysis
  4. What is lapply and sapply?

    Lapply applies a function to each element of a list and returns the results as a list Sapply applies a function to each element of a list and returns the result in a vector.
  5. Guesstimate – What is the size of the market for disposable diapers in India?

    1.2 billion people x 60% childbearing age = 0.72 B people
    0.72 people x 1/2 are women = 0.36 B women of childbearing age 0.36 women x 2/3 have children = 0.24 women with children
    0.24 women x 1.5 children each = 0.36 children
    0.36 B children x 1/10 under age 2 = 36 million
  6. Count the total salary department number-wise where more than 2 employees exist.

    SELECT deptno, sum(sal) As totalsal
    FROM emp
    GROUP BY deptno
    HAVING COUNT(empno) > 2
  7. How to retrieve the 3 Minimum salaries ?

    SELECT DISTINCT sal
    FROM emp a
    WHERE 3 >= (SELECT COUNT(DISTINCT sal) FROM emp b WHERE a.sal >= b.sal);
  8. Case Study 1 – A client has a Diwali-themed e-commerce shop that sells five items. What are some potential problems you foresee with their revenue streams?

    a. The immediate issue with the client’s revenue stream is that it will take a severe hit once the holiday season is over.
    b. How to generate revenue outside of the holiday season would be a key point to address with the client.
    c. The other concern is with only offering five items.
    d. The client is severely limiting their opportunity to generate revenue
    e. A couple of bad reviews might create a lot of problems for them as they have very limited items
    f. These products are mostly around lighting and crackers, these products have brief shelf-life and the defect in the product is also more than usual
    g. Competitor issue – Since these are themed product that are released once an year, so a competitor might provide a sub-standard product at lower cost to kill the competition
  9. How do you remove your own list of stop words from a line of text given below ‘Book My Show is the best website to book a show’

    dict = [“is”,”the”,”and”,”are”,”you”,”to”,”here”,”this”,”we”,”This”,”a”,”best”]
    def stopy(text):
    words = text.split()
    no_noise = [word for word in words if word not in dict]
    final = ” “.join(no_noise)
    return final

    x = stopy(“Book My Show is the best website to book a show”)
  10. What are the steps involved in a typical Text-Analytics project

    We mostly follow the below steps:-
    -Get the raw data
    -Remove special characters and punctuations after converting the text into tokens
    -Remove stop words. These are the common words which are present in text
    -Stemming and Lemmatization to remove the noise from the filtered data
    -Do a TF-IDF to find out the important words
    -We mostly go for n-gram to see the correlated words
    -Word correlation

    – After this point, it’s mostly about the requirement of the project. There are multiple algorithms that we followed at different points in time
    *Part of Speech Tagging
    *Named Entity Recognition
    *Text Classification
    *Sentiment Analysis

    -How many bi-grams can be generated from a given sentence:
    “Sachin Tendulkar is the best batsman in the World”
    Sachin Tendulkar, Tendulkar is, is the, the best, best batsman, batsman in, in the, the World

The Data Monk services

We are well known for our interview books and have 70+ e-book across Amazon and The Data Monk e-shop page . Following are best-seller combo packs and services that we are providing as of now

  1. YouTube channel covering all the interview-related important topics in SQL, Python, MS Excel, Machine Learning Algorithm, Statistics, and Direct Interview Questions
    Link – The Data Monk Youtube Channel
  2. Website – ~2000 completed solved Interview questions in SQL, Python, ML, and Case Study
    Link – The Data Monk website
  3. E-book shop – We have 70+ e-books available on our website and 3 bundles covering 2000+ solved interview questions. Do check it out
    Link – The Data E-shop Page
  4. Instagram Page – It covers only Most asked Questions and concepts (100+ posts). We have 100+ most asked interview topics explained in simple terms
    Link – The Data Monk Instagram page
  5. Mock Interviews/Career Guidance/Mentorship/Resume Making
    Book a slot on Top Mate

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

You can check out all the other e-books on our e-shop page – Do not miss it


For any information related to courses or e-books, please send an email to nitinkamal132@gmail.com

Data Analytics Interview Questions | Day 2

Welcome to the 2nd Day of Data Analytics Interview Questions Quiz.
You can go through the question and answer for Day 1 Here
The best answer to the quiz was provided by Raahul
Data Analytics 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-analytics 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.

