11 monday.com SQL Interview Questions (Updated 2024)
At Monday.com, SQL is used across the company for analyzing user behavior data to drive product improvements and managing diverse datasets for effective business intelligence reporting. Because of this, monday.com LOVES to ask SQL questions in interviews for Data Analytics, Data Science, and Data Engineering jobs.
As such, to help you study for the monday.com SQL interview, this blog covers 11 monday.com SQL interview questions – able to answer them all?
11 monday.com SQL Interview Questions
SQL Question 1: Identify Power Users of Monday.com
Suppose you are working with a hypothetical customer dataset for the productivity software company, Monday.com. As part of your data analysis tasks, you have been tasked with identifying the "power users" of the Monday.com software.
A power user in this context is a user who has created a significant number of projects within the last six months. Let's define "significant" as users who are in the top 5% in terms of projects created.
Here are two tables you'll be using:
Users Table:
Projects Table:
Answer:
User the PostgreSQL query below to solve the question:
This query first selects the number of projects created by each user in the last six months. It then calculates the 95th percentile of the number of projects created (the cutoff for power users), and finally selects the users who have created more projects than the cutoff. These users are the "power users" of Monday.com.
To practice another SQL customer analytics question where you can solve it right in the browser and have your SQL query instantly graded, try this Walmart SQL Interview Question:
SQL Question 2: Compute the Ratio of Active to Inactive Users for Each Workspace
Suppose you are a data analyst at monday.com and your manager wants to understand the user engagement for different workspaces. Each user can belong to multiple workspaces. A user is considered active in a workspace if they have performed any actions, such as adding, deleting, or modifying a task, in the past 90 days. Otherwise, they are considered inactive.
Write a SQL query to compute the ratio of active to inactive users for each workspace in the database. If a workspace has no inactive users, let's consider the ratio to be 1.
Example Input
| workspace_id | workspace_name |
|---|---|
| 101 | "Marketing" |
| 102 | "Product" |
| 103 | "Engineering" |
Example Input
| user_id | user_name |
|---|---|
| 2001 | "Alice" |
| 2002 | "Bob" |
| 2003 | "Charlie" |
Example Input
| workspace_id | user_id | last_action_date |
|---|---|---|
| 101 | 2001 | "2023-01-01" |
| 101 | 2002 | "2023-03-15" |
| 102 | 2001 | "2022-12-01" |
| 102 | 2003 | "2023-02-01" |
| 103 | 2003 | "2022-08-01" |
| 103 | 2002 | "2023-02-10" |
Example Output:
| workspace_name | active_to_inactive_ratio |
|---|---|
| "Marketing" | 1.00 |
| "Product" | 0.50 |
| "Engineering" | 0.50 |
Answer:
This query first joins the table with the based on . It then groups by and . For each group, it calculates the count of active and inactive users. Active users are those whose is within the past 90 days from the current date. Inactive users have a longer than 90 days ago. The ratio of active to inactive users is then calculated. If there are no active users, the ratio is 0. If there are no inactive users, the ratio is 1.
For more window function practice, solve this Uber SQL Interview Question on DataLemur's online SQL coding environment:
SQL Question 3: What's the difference between and ?
The clause is used to filter rows from the result set of a , , or statement. It allows you to specify a condition that must be met for a row to be included in the result set.
The clause is used to filter groups created by the clause. It is similar to the clause, but it is used to specify conditions on the groups created by the clause, rather than on the individual rows of the table.
Say you were working on a social media analytics project for monday.com.
Here is an example of a SQL query that you might write which uses both the WHERE and HAVING clauses:
This query retrieves the total impressions and average conversions for each platform in the table, the date of the campaign is in January 2023. The rows are grouped by platform and the clause filters the groups to include only those with more than 5000 impressions and an average conversion rate above 0.2.
SQL Question 4: Calculate the Click-Through-Conversions
At monday.com, we encourage users to view our products then add the product into their carts, which we call a "conversion". We make use of digital marketing strategies and display ads on various digital platforms. For the following two tables:
Example Input:
| view_id | user_id | view_date | product_id |
|---|---|---|---|
| 3051 | 107 | 06/08/2022 00:00:00 | 10001 |
| 2362 | 148 | 06/10/2022 00:00:00 | 10001 |
| 6293 | 148 | 06/11/2022 00:00:00 | 10002 |
| 7012 | 225 | 07/26/2022 00:00:00 | 10002 |
| 9281 | 225 | 07/26/2022 00:00:00 | 10001 |
Example Input:
| cart_id | user_id | add_date | product_id |
|---|---|---|---|
| 9871 | 107 | 06/11/2022 00:00:00 | 10001 |
| 2468 | 148 | 06/12/2022 00:00:00 | 10001 |
| 7567 | 148 | 06/13/2022 00:00:00 | 10002 |
| 1902 | 225 | 07/30/2022 00:00:00 | 10002 |
Write an SQL query to find out the click-through-conversion rate for each product. That is, for each product, calculate the number of users who added the product into their cart after viewing it divided by the total number of users who viewed the product.
Answer:
The above query first creates two temporary views - and - which count the number of distinct viewers and converters for each product. It then joins these two views on and calculates the conversion rate.
To solve a similar problem about calculating rates, solve this SQL interview question from TikTok within DataLemur's interactive coding environment:
SQL Question 5: Can you list the various types of joins in SQL, and describe their purposes?
A join in SQL allows you to retrieve data from multiple tables and combine it into a single set of results.
In SQL, there are four different types of JOINs. To demonstrate each kind, imagine you had two database tables, an advertising campaigns table which had information on Google Ads keywords and how much was bid for each keyword, and a sales table, which has data on how many products were sold and which Google Ads keyword drove that sale.
: An INNER JOIN retrieves rows from both tables where there is a match in the shared key or keys. For example, an INNER JOIN between the table and the table could be performed using the keyword column as the shared key. This would retrieve only the rows where the keyword in the table matches the keyword in the table.
: A LEFT JOIN retrieves all rows from the left table (in this case, the table) and any matching rows from the right table (the Sales table). If there is no match in the right table, values will be returned for the right table's columns.
: A RIGHT JOIN retrieves all rows from the right table (in this case, the Sales table) and any matching rows from the left table (the table). If there is no match in the left table, values will be returned for the left table's columns.
: A FULL OUTER JOIN retrieves all rows from both tables, regardless of whether there is a match in the shared key or keys. If there is no match, values will be returned for the columns of the non-matching table.
SQL Question 6: Get the Average Time Spent on Projects by Teams
Monday.com is a teamwork platform that allows teams to manage projects and tasks. For this question, let's consider that we have two tables. One is a table, which contains information on all projects, like project_id, team_id, and the time spent on them. The second is a table, which contains information on each team, such as team_id and team_name.
Given these two tables, write an SQL query to find the average time spent on projects by each team.
Example Input:
| project_id | team_id | time_spent_hours |
|---|---|---|
| 101 | 1 | 100 |
| 102 | 2 | 150 |
| 103 | 1 | 200 |
| 104 | 3 | 120 |
| 105 | 1 | 180 |
Example Input:
| team_id | team_name |
|---|---|
| 1 | "TeamAlpha" |
| 2 | "TeamBeta" |
| 3 | "TeamGamma" |
Example Output:
| team_name | avg_time_spent_hours |
|---|---|
| "TeamAlpha" | 160.00 |
| "TeamBeta" | 150.00 |
| "TeamGamma" | 120.00 |
Answer:
This query first joins the projects table and teams table based on the team_id. Then it groups the projects by team_name and calculates the average time spent on projects for each team.
SQL Question 7: Do NULLs in SQL mean the same thing as a zero?
A NULL value represents a missing or unknown value. It is different from a zero or a blank space, which are actual values. It is important to handle NULL values properly in SQL because they can cause unexpected results if not treated correctly.
SQL Question 8: Finding Specific Customer Information
In an attempt to grow the business, monday.com wants to offer a promotion to its current customers who work in the tech industry. As a data specialist, you have been asked to find all customer records where the company name includes 'tech'.
Example Input:
| customer_id | first_name | last_name | company_name | |
|---|---|---|---|---|
| 1023 | John | Doe | jdoe@mail.com | Techcorp Inc |
| 4528 | Sally | Smith | ssmith@mail.com | Suntech Solutions |
| 9827 | Bob | Johnson | bjohnson@mail.com | Lightspeed Logistics |
| 8391 | Alice | Williams | awilliams@mail.com | Grape Tech |
| 6925 | Charlie | Brown | cbrown@mail.com | BlueTech |
Answer:
This query uses the SQL statement with the wildcard symbol on both sides of 'tech'. This will match any text before or after 'tech' in the column, helping us identify all customers working at tech companies.
SQL Question 9: Total Customer Spending Per Project
Assume monday.com would like to analyze the total spending of customers per project. They have two tables - a 'Customers' table that records client information and a 'Projects' table that records project information and expenditures. Write an SQL query that will join the Customers and Projects tables on the customer_id field, and compute the total money spent per project for each customer.
