# 11 Pinduoduo SQL Interview Questions (Updated 2024)

Updated on

February 7, 2024

At Pinduoduo, SQL is used often for analyzing customer shopping patterns and managing their large product inventory database. Unsurprisingly this is why Pinduoduo asks SQL questions during interviews for Data Science and Data Engineering positions.

So, if you're studying for a SQL Assessment, we've collected 11 Pinduoduo SQL interview questions to practice, which are similar to commonly asked questions at Pinduoduo – how many can you solve?

## 11 Pinduoduo SQL Interview Questions

### SQL Question 1: Compute CTR for Pinduoduo

Pinduoduo is one of the largest e-commerce platforms in China, selling various kinds of products. The click-through conversion rate is a critical metric for Pinduoduo, showing the rate at which users who viewed a product ended up adding it to their shopping cart.

Given two tables and , where has columns , , and , and has columns , , and , Write a SQL query that computes the daily click-through conversion rate (the number of unique users who add a product to their cart after viewing it divided by the number of unique users who viewed the product) for the last 30 days.

##### Example Input:
dateuser_idproduct_id
2019-06-0112350001
2019-06-0123450001
2019-06-0234550001
2019-06-0245669852
2019-06-0356769852
##### Example Input:
dateuser_idproduct_id
2019-06-0112350001
2019-06-0234550001
2019-06-0356769852
2019-06-0367869852

This query calculates the click-through conversion rate by joining the and tables on , , and . The query also includes a filter clause to restrict the output to the last 30 days. After that, it divides the count of unique users who added a product to their cart by the count of unique users who viewed the product per date per product. The result is then ordered by and .

To solve a related SQL interview question on DataLemur's free interactive SQL code editor, solve this Facebook SQL Interview question:

### SQL Question 2: Supplier Management at Pinduoduo

Pinduoduo needs to track and manage their supplier interactions, including keeping an inventory of products and their suppliers, as well as the different interactions made with these suppliers. Suppose we have these tables in our system:

##### Example Input
product_idproduct_namesupplier_id
1Apple iPhone 122001
2Samsung Galaxy Note 202002
3OnePlus 8T2003
4Apple iPhone 112001
##### Example Input
supplier_idsupplier_namelocation
2002Mobile Master.Beijing
2003One Plus SupplierShenzhen
##### Example Input
interaction_idsupplier_idinteraction_dateinteraction_type
1001200106/08/2022 00:00:00Order
1002200106/09/2022 00:00:00Payment
1003200206/10/2022 00:00:00Order
1004200206/15/2022 00:00:00Payment
1005200106/18/2022 00:00:00Order

Suppose Pinduoduo wants an up-to-date report that includes the supplier ID, supplier name, their location, the latest interaction date with each supplier, and the type of this latest interaction. In case there are multiple interactions on the same latest date for a supplier, arbitrarily pick any one (i.e., it's not specified which one should be selected).

Here is a PostgreSQL query to solve this problem:

This query uses the function to generate a row number for each supplier interaction in descending order of the date. It then joins the result with the suppliers table and only keeps the rows with a row number equal to 1, which means keeping only the latest interaction for each supplier.

### SQL Question 3: Can you explain what a cross-join is and the purpose of using them?

A cross-join, also known as a cartesian join, is a JOIN that produces the cross-product of two tables. In a cross-join, each row from the first table is matched with every row from the second table, resulting in a new table with a row for each possible combination of rows from the two input tables.

Let's say you were building a Machine Learning model that attempts to score the probability of a customer purchasing a Pinduoduo product. Before working in Pandas and Tensorflow, you might want to do some Exploratory Data Analysis (EDA) in SQL, and generate all pairs of customers and Pinduoduo products.

Here's a cross-join query you could run:

Cross-joins are useful for generating all possible combinations, but they can also create huge tables if you're not careful. For instance, if you had 10,000 potential customers and Pinduoduo had 500 different product SKUs, the resulting cross-join would have 5 million rows!

### SQL Question 4: Finding Active Customers from Pinduoduo

As a data analyst for Pinduoduo, you've been asked to find all customers that have made at least one purchase in each of the last 3 months (May, June, July in 2022). We would like to know the of these active customers. The database has a table with the following schema:

##### Example Input:
purchase_iduser_idpurchase_dateproduct_idprice
100112305/08/2022 00:00:005000122.5
230145606/12/2022 00:00:006985245.0
319212306/18/2022 00:00:005000122.5
522278907/26/2022 00:00:006985245.0
715912307/05/2022 00:00:006985222.5
##### Example Output:
user_id
123

For this question, assuming we don't have an upper limit on the number of months, a month-based partition window function can be used in conjunction with a rule excluding rows with NULL values in the lag columns for each customer

A solution to the problem in PostgreSQL would be:

This PostgreSQL query finds all unique users that have made at least one purchase in each of the three months by using the condition. For each month, it checks if there is a record in the purchases table for that user within the specified date range. If all three conditions are true, the is included in the results. undefined

### SQL Question 5: What's the difference between and ?

The operator merges the output of two or more statements into a single result set. It ignores duplicated rows, and makes sure each row in the result set is unique.

For a concrete example, say you were a Data Analyst at Pinduoduo working on a Marketing Analytics project. If you needed to get the combined result set of both Pinduoduo's Google and Facebook ads you could execute this SQL query:

The operator works in a similar way to combine data from multiple statements, but it differs from the operator when it comes to handling duplicate rows. Whereas filters out duplicates (so if the same ad_name is run on both Facebook and Google, it only shows up once), outputs duplicate rows.

### SQL Question 6: Analyze Monthly Average Product Ratings

As a data analyst at Pinduoduo, you are tasked with analyzing the performance of the products. You have access to a table which lists down all product reviews by customers.

Each review contains the following fields:

• : The unique identifier of the review
• : The user who posted the review
• : The date of the review submission
• : The product that was reviewed
• : The number of stars the user rated the product, from 1 (very poor) to 5 (very good)

Write a SQL query that returns for each month, the product id and its average rating for that month. Include only the products which were reviewed in that month.

##### Example Input:
review_iduser_idsubmit_dateproduct_idstars
61901232022-01-032344
51947892022-02-151212
39124562022-03-052345
89218902022-03-131213
23675672022-04-012343
##### Example Output:
monthproduct_idaverage_rating
012344.00
021212.00
032345.00
031213.00
042343.00

This SQL query uses the AVG function to calculate the mean value of for each for every month. The TO_CHAR function is used to format the date and create a column. The GROUP BY statement groups the results by and , while the ORDER BY sorts the output by and .

Pro Tip: Window functions are a frequent SQL interview topic, so practice every window function problem on DataLemur

### SQL Question 7: Can you explain the distinction between a correlated and a non-correlated sub-query?

hile a correlated subquery relies on columns in the main query's FROM clause and cannot function independently, a non-correlated subquery operates as a standalone query and its results are integrated into the main query.

An example correlated sub-query:

This correlated subquery retrieves the names and salaries of Pinduoduo employees who make more than the average salary for their department. The subquery references the department column in the main query's FROM clause (e1.department) and uses it to filter the rows of the subquery's FROM clause (e2.department).

An example non-correlated sub-query:

This non-correlated subquery retrieves the names and salaries of Pinduoduo employees who make more than the average salary for the Data Science department (which honestly should be very few people since Data Scientists are awesome and deserve to be paid well).The subquery is considered independent of the main query can stand alone. Its output (the average salary for the Data Science department) is then used in the main query to filter the rows of the Pinduoduo employees table.

### SQL Question 8: Calculate the Maximum Total Amount Spent Per Category for Each Month

As an analyst for Pinduoduo, you are interested in analyzing customer spending habits. Each customer can purchase different products and each product falls under a specific category. Write a SQL query to find the maximum total amount spent per category for each month.

##### Example Input:
transaction_iduser_idtransaction_dateproduct_idquantityprice_unit
52112306/08/202250001210.5
63426506/10/202269852120.0
12736206/18/202250001410.5
43319207/26/202269852220.0
90198107/05/202269852120.0
##### Example Input:
product_idcategory_id
50001100
69852200
##### Example Output:
mthcategorymax_total_spend
610050.0
620020.0
720040.0

The SQL statement above joins the table with the table based on the first. Then it groups the data by the month part of the transaction date and the , and finally calculates the maximum total amount spent per category for each month. The total spend for a transaction is calculated as the quantity of the product times the unit price.

### SQL Question 9: Search for specific customer purchases

You are working as a data analyst at Pinduoduo. Your task is to review the database and find purchases made by customers that have a specific name pattern. For this task, you are required to find all purchases made by customers whose names begin with "Li". To assist your investigation, use the and tables shown below.

##### Example Input:
customer_idfirst_namelast_name
101LiWang
102LindaChan
103SamLee
104LisaSmith
105LilyZhang
106JohnDoe
##### Example Input:
purchase_idcustomer_idproduct_idpurchase_dateproduct_price
20111015000102/08/2022500
20121026985204/10/2022150
20131035000106/18/2022300
20141046985207/26/2022250
20151056985209/05/2022100
20161065000111/22/2022350

This query joins the and tables on the column. It then filters the resulting table to include only those rows where the in the table starts with "Li". The keyword in SQL is used to search for a specified pattern in a column. The "%" sign is used to define wildcards (missing letters) both before and after the pattern. Also notice that SQL is case insensitive.

### SQL Question 10: What does / SQL commands do?

The / operator is used to remove to return all rows from the first SELECT statement that are not returned by the second SELECT statement.

Note that is available in PostgreSQL and SQL Server, while MINUS is available in MySQL and Oracle (but don't stress about knowing which DBMS supports what exact commands since the interviewers at Pinduoduo should be lenient!).

For a tangible example in PostgreSQL, suppose you were doing an HR Analytics project for Pinduoduo, and had access to Pinduoduo'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 contractors who never were a employee using this query:

### SQL Question 11: Analyzing Customer Purchases and Feedback

Imagine you are a data analyst at Pinduoduo, a popular e-commerce platform. You are given two tables - 'Users' which provides information about all registered users, and 'Orders' which captures details on all orders placed on the platform.

The 'Users' table has the following columns: user_id (unique identifier for users), user_name (names of users), and registration_date (date when the user registered).

The 'Orders' table has the following columns: order_id (unique identifier for each order), user_id (identifier for user who made the purchase), product_id (identifier for product), purchase_date (date when the product was purchased), and review_score (score given by the user to the product on a scale of 1 to 5).

Here is some sample data:

Example Input:

user_iduser_nameregistration_date
1Alice2020-01-01
2Bob2020-03-01
3Charlie2021-01-01
4David2021-06-01

Example Input:

order_iduser_idproduct_idpurchase_datereview_score
101A2022-02-013
112B2022-03-014
123A2022-04-015
134C2022-05-012
141B2022-05-024

Your task is to write a SQL query that calculates the average review_score for each product_id among users who registered in 2020 only. Sort the results by average review_score in descending order.

Example Output:

product_idavg_review_score
B4.00
A3.00

This query first filters out the users who registered in 2020. It then joins this data with the 'Orders' table using a standard . This results in a dataset comprising only of orders from 2020 users. Grouping by and taking the average of the for each group gives us the desired output. Finally, the data is sorted by the in descending order.

Because joins come up frequently during SQL interviews, try this interactive Snapchat JOIN SQL interview question:

### How To Prepare for the Pinduoduo SQL Interview

Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Pinduoduo SQL interview is to solve as many practice SQL interview questions as you can! Beyond just solving the earlier Pinduoduo SQL interview questions, you should also solve the 200+ SQL questions on DataLemur which come from companies like Netflix, Google, and Amazon.

Each interview question has multiple hints, fully explained answers along with a discussion board to see how others solved it and most importantly, there is an interactive SQL code editor so you can instantly run your SQL query and have it executed.

To prep for the Pinduoduo SQL interview it is also helpful to practice SQL problems from other tech companies like:

However, if your SQL foundations are weak, don't worry about jumping right into solving questions – improve your SQL foundations with this SQL tutorial for Data Analytics.

This tutorial covers SQL concepts such as inner vs. outer JOIN and aggregate functions – both of these show up often in Pinduoduo interviews.

### Pinduoduo Data Science Interview Tips

#### What Do Pinduoduo Data Science Interviews Cover?

For the Pinduoduo Data Science Interview, in addition to SQL query questions, the other types of questions to prepare for are:

#### How To Prepare for Pinduoduo Data Science Interviews?

To prepare for Pinduoduo Data Science interviews read the book Ace the Data Science Interview because it's got:

• 201 interview questions taken from FAANG & startups
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