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9 Donnelley Financial Solutions SQL Interview Questions (Updated 2024)

Updated on

March 3, 2024

At Donnelley Financial Solutions, SQL is used often for analyzing financial data patterns and managing large datasets for client reporting. So, it shouldn't surprise you that Donnelley Financial Solutions frequently asks SQL questions in interviews for Data Science, Analytics, and & Data Engineering jobs.

As such, to help you study for the Donnelley Financial Solutions SQL interview, we've collected 9 Donnelley Financial Solutions SQL interview questions – able to solve them?

9 Donnelley Financial Solutions SQL Interview Questions

SQL Question 1: Identify the Most Active Customers

Donnelley Financial Solutions is interested in understanding which customers are the most active. They define 'activity' as the total value of financial transactions processed by a customer. A "VIP" customer is one who processes over $1,000,000 US Dollars. Write a SQL query to identify these VIP customers, listing the customer_id, name, and total transaction value.

Example Input:

Example Input:

Answer:


This query joins the table with the table on the field. Then it groups the data by and , calculates the total transaction value for each customer using the function. The clause applies the condition that total transaction value more than $1,000,000 to filter the grouped data. If the condition is matched, the customer is selected as a VIP customer.

Example Output:


This means that Acme Corp and Beta Ltd are VIP customers based on their total transaction value.

To practice a similar customer analytics SQL question where you can solve it right in the browser and have your SQL query instantly executed, try this Walmart SQL Interview Question: Walmart SQL Interview Question

SQL Question 2: Analyze Customer Subscriptions

Donnelley Financial Solutions deals with financial communications and data services. It has customers that subscribe to different services. A common type of question might be to write a SQL query to find out the busiest month for new subscriptions.

Assume you have a table named that logs all subscriptions. Write a SQL query that will return each service id, the month with the most new subscriptions for that service, and how many subscriptions there were in that month. We will define the month based on the :

Example Input:
subscription_idcustomer_idservice_idsubscribe_date
312442810101/15/2022
768559320101/22/2022
421863510101/28/2022
954742830102/05/2022
322391120102/12/2022
439031120102/18/2022
599759330103/03/2022
289591110103/15/2022
924731130103/21/2022

Answer:


In the query above, we use window function to count the number of subscriptions for each service in each month. We then present results by service and month order while also only showing the month with the most subscriptions for each service. Be aware that if there's a tie in number of subscriptions in different months for the same service, only the first month will be shown due to .

p.s. Window functions show up pretty often during SQL interviews, so practice the 27+ window function questions on DataLemur

DataLemur SQL Questions

SQL Question 3: Give a few ways in SQL that you can identify duplicate records in a table?

"One creative way is to use the window function ROW_NUMBER() and partition by whichver columns you are loooking for duplicates in. If any row has a row_number() more than 1, then it's a duplicate!


You could also use COUNT(DISTINCT col1) vs. COUNT(*) and see if they return different numbers, indicating the prescence of duplicates!

Donnelley Financial Solutions SQL Interview Questions

SQL Question 4: Financial Transactions Analytics

Donnelley Financial Solutions uses two tables for tracking financial transactions: a client's table that includes the client information and a transactions table containing transactions details. Design these tables and write a SQL query to calculate the total transaction amount each client has made, from highest to lowest. Evaluate data from the month of March 2022.

Example Input:
client_idfirst_namelast_nameemail
101JohnDoejohn.doe@example.com
102JaneSmithjane.smith@example.com
103RobertBrownrobert.brown@example.com
104MariaGarciamaria.garcia@example.com
105JamesJohnsonjames.johnson@example.com
Example Input:
transaction_idclient_idtransaction_datetransaction_amount
100110103/02/2022 00:00:00500.00
100210203/05/2022 00:00:001500.00
100310303/13/2022 00:00:00800.00
100410103/20/2022 00:00:00700.00
100510403/27/2022 00:00:001000.00
100610503/09/2022 00:00:002000.00
100710503/15/2022 00:00:001500.00

Answer:


This query joins the and tables on the common column. It then groups the data by client, calculates the sum of each client's transaction amount, and filters for the transactions made in March 2022. The output is ordered in descending order of the total transaction amount per client.

SQL Question 5: Why would you use the SQL constraint?

The CHECK constraint is used to set a rule for the data in a column. If a row is inserted or updated and the data in the column does not follow the rule specified by the CHECK constraint, the operation will be unsuccessful.

For example, say you had Donnelley Financial Solutions customer data. You could use a CHECK constraint to ensure that the email column contains only properly formatted email addresses, or that the age column contains only positive integer. Here's an example of that:


SQL Question 6: Calculating The Average Revenue Generated From Different Financial Services

Donnelley Financial Solutions is a company that offers a range of financial services, including equity services, investment banking, and tax services.

They want to understand their revenue streams better and are interested in finding out the average revenue generated monthly from each of their services.

They have a table where they record each transaction along with its generated revenue.

The table has the following schema:

Example Input:
transaction_idtransaction_dateservice_typerevenue
120101/05/2022Equity Services1000
120202/05/2022Investment Banking2000
120310/05/2022Equity Services800
120415/07/2022Tax Services1500
120520/07/2022Investment Banking2500

Your task is to write a SQL query that groups transactions by , and , then calculates the average for each group.

Answer:


Example Output:
mthservice_typeavg_revenue
5Equity Services900
5Investment Banking2000
7Tax Services1500
7Investment Banking2500

This query extracts the month from the transaction date and groups the transactions by the extracted month as well as by the service type. Then it calculates the average revenue for each group. This result provides a summary of how much revenue on average each service brings per month.

SQL Question 7: DBMS transactions are expected to follow the ACID properties. What are they, and what does each property mean?

To ensure the reliability and integrity of data, a database management system (DBMS) strives to maintain the ACID properties: Atomicity, Consistency, Isolation, and Durability. To illustrate these concepts, consider the following examples of how ACID properties apply to banking transactions:

  1. Atomicity: A transaction is either completed in full or not at all. For example, if a customer is transferring money between accounts, the transaction should either transfer the entire amount or none at all.
  2. Consistency: A transaction is only allowed to complete if it follows all rules and constraints within the database. For example, if a customer is withdrawing money from an account, the transaction should only be allowed to proceed if there are sufficient funds available. Otherwise, the transaction is rejected.
  3. Isolation: Concurrent transactions are kept separate from each other, so that the changes made by one transaction cannot be seen by another transaction until the first one is complete. This helps prevent conflicts, such as two customers attempting to withdraw money from the same account at the same time.
  4. Durability: Once a transaction has been committed and completed, the changes made by the transaction are permanently stored in the database and will not be lost even if the database or system crashes. For example, if a customer makes a deposit, the transaction should be durable so that the deposit is not lost in the event of a system failure.

SQL Question 8: Filtering Customer Records via SQL LIKE Keyword

As a data analyst working for Donnelley Financial Solutions, you've been tasked with retrieving customer record data with specific name patterns. Find customer records whose names start with "Don".

To illustrate the problem, let's use the following markdown formatted sample table:

Example Input:
customer_idfirst_namelast_nameemailaccount_balance
101DonaldSmithdsmith@email.com5000.00
102DonnaJohnsondjohnson@email.com4000.00
103RonDonnellyrdonnelly@email.com6000.00
104ThompsonKellertkeller@email.com5500.00
105DavidDonnerddonner@email.com4500.00

The query should return the customer records with names starting with "Don".

Example Output:
customer_idfirst_namelast_nameemailaccount_balance
101DonaldSmithdsmith@email.com5000.00
102DonnaJohnsondjohnson@email.com4000.00

Answer:

Here is the proposed answer in a SQL(PgSQL) block:


This PostgreSQL code retrieves all records from the table where the first name starts with "Don". The '%' in the LIKE statement acts as a wildcard, meaning it will match any characters following "Don". Hence, this will filter out all the names that start with "Don".

It is important to note that SQL is case-insensitive, so 'Don%' will match names like 'Donald','Donna','Don' etc. The capitalization does not matter in this case.

SQL Question 9: Extract Customer Transaction and Product Information

Donnelley Financial Solutions tracks customer transactions and the products that they purchase. They store this data in two different tables: and . The table contains transaction-specific information, while the table contains data related to the products.

The structure of the table is as follows:

Example Input:
transaction_idcustomer_idproduct_idpurchase_datetransaction_amount
1023001356706/15/2022$85
2075002453607/02/2022$150
3194003356706/21/2022$85
4102004765407/25/2022$300
5146005453606/29/2022$150

In the table, the product_id field is linked with the product_id in the customer_transactions table. The structure of the table is as follows:

Example Input:
product_idproduct_nameproduct_typeproduct_price
3567Financial Statement AnalysisBook$85
453614+ IPOSoftware$150
7654VenueData Room$300

The company wants a detailed report of all transactions including the name and type of product bought. Write a SQL query to join these two tables and provide the required information.

Answer:


This query joins the (alias as 't') table and the (alias as 'p') table on the field. The select statement brings up transaction ID, customer ID, product name, product type, transaction amount, and purchase date. The final view is sorted by the purchase date.

Because joins come up so often during SQL interviews, try this interactive Snapchat JOIN SQL interview question: Snapchat SQL Interview question using JOINS

Donnelley Financial Solutions 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. Beyond just solving the earlier Donnelley Financial Solutions SQL interview questions, you should also solve the 200+ SQL questions from real Data Science & Analytics interviews which come from companies like Facebook, Google and unicorn tech startups. DataLemur SQL Interview Questions

Each DataLemur SQL question has hints to guide you, detailed solutions and best of all, there's an online SQL code editor so you can right online code up your query and have it checked.

To prep for the Donnelley Financial Solutions SQL interview it is also useful to solve interview questions from other tech companies like:

But if your SQL foundations are weak, forget about going right into solving questions – improve your SQL foundations with this DataLemur SQL tutorial.

SQL tutorial for Data Analytics

This tutorial covers SQL concepts such as grouping by multiple columns and manipulating string/text data – both of which come up routinely in Donnelley Financial Solutions SQL assessments.

Donnelley Financial Solutions Data Science Interview Tips

What Do Donnelley Financial Solutions Data Science Interviews Cover?

Besides SQL interview questions, the other topics covered in the Donnelley Financial Solutions Data Science Interview are:

Donnelley Financial Solutions Data Scientist

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