Example Input:
| customer_id | customer_name | company_name |
|---|---|---|
| 1001 | John Doe | XYZ Corp |
| 1002 | Jane Smith | ABC Inc |
| 1003 | Jim Brown | DEF Ltd |
Example Input:
| project_id | customer_id | project_name | money_spent |
|---|---|---|---|
| 2001 | 1001 | Website Redevelopment | 5000 |
| 2002 | 1001 | SEO Campaign | 3000 |
| 2003 | 1002 | Brand Consultation | 7000 |
| 2004 | 1003 | Product Launch | 10000 |
| 2005 | 1003 | Market Research | 5000 |
Example Output:
| customer_name | project_name | total_spent |
|---|---|---|
| John Doe | Website Redevelopment | 5000 |
| John Doe | SEO Campaign | 3000 |
| Jane Smith | Brand Consultation | 7000 |
| Jim Brown | Product Launch | 10000 |
| Jim Brown | Market Research | 5000 |
Answer:
The above SQL command is joining the two tables on their common attribute, customer_id. It then selects the customer's name from the customer table, along with each project they have worked on and the total money spent on each project from the project table.
Since joins come up frequently during SQL interviews, try an interactive Spotify JOIN SQL question:
SQL Question 10: What does adding 'DISTINCT' to a SQL query do?
"The clause in SQL allows you to select records that are unique, eliminating duplicates.
For a tangible example, say you had a table of monday.com employees:
| first_name | job_title |
|---|---|
| Akash | Data Analyst |
| Brittany | Data Scientist |
| Carlos | Data Engineer |
| Diego | Data Engineer |
| Eva | Data Analyst |
If you were doing an HR Analytics project and you wanted to get all the unique job titles that currently worked at the company, you would write the following SQL query:
The output would give you 3 distinct job titles at monday.com:
| job_title |
|---|
| Data Analyst |
| Data Scientist |
| Data Engineer |
SQL Question 11: Calculate Statistics From Project Time Logs
Assume that monday.com wants to get some insights from their project time logs. They are interested in the average hours worked on each project per day, rounded to the nearest hour. They also want to find out the absolute difference between the maximum hours and the minimum hours spent on a project per day. Finally, they want to get the square root of the total hours worked on each project, rounded to the nearest whole number.
Here are some sample tables:
Example Input:
| log_id | project_id | user_id | log_date | hours_worked |
|---|---|---|---|---|
| 1001 | 2005 | 3001 | 06/15/2022 | 3 |
| 1002 | 2005 | 3002 | 06/15/2022 | 5 |
| 1003 | 2005 | 3003 | 06/15/2022 | 4 |
| 1004 | 2006 | 3001 | 06/15/2022 | 8 |
| 1005 | 2006 | 3002 | 06/15/2022 | 6 |
| 1006 | 2005 | 3001 | 06/16/2022 | 6 |
| 1007 | 2006 | 3002 | 06/16/2022 | 7 |
Example Output:
| project_id | avg_hours_per_day | abs_diff_max_min_hours_per_day | sqrt_total_hours |
|---|---|---|---|
| 2005 | 5 | 2 | 6 |
| 2006 | 7 | 1 | 4 |
Answer:
You can tackle this question with several SQL commands using PostgreSQL:
Here we first group by both the project_id and the log_date to calculate average, maximum, minimum and total hours per day for each project. Then in the main query, we take the average of daily average hours and daily difference hours (calculated as the absolute difference of maximum and minimum hours per day), and square root of total hours for each project. The final output is rounded to nearest whole number using ROUND() function.
To practice a very similar question try this interactive Twitter Tweets' Rolling Averages Question which is similar for calculating aggregated metrics or this Amazon Average Review Ratings Question which is similar for finding average and rounding it.
How To Prepare for the monday.com SQL Interview
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the monday.com SQL interview is to solve as many practice SQL interview questions as you can!
Beyond just solving the earlier monday.com SQL interview questions, you should also solve the 200+ SQL coding questions which come from companies like FAANG (FB, Apple, Amazon, Netflix, Google).
Each DataLemur SQL question has multiple hints, step-by-step solutions and best of all, there is an online SQL coding environment so you can instantly run your query and have it checked.
To prep for the monday.com SQL interview you can also be a great idea 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 topics like creating pairs via SELF-JOINs and LEFT vs. RIGHT JOIN – both of which come up frequently during SQL interviews at monday.com.
monday.com Data Science Interview Tips
What Do monday.com Data Science Interviews Cover?
In addition to SQL query questions, the other types of problems covered in the monday.com Data Science Interview are:
- Statistics and Probability Questions
- Python or R Programming Questions
- Product Data Science Interview Questions
- ML Interview Questions
- Behavioral & Resume-Based Questions
How To Prepare for monday.com Data Science Interviews?
The best way to prepare for monday.com Data Science interviews is by reading Ace the Data Science Interview. The book's got:
- 201 Interview Questions from Facebook, Google & startups
- A Refresher covering SQL, Product-Sense & ML
- Great Reviews (900+ 5-star reviews on Amazon)