# 10 Mixi SQL Interview Questions (Updated 2024)

Updated on

June 23, 2024

Mixi employees use SQL often for analyzing user interaction data for product improvements and managing gaming data to target user engagement strategies. For this reason Mixi often tests SQL query questions in interviews for Data Analytics, Data Science, and Data Engineering jobs.

## 10 Mixi SQL Interview Questions

### SQL Question 1: Daily Active Users Performance

Imagine that you are a Data Analyst at Mixi, a fictitious social networking company. You have been tasked with analyzing the daily user activity on the platform. The company wants to identify the average number of users who log in daily and compare it with the previous day's activity to understand trends.

Specifically, we have a table that records each user's login activity with the following structure:

##### Example Input:
362012009/01/2022
382125509/01/2022
500210209/02/2022
425325509/02/2022
109330009/03/2022
193010209/03/2022
227530009/03/2022
336212009/04/2022

Your task is to write a SQL query that returns a list of dates with the average and previous day's active users.

Example Output:

dateaverage_usersprevious_day_users
09/02/202222
09/03/202221
09/04/202212

This query first calculates the number of daily active users using a common table expression (CTE). Then, it uses window functions to calculate the average number of active users up to the current day and also retrieves the number of active users on the previous day. The AVG window function gets the average daily active user by scanning all the rows from the start to the current one, and the LAG function retrieves the active users count from the previous day.

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

### SQL Question 2: Second Highest Salary

Suppose there was a table of Mixi employee salary data. Write a SQL query to find the 2nd highest salary at the company.

#### Mixi Example Input:

employee_idsalary
12500
2800
31000
41200

#### Example Output:

second_highest_salary
1200

You can solve this question directly within the browser on DataLemur:

You can find a step-by-step solution with hints here: 2nd Highest Salary.

### SQL Question 3: In SQL, what's the primary difference between the 'BETWEEN' and 'IN' operators?

While both the and operators are used to filter data based on some criteria, selects for values within a given range, whereas for it checks if the value is in a given list of values.

For example, say you had a table called , which had the salary of the employee, along with the country in which they reside.

To find all employees who made between $80k and$120k, you could use the operator:

To find all employees that reside in the US or Canada, you could use the operator:

### SQL Question 4: User Activity on Mixi

Mixi is a social networking service where users can share status updates, photos and interact with their friends. As a Data Engineer at Mixi, you are asked to analyze the user activities on the platform. Your task is to find the number of posts and comments made by users for each month. Also, find the user who made the most posts in each month.

For this problem consider the following tables:

user_iduser_name
101Alice
102Bob
103Charlie
##### Sample Input:
post_iduser_idpost_date
10011012022-06-01
10021022022-06-15
10031022022-06-20
10041032022-07-01
10051012022-07-20
##### Sample Input:
comment_idpost_iduser_idcomment_date
200110011012022-06-02
200210021032022-06-16
200310021012022-06-21
200410041022022-07-02
200510051032022-07-21
##### Example Output:
2022-0633Bob
2022-0722Alice

This query first calculates the total posts and comments made by users for each month and then finds the most active user in each month (i.e., the user with the maximum number of posts).

### SQL Question 5: What is a cross-join, and when would you use one?

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

For example, say you worked on the Marketing Analytics team at Mixi, and needed to understand what advertising copy (text) goes best with what advertising creative (the photo that gets posted with the advertisement copy).

Here is an example of a cross-join between a table of ad_copy and a table of ad_creative:

You could this query to generate all possible combinations of ad copy and ad creative to help you create more effective ads for Mixi. Just be careful: if you had 1,000 rows of ad copy and 1,000 different image creatives, the resulting cross-join would have 1 million rows!

### SQL Question 6: Extract raving customer data

Mixi is an online retail company with a growing customer base. They have a database of all their customer transactions. However, they now want to focus more on their loyal and satisfied customers. They define raving customers as customers who have placed more than 10 orders and given an average rating of 4.5 and above.

Write an SQL query to filter down the customers based on these conditions from the and tables. The output should include the customer's id, their total number of orders and their average rating.

##### Example Input:
order_idcustomer_idproduct_idorder_date
121002022-06-10
231012022-06-11
321022022-06-12
441032022-06-13
521042022-06-14
621052022-06-15
721062022-06-16
821072022-06-17
921082022-06-18
1021092022-06-19
1121102022-06-20
##### Example Input:
review_idcustomer_idproduct_idstars
121004.5
231014.3
321024.7
441033.6
521044.6
621054.5
721064.7
821074.6
921084.5
1021094.7
1121104.8

This query joins the and table on and and then groups the combined data by . The clause is then used to filter out all the customers who have placed more than 10 orders and have an average rating of 4.5 or higher.

### SQL Question 7: What are the different normal forms (NF)?

There are several normal forms that define the rules for normalizing a database:

A database is in first normal form (1NF) if it meets the following criteria:

• Each column in a table contains a single value (no lists or containers of data)
• Each column should contain the same type of data (no mixing strings vs. integers)
• Each row in the table is unique

A database is in second normal form (2NF) if it meets the following criteria:

• It is in first normal form.
• All non-key attributes in a table are fully dependent on the primary key.

Said another way, to achieve 2NF, besides following all the rules from 1NF all the columns in a given table should be dependent only on that table's primary key.

A database is in third normal form (3NF) if it meets the following criteria:

• It is in second normal form.
• There are no transitive dependencies in the table.

A transitive dependency means that a piece of data in one column is derived from another column. For example, it wouldn't make sense to keep a column called "user's age" and "user's birthdate" (because age can be derived from birthdate.

While there's also a 4th and 5th normal form, it's too pedantic and hence out-of-scope to know for the Mixi SQL interview.

### SQL Question 8: Click-Through-Rates and Conversion Rates

In Mixi, a digital product company, they use two main tables to track detailed steps of user actions. The first is the table, which records every time a visitor clicks an ad, and the second is the table, which records every time a user adds an item to their cart after clicking on an ad.

The goal of Mixi is to have as many click conversions as possible. Could you analyze the click-through-rate (CTR) and the conversion rate from click to purchase for different product categories in the and tables?

##### Example Input:
click_iduser_idclick_dateproduct_idproduct_category
100111106/01/2022 00:00:00101Electronics
100211206/02/2022 00:00:00101Electronics
100311306/04/2022 00:00:00102Clothing
100411406/07/2022 00:00:00103Books
100511506/09/2022 00:00:00104Sports Equipment
##### Example Input:
conversion_iduser_idconversion_dateproduct_idproduct_category
101111106/01/2022 00:00:00101Electronics
101211206/03/2022 00:00:00101Electronics
101311606/10/2022 00:00:00104Sports Equipment

In this SQL query, the statement is used to divide the data into groups of distinct rows, where each group represents data of a specific product category. The returns the number of unique clicks for different product categories and the returns the number of unique conversions for each product category. The is calculated by dividing the count of unique conversions by the count of unique clicks.

To solve another question about calculating rates, try this SQL interview question from TikTok within DataLemur's interactive SQL code editor:

### SQL Question 9: Highest Selling Products per Month for Mixi

Given a table named "sales" with the following columns: , , , and ; You are expected to write a query to determine the highest selling product for each month in terms of the total sales amount (which can be calculated as * ).

##### Example Input:
transaction_idproduct_iddate_of_salequantity_soldunit_price
579110105/12/2022 00:00:002510
130210205/15/2022 00:00:001520
951310105/28/2022 00:00:002010
491710206/05/2022 00:00:003020
692310306/21/2022 00:00:001850
##### Example Output:
monthproducttotal_sales_amount
5101450
6102600

This query works by first creating a common table expression (CTE) that calculates the total sales amount for each product for each month. The second CTE, , then determines the highest sales amount for every month, and the final SELECT statement joins these two CTEs together to return the month, product id, and the maximum sales amount for the highest selling product during that month.

### SQL Question 10: What's the difference between a clustered and non-clustered index?

Both clustered and non-clustered indexes are used to improve query performance, but they have different characteristics.

A clustered index determines the physical order of the data rows in a table, while a non-clustered index does not.This means that a table can have only one clustered index, but it can have multiple non-clustered indexes.

In terms of query performance, a clustered index is usually faster for searches that return a large number of records, while a non-clustered index is faster for searches that return a small number of records. However, updates to a clustered index are slower, as they require the data rows to be physically rearranged, while updates to a non-clustered index are faster, as they only require the index data structure to be updated.

### How To Prepare for the Mixi SQL Interview

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

Each exercise has multiple hints, detailed solutions and most importantly, there's an interactive coding environment so you can right online code up your query and have it checked.

To prep for the Mixi SQL interview it is also helpful to solve interview questions from other tech companies like:

However, if your SQL skills are weak, don't worry about jumping right into solving questions – strengthen your SQL foundations with this interactive SQL tutorial.

This tutorial covers SQL concepts such as filtering strings using LIKE and window functions – both of these come up frequently during Mixi SQL assessments.

### Mixi Data Science Interview Tips

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

In addition to SQL query questions, the other question categories to prepare for the Mixi Data Science Interview are:

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

I'm sorta biased, but I believe the best way to prep for Mixi Data Science interviews is to read the book I wrote: Ace the Data Science Interview.

The book has 201 data interview questions taken from companies like Google, Tesla, & Goldman Sachs. It also has a refresher covering Python, SQL & ML. And finally it's vouched for by the data community, which is why it's earned 1000+ reviews on Amazon and a 4.5-star rating.