At ACI Worldwide, SQL is used across the company for analyzing vast transactional datasets for fraud detection, and managing customer databases to provide personalized payment solutions. Unsurprisingly this is why ACI Worldwide typically asks SQL problems in interviews for Data Science, Data Engineering and Data Analytics jobs.
Thus, to help you prepare for the ACI Worldwide SQL interview, we'll cover 10 ACI Worldwide SQL interview questions – able to solve them?
ACI Worldwide is a payment systems company, so its power users, VIP users, or whale users might be identified as the customers who make the most transactions or the largest transactions.
Given tables (with columns ) and (with columns ), write a SQL query to identify customers who, over the last year, have made more than a certain threshold of transactions or have a total transaction amount that exceeds a certain threshold.
customer_id | register_date | location |
---|---|---|
321 | 2019-07-21 00:00:00 | USA |
456 | 2020-06-25 00:00:00 | UK |
897 | 2019-10-30 00:00:00 | Germany |
562 | 2020-11-01 00:00:00 | Australia |
238 | 2018-06-01 00:00:00 | USA |
transaction_id | customer_id | transaction_date | amount |
---|---|---|---|
51872 | 321 | 2022-04-26 00:00:00 | 500.25 |
65890 | 456 | 2022-02-25 00:00:00 | 750.50 |
46932 | 897 | 2022-10-30 00:00:00 | 120.00 |
93652 | 562 | 2021-11-15 00:00:00 | 400.00 |
15472 | 238 | 2022-07-01 00:00:00 | 650.00 |
64378 | 321 | 2022-12-02 00:00:00 | 575.25 |
Assume that the thresholds for the number of transactions and the total transaction amount are 50 times and $10,000, respectively.
This query first joins the and tables on the field. It then filters the data to include only transactions from the last year. The query groups the data by before applying a clause to filter for customers who have made more than 50 transactions or whose total transaction amount is more than $10,000 in the last year. The result is a list of whale users for ACI Worldwide.
To solve a similar VIP customer analysis question on DataLemur's free online SQL coding environment, try this Microsoft SQL Interview problem:
ACI Worldwide is a global provider of electronic banking and payment solutions. One of their key responsibilities could be to acquire transactional data to examine month-over-month trends for each product.
Suppose you have given a table named "transactions" containing historical transaction data with the following schema:
transaction_id | product_id | transaction_date | transaction_amount |
---|---|---|---|
1 | A | 2022-01-05 | 100.00 |
2 | A | 2022-01-28 | 150.00 |
3 | B | 2022-01-15 | 200.00 |
4 | B | 2022-02-19 | 400.00 |
5 | A | 2022-02-20 | 250.00 |
In the table, is a unique id for each transaction, is the id for each product, is the date when the transaction occurred, and is the amount of each transaction.
The objective of this SQL question is to write a query that can calculate the average transaction amount per month for each product.
month | product_id | average_transaction_amount |
---|---|---|
1 | A | 125 |
1 | B | 200 |
2 | A | 250 |
2 | B | 400 |
Below is the PostgreSQL query to solve the problem:
This query works by grouping all transactions based on the month of and . Then, it calculates the average for each group. The function extracts the month from the . The function calculates the average transaction amount for each group. The resulting table is sorted by and to make it easy to follow transaction trends for each product across different months.
Pro Tip: Window functions are a popular SQL interview topic, so practice every window function problem on DataLemur
NULLs are NOT the same as zero or blank spaces in SQL. NULLs are used to represent a missing value or the abscence of a value, whereas zero and blank space are legitimate values.
It's important to handle NULLs carefully, because they can mess up your analysis very easily. For example, if you compare a NULL value using the = operator, the result will always be NULL (because just like Drake, nothing be dared compared to NULL). That's why many data analysis in SQL start with removing NULLs using the function.
ACI Worldwide is a universal payments (UP) company powering electronic payments for businesses around the globe. They manage several forms of transactions across diverse regions and customers. Given the tables representing customer transactions and customer metadata, the business analyst would like to know the different transaction types each customer from each region prefers. This information would be used to tailor ACI’s future strategies and offerings.
Suppose we have two tables, and . The table captures all the transaction information, and the table contains customer-related details, including the ‘region’ they are from.
transaction_id | customer_id | transaction_type | transaction_date |
---|---|---|---|
6781 | 100 | Credit Card | 01/01/2022 00:00:00 |
6728 | 200 | Debit Card | 01/02/2022 00:00:00 |
6257 | 300 | Internet Banking | 01/08/2022 00:00:00 |
9901 | 400 | Mobile Wallet | 02/16/2022 00:00:00 |
3462 | 500 | Credit Card | 03/10/2022 00:00:00 |
customer_id | region |
---|---|
100 | Asia |
200 | Africa |
300 | Europe |
400 | North America |
500 | South America |
####Answer:
To find the most preferred transaction type for each customer from each region we do:
This query joins the table with table on and groups by , and . The function is used to get the count of each transaction type grouped by region and customer_id. The clause is used to order the final result by , and the count of transaction types in descending order. This way, for each customer from each region, their most preferred transaction type(one with maximum count) will be displayed first.
In database schema design, a one-to-one relationship between two entities means that each entity is linked to a single instance of the other. For example, the relationship between a car and a license plate is one-to-one because each car has only one license plate, and each license plate belongs to one car.
In contrast, a one-to-many relationship is when one entity can be associated with multiple instances of the other entity. An example of this is the relationship between a teacher and their classes - a teacher can teach multiple classes, but each class is only associated with one teacher.
ACI Worldwide is a payment systems company. Its data consists of a large number of customer transactions. Your task is to write an SQL query to filter the database to only include transactions by customers from the USA or Canada, made in the last year, that were not disputed.
transaction_id | customer_id | transaction_date | amount | country | disputed |
---|---|---|---|---|---|
6543 | 123 | 08/06/2021 | 200 | USA | False |
8374 | 456 | 12/30/2020 | 150 | Canada | False |
7655 | 789 | 10/26/2020 | 300 | UK | False |
3487 | 321 | 02/14/2021 | 500 | USA | True |
1235 | 654 | 03/25/2021 | 250 | Canada | False |
You should aim to get the following output, which only includes transactions made by customers in USA or Canada, within the last year, and were not disputed.
transaction_id | customer_id | transaction_date | amount | country | disputed |
---|---|---|---|---|---|
6543 | 123 | 08/06/2021 | 200 | USA | False |
8374 | 456 | 12/30/2020 | 150 | Canada | False |
1235 | 654 | 03/25/2021 | 250 | Canada | False |
This PostgreSQL query uses multiple WHERE conditions to filter the transactions table. It first filters for transactions from the USA or Canada using the OR clause. It then limits to transactions from the last year using a date operation, and finally to transactions that were not disputed. The output is all the columns from the filtered transactions.
The clause is used to filter the groups created by the clause. It's similar to the clause, but it is used to specify conditions on the groups created by the clause, rather than on the individual rows of the table.
For example, say you were analyzing salaries for analytics employees at ACI Worldwide:
This query retrieves the total salary for each Analytics department at ACI Worldwide and groups the rows by the specific department (i.e. "Marketing Analytics", "Business Analytics", "Sales Analytics" teams).
The clause then filters the groups to include only ACI Worldwide departments where the total salary is greater than $1 million
As a data analyst at ACI Worldwide, which provides software products for electronic payment systems, your task is to find out the average transaction amount processed per bank on a daily basis. For each bank, determine the average amount of all transactions processed in each day.
transaction_id | transaction_date | bank_id | transaction_amount |
---|---|---|---|
101 | 2022-08-01 | 1 | 1000 |
102 | 2022-08-01 | 2 | 500 |
103 | 2022-08-02 | 1 | 1500 |
104 | 2022-08-02 | 2 | 2000 |
105 | 2022-08-03 | 1 | 1000 |
106 | 2022-08-03 | 2 | 3000 |
transaction_date | bank_id | avg_transaction_amount |
---|---|---|
2022-08-01 | 1 | 1000.00 |
2022-08-01 | 2 | 500.00 |
2022-08-02 | 1 | 1500.00 |
2022-08-02 | 2 | 2000.00 |
2022-08-03 | 1 | 1000.00 |
2022-08-03 | 2 | 3000.00 |
In this query, the GROUP BY statement is used to group the transactions by date and bank. Then, the AVG function calculates the average transaction amount for each group. Finally, the result is ordered by transaction_date and bank_id for better readability. This query helps in understanding the daily average transaction processed per bank which can be instrumental in assessing bank performance and detecting any unusual activities.
To practice a very similar question try this interactive Stripe Repeated Payments Question which is similar for handling transaction data or this Uber User's Third Transaction Question which is similar for dealing with transaction sequences.
As part of ACI Worldwide's efforts to better understand customer behavior and optimizing sales, they would like to analyze the purchase history of their customers. They are particularly interested in finding out, for each customer, what's the total amount they spent and the total number of products they bought in each product category they have shopped for.
For this analysis, ACI Worldwide have provided two tables, and . The table contains information about the customers. The table contains the details about the purchases made by the customers, including the product category.
ACI Worldwide requests you to write a SQL query that joins the two tables and calculates, for each customer, the total amount spent () and total number of products bought () in each product category.
customer_id | first_name | last_name |
---|---|---|
1 | John | Doe |
2 | Jane | Smith |
3 | Emily | Rogers |
4 | Michael | Johnson |
purchase_id | customer_id | product_category | purchase_amount | quantity |
---|---|---|---|---|
100 | 1 | Electronics | $500.00 | 2 |
101 | 2 | Books | $30.00 | 3 |
102 | 1 | Books | $60.00 | 1 |
103 | 2 | Electronics | $1000.00 | 1 |
104 | 3 | Clothing | $250.00 | 5 |
first_name | last_name | product_category | total_spent | total_items |
---|---|---|---|---|
John | Doe | Electronics | $500.0 | 2 |
Jane | Smith | Books | $30.0 | 3 |
John | Doe | Books | $60.0 | 1 |
Jane | Smith | Electronics | $1000.0 | 1 |
Emily | Rogers | Clothing | $250.0 | 5 |
This query uses a JOIN operation to combine relevant data from and tables. It then groups the data by customer name (, ) and . The aggregate functions SUM are used to compute the and for each group.
Since joins come up so often during SQL interviews, take a stab at this Snapchat SQL Interview question using JOINS:
"The clause in SQL allows you to select records that are unique, eliminating duplicates.
For a tangible example, say you had a table of ACI Worldwide employees:
first_name | job_title |
---|---|
Akash | Data Analyst |
Brittany | Data Scientist |
Carlos | Data Engineer |
Diego | Data Engineer |
Eva | Data Analyst |
If you were doing an HR Analytics project and you wanted to get all the unique job titles that currently worked at the company, you would write the following SQL query:
The output would give you 3 distinct job titles at ACI Worldwide:
job_title |
---|
Data Analyst |
Data Scientist |
Data Engineer |
The key to acing a ACI Worldwide SQL interview is to practice, practice, and then practice some more! In addition to solving the earlier ACI Worldwide SQL interview questions, you should also solve the 200+ SQL questions from real Data Science & Analytics interviews which come from companies like Google, Facebook, Microsoft and Amazon.
Each interview question has multiple hints, step-by-step solutions and most importantly, there's an interactive SQL code editor so you can easily right in the browser your SQL query answer and have it executed.
To prep for the ACI Worldwide SQL interview you can also be useful to practice SQL problems from other tech companies like:
But if your SQL skills are weak, forget about jumping right into solving questions – strengthen your SQL foundations with this SQL interview tutorial.
This tutorial covers SQL topics like filtering data with WHERE and filtering on multiple conditions using AND/OR/NOT – both of which show up often during SQL job interviews at ACI Worldwide.
Beyond writing SQL queries, the other types of problems covered in the ACI Worldwide Data Science Interview are:
To prepare for ACI Worldwide Data Science interviews read the book Ace the Data Science Interview because it's got: