9 The Hut Group SQL Interview Questions (Updated 2024)
The Hut Group employees write SQL queries daily for extracting e-commerce transaction data for further analysis and creating performance reports for marketing strategies. That's why The Hut Group frequently asks SQL problems during interviews for Data Science and Data Engineering positions.
As such, to help you practice for the The Hut Group SQL interview, here’s 9 THG (The Hut Group) SQL interview questions in this article.
9 THG (The Hut Group) SQL Interview Questions
SQL Question 1: Calculate the Average Rating per Product per Month
The Hut Group is an e-commerce company that sells a variety of products. They are interested in tracking how the average ratings for their products are changing over time. As a Data Analyst, you are tasked with writing a SQL query that calculates the average rating by product for every month. Use PostgreSQL syntax.
Product Table
The table provides relevant product data.
Example Input:
| product_id | product_name | brand_id | category_id |
|---|---|---|---|
| 50001 | Eyeliner | 1001 | 3001 |
| 69852 | Mascara | 2001 | 3002 |
Reviews Table
The table logs each review, its rating, and the user who submitted it.
Example Input:
| review_id | user_id | submit_date | product_id | stars |
|---|---|---|---|---|
| 6171 | 123 | 06/08/2022 00:00:00 | 50001 | 4 |
| 7802 | 265 | 06/10/2022 00:00:00 | 69852 | 4 |
| 5293 | 362 | 06/18/2022 00:00:00 | 50001 | 3 |
| 6352 | 192 | 07/26/2022 00:00:00 | 69852 | 3 |
| 4517 | 981 | 07/05/2022 00:00:00 | 69852 | 2 |
Answer:
Here's the SQL query you would write to solve this problem:
This query first truncates the 'submit_date' timestamp down to the month level. It then computes the average rating ('stars') for each product and for each month. The use of the GROUP BY clause on the 'mth' and 'product' fields ensures the averages are computed separately for each unique combination of product and month. These results are then returned in ascending order of month and product name.
Pro Tip: Window functions are a popular SQL interview topic, so practice every window function problem on DataLemur
SQL Question 2: Top Three Salaries
Suppose there was a table of The Hut Group employee salary data. Write a SQL query to find the top 3 highest paid employees within each department.
The Hut Group Example Input:
| employee_id | name | salary | department_id |
|---|---|---|---|
| 1 | Emma Thompson | 3800 | 1 |
| 2 | Daniel Rodriguez | 2230 | 1 |
| 3 | Olivia Smith | 2000 | 1 |
| 4 | Noah Johnson | 6800 | 2 |
| 5 | Sophia Martinez | 1750 | 1 |
| 8 | William Davis | 6800 | 2 |
| 10 | James Anderson | 4000 | 1 |
Example Input:
| department_id | department_name |
|---|---|
| 1 | Data Analytics |
| 2 | Data Science |
Example Output:
| department_name | name | salary |
|---|---|---|
| Data Analytics | James Anderson | 4000 |
| Data Analytics | Emma Thompson | 3800 |
| Data Analytics | Daniel Rodriguez | 2230 |
| Data Science | Noah Johnson | 6800 |
| Data Science | William Davis | 6800 |
Code your solution to this interview question directly within the browser on DataLemur:
Answer:
We use the DENSE_RANK() window function to generate unique ranks for each employee's salary within their department, with higher salaries receiving lower ranks. Then, we wrap this up in a CTE and filter the employees with a ranking of 3 or lower.
If the solution above is confusing, you can find a step-by-step solution with hints here: Top 3 Department Salaries.
SQL Question 3: What's a cross-join, and why are they used?
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 The Hut Group, 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 The Hut Group. 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 4: Analyze the Click-Through Conversion Rate for The Hut Group
As a data analyst at The Hut Group, you are tasked with calculating the click-through conversion rate of customers - from the moment they view a product, to when they add it to their shopping cart. The time interval you are asked to calculate this for is the month of August 2022.
Example Input:
| view_id | user_id | view_date | product_id |
|---|---|---|---|
| 1001 | 111 | 08/01/2022 00:00:00 | 20001 |
| 1002 | 222 | 08/02/2022 00:00:00 | 20002 |
| 1003 | 333 | 08/03/2022 00:00:00 | 20003 |
| 1004 | 111 | 08/04/2022 00:00:00 | 20001 |
| 1005 | 222 | 08/05/2022 00:00:00 | 20002 |
Example Input:
| add_id | user_id | add_date | product_id |
|---|---|---|---|
| 5001 | 111 | 08/01/2022 00:00:00 | 20001 |
| 5002 | 222 | 08/06/2022 00:00:00 | 20002 |
| 5003 | 333 | 08/07/2022 00:00:00 | 20003 |
| 5004 | 111 | 08/08/2022 00:00:00 | 20001 |
| 5005 | 222 | 08/09/2022 00:00:00 | 20002 |
Answer:
This PostgreSQL query first joins and on and to align views and adds to the shopping cart for each user and product. It then filters for views in the month of August 2022. For each product, it calculates the click-through conversion rate as the count of distinct users who added the product to their cart divided by the count of distinct users who viewed the product, multiplied by 100 (to get a percentage). The results are ordered in descending order of conversion rate.
To practice another question about calculating rates, solve this TikTok SQL Interview Question within DataLemur's online SQL code editor:
SQL Question 5: What does database normalization mean?
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 6: Calculate the average sales per product category every month
The Hut Group is a global leader in health and beauty products. The company wants to assess the sales performance of its various product categories. Can you write a SQL query that calculates the average sales per category, on a month-to-month basis?
Example Input:
| sales_id | product_category | sales_date | unit_sold |
|---|---|---|---|
| 858 | Vitamins | 06/10/2022 00:00:00 | 30 |
| 912 | Skincare | 06/15/2022 00:00:00 | 45 |
| 673 | Vitamins | 06/30/2022 00:00:00 | 20 |
| 289 | Skincare | 06/18/2022 00:00:00 | 50 |
| 749 | Skincare | 07/01/2022 00:00:00 | 70 |
| 426 | Makeup | 07/15/2022 00:00:00 | 40 |
| 316 | Makeup | 07/30/2022 00:00:00 | 35 |
| 490 | Vitamins | 07/18/2022 00:00:00 | 50 |
Example Output:
| month | product_category | avg_sales |
|---|---|---|
| 06 | Vitamins | 25.00 |
| 06 | Skincare | 47.50 |
| 07 | Skincare | 70.00 |
| 07 | Makeup | 37.50 |
| 07 | Vitamins | 50.00 |
Answer:
To solve this problem, one needs to utilize the GROUP BY clause along with the AVG aggregate function and EXTRACT function, like this:
For each row in the result set, it gives the month of the sales date, the product category, and the average units sold for that product category during that month. The result is ordered by month and by product category.
SQL Question 7: When would you use a clustered index vs. a non-clustered index?
Clustered and non-clustered indexes are both used to improve query performance, but they differ in how they are implemented.
A clustered index determines the physical order of the data rows in a table, while a non-clustered index does not. As a result, a table can have only one clustered index, but it can have multiple non-clustered indexes.
In terms of query efficiency & performance, a clustered index is generally faster for SELECT queries (reads) but updates (writes) to a clustered index are slower, as they require the data rows to be physically rearranged.
SQL Question 8: Find the Average Order Value by Country
Given a customer table where each row represents a customer with fields for , , and , and an orders table where each row represents an order with fields for , , , and . Join these tables and find the average order value () for each country.
Here is the schema of tables and :
Example Input:
| customer_id | country | signup_date |
|---|---|---|
| 765 | USA | 05/11/2021 |
| 982 | UK | 07/22/2020 |
| 521 | USA | 08/13/2019 |
| 632 | Ger | 01/05/2022 |
| 125 | UK | 11/14/2021 |
Example Input:
| order_id | customer_id | order_amount | order_date |
|---|---|---|---|
| 1234 | 765 | 256.73 | 07/18/2022 |
| 3463 | 982 | 102.67 | 07/25/2022 |
| 7890 | 521 | 136.99 | 07/21/2022 |
| 5412 | 632 | 82.33 | 07/10/2022 |
| 3675 | 125 | 259.74 | 07/20/2022 |
You are expected to write a SQL query to solve this problem, and the result should look like:
Example Output:
| country | avg_order_amount |
|---|---|
| USA | 196.86 |
| UK | 181.21 |
| Ger | 82.33 |
Answer:
This query joins the table with the table using the field which is common between them. After joining the tables, we calculate the average order amount per country and display the results grouped by country.
Because join questions come up so often during SQL interviews, take a stab at this Snapchat SQL Interview question using JOINS:
SQL Question 9: Calculate Weighted Score per product by Month
Given a product's rating and the number of times it is sold, calculate its weighted average rating and overall rating per month rounded to two decimal places. Use the product sales and weights as 5-Star (weight 5), 4-Star (weight 4), 3-Star (weight 3), 2-Star (weight 2) and 1-Star (weight 1).
Example Input:
| sale_id | product_id | sale_date | user_id |
|---|---|---|---|
| 1 | 10002 | 06/08/2022 | 123 |
| 2 | 10003 | 06/10/2022 | 265 |
| 3 | 10001 | 06/18/2022 | 362 |
| 4 | 10003 | 07/26/2022 | 192 |
| 5 | 10001 | 07/05/2022 | 981 |
Example Input:
| review_id | user_id | submit_date | product_id | stars |
|---|---|---|---|---|
| 1 | 123 | 06/08/2022 | 10002 | 4 |
| 2 | 265 | 06/10/2022 | 10003 | 3 |
| 3 | 362 | 06/18/2022 | 10001 | 5 |
| 4 | 192 | 07/26/2022 | 10003 | 4 |
| 5 | 981 | 07/05/2022 | 10001 | 5 |
Example Output:
| month | product_id | weighted_avg | overall_avg |
|---|---|---|---|
| 6 | 10001 | 5.00 | 5.00 |
| 6 | 10002 | 4.00 | 4.00 |
| 6 | 10003 | 3.00 | 3.00 |
| 7 | 10001 | 5.00 | 5.00 |
| 7 | 10003 | 4.00 | 4.00 |
Answer:
The query calculates the average of ratings (stars) for each product in each month and it also calculates the overall average normalized to a scale of 1 (divided by 5), both rounded to 2 decimal places. First, we extract the month from the date using the EXTRACT function, then the sales and review tables are joined on the product_id, and finally, the average is calculated with the AVG function and rounded with the ROUND function.
To practice a very similar question try this interactive Amazon Average Review Ratings Question which is similar for calculating average product metrics by month or this Wayfair Y-on-Y Growth Rate Question which is similar for <Understanding sales performance across time.
The Hut Group SQL Interview Tips
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the The Hut Group SQL interview is to solve as many practice SQL interview questions as you can!
In addition to solving the above The Hut Group SQL interview questions, you should also solve the 200+ DataLemur interview questions which come from companies like Google, Facebook, Microsoft and Amazon.
Each SQL question has hints to guide you, detailed solutions and most importantly, there is an online SQL coding environment so you can right online code up your SQL query answer and have it checked.
To prep for the The Hut Group SQL interview you can also be a great idea to practice SQL problems from other tech companies like:
However, if your SQL skills are weak, don't worry about diving straight into solving questions – improve your SQL foundations with this SQL tutorial for Data Scientists & Analysts.
This tutorial covers SQL concepts such as handling missing data (NULLs) and math functions – both of which show up often in The Hut Group SQL interviews.
THG (The Hut Group) Data Science Interview Tips
What Do The Hut Group Data Science Interviews Cover?
Besides SQL interview questions, the other types of problems to prepare for the The Hut Group Data Science Interview include:
- AB Testing Questions
- Python or R Coding Questions
- Product & Business Analytics Questions
- Machine Learning Questions
- Behavioral Interview Questions
How To Prepare for The Hut Group Data Science Interviews?
I'm a bit biased, but I think the optimal way to prep for The Hut Group Data Science interviews is to read the book Ace the Data Science Interview.
The book covers 201 interview questions sourced from companies like Microsoft, Google & Amazon. It also has a refresher on SQL, Product-Sense & ML. And finally it's helped thousands of people land their dream job in data, which is why it's earned 1000+ reviews on Amazon and a 4.5-star rating.