11 MongoDB SQL Interview Questions (Updated 2024)

Updated on

February 14, 2024

Although MongoDB uses Mongo Query Language (MQL) to interact with MongoDB databases, they still heavily invest in SQL and offer tutorials and tools to convert between SQL and MQL. That's why MongoDB often tests SQL problems during interviews for Data Analytics, Data Science, and Data Engineering jobs.

So, to help you practice for the MongoDB SQL interview, we've curated 11 MongoDB SQL interview questions – able to answer them all?

11 MongoDB SQL Interview Questions

SQL Question 1: Identifying MongoDB Power Users

MongoDB often identifies its power users or VIPs as users who are making a high number of read and write operations. Let's say a power user is defined as a user who makes more than 5000 operations in a single day. Write a SQL query to find all the power users that exist in the database based on their daily operations.

The database contains two tables - 'users' and 'operations'. The 'users' table contains information about each user, with columns for 'user_id' and 'user_name'. The 'operations' table records each operation made by a user, with columns for 'op_id', 'user_id', 'operation_type' ('read' or 'write') and 'op_time' (timestamp of when the operation was made).

Example Input:
Example Input:
1011read06/08/2022 00:00:00
1021write06/08/2022 00:01:00
1031read06/08/2022 00:02:00
1042write06/10/2022 00:00:00
1052read06/10/2022 00:01:00


This query first joins the 'users' and 'operations' tables based on 'user_id'. It then filters the operations for a specific date, in this case '2022-06-08'. The query then groups by 'user_id', and only includes those users who have made more than 5000 operations on that day. The result is a list of power users for the specified date.

To practice a related customer analytics question on DataLemur's free interactive coding environment, try this Microsoft Teams Power User SQL Interview Question: Microsoft SQL Interview Question: Teams Super User

SQL Question 2: Find the Average Star Rating Per Month for Each Product

Write a SQL query to find the average stars given by users for each product for each month in the year 2022. For simplicity, assume that each review is uniquely identified by review_id, and each user and product is uniquely identified by user_id and product_id, respectively.

You are provided with the following table:

Example Input:

Now, write a SQL query to find the average star rating given to each product per month. The date is given in the format 'YYYY-MM-DD'.

Expected Output:


To solve the problem, we can use the SQL function to extract the month from the column and the function to calculate the average of . The clause is used to group the result by month and product_id.

Here's the PostgreSQL query:

The query works by first extracting the month from the for each row. Then we group by the extracted month and the . For each group, we calculate the average which gives us the average star rating given to each product per month within the year 2022. The result is then ordered by and .

For more window function practice, try this Uber SQL Interview Question on DataLemur's interactive SQL code editor:

Uber SQL problem

SQL Question 3: What would you do to speed up 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. You might discover that your query is inefficient, or that there are many database writes at the same time you are doing a read, or maybe too many people are concurrently running queries on the same database server.

For Data Analyst and Data Science positions, knowing the ins-and-outs of SQL performance tuning is out-of-scope for the SQL interview round. However, knowing that joins are expensive, and indexes can speed up queries, is generally enough of an answer for MongoDB SQL interviews.

MongoDB SQL Interview Questions

SQL Question 4: Analyzing Customer Query Trends

MongoDB Inc. is interested in better understanding how their customers are using their product. Specifically, they want to know which of their top database products (Product A, B, C, D, E) are queried most often by users per day. This information will help the company prioritize developing new features and improvements for the most popular products.

To conduct this analysis, you have access to two tables in their PostgreSQL database, and .

11002022-08-12 09:12:33'SELECT * FROM product_a'
21502022-08-12 10:22:02'UPDATE product_a SET...'
31202022-08-13 11:33:44'DELETE FROM product_b WHERE...'
41002022-08-13 18:45:52'INSERT INTO product_c VALUES...'
51502022-08-14 07:25:22'SELECT * FROM product_d'
100'Product A'
150'Product B'
120'Product C'
100'Product D'
150'Product E'

Write a SQL query in PostgreSQL to calculate the total queries for each product per day.


This query extracts the date from the query time, includes the product name, then counts the total queries for that combination. Joining the table to the table allows us to match a query to its product by user_id, provided the query text contains the product name.

Please note, this solution might not be perfect as the query assumes product name matches part of the query text and there might be situations where that assumption doesn't hold true. Depending on the specifics of the data and business needs, different approaches could be developed.

SQL Question 5: What is the difference between cross join and natural join?

Cross joins and natural joins are two types of JOIN operations in SQL that are used to combine data from multiple tables. A cross join creates a new table by combining each row from the first table with every row from the second table, and is also known as a cartesian join. On the other hand, a natural join combines rows from two or more tables based on their common columns, forming a new table. One key difference between these types of JOINs is that cross joins do not require common columns between the tables being joined, while natural joins do.

Here's an example of a cross join:

If you have 20 products and 10 colors, that's 200 rows right there!

Here's a natural join example using two tables, MongoDB employees and MongoDB managers:

This natural join returns all rows from MongoDB employees where there is no matching row in managers based on the column.

SQL Question 6: Filter and Aggregate Customer Reviews Data

A business has been collecting reviews on its products and wants to understand how the products have been received by the customers. Specifically, they would like to see a monthly average rating for each product during the past year. The business uses PostgreSQL for their database.

Can you write a SQL query that filters and aggregates the data from the table to display the month of review submission, product id, and average stars received for each product on a monthly basis?

Example Input:


Example Output:



In the SQL block above, we first create a common table expression (CTE) to extract month from . Then we select and from the CTE and calculate the average of grouping by and . The final result is ordered first by and then by .

SQL Question 7: What does it mean to normalize a database?

