Data Analysts & Data Scientists at PNC Financial Services Group use SQL for analyzing banking transaction patterns and for managing client data efficiently. That's why PNC Financial Services Group LOVES to ask folks interviewing at the company SQL interview problems.
Thus, to help you prepare, we've curated 8 PNC Financial Services Group SQL interview questions – able to answer them all?
Based on the data in the table containing all transactions made by customers, each corresponding to a specific , create a SQL query that identifies 'VIP' customers. For this question, let's define 'VIP' customers as those with a total transaction value above $10,000 in a quarter.
The sample data is as follows:
transaction_id | customer_id | amount | transaction_date |
---|---|---|---|
1001 | 2345 | 3000 | 2022-01-01 00:00:00 |
1002 | 2345 | 4000 | 2022-01-02 00:00:00 |
1003 | 4567 | 50 | 2022-01-05 00:00:00 |
1004 | 4567 | 50 | 2022-01-06 00:00:00 |
1005 | 2345 | 4500 | 2022-04-01 00:00:00 |
1006 | 4567 | 7000 | 2022-04-02 00:00:00 |
1007 | 7890 | 3000 | 2022-01-01 00:00:00 |
1008 | 7890 | 8000 | 2022-04-02 00:00:00 |
Here is a PostgreSQL query to solve it:
This query works by grouping the table by customer ID and quarter. It then calculates the total transaction value made by each customer in each quarter. If the summed amount for a customer in a quarter is greater than $10000, the customer ID, quarter, and total transaction value are returned. The results are sorted by descending .
Please note this query does not take into consideration the fact that customers who did not perform any transactions in a quarter will not show up on the results, as well as it does not account for possibly considering the customers VIP if they consistently do a lot of transactions even though they may not go over the set $10,000 limit.
To practice a super-customer analysis question on DataLemur's free online SQL coding environment, try this Microsoft SQL Interview problem:
Imagine you had a table of PNC Financial Services Group employee salary data. Write a SQL query to find the top 3 highest earning employees within each department.
employee_id | name | salary | department_id |
---|---|---|---|
1 | Emma Thompson | 3800 | 1 |
2 | Daniel Rodriguez | 2230 | 1 |
3 | Olivia Smith | 2000 | 1 |
4 | Noah Johnson | 6800 | 2 |
5 | Sophia Martinez | 1750 | 1 |
8 | William Davis | 6800 | 2 |
10 | James Anderson | 4000 | 1 |
department_id | department_name |
---|---|
1 | Data Analytics |
2 | Data Science |
department_name | name | salary |
---|---|---|
Data Analytics | James Anderson | 4000 |
Data Analytics | Emma Thompson | 3800 |
Data Analytics | Daniel Rodriguez | 2230 |
Data Science | Noah Johnson | 6800 |
Data Science | William Davis | 6800 |
You can solve this interview question interactively on DataLemur:
We use the DENSE_RANK() window function to generate unique ranks for each employee's salary within their department, with higher salaries receiving lower ranks. Then, we wrap this up in a CTE and filter the employees with a ranking of 3 or lower.
If the code above is tough, you can find a step-by-step solution with hints here: Top 3 Department Salaries.
The clause works similarly to the clause, but it is used to filter the groups of rows created by the clause rather than the rows of the table themselves.
For example, say you were analyzing PNC Financial Services Group sales data:
This query retrieves the total sales for all products in each region, and uses the clause to only sales made after January 1, 2023. The rows are then grouped by region and the clause filters the groups to include only those with total sales greater than $500k.
PNC Financial Services Group is interested in identifying their most high-value customers. They want you to help them rank each customer by their total transaction value within each quarter, using their table.
The dataset consists of customer transactions with the following fields: , , , and .
Within each quarter, if two or more customers have the same transaction value, they would like them to be ranked next to each other (i.e., same rank value).
You are asked to provide a query that adds a new column that holds the rank of each customer within each quarter based on their total transaction value.
transaction_id | customer_id | transaction_date | transaction_value |
---|---|---|---|
1234 | 56 | 2022-01-10 | 200 |
5678 | 90 | 2022-02-02 | 300 |
9101 | 12 | 2022-03-22 | 500 |
1314 | 56 | 2022-04-13 | 150 |
1617 | 90 | 2022-02-25 | 200 |
2021 | 12 | 2022-04-02 | 300 |
2425 | 90 | 2022-07-14 | 400 |
quarter | customer_id | total_transaction_value | customer_rank |
---|---|---|---|
1 | 90 | 500 | 1 |
1 | 56 | 200 | 2 |
1 | 12 | 500 | 1 |
2 | 56 | 150 | 2 |
2 | 12 | 300 | 1 |
3 | 90 | 400 | 1 |
This query first groups the transactions by quarter and customer_id, and calculates the sum of transaction_value in each group. It then applies a window function () that ranks these groupings within each quarter () by the sum of transaction_values in descending order. The cast is to format the date to 'YYYY-MM-DD' standard, as window functions require ordering fields to be of the same type.
To practice another window function question on DataLemur's free interactive coding environment, solve this Amazon SQL Interview Question:
Check out some of the awards PNC bank has been awarded in the past few years, pretty impressive!
Both window functions are used to find rows at a given offset from the current row. However, will give you the rows AFTER the current row you. On the other hand, will give you the rows BEFORE the current row.
As a data analyst at PNC Financial Services Group, you are tasked to extract valuable information from the customer transactions database. Generate a list of all customers who have made more than five transactions, each of more than $500 in the last month. Additionally, filter out customers who have a negative account balance or have opened their account within the last 30 days.
customer_id | account_balance | account_start_date |
---|---|---|
2201 | $6000 | 04/09/2020 |
2903 | $-200 | 06/01/2021 |
3809 | $7000 | 07/27/2021 |
4750 | $5000 | 10/08/2019 |
5852 | $300 | 12/06/2020 |
|transaction_id|customer_id|transaction_date|transaction_amount| |:----|:----|:----|:----|:----| |1871|2201|07/02/2022|550| |3299|3809|07/28/2022|500| |5683|2201|07/31/2022|510| |7652|3809|07/29/2022|400| |9345|2201|07/07/2022|550|
This query first joins the and tables on the field. It then filters the resulting table for transactions from the past month, with a transaction amount greater than $500. It further filters the customers who currently have a positive account balance and have been customers for more than a month. Finally, it groups the results by , and retains only the groups with more than five records, which corresponds to the customers who have made more than five transactions in the last month.
The UNION operator combines the results from multiple SELECT statements into a single result set.
Here's an example of using the UNION operator to combine the results of two SELECT statements that retrieve data from tables of PNC Financial Services Group's Facebook ads and their Google ads:
This statement would return a combined result set of both Facebook and Google ads that have a spend greater than $200. The result set would include the following columns: ad_id, ad_name, ad_type, impressions, clicks, and spend.
Note that the two SELECT statements within the UNION must have the same number of columns and the columns must have similar data types. In this example, both SELECT statements have the same number of columns and the data types of the columns are all compatible.
We would like to analyze the flow of funds into our bank. For each month in 2022, could you provide the total amount deposited into all accounts? The data is available in the table with the following structure:
transaction_id | account_id | transaction_date | transaction_type | amount |
---|---|---|---|---|
1051 | 1000 | 02/03/2022 | Deposit | 500.00 |
2699 | 1001 | 02/10/2022 | Deposit | 1500.00 |
8477 | 1002 | 03/15/2022 | Deposit | 700.00 |
7952 | 1000 | 05/22/2022 | Deposit | 1000.00 |
3870 | 1002 | 06/26/2022 | Withdrawal | 300.00 |
4637 | 1001 | 07/07/2022 | Deposit | 600.00 |
The above SQL query first filters the table for the year 2022 and transaction type 'Deposit'. It then uses the function to extract the month from the . The function is used in conjunction with the clause to calculate the total amount deposited each month. The results are then ordered by the month.
The best way to prepare for a PNC Financial Services Group SQL interview is to practice, practice, practice. Besides solving the earlier PNC Financial Services Group SQL interview questions, you should also solve the 200+ SQL questions on DataLemur which come from companies like Netflix, Google, and Amazon.
Each problem on DataLemur has hints to guide you, fully explained answers along with a discussion board to see how others solved it and most importantly, there's an online SQL code editor so you can right in the browser run your SQL query answer and have it executed.
To prep for the PNC Financial Services Group SQL interview you can also be helpful to solve SQL questions from other banking & finanacial services companies like:
But if your SQL skills are weak, don't worry about diving straight into solving questions – improve your SQL foundations with this SQL tutorial for Data Analytics.
This tutorial covers things like working with string/text data and INTERCEPT/EXCEPT – both of these show up routinely in SQL job interviews at PNC Financial Services Group.
Beyond writing SQL queries, the other types of questions to practice for the PNC Financial Services Group Data Science Interview include:
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The book has 201 data interview questions sourced from Facebook, Google & startups. It also has a crash course on Stats, ML, & Data Case Studies. And finally it's helped a TON of people, which is why it's got over 1000+ 5-star reviews on Amazon.