9 Berkshire Hathaway SQL Interview Questions (Updated 2024)
Data Science, Data Engineering and Data Analytics employees at Berkshire Hathaway code up SQL queries all the time for work. They use SQL for analyzing large financial data sets for investment insights and maintaining databases of mergers, acquisitions, and other corporate events. That's why Berkshire Hathaway asks prospective hires SQL interview problems.
Thus, to help you prepare, we've curated 9 Berkshire Hathaway SQL interview questions – how many can you solve?
9 Berkshire Hathaway SQL Interview Questions
Sure, here is a SQL window function interview question related to Berkshire Hathaway's insurance business.
SQL Question 1: Calculate the Total Premium Collected by Each Agent Month Over Month.
Given a dataset of the insurance policies sold by Berkshire Hathaway, your task is to calculate the total premium collected by each agent month over month. This will require a combination of the GROUP BY clause for agent_id and policy_start_date (month), and the SUM function for policy_premium.
Sample Input:
| policy_id | agent_id | policy_start_date | policy_premium |
|---|---|---|---|
| 1201 | 4 | 2020-03-15 | 5000 |
| 1830 | 2 | 2020-04-10 | 7000 |
| 2394 | 1 | 2020-04-20 | 8000 |
| 3812 | 3 | 2020-05-01 | 5000 |
| 4521 | 2 | 2020-05-20 | 6000 |
Sample Output:
| month | agent_id | total_premium |
|---|---|---|
| 2020-03-01 | 4 | 5000 |
| 2020-04-01 | 2 | 7000 |
| 2020-04-01 | 1 | 8000 |
| 2020-05-01 | 3 | 5000 |
| 2020-05-01 | 2 | 6000 |
Answer:
This query uses the function to get the first day of the month for each policy_start_date. It then groups by this date and the agent_id, and sums the policy_premium for each group. The clause is used to define the window on which the sum is calculated, in this case the window corresponds to each agent each month. The clause ensures the output is sorted by month and agent for easy review.
Pro Tip: Window functions are a frequent SQL interview topic, so practice every window function problem on DataLemur
SQL Question 2: Department Salaries
Imagine you had a table of Berkshire Hathaway employee salary data, along with which department they belonged to. Write a query to compare the average salary of employees in each department to the company's average salary for March 2024. Return the comparison result as 'higher', 'lower', or 'same' for each department. Display the department ID, payment month (in MM-YYYY format), and the comparison.
You can solve this question directly within the browser on DataLemur:
The solution is LONG – 30+ lines of SQL. You can find a step-by-step solution here: Department vs. Company Salary.
Read about Berkshire Hathaway's history and see how they became the company they're known as today.
SQL Question 3: What's the purpose of a foreign key?
A foreign key is a column or group of columns in a table that refers to the primary key in another table. The foreign key constraint helps maintain referential integrity between the two tables.
For example, let's look at the Berkshire Hathaway sales database:
berkshire_hathaway_sales:
+------------+------------+------------+------------+ | order_id | product_id | customer_id| quantity | +------------+------------+------------+------------+ | 1 | 222 | 1 | 2 | | 2 | 333 | 1 | 1 | | 3 | 444 | 2 | 3 | | 4 | 555 | 3 | 1 | +------------+------------+------------+------------+
In this table, and could both be foreign keys. They reference the primary keys of other tables, such as a Products table and a Customers table, respectively. This establishes a relationship between the table and the other tables, such that each row in the sales database corresponds to a specific product and a specific customer.
"
SQL Question 4: Calculate Average Net Income for Various Divisions
Berkshire Hathaway is a holding company comprising a multitude of businesses. For this task, you are attempting to find the average annual net income for each of its divisions over the past 5 years. You have access to the 'divisions_financials' table that hosts net income data for each year and division.
Example Input:
| division_id | division_name | year | net_income |
|---|---|---|---|
| 1 | GEICO | 2017 | 1500 |
| 1 | GEICO | 2018 | 1430 |
| 1 | GEICO | 2019 | 1320 |
| 1 | GEICO | 2020 | 1410 |
| 1 | GEICO | 2021 | 1450 |
| 2 | Burlington Northern | 2017 | 2300 |
| 2 | Burlington Northern | 2018 | 2500 |
| 2 | Burlington Northern | 2019 | 2600 |
| 2 | Burlington Northern | 2020 | 2450 |
| 2 | Burlington Northern | 2021 | 2400 |
Answer:
This SQL query groups records by their division ID and name, and then calculates the average net income for each division over the years. AVG is an aggregate function in SQL that calculates the average value of a group of selected values. The SELECT clause selects the division ID, the division name, and the average net income from the 'divisions_financials' table. The GROUP BY clause groups the records by division ID and name so that the AVG function can calculate the average net income for each separate division.
Example Output:
| division_id | division_name | avg_net_income |
|---|---|---|
| 1 | GEICO | 1422 |
| 2 | Burlington Northern | 2450 |
To practice a very similar question try this interactive Wayfair Y-on-Y Growth Rate Question which is similar for calculating year-on-year averages or this Amazon Average Review Ratings Question which is similar for calculating average value based on groups.
SQL Question 5: What is a cross-join?
A cross-join, also known as a cartesian join, is a JOIN that produces the cross-product of two tables. In a cross-join, each row from the first table is matched with every row from the second table, resulting in a new table with a row for each possible combination of rows from the two input tables.
Let's say you were building a Machine Learning model that attempts to score the probability of a customer purchasing a Berkshire Hathaway product. Before working in Pandas and Tensorflow, you might want to do some Exploratory Data Analysis (EDA) in SQL, and generate all pairs of customers and Berkshire Hathaway products.
Here's a cross-join query you could run:
Cross-joins are useful for generating all possible combinations, but they can also create huge tables if you're not careful. For instance, if you had 10,000 potential customers and Berkshire Hathaway had 500 different product SKUs, the resulting cross-join would have 5 million rows!
SQL Question 6: Analyzing Click-Through Rates for Berkshire Hathaway Ads
Berkshire Hathaway, a multinational conglomerate company, launched several online advertisement campaigns for its various products and services. Each time a user clicks on an ad, the click is recorded in a table. The company also records when the user purchases a product or service after clicking on the ad in a table. The analysts at Berkshire Hathaway want to understand the overall click-through rate (CTR), defined as the number of unique users who made a purchase after clicking on an ad divided by the total number of unique users who clicked on an ad. Can you help in finding the CTR for the given data?
Example Input:
| click_id | user_id | click_time | ad_id |
|---|---|---|---|
| 1 | 101 | 01/01/2022 10:00:00 | 1 |
| 2 | 102 | 01/01/2022 11:00:00 | 1 |
| 3 | 103 | 01/01/2022 12:00:00 | 1 |
| 4 | 101 | 01/01/2022 13:00:00 | 1 |
| 5 | 104 | 01/01/2022 14:00:00 | 1 |
Example Input:
| purchase_id | user_id | purchase_time | ad_id |
|---|---|---|---|
| 1 | 101 | 01/01/2022 11:00:00 | 1 |
| 2 | 102 | 01/01/2022 12:00:00 | 1 |
| 3 | 101 | 01/01/2022 15:00:00 | 1 |
Note: A click is considered to have resulted in a purchase if the purchase happened on the same day as the click.
Answer:
The below PostgreSQL query will calculate the CTR based on the provided tables:
This solution first eliminates duplicate click and purchase records within the same day from the same user using CTE (Common Table Expressions) and DISTINCT keyword in and . Then, it joins these two CTEs based on both and the same day (in case of purchases happening on the same day as clicks). Finally, it calculates the CTR by counting the number of unique users who clicked on the ad and made a purchase divided by the count of unique users who clicked the ad. The function is used to prevent division by zero. If no user has clicked the ad, the CTR is null. The result of the division is then multiplied by 100.0 to convert it into a percentage.
To solve a related problem on DataLemur's free interactive SQL code editor, solve this SQL interview question asked by Facebook:
SQL Question 7: What is denormalization?
Denormalization is the practice of altering a database schema in a way that breaks the normalization rules (1st, 2nd, 3rd normal forms).
Denormalization is done to improve the performance of the database when it is being used for online analytics processing (OLAP), rather than online transaction processing (OLTP) use cases.
For these OLAP use cases, you're bottleneck frequently is joining multiple tables, but de-normalizing your database cuts these outs.
Denormalization can improve query performance by reducing the number of expensive joins required to retrieve data, but it comes with its own drawbacks too. By adding redundant columns, you incur more data storage costs, and there's need for more complex update and delete operations in order to maintain data integrity across all the duplicated data. Thus, it's important to carefully consider the trade-offs involved before implementing denormalization.