Normalization involves breaking up your tables into smaller, more specialized ones and using primary and foreign keys to define relationships between them. Not only does this make your database more flexible and scalable, it also makes it easier to maintain. Plus, normalization helps to keep your data accurate by reducing the chance of inconsistencies and errors.

The only downside is now is that your queries will involve more joins, which are slow AF and often a DB performance botteleneck.

SQL Question 8: Average Response Time Per Customer Request

In the technical support team at MongoDB, the support tickets are stored in a table. For each request, the time from when it's received to when it's addressed and closed may vary. You have been asked to determine the average response time, in hours, for each ticket priority level ("High", "Medium", "Low"). Breakdown this statistic by the priority of the requests.

Assume we have a table with the following structure:

Example Input:
47892026-08-08 00:00:002026-08-08 05:29:13High
63522026-08-07 11:04:282026-08-08 02:45:30Low
78132026-08-08 15:20:152026-08-08 18:31:22High
52962026-08-07 16:07:122026-08-08 00:46:30Medium
34592026-08-07 22:35:202026-08-08 01:04:50Low

We will calculate the difference between when the request was submitted () and when it was closed () to find out the response time for each request.


This query calculates the average response time for each request priority. It first extracts the difference in the UNIX timestamp epoch between the times the request was closed and the request was submitted. This difference is in seconds so it is then divided by 3600 to convert it to hours. The AVG function is then used to find the average of these response times. We use to get these average response times for each distinct priority level.

To practice a very similar question try this interactive Amazon Average Review Ratings Question which is similar for calculating averages or this Alibaba Compressed Mean Question which is similar for handling time-based averages.

SQL Question 9: Calculating Click-Through Rates for MongoDB Ads

You are a data analyst at MongoDB, a document database company providing the versatility needed to work with data in any structure. Marketing team has been running campaigns online and they want to understand how effective their campaigns are.

Specifically, they want to find out the "Click Through Rate (CTR)" for each campaign they have run in last month. The click-through rate is simply calculated by taking the number of users who clicked on the ad divided by the number of users who have seen the ad.

You are given two tables, impressions and clicks. The impressions table contains information about when and which user saw the advertisement. The clicks table contains information about when and which user clicked on the advertisement.

Table Example Input:
17212306/08/2022 00:00:003
18223406/10/2022 00:00:003
21315006/18/2022 00:00:005
10911006/18/2022 00:00:005
12712307/05/2022 00:00:003
Table Example Input:
71712306/08/2022 00:00:003
72226506/18/2022 00:00:005
51919207/05/2022 00:00:003


With PostgreSQL, you can perform this calculation by using the subquery and join syntax.

This query first calculates the count of impressions and clicks per campaign in the last month separately. It then joins these counts on the basis of campaign_id and calculates click through rate by dividing the count of clicks by count of impressions.

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

SQL Question 10: Can you describe the concept of a database index and the various types of indexes?

An index in a database is a data structure that helps to quickly find and access specific records in a table.

For example, if you had a database of MongoDB customers, you could create a primary index on the column.

Having a primary index on the column can speed up performance in several ways. For example, if you want to retrieve a specific customer record based on their , the database can use the primary index to quickly locate and retrieve the desired record. The primary index acts like a map, allowing the database to quickly find the location of the desired record without having to search through the entire table.

Additionally, a primary index can also be used to enforce the uniqueness of the column, ensuring that no duplicate values are inserted into the table. This can help to prevent errors and maintain the integrity of the data in the table.

SQL Question 11: Average Sales By Product Per Month

As a Data Analyst at MongoDB, you'd want to oversee sales performance by month for multiple products. Your task is to write a SQL query that calculates the average sales per month for each product. We are interested in getting both the total sales and the average value per transaction.

Consider the following table :

Example Input:
101112506/02/2022 00:00:0010001300
102378906/10/2022 00:00:0020052800
301421506/29/2022 00:00:0010001500
401647307/05/2022 00:00:0020052400
501331307/22/2022 00:00:0010001600

We want an output table that shows the average sales by month for each product:

Example Output:


This code uses the function to pull the month from the field. It then both and to find the average amount. Finally, is used to arrange the result in a sensible order.

Preparing For The MongoDB SQL Interview

The best way to prepare for a SQL interview, besides making sure you have strong SQL fundamentals, is to practice a ton of real SQL questions that were asked in recent job interviews. Beyond just solving the above MongoDB SQL interview questions, you should also solve the 200+ SQL coding questions which come from companies like Facebook, Google, and VC-backed startups. DataLemur Question Bank

Each interview question has multiple hints, full answers and crucially, there is an interactive SQL code editor so you can instantly run your query and have it graded.

To prep for the MongoDB SQL interview you can also be useful to practice SQL questions from other tech companies like:

But if your SQL coding skills are weak, forget about going right into solving questions – improve your SQL foundations with this free SQL for Data Analytics course. You can also improve your Mongo Query Language knowledge with this MongoDB MQL tutorial.

MongoDB Data Science Interview Tips

What Do MongoDB Data Science Interviews Cover?

In addition to SQL interview questions, the other question categories to practice for the MongoDB Data Science Interview are:

  • Statistics Interview Questions
  • Python Pandas or R Coding Questions
  • Analytics and Product-Metrics Questions
  • Machine Learning and Predictive Modeling Questions
  • Behavioral Based Interview Questions

MongoDB Data Scientist

How To Prepare for MongoDB Data Science Interviews?

The best way to prepare for MongoDB Data Science interviews is by reading Ace the Data Science Interview. The book's got:

  • 201 Interview Questions from Facebook, Google, & Amazon
  • A Refresher covering Stats, ML, & Data Case Studies
  • Great Reviews (900+ reviews, 4.5-star rating)

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