11 Zoom SQL Interview Questions (Updated 2024)
At Zoom, SQL is used across the company for analyzing customer usage patterns for service optimization, and extracting data insights to improve video conferencing features and performance. That's why Zoom, as part of their interview process, calls you on Zoom and asks you multiple SQL problems for Data Analytics, Data Science, and Data Engineering jobs.
So, to help you ace the Zoom SQL interview, this blog covers 11 Zoom SQL interview questions – how many can you solve?
11 Zoom SQL Interview Questions
SQL Question 1: Identify Power Users in Zoom
In a company like Zoom, we can define a power user as someone who hosts meetings very frequently, with a large number of participants and long durations. Power users are also likely to use the premium features of Zoom.
Assuming we have tables that capture users (), meetings () and premium feature usage (), write an SQL query to identify the power users. A power user could be defined as someone who has hosted more than 10 meetings in the last month, with an average of more than 5 participants per meeting and a total of more than 20 premium features used in the last month.
Example Input:
| user_id | name |
|---|---|
| 1 | Alice |
| 2 | Bob |
| 3 | Carlos |
Example Input:
| meeting_id | host_user_id | participants | start_time | end_time |
|---|---|---|---|---|
| 101 | 1 | 10 | 2022-07-01 09:00:00 | 2022-07-01 10:00:00 |
| 102 | 1 | 7 | 2022-07-05 14:00:00 | 2022-07-05 15:30:00 |
| 103 | 2 | 4 | 2022-07-06 10:00:00 | 2022-07-06 11:00:00 |
Example Input:
| feature_id | user_id | usage_time |
|---|---|---|
| 201 | 1 | 2022-07-01 09:30:00 |
| 202 | 1 | 2022-07-05 14:45:00 |
| 203 | 2 | 2022-07-06 10:30:00 |
Answer:
This SQL query first joins the , , and tables based on . It then filters out records where the meeting start time and premium feature usage time are within the last month. It groups by the user's name and filters out users who have not hosted more than 10 meetings, have an average participants less than 5, and have used premium features less than 20 times in the last month. The query returns the name of the power users, along with the count of meetings they have hosted, their average number of participants, and the count of premium features they have used within the last month.
To solve a related customer analytics question on DataLemur's free online SQL code editor, try this recently asked Microsoft SQL interview question:
SQL Question 2: Calculate the Monthly Average Ratings For Each Product
As a data analyst at Zoom, you are asked to monitor the changes in user sentiment for each product by tracking monthly average ratings. Sort your final result by in ascending order and in ascending order within each .
For instance, if a product received a rating of 4 stars on June 1st and a rating of 3 stars on June 30th, the average rating for that product in June would be .
Example Input:
| review_id | user_id | submit_date | product_id | stars |
|---|---|---|---|---|
| 201 | 11 | 02/01/2022 00:00:00 | 101 | 5 |
| 202 | 12 | 02/10/2022 00:00:00 | 102 | 3 |
| 203 | 13 | 03/01/2022 00:00:00 | 101 | 4 |
| 204 | 11 | 04/01/2022 00:00:00 | 101 | 2 |
| 205 | 14 | 05/01/2022 00:00:00 | 102 | 4 |
Example Output:
| month | product_id | avg_stars |
|---|---|---|
| 2 | 101 | 5.00 |
| 2 | 102 | 3.00 |
| 3 | 101 | 4.00 |
| 4 | 101 | 2.00 |
| 5 | 102 | 4.00 |
Answer:
Assuming that the table name is , you can use the following SQL to get the average monthly ratings
In this SQL query, we use the function to get the month from the . We then group our data by and , and use the function to calculate the average of for each group. Finally, we sort our result by and in ascending order.
Pro Tip: Window functions are a popular SQL interview topic, so practice all the window function problems on DataLemur
SQL Question 3: What does the constraint do?
A is like a secret code that unlocks the door to another table. It's a field in one table that points to the (the master key) in another table. This helps keep the data in your database organized and tidy, because it won't let you add new rows to the table unless they have the correct secret code (a corresponding entry in the table).
It's also like a special bond between the two tables - if you try to delete the data, the will be like "Whoa, hold on! I still need that information!" and prevent the deletion from happening.
SQL Question 4: Zoom Meeting Data Analysis
Zoom is a popular platform for conducting online meetings, webinars and providing collaborative workspace. As a data scientist in Zoom, you have been tasked with designing a database schema that captures details about users, meetings and the interaction between them.
Users Table
The table captures detail about each user. Columns would include , (Individual, Business, Enterprise), and .
