Dream11 Product Analyst Interview Questions
Dream11 Product Analyst Interview Questions
Dream11 Product Analyst Interview Questions
We have 100 case studies and guesstimate completely solved that are repeatedly asked in Analytics Interview. Do check it out.
Why Analytics as a Career?
Target job roles – Business Analyst, Data Analyst, Data Scientist, Business Intelligence Engineer, Product Analyst, Machine Learning Engineer, Data Engineer
Target Companies – FAANG and only product-based companies
CTC offered –
12 to 20 LPA for Level 1 (0 to 4 YOE)
20 to 35 LPA for Level 2 (Senior level – 4 to 7 YOE )
35 to 50 LPA for Level 3 (Team Lead or Manager – 7 to 9 YOE)
50 to 80 LPA for Level 4 (Manager or Senior Manager – 9 to 12 YOE)
Tools and Technologies required
SQL – 9/10
Python – 7/10
Visualization tool (Power BI or Tableau) – Good to have
Machine Learning Algorithm – Expert in at least a couple of algorithms (if going for Data Science role)
Why The Data Monk?
We are a group of 30+ people with ~8 years of Analytics experience in product-based companies. We take interviews on a daily basis for our organization and we very well know what is asked in the interviews.
Other skill enhancer website charge 2lakh+ GST for courses ranging from 10 to 15 months.
We only focus on making you a clear interview with ease. We have released our Become a Full Stack Analytics Professional for anyone in 2nd year of graduation to 8-10 YOE. This book contains 23 topics and each topic is divided into 50/100/200/250 questions and answers. Pick the book and read it thrice, learn it, and appear in the interview.
We also have a complete Analytics interview package
– 2200 questions ebook (Rs.1999) + 23 ebook bundle for Data Science and Analyst role (Rs.1999)
– 4 one-hour mock interviews, every Saturday (top mate – Rs.1000 per interview)
– 4 career guidance sessions, 30 mins each on every Sunday (top mate – Rs.500 per session)
– Resume review and improvement (Top mate – Rs.500 per review)
Total cost – Rs.10500
Discounted price – Rs. 9000
How to avail of this offer?
Send a mail to nitinkamal132@gmail.com
We have been curating interview questions for the Top Product based companies. Following are the interview questions shared so far:-
Day 1 – Amazon Business Intelligence Engineer Interview Questions – https://thedatamonk.com/amazon-business-intelligence-engineer-interview-questions/
Day 2 – Myntra Senior Business Analyst Questions – https://thedatamonk.com/myntra-senior-business-analyst-questions/
Day 3 – OYO Data Analyst Interview Questions – https://thedatamonk.com/oyo-data-analyst-interview-questions/
Day 4 – Swiggy Business Analyst Interview Questions – https://thedatamonk.com/swiggy-business-analyst-interview-questions/
Day 5 – Flipkart Business Analyst Interview Questions – https://thedatamonk.com/flipkart-business-analyst-interview-questions-2/
Dream11 Product Analyst Interview Questions
Company – Dream11
Designation – Product Analyst
Year of Experience required – 2 to 5 years
Technical expertise – SQL, Python, Case Study
Salary offered – 18 to 24 LPA (10% variable)
Number of Rounds – 5
Dream11 SQL Interview Questions
What is the difference between DBMS and RDBMS?
What are the examples of DBMS and RDBMS?
DBMS – XML, Microsoft Access, etc.
RDBMS – Oracle, SQL Server, etc
What is the difference between SQL and MySQL?
SQL is a standard language for retrieving and manipulating structured databases. On the contrary, MySQL is a relational database management system, like SQL Server, Oracle, or IBM DB2, that is used to manage SQL databases.
Write a SQL query to get the second highest query using Ranking
Note: Dense_rank() has been used to handle duplicate salaries if there are any.
With result as
{
select salary,
dense_rank() over (order by salary desc) as salaryrank
from employees
}
select top 1 salary
from result
where salaryrank = 2
What is the use of FETCH command?
The FETCH command cannot be used alone. It has to be used in conjunction with the OFFSET command.
It is used to return a set of number of rows.
The OFFSET argument is used to identify the starting point to return rows from a result set.
Basically, it exclude the first set of records.
Example
SELECT * FROM Employee
ORDER BY Salary
OFFSET 5 ROWS
FETCH NEXT 10 ROWS ONLY;The above query will skip the first 5 rows and return the next 10 rows.
ORDER BY clause is mandatory to be used with OFFSET and FETCH.
OFFSET value must be greater than or equal to 0. It cannot be negative.
Arrange the employees with respect to their Joining date, most experienced employee coming on the top followed by others.
(Hint- In case of same date, preference should be HR>Admin>Accountant)
Ans.
SELECT*FROM Employees
ORDER BY Joining_Date, FIELD( Designation, ‘HR’,’Admin’,’Accountant’);
What is Cursor? How to use a Cursor?
After any variable declaration, DECLARE a cursor. A SELECT Statement must always be coupled with the cursor definition.
To start the result set, move the cursor over it. Before obtaining rows from the result set, the OPEN statement must be executed.
To retrieve and go to the next row in the result set, use the FETCH command.
To disable the cursor, use the CLOSE command.Finally, use the DEALLOCATE command to remove the cursor definition and free up the resources connected with it.
How to create a temp table in SQL Server?
Temporary tables are created in TempDB and are erased automatically after the last connection is closed. We may use Temporary Tables to store and process interim results. When we need to store temporary data, temporary tables come in handy.
The following is the syntax for creating a Temporary Table:CREATE TABLE #Employee (id INT, name VARCHAR(25))
INSERT INTO #Employee VALUES (01, ‘Ashish’), (02, ‘Atul’)
Dream11 Case Study
Create a dashboard with all the important KPIs for Dream11
Registered Users – Total number of users registered on the platform
Active Users – Number of users who log in and participate on the platform in a given period
Retention Rate – Percentage of users who continue to use the platform after a certain period of time
Churn Rate – Percentage of users who stop using the platform after a certain period of time
Daily Active Users (DAU) – Number of unique users who log in and participate on the platform in a day
Monthly Active Users (MAU) – Number of unique users who log in and participate on the platform in a month
Average Revenue Per User (ARPU) – Total revenue generated divided by the number of active users
Gross Merchandise Value (GMV) – Total value of transactions on the platform in a given period
Customer Acquisition Cost (CAC) – Total cost of acquiring new users divided by the number of new users
Customer Lifetime Value (CLV) – Total value a customer is expected to generate over their lifetime on the platform
Customer Engagement – Measures the level of engagement users have with the platform (e.g., number of contests entered, time spent on the platform)
Contest Conversion Rate – Percentage of users who convert from viewing a contest to participating in it
Average Contest Size – Average number of users who participate in a contest
Number of Contests – Total number of contests available on the platform
Winning Percentages – Percentage of users who win a prize in a contest
User Ratings – Average rating given by users on the platform (e.g., app rating, customer service rating)
Time to First Deposit – Amount of time it takes for a user to make their first deposit on the platform
Payment Conversion Rate – Percentage of users who complete a payment transaction on the platform
Referral Rate – Percentage of new users who join the platform through referrals
User Acquisition Source – Breakdown of where new users come from (e.g., social media, search engines, referral programs)
Dream11 Leadership Round
There were questions like:
– Why you want to quit your current organisation?
– How do you lead a team of 100 people ?
– How are you trying to upgrade your technical skills?
– How much time do you invest in data cleaning ?
– Project description
The Data Monk Product and Services
- 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 - Website – ~2000 completed solved Interview questions in SQL, Python, ML, and Case Study
Link – The Data Monk website - 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 - Mock Interviews
Book a slot on Top Mate - Career Guidance/Mentorship
Book a slot on Top Mate - 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 (ARIM