Data Analytics Interview Questions

SQL

  1. Write the sequence of the commands in which it is executed in the backend of SQL. (Most asked interview question)
  2. What is the difference between HAVING and WHERE in SQL?
  3. What is the use of Coalesce() function in SQL?

Python

  1. Create a list of a list
  2. Create a dictionary of list
  3. Is string mutable or immutable? Give an example

Case study

  1. KFC wants to open its first branch in India, what data points should be considered before it entering in a new market?

Machine Learning

  1. Define precision and recall using a simple example
  2. Can we use linear regression for a classification problem?

The Data Monk services

We are well known for our interview books and have 70+ e-book across Amazon and The Data Monk e-shop page . Following are best-seller combo packs and services that we are providing as of now

  1. YouTube channel covering all the interview-related important topics in SQL, Python, MS Excel, Machine Learning Algorithm, Statistics, and Direct Interview Questions
    Link – The Data Monk Youtube Channel
  2. Website – ~2000 completed solved Interview questions in SQL, Python, ML, and Case Study
    Link – The Data Monk website
  3. E-book shop – We have 70+ e-books available on our website and 3 bundles covering 2000+ solved interview questions. Do check it out
    Link – The Data E-shop Page
  4. Instagram Page – It covers only Most asked Questions and concepts (100+ posts). We have 100+ most asked interview topics explained in simple terms
    Link – The Data Monk Instagram page
  5. Mock Interviews/Career Guidance/Mentorship/Resume Making
    Book a slot on Top Mate

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

You can check out all the other e-books on our e-shop page – Do not miss it


For any information related to courses or e-books, please send an email to nitinkamal132@gmail.com

Sapient Business Analyst Interview Questions

Sapient Business Analyst Interview Questions
Publicis Sapient, formerly Sapient, is a digital transformation partner helping established organizations get to their future, digitally-enabled state by fusing strategy, consulting, and customer experience with agile engineering. It was founded in 1990.

Location – Bangalore
Job Title – Business Analyst
Experience required – 1-3 years
Number of Rounds – 4
 
Salary Offered – ~10 LPA

Sapient Business Analyst Interview Questions

Sapient Business Analyst Interview Questions
Sapient Business Analyst Interview Questions



Round 1 – Telephonic Round

The telephonic interview lasted for ~45 minutes where the questions were mostly on the tools and technologies I have worked on in my previous organization. Slowly, the questions shifted to SQL and statistics. Following are the questions which were asked:-

  1. What is the output of SELECT NULL+0?
    NULL
  2. What are the ranking functions in SQL?
    There are mainly 3 types of ranking functions:-
    Rank()
    Row_Number()
    Dense_Rank()
  3. What is a partition by clause and how is it used?
    Partition by clause is used to create to divide the whole data in different parts depending on the column on which it is partitioned. Suppose the data contains 50 rows and have data for 6 States, then if you do a partition by on state, the whole data set will be treated differently on all the 6 partitions. The syntax for partition by in ROW_NUMBER() is given below SELECT *, ROW_NUMBER() OVER (PARTITION BY State ORDER BY population DESC) AS row_num
    FROM Table_Name So, a new column will be added in the result as row_num and it will give a row number to all the state row starting from 1. Once the rows of a particular state is over, then it will again take up another state and will start the counting from there
  4. What is A/B Testing?
    A/B testing is a form of statistical hypothesis testing with two variants leading to the technical term, two-sample hypothesis testing, used in the field of statistics. In simple words, A/B Testing in web analytics is used to compare the performance of 2 web design to get a better design. Suppose you have 2 designs to display an advertisement on your website, one being a picture and other a text or link. So, you can compare the performance of the two design by A/B Testing.
  5. What is regression?
    Regression is a form of predictive modeling technique to determine the strength of the relationship between a dependent and independent variable. One of these variables is called a predictor variable whose value is gathered through experiments. The other variable is called the response variable whose value is derived from the predictor variable.Y=aX+b – Linear regression (X is predictor variable and Y is response variable)
  6. Give some example of regression?
    Regression is used for forecasting, time series modeling and finding the casual effect relationship between the variables. For example, the relationship between rash driving and the number of road accidents by a driver is best studied through regression.
  7. What is a multiple regression?
    Multiple regression is an extension of linear regression into the relationship between more than two variables. In simple linear relation we have one predictor and one response variable, but in multiple regression, we have more than one predictor variable and one response variable.Y=a1x1+a2x2+..+b
  8. What is DENSE_RANK() function?
    DENSE_RANK() again is a ranking function which is very similar to RANK() function. The only difference is that it does not miss any rank even if there are duplicates in the table.
  9. Syntax of DENSE_RANK() function
    SELECT *, DENSE_RANK() OVER (PARTITION BY Column1 ORDER BY Column2 DESC)
    FROM Table_Name
  10. There was a question on self-join where you have to get the employee name and manager name from a table having 3 columns, EmployeeID, EmployeeNameManagerID
    SELECT e1.Name AS EmployeeName, e2.Name AS ManagerName
    FROM Employee AS e1
    INNER JOIN Employee AS e2
    ON e1.ManagerID = e2.EmplyeeID

There were a few questions on the project you are working on right now.

Round 2 – Case Study

The Case Study topic was to recommend two food items to a customer who is new to the restaurant. You can find the complete analysis of this case study and other case studies here

Round 3 – Face to Face Technical Round


This round was mostly about past projects. I had a Natural Language Processing project, so the interview revolved around the same topic. Following questions were asked in this round:- 

1. What was the project for?

A. The project was to do sentiment analysis on the survey data filled by online customers. 

2. What algorithms/methods did you try?
A. We tried multiple algorithms, starting from TF-IDF, Part-Of-Speech tagging, n-gram, Lemmatization, Stemming, Tokenization, Latent Semantic Indexing, Sentiment Analysis. 

3. What all methods do you need to perform in order to convert a keyword into its base form(Normalization)?
A. Lemmatization and Stemming 

4. What is N-gram?
A. N-grams are simply all combinations of adjacent words or letters of length n that you can find in your text file.
For example
This is a sentence
N-grams = This is, is a, a sentence 

5. What is the use of TF-IDF?
A. TF-IDF stands for Term Frequency and Inverse Document Frequency. TF-IDF is numerical statistics that help to understand the importance of a particular word in a document. Term frequency gets you the number of times a particular word has occurred in a document and Inverse Document Frequency gets you the importance of the words. It helps out in filtering out the most common words like a, an, the, was, etc.. So, you get only the important terms. 

6. What is Lemmatization?
Lemmatization takes into account the morphological analysis of the word. It converts a word into its pure root form by looking into the morphological information studies – Third-person, singular number, present tense of verb study
Lemma – study
studying – Gerund of the verb study
Lemma – study

As you can see, both the words studies and studying has been narrowed down to the lemma study.


7. Explain the complete flow of your NLP project

A. The brief of the process is given below with some coding examples:-
Step 1 – Get the text dataset
Step 2 – Tokenize the text using get_text() in Python
Step 3 – Split the text using
tokens = [t for t in text.split()]
Step 4 – Get the count of the word frequency using the NLTK package in Python
freq = nltk.FreqDist(tokens)
Step 5 – Remove stop words. Code below
for token in tokens:
          if token in stopwords.words(‘english’):
             clean_tokens.remove(token) Step 6 – Tokenize non-English words
Step 7 – Get synonyms and antonyms using WordNet package from NLTK in Python
Step 8 – Stemming of words. I used PorterStemmer algorithm
stem_Word = PorterStemmer
Step 9 – Once we are done with stemming, go for Lemmatization. WordNet package
lemma = WordNetLemmatizer()
Step 10 – Build a classifier. We can you Logistic Regression to create a baseline model. Later we used Naive Bayes Classification.

There were questions only on the logical part of the process and not on the code implementation. But, it’s always better to infuse coding examples wherever you can. The interview lasted for around 1 hour.

Round 4 – Hiring Manager Round


Basic questions, like:-
1. Why are you quitting your present job?
2. What are your expectations with the company? and the company’s expectation
3. Salary negotiation
4. Have you ever lead a team?

Machine Learning Algorithm in their resume and to learn/revise the 

The Data Monk services

We are well known for our interview books and have 70+ e-book across Amazon and The Data Monk e-shop page . Following are best-seller combo packs and services that we are providing as of now

  1. YouTube channel covering all the interview-related important topics in SQL, Python, MS Excel, Machine Learning Algorithm, Statistics, and Direct Interview Questions
    Link – The Data Monk Youtube Channel
  2. Website – ~2000 completed solved Interview questions in SQL, Python, ML, and Case Study
    Link – The Data Monk website
  3. E-book shop – We have 70+ e-books available on our website and 3 bundles covering 2000+ solved interview questions. Do check it out
    Link – The Data E-shop Page
  4. Instagram Page – It covers only Most asked Questions and concepts (100+ posts). We have 100+ most asked interview topics explained in simple terms
    Link – The Data Monk Instagram page
  5. Mock Interviews/Career Guidance/Mentorship/Resume Making
    Book a slot on Top Mate

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

You can check out all the other e-books on our e-shop page – Do not miss it


For any information related to courses or e-books, please send an email to nitinkamal132@gmail.com

Box8 Business Analyst Interview Questions

Box8 Business Analyst Interview Questions
Company Name – Box8
Position – Business Analyst
Salary – 12 – 16 LPA depending on experience and last compensation
Difficulty Level – Hard
How to apply – Mostly Referrals and uploading resumes on their website
Number of rounds – It ranges from 4 (depending on the team you are interviewing for)
Job Location – Bangalore
Box8 Business Analyst Interview Questions

Box8 Business Analyst Interview Questions
Box8 Business Analyst Interview Questions

There are a few companies where the work is good and also you get a good amount of business exposure to understand the way astart-up works, in most of the business analytics jobs the questions are asked around simple SQL and Python, but at Box8 our mentor experienced some difficult and not so normally asked questions. Let’s start with the complete interview experience

In total there were 4 rounds, the first two rounds were purely technical, the third was based on a case study, and the fourth one was mostly around managerial questions

Round 1 – SQL Questions

Round 1 started with a brief about me and about the interviewer. One of my projects which was based out on data engineering was discussed in brief. Then the questions on SQL were asked, it started with basic join questions and later shifted to Regular Expression. The questions asked on Regular Expression were quite good, we have put similar question links where these are mostly solved, Try to go through all the questions and attempt the questions in the comment section.

Split Paragraph – https://thedatamonk.com/question/box8-write-a-regular-expression-to-split-a-paragraph-every-time-it-finds-an-exclamation-mark/
Regular Expression Output – https://thedatamonk.com/question/box8-find-the-output-of-the-following-code/
Tokenization – https://thedatamonk.com/question/box8-what-is-tokenization-and-what-are-the-important-nltk-tokenizer/
RegEx to match commas and spaces – https://thedatamonk.com/question/box8-how-to-write-a-regex-to-match-spaces-or-commas/
Ranking in SQL – https://thedatamonk.com/question/box8-ranking-in-sql/
Processing time in SQL – https://thedatamonk.com/question/box8-sql-query/
Topic Modeling in NLP – https://thedatamonk.com/question/box8-what-is-topic-modeling/
Bag of Words – https://thedatamonk.com/question/box8-what-is-bag-of-words/
RegEx in SQL – https://thedatamonk.com/question/box8-regular-expression-in-sql/
RegEx in SQL – https://thedatamonk.com/question/box8-sql-query-on-regular-expression/

Round 2 – Python and SQL

My feedback was decent and was called for the second round of interviews. It was with a Lead Business Analyst. It started with an introduction and later shifted on to the type of projects which were mentioned in my resume.


My understanding of the Data Pipeline was tested where I was asked to draw the complete data pipeline for my current organization. We already have a good detailed blog on the data pipeline, do go through it if you have worked closely with the data engineering team.

On the Python front, I was asked about mutable, immutable concepts, about the OS module (it’s very important to understand the OS module in Python, do explore it either on Google or Here) and some very basic questions on Machine Learning like, what is the p-value, correlation, cross-validation. Only the concepts were asked and not the detailed syntax.

In SQL again some regex questions were asked which I have provided the link above, do go through these.

Round 3 – Case Study

There was only one question which was discussed for 1 hour. The case study was “If you have a restaurant which sells only Biryani and somedays you get sold out, somedays you are left with 50 kgs of Biryani, then how do you optimize this process so that the loss is minimized”


Think and comment your approach below. There are multiple things that we discussed like getting the busiest hour, taking a rolling average of 7 days, and seasonality of the month. Keep a check on the sold plates in the interim period, etc. Do comment your approach below for evaluation

Round 4 – Managerial Round

I guess all three rounds of mine were good, I was asked questions on my family background, education, whether I am a customer of Box8 or not, etc.

Everything was wrapped up in 4-5 days, Offer letter was released within 7 days with a good 70% hike 🙂


The Data Monk services

We are well known for our interview books and have 70+ e-book across Amazon and The Data Monk e-shop page . Following are best-seller combo packs and services that we are providing as of now

  1. YouTube channel covering all the interview-related important topics in SQL, Python, MS Excel, Machine Learning Algorithm, Statistics, and Direct Interview Questions
    Link – The Data Monk Youtube Channel
  2. Website – ~2000 completed solved Interview questions in SQL, Python, ML, and Case Study
    Link – The Data Monk website
  3. E-book shop – We have 70+ e-books available on our website and 3 bundles covering 2000+ solved interview questions. Do check it out
    Link – The Data E-shop Page
  4. Instagram Page – It covers only Most asked Questions and concepts (100+ posts). We have 100+ most asked interview topics explained in simple terms
    Link – The Data Monk Instagram page
  5. Mock Interviews/Career Guidance/Mentorship/Resume Making
    Book a slot on Top Mate

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

You can check out all the other e-books on our e-shop page – Do not miss it


For any information related to courses or e-books, please send an email to nitinkamal132@gmail.com

Amazon Business Analyst Interview Questions

Amazon Business Analyst Interview Questions

Company Name – Amazon
Position – Business Analyst
Salary – 12 to 25 LPA depending on experience and last compensation
Difficulty Level – Moderate+
How to apply – Naukri.com, Linkedin, Amazon job portal, and Referrals
Number of rounds – It ranges from 3 to 5 rounds (depending on the team you are interviewing for)
Job Location – Bangalore


Amazon Business Analyst Interview Questions

Amazon Business Analyst Interview Questions
Amazon Business Analyst Interview Questions

The author of this post was interviewed for a team in Singapore. It was a four-round process. People do believe that Amazon asks only the 14 principles and no technicals in the Analyst domain but it was not the case with the author.

Round 1SQL written test

You were given a link to join the test, there might be 6-8 more candidates taking the test at the same time. You will have a simple 10 minutes Dos and Don’t discussion.
Then you will be given a test link and now it’s just you in a separate virtual room.

SQL test – There were 5 tables (Revenue, Item, Orders, Departments, and Users) and an ER diagram was given.
You will have 40 minutes to solve 5 questions.

Level of questions – Moderate, the emphasis was on which all tables to join and then how to get particular metrics. There were questions on Year over Year trend which was a bit tricky.

Overall it will take you 40 minutes to complete the code. There was no compiler, so you have to write the code and make sure that it’s executable.

I did pretty decently I guess and was able to solve all the questions but later realized that there was a better way to solve one particular question.

Questions were like:-
1. Get the name of the customers who have made 2 orders each month from Sydney and have canceled at least 5 orders
2. Make a Year over Year report for the sales of XYZ department

Round 2 – Project Discussion, SQL, and Python

Round 2 was all about your project, but you always need to remember the 14 Amazon Principles. We have a complete dedicated video on Amazon’s 14 principles and their STAR technique. Do take a look if you ever want to appear for Amazon’s technical or business role.

SQL – The SQL part was moderate in level. It starts with the same questions which you did in the first round. It is followed by some direct questions on the basic window functions, optimization techniques, building data pipelines, etc. Some questions were on the line of:-

1. How to create a table in which can read the data for 2 days and write it for 6 hours and run 4 times a day?
2. How to make sure that you do not have any duplicates in a table?
3. What is the best way to store a variable that can have many values (Array/JSON)

Python – Basic questions on any project which you have done in Python in the past. It need not be a data science or machine learning project but it should have a good use case.

Hint – Brownie point if you have automated something in past using Python

The project was discussed in every detail. The complete interview went on for around 2 hours. The screen was shared for writing the codes. You need to be good with SQL syntax.

Hint – Be very comfortable with your project, practice everything written in your resume.


Round 3 – Project, Responsibilities, Case study and Hiring Manager round
I was of the opinion that Amazon’s interview will have at least 5 rounds. But surprisingly the third was the final round. A very senior member took the interview, projects were asked on an overview level. there were some technical discussions also on some points, but it was mainly on an uber level.

The interviewer was very keen to understand the ecosystem in which you are working right now and the ecosystem and culture of Amazon (specifically their team)

There was a case study or business problem “If a person has 100s of items in their cart and he is not buying anything. Do we need to consider him as a potential customer? What data do you need to justify your answer”

The point is to look for all the cases like, did the person add everything in the last 2-3 days? If yes then he can be a potential customer.
Did he put things in a random fashion or does that belong to a specific product type?
How much time did he invest to select an item?
Has he bought anything in the past? If yes, then what is the volume

You can comment on your ideas below and we will try to evaluate them. Try to work in a methodological way, above is just a few examples (I presented the analysis in a very simple and structured way)

If you want to crack case study round then please go and check out this video from The Data Monk –

Solve Case Study and Guesstimates in any Analytics Interview | Round 3 | The Data Monk

Overall it was a quick interview with moderate technical skills. The complete process took around 8 days.

The Data Monk services

We are well known for our interview books and have 70+ e-book across Amazon and The Data Monk e-shop page . Following are best-seller combo packs and services that we are providing as of now

  1. YouTube channel covering all the interview-related important topics in SQL, Python, MS Excel, Machine Learning Algorithm, Statistics, and Direct Interview Questions
    Link – The Data Monk Youtube Channel
  2. Website – ~2000 completed solved Interview questions in SQL, Python, ML, and Case Study
    Link – The Data Monk website
  3. E-book shop – We have 70+ e-books available on our website and 3 bundles covering 2000+ solved interview questions. Do check it out
    Link – The Data E-shop Page
  4. Instagram Page – It covers only Most asked Questions and concepts (100+ posts). We have 100+ most asked interview topics explained in simple terms
    Link – The Data Monk Instagram page
  5. Mock Interviews/Career Guidance/Mentorship/Resume Making
    Book a slot on Top Mate

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

You can check out all the other e-books on our e-shop page – Do not miss it


For any information related to courses or e-books, please send an email to nitinkamal132@gmail.com