Example Input:
| user_id | account_type | signup_date | location |
|---|---|---|---|
| 123 | Individual | 12/18/2020 | Los Angeles, CA |
| 256 | Business | 06/08/2020 | New York, NY |
| 789 | Enterprise | 01/25/2021 | San Francisco, CA |
| 973 | Individual | 07/20/2021 | Chicago, IL |
| 567 | Business | 11/22/2020 | Houston, TX |
Meetings Table
The table captures details about each meeting. Columns would include , , , , (Scheduled, Instant, Recurring), and .
Example Input:
| meeting_id | host_user_id | start_time | end_time | meeting_type | participants_count |
|---|---|---|---|---|---|
| 70012 | 123 | 08/10/2022 09:00:00 | 08/10/2022 10:30:00 | Scheduled | 15 |
| 98052 | 789 | 08/10/2022 13:00:00 | 08/10/2022 14:00:00 | Recurring | 100 |
| 64565 | 256 | 08/11/2022 15:00:00 | 08/11/2022 15:45:00 | Instant | 7 |
| 24680 | 973 | 08/11/2022 11:30:00 | 08/11/2022 12:30:00 | Scheduled | 20 |
| 56789 | 567 | 08/12/2022 09:30:00 | 08/12/2022 10:00:00 | Instant | 10 |
A common analysis might be to determine the for each and to better understand how different account types use Zoom meetings.
Answer:
Here is the PostgreSQL query that will provide this information:
This query joins the table with the table on the (or host_id in meetings table). It then uses an aggregation function, , to calculate the average participant count for each combination of and . The statement is used to specify the columns that we want to group by. Finally, the statement is used to order the output by and .
SQL Question 5: What does the SQL command do?
When using , only rows that are identical in both sets will be returned.
For a concrete example, say you were a Data Analyst supporting the Sales Analytics team at Zoom, and data on potential sales leads lived in both Salesforce and Hubspot CRMs. To write a query to analyze leads created before 2023 started, that show up in BOTH CRMs, you would use the command:
SQL Question 6: Filtering Zoom Users based on Meeting Attendance
As an analyst at Zoom, you are given a task to filter out customers who attended more than 5 meetings in the last week and spent more than 60 minutes in total, and the meetings were not on a weekend. Here, a 'meeting' refers to a video conferencing session and the 'duration' refers to the total length of the meeting in minutes.
Write a SQL query that filters these users from the and tables.
Sample Input
| meeting_id | date | duration |
|---|---|---|
| 102 | 2022-09-01 | 30 |
| 106 | 2022-09-02 | 60 |
| 110 | 2022-09-03 | 45 |
| 115 | 2022-09-04 | 90 |
| 120 | 2022-09-05 | 50 |
Sample Input
| user_id | meeting_id |
|---|---|
| 7865 | 102 |
| 7865 | 106 |
| 9032 | 110 |
| 7865 | 110 |
| 7865 | 115 |
| 9032 | 115 |
| 7865 | 120 |
| 9032 | 120 |
Answer:
This query joins the attendance and meetings tables on the meeting_id. It then filters for meetings in the last week that were not on weekends (where the ISO day of the week is less than 6). The query then groups the results by user_id, and uses the HAVING clause to filter out groups of rows (users) who attended more than 5 meetings and whose total meeting duration was over 60 minutes. This list will contain the user_ids of the users who meet these conditions.
SQL Question 7: What does it mean to use a UNIQUE constraint in a database?
A UNIQUE constraint ensures that all values in a column are different. It is often used in conjunction with other constraints, such as NOT NULL, to ensure that the data meets certain conditions.
For example, if you had Zoom sales leads data stored in a database, here's some constraints you'd use:
In this example, the UNIQUE constraint is applied to the "email" and "phone" fields to ensure that each Zoom lead has a unique email address and phone number. This helps to ensure the integrity of the data in the database and prevents errors that could occur if two leads had the same email address or phone number.
SQL Question 8: Calculate the Average Duration of Zoom Meetings
You are working as a data scientist at Zoom. The company wants to understand more about how long users are spending in meetings to drive business decisions. Can you write a SQL query to find the average duration of meetings hosted on Zoom platform in hours, grouped by month and year?
Note that duration is calculated as - and the result should be in hours.
Example Input:
| meeting_id | user_id | start_time | end_time |
|---|---|---|---|
| 101 | 500 | 2022-04-02 10:00:00 | 2022-04-02 12:00:00 |
| 102 | 501 | 2022-04-15 09:00:00 | 2022-04-15 10:05:00 |
| 103 | 502 | 2022-05-01 14:00:00 | 2022-05-01 15:00:00 |
| 104 | 503 | 2022-05-22 16:00:00 | 2022-05-22 18:00:00 |
| 105 | 504 | 2022-05-30 08:00:00 | 2022-05-30 08:50:00 |
Example Output:
| year | month | avg_duration_hr |
|---|---|---|
| 2022 | 04 | 1.52 |
| 2022 | 05 | 1.33 |
Answer:
In PostgreSQL, we can first extract the year and month from start_time using the function. Then we can calculate the duration in hours with . Here is the SQL query to get the result:
This query will first calculate the duration of each meeting in hours, then it will group these durations by year and month of the . The function is then applied to these groups to calculate the average duration of meetings for each month and year. The results are ordered by year and month for easier interpretation.
To practice a very similar question try this interactive Amazon Server Utilization Time Question which is similar for calculating total time usage in a period or this Facebook Active User Retention Question which is similar for calculating data grouped by month and year.
SQL Question 9: Get Average Meeting Duration per User
Given a table named of Zoom's records, where each row represents an online meeting. Each meeting has an ID, the ID of the user who initiated the meeting, and the start and end times. Can you query the average duration (in minutes) of meetings initiated by each user?
Example Input:
| meeting_id | user_id | start_time | end_time |
|---|---|---|---|
| 1 | 101 | 2022-08-10 10:30:00 | 2022-08-10 10:50:00 |
| 2 | 102 | 2022-08-10 11:00:00 | 2022-08-10 11:45:00 |
| 3 | 101 | 2022-08-10 14:00:00 | 2022-08-10 14:30:00 |
| 4 | 102 | 2022-08-10 15:00:00 | 2022-08-10 16:30:00 |
| 5 | 103 | 2022-08-10 16:00:00 | 2022-08-10 16:15:00 |
Example Output:
| user_id | avg_duration |
|---|---|
| 101 | 25.00 |
| 102 | 67.50 |
| 103 | 15.00 |
Answer:
This query first calculates the duration of each meeting in minutes (subtracting from and converting the result to seconds then to minutes using ). The function is then used to calculate the average duration for each user (), which are grouped together using the clause.
SQL Question 10: What's a correlated sub-query? How does it differ from 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 Zoom 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 Zoom 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 Zoom employees table.
SQL Question 11: Analyzing Zoom Customer Meeting Durations and Participations
Please write a SQL query that will tell us the average meeting duration per customer and the total number of participants in their meetings. For this question, assume that we have two tables - and . It is also assumed that a meeting could have multiple participants but each has a unique .
Here's some sample data for our and tables:
Example Input:
| customer_id | first_name | last_name | signup_date |
|---|---|---|---|
| 101 | John | Doe | 2018-05-01 |
| 102 | Jane | Smith | 2019-03-17 |
| 103 | Alice | Johnson | 2020-12-04 |
Example Input:
| meeting_id | customer_id | duration_mins | participants |
|---|---|---|---|
| 201 | 101 | 30 | 6 |
| 202 | 101 | 60 | 10 |
| 203 | 102 | 45 | 8 |
| 204 | 103 | 30 | 5 |
| 205 | 103 | 60 | 7 |
Example Output:
| customer_id | avg_duration_mins | total_participants |
|---|---|---|
| 101 | 45.00 | 16 |
| 102 | 45.00 | 8 |
| 103 | 45.00 | 12 |
Answer:
In this query, we join the and tables on the field. We then group the results by . For each customer, we compute the average meeting duration () and the total number of participants (). We order the results by for clarity.
Because joins come up so often during SQL interviews, try an interactive Spotify JOIN SQL question:
Zoom SQL Interview Tips
The key to acing a Zoom SQL interview is to practice, practice, and then practice some more!
Beyond just solving the above Zoom SQL interview questions, you should also solve the 200+ FAANG SQL Questions on DataLemur which come from companies like Amazon, Microsoft, Meta, and smaller tech companies.
Each problem on DataLemur has multiple hints, step-by-step solutions and most importantly, there's an online SQL coding environment so you can right in the browser run your SQL query answer and have it executed.
To prep for the Zoom SQL interview it is also wise to solve SQL questions from other tech companies like:
However, if your SQL foundations are weak, don't worry about diving straight into solving questions – refresh your SQL knowledge with this SQL interview tutorial.
This tutorial covers SQL topics like SQL joins with practice exercises and math functions like ROUND()/CEIL() – both of which pop up often during Zoom SQL interviews.
Zoom Data Science Interview Tips
What Do Zoom Data Science Interviews Cover?
In addition to SQL interview questions, the other types of questions to practice for the Zoom Data Science Interview are:
- Statistics & Probability Questions
- Python or R Coding Questions
- Analytics and Product-Metrics Questions
- ML Interview Questions
- Resume-Based Behavioral Questions
How To Prepare for Zoom Data Science Interviews?
To prepare for Zoom Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions sourced from FAANG, tech startups, and Wall Street
- a crash course covering SQL, Product-Sense & ML
- over 900+ reviews on Amazon & 4.5-star rating