9 NVIDIA SQL Interview Questions (Updated 2024)
At NVIDIA, SQL is used for driving data-driven insights for enhancing graphical processor unit's performance. They also support SQL, by creating solutions to accelerate querying workflows for Data Scientists. This is why NVIDIA asks SQL problems in interviews for Data Science, Data Engineering and Data Analytics jobs.
To help you ace the NVIDIA SQL interview, we've curated 9 NVIDIA SQL interview questions to practice – can you solve them?
9 NVIDIA SQL Interview Questions
SQL Question 1: Product Line Revenue
As a business analyst on the revenue forecasting team at NVIDIA, you are given a table of NVIDIA transactions in 2021.
Write a query to summarize the total sales revenue for each product line. The product line with the highest revenue should be at the top of the results.
Assumption:
- There will be at least one sale of each product line.
Table:
| Column Name | Type |
|---|---|
| product_id | integer |
| product_name | varchar |
| product_line | varchar |
Example Input:
| product_id | product_name | product_line |
|---|---|---|
| 1 | Quadro RTX 8000 | GPU |
| 2 | Quadro RTX 6000 | GPU |
| 3 | GeForce RTX 3060 | GPU |
| 4 | BlueField-3 | DPU |
Table:
| Column Name | Type |
|---|---|
| transaction_id | integer |
| product_id | integer |
| amount | integer |
Example Input:
| transaction_id | product_id | amount |
|---|---|---|
| 101 | 1 | 5000 |
| 102 | 2 | 4200 |
| 103 | 3 | 9000 |
| 104 | 4 | 7000 |
Example Output:
| product_line | total_revenue |
|---|---|
| GPU | 18200 |
| DPU | 7000 |
Answer:
Here is the SQL query that can be used to calculate the monthly average reviews for NVIDIA products:
To practice this problem, go to this NVIDIA SQL question on DataLemur's online SQL coding environment:
SQL Question 2: Find Customers Interested in AI
As an employee at NVIDIA, you've been given access to the customer records database. The Sensors team is interested in understanding which customers are interested in Artificial Intelligence (AI). The relevant fields are:
- - A unique identifier for the customer
- - The name of the customer
- - The email address of the customer
- - A list of customer's interests (e.g. 'Gaming', 'AI', 'Autonomous Machines')
Write a SQL query to find customers who have shown an interest in AI.
Example Input:
| customer_id | name | interests | |
|---|---|---|---|
| 1234 | John Smith | johnsmith@email.com | Gaming, AI, Data Center |
| 5678 | Mary Johnson | maryj@email.com | Gaming, Autonomous Machines |
| 9123 | James Williams | jamesw@email.com | AI, Autonomous Machines |
| 4567 | Patricia Brown | patriciab@email.com | Data Center, AI |
Example Output:
| customer_id | name | |
|---|---|---|
| 1234 | John Smith | johnsmith@email.com |
| 9123 | James Williams | jamesw@email.com |
| 4567 | Patricia Brown | patriciab@email.com |
Answer:
In PostgreSQL, you can use the LIKE operator to check if a column contains a specific string. In this case, you want to find all rows in the table where the column contains the string 'AI'.
The percentage signs (%) before and after 'AI' are wildcards that match any sequence of characters. So, the query matches any that contain 'AI', no matter what characters come before or after it.
To practice a similar SQL interview question on DataLemur's free interactive coding environment, solve this Facebook SQL Interview question:
SQL Question 3: What's the difference between a left and right join?
A join in SQL allows you to retrieve data from multiple tables and combine it into a single set of results.
To demonstrate the difference between left vs. right join, 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.
: 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.
SQL Question 4: Average GPU Temperatures per Model
As an SQL analyst at NVIDIA, you are given task to monitor the performance of various GPU models. You have been asked to write a SQL query to find the average running temperature for each GPU model in the database.
Sample data for Database:
Input:
| record_id | timestamp | model_id | temperature |
|---|---|---|---|
| 1 | 06/08/2022 00:00:00 | 1001 | 60 |
| 2 | 06/10/2022 00:00:00 | 1001 | 65 |
| 3 | 06/18/2022 00:00:00 | 1002 | 58 |
| 4 | 07/26/2022 00:00:00 | 1002 | 60 |
| 5 | 07/05/2022 00:00:00 | 1002 | 62 |
Expected Output:
| model_id | avg_temperature |
|---|---|
| 1001 | 62.50 |
| 1002 | 60.00 |
Answer:
In this query, we use the AVG function to calculate the average temperature for each GPU model. The GROUP BY clause is used to divide the data into groups per GPU model. The AVG function is then applied to each group separately.
To practice a similar problem about calculating rates, solve this SQL interview question from TikTok on DataLemur's online SQL code editor:
SQL Question 5: Can you describe the difference between a unique and a non-unique index?
While both types of indexes improve the performance of SQL queries by providing a faster way to lookup rows of data, a unique index enforces the uniqueness of the indexed columns while a non-unique index allows duplicate values in the indexed columns.
Suppose you had a table of NVIDIA employees. Here's an example of a unique index on the column:
This index would ensure that no two NVIDIA employees have the same , which could be used as a unique identifier for each employee.
Here's a non-unique index example example on the column:
This index would not enforce uniqueness, but it could be used to improve the performance of queries that filter or sort the data based on the column. For example, if you want to quicklly retreive all Data Scientists, the database can use the index to efficiently locate and retrieve the desired records without having to do a full table scan on all NVIDIA employees.
SQL Question 6: Calculate the Click-Through-Rates for NVIDIA Products
At NVIDIA, we utilize digital marketing strategies to ensure our customers are aware of our products. One essential metric we use to analyze the effectiveness of these strategies is the click-through rate (CTR). We would like you to calculate the CTR for every NVIDIA product for the past month.
Our table records every time a user views one of our products:
Example Input:
| view_id | user_id | view_date | product_id |
|---|---|---|---|
| 1 | 123 | 09/01/2022 09:30:00 | 50001 |
| 2 | 265 | 09/01/2022 10:00:00 | 69852 |
| 3 | 362 | 09/02/2022 14:00:00 | 50001 |
| 4 | 192 | 09/03/2022 16:30:00 | 69852 |
| 5 | 981 | 09/04/2022 20:00:00 | 69852 |
Our table records every time a user clicks on one of our products:
Example Input:
| click_id | user_id | click_date | product_id |
|---|---|---|---|
| 9076 | 123 | 09/01/2022 09:31:00 | 50001 |
| 7803 | 265 | 09/01/2022 10:02:00 | 69852 |
| 5294 | 362 | 09/02/2022 14:01:00 | 50001 |
| 6353 | 981 | 09/04/2022 20:03:00 | 69852 |
You should join the views and clicks according to user_id and product_id and then calculate CTR as the number of clicks divided by the number of views for each product.
Answer:
This query uses a to combine the and tables according to the matching and . The clause filters out the views from the last month. The clause is used to split the data according to the different products. Finally, is used to convert the count of clicks to a float so we can calculate the percentage as click-through rate. The multiplication by 100 is for representing the click through rate in percentage form.
To solve another question about calculating rates, solve this TikTok SQL Interview Question within DataLemur's interactive SQL code editor:
SQL Question 7: What does the constraint do?
The constraint is used to establish a relationship between two tables in a database. This ensures the referential integrity of the data in the database.
For example, if you have a table of NVIDIA customers and an orders table, the customer_id column in the orders table could be a that references the id column (which is the primary key) in the NVIDIA customers table.
SQL Question 8: Customers and Purchases Data Analysis at NVIDIA
As a data analyst at NVIDIA, you are tasked with analyzing customer database and their purchases. There are two tables you can work with:
Customers:
| customer_id | customer_name | customer_email |
|---|---|---|
| 1 | John Doe | johndoe@example.com |
| 2 | Jane Smith | janesmith@example.com |
| 3 | Bob Johnson | bob_johnson@example.com |
| 4 | Alice Davis | alice_davis@example.com |
| 5 | Charlie White | charlie_white@example.com |
Purchases:
| purchase_id | customer_id | product_id | purchase_date | product_price |
|---|---|---|---|---|
| 101 | 1 | 50001 | 05/10/2022 | $1500 |
| 102 | 2 | 50002 | 05/15/2022 | $2500 |
| 103 | 3 | 50001 | 05/20/2022 | $1500 |
| 104 | 2 | 50001 | 05/22/2022 | $1500 |
| 105 | 1 | 50002 | 05/25/2022 | $2500 |
Write a SQL query to get a list of all customers who bought the product with product_id = 50001 and the total amount they spent on that product. Also, sort the output by total amount spent in descending order.
Answer:
This SQL block first joins the Customers table with the Purchases table using the column in both tables. It then filters the rows where is 50001. Next, it groups by and calculates the sum of for each customer. Finally, it orders the result by in descending order, to give the customers who spent the most at the top of the table.
To solve a related SQL problem on DataLemur's free interactive SQL code editor, solve this Meta SQL interview question:
SQL Question 9: Obtaining Monthly Sales for each NVIDIA Product
As a Data Analyst in NVIDIA, you are asked to get a analysis of every product sold in the past year. You need to retrieve the month, product ID and the total quantity sold for each product listed by NVIDIA on a monthly basis. Consider that fiscal year begins in January and ends in December. Use the table for your analysis.
Provided below are sample inputs and output for the problem:
Example Input:
| sale_id | sale_date | product_id | quantity |
|---|---|---|---|
| 1001 | 02/20/2021 12:14:00 | 50001 | 2 |
| 1002 | 02/25/2021 15:35:00 | 69852 | 1 |
| 1003 | 03/08/2021 11:11:00 | 50001 | 3 |
| 1004 | 03/14/2021 18:30:00 | 69852 | 5 |
| 1005 | 04/01/2021 09:09:00 | 50001 | 7 |
Example Output:
| month | product_id | total_quantity |
|---|---|---|
| 2 | 50001 | 2 |
| 2 | 69852 | 1 |
| 3 | 50001 | 3 |
| 3 | 69852 | 5 |
| 4 | 50001 | 7 |
Answer:
This query extracts the month from the sale_date column. It then groups the data by month and product_id. The quantity of products sold is added up using the SUM function. The WHERE clause filters out the records relevant to the fiscal year 2021.
To solve a similar SQL problem on DataLemur's free interactive coding environment, attempt this Facebook SQL Interview question:
How To Prepare for the NVIDIA SQL Interview
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the NVIDIA SQL interview is to solve as many practice SQL interview questions as you can!
Besides solving the earlier NVIDIA SQL interview questions, you should also solve the 200+ SQL questions on DataLemur which come from companies like Facebook, Google and unicorn tech startups.
Each problem on DataLemur has multiple hints, full answers and crucially, there's an interactive coding environment so you can instantly run your SQL query answer and have it checked.
To prep for the NVIDIA SQL interview it is also helpful to practice SQL questions from other tech companies like:
However, if your SQL query skills are weak, forget about jumping right into solving questions – improve your SQL foundations with this SQL interview tutorial.
This tutorial covers things like Union vs. UNION ALL and RANK vs. DENSE RANK – both of these pop up often during SQL job interviews at NVIDIA.
NVIDIA Data Science Interview Tips
What Do NVIDIA Data Science Interviews Cover?
For the NVIDIA Data Science Interview, beyond writing SQL queries, the other types of questions which are covered:
- Probability & Statistics Questions
- Python or R Programming Questions
- Data Case Study Questions
- ML Modelling Questions
- Resume-Based Behavioral Questions
How To Prepare for NVIDIA Data Science Interviews?
To prepare for NVIDIA Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions sourced from companies like Microsoft, Google & Amazon
- a refresher on Python, SQL & ML
- over 900+ reviews on Amazon & 4.5-star rating
You should also look into how NVIDIA accelerates Data Science workflows which could make great fodder for the behavioral interview.