SQL Question 8: Calculate the total insurance claims per policy type
As a data professional at Berkshire Hathaway, specifically for their insurance division, you are tasked to analyze the insurance claims database. You're required to write a SQL query to calculate the total insurance claims per policy type from the table and join it with the table.
table Example Input:
| claim_id | policy_id | claim_amount | claim_date |
|---|---|---|---|
| 5210 | 344 | 800 | 2022-05-10 |
| 5349 | 239 | 1000 | 2022-05-15 |
| 5542 | 183 | 1200 | 2022-06-01 |
| 6704 | 239 | 700 | 2022-06-07 |
| 6789 | 477 | 1500 | 2022-06-10 |
table Example Input:
| policy_id | policy_type |
|---|---|
| 344 | Home |
| 239 | Auto |
| 183 | Life |
| 477 | Auto |
You are expected to return a table with two columns: and , where is the sum of all insurance claims for that policy type.
Answer:
This query joins the table with table using the as the common key. After joining, it groups the resulting records by and calculates the sum of for each policy type.
Example Output:
| policy_type | total_claimed_amount |
|---|---|
| Home | 800 |
| Auto | 2200 |
| Life | 1200 |
Since joins come up so often during SQL interviews, try an interactive SQL join question from Spotify:
SQL Question 9: Calculating Average Investment Return
As a data analyst at Berkshire Hathaway, you are tasked with a job to evaluate the annual return rate of some investment portfolios. Each portfolio is composed of many different stocks and the return of each investment is logged in a separate table. For this task, you have been provided with two tables: boards and returns.
The 'boards' table contains information about the investment Board ID, Stock Name, and Purchase Price. The 'returns' table contains the Board ID, Year, and Return.
You are asked to calculate the average yearly return percentage for each portfolio (Board ID), rounded to 2 decimal places, and sorted in descending order. You should also provide the square root (rounded to 2 decimal places) of each averaged value for more insightful analysis.
Example Input:
| board_id | stock_name | purchase_price |
|---|---|---|
| 1 | Bennett | 110.50 |
| 1 | Orion | 95.75 |
| 2 | Maddox | 107.30 |
| 2 | Vance | 102.98 |
| 3 | Channing | 125.60 |
| 3 | Nash | 112.80 |
Example Input:
| board_id | year | return |
|---|---|---|
| 1 | 2022 | 154.10 |
| 2 | 2022 | 222.80 |
| 3 | 2022 | 267.00 |
| 1 | 2023 | 175.20 |
| 2 | 2023 | 245.00 |
| 3 | 2023 | 305.80 |
Answer:
This SQL query first performs a join operation on the two tables based on the board_id. It then calculates the yearly return percentage for each investment, averages them, and orders the result in descending order. For a more insightful analysis, it also calculates the square root of each averaged value.
To practice a very similar question try this interactive Wayfair Y-on-Y Growth Rate Question which is similar for calculating yearly metrics or this Amazon Average Review Ratings Question which is similar for calculating average returns.
Preparing For The Berkshire Hathaway SQL Interview
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Berkshire Hathaway SQL interview is to solve as many practice SQL interview questions as you can!
In addition to solving the above Berkshire Hathaway SQL interview questions, you should also solve the 200+ SQL exercises on DataLemur which come from companies like Google, Facebook, Microsoft and Amazon.
Each interview question has multiple hints, fully explained answers along with a discussion board to see how others solved it and crucially, there is an online SQL code editor so you can right in the browser run your SQL query and have it executed.
To prep for the Berkshire Hathaway SQL interview it is also a great idea to solve interview questions from other conglomerate & insurance companies like:
However, if your SQL coding skills are weak, don't worry about diving straight into solving questions – strengthen your SQL foundations with this DataLemur SQL tutorial.
This tutorial covers things like AND/OR/NOT and working with string/text data – both of which come up frequently in Berkshire Hathaway interviews.
Berkshire Hathaway Data Science Interview Tips
What Do Berkshire Hathaway Data Science Interviews Cover?
In addition to SQL interview questions, the other topics to prepare for the Berkshire Hathaway Data Science Interview are:
- Probability & Statistics Questions
- Python or R Coding Questions
- Product Analytics Questions
- ML Modelling Questions
- Resume-Based Behavioral Questions
How To Prepare for Berkshire Hathaway Data Science Interviews?
I'm a tad biased, but I believe the best way to prep for Berkshire Hathaway Data Science interviews is to read the book Ace the Data Science Interview.
The book has 201 interview questions sourced from Microsoft, Amazon & startups. It also has a crash course covering Python, SQL & 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.