# 10 Xiaomi SQL Interview Questions (Updated 2024)

Updated on

February 14, 2024

At Xiaomi, SQL is used day-to-day for analyzing user behavior across their ecosystem of devices and optimizing supply chain management for their IoT & smart home products. Unsurprisingly this is why Xiaomi often tests SQL coding questions during interviews for Data Science, Data Engineering and Data Analytics jobs.

As such, to help you study for the Xiaomi SQL interview, this blog covers 10 Xiaomi SQL interview questions – can you solve them?

## 10 Xiaomi SQL Interview Questions

### SQL Question 1: Monthly Average Ratings of Xiaomi Products

You are analyzing customer reviews data for Xiaomi. Each row in the table indicates the review for a specific product by a user submitted on a given date.

The task here is to write a SQL query to calculate the monthly average rating of each product. Ratings are from 1 to 5 stars (5 means the best score, 1 the worst).

The table has the following schema:

review_iduser_idsubmit_dateproduct_idstars
61711232022-06-08500014
78022652022-06-10698524
52933622022-06-18500013
63521922022-07-26698523
45179812022-07-05698522

This query uses the function of PostgreSQL to get the month from the . Then, it simply calculates the average () number of stars for each product, grouped by month and product_id. The clause sorts the output by month and product, making it easier to follow.

Pro Tip: Window functions are a popular SQL interview topic, so practice all the window function problems on DataLemur

### SQL Question 2: Click-through Conversion Rate for Xiaomi Products

Xiaomi, a digital product company, given the tables and . The table tracks the time that a user views a product and tracks the time a user adds the product to the cart. Create a SQL query which finds the overall click-through conversion rate (the number of times a product added to the cart, divided by the number of times it was viewed) for each product.

#### Example Input:

view_iduser_idproduct_idview_time
11001206/08/2022 00:00:00
21002206/08/2022 01:00:00
31003306/19/2022 00:00:00
41004107/26/2022 00:00:00
51001107/05/2022 00:00:00

#### Example Input:

11001206/08/2022 00:05:00
21003306/19/2022 00:05:00
31005107/26/2022 00:05:00
41001107/05/2022 00:05:00

This query first creates a subquery on the table to count how many times each product was added to the cart. It then joins this data with the table on to determine how many times each product was viewed. The conversion rate is calculated by dividing the number of times added to the cart by the number of views, and expressed as a decimal for readability. If a product was not added to the cart at all, is used to replace the NULL value with 0 in the calculations.

To practice another question about calculating rates, solve this TikTok SQL Interview Question within DataLemur's online SQL coding environment:

### SQL Question 3: DBMS transactions are expected to follow the ACID properties. What are they, and what does each property mean?

A DBMS (database management system), in order to ensure transactions are relaible and correct, tries to mantain the following ACID properties: Atomicity, Consistency, Isolation, and Durability

Here is what each of the ACID properties stands for:

Atomicity: ensures that a transaction is either completed in its entirety, or not completed at all. If a transaction fails halfway, the database does a rollback on the commit.

Consistency: ensures that a transaction will only be completed if adheres to the constraints defined in the database of the DB.

Isolation: ensures that concurrent transactions are isolated from each one another, so that the changes made by one transaction cannot be seen by a 2nd transaction until the 1st transaction is done.

**Durability: ** ensures that once a transaction has been committed, the database permanently stores the results in the DB.

As you can see, it's pretty important for Xiaomi's data systems to be ACID compliant, else they'll be a big problem for their customers!

### SQL Question 4: Xiaomi Products Sales Analysis

Suppose, as a data analyst at Xiaomi, your manager has requested you find out the total sales and the average price of each product type (like smartphone, accessories, IoT & lifestyle, etc) per year. Create the SQL query that will achieve this for the Xiaomi company.

##### Example Input:
sale_idproduct_idproduct_typesale_dateprice
1101smartphone2020-03-15 00:00:00600
2102accessories2020-06-18 00:00:0050
3103IoT & lifestyle2020-09-20 00:00:00200
4101smartphone2021-05-22 00:00:00620
5104smartphone2021-07-15 00:00:00650
##### Example Output:
yearproduct_typetotal_salesaverage_price
2020smartphone1600.00
2020accessories150.00
2020IoT & lifestyle1200.00
2021smartphone2635.00

This query first extracts the year from the sale_date. It then groups by the extracted year and product type. For each group, it counts the number of product_id, which represents the total sales of each product type per year, and it also calculates the average price of each product type per year.

### SQL Question 5: What are stored procedures, and why are they useful?

Stored procedures in SQL are like recipes in a cookbook. Just like a recipe tells you the ingredients and instructions for making a particular dish, a stored procedure tells the DBMS the logic/statements needed to perform a specific task. Just like you can use a recipe to make the same dish over and over again, you can use a stored procedure to repeat the same task multiple times with different input parameters (which is why stored procedures are so damn useful!).

Say you were a Data Analyst working on a HR analytics project. A common sub-task you might have to do is calculate the average salary for a given department at Xiaomi, which would be perfect for a stored procedure:

To call this stored procedure and find the average salary for the Data Science department you'd execute a query like this:

### SQL Question 6: Identify Customers of Xiaomi Products Containing Specific String

As a global electronic and smartphone manufacturer, Xiaomi has introduced many products in various markets. For this question, you are required to write an SQL query that allows us to identify the customer records for a specified product that contains a particular string in its name.

Suppose, we want to get customer records for those who have purchased a product whose name contains the string 'Mi Mix'.

