(Ex-Facebook & Best-Selling Data Science Author)
February 6, 2024
At MercadoLibre, SQL (BigQuery) is used day-to-day for analyzing e-commerce trends and identifying consumer behavior. Because of this, MercadoLibre almost always asks SQL questions during interviews for Data Analyst, Data Science, and BI jobs.
So, if you're preparing for a SQL Assessment, here’s 8 MercadoLibre SQL interview questions to practice, which are similar to commonly asked questions at MercadoLibre – can you solve them?
At MercadoLibre, let's assume that the 'VIP' customers are those who regularly make high-volume purchases.
Given the following table , write a SQL query that identifies the top 5 customers who made the highest total purchases over the latest one year period.
You can use SQL aggregation functions (like and ) to answer this question.
Here's the SQL query:
In this query, first we filter out the purchases that happened in the latest one year. Then, for each user, we calculate the total purchase by multiplying the with the , and then add up these values. Then we order the users in descending order of the total purchase value, and choose top 5 from them.
Please note that the provided sample input and output are quite small for brevity, and they don't actually contain information from one year. The SQL query assumes that the table contains data from more than a year.
To solve a super-customer analysis question on DataLemur's free online SQL coding environment, try this Microsoft Azure Cloud SQL Interview Question:
MercadoLibre is an Argentina-based online marketplace that allows users to buy and sell items. Each item is rated by users who purchase them. Your role as a data analyst is to provide insights on product performance.
Given a table , write a SQL query that calculates the monthly average rating (stars) of each product. The results should be ordered by month and then product. Assume that is a timestamp formatted as .
This SQL script employs the function to obtain the month from the field. The is used to convert the string to a timestamp so the function can pull out the month. The function is employed to calculate the average star rating for every combination of month and product_id. The results are grouped by the month and product_id and ordered in the same way.
Pro Tip: Window functions are a frequent SQL interview topic, so practice all the window function problems on DataLemur
Database denormalization is when you add redundancy to a database, and break typical normalization rules (codified by the 1st, 2nd, 3rd normal forms).
Denormalization is typically used to improve the performance of a database, particularly when the database is being for OLAP (Online Analytical Processing) purposes. By denormalizing a database, you can reduce the number of joins that are required to retrieve data, which can greatly improve the speed of queries since joins are costly and slow. However, denormalization can also introduce some problems, such as increased data redundancy and the need for more complex update and delete operations since data has been duplicated into multiple tables.
In general, denormalization should be used with caution and only after careful consideration of the trade-offs involved. It's typically better to start with a well-normalized database design then denormalize only if your database isn't scaling as well as you want.
As a data analyst at MercadoLibre, you are tasked to help the marketing team identify the customers who made purchases exceeding $1000 in total within any single month in 2022, and who live in either Argentina or Brazil. The team is interested in studying the purchase behavior of these high-value customers to better customize the marketing strategies.
You have been given access to two tables: the table that records all transactions on the platform, and the table that stores customer details.
The table has the following structure:
The table is formatted as follows:
The above PostgreSQL query first joins the and tables on the . It then filters the records for the year 2022 and for customers who live either in Argentina or Brazil. The clause is used to group the transactions by the month, and the clause ensures that we are only looking at customers whose total purchase amount in any single month exceeds $1000. undefined
A full outer join returns all rows from both tables, including any unmatched rows, whereas an inner join only returns rows that match the join condition between the two tables.
For an example of each one, say you had sales data exported from MercadoLibre's Salesforce CRM stored in a datawarehouse which had two tables: and .
: retrieves rows from both tables where there is a match in the shared key or keys.
This query will return rows from the sales and tables that have matching customer id values. Only rows with matching values will be included in the results.
: retrieves all rows from both tables, regardless of whether there is a match in the shared key or keys. If there is no match, NULL values will be returned for the columns of the non-matching table.
Here is an example of a SQL full outer join using the sales and tables:
You are given a database of all transactions on the MercadoLibre platform. Each transaction contains the ID of the seller, the ID of the buyer, the item's ID, the item's selling price, and the date of the transaction. Write a SQL query to calculate the average sales per seller for the last month.
This question is asking for the average sales per seller for the last month, so we need to average the for each where the is within the last month. The is used to truncate the current date to the start of the current month and the start of the previous month to establish the range for the last month. The subtracts one month from the current date. The clause is used to calculate the average for each seller independently.
To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for dealing with sales and revenue data on a platform or this Stripe Repeated Payments Question which is similar for working with transaction data.
combines the results from two or more SELECT statements, but only returns the rows that are exactly the same between the two sets.
For example, say you were doing an HR Analytics project for MercadoLibre, and had access to MercadoLibre's contractors and employees data. Assume that some employees were previously contractors, and vice versa, and thus would show up in both tables. You could use operator to find all employees who also show up in the contractors table:
Given the and the tables, write a SQL query that returns a list of customers along with the most expensive product they have purchased. If the customer has purchased more than one product with the same maximum price, return any one of them.
The output should display the customer name and the name of the most expensive product they have purchased.
The PostgreSQL query that can be used to solve this problem is:
The subquery retrieves a list of maximum prices for each customer along with customer_id. The main query joins table and the subquery on customer_id, and returns the desired output.
Because joins come up so often during SQL interviews, try an interactive SQL join question from Spotify:
The best way to prepare for a MercadoLibre SQL interview is to practice, practice, practice. In addition to solving the above MercadoLibre SQL interview questions, you should also solve the 200+ SQL Interview Questions on DataLemur which come from companies like FAANG (FB, Apple, Amazon, Netflix, Google).
Each SQL question has multiple hints, fully explained answers along with a discussion board to see how others solved it and crucially, there's an interactive SQL code editor so you can right in the browser run your SQL query and have it checked.
To prep for the MercadoLibre SQL interview it is also a great idea to practice SQL problems from other tech companies like:
However, if your SQL skills are weak, forget about going right into solving questions – strengthen your SQL foundations with this DataLemur SQL Tutorial.
For the MercadoLibre Data Science Interview, beyond writing SQL queries, the other types of questions to prepare for are:
The best way to prepare for MercadoLibre Data Science interviews is by reading Ace the Data Science Interview. The book's got: