10 Qualcomm SQL Interview Questions (Updated 2024)
At Qualcomm, SQL does the heavy lifting for analyzing chip design and chip power efficiency data. That's why Qualcomm asks SQL questions in interviews for Data Analytics, Data Science, and Data Engineering jobs.
So, if you're trying to prepare for the SQL Assessment, here’s 10 Qualcomm SQL interview questions to practice – how many can you solve?
10 Qualcomm SQL Interview Questions
SQL Question 1: Identify Qualcomm's top spending customers
Qualcomm is a company that sells a variety of tech-related products. They are interested in finding who their top spending (whale) customers are. Given the sales data, write an SQL query that will identify the top 3 customers who have spent the most money on Qualcomm products in the last year.
Assume a PostgreSQL database with the following tables:
Example Input:
| customer_id | name | signup_date |
|---|---|---|
| 1001 | John Doe | 01/01/2021 |
| 1002 | Jane Smith | 03/03/2021 |
| 1003 | Mary Johnson | 04/04/2021 |
Example Input:
| sale_id | customer_id | product_id | sale_date | price |
|---|---|---|---|---|
| 501 | 1001 | 2001 | 05/08/2021 | 100 |
| 502 | 1002 | 2002 | 06/10/2021 | 200 |
| 503 | 1003 | 2003 | 07/10/2021 | 150 |
| 504 | 1001 | 2004 | 08/20/2021 | 250 |
| 505 | 1001 | 2005 | 09/30/2021 | 200 |
Answer:
The query first joins the and tables on the field. It then filters only the sales made in the last year by checking the sale date. It groups the result by customer name and calculates the total amount spent by each customer using the function. It orders the result set by total amount spent in descending order and finally, it limits the result to the top 3 customers.
To work on another SQL customer analytics question where you can solve it interactively and have your SQL query instantly executed, try this Walmart SQL Interview Question:
SQL Question 2: Calculate Rolling Monthly Average Ratings for Each Product
Imagine you are a data analyst at Qualcomm. The company wants to understand how each of their Qualcomm chipsets is rated on average every month, to assess and improve their product quality over time.
Given a table named , which includes columns for , , , , and , write a SQL query to calculate a rolling average rating for each for every month. The rolling average should be calculated from the start of the dataset up to the current month.
Example Input:
| review_id | user_id | submit_date | product_id | stars |
|---|---|---|---|---|
| 1 | 123 | 02/01/2022 | 101 | 4 |
| 2 | 456 | 02/15/2022 | 102 | 5 |
| 3 | 789 | 02/21/2022 | 103 | 3 |
| 4 | 123 | 03/05/2022 | 101 | 5 |
| 5 | 456 | 03/10/2022 | 102 | 4 |
| 6 | 789 | 03/25/2022 | 103 | 2 |
| 7 | 123 | 04/01/2022 | 101 | 3 |
| 8 | 456 | 04/15/2022 | 102 | 4 |
| 9 | 789 | 04/20/2022 | 103 | 5 |
Example Output:
| month_year | product_id | avg_stars |
|---|---|---|
| 2022-02 | 101 | 4.00 |
| 2022-02 | 102 | 5.00 |
| 2022-02 | 103 | 3.00 |
| 2022-03 | 101 | 4.50 |
| 2022-03 | 102 | 4.50 |
| 2022-03 | 103 | 2.50 |
| 2022-04 | 101 | 4.00 |
| 2022-04 | 102 | 4.33 |
| 2022-04 | 103 | 3.33 |
Answer:
This PostgreSQL query uses a window function to calculate the rolling average rating for each product per month. The PARTITION BY clause divides the dataset into groups based on . The ORDER BY clause within the window function organizes these groups by year and month. RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW includes all rows from the start of the dataset to the current row in the calculation. As the window moves to the next row, the boundaries of the window adjust to include the new record and hence, re-calculating the average.
Pro Tip: Window functions are a frequent SQL interview topic, so practice all the window function problems on DataLemur
SQL Question 3: What is database normalization?
Database normalization is the process of breaking down a table into smaller and more specific tables and defining relationships between them via foreign keys. This minimizes redundancy, and creates a database that's more flexible, scalable, and easier to maintain. It also helps to ensure the integrity of the data by minimizing the risk of data inconsistencies and anomalies.
SQL Question 4: Filter Customer Records
Qualcomm is conducting an internal review to study the data usage patterns of its customers. They would like to filter their customer database based on the customers who are on a "Premium" plan and have their data usage level above 50 GB for the month of July 2022.
Assume we have the following available tables:
Example Input:
| customer_id | name | plan_type |
|---|---|---|
| 1001 | John Doe | Basic |
| 1002 | Jane Smith | Premium |
| 1003 | Harry Potter | Premium |
| 1004 | Hermione Granger | Basic |
Example Input:
| usage_id | customer_id | usage_date | data_used_gb |
|---|---|---|---|
| 1 | 1001 | 07/01/2022 | 40 |
| 2 | 1002 | 07/15/2022 | 60 |
| 3 | 1003 | 07/20/2022 | 55 |
| 4 | 1004 | 07/05/2022 | 30 |
| 5 | 1002 | 07/25/2022 | 20 |
Example Output:
| customer_id | name |
|---|---|
| 1002 | Jane Smith |
| 1003 | Harry Potter |
Answer:
For this task, you can write your PostgreSQL query like this:
This query joins the and tables on the field. It looks for customers who are in the 'Premium' plan (specified by the field) and who used more than 50 GB of data (specified by the field) in the month of July 2022. The function is used to determine the month and year of the data usage. The clause is used to avoid duplication in customers. undefined
SQL Question 5: What is the difference between cross join and natural join?
Imagine you are organizing a party and have two database tables: one table of people you want to invite and another list of food items you want to serve.
A cross join would be like inviting every person on your list to the party and serving them every food item on the menu, regardless of whether they like the food or not. So, if you had 10 people on your invite list and 5 food items on the menu, you would generate all 50 different combinations of people and food (10 x 5 = 50).
On the other hand, a natural join would be like inviting only the people who like the food items on the menu (based on doing a inner/left/right/outer JOIN on a common key like ).
SQL Question 6: Calculate the Average Power Consumption of Qualcomm Chipsets
As an Electrical Design Engineer at Qualcomm, understanding how much power chipsets consume on average is crucial. Write a SQL query to find the average power consumption (mW) by chipset category for all Qualcomm chipsets recorded in our database.
Example Input:
| chipset_id | category | model | power_consumption_mW |
|---|---|---|---|
| 1 | "5G" | "Snapdragon X55" | 650 |
| 2 | "5G" | "Snapdragon X60" | 700 |
| 3 | "Bluetooth" | "QCC5100" | 150 |
| 4 | "Bluetooth" | "QCC3040" | 120 |
| 5 | "4G" | "MDM9x07" | 500 |
| 6 | "4G" | "Snapdragon 210" | 540 |
| 7 | "Wifi" | "QCA6390" | 280 |
Example Output:
| category | average_power_consumption_mW |
|---|---|
| 5G | 675 |
| Bluetooth | 135 |
| 4G | 520 |
| Wifi | 280 |
Answer:
This SQL query groups data by the of the chipsets and calculates the average power consumption in milliwatts () for each category by using the function. This result provides an overview of power requirements for the different categories of Qualcomm chipsets and can help to inform design decisions and optimization for power consumption.
To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for data aggregation across categories or this Google Median Google Search Frequency Question which is similar for calculating average metrics.
SQL Question 7: How does differ from ?
The operator combines two or more results from multiple SELECT queries into a single result. If it encounters duplicate rows, the multiple copies are removed (so there's only one instance of each would-be duplicate in the result set). Here's an example of a operator which combines all rows from and (making sure each row is unique):
The operator is similar to the operator but it does NOT remove duplicate rows!
SQL Question 8: Analyzing Click-through Conversion Rate
As part of the marketing analytics team at Qualcomm, one main metric of interest is the click-through conversion rate. This rate refers to the proportion of customers who not only clicked on a digital advertisement, but also proceeded to purchase the advertised product. Assume you have two tables:
- ad_clicks, which records whenever a customer clicks on an ad.
- purchases, which records whenever a customer purchases a product that was advertised.
Given the data in these tables, write a SQL query that provides the click-through conversion rate, defined as the total number of ad clicks that resulted in a purchase divided by the total number of ad clicks, for each unique advertisement.
Example Input:
| click_id | user_id | click_date | ad_id |
|---|---|---|---|
| 101 | 1 | 2022-06-20 | 202 |
| 102 | 2 | 2022-06-21 | 203 |
| 103 | 3 | 2022-06-22 | 202 |
| 104 | 4 | 2022-06-23 | 204 |
| 105 | 5 | 2022-06-24 | 203 |
Example Input:
| purchase_id | user_id | purchase_date | ad_id |
|---|---|---|---|
| 501 | 1 | 2022-06-21 | 202 |
| 502 | 2 | 2022-06-22 | 203 |
| 503 | 6 | 2022-06-23 | 204 |
| 504 | 7 | 2022-06-24 | 205 |
| 505 | 8 | 2022-06-25 | 206 |
Answer:
The answer is achieved by first creating sub-queries to individually count the total clicks and total purchases for each ad. Then, these two sub-queries are joined together using the field as the joining column. The conversion rate is calculated by dividing by . The is used to handle scenarios when there are clicks but no purchases for certain ads by treating those as zero purchases.
To solve a related SQL interview question on DataLemur's free interactive coding environment, try this Meta SQL interview question:
SQL Question 9: Calculating the Average Sales Per Region of Qualcomm Products
As the data analyst for Qualcomm, your task is to calculate the average sales per region for each product every month. Each row in the table represents a sale of a certain product in a specific region. The columns identify the region where the sale was made.
Example Input:
| sale_id | product_id | date_sold | price | region_id |
|---|---|---|---|---|
| 2567 | SQN6720 | 2021-06-08 | 500 | North America |
| 1984 | SQN6731 | 2021-06-10 | 600 | Asia Pacific |
| 5249 | SQN6720 | 2021-07-18 | 500 | Europe |
| 3675 | SQN6731 | 2021-07-26 | 600 | North America |
| 4421 | SQN6712 | 2021-08-05 | 700 | Europe |
Answer:
The following SQL query can be used to calculate the average sales per region for each product every month:
Example Output:
| month | region_id | product_id | avg_sales |
|---|---|---|---|
| 2021-06 | Asia Pacific | SQN6731 | 600.00 |
| 2021-06 | North America | SQN6720 | 500.00 |
| 2021-07 | Europe | SQN6720 | 500.00 |
| 2021-07 | North America | SQN6731 | 600.00 |
| 2021-08 | Europe | SQN6712 | 700.00 |
This SQL query first formats the into a string representing the year and month in the format . It then groups by this value, the , and the , and calculates the average for each group. Finally, the results are ordered by , , and to make the output easier to read. undefined
SQL Question 10: How can you select records without duplicates from a table?
The clause is used to remove all duplicate records from a query.
For example, if you had a table of open jobs Qualcomm was hiring for, and wanted to see what are all the unique job titles that were currently available at the company, you could write the following query:
Qualcomm SQL Interview Tips
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 earlier Qualcomm SQL interview questions, you should also solve the 200+ FAANG SQL Questions on DataLemur which come from companies like FAANG and tech startups.
Each DataLemur SQL question has hints to guide you, full answers and crucially, there's an online SQL code editor so you can right in the browser run your query and have it executed.
To prep for the Qualcomm SQL interview it is also wise to solve SQL problems from other tech companies like:
But if your SQL query skills are weak, don't worry about jumping right into solving questions – go learn SQL with this DataLemur SQL tutorial.
This tutorial covers things like filtering data with WHERE and sorting data with ORDER BY – both of these pop up often in Qualcomm SQL assessments.
Qualcomm Data Science Interview Tips
What Do Qualcomm Data Science Interviews Cover?
For the Qualcomm Data Science Interview, beyond writing SQL queries, the other types of questions which are covered:
- Probability & Stats Questions
- Coding Questions in Python or R
- Open-Ended Data Case Studies
- Machine Learning Questions
- Resume-Based Behavioral Questions
How To Prepare for Qualcomm Data Science Interviews?
To prepare for Qualcomm 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 Product Analytics, SQL & ML
- over 900+ reviews on Amazon & 4.5-star rating