Our customer records and product data are stored in two tables with the following structure:

##### Example Input:
customer_idfirst_namelast_nameemail
1JohnDoejohn.doe@example.com
2JaneSmithjane.smith@example.com
4EmilyClarkemily.clark@example.com
##### Example Input:
product_idproduct_namecustomer_id
1Xiaomi Mi Mix 21
2Xiaomi Redmi 92
3Xiaomi Mi Mix 33
4Xiaomi Mi 11 Ultra4
5Xiaomi Mi Mix Fold1

This provided SQL query will return all customer records where the corresponding product name contains the string 'Mi Mix'. The use of the 'LIKE' keyword combined with wildcard characters ('%') allows us to match any products where 'Mi Mix' appears anywhere in the name.

### SQL Question 7: How would you improve the performance of a slow SQL query?

There's several steps you can take to troubleshoot a slow SQL query.

First things first, figure out why the query is slow! You can use and commands in PostgreSQL to identify any performance bottlenecks. After that, you can start changing your query, depending on what the source of the query performance issue is.

Generally, indexes can help speed up queries. Also de-normalizing your tables might help, to remove slow joins.

Lastly, you could always just upgrade your hardware! Time and money wasted improving query performance could just be better spent on mroe powerful database servers!

While this is a very surface-level approach to optimizing SQL query performance, it should do the trick for Data Analyst interviews and Data Science interviews at Xiaomi. Data Engineers should know a bit more about the and before the interview.

### SQL Question 8: Find the Most Popular Product in Each City

As an Data Analyst in Xiaomi, your task is to analyze the customer purchasing behavior in different cities. You have two tables at your disposal: and .

The table has four columns: (the ID of the purchase), (the ID of the customer who made the purchase), (the ID of the purchased product), and (the date of the purchase).

The table has three columns: (the ID of the customer), (the customer's first name), and (the city where the customer lives).

You need to write a SQL query to find the most popular product (in terms of the number of purchases) in each city.

##### Example Input:
purchase_idcustomer_idproduct_idpurchase_date
10113012022-01-12
10224022022-03-17
10313012022-04-05
10435012022-02-20
10524022022-08-15
##### Example Input:
customer_idfirst_namecity
1JohnLondon
2AnnaParis
3MikeNew York

This query joins the and tables on the field, then groups by and . For each group, it counts the number of rows (i.e., the number of purchases), and orders the results by , then by in descending order. The first row for each city is the most popular product in that city.

If there is a need to only display each city with the most popular product in each city, you can use the clause which is specific to PostgreSQL:

This will only return the first row for each distinct city, which will be the one with the most popular product, as rows are ordered by the number of purchases in descending order.

Since joins come up so often during SQL interviews, try this interactive Snapchat SQL Interview question using JOINS:

### SQL Question 9: Calculate Product Profit Margin

Xiaomi, the multinational electronics company, sells various products from smartphones to smart televisions. It would like to evaluate the profitability of their products. They are particularly interested in understanding the profit margin for each product sold in the year 2022. The profit margin is calculated as (Selling Price - Cost Price)/Selling Price * 100. The rounded profit margin should then be grouped by the product type.

For this problem, consider the following two tables in the Xiaomi database:

Example Input:

sale_idproduct_idsold_dateselling_price
1290100102/10/2022 00:00:00250
1835100203/20/2022 00:00:00500
2436100305/15/2022 00:00:00200
2754100407/01/2022 00:00:00150
3450100104/16/2022 00:00:00260

Example Input:

product_idproduct_typecost_price
1001Smartphone150
1002Television400
1004Smartwatch100

### Example Output:

product_typeprofit_margin
Smartphone44
Television20
Smartwatch33

This query calculates the profit margin for each product. It first joins the and tables on the field. It then filters the joined data for the sales happening in the year 2022. The calculation for the profit margin is performed in the statement, and the result is rounded to give a cleaner output. Lastly, the results are grouped by the field, providing an average profit margin for each type of product sold.

To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for requiring product profitability analysis or this CVS Health Pharmacy Analytics (Part 1) Question which is similar for calculating most profitable items.

### SQL Question 10: What are the main differences between foreign and primary keys in a database?

A primary key is a column (or set of columns) in a table that uniquely identifies each row in the table. It cannot contain null values and must be unique across all rows in the table.

A foreign key is a column (or set of columns) in a table that references the primary key of another table. It is used to establish a relationship between the two tables. A foreign key can contain null values, and multiple rows in the referencing table can reference the same row in the referenced table.

For example, consider a database with two tables: and . The Xiaomi customers table might have a primary key column called , while the Xiaomi orders table might have a foreign key column called that references the column in the table. This establishes a relationship between the two tables, such that each row in the orders table corresponds to a specific Xiaomi customer.

### How To Prepare for the Xiaomi SQL Interview

The key to acing a Xiaomi SQL interview is to practice, practice, and then practice some more! In addition to solving the earlier Xiaomi SQL interview questions, you should also solve the 200+ FAANG SQL Questions on DataLemur which come from companies like FAANG (FB, Apple, Amazon, Netflix, Google).

Each interview question has hints to guide you, detailed solutions and most importantly, there's an interactive coding environment so you can easily right in the browser your query and have it checked.

To prep for the Xiaomi SQL interview you can also be wise to solve interview questions from other tech companies like:

But if your SQL skills are weak, forget about going right into solving questions – go learn SQL with this DataLemur SQL Tutorial.

This tutorial covers SQL concepts such as joining a table to itself and grouping by multiple columns – both of which show up routinely during SQL interviews at Xiaomi.

### Xiaomi Data Science Interview Tips

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

Beyond writing SQL queries, the other types of questions to prepare for the Xiaomi Data Science Interview are:

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

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