At Broadcom, SQL is used often for analyzing complex networking data and doing sales analytics for the variety of enterprise solutions Broadcom sells. Because of this, Broadcom often tests SQL coding questions during interviews for Data Analyst, Data Science, and BI jobs.
So, if you're preparing for a SQL Interview, we've collected 11 Broadcom SQL interview questions to practice – can you solve them?
Broadcom sells an insane variety of products. Given a database table named containing Broadcom product reviews write a SQL query that can calculate the average rating () for each product () per calendar month. For this task, disregard the year in your calculation and only focus on the month of the 'submit_date'.
The table is structured as below:
Your result should be a table of the following format:
This PostgreSQL query first extracts the month from the for each row. Then, it groups the rows by the calendar month and , figures out the average value () of for each group, and converts the values to numeric type with 3 digits in total, where 2 digits are after decimal point (). The resulting table is ordered by and in ascending order. The clause along with the aggregate function essentially create a window over which the average calculation is made.
To practice a related window function SQL problem on DataLemur's free interactive SQL code editor, try this Google SQL Interview Question:
Broadcom is a major player in the semiconductor industry and probably has a variety of products. Suppose Broadcom wants to analyze the sales of their major product segments over time and identify the product segment with the highest revenue and the number of units sold in each region to better understand their market reach and demand. They want to calculate the total sales each product segment achieved per region over a specific time period.
Assume the following three tables:
product_id | product_name | product_segment |
---|---|---|
1001 | Product A | Segment 1 |
1002 | Product B | Segment 2 |
1003 | Product C | Segment 1 |
sale_id | product_id | region | date_of_sale | units_sold | unit_price |
---|---|---|---|---|---|
5001 | 1001 | North America | 2022-07-01 | 1000 | 50 |
5002 | 1002 | Europe | 2022-07-03 | 500 | 100 |
5003 | 1003 | Asia | 2022-07-05 | 500 | 75 |
5004 | 1001 | Europe | 2022-07-07 | 750 | 50 |
region_name |
---|
North America |
Europe |
Asia |
Write a SQL query to obtain the total sale revenue (units sold * unit price) and total units sold per product segment and region for the year 2022.
The query joins the and tables on . It then aggregates the data by and , summing both the total revenue and units sold for each grouping. The WHERE clause filters the sales data to only include sales from the year 2022.
This kind of analysis can be useful for Broadcom to understand the distribution of their product sales across different regions and in different product segments. undefined
Database normalization has several benefits:
Reduces Redundancy: Normalization can minimize redundancy by breaking down a larger, general table into smaller, more granular tables. This often reduces the amount of data that needs to be accessed for particular queries, since some duplicated columns can be removed.
Improves Data Integrity: Normalization can help to ensure the integrity of the data by minimizing the risk of data inconsistencies and errors. By establishing clear relationships between the tables via primary and foreign keys, and enforcing these constraints, you can have more reliable records and relationships stored in your DB.
Improves Performance: Normalization can improve the performance of the database by reducing the amount of data that needs to be stored and accessed in a single table. This results in faster query times and better overall performance.
Broadcom, a global supplier of semiconductor and infrastructure software products, has a customer base that includes a diverse range of industries. As a data analyst, you have been asked to filter a customer data based on multiple conditions:
The company wants to know these specific customer details to tailor their marketing and sales strategies.
The table provides information about the customers:
customer_id | industry | last_purchase_date | total_spent |
---|---|---|---|
101 | Telecommunication | 11/20/2019 | $600,000 |
102 | Retail | 08/15/2020 | $400,000 |
103 | Telecommunication | 06/10/2021 | $510,000 |
104 | Oil & Gas | 03/25/2020 | $600,000 |
105 | Telecommunication | 08/10/2018 | $650,000 |
The goal is to write a PostgreSQL query that filters the data based on the above conditions.
In the above query, we used the clause along with to filter the customer records. We are looking for customers in the 'Telecommunication' industry, those with a within the last two years, and with a more than 500,000.
Normalization is the process of dividing a larger table into smaller tables, to eliminate redundancy and dependency. Although there are 5 levels of normalization (normal forms), the 3 most important ones that you should know for the Broadcom SQL interview are:
Broadcom, a leading infrastructure technology company, is interested in understanding the click-through rates from its digital marketing efforts. The company has two categories of digital ads, namely 'Software Products' and 'Hardware Products'. For this question, we are interested in determining the click-through-rate (CTR) for these two categories of adverts for the month of April, where CTR is defined as the number of users who click on an ad, among the total number of users who view the ad, expressed as a percentage.
view_id | user_id | view_date | ad_category |
---|---|---|---|
237 | 563 | 04/01/2022 00:00:00 | Software Products |
985 | 672 | 04/15/2022 00:00:00 | Hardware Products |
124 | 395 | 04/20/2022 00:00:00 | Software Products |
428 | 205 | 04/25/2022 00:00:00 | Hardware Products |
352 | 766 | 04/07/2022 00:00:00 | Software Products |
click_id | user_id | click_date | ad_category |
---|---|---|---|
675 | 563 | 04/01/2022 00:00:00 | Software Products |
709 | 672 | 04/15/2022 00:00:00 | Hardware Products |
814 | 395 | 04/20/2022 00:00:00 | Software Products |
394 | 766 | 04/07/2022 00:00:00 | Software Products |
In this example, the query calculates the number of views and clicks for each ad category within April. Then, it calculates CTR as the percentage of clicks over the total views. Using a CTE (Common Table Expression) makes the query easier to read and maintain while ensuring computational accuracy. Remember to convert counts to float before calculating the CTR, which will avoid integer division ending up in 0 or 1 for CTR.
To solve a related SQL interview question on DataLemur's free online SQL coding environment, try this Facebook SQL Interview question:
A transaction is a one or more SQL commands which are executed as a singular unit if the transaction -commits- (or no execution hapens if the transaction -aborts-).
For transactions, a DBMS is supposed to enforce the follwing ACID properties: Atomicity, Consistency, Isolation, & Durability.
Here's what each one means:
As you can see, it's pretty important for the multiple databases where Broadcom store's it's data to be ACID-compliant!
Given the table, write an SQL query that identifies the product with the maximum sales in every quarter of the year 2021. Assume that the company's financial year follows the calendar year.
Please consider the below transaction data:
transaction_id | transaction_date | product_id | units_sold |
---|---|---|---|
1106 | 01/05/2021 | BRCM01 | 150 |
1803 | 03/15/2021 | BRCM02 | 200 |
5296 | 02/20/2021 | BRCM01 | 250 |
6355 | 04/27/2021 | BRCM02 | 100 |
4520 | 07/10/2021 | BRCM01 | 250 |
quarter | product | units_sold |
---|---|---|
Q1 | BRCM01 | 400 |
Q2 | BRCM02 | 100 |
Q3 | BRCM01 | 250 |
This query first generates a temporary table () that contains the total units sold for every product in each quarter of the year 2021. The main query then takes the maximum units sold for each quarter. This result indicates the product with the highest sales in each quarter of 2021. undefined
Broadcom maintains two database tables and . There is a business requirement of identifying total sales volume of each product for each customer.
Consider two data tables below.
customer_id | first_name | last_name | |
---|---|---|---|
1 | John | Doe | john.doe@gmail.com |
2 | Jane | Smith | jane.smith@gmail.com |
3 | Mike | Brown | mike.brown@gmail.com |
transaction_id | customer_id | product_id | quantity |
---|---|---|---|
5001 | 1 | 40001 | 3 |
5002 | 2 | 40002 | 2 |
5003 | 3 | 40001 | 1 |
5004 | 1 | 40003 | 4 |
5005 | 2 | 40002 | 2 |
5006 | 3 | 40001 | 5 |
Write a SQL query that will join these two tables and calculate the total sales volume for each product for each customer.
Here's how you can perform this operation using a SQL query in Postgres:
This query initially joins the two tables based on the field which is common in both the tables. The result of the join operation is then grouped by both and fields so we can calculate the sum of for each combination of and . The final result will show total sales volume for each product for each customer.
Since joins come up so often during SQL interviews, try this interactive Snapchat JOIN SQL interview question:
Denormalization is the process of modifying a database schema in a way that deviates from the typical rules of normalization (1NF, 2NF, 3NF, etc.). There's a few reasons to denormalize a database:
Improved performance: Joins are slow AF, especially when dealing with the massive datasets that are typically used at Broadcom. Denormalization can improve the performance of a database by reducing the number of joins that are required to retrieve data. This can be particularly useful when the database is being used for querying and reporting purposes, as joins can be expensive and slow.
Scalability: By reducing the amount of data that needs to be read and processed to execute a query, denormalization can enhance the scalability of a database. This can be useful when the database is anticipated to handle a large number of read-only queries (such as in OLAP use cases).
Ease of use: Denormalization can also make it easier for users to work with a database by providing them with a more intuitive and straightforward data model.
Because denormalization can create more complex update and delete operations, and pose potential data integrity issues, consider denormalization only if joins are causing performance bottlenecks.
In Broadcom, a semiconductor manufacturing company, an engineer needs an analysis of the silicon wafers' production efficiency. He wants to compare the efficiency of the different machines used in the manufacturing process. For this, he needs to perform a few mathematical calculations using the database.
The data has two tables:
The first table, , provides details about each machine, including its identity, production unit, and certain technical specifications.
machine_id | unit | power_capacity | process_node |
---|---|---|---|
1001 | 5 | 100 | 7 |
1002 | 5 | 120 | 5 |
1003 | 6 | 150 | 5 |
1004 | 7 | 180 | 10 |
1005 | 6 | 100 | 7 |
The second table, , contains logging data, which includes the machine id, the date of the operation, the number of wafers produced, and the production duration.
machine_id | operation_date | wafers_produced | duration_hrs |
---|---|---|---|
1001 | 2022/01/01 | 300 | 5 |
1002 | 2022/01/02 | 450 | 4 |
1003 | 2022/01/05 | 600 | 6 |
1004 | 2022/01/02 | 400 | 7 |
1005 | 2022/01/03 | 550 | 5 |
The engineer wants to know the average wafer production per hour (rounded to 2 decimal places) and the power efficiency (defined as the wafers produced divided by power capacity) for each machine. This output data should be arranged in ascending order by the machine_id.
Please write a SQL query to fulfill this requirement.
machine_id | avg_wafers/hr | power_efficiency |
---|---|---|
1001 | 60.00 | 3.00 |
1002 | 112.50 | 3.75 |
1003 | 100.00 | 4.00 |
1004 | 57.14 | 2.22 |
1005 | 110.00 | 5.50 |
This query first joins the two tables on the . Then it calculates the average wafers produced per hour by dividing the total wafers produced by the duration in hours. It also computes the power efficiency by dividing the total wafers produced by the power capacity of the machine. These results are averaged and rounded to two decimal places for accuracy. The resultant data is then grouped by the and ordered in ascending order.
To practice a very similar question try this interactive Tesla Unfinished Parts Question which is similar for analyzing production efficiency or this Amazon Server Utilization Time Question which is similar for performing analysis by using math calculations.
The best way to prepare for a Broadcom SQL interview is to practice, practice, practice. In addition to solving the above Broadcom SQL interview questions, you should also solve the 200+ SQL coding questions which come from companies like Google, Uber, and Microsoft.
Each problem on DataLemur has hints to guide you, detailed solutions and most importantly, there is an interactive coding environment so you can right in the browser run your query and have it executed.
To prep for the Broadcom SQL interview you can also be wise to solve SQL problems from other tech companies like:
In case your SQL coding skills are weak, forget about jumping right into solving questions – go learn SQL with this DataLemur SQL tutorial.
This tutorial covers SQL topics like LEAD/LAG and filtering on multiple conditions using AND/OR/NOT – both of these come up routinely during Broadcom SQL interviews.
For the Broadcom Data Science Interview, besides SQL questions, the other types of questions to prepare for are:
To prepare for Broadcom Data Science interviews read the book Ace the Data Science Interview because it's got: