11 Sterling Check SQL Interview Questions (Updated 2024)
Sterling Check employees write SQL queries all the damn time for analyzing criminal background check data and optimizing the efficiency of identity verification processes. For this reason Sterling Check asks SQL query questions during interviews for Data Science, Analytics, and & Data Engineering jobs.
So, to help you prepare, we've curated 11 Sterling Check SQL interview questions – able to solve them?
11 Sterling Check SQL Interview Questions
SQL Question 1: Identify the Power Users
Sterling Check provides background checking services to its customers. An essential metric for the business is the frequency and volume of checks ordered by a customer. A "Power User" is defined as a customer who has ordered more than 50 background checks in the last 30 days.
Given a table with columns , , , and (indicating how many background checks were ordered in that order), write a SQL query to identify all the Power Users.
Example Input:
| order_id | customer_id | order_date | num_checks |
|---|---|---|---|
| 1001 | 1 | 2022-11-01 | 30 |
| 1002 | 2 | 2022-11-02 | 55 |
| 1003 | 3 | 2022-11-03 | 35 |
| 1004 | 2 | 2022-11-10 | 60 |
| 1005 | 4 | 2022-11-15 | 80 |
Example Output:
| customer_id |
|---|
| 2 |
| 4 |
Answer:
This query filters the orders to the last 30 days and sums the for each . If the sum of is greater than 50, the customer is considered a Power User. Thus, we retrieve a list of customer_ids fulfilling this condition.
To practice a related super-user data analysis question on DataLemur's free online SQL code editor, try this recently asked Microsoft SQL interview question:
SQL Question 2: Employee Salaries Higher Than Their Manager
Suppose you had a table of Sterling Check employee salaries. Write a SQL query to find the employees who earn more than their direct manager.
Sterling Check Example Input:
| employee_id | name | salary | department_id | manager_id |
|---|---|---|---|---|
| 1 | Emma Thompson | 3800 | 1 | |
| 2 | Daniel Rodriguez | 2230 | 1 | 10 |
| 3 | Olivia Smith | 8000 | 1 | 8 |
| 4 | Noah Johnson | 6800 | 2 | 8 |
| 5 | Sophia Martinez | 1750 | 1 | 10 |
| 8 | William Davis | 7000 | 2 | NULL |
| 10 | James Anderson | 4000 | 1 | NULL |
Example Output:
| employee_id | employee_name |
|---|---|
| 3 | Olivia Smith |
This is the output because Olivia Smith earns 8,000, surpassing her manager, William Davis who earns $7,800.
Code your solution to this interview question directly within the browser on DataLemur:
Answer:
First, we perform a SELF-JOIN where we treat the first table () as the managers' table and the second table () as the employees' table. Then we use a clause to filter the results, ensuring we only get employees whose salaries are higher than their manager's salary.
If the solution above is tough, you can find a detailed solution with hints here: Employees Earning More Than Their Boss.
SQL Question 3: How do cross joins and natural joins differ?
Cross join and natural join are like two sides of a coin in the world of SQL.
Cross joins is like the wild and reckless cousin who creates a giant new table by combining every row from table A with every row from table B, no questions asked, no common key needed.
Natural joins are like the more refined and selective cousin who only combines rows from multiple tables if they have something in common (i.e., common columns/keys).
While cross join doesn't discriminate and will create a massive table if given the chance, natural join is more selective and only returns a table with the number of rows equal to the number of matching rows in the input tables. So, choose your JOIN wisely!
SQL Question 4: Calculate the Average Time Difference Between Users' Checks
Sterling Check provides a variety of background and identity services, one being the SterlingONE service. This service allows users to perform various checks on individuals.
Considering this, let's say we have a dataset of all individual checks performed on SterlingONE within a particular time frame. Each row represents an individual check performed by a user, including the user_id, the check_id, the checked_person_id, and the check_time.
The dataset, , is as follows:
Example Input:
| check_id | user_id | checked_person_id | check_time |
|---|---|---|---|
| 1001 | 145 | 757432 | 2022-02-15 08:26:10 |
| 1002 | 145 | 748291 | 2022-02-15 08:45:03 |
| 1003 | 167 | 821745 | 2022-02-15 09:01:56 |
| 1004 | 145 | 925186 | 2022-02-15 09:58:10 |
| 1005 | 167 | 821745 | 2022-02-15 09:01:56 |
| 1006 | 145 | 748163 | 2022-02-15 11:02:43 |
| 1007 | 167 | 833362 | 2022-02-15 12:58:28 |
The task is to calculate the average time (in minutes) between consecutive checks performed by the same user. How long does each user spend between checks on average?
For clarity, only consider time gaps that are less than or equal to 1 day (24 hours). So if a user performs a check at 1 PM on one day, and their next check is not until 2 PM the following day, ignore this time gap in your calculation. If a user only performed one check, the average time gap is null.
Answer:
This SQL query first calculates the time difference in minutes between checks from the same user using the window function and saves it in a CTE (). Then, it calculates and returns the average time difference per user, ignoring gaps more than 1 day ( minutes).
Pro Tip: Window functions are a popular SQL interview topic, so practice all the window function problems on DataLemur
SQL Question 5: Can you explain the difference between the and window functions in SQL?
In SQL, both and are used to rank rows within a result set. The key difference between the two functions is how deal with two or more rows having the same value in the ranked column (aka how the break ties).
RANK(): When there's a tie, leaves a gap in the ranking. For example, if three rows are tied for 2nd place, the RANK() function will assign a rank of 2 to the first of these rows, a rank of 3 to the second row, and a rank of 4 to the third row.
DENSE_RANK(): For ties, does not leave a gap in the ranking. Instead, it assigns the same rank to all tied rows, and then makes the next row 1 bigger. Confusing, I know, but here's an example to make it more clear: if three rows are tied for 3rd place, the function will assign a rank of 3 to all three rows, and then assign a rank of 4 to the next row.
SQL Question 6: Employee Background Check Efficiency
Sterling Check is a company that deals with background checks. Suppose they handle background checks for various employers. To maintain the efficiency of their services, they need to monitor how long the average background check takes and the status of background checks.
Given two tables and , design a PostgreSQL query to find the following for each employer -
- Average check time, that is, the average of (check_end_time - check_start_time)
- Number of checks that are 'Completed'
- Number of checks that are 'In-Progress'
The table has the columns: 'check_id'(Primary Key), 'employer_id'(Foreign Key), 'check_start_time', 'check_end_time', and 'status' and the table has the columns: 'employer_id'(Primary Key), 'employer_name'.
Sample Data:
Table:
| check_id | employer_id | check_start_time | check_end_time | status |
|---|---|---|---|---|
| 101 | 1 | 2022-06-08 09:00:00 | 2022-06-10 14:00:00 | Completed |
| 102 | 1 | 2022-06-10 11:00:00 | 2022-06-12 16:00:00 | Completed |
| 103 | 2 | 2022-06-11 12:00:00 | NULL | In-Progress |
| 104 | 2 | 2022-06-12 10:30:00 | 2022-06-14 11:00:00 | Completed |
| 105 | 3 | 2022-06-13 14:00:00 | NULL | In-Progress |
Table:
| employer_id | employer_name |
|---|---|
| 1 | Company A |
| 2 | Company B |
| 3 | Company C |
Answer:
This query first joins the and tables on the field. It then groups by and calculates the average check time, considering only completed checks. It also counts the number of 'Completed' and 'In-Progress' checks for each employer. The average check time is given in hours.
SQL Question 7: How do you determine which records in one table are not present in a second table?
To find records in one table that aren't in another, you can use a and check for values in the right-side table.
Here's an example using two tables, Sterling Check employees and Sterling Check managers:
This query returns all rows from Sterling Check employees where there is no matching row in managers based on the column.
You can also use the operator in PostgreSQL and Microsoft SQL Server to return the records that are in the first table but not in the second. Here is an example:
This will return all rows from employees that are not in managers. The operator works by returning the rows that are returned by the first query, but not by the second.
Note that isn't supported by all DBMS systems, like in MySQL and Oracle (but have no fear, since you can use the operator to achieve a similar result).
SQL Question 8: Filter Customer Records for Sterling Check
Sterling Check is a company that specializes in background and identity services. You are given a database of customer records to work with. For the purpose of targeted marketing, the company would like to extract specific data on its customers. Write a SQL query to filter customers with a salary greater than $50,000, live in the United States, and are not currently employed in the Technology sector.
Here are sample inputs and outputs for your query:
Example Input:
Example Output:
Answer:
The query filters customers based on three conditions: salary greater than $50,000, country being United States, and not currently working in the Technology industry. The output will contain only customers who satisfy all these conditions.
SQL Question 9: Click-through and Conversion Rates for Sterling Check Ads
Sterling Check runs a number of digital ads daily and tracks user interactions. They are interested in knowing the click-through rates (CTR) for their ads and how this corresponds to user conversions (i.e., users that add a product to cart). You are given two tables: and . The table keeps track of all ads viewed by users. The table tracks all users who view an ad and subsequently add a service to their cart. Write a query to calculate the click-through rate and conversion rate for each Ad_ID.
Example Input:
| view_id | user_id | view_date | Ad_ID |
|---|---|---|---|
| 9513 | 135 | 06/11/2022 00:00:00 | 70101 |
| 5684 | 245 | 06/12/2022 00:00:00 | 82763 |
| 7495 | 305 | 06/13/2022 00:00:00 | 70101 |
| 8486 | 152 | 06/20/2022 00:00:00 | 82763 |
| 7324 | 346 | 06/26/2022 00:00:00 | 70103 |
Example Input:
| conversion_id | user_id | conversion_date | Ad_ID |
|---|---|---|---|
| 2849 | 135 | 06/11/2022 00:00:00 | 70101 |
| 3789 | 305 | 06/13/2022 00:00:00 | 70101 |
| 4652 | 152 | 06/20/2022 00:00:00 | 82763 |
Answer:
This query first builds two Common Table Expressions(CTEs) to get the ad views and conversions count for each Ad. The resulting tables are then joined on the Ad_ID. The counts are then used to calculate the conversion rate for each Ad.
To solve a related SQL interview question on DataLemur's free online SQL coding environment, solve this Facebook SQL Interview question:
SQL Question 10: What do the / operators do, and can you give an example?
The / operator is used to remove to return all rows from the first SELECT statement that are not returned by the second SELECT statement.
Note that is available in PostgreSQL and SQL Server, while MINUS is available in MySQL and Oracle (but don't stress about knowing which DBMS supports what exact commands since the interviewers at Sterling Check should be lenient!).
Here's a PostgreSQL example of using EXCEPT to find all of Sterling Check's Facebook video ads with more than 10k views that aren't also being run on YouTube:
If you want to retain duplicates, you can use the EXCEPT ALL operator instead of EXCEPT. The EXCEPT ALL operator will return all rows, including duplicates.
SQL Question 11: Find Customers with Specific Email Domains
As a part of Sterling Check's business, the company frequently sorts and filters customer records based on specific patterns and strings to conduct effective customer relationship management. You are tasked with the responsibility of finding all the customers who are using a particular email service.
Given a table that has columns , , , , and , write a SQL query that finds all customers whose email address ends with '@sterlingcheck.com'.
Example Input:
| customer_id | name | registration_date | service_needed | |
|---|---|---|---|---|
| 001 | John Doe | johndoe@yahoo.com | 2020-06-02 00:00:00 | Background Check |
| 002 | Jane Doe | janedoe@sterlingcheck.com | 2020-10-05 00:00:00 | Drug Testing |
| 003 | Sarah Smith | sarahsmith@sterlingcheck.com | 2019-12-22 00:00:00 | Identity Verification |
| 004 | Dylan Johnson | djohnson@gmail.com | 2022-01-01 00:00:00 | Background Check |
| 005 | Rachel Green | rachel.green@sterlingcheck.com | 2021-05-05 00:00:00 | Education Verification |
Answer:
This query filters the table and selects all records where the column ends with '@sterlingcheck.com'. This is achieved by using the keyword and the '%' wildcard to match any characters before '@sterlingcheck.com' in the column.
Preparing For The Sterling Check SQL Interview
The key to acing a Sterling Check SQL interview is to practice, practice, and then practice some more!
Besides solving the earlier Sterling Check SQL interview questions, you should also solve the 200+ DataLemur SQL Interview Questions which come from companies like FAANG and tech startups.
Each SQL question has hints to guide you, fully explained answers along with a discussion board to see how others solved it and crucially, there is an interactive SQL code editor so you can right in the browser run your SQL query answer and have it checked.
To prep for the Sterling Check SQL interview it is also wise to practice SQL problems from other tech companies like:
But if your SQL foundations are weak, forget about going right into solving questions – strengthen your SQL foundations with this DataLemur SQL Tutorial.
This tutorial covers SQL concepts such as Union vs. UNION ALL and WHERE vs. HAVING – both of which show up often in SQL interviews at Sterling Check.
Sterling Check Data Science Interview Tips
What Do Sterling Check Data Science Interviews Cover?
Besides SQL interview questions, the other question categories to prepare for the Sterling Check Data Science Interview include:
- Statistics and Probability Questions
- Python or R Programming Questions
- Business Sense and Product-Sense Questions
- Machine Learning Questions
- Behavioral & Resume-Based Questions
How To Prepare for Sterling Check Data Science Interviews?
I'm a bit biased, but I think the best way to study for Sterling Check Data Science interviews is to read the book I wrote: Ace the Data Science Interview.
The book has 201 data interview questions sourced from FAANG (FB, Apple, Amazon, Netflix, Google). It also has a crash course covering Python, SQL & ML. And finally it's helped a TON of people, which is why it's earned 1000+ reviews on Amazon and a 4.5-star rating.