8 Logitech SQL Interview Questions (Updated 2024)
At Logitech, SQL is used all the damn time for analyzing user interactions with hardware products, and optimizing supply chain logistics through data-driven insights. Unsurprisingly this is why Logitech often tests SQL questions during interviews for Data Analytics, Data Science, and Data Engineering jobs.
To help you practice for the Logitech SQL interview, we've collected 8 Logitech SQL interview questions – able to answer them all?
8 Logitech SQL Interview Questions
SQL Question 1: Average product rating per month
Logitech is interested in understanding the average user rating given to each of their products on a monthly basis. Write a SQL query to analyze the table and return each product's average rating per month.
The table is structured as follows:
Sample Data:
| review_id | user_id | submit_date | product_id | stars |
|---|---|---|---|---|
| 1001 | 763 | 03/15/2022 00:00:00 | 001 | 5 |
| 1002 | 461 | 03/15/2022 00:00:00 | 002 | 4 |
| 1003 | 881 | 03/18/2022 00:00:00 | 002 | 3 |
| 1004 | 361 | 04/02/2022 00:00:00 | 003 | 4 |
| 1005 | 712 | 04/05/2022 00:00:00 | 001 | 2 |
| 1006 | 635 | 05/20/2022 00:00:00 | 001 | 4 |
| 1007 | 114 | 05/25/2022 00:00:00 | 002 | 5 |
| 1008 | 543 | 05/30/2022 00:00:00 | 003 | 1 |
To keep the data manageable for this scenario, assume that each corresponds to a different Logitech product and the column indicates the user's rating for that product, ranging from 1 (worst) to 5 (best).
Answer:
Here's how you can solve this with a PostgreSQL query:
This PostgreSQL query extracts the month from the column, groups the table by this month along with after which it calculates the average rating. The command is used to limit the average to two decimal places. The result is then ordered by month and product id. The function is used to get the month part from the column date in PostgreSQL.
p.s. Window functions show up super often during SQL interviews, so practice the 27+ window function questions on DataLemur
SQL Question 2: Average mouse usage time per day
Logitech is a company that sells computer accessories, including computer mice. Many of these mice report usage data back to Logitech's servers for analysis.
Logitech is interested in figuring out the average daily usage time for their computer mice. As part of a team of data analysts, your task is to find the average daily usage time of a specific mouse model. In the 'usage' table below, each row represents a single instant of usage.
Example Input:
| usage_id | user_id | usage_date | mouse_id | usage_time |
|---|---|---|---|---|
| 2761 | 122 | 02/11/2022 00:00:00 | 3001 | 30 |
| 3488 | 233 | 02/12/2022 00:00:00 | 3001 | 45 |
| 5472 | 189 | 02/12/2022 00:00:00 | 3001 | 60 |
| 4825 | 122 | 02/13/2022 00:00:00 | 3001 | 30 |
| 3725 | 233 | 02/14/2022 00:00:00 | 3001 | 60 |
where is in minutes.
Example SQL Output:
| date | mouse_model | avg_usage_time |
|---|---|---|
| 02/11/2022 | 3001 | 30.00 |
| 02/12/2022 | 3001 | 52.50 |
| 02/13/2022 | 3001 | 30.00 |
| 02/14/2022 | 3001 | 60.00 |
Answer:
This SQL query groups the 'usage' table by date and mouse model, and for each group, it calculates the average usage time using the AVG function. The WHERE clause is used to filter the usage data to only include data for the mouse model 3001. The ORDER BY clause then sorts the results by date.
SQL Question 3: How would you improve the performance of a slow SQL query?
There's several steps you can take to troubleshoot 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. After that, you can start changing your query, depending on what the source of the query performance issue is.
Generally, indexes can help speed up queries. Also de-normalizing your tables might help, to remove slow joins.
Lastly, you could always just upgrade your hardware! Time and money wasted improving query performance could just be better spent on mroe powerful database servers!
While this is a very surface-level approach to optimizing SQL query performance, it should do the trick for Data Analyst interviews and Data Science interviews at Logitech. Data Engineers should know a bit more about the and before the interview.
SQL Question 4: Logitech's Best Selling Product
Assume Logitech wants to analyze their sales data and find out the product with the maximum sales for each month. The company records its sales data in a table called with following columns: , , and .
Example Input:
| sales_id | product_id | sale_date | quantity |
|---|---|---|---|
| 7865 | 101 | 06/08/2022 00:00:00 | 345 |
| 1825 | 201 | 06/21/2022 00:00:00 | 200 |
| 3562 | 101 | 06/19/2022 00:00:00 | 178 |
| 4563 | 301 | 07/01/2022 00:00:00 | 275 |
| 8912 | 201 | 07/04/2022 00:00:00 | 325 |
They would like to see the result as a table with columns: , and , where is the year and month of the sale date, is the product_id with maximum total quantity and is the total quantity of that product sold in the month.
Example Output:
| month | product | max_sales |
|---|---|---|
| 6 | 101 | 523 |
| 7 | 201 | 325 |
Your job is to write a SQL query that answers the posed question.
Answer:
This query groups the sales data by month and product_id, and then sums up the quantity for each group. The result is ordered by the total quantity in descending order, so the product with the highest total sales for each month appears at the top.
SQL Question 5: What is the difference between a correlated subquery and non-correlated subquery?
A correlated sub-query is a sub-query that depends on the outer query and cannot be executed separately. It uses the outer query to filter or transform data by referencing a column from it, and the outer query uses the results of the inner query. In contrast, a non-correlated sub-query is independent of the outer query and can be executed on its own. It does not reference any columns from the outer query and is used to retrieve additional data needed by the outer query.
