10 Akuna Capital SQL Interview Questions (Updated 2024)
Data Analytics, Data Science, and Data Engineering employees at Akuna Capital uses SQL for analyzing trading data, including trade volumes, market prices, and risk metrics, as well as, managing and maintaining the financial databases, including those containing trade histories, market data, and risk models, for improved decision-making. Because of this, Akuna Capital asks interviewees SQL coding interview questions.
As such, to help prep you for the Akuna Capital SQL interview, we've curated 10 Akuna Capital SQL interview questions – can you solve them?
10 Akuna Capital SQL Interview Questions
SQL Question 1: Identify Whale Traders
Akuna Capital needs to identify its 'Whale Traders'. A 'Whale Trader' is a customer who has traded above one million dollars in total trade volume within a calendar month. Analyze the customer database and write a SQL query to find customer IDs that fit the 'Whale Trader' profile.
For the context of this question, assume we have a trades table with columns , , , , and .
Example Input:
| trade_id | customer_id | trade_date | trade_volume | trade_price |
|---|---|---|---|---|
| 2189 | 139 | 06/23/2022 | 250000 | 4 |
| 3987 | 295 | 07/01/2022 | 500000 | 8 |
| 3245 | 139 | 06/29/2022 | 800000 | 10 |
| 1267 | 654 | 07/15/2022 | 400000 | 12 |
| 0971 | 295 | 07/02/2022 | 520000 | 14 |
Answer:
The SQL query can calculate the total amount of trading volume (trade_volume*trade_price) for each customer, per month, and then filter only the ones higher than 1 million dollars.
Here's the complete query in PostgreSQL:
This query groups all trades by month and customer_id, totaling the volume traded on each group. The HAVING clause then filters only the groups where the total trade volume exceeds one million dollars, finding the 'Whale Traders' for each month.
To work on another SQL customer analytics question where you can solve it right in the browser and have your SQL code instantly executed, try this Walmart Labs SQL Interview Question:
SQL Question 2: 2nd Highest Salary
Given a table of Akuna Capital employee salary data, write a SQL query to find the 2nd highest salary at the company.
Akuna Capital Example Input:
| employee_id | salary |
|---|---|
| 1 | 2500 |
| 2 | 800 |
| 3 | 1000 |
| 4 | 1200 |
Example Output:
| second_highest_salary |
|---|
| 1200 |
Try this question directly within the browser on DataLemur:
Answer:
You can find a step-by-step solution here: 2nd Highest Salary.
SQL Question 3: In database design, what do foreign keys do?
A foreign key is a field in a database table that links to the primary key of another table, establishing a connection between the two tables.
To demonstrate this concept, let's analyze Akuna Capital's marketing analytics database which stores data from Google Ads campaigns:
:
| ad_id | campaign_id | keyword | click_count |
|---|---|---|---|
| 1 | 100 | Akuna Capital pricing | 10 |
| 2 | 100 | Akuna Capital reviews | 15 |
| 3 | 101 | Akuna Capital alternatives | 7 |
| 4 | 101 | buy Akuna Capital | 12 |
is a foreign key. It references the of the Google Ads campaign that each ad belongs to, establishing a relationship between the ads and their campaigns. This foreign key allows you to easily query the table to find out which ads belong to a specific campaign, or to find out which campaigns a specific ad belongs to.
It is also possible for a table to have multiple foreign keys that reference different primary keys in other tables. For example, the table could have additional foreign keys for the of the ad group that each ad belongs to, and the of the Google Ads account that the campaigns belong to.
SQL Question 4: Analyzing Trading Data
You're given trading data in table from Akuna Capital. Your task is to write a SQL query that will compute the moving average price over the past 3 days for each trading day and each stock symbol (which should not include the current row's price). To make it more realistic, let's work with business days only, i.e., Monday to Friday.
example input:
| TradeDate | Symbol | TradePrice |
|---|---|---|
| 2022-08-01 | AAPL | 146.00 |
| 2022-08-02 | AAPL | 147.00 |
| 2022-08-03 | AAPL | 148.00 |
| 2022-08-04 | AAPL | 149.00 |
| 2022-08-05 | AAPL | 150.00 |
| 2022-08-01 | GOOG | 2720.00 |
| 2022-08-02 | GOOG | 2730.00 |
| 2022-08-03 | GOOG | 2740.00 |
| 2022-08-04 | GOOG | 2750.00 |
| 2022-08-05 | GOOG | 2760.00 |
Answer:
This query creates a window of rows for each Symbol that contains the current row and the preceding 3 business days' rows (excluding the current row for moving average calculation) and calculates the average TradePrice. The main query then simply returns all rows from the subquery and orders them by & to present data in an understandable way.
Please clarify if you'd like the moving 3-day averages for non-business days to not consider non-business days too. The current answer includes them in counting the preceding 3 days.
Pro Tip: Window functions are a frequent SQL interview topic, so practice all the window function problems on DataLemur
SQL Question 5: Can you provide a comparison of cross join and natural join?
A cross join is a type of JOIN operation in SQL that creates a new table by combining each row from the first table with every row from the second table. It is also known as a cartesian join.
For example, say you worked on the Marketing Analytics team at Akuna Capital, and needed to understand what advertising copy (text) goes best with what advertising creative (the photo that gets posted with the advertisement copy).
Here is an example of a cross-join between a table of ad_copy and a table of ad_creative:
A natural join, on the other hand, is a type of JOIN that combines rows from two or more tables based on their common columns. It is called a "natural" join because it is based on the natural relationship that exists between the common columns in the tables being joined.
For an example of each one, say you had sales data exported from Akuna Capital's Salesforce CRM stored in a datawarehouse which had two tables: and .
An (which is a type of natural join) combines the two tables on the common
This query will return rows from the sales and tables that have matching customer id values. Only rows with matching values will be included in the results.
One main difference between cross joins and natural joins is that cross joins do not require any common columns between the tables being joined, while natural joins do. Another difference is that cross joins can create very large tables if the input tables have a large number of rows, while natural joins will only return a table with the number of rows equal to the number of matching rows in the input tables.
SQL Question 6: Design a Trading Database for Akuna Capital
Akuna Capital is a proprietary trading firm with a strong focus on cutting-edge technology, data driven decisions, and automation. Imagine you are in charge of designing the database system that keeps track of all trading activities. Your database needs to follow these details:
- Each trader can either buy or sell multiple different assets.
- Every asset has a unique identifier.
- A trade consists of which asset was traded, who the trader was, how many units of that asset were traded, the date of trade, and the price at which was traded.
- The company needs to keep track of how many assets of each type each trader currently owns which should be updated after every buy/sell operation.
- The firm needs to calculate total day trading profit for each asset by subtracting the total cost of all buy operations from the total revenue from all sell operations (i.e., daily gain = total sell price - total buy price).
Design the necessary database tables including specifying the table names, column names, data types, and column relationships (PK, FK matching). Based on your design, write a SQL query to extract the total day trading profit for each asset on .
Sample Tables:
Example Input:
| asset_id | asset_name |
|---|---|
| 1 | Apple |
| 2 |
Example Input:
| trader_id | trader_name |
|---|---|
| 1 | John Doe |
| 2 | Jane Smith |
Example Input:
| trade_id | asset_id | trader_id | trade_date | trade_type | trade_units | trade_price |
|---|---|---|---|---|---|---|
| 1 | 1 | 1 | 2022-09-01 | Buy | 10 | 150 |
| 2 | 1 | 2 | 2022-09-01 | Sell | 5 | 180 |
| 3 | 2 | 1 | 2022-09-01 | Sell | 10 | 1000 |
| 4 | 2 | 2 | 2022-09-01 | Buy | 5 | 1200 |
Answer:
Here is an example SQL query in PostgreSQL.
This query joins the and tables on with a condition that only considers trade records on . It then groups the records by and , and uses a conditional function to calculate the daily gain for each asset. The condition in checks the - if the trade type is 'Sell', it multiplies the by (as an outgoing profit), but if it's 'Buy', it calculates the cost as negative. The of these values gives the daily gain.
SQL Question 7: In SQL, are values same the same as zero or a blank space?
In SQL, a value is NOT the same as a zero or a blank space. A NULL value represents the absence of a value or a missing value, whereas both zero and blank space are actually values.
To handle nulls, the function is often used to check for a null, and if there is a null, you can assign a default value.
SQL Question 8: Filter Customers with Unusually High Trade Volume
Akuna Capital has a database of all customers' trades and wants to investigate potential fraudulent activities. Write a SQL query to filter down customers in the table who made trades of over $100,000, and execute the trades more than 10 times in a given month.
Example Input:
| trade_id | customer_id | trade_date | trade_amount |
|---|---|---|---|
| 829 | 125 | 2022-08-15 | 105348 |
| 654 | 255 | 2022-08-16 | 54800 |
| 514 | 125 | 2022-08-19 | 123654 |
| 834 | 992 | 2022-07-20 | 87963 |
| 960 | 255 | 2022-08-25 | 130000 |
| 677 | 125 | 2022-08-27 | 97856 |
| 732 | 992 | 2022-07-05 | 125700 |
| 680 | 125 | 2022-08-10 | 120530 |
Example Output:
| mth | customer | total_trade | trade_count |
|---|---|---|---|
| 8 | 125 | 441388 | 4 |
| 8 | 255 | 184800 | 2 |
Answer:
The query extracts the month from the trade date and the customer ID from the trades table, sums the trade amount for trades over $100,000, and counts the number of trades per customer. It only returns customers who have made more than 10 such trades in a given month.
SQL Question 9: Average Quantities Per Trade
You are given data that consists of trade performances for Akuna Capital. The data provides information about each trade including the quantity used in the trade. Write a SQL query to calculate the average quantity per trade for each unique ticker(trade code).
We'll use a hypothetical table as an example:
Example Input:
| trade_id | ticker | trade_date | quantity |
|---|---|---|---|
| 1 | AAPL | 2019-07-01 | 300 |
| 2 | AAPL | 2019-07-02 | 500 |
| 3 | AAPL | 2019-07-03 | 400 |
| 4 | MSFT | 2019-07-01 | 200 |
| 5 | MSFT | 2019-07-02 | 600 |
| 6 | GOOG | 2019-07-01 | 100 |
| 7 | GOOG | 2019-07-03 | 300 |
Example Output:
| ticker | avg_quantity |
|---|---|
| AAPL | 400 |
| MSFT | 400 |
| GOOG | 200 |
Answer:
This query groups rows by the 'ticker' column and applies the AVG function on the 'quantity' column for each group. The result is the average quantity per trade for each unique ticker. This type of question tests your understanding of the AVG function in conjunction with the GROUP BY clause.
To practice a very similar question try this interactive Robinhood Cities With Completed Trades Question which is similar for analyzing trade data or this Stripe Repeated Payments Question which is similar for handling transactional quantities.
SQL Question 10: What are the different normal forms (NF)?
The three levels of database normalization (also known as normal forms) are:
First Normal Form (1NF):
- Each column should contain the same type of data (no mixing strings vs. integers)
- Each column in a table contains a single value (no lists or containers of data)
- Each row in the table is unique
Second Normal Form (2NF)
- It's in 1st Normal Form
- All non-key attributes are dependent on the primary key
Said another way, to achieve 2NF, besides following the 1NF criteria, the columns should also be dependent only on that table's primary key.
Third normal form (3NF) if it meets the following criteria:
- It's in 2nd Normal Form
- There are no transitive dependencies in the table.
A transitive dependency means values in one column are derived from data in another column. For example, it wouldn't make sense to keep a column called ""vehicle's age"" and ""vehicle's manufacture date" (because the vehicle's age can be derived from the date it was manufactured).
Although there is a 4NF and 5NF, you probably don't need to know it for the Akuna Capital interview.
Preparing For The Akuna Capital 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 Akuna Capital SQL interview questions, you should also solve the 200+ DataLemur SQL Interview Questions which come from companies like Google, Facebook, Microsoft and Amazon.
Each SQL question has hints to guide you, detailed solutions and most importantly, there's an interactive SQL code editor so you can right online code up your query and have it graded.
To prep for the Akuna Capital SQL interview you can also be helpful to solve SQL questions from other finance companies like:
Find out how AQR is using Machine Learning to drive investment innovation!
However, if your SQL skills are weak, don't worry about diving straight into solving questions – refresh your SQL knowledge with this SQL interview tutorial.
This tutorial covers things like aggregate window functions and handling strings – both of these show up frequently during Akuna Capital SQL interviews.
Akuna Capital Data Science Interview Tips
What Do Akuna Capital Data Science Interviews Cover?
In addition to SQL query questions, the other types of questions tested in the Akuna Capital Data Science Interview are:
- AB Testing Questions
- Python Pandas or R Coding Questions
- Data Case Study Questions
- Machine Learning Questions
- Behavioral Based Interview Questions
How To Prepare for Akuna Capital Data Science Interviews?
To prepare for the Akuna Capital Data Science interview make sure you have a firm understanding of the company's culture and values – this will be clutch for acing the behavioral interview. For the technical Data Science interviews, get ready by reading Ace the Data Science Interview. The book's got:
- 201 Interview Questions from tech companies like Netflix, Google, & Airbnb
- A Refresher covering SQL, Product-Sense & ML
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