Here is an example of a correlated sub-query:
This query selects the and total sales of all Logitech customers in the sales table whose are greater than the average of their own customer group. The sub-query in this case is correlated with the outer query, as it references the column from the outer query ().
Here is an example of a non-correlated sub-query:
The sub-query in this case is non-correlated, as it does not reference any columns from the outer query.
Performance-wise, correlated sub-queries are generally slower to execute, as they have to be re-evaluated for each row of the outer query, while non-correlated sub-queries are faster, as they only have to be executed once.
SQL Question 6: Average Purchase Size per Customer
Given two tables, and , write a SQL query to find the average purchase amount for each customer. Assume that the table has a primary key and the table has a foreign key . In the table, each purchase has a .
Example Input:
| customer_id | first_name | last_name |
|---|---|---|
| 1 | John | Doe |
| 2 | Jane | Smith |
| 3 | Bob | Johnson |
Example Input:
| purchase_id | purchase_amount | customer_id |
|---|---|---|
| 101 | 200.00 | 1 |
| 102 | 150.00 | 1 |
| 103 | 500.00 | 2 |
| 104 | 300.00 | 3 |
| 105 | 250.00 | 3 |
Expected output should show the customer id, customer name (both first and last name), and their average purchase size.
Example Output:
| customer_id | customer_name | avg_purchase_size |
|---|---|---|
| 1 | John Doe | 175.00 |
| 2 | Jane Smith | 500.00 |
| 3 | Bob Johnson | 275.00 |
Answer:
This query joins the customers and purchases table on the field. It then groups the data by , thus grouping all purchases by customer. The function is then used to find the average for each customer. The function is used to combine the and into a single column .
Because joins come up so often during SQL interviews, try this interactive Snapchat SQL Interview question using JOINS:
SQL Question 7: Can you explain the distinction between an inner and a full outer join?
An inner join only includes rows from both tables that satisfy the join condition, whereas a full outer join includes all rows from both tables, regardless of whether they match the join condition or not.
For a concrete example, 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 Logitech sales table, which has data on how many products were sold and which Google Ads keyword drove that sale.
: 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.
: 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 8: Use Math Functions to Calculate Product Weights
Logitech, as a renowned technology company, deals with different types of products with varying weights. For some mathematical analysis, suppose we want to find out the average weight of each product category, round it to 1 decimal place, find out the absolute difference between the weight and average weight in each category, and also calculate the square root of the absolute difference.
Assume that all weights are purposefully exaggerated for the calculations.
Additionally, we want to calculate the modulus result when the product_id is divided by 100 and also find out the power of 2 for each average weight.
For this task, you need to create these columns - product_id, product_category, weight, avg_weight, weight_diff, sqrt_diff, mod_res and weight_powered.
Use the following table:
Example Input:
| product_id | product_category | weight |
|---|---|---|
| 1001 | Keyboard | 1500 |
| 1002 | Keyboard | 1300 |
| 2001 | Mouse | 500 |
| 2002 | Mouse | 700 |
| 3001 | Headphone | 1000 |
Example Output:
| product_id | product_category | weight | avg_weight | weight_diff | sqrt_diff | mod_res | weight_powered |
|---|---|---|---|---|---|---|---|
| 1001 | Keyboard | 1500 | 1400.0 | 100.0 | 10.0 | 1 | 1960000.0 |
| 1002 | Keyboard | 1300 | 1400.0 | 100.0 | 10.0 | 2 | 1960000.0 |
| 2001 | Mouse | 500 | 600.0 | 100.0 | 10.0 | 1 | 360000.0 |
| 2002 | Mouse | 700 | 600.0 | 100.0 | 10.0 | 2 | 360000.0 |
| 3001 | Headphone | 1000 | 1000.0 | 0.0 | 0.0 | 1 | 1000000.0 |
Answer:
In the query, we firstly calculate the average weight of each category using window function. Then, the absolute difference is found between the product weight and the average weight. This difference is further used to find the square root of it. The modulus operator is used to calculate the remainder when product_id is divided by 100. Finally, we calculate the power of 2 for the average weight. This query allows us to use multiple math functions and arithmetic operators in SQL.
To practice a very similar question try this interactive Google Median Google Search Frequency Question which is similar for usage of math functions & rounding to a decimal place or this Alibaba Compressed Mean Question which is similar for calculating an average and rounding it.
Preparing For The Logitech 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.
Besides solving the above Logitech SQL interview questions, you should also solve the 200+ FAANG SQL Questions on DataLemur which come from companies like Google, Uber, and Microsoft.
Each SQL question has multiple hints, step-by-step solutions and most importantly, there is an online SQL coding environment so you can instantly run your query and have it executed.
To prep for the Logitech SQL interview it is also helpful to practice SQL questions from other tech companies like:
In case your SQL coding skills are weak, forget about diving straight into solving questions – improve your SQL foundations with this interactive SQL tutorial.
This tutorial covers SQL topics like Self-Joins and filtering groups with HAVING – both of these show up frequently during Logitech SQL assessments.
Logitech Data Science Interview Tips
What Do Logitech Data Science Interviews Cover?
Beyond writing SQL queries, the other types of questions covered in the Logitech Data Science Interview are:
- Statistics and AB Testing Questions
- Python or R Coding Questions
- Product-Sense and Business Case Questions
- Machine Learning Questions
- Resume-Based Behavioral Questions
How To Prepare for Logitech Data Science Interviews?
The best way to prepare for Logitech Data Science interviews is by reading Ace the Data Science Interview. The book's got:
- 201 Interview Questions from Facebook, Google, & Amazon
- A Refresher on SQL, Product-Sense & ML
- Great Reviews (900+ reviews, 4.5